A Method and System for Fish Propagation Quota in Plateau Lakes and Reservoirs Based on Ecological Carrying Capacity Analysis

Abstract

This invention discloses a method and system for allocating fish stock enhancement quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis. The method includes: acquiring lake / reservoir environmental perception information; preprocessing the information and constructing a distribution map of lake / reservoir environmental parameters; extracting multimodal features from the environmental parameter distribution map to generate multimodal fusion features, constructing an ecological carrying capacity analysis model, and performing ecological carrying capacity analysis on the target plateau lake / reservoir to obtain ecological carrying capacity analysis information; introducing a quantum evolutionary algorithm to combine the ecological carrying capacity analysis information and the multimodal fusion features to perform fish stock enhancement quota analysis and generate a stock enhancement quota scheme; conducting spatial value assessment of the target plateau lake / reservoir to generate a value heatmap; and using an ant colony optimization algorithm to optimize the stock enhancement quota allocation path to obtain a stock enhancement allocation path network to assist in fish stock enhancement quota allocation in the lake / reservoir. This improves the effectiveness and ecological stability of fish stock enhancement in plateau lakes and reservoirs.

Description

Technical Field

[0001] This invention relates to the field of fish propagation technology in plateau lakes and reservoirs, and in particular to a method and system for allocating fish propagation quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis. Background Technology

[0002] Currently, fish propagation management in high-altitude lakes and reservoirs generally faces the severe challenge of insufficient ecological adaptability. Traditional quota methods mainly rely on linear extrapolation of historical catch volumes or simple modeling based on a single water quality indicator (such as chlorophyll a), which cannot adapt to the multidimensional dynamic constraint system unique to high-altitude lakes and reservoirs. The ecosystems of high-altitude lakes and reservoirs exhibit extreme environmental characteristics such as low water temperature, strong radiation, and drastic vertical variations in dissolved oxygen, leading to systematic biases in conventional carrying capacity models.

[0003] Existing technologies, such as hydrological analogy, apply parameters from plain lakes and reservoirs to plateau scenarios, neglecting the physiological inhibitory effect of oxygen partial pressure dropping to 60% of sea level at high altitudes. Empirical propagation methods, based on fisherman interviews, set fixed stocking volumes but fail to quantify the trophic level competition losses between native and introduced species. Current fish propagation programs largely focus on optimizing aquaculture cages or genetic selection, neglecting lake- and reservoir-level systemic solutions that consider real-time ecological carrying capacity analysis, environmental adaptability, and hydrodynamic release pathways. There is an urgent need to construct an intelligent propagation technology system adapted to the unique habitats of plateau regions. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides a method and system for allocating fish breeding quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis.

[0005] To achieve the above objectives, the first aspect of this invention provides a method for allocating fish propagation quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis, comprising:

[0006] Multi-source data collection was performed on the target plateau lakes and reservoirs to obtain lake and reservoir environmental perception information. The lake and reservoir environmental perception information was preprocessed and a lake and reservoir environmental parameter distribution map was constructed.

[0007] Based on the distribution map of environmental parameters of the lake and reservoir, multimodal features are extracted to generate multimodal fusion features, an ecological carrying capacity analysis model is constructed, and ecological carrying capacity analysis is performed on the target plateau lake and reservoir to obtain ecological carrying capacity analysis information;

[0008] A quantum evolutionary algorithm is introduced to combine the ecological carrying capacity analysis information and multimodal fusion features to analyze the fish breeding quota in lakes and reservoirs, and to generate a breeding quota scheme.

[0009] Spatial value assessment of target plateau lakes and reservoirs is conducted to generate value heat maps. Ant colony optimization algorithm is used to optimize the propagation quota distribution path, resulting in a propagation distribution path network to assist in the propagation quota distribution of fish in lakes and reservoirs.

[0010] In this scheme, the process of acquiring multi-source data from the target plateau lake / reservoir to obtain lake / reservoir environmental perception information, preprocessing the lake / reservoir environmental perception information, and constructing a lake / reservoir environmental parameter distribution map specifically includes:

[0011] A sensor network is set up in the target plateau lake / reservoir. Regional water environment data of the target plateau lake / reservoir are collected through the set sensor network. The density of plankton clusters and fish activity hotspots in the target plateau lake / reservoir are captured by an underwater sonar array to obtain the first collection information.

[0012] Subsequently, regional remote sensing monitoring data of the target plateau lake and reservoir were acquired through satellite remote sensing. The hydrological change characteristics of the target plateau lake and reservoir and the chlorophyll fluorescence intensity and surface temperature field of the entire lake surface were extracted. Combined with the first collected information, the lake and reservoir environmental perception information was constructed.

[0013] Abnormal data is detected and removed from the lake and reservoir environmental perception information. After removing the abnormal data, the missing values ​​are filled in by predicting the missing states through the state prediction equation using the adaptive Kalman filter algorithm, so as to obtain the preprocessed lake and reservoir environmental perception information.

[0014] A polar coordinate system is established with the center of the target plateau lake as the origin. The collection coordinates of each data point are extracted from the preprocessed lake environment perception information and mapped to the polar coordinate system.

[0015] A radial-circular dual-path interpolation method is introduced. The parameter gradient change is fitted by a cubic spline function along the radial path, and the monitoring blind spot caused by the lake bay topography is compensated by the Kriging pan-Kriging algorithm along the circumferential path. The grid is then processed to generate a distribution map of lake and reservoir environmental parameters.

[0016] In this scheme, the step of extracting multimodal features based on the distribution map of environmental parameters of the lake and reservoir to generate multimodal fusion features, constructing an ecological carrying capacity analysis model, and performing ecological carrying capacity analysis on the target plateau lake and reservoir to obtain ecological carrying capacity analysis information specifically includes:

[0017] Obtain the distribution map of lake and reservoir environmental parameters, and extract multimodal features from the lake and reservoir environmental parameter distribution map, including hydrological modal features, biological modal features and environmental modal features, to obtain a single modal feature set;

[0018] The single-modal feature set is input into a preset multimodal feature fusion network. In the multimodal feature fusion network, the hydrological modal features and biological modal features are concatenated by tensors and input into a cross-attention layer to calculate the degree of inhibition of the hydrological modal features on the biological modal features, and the first weight is obtained.

[0019] The correlation strength between environmental modal features and biological modal features is analyzed using a gated fusion unit to form a second weight. Multimodal feature fusion is then performed based on the first and second weights to obtain multimodal fused features.

[0020] Based on the multimodal fusion features, the light energy conversion efficiency of each grid cell is calculated through the radiation-temperature response function, chlorophyll concentration is defined as the biomass carrier, the transmitted light attenuation gradient is calculated in combination with the euphotic layer depth, and the daily net primary productivity value is obtained using the vertical generalized productivity model.

[0021] A random forest algorithm is introduced to construct an ecological carrying capacity analysis model. The daily net primary productivity value and multimodal fusion features are input into the ecological carrying capacity analysis model for analysis to obtain ecological carrying capacity analysis information.

[0022] In this scheme, the introduction of the random forest algorithm to construct an ecological carrying capacity analysis model, and the input of the daily net primary productivity value and multimodal fusion features into the ecological carrying capacity analysis model for analysis, to obtain ecological carrying capacity analysis information, specifically includes:

[0023] Spatial registration is performed between the daily net primary productivity value and the multimodal fusion feature, so that each grid cell corresponds to a unique primary productivity benchmark value, forming a training sample set;

[0024] An ecological carrying capacity analysis model is constructed using the random forest algorithm. The random forest regression model architecture is initialized and the training sample set is imported for model training. A decision tree forest is constructed, and a preset number of samples are extracted using the bootstrap sampling method as the training subset of a single tree. The remaining samples are used as the out-of-bag validation set.

[0025] During the node splitting process of each tree, a preset number of candidate features are randomly selected as candidate splitting variables. The decrease in Gini impurity of each candidate splitting variable is calculated. The candidate splitting variable corresponding to the largest decrease in Gini impurity is selected for node splitting. When the splitting stopping criterion is met, the splitting is terminated and the predicted mean of primary productivity in the region is recorded, thus completing the construction of the decision tree forest.

[0026] After the decision tree forest is constructed, feature perturbation is performed based on the out-of-bag validation set. For each feature channel, the value of the feature in the out-of-bag sample is randomly replaced. The increase in prediction error after replacement is calculated as the feature importance score, and normalization is performed to obtain the feature importance score.

[0027] Feature weights are generated based on the feature importance scores, and an analytical equation for ecological carrying capacity is constructed based on the feature weights. The ecological carrying capacity of the target plateau lake and reservoir is solved using the analytical equation for ecological carrying capacity, and ecological carrying capacity analysis information is obtained.

[0028] In this scheme, the introduction of a quantum evolutionary algorithm, combined with the ecological carrying capacity analysis information and multimodal fusion features, is used to analyze the fish stocking quota in lakes and reservoirs, generating a stocking quota scheme. Specifically, this includes:

[0029] Ecological carrying capacity analysis information and multimodal fusion features are obtained. Based on the multimodal fusion features, underwater biological detection features of the target plateau lake and reservoir are extracted. Regional biomass is estimated through the underwater biological detection features to obtain regional biomass prediction information.

[0030] An ecological carrying capacity heat map is generated using the ecological carrying capacity analysis information and then gridded. The regional biomass prediction information is spatially registered with the ecological carrying capacity heat map to obtain a registered ecological carrying capacity heat map.

[0031] Based on the registered ecological carrying capacity heat map, a space analysis is performed. For each grid cell, the existing fish biomass is deducted from its carrying capacity threshold. If the result is positive, it is marked as an effective stocking space. If the result is negative or zero, the grid freezing mechanism is triggered, and it is marked as a prohibited area.

[0032] A quantum evolutionary algorithm was introduced to analyze the fish breeding quota in lakes and reservoirs. All grid cells in the registered ecological carrying capacity heatmap were converted into quantum chromosomes, and a corresponding gene segment was set for each chromosome to initialize the population and generate an initial quantum population.

[0033] The objective function and constraints are preset, and iterative evolution is carried out in combination with the initial quantum population. The fitness value of the initial quantum population is calculated through the objective function. Individual selection, crossover and mutation operations are performed based on the calculated fitness value to generate a new quantum population for the next round of iterative optimization.

[0034] In each round of iterative evolution, the direction of population evolution is dynamically controlled by a rotating gate. The rotation angle is dynamically generated by the deviation of the objective function. After repeated iterative evolution until the stopping criterion is met, the optimal solution set is output, and a proliferation quota scheme is generated based on the optimal solution set.

[0035] In this scheme, the spatial value assessment of the target plateau lake / reservoir to generate a value heatmap, and the optimization of the fish stocking quota distribution path using an ant colony optimization algorithm to obtain a fish stocking path network for fish stocking quota assistance in the lake / reservoir, specifically include:

[0036] Obtain the distribution map of lake and reservoir environmental parameters, extract the hydrological environmental characteristics of the target plateau lake and reservoir from the lake and reservoir environmental parameter distribution map, divide the region into functions based on the extracted lake and reservoir hydrological environmental characteristics, and generate a region function division map;

[0037] A spatial value assessment function is constructed, which consists of three decision factors: the remaining carrying capacity in the quota scheme, the spatial proximity of the indigenous protection area, and the hydrological diffusion efficiency index.

[0038] The spatial value assessment function is used to assess the value of the regional functional division map, and regional value is marked based on the spatial value assessment results of different regions to generate a value heat map.

[0039] An ant colony optimization algorithm is introduced to optimize the distribution path of the proliferation quota. Each area grid in the value heatmap is defined as a single node. A starting point is set and the ant population is initialized. The initial pheromone concentration is set by the regional value.

[0040] Construct path search constraints, perform deployment path search using an initial ant population, restrict ant movement trajectories using the path search constraints, and obtain the pheromone concentration field after repeated iterations.

[0041] A propagation quota scheme is obtained. By extracting path segments with pheromone concentrations greater than a preset threshold through the pheromone concentration field, a main distribution network is constructed. The propagation quota scheme is marked in the main distribution network, and a propagation distribution path network is generated to assist in the propagation quota of target plateau lakes and reservoirs.

[0042] A second aspect of this invention provides a fish stock enhancement quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis. The system includes a memory and a processor. The memory contains a program for a fish stock enhancement quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis. When executed by the processor, the program for the fish stock enhancement quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis performs the following steps:

[0043] Multi-source data collection was performed on the target plateau lakes and reservoirs to obtain lake and reservoir environmental perception information. The lake and reservoir environmental perception information was preprocessed and a lake and reservoir environmental parameter distribution map was constructed.

[0044] Based on the distribution map of environmental parameters of the lake and reservoir, multimodal features are extracted to generate multimodal fusion features, an ecological carrying capacity analysis model is constructed, and ecological carrying capacity analysis is performed on the target plateau lake and reservoir to obtain ecological carrying capacity analysis information;

[0045] A quantum evolutionary algorithm is introduced to combine the ecological carrying capacity analysis information and multimodal fusion features to analyze the fish breeding quota in lakes and reservoirs, and to generate a breeding quota scheme.

[0046] Spatial value assessment of target plateau lakes and reservoirs is conducted to generate value heat maps. Ant colony optimization algorithm is used to optimize the propagation quota distribution path, resulting in a propagation distribution path network to assist in the propagation quota distribution of fish in lakes and reservoirs.

[0047] This invention discloses a method and system for allocating fish stock enhancement quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis. The method includes: acquiring lake / reservoir environmental perception information; preprocessing the information and constructing a distribution map of lake / reservoir environmental parameters; extracting multimodal features from the environmental parameter distribution map to generate multimodal fusion features, constructing an ecological carrying capacity analysis model, and performing ecological carrying capacity analysis on the target plateau lake / reservoir to obtain ecological carrying capacity analysis information; introducing a quantum evolutionary algorithm to combine the ecological carrying capacity analysis information and the multimodal fusion features to perform fish stock enhancement quota analysis and generate a stock enhancement quota scheme; conducting spatial value assessment of the target plateau lake / reservoir to generate a value heatmap; and using an ant colony optimization algorithm to optimize the stock enhancement quota allocation path to obtain a stock enhancement allocation path network to assist in fish stock enhancement quota allocation in the lake / reservoir. This improves the effectiveness and ecological stability of fish stock enhancement in plateau lakes and reservoirs. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments or examples of the present invention, the drawings used in the embodiments or examples 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 according to these drawings without creative effort.

[0049] Figure 1 A flowchart of a method for allocating fish breeding quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis, provided in an embodiment of the present invention;

[0050] Figure 2 A flowchart of an ecological carrying capacity analysis method for plateau lakes and reservoirs provided in an embodiment of the present invention;

[0051] Figure 3 A block diagram of a fish propagation quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis is provided in one embodiment of the present invention.

[0052] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 A flowchart of a method for allocating fish breeding quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis, provided in an embodiment of the present invention;

[0056] like Figure 1 As shown, this invention provides a flowchart of a method for allocating fish stock enhancement quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis, including:

[0057] S102, Multi-source data collection is performed on the target plateau lake and reservoir to obtain lake and reservoir environmental perception information, the lake and reservoir environmental perception information is preprocessed and a lake and reservoir environmental parameter distribution map is constructed;

[0058] S104, Based on the distribution map of environmental parameters of the lake and reservoir, multimodal features are extracted to generate multimodal fusion features, an ecological carrying capacity analysis model is constructed, and ecological carrying capacity analysis is performed on the target plateau lake and reservoir to obtain ecological carrying capacity analysis information;

[0059] S106, Introduce a quantum evolution algorithm to combine the ecological carrying capacity analysis information and multimodal fusion features to analyze the fish breeding quota in lakes and reservoirs, and generate a breeding quota scheme;

[0060] S108. Spatial value assessment of target plateau lakes and reservoirs is performed to generate value heat maps. Ant colony optimization algorithm is used to optimize the propagation quota distribution path, and the propagation distribution path network is obtained to assist in the propagation quota of lake and reservoir fish.

[0061] Furthermore, in a preferred embodiment of the present invention, the step of acquiring multi-source data from the target plateau lake / reservoir to obtain lake / reservoir environmental perception information, preprocessing the lake / reservoir environmental perception information, and constructing a lake / reservoir environmental parameter distribution map specifically includes:

[0062] A sensor network is set up in the target plateau lake / reservoir. Regional water environment data of the target plateau lake / reservoir are collected through the set sensor network. The density of plankton clusters and fish activity hotspots in the target plateau lake / reservoir are captured by an underwater sonar array to obtain the first collection information.

[0063] Subsequently, regional remote sensing monitoring data of the target plateau lake and reservoir were acquired through satellite remote sensing. The hydrological change characteristics of the target plateau lake and reservoir and the chlorophyll fluorescence intensity and surface temperature field of the entire lake surface were extracted. Combined with the first collected information, the lake and reservoir environmental perception information was constructed.

[0064] Abnormal data is detected and removed from the lake and reservoir environmental perception information. After removing the abnormal data, the missing values ​​are filled in by predicting the missing states through the state prediction equation using the adaptive Kalman filter algorithm, so as to obtain the preprocessed lake and reservoir environmental perception information.

[0065] A polar coordinate system is established with the center of the target plateau lake as the origin. The collection coordinates of each data point are extracted from the preprocessed lake environment perception information and mapped to the polar coordinate system.

[0066] A radial-circular dual-path interpolation method is introduced. The parameter gradient change is fitted by a cubic spline function along the radial path, and the monitoring blind spot caused by the lake bay topography is compensated by the Kriging pan-Kriging algorithm along the circumferential path. The grid is then processed to generate a distribution map of lake and reservoir environmental parameters.

[0067] It should be noted that a multi-source sensor network system was deployed in the target plateau lake / reservoir. This network includes an underwater sonar array and a water quality sensing matrix. High-frequency acoustic scanning captures the spatial density distribution characteristics of plankton clusters, and simultaneously records the three-dimensional trajectory coordinates of fish aggregation hotspots, forming the first set of collected information covering the physicochemical properties and biological activity status of the water body. Simultaneously, multispectral monitoring data streams from satellite remote sensing platforms are received, and the hydrological change characteristic parameters of the lake / reservoir (such as inflow water exchange rate and evaporation flux) and the chlorophyll fluorescence intensity and surface temperature field of the entire lake surface are analyzed. The satellite remote sensing data and underwater in-situ sensing data are spatially calibrated and fused into lake / reservoir environmental perception information. An outlier cleaning procedure is performed on this perception information: a dynamic threshold algorithm is applied to identify data drift points (such as biochemical anomalies where dissolved oxygen concentration is greater than 300% of saturation, or physical distortion signals where surface water temperature drops by more than 5°C), and these anomalies are removed after box plot statistical verification. To address the issue of missing time-series data caused by sensor breakpoints or cloud cover, an adaptive Kalman filter algorithm is employed for repair. The environmental parameter change trend at the missing location is calculated using a state prediction equation (e.g., predicting the current missing value based on the dissolved oxygen decay coefficient of the previous 6 hours). Then, the prediction bias is corrected through residual feedback from the measured sensor group, generating a spatiotemporally continuous seamless sensing information matrix.

[0068] Subsequently, a polar coordinate spatial model was established based on the complete environmental dataset: a radial-circular dual-axis system was constructed with the lake center as the origin, mapping the original geographic coordinates (longitude and latitude) to polar coordinates using coordinate transformation formulas. The ρ-axis represents the straight-line distance from the lake center, and the θ-axis represents the angular distribution along the lake circumference. A radial-circular dual-path interpolation algorithm was introduced: on the radial path (radius ρ direction), a cubic spline function was used to accurately fit the nonlinear changes of parameters along the water depth gradient (e.g., dissolved oxygen exhibits an exponential decay curve characteristic with depth); on the circumferential path (angle θ direction), the Kriging pan-Kriging algorithm was applied to compensate for monitoring blind spots caused by topographical obstruction such as bays and headlands (the regional correlation between adjacent measuring points was quantified using a spatial covariance function). Finally, a distribution map of lake and reservoir environmental parameters was generated, integrating two-dimensional polar coordinate raster data of parameters such as water temperature, dissolved oxygen, and chlorophyll, providing a spatially standardized data base for subsequent ecological carrying capacity analysis.

[0069] Furthermore, in a preferred embodiment of the present invention, the step of extracting multimodal features based on the distribution map of environmental parameters of the lake / reservoir to generate multimodal fusion features, constructing an ecological carrying capacity analysis model, and performing ecological carrying capacity analysis on the target plateau lake / reservoir to obtain ecological carrying capacity analysis information specifically includes:

[0070] Obtain the distribution map of lake and reservoir environmental parameters, and extract multimodal features from the lake and reservoir environmental parameter distribution map, including hydrological modal features, biological modal features and environmental modal features, to obtain a single modal feature set;

[0071] The single-modal feature set is input into a preset multimodal feature fusion network. In the multimodal feature fusion network, the hydrological modal features and biological modal features are concatenated by tensors and input into a cross-attention layer to calculate the degree of inhibition of the hydrological modal features on the biological modal features, and the first weight is obtained.

[0072] The correlation strength between environmental modal features and biological modal features is analyzed using a gated fusion unit to form a second weight. Multimodal feature fusion is then performed based on the first and second weights to obtain multimodal fused features.

[0073] Based on the multimodal fusion features, the light energy conversion efficiency of each grid cell is calculated through the radiation-temperature response function, chlorophyll concentration is defined as the biomass carrier, the transmitted light attenuation gradient is calculated in combination with the euphotic layer depth, and the daily net primary productivity value is obtained using the vertical generalized productivity model.

[0074] A random forest algorithm is introduced to construct an ecological carrying capacity analysis model. The daily net primary productivity value and multimodal fusion features are input into the ecological carrying capacity analysis model for analysis to obtain ecological carrying capacity analysis information.

[0075] It should be noted that after obtaining the distribution map of lake and reservoir environmental parameters, multimodal feature extraction and analysis are first performed: Vertical stratification of water temperature and dissolved oxygen gradient jump characteristics are extracted from the hydrological tomography data in the distribution map to form a hydrological modal feature set; the symbiotic relationship between chlorophyll concentration field and plankton density hotspots is dissected based on biodistribution tomography to construct a biological modal feature set; and the spatial synergistic effect between substrate type and submerged vegetation coverage is focused to form an environmental modal feature set. The single-modal feature set is then imported into a multimodal feature fusion network: In the first layer of the network, the hydrological modal feature tensor and the biological modal feature tensor are fused in concatenation along the depth dimension and input into a three-dimensional cross-attention layer. This layer quantifies the inhibitory effect of hydrological parameters on biological activity (e.g., the inhibition coefficient of dissolved oxygen gradient descent on plankton density) through a key-value query mechanism, outputting a first weight matrix representing the intensity of environmental constraints. Simultaneously, in the second branch, a gated fusion unit is used to analyze the spatial coupling relationship between environmental modal features and biological modal features, and a sigmoid activation function is used to generate the vegetation-substrate gain coefficient for the biological community, constituting a second weight matrix. Finally, the first weight is multiplicatively modulated to modulate the biomarker intensity, and the second weight is additively used to compensate for environmental gains, achieving deep coupling of multimodal features. Subsequently, primary productivity modeling is initiated based on the fused feature tensor: within each grid cell, light energy conversion efficiency is calculated using the radiation-temperature response function; chlorophyll a concentration is defined as the phytoplankton biomass carrier; and combined with euphotic depth data, the Beer-Lambert law is applied to calculate the transmitted light attenuation gradient (attenuation coefficient = 0.2 × Chla^0.6). These three parameters are input into a Vertical Generalized Productivity Model (VGPM) calculator, outputting the daily net primary productivity value to quantify the basic energy supply capacity of the water body. A random forest algorithm is introduced to construct an ecological carrying capacity analysis model. The daily net primary productivity value and the multimodal fused features are input into this model for analysis to obtain ecological carrying capacity analysis information.

[0076] Furthermore, in a preferred embodiment of the present invention, the introduction of a quantum evolutionary algorithm combined with the ecological carrying capacity analysis information and multimodal fusion features to analyze the fish stocking quota in lakes and reservoirs and generate a stocking quota scheme specifically includes:

[0077] Ecological carrying capacity analysis information and multimodal fusion features are obtained. Based on the multimodal fusion features, underwater biological detection features of the target plateau lake and reservoir are extracted. Regional biomass is estimated through the underwater biological detection features to obtain regional biomass prediction information.

[0078] An ecological carrying capacity heat map is generated using the ecological carrying capacity analysis information and then gridded. The regional biomass prediction information is spatially registered with the ecological carrying capacity heat map to obtain a registered ecological carrying capacity heat map.

[0079] Based on the registered ecological carrying capacity heat map, a space analysis is performed. For each grid cell, the existing fish biomass is deducted from its carrying capacity threshold. If the result is positive, it is marked as an effective stocking space. If the result is negative or zero, the grid freezing mechanism is triggered, and it is marked as a prohibited area.

[0080] A quantum evolutionary algorithm was introduced to analyze the fish breeding quota in lakes and reservoirs. All grid cells in the registered ecological carrying capacity heatmap were converted into quantum chromosomes, and a corresponding gene segment was set for each chromosome to initialize the population and generate an initial quantum population.

[0081] The objective function and constraints are preset, and iterative evolution is carried out in combination with the initial quantum population. The fitness value of the initial quantum population is calculated through the objective function. Individual selection, crossover and mutation operations are performed based on the calculated fitness value to generate a new quantum population for the next round of iterative optimization.

[0082] In each round of iterative evolution, the direction of population evolution is dynamically controlled by a rotating gate. The rotation angle is dynamically generated by the deviation of the objective function. After repeated iterative evolution until the stopping criterion is met, the optimal solution set is output, and a proliferation quota scheme is generated based on the optimal solution set.

[0083] It should be noted that the acquisition of ecological carrying capacity analysis information and multimodal fusion features first involves analyzing underwater biological detection characteristics to extract fish distribution hotspots, biomass density, and population structure ratios. A biomass-trophic level conversion model is then established using existing plankton data to achieve spatial prediction of biomass across the entire lake area, generating regional biomass prediction information with annotations of existing fish biomass and species composition. Simultaneously, the carrying capacity threshold matrix from the ecological carrying capacity analysis information is converted into a heat map, which, after georegistration, is spatially overlaid with the regional biomass distribution map. Subsequently, a gridded capacity calculation is performed: for each registered grid cell, the actual existing fish biomass data is subtracted from its carrying capacity threshold. When the calculation result is positive, it is marked as an effective stocking space, with the value representing the remaining biological carrying capacity that grid can add; if the result is zero or negative, a freezing mechanism is triggered, and the area is marked as a prohibited area (e.g., deep-water anoxic areas are automatically disabled due to existing biomass overload). The current status of native fish proportions is simultaneously assessed; when the native proportion in a specific grid falls below 25%, its protection priority is forcibly increased.

[0084] Then, the quantum evolutionary decision engine is activated: all grid cells are transformed into quantum chromosome populations, with each chromosome designed with four gene segments: the first segment encodes the current fish status (discretized into four biomass ranges), the second segment regulates the native fish ratio (30-70% probability amplitude), the third segment sets the stocking plan for economically important fish species, and the final segment configures the environmental risk buffer coefficient. Population initialization adopts a global equiprobability superposition state, covering the solution space from zero stocking to the maximum expandable space. Preset three-dimensional optimization objectives and rigid constraints: the economic objective integrates the fish growth model and market price curve to calculate expected returns; the ecological objective quantifies the recovery rate of the native population; and the risk objective assesses stability through an interspecific competition index. Constraints include total biomass not exceeding the carrying capacity ceiling and the native fish ratio meeting the dynamic protection red line. In iterative evolution, each generation of the population adjusts the gene probability amplitude distribution through a quantum rotation gate: the rotation angle is dynamically generated by the deviation of the objective function (e.g., increasing the gene amplitude of economically important fish species when economic returns are insufficient). After generational evolution and constraint filtering, the calculation terminates when the Pareto solution improvement rate of the population stabilizes within a preset threshold for 40 consecutive generations, and the optimal solution set is output to generate the proliferation quota scheme.

[0085] Furthermore, in a preferred embodiment of the present invention, the step of performing spatial value assessment on the target plateau lake / reservoir to generate a value heatmap, and using an ant colony optimization algorithm to optimize the propagation quota distribution path to obtain a propagation distribution path network for assisting in the propagation quota of lake / reservoir fish, specifically includes:

[0086] Obtain the distribution map of lake and reservoir environmental parameters, extract the hydrological environmental characteristics of the target plateau lake and reservoir from the lake and reservoir environmental parameter distribution map, divide the region into functions based on the extracted lake and reservoir hydrological environmental characteristics, and generate a region function division map;

[0087] A spatial value assessment function is constructed, which consists of three decision factors: the remaining carrying capacity in the quota scheme, the spatial proximity of the indigenous protection area, and the hydrological diffusion efficiency index.

[0088] The spatial value assessment function is used to assess the value of the regional functional division map, and regional value is marked based on the spatial value assessment results of different regions to generate a value heat map.

[0089] An ant colony optimization algorithm is introduced to optimize the distribution path of the proliferation quota. Each area grid in the value heatmap is defined as a single node. A starting point is set and the ant population is initialized. The initial pheromone concentration is set by the regional value.

[0090] Construct path search constraints, perform deployment path search using an initial ant population, restrict ant movement trajectories using the path search constraints, and obtain the pheromone concentration field after repeated iterations.

[0091] A propagation quota scheme is obtained. By extracting path segments with pheromone concentrations greater than a preset threshold through the pheromone concentration field, a main distribution network is constructed. The propagation quota scheme is marked in the main distribution network, and a propagation distribution path network is generated to assist in the propagation quota of target plateau lakes and reservoirs.

[0092] It should be noted that after obtaining the distribution map of lake and reservoir environmental parameters, key hydrological environmental characteristics of the target plateau lake and reservoir are extracted: based on the velocity field output by the hydrodynamic model, high diffusion zones and biological lagoon zones are divided; based on the dissolved oxygen concentration field, anoxic restricted areas are marked; and combined with the thermocline structure, deep-water low-temperature risk zones are identified. Based on these characteristics, the lake area is divided into functionally differentiated units: high diffusion corridor zone, native species core protection zone, still water lagoon zone, and near-shore deep-water buffer zone, generating a regional functional division map with spatial constraints. Based on the functional division map, a spatial value assessment function is constructed. This function integrates three decision factors for value quantification: the first factor refers to the remaining carrying capacity of the propagation quota scheme (the spatial gradient value of the stocking space); the second factor calculates the spatial proximity of each grid to the native protection zone (the weight increases by 8% for every 100 meters closer); and the third factor injects the hydrological diffusion efficiency index (1.5 times the coefficient for high diffusion zones and only 0.3 times for lagoon zones). The three factors are integrated into a spatial value score through raster operations. The high diffusion corridor in the center of the lake and the intersection of the indigenous protection area form a value peak area, while the nearshore deep water area presents a value depression due to the superimposed 30% risk attenuation, and finally a value heat map is generated.

[0093] Subsequently, the spatial grid of the value heatmap was defined as a path node network, with the dock coordinates set as the starting point of each path. The initial ant colony size was 500 artificial ants, with each ant representing a candidate delivery path. The initial pheromone concentration of each node was strictly proportional to its spatial value score (1.8 times the baseline concentration was set in the peak area). The ant movement followed three constraint mechanisms: forced avoidance of dissolved oxygen restricted areas (automatic skipping of red nodes), deceleration exploration in deep water buffer zones, and pheromone growth triggered by high-value nodes. When the cumulative value of a single path exceeded 85% of the total quota for the entire region, a positive feedback mechanism was activated to double the pheromone concentration of that path. After multiple rounds of ant colony iterations to obtain the pheromone concentration field, path segments with concentration values ​​greater than a preset threshold were extracted, and a main delivery network consisting of three main delivery axes and five auxiliary branches was constructed. The quota expansion scheme was spatially mapped onto this network: high-value nodes carried 60% of the quota (core delivery zone), medium-value zones allocated 30% (secondary adaptation zone), and edge safety zones configured with 10% (risk buffer zone). The final output is a network of propagation and release pathways, enabling precise spatial matching of propagation quotas in plateau lakes and reservoirs.

[0094] Figure 2 A flowchart of an ecological carrying capacity analysis method for plateau lakes and reservoirs provided in an embodiment of the present invention;

[0095] like Figure 2 As shown, this invention provides a flowchart of a method for analyzing the ecological carrying capacity of plateau lakes and reservoirs, including:

[0096] S202, Spatial registration is performed between the daily net primary productivity value and the multimodal fusion feature, so that each grid cell corresponds to a unique primary productivity benchmark value, forming a training sample set;

[0097] S204, construct an ecological carrying capacity analysis model through random forest algorithm, initialize random forest regression model architecture, import the training sample set for model training, construct decision tree forest, use bootstrap sampling method to extract a preset number of samples as training subset of a single tree, and use the remaining samples as out-of-bag validation set.

[0098] S206. During the node splitting process of each tree, a preset number of candidate features are randomly selected as candidate splitting variables. The decrease in Gini impurity of each candidate splitting variable is calculated. The candidate splitting variable corresponding to the largest decrease in Gini impurity is selected for node splitting. When the splitting stopping criterion is met, the splitting is terminated and the predicted mean of primary productivity in the region is recorded, thus completing the construction of the decision tree forest.

[0099] S208. After completing the construction of the decision tree forest, feature perturbation is performed based on the out-of-bag validation set. For each feature channel, the value of the feature in the out-of-bag sample is randomly replaced. The increase in prediction error after replacement is calculated as the feature importance score, and normalization is performed to obtain the feature importance score.

[0100] S210, generate feature weights based on the feature importance score, construct an analytical equation for ecological carrying capacity based on the feature weights, and use the analytical equation for ecological carrying capacity to solve for the ecological carrying capacity of the target plateau lake and reservoir, thereby obtaining ecological carrying capacity analysis information.

[0101] It should be noted that the daily net primary productivity value and multimodal fusion features are spatially gridded and registered to ensure that each grid cell is associated with a unique primary productivity baseline value and multimodal feature vector, forming a training sample set containing feature data and baseline values. The random forest regression model architecture is initialized: a forest size of 500 decision trees is set, and 80% of the samples in the full sample set are randomly selected as the training subset for each tree using a bootstrap sampling method. The remaining 20% ​​of the samples are reserved as an out-of-bag validation set to ensure the model's generalization ability. During the construction of a single decision tree, when a node needs to split, three candidate features are randomly selected as splitting variables. The Gini impurity reduction of each candidate feature is calculated, and the feature with the largest reduction is selected as the splitting criterion. For continuous features, all possible split points are traversed to determine the optimal splitting threshold; for discrete features, purity gain is calculated by grouping by category. When the number of samples in a node is less than 100 or the purity increase rate is less than the threshold, splitting is stopped, and the mean primary productivity of the samples covered by that node is recorded as the predicted value. After the growth of all decision trees is completed, the forest architecture is formed. Subsequently, feature importance quantification is performed based on the out-of-bag validation set: each feature channel is operated independently, and the feature value is randomly permuted in the out-of-bag samples while keeping other features unchanged, and the prediction error after permutation is recalculated. The increase in the mean squared error between the original prediction and the prediction after permutation is the original importance score of the feature. The scores of all decision trees are aggregated and averaged, and the scores are mapped to the [0,1] interval through range normalization to obtain a standardized feature importance weight matrix. An analytical equation for ecological carrying capacity is constructed based on the feature importance weights: a feature weight adjustment mechanism is introduced based on the primary productivity benchmark value. Key environmental constraints (such as dissolved oxygen gradient features) are modeled using an exponential decay function, biological promotion factors use a linear gain coefficient, and a 30% carrying capacity compensation is superimposed on the indigenous reserve grid. This equation is applied to solve the whole lake spatial grid and outputs ecological carrying capacity analysis information. This achieves a precise conversion from primary productivity to actual carrying capacity, providing a quantitative constraint boundary for fish propagation decisions.

[0102] Figure 3 An embodiment of the present invention provides a fish propagation quota system 3 for plateau lakes and reservoirs based on ecological carrying capacity analysis. The system includes a memory 31 and a processor 32. The memory 31 contains a program for a fish propagation quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis. When the processor 32 executes the program for the fish propagation quota system for plateau lakes and reservoirs based on ecological carrying capacity analysis, it performs the following steps:

[0103] Multi-source data collection was performed on the target plateau lakes and reservoirs to obtain lake and reservoir environmental perception information. The lake and reservoir environmental perception information was preprocessed and a lake and reservoir environmental parameter distribution map was constructed.

[0104] Based on the distribution map of environmental parameters of the lake and reservoir, multimodal features are extracted to generate multimodal fusion features, an ecological carrying capacity analysis model is constructed, and ecological carrying capacity analysis is performed on the target plateau lake and reservoir to obtain ecological carrying capacity analysis information;

[0105] A quantum evolutionary algorithm is introduced to combine the ecological carrying capacity analysis information and multimodal fusion features to analyze the fish breeding quota in lakes and reservoirs, and to generate a breeding quota scheme.

[0106] Spatial value assessment of target plateau lakes and reservoirs is conducted to generate value heat maps. Ant colony optimization algorithm is used to optimize the propagation quota distribution path, resulting in a propagation distribution path network to assist in the propagation quota distribution of fish in lakes and reservoirs.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0108] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0110] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for highland lake and reservoir fish propagation quota based on ecological carrying capacity analysis, characterized in that, The method comprises the following steps: Collecting lake environment perception information of the target plateau lake by multi-source data acquisition, preprocessing the lake environment perception information, and constructing a lake environment parameter distribution map; Based on the lake environment parameter distribution map, multi-modal feature extraction is performed to generate multi-modal fusion features, an ecological carrying capacity analysis model is constructed, and ecological carrying capacity analysis of the target plateau lake is performed to obtain ecological carrying capacity analysis information; Introducing a quantum evolutionary algorithm to analyze the lake fish propagation quota based on the ecological carrying capacity analysis information and multi-modal fusion features, and generating a propagation quota scheme; The spatial value of the target plateau lake is evaluated to generate a value heat map, and an ant colony optimization algorithm is used to optimize the propagation quota release path to obtain a propagation release path network for lake fish propagation quota assistance; The lake environment perception information of the target plateau lake is obtained by multi-source data acquisition, and the lake environment perception information is preprocessed and a lake environment parameter distribution map is constructed, which specifically comprises: A sensor network is set up in the target plateau lake, and regional water environment data of the target plateau lake is collected through the set sensor network. An underwater sonar array is used to capture the plankton cluster density and fish activity hot zone of the target plateau lake to obtain first collection information; Subsequently, satellite remote sensing is used to obtain regional remote sensing monitoring data of the target plateau lake, and the hydrological change characteristics, chlorophyll fluorescence intensity and surface temperature field of the target plateau lake are extracted, and the first collection information is combined to form lake environment perception information; The lake environment perception information is subjected to abnormal data detection and elimination. After the elimination of abnormal data, the adaptive Kalman filter algorithm is used to predict the missing state through the state prediction equation to supplement the missing values, and the preprocessed lake environment perception information is obtained; An polar coordinate system is established with the center of the target plateau lake as the origin, and the collection coordinates of each collection data are extracted from the preprocessed lake environment perception information and mapped into the polar coordinate system; A radial-tangential double-path interpolation method is introduced, a cubic spline function is used to fit the parameter gradient change along the radial path, and a Kriging universal Kriging algorithm is used to compensate for the monitoring blind area caused by the lake bay terrain, and a grid processing is performed to generate a lake environment parameter distribution map.

2. The method of claim 1, wherein the method is characterized by, The lake environment parameter distribution map is obtained, and multi-modal feature extraction is performed on the lake environment parameter distribution map, including hydrological modal features, biological modal features, and environmental modal features, to obtain a single-modal feature set; The single-modal feature set is input into a pre-set multi-modal feature fusion network. In the multi-modal feature fusion network, the hydrological modal features and the biological modal features are concatenated in a tensor, and are input into a cross-attention layer to calculate the inhibition degree of the hydrological modal features on the biological modal features, to obtain a first weight; ​ The correlation strength between the environmental modal features and the biological modal features is analyzed by using a gated fusion unit, and a second weight is formed, and multi-modal feature fusion is performed according to the first weight and the second weight to obtain multi-modal fusion features; Based on the multi-modal fusion features, the light energy conversion efficiency is calculated for each grid cell by a radiation-temperature response function, the chlorophyll concentration is defined as a biomass carrier, the transmittance light attenuation gradient is calculated in combination with the true light layer depth, and the daily net primary productivity value is obtained by using a vertical generalized productivity model; A random forest algorithm is introduced to construct an ecological carrying capacity analysis model, and the daily net primary productivity value and the multi-modal fusion features are input into the ecological carrying capacity analysis model for analysis to obtain ecological carrying capacity analysis information.

3. The method of claim 2, wherein the method is characterized by, The random forest algorithm is introduced to construct an ecological carrying capacity analysis model, and the daily net primary productivity value and the multi-modal fusion features are input into the ecological carrying capacity analysis model for analysis to obtain ecological carrying capacity analysis information, specifically including: The daily net primary productivity value is spatially registered with the multi-modal fusion features, so that each grid cell corresponds to a unique primary productivity reference value, forming a training sample set; An ecological carrying capacity analysis model is constructed by a random forest algorithm, a random forest regression model architecture is initialized, and the training sample set is imported for model training to construct a decision tree forest. A self-sampling method is used to extract a preset number of samples as a training subset for a single tree, and the remaining samples are used as an out-of-bag validation set; During the node splitting process of each tree, a preset number of candidate features are randomly selected as candidate split variables, the Gini impurity reduction of each candidate split variable is calculated, and the candidate split variable corresponding to the maximum Gini impurity reduction is selected for node division. When the split stopping criteria are met, the division is terminated and the predicted mean value of the primary productivity in the region is recorded, and the decision tree forest construction is completed; After the decision tree forest construction is completed, feature perturbation is performed based on the out-of-bag validation set. For each feature channel, the values of the feature in the out-of-bag samples are randomly replaced, the prediction error growth rate after replacement is calculated as the feature importance score, and normalization processing is performed to obtain the feature importance score; A feature weight is generated according to the feature importance score, an ecological carrying capacity analysis equation is constructed based on the feature weight, and the ecological carrying capacity of the target plateau lake is solved by using the ecological carrying capacity analysis equation to obtain the ecological carrying capacity analysis information.

4. The method of claim 1, wherein the method is characterized by, The quantum evolutionary algorithm is introduced in combination with the ecological carrying capacity analysis information and the multi-modal fusion features to analyze the lake fish propagation quota, and a propagation quota scheme is generated, specifically including: The ecological carrying capacity analysis information and the multi-modal fusion features are obtained, the underwater biological detection features of the target plateau lake are extracted based on the multi-modal fusion features, the regional biomass is estimated by using the underwater biological detection features, and regional biomass estimation information is obtained; An ecological carrying capacity thermal map is generated based on the ecological carrying capacity analysis information and is subjected to grid processing. The regional biomass estimation information is spatially registered with the ecological carrying capacity thermal map to obtain a registered ecological carrying capacity thermal map. Based on the registered ecological carrying capacity thermal map, the available space is analyzed. For each grid cell, the existing fish biomass is deducted from the carrying capacity threshold. If it is positive, it is marked as valid stocking space. If it is negative or zero, the grid freezing mechanism is triggered, and it is marked as a forbidden area. A quantum evolutionary algorithm is introduced to analyze the lake fish stocking quota. All grid cells in the registered ecological carrying capacity thermal map are converted into quantum chromosomes, and each chromosome is set to correspond to a gene segment for population initialization, generating an initial quantum population. A preset objective function and constraint condition are combined with the initial quantum population for iterative evolution. The fitness value of the initial quantum population is calculated by the objective function. Individual selection, crossover, and mutation operations are performed based on the calculated fitness value to generate a new quantum population for the next round of iterative optimization. In each round of iterative evolution, the population evolution direction is dynamically regulated by the rotation gate, and the rotation angle is dynamically generated by the objective function deviation. The optimal solution set is output after repeated iterative evolution until the stopping criteria are met. Based on the optimal solution set, the stocking quota scheme is generated.

5. The method of claim 1, wherein the method is characterized by, The spatial value of the target highland lake is evaluated to generate a value thermal map. The ant colony optimization algorithm is used to optimize the stocking path to obtain a stocking path network for lake fish stocking quota assistance. Specifically, it includes: Obtain the lake environmental parameter distribution map. Extract the lake hydrological environmental characteristics of the target highland lake from the lake environmental parameter distribution map. According to the extracted lake hydrological environmental characteristics, the regional function is divided, and a regional function division map is generated. Construct a spatial value evaluation function. The spatial value evaluation function is composed of three decision factors: residual carrying capacity, indigenous protection area spatial proximity, and hydrological diffusion efficiency index. Evaluate the regional function division map based on the spatial value evaluation function. Based on the spatial value evaluation results of different regions, the regional value is marked to generate a value thermal map. Introduce the ant colony optimization algorithm to optimize the stocking path. Define each region grid in the value thermal map as a single node, set the starting point, and initialize the ant population. The initial pheromone concentration is set by the regional value. Construct a path search constraint condition. Search for the stocking path using the initial ant population. Limit the ant movement trajectory using the path search constraint condition. After repeated iterations, obtain the pheromone concentration field. Obtain the stocking quota scheme. Extract the path segments with pheromone concentration greater than the preset threshold from the pheromone concentration field to form the main stocking network. Mark the stocking quota scheme on the main stocking network to generate a stocking path network for the target highland lake fish stocking quota assistance.

6. A highland lake and reservoir fish propagation quota system based on ecological carrying capacity analysis, characterized in that, The system includes a memory and a processor. The memory contains a highland lake fish stocking quota method based on ecological carrying capacity analysis program. When the program is executed by the processor, it implements the steps of the highland lake fish stocking quota method based on ecological carrying capacity analysis as claimed in any one of claims 1-5.

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