A simulation method, system, equipment, and medium for fish community survey sampling.

Abstract

This application discloses a simulation method, system, device, and storage medium for fish community survey sampling. The method includes: acquiring hydrodynamic environmental data and historical ecological baseline data of fish in a target sea area; determining the adaptive grid topology and fish species migration response coefficients of each monitoring unit in the target sea area based on the hydrodynamic environmental data and the historical ecological baseline data of fish; identifying the core area of ​​fish resource aggregation; constructing a multi-species coupled dynamic model of fish; generating a temporal series of the spatial distribution of community structure for each monitoring unit; fusing and constructing a multi-dimensional state temporal series of the ecosystem; generating a species diversity thermodynamic field temporal series based on the multi-dimensional state temporal series of the ecosystem and a preset uncertainty quantification algorithm, and outputting a sampling station layout scheme that prioritizes coverage of highly heterogeneous areas of the fish community. This invention improves the accuracy and efficiency of marine fish community surveys through dynamic simulation and uncertainty analysis, and can support efficient sampling station optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fish community survey, more particularly, the present application relates to a fish community survey sampling simulation method, system, device and medium. BACKGROUND

[0002] Simulation modeling simulates the behavior of real systems by establishing mathematical or computer models. Simulation modeling is widely used in engineering, ecology, economics and other fields. In simulation modeling, the system to be studied needs to be abstracted and simplified, and the main characteristics and mutual relationships of the system are extracted to establish a model that can describe its behavior. The simulation model can evolve dynamically in the computer and simulate the performance of the system in different situations. In fish management, simulation modeling technology can simulate the distribution process and population dynamics of fish, helping to develop scientific prevention and control strategies.

[0003] Fish community survey sampling is a key means to assess the health of aquatic ecosystems. By monitoring fish species composition, abundance, diversity and distribution pattern, it provides important basis for identifying invasive alien species, tracking resource decline trends, supporting fisheries management and biodiversity conservation. Traditionally, such surveys mainly rely on field fishing, acoustic detection or environmental DNA (eDNA) technology to collect samples to reveal the impact of environmental factors such as hydrodynamics, pollution and habitat changes on fish. However, these methods generally face the challenges of limited coverage and low spatio-temporal resolution, which restricts the efficiency and comprehensiveness of the survey.

[0004] Adopting simulation methods is considered as a key way to optimize fish community survey sampling strategies (such as station layout), which can significantly improve the efficiency of the survey and promote the sustainable use of marine ecological resources. However, existing fish community survey sampling simulation models mostly focus on a single species or use static grid structures, lacking the ability to analyze complex community adaptive topological relationships and multi-dimensional temporal dynamic changes, which makes them difficult to effectively support efficient sampling station optimization design. SUMMARY

[0005] The present application provides a fish community survey sampling simulation method, system, device and medium, which can support efficient sampling station optimization.

[0006] In a first aspect, the present application provides a fish community survey sampling simulation method, which comprises:

[0007] obtaining water dynamic environment data of a target sea area and fish historical ecological baseline data of a preset section;

[0008] determining the adaptive grid topology of each monitoring unit in the target sea area and the fish species migration response coefficient according to the water dynamic environment data and the fish historical ecological baseline data;

[0009] identify a fish resource aggregation core area of each monitoring unit based on the hydrodynamic environment data, the fish species migration response coefficient, and fish historical ecological baseline data;

[0010] construct a fish multi-species coupled dynamics model in combination with the fish resource aggregation core area, the fish historical ecological baseline data, and a water volume of each monitoring unit;

[0011] generate a community structure spatial distribution time sequence of each monitoring unit according to the adaptive grid topology, the fish multi-species coupled dynamics model, and a preset continuous observation period;

[0012] fuse community structure spatial distribution time sequences of all monitoring units to construct an ecosystem multi-dimensional state time sequence of the target sea area;

[0013] generate a species diversity thermal field time sequence based on the ecosystem multi-dimensional state time sequence and a preset uncertainty quantification algorithm, and output a sampling station layout scheme that preferentially covers fish community high-heterogeneity areas.

[0014] Further, determining the adaptive grid topology and the fish species migration response coefficient of each monitoring unit in the target sea area according to the hydrodynamic environment data and the fish historical ecological baseline data specifically comprises:

[0015] dividing the target sea area into multiple monitoring units by analyzing flow velocity, temperature, and salinity distribution in the hydrodynamic environment data;

[0016] calculating fish habitat density and migration path sensitivity of each monitoring unit in combination with the fish historical ecological baseline data;

[0017] generating an adaptive grid topology and determining a fish species migration response coefficient of fish species to environmental gradients based on the habitat density and the migration path sensitivity.

[0018] Further, identifying the fish resource aggregation core area of each monitoring unit based on the hydrodynamic environment data, the fish species migration response coefficient, and fish historical ecological baseline data specifically comprises:

[0019] spatiotemporally interpolating the hydrodynamic environment data to obtain a dynamic environment field of each monitoring unit;

[0020] simulating diffusion and aggregation behavior of fish species in the dynamic environment field based on the fish species migration response coefficient, and calculating fish density distribution of each monitoring unit accordingly;

[0021] In combination with the fish historical baseline data and the fish density distribution, a region with a gathering density exceeding a preset threshold is identified as a fish resource gathering core area.

[0022] Further, in combination with the fish resource gathering core area, the fish historical baseline data, and the water volume of each monitoring unit, a fish multi-species coupled dynamics model is constructed, specifically including:

[0023] The species composition and abundance characteristics of the fish resource gathering core area are extracted, and in combination with the fish historical baseline data, parameters of interspecies competition and predation relationships are estimated;

[0024] Based on the species composition and abundance characteristics and the interspecies competition and predation relationship parameters, the initial state and interaction terms in the model are defined;

[0025] According to the water volume of each monitoring unit, a multi-species coupled dynamics model containing diffusion, growth, interaction, and volume constraints is constructed.

[0026] Further, according to the adaptive grid topology, the fish multi-species coupled dynamics model, and a preset continuous observation period, a community structure spatial distribution time series of each monitoring unit is generated, specifically including:

[0027] Using the initial state and interaction terms in the fish multi-species coupled dynamics model, the fish community state is initialized on the adaptive grid topology;

[0028] Through the fish multi-species coupled dynamics model, the community dynamic evolution in each observation period is simulated, including species composition changes and abundance adjustments based on the interspecies competition and predation relationship parameters;

[0029] The community structure spatial distribution time series data of each monitoring unit in the continuous observation period is generated.

[0030] Further, the community structure spatial distribution time series of all monitoring units is fused to construct an ecosystem multi-dimensional state time series of the target sea area, specifically including:

[0031] The community structure spatial distribution time series of all monitoring units is spatially spliced and temporally aligned;

[0032] The fused species diversity, biomass, and functional group indicators are calculated, and the spatiotemporal heterogeneity of the community structure is quantified based on the influence of interspecies competition and predation relationships;

[0033] The target sea area ecosystem multi-dimensional state time series is constructed, including spatial, temporal, and ecological dimensions.

[0034] Further, based on the ecosystem multi-dimensional state time sequence and a preset uncertainty quantification algorithm, a species diversity thermal field time sequence is generated, and a sampling station layout scheme that preferentially covers a high heterogeneity area of a fish community is output, and the specific steps include:

[0035] The uncertainty quantification algorithm is used to perform Monte Carlo simulation on the ecosystem multi-dimensional state time sequence to generate a species diversity thermal field time sequence.

[0036] The spatiotemporal distribution of the high heterogeneity area in the thermal field time sequence is identified, and the area with significant spatiotemporal heterogeneity of the community structure is preferentially considered.

[0037] Based on the high heterogeneity area, a sampling station layout scheme is optimized and output to ensure that the high heterogeneity area is preferentially covered.

[0038] In a second aspect, the present application also provides a simulation system for fish community investigation and sampling, and the system includes:

[0039] An acquisition module is configured to acquire water dynamic environment data of a target sea area and fish historical ecological baseline data of a preset section.

[0040] A processing module is configured to determine an adaptive grid topology and a fish species migration response coefficient of each monitoring unit in the target sea area according to the water dynamic environment data and the fish historical ecological baseline data.

[0041] The processing module is further configured to identify a fish resource aggregation core area of each monitoring unit based on the water dynamic environment data, the fish species migration response coefficient and the fish historical ecological baseline data.

[0042] The processing module is further configured to construct a fish multi-species coupled dynamics model in combination with the fish resource aggregation core area, the fish historical ecological baseline data and a water volume of each monitoring unit.

[0043] The processing module is further configured to generate a community structure spatial distribution time sequence of each monitoring unit according to the adaptive grid topology, the fish multi-species coupled dynamics model and a preset continuous observation period.

[0044] The processing module is further configured to construct an ecosystem multi-dimensional state time sequence of the target sea area by fusing the community structure spatial distribution time sequences of all monitoring units.

[0045] An execution module is configured to generate a species diversity thermal field time sequence based on the ecosystem multi-dimensional state time sequence and a preset uncertainty quantification algorithm, and output a sampling station layout scheme that preferentially covers a high heterogeneity area of a fish community.

[0046] Thirdly, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores code and the processor is configured to acquire the code and execute the above-described simulation method for fish community survey sampling.

[0047] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described simulation method for fish community survey sampling.

[0048] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0049] This application acquires hydrodynamic environmental data of the target sea area and historical ecological baseline data of fish at a preset cross-section; based on the hydrodynamic environmental data and the historical ecological baseline data of fish, it determines the adaptive grid topology and fish species migration response coefficient of each monitoring unit in the target sea area; based on the hydrodynamic environmental data, the fish species migration response coefficient, and the historical ecological baseline data of fish, it identifies the core area of ​​fish resource aggregation in each monitoring unit; combining the core area of ​​fish resource aggregation, the historical ecological baseline data of fish, and the water volume of each monitoring unit, it constructs a multi-species coupled dynamics model of fish; based on the adaptive grid topology and the multi-species fish... By coupling a dynamic model and a preset continuous observation period, a temporal series of the spatial distribution of community structure for each monitoring unit is generated. The temporal series of the spatial distribution of community structure for all monitoring units are then fused to construct a multidimensional state temporal series of the ecosystem in the target sea area. Based on the multidimensional state temporal series of the ecosystem and a preset uncertainty quantification algorithm, a species diversity thermodynamic field temporal series is generated, and a sampling station layout scheme that prioritizes coverage of highly heterogeneous areas of the fish community is output. In other words, this application generates a species diversity thermodynamic field temporal series by fusing the multidimensional state temporal series of the ecosystem and an uncertainty quantification algorithm, and identifies the spatiotemporal distribution of highly heterogeneous areas accordingly, thereby outputting a sampling station layout scheme that prioritizes coverage of these highly heterogeneous areas. This scheme optimizes the station layout, ensuring that sampling resources are concentrated in hotspots where fish community dynamics change significantly, avoiding the inefficiency of traditional uniform or random distribution, and significantly reducing the cost and time consumption of field surveys, thus supporting efficient sampling station optimization. Attached Figure Description

[0050] Figure 1 This is an exemplary flowchart of a simulation method for fish community survey sampling according to some embodiments of this application;

[0051] Figure 2 This is a schematic diagram of the structure of a simulation system for fish community survey sampling according to some embodiments of this application;

[0052] Figure 3This is a schematic diagram of the structure of a computer device for implementing a simulation method for fish community survey sampling according to some embodiments of this application. Detailed Implementation

[0053] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0054] refer to Figure 1 The figure is an exemplary flowchart of a simulation method for fish community survey sampling according to some embodiments of this application. The method mainly includes the following steps:

[0055] Step 101: Obtain hydrodynamic environmental data of the target sea area and historical ecological baseline data of fish at the preset cross section.

[0056] Specifically, hydrodynamic environmental data refers to the spatiotemporal distribution data reflecting the water movement and physical characteristics of the target sea area. Hydrodynamic environmental data may include spatiotemporal distribution data such as flow velocity, temperature, and salinity. There are no specific limitations here. In practice, hydrodynamic environmental data can be acquired in real time through satellite remote sensing or on-site sensors. In addition, fish historical ecological baseline data refers to the benchmark data of fish species composition, abundance, and distribution in historical surveys.

[0057] Step 102: Based on the hydrodynamic environment data and the historical ecological baseline data of fish, determine the adaptive grid topology and fish species migration response coefficient of each monitoring unit in the target sea area.

[0058] In some embodiments, determining the adaptive grid topology of each monitoring unit in the target sea area based on the hydrodynamic environmental data and the historical ecological baseline data of fish can be achieved in the following manner:

[0059] The distribution of flow velocity, temperature and salinity in the hydrodynamic environmental data is analyzed to divide the target sea area into multiple monitoring units, where each monitoring unit is a sub-region within the target sea area with similar environmental characteristics.

[0060] Based on the historical ecological baseline data of fish, the fish habitat density and migration path sensitivity of each monitoring unit are calculated. Fish habitat density is the number of fish individuals per unit volume, and migration path sensitivity is the intensity of fish response to changes in environmental gradients. Specifically, the number of individual fish species, distribution range, and historical migration records within each monitoring unit are obtained from the historical ecological baseline data. Using the water volume of the monitoring unit as a benchmark, the number of fish individuals per unit volume, i.e., the fish habitat density, is obtained by dividing the total number of fish individuals in the unit by the water volume. Additionally, the migration distance, frequency, or rate of fish under environmental gradients (such as changes in temperature and salinity) in the historical ecological baseline data can be analyzed. The intensity of fish response to environmental changes is quantified using statistical models (such as linear regression models). The quantification result serves as the migration path sensitivity. It should be noted that a higher migration path sensitivity value indicates a more significant response of fish to changes in environmental gradients; this will not be elaborated further here.

[0061] In this application, an adaptive grid topology is generated based on the aforementioned habitat density and migration path sensitivity. The adaptive grid topology is a grid structure that is dynamically adjusted according to density. In specific implementation, uniform grid cells are initially divided according to the target sea area. For areas with high habitat density (i.e., concentrated fish) and high migration path sensitivity (i.e., environmental changes have a significant impact on migration), the grid cells are subdivided (e.g., adjusted from 1km×1km to 500m×500m) to improve local simulation accuracy. For areas with low habitat density and low sensitivity, the grid cells are merged (e.g., adjusted to 2km×2km) to reduce computational load. The adjacency relationship of the adjusted grid cells is recorded to form an adaptive grid topology. The grid density changes dynamically with fish distribution and migration characteristics, which will not be elaborated here.

[0062] In addition, this application defines a fish species migration response coefficient, which is a parameter that quantifies the probability of fish moving in response to gradients such as temperature and salinity. Specifically, this can be achieved by obtaining the gradient values ​​of environmental parameters such as temperature and salinity from hydrodynamic environmental data, extracting the migration probability of fish under the corresponding environmental gradient (e.g., the proportion of fish migrating when the temperature increases by 1°C) from historical ecological baseline data, and using a statistical model (e.g., a logistic regression model) with the environmental gradient as the independent variable and the migration probability as the dependent variable to fit the model and obtain the fish species migration response coefficient. For example, a migration response coefficient of 0.3 indicates that the migration probability increases by 30% for every 1 unit increase in the environmental gradient, but no specific limitation is made here.

[0063] Step 103: Based on the hydrodynamic environment data, the fish species migration response coefficient, and the historical ecological baseline data of fish, identify the core area of ​​fish resource aggregation in each monitoring unit.

[0064] In some embodiments, the identification of the core area of ​​fish resource aggregation in each monitoring unit based on the hydrodynamic environmental data, the fish species migration response coefficient, and the historical ecological baseline data of fish can be carried out in the following manner:

[0065] Spatiotemporal interpolation is performed on the hydrodynamic environment data (for example, spatiotemporal interpolation can use the Kriging method to fill data gaps) to obtain the dynamic environmental field of each monitoring unit;

[0066] Based on the aforementioned fish species migration response coefficient, the diffusion and aggregation behavior of fish species in the dynamic environmental field is simulated, and the fish density distribution of each monitoring unit is calculated accordingly. Specifically, the simulation of diffusion behavior can be based on a random walk model, and aggregation behavior can be based on the attractor principle. This can be achieved as follows: An initial fish density is set for each monitoring unit based on historical ecological baseline data; based on the random walk model and the aforementioned fish species migration response coefficient, the diffusion of fish in the dynamic environmental field is simulated: when environmental gradients trigger migration, individuals determine their movement direction according to the fish species migration response coefficient (e.g., towards a suitable temperature area), and the movement distance follows a random distribution. It should be noted that in the above diffusion simulation, an initial fish density is assigned to each monitoring unit based on historical ecological baseline data (e.g., past fish distribution records in each monitoring unit). This step is the "starting point" of the simulation, ensuring that the simulation begins in a state consistent with the actual ecological background. The movement direction of the fish is not completely random, but is jointly determined by the "environmental gradient" and the "fish species migration response coefficient." For example, when an environmental gradient indicates that "the temperature in a certain area is more suitable" (such as the difference between the target temperature and the current temperature), the fish species migration response coefficient (which can be understood as the fish's sensitivity to this environmental factor; the higher the coefficient, the stronger the response to the gradient) will drive the fish to move more towards this suitable area (rather than randomly turning). This aligns with the ecological instinct of fish to "seek advantage and avoid harm"—actively migrating towards favorable environments (such as areas with suitable temperatures and high dissolved oxygen levels). Although there is a preference for direction, the distance of a single movement is random (such as following a normal or uniform distribution). This is because, in reality, the distance fish move is affected by random factors such as individual physical strength, water flow disturbance, and obstacles (for example, for the same species of fish responding to the same environmental gradient, individual A may move 5 meters, while individual B may move 3 meters). Retaining randomness is intended to reflect the uncertainty in nature.

[0067] Furthermore, the simulated aggregation behavior is based on the attractor principle, identifying suitable environmental areas (such as food-rich areas) as attractors. Fish are more likely to move toward the attractor than to disperse randomly. This means that there are "key areas" (i.e., attractors) in the environment that strongly attract fish, such as food-rich waters, breeding grounds, and hiding places to avoid predators. The environmental characteristics of these areas (such as food density and substrate type) will form an "attraction gradient." When fish move to the vicinity of the attractor, they enhance their "probability of staying" or "centripetal tendency" (for example, when approaching the attractor, the probability of fish turning toward the center of the attractor increases, or the movement distance is shortened to reduce the possibility of leaving), which ultimately leads to a significant increase in the fish density of the monitoring unit where the attractor is located, thus forming aggregation.

[0068] In this application, after simulating the diffusion and aggregation behavior of fish, the total number of fish individuals in each monitoring unit after the simulation is counted, and then divided by the water volume of the monitoring unit to obtain the fish density distribution of that monitoring unit; the diffusion and aggregation simulation is repeated according to the time step (e.g., daily) to update the density distribution;

[0069] By combining the historical ecological baseline data of the fish and the fish density distribution, areas where the aggregation density exceeds a preset threshold are identified as core areas for fish resource aggregation. For example, the core area can be defined as a hotspot area with a density higher than the average value, which will not be elaborated here.

[0070] Step 104: Combine the core area of ​​fish resource aggregation, the historical ecological baseline data of fish, and the water volume of each monitoring unit to construct a multi-species coupled dynamic model of fish.

[0071] The fish multi-species coupling dynamics model is a mathematical model used to describe the interaction and population change of multiple species within a fish community in a dynamic environment. In some embodiments, the fish multi-species coupling dynamics model can be constructed by combining the core area of ​​fish resource aggregation, the historical ecological baseline data of fish, and the water volume of each monitoring unit in the following ways:

[0072] Extract the species composition and abundance characteristics of the core area of ​​fish resource aggregation, and combine them with the historical ecological baseline data of fish to estimate the parameters of competition and predation relationships among species. In specific implementation, for example, from the data of the core area of ​​fish resource aggregation, count the species composition of fish (e.g., there are three types of fish, A, B and C) and abundance (e.g., the abundance of fish A is 50 individuals / cubic meter).

[0073] By combining species coexistence records from historical baseline data, competition parameters are calculated. In practice, for example, when fish A and fish B coexist, if the growth rate of fish A decreases by 20%, the competition parameter of fish A against fish B is estimated to be 0.2. Finally, using historical food web data, predator-prey relationships are determined (e.g., fish C preys on fish A), and the ratio of the number of prey (fish A) to the number of predators (fish C) is calculated to fit the predation rate (e.g., if each fish C preys on 0.5 fish A per day, then the predation parameter is 0.5).

[0074] Based on the species composition and abundance characteristics, as well as the interspecies competition and predation relationship parameters, the initial state and interaction terms in the model are defined.

[0075] Based on the water volume of each monitoring unit (where water volume is defined as the amount of water within the monitoring unit, used to constrain biomass), a multi-species coupling dynamics model is constructed, incorporating diffusion, growth, interaction, and volume constraints. Specifically, when constructing the multi-species coupling dynamics model, the core terms can be defined first, namely:

[0076] Diffusion term: Based on the fish species migration response coefficient, it describes the diffusion rate of fish species between monitoring units;

[0077] Growth: Natural growth rate of each fish species (e.g., the annual growth rate of fish A is 0.1);

[0078] Interaction parameters: Incorporating competition and predation parameters to describe interspecific interactions among fish species;

[0079] Then, add volume constraints: set the maximum biomass corresponding to the volume of a simple unit water body (e.g., for a unit with a volume of 1000 cubic meters, the total biomass ≤ 500 kg), and inhibit growth when the biomass exceeds the threshold;

[0080] Finally, a system of differential equations is established: taking the abundance of each species as variables, the terms of diffusion, growth, interaction, and volume constraints are integrated into a system of equations to form a multi-species coupled dynamic model. For example, the rate of change of the abundance of species A over time = growth term - competition term - predation term + diffusion term. The rate of change of the abundance of species A over time is represented by the growth term, which represents the natural growth of the species; the competition term, which represents the competitive inhibition with other species; the predation term, which represents the loss due to predation by other species; and the diffusion term, which represents the migration and changes of the species between monitoring units. The above system of differential equations can comprehensively describe the dynamic evolution of the abundance of species A over time, which will not be elaborated here.

[0081] Step 105: Based on the adaptive grid topology, the multi-species coupling dynamics model of fish, and the preset continuous observation period, generate the temporal sequence of the community structure spatial distribution of each monitoring unit.

[0082] In some embodiments, the generation of the spatial distribution time series of the community structure of each monitoring unit based on the adaptive grid topology, the multi-species coupling dynamics model of fish, and the preset continuous observation period can be specifically carried out in the following manner:

[0083] Using the initial state and interaction terms in the fish multi-species coupled dynamics model, the fish community state is initialized on the adaptive grid topology. Specifically, in each cell of the adaptive grid, the species composition and abundance at the starting time are set according to the initial state parameters (e.g., species A abundance 30, species B abundance 20) and interaction term parameters (e.g., competition and predation coefficients) as the starting point of the simulation.

[0084] The multi-species coupled dynamics model of fish is used to simulate the dynamic evolution of the community within each observation period. This dynamic evolution includes changes in species composition and abundance adjustments based on parameters related to interspecies competition and predation relationships. Specifically, the simulation of community dynamic evolution, for example, within a one-month observation period:

[0085] Competition impact: Species A is highly competitive, causing the abundance of species B to decrease from 20 to 15;

[0086] Predation effect: Species C preys on species A, causing the abundance of species A to decrease from 30 to 25, and the abundance of species C to increase from 10 to 12;

[0087] Final adjustment: At the end of this cycle, A=25, B=15, and C=12 within the cell, completing one community structure update;

[0088] Generate time-series data on the spatial distribution of community structure for each monitoring unit within a continuous observation period. This time-series data is a spatiotemporal sequence of fish community structure changes over time within a preset continuous observation period. Specifically, at the end of each observation period (e.g., each week), record the species composition (e.g., which species are present) and abundance of each species within each monitoring unit. Then, add spatial coordinates (e.g., latitude and longitude) and timestamps (e.g., week 1, week 2) to the data for each monitoring unit. Finally, generate time-series data on the spatial distribution of community structure, which is the observation results of each monitoring unit arranged in chronological order, forming a table of "unit ID-time-species composition-abundance". In practice, a series of spatial distribution maps (one map per time point, using color intensity to represent abundance) generated in chronological order of continuous observation periods can also be generated, which can also serve as time-series data on the spatial distribution of community structure. No specific limitations are imposed here.

[0089] Step 106: Integrate the temporal series of community structure spatial distribution of all monitoring units to construct a multidimensional temporal series of ecosystem status in the target sea area.

[0090] The multidimensional state time series of the ecosystem in the target sea area is a three-dimensional tensor data reflecting the dynamics of the ecosystem in terms of spatial location, temporal changes, and ecological characteristics (such as species diversity and biomass). In some embodiments, the multidimensional state time series of the ecosystem in the target sea area is constructed by fusing the spatial distribution time series of community structure of all monitoring units. The specific methods are as follows:

[0091] Spatial splicing and temporal alignment of the spatial distribution time series of community structure of all monitoring units are performed to obtain a spatiotemporal data set of community structure that covers the entire area and is consistent in time. That is, spatial splicing is used to form a complete sea area range, and temporal alignment is used to ensure that the time nodes of each monitoring unit are consistent, providing basic data for the whole area and synchronous ecological analysis.

[0092] The fused species diversity, biomass, and functional group indices are calculated using the spatiotemporal data set of the aforementioned community structure. Based on the influence of interspecies competition and predation relationships, the spatiotemporal heterogeneity of the community structure is quantified. Spatiotemporal heterogeneity refers to the differences and fluctuations in the spatial distribution and time series of fish community structure, specifically including two aspects: Spatial heterogeneity: the difference in community structure (such as species diversity) between different monitoring units at the same time point, quantified by the coefficient of variation (the ratio of the standard deviation to the mean of the diversity index of different monitoring units at the same time point). The larger the value, the more significant the spatial difference. Temporal heterogeneity: the fluctuation of community structure (such as species diversity) of the same monitoring unit at different time points, quantified by the standard deviation (the degree of dispersion of the diversity index of the same unit at different times). The larger the value, the more significant the temporal fluctuation. In practice, for example: at the same time point, calculate the species diversity index of different monitoring units, and quantify spatial differences using the coefficient of variation (standard deviation / mean) (the larger the coefficient of variation, the higher the spatial heterogeneity); for the same monitoring unit, calculate the standard deviation of the diversity index at different time points to quantify temporal fluctuations (the larger the standard deviation, the higher the temporal heterogeneity); finally, combine competition and predation relationships to analyze the sources of heterogeneity (e.g., strong competition between fish A and fish B leads to significant spatial differences in the distribution of fish B), and finally use the spatial coefficient of variation and temporal standard deviation to comprehensively quantify the spatiotemporal heterogeneity of the community structure. That is, by combining the spatial coefficient of variation and the temporal standard deviation, the overall differences and dynamic changes of fish community structure in spatiotemporal space can be comprehensively described from two dimensions: spatial differences and temporal fluctuations, thus quantifying the spatiotemporal heterogeneity of the community structure.

[0093] A multidimensional state time series of the target marine ecosystem is constructed, encompassing spatial, temporal, and ecological dimensions. This multidimensional state time series is a three-dimensional tensor data set. Specifically, the three-dimensional tensor data of the ecosystem multidimensional state time series is structured data integrating information from the spatial, temporal, and ecological dimensions. The spatial dimension corresponds to all monitoring units in the target marine area (e.g., sub-regions at different latitudes and longitudes); the temporal dimension corresponds to continuous observation periods (e.g., various time points and time periods); and the ecological dimension includes key ecological indicators such as species diversity (e.g., Shannon index), biomass, and functional groups. These three dimensions constitute a three-dimensional tensor of "space-time-ecological indicators," which can intuitively present the ecological state of the target marine area at different spatial locations and time points (e.g., the species diversity value of a monitoring unit at a certain time), comprehensively reflecting the dynamic changes of the ecosystem.

[0094] Step 107: Generate a species diversity thermodynamic field time series based on the multidimensional state time series of the ecosystem and a preset uncertainty quantification algorithm, and output a sampling station layout scheme that prioritizes coverage of highly heterogeneous areas of fish communities.

[0095] Among them, the species diversity thermogram time series is a heat map sequence that reflects the spatial distribution of species diversity and its dynamic changes over time. High heterogeneity areas refer to areas with significant spatiotemporal heterogeneity in fish community structure. That is, the species composition and abundance characteristics of fish communities in these areas vary greatly in spatial distribution (high spatial heterogeneity) and fluctuate significantly in time series (high temporal heterogeneity). These areas are regions with significant dynamic changes in ecological characteristics such as species diversity, and are also areas that sampling stations should prioritize covering.

[0096] In some embodiments, the following methods can be used to generate a species diversity thermodynamic field time series based on the multidimensional state time series of the ecosystem and a preset uncertainty quantification algorithm, and to output a sampling station layout scheme that prioritizes coverage of highly heterogeneous areas of fish communities:

[0097] An uncertainty quantification algorithm (such as the Monte Carlo method in this application, which can simulate parameter perturbations) is used to perform Monte Carlo simulation on the multidimensional state time series of the ecosystem to generate a species diversity thermodynamic field time series, wherein the thermodynamic field time series is a diversity heat map sequence;

[0098] Identify the spatiotemporal distribution of highly heterogeneous regions in the thermal field time series, prioritizing regions with significant spatiotemporal heterogeneity in the community structure;

[0099] Based on the aforementioned highly heterogeneous regions, an optimized sampling station layout scheme is generated, prioritizing the placement of sampling stations in areas with significant spatiotemporal heterogeneity in fish communities (i.e., marked dynamic changes in species composition, abundance, etc.). Specifically, after identifying the spatiotemporal distribution of highly heterogeneous regions, the location and number of sampling stations are adjusted to ensure that the sampling station layout scheme can maximize coverage of these key areas, concentrating sampling resources in hotspots of significant community dynamic changes, thereby improving the efficiency and accuracy of fish community surveys.

[0100] In another aspect, in some embodiments, this application provides a simulation system for fish community survey sampling, with reference to... Figure 2 The figure is a schematic diagram of the structure of a simulation system for surveying and sampling fish communities in forests, according to some embodiments of this application. The simulation system for surveying and sampling fish communities includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below:

[0101] The acquisition module 401 in this application is mainly used to acquire hydrodynamic environmental data of the target sea area and historical ecological baseline data of fish at the preset cross section.

[0102] Processing module 402, in this application, is used to determine the adaptive grid topology and fish species migration response coefficient of each monitoring unit in the target sea area based on the hydrodynamic environment data and the fish historical ecological baseline data;

[0103] It should be noted that the processing module 402 in this application also identifies the core area of ​​fish resource aggregation in each monitoring unit based on the hydrodynamic environment data, the fish species migration response coefficient and the fish historical ecological baseline data;

[0104] In addition, the processing module 402 also combines the core area of ​​fish resource aggregation, the historical ecological baseline data of fish and the water volume of each monitoring unit to construct a multi-species coupled dynamic model of fish.

[0105] In addition, the processing module 402 also generates the temporal sequence of the community structure spatial distribution of each monitoring unit based on the adaptive grid topology, the fish multi-species coupling dynamics model and the preset continuous observation period;

[0106] In addition, the processing module 402 also integrates the spatial distribution time series of community structure of all monitoring units to construct a multi-dimensional state time series of the ecosystem of the target sea area.

[0107] The execution module 403 in this application is mainly used to generate a species diversity thermodynamic field time series based on the multidimensional state time series of the ecosystem and a preset uncertainty quantification algorithm, and output a sampling station layout scheme that prioritizes coverage of highly heterogeneous areas of fish communities.

[0108] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described simulation method for fish community survey sampling.

[0109] In some embodiments, reference Figure 3 The figure is a schematic diagram of the structure of a computer device for implementing a simulation method for fish community survey sampling according to some embodiments of this application. The method in the above embodiments can be achieved through... Figure 3 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0110] The processor 501 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more simulation methods for controlling the execution of fish community survey sampling in this application.

[0111] The communication bus 502 may include a path for transmitting information between the aforementioned components.

[0112] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0113] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The simulation method for fish community survey sampling in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0114] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0115] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core processor or a multi-core processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0116] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0117] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described simulation method for fish community survey sampling.

[0118] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0119] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method of simulating fish community survey sampling, characterized by, The method comprises the following steps: acquiring water dynamic environment data of a target sea area and fish historical ecological baseline data of a preset section; determining an adaptive grid topology and a fish species migration response coefficient of each monitoring unit in the target sea area according to the water dynamic environment data and the fish historical ecological baseline data; identifying a fish resource aggregation core area of each monitoring unit based on the water dynamic environment data, the fish species migration response coefficient and the fish historical ecological baseline data; constructing a fish multi-species coupled dynamics model in combination with the fish resource aggregation core area, the fish historical ecological baseline data and a water volume of each monitoring unit; generating a community structure spatial distribution time sequence of each monitoring unit according to the adaptive grid topology, the fish multi-species coupled dynamics model and a preset continuous observation period; fusing community structure spatial distribution time sequences of all monitoring units to construct an ecosystem multi-dimensional state time sequence of the target sea area; generating a species diversity thermal field time sequence based on the ecosystem multi-dimensional state time sequence and a preset uncertainty quantification algorithm, and outputting a sampling station layout scheme that preferentially covers a fish community high-heterogeneity area; wherein fusing community structure spatial distribution time sequences of all monitoring units to construct an ecosystem multi-dimensional state time sequence of the target sea area specifically comprises: spatially splicing and temporally aligning the community structure spatial distribution time sequences of all monitoring units; calculating fused species diversity, biomass and functional group indexes, and quantifying spatio-temporal heterogeneity of the community structure based on influences of interspecific competition and predation relationship; constructing an ecosystem multi-dimensional state time sequence of the target sea area including spatial, temporal and ecological dimensions.

2. The method of claim 1, wherein, determining an adaptive grid topology and a fish species migration response coefficient of each monitoring unit in the target sea area according to the water dynamic environment data and the fish historical ecological baseline data specifically comprises: analyzing flow velocity, temperature and salinity distribution in the water dynamic environment data to divide the target sea area into multiple monitoring units; calculating fish habitat density and migration path sensitivity of each monitoring unit in combination with the fish historical ecological baseline data; generating an adaptive grid topology and determining a fish species migration response coefficient of fish species to an environmental gradient based on the habitat density and the migration path sensitivity.

3. The method of claim 1, wherein, identifying a fish resource aggregation core area of each monitoring unit based on the water dynamic environment data, the fish species migration response coefficient and the fish historical ecological baseline data specifically comprises: spatio-temporally interpolating the water dynamic environment data to obtain a dynamic environment field of each monitoring unit; simulating diffusion and aggregation behaviors of fish species in the dynamic environment field based on the fish species migration response coefficient; identifying, as a fish resource aggregation core area, an area with an aggregation density exceeding a preset threshold through the fish historical ecological baseline data.

4. The method of claim 1, wherein, constructing a fish multi-species coupled dynamics model in combination with the fish resource aggregation core area, the fish historical ecological baseline data and a water volume of each monitoring unit specifically comprises: extracting species composition and abundance characteristics of the fish resource aggregation core area, and combining the fish historical ecological baseline data to estimate the parameters of interspecific competition and predation relationship; defining the initial state and interaction term in the model based on the species composition and abundance characteristics and the parameters of interspecific competition and predation relationship; constructing a multispecies coupled dynamics model containing diffusion, growth, interaction and volume constraints according to the water volume of each monitoring unit.

5. The method of claim 4, wherein, According to the adaptive grid topology, the fish multispecies coupled dynamics model and the preset continuous observation period, the community structure spatial distribution time sequence of each monitoring unit specifically includes: initializing the fish community state on the adaptive grid topology using the initial state and interaction term in the fish multispecies coupled dynamics model; simulate the community dynamic evolution in each observation period through the fish multispecies coupled dynamics model, which includes species composition change and abundance adjustment based on the parameters of interspecific competition and predation relationship; generate the community structure spatial distribution time sequence data of each monitoring unit in the continuous observation period.

6. The method of claim 1, wherein, Based on the ecosystem multi-dimensional state time sequence and the preset uncertainty quantification algorithm, generate the species diversity thermal field time sequence, and output the sampling station layout scheme which preferentially covers the high heterogeneity area of fish community specifically includes: using the uncertainty quantification algorithm, monte carlo simulation is performed on the ecosystem multi-dimensional state time sequence to generate the species diversity thermal field time sequence; identify the spatiotemporal distribution of high heterogeneity area in the thermal field time sequence, and preferentially consider the area with significant spatiotemporal heterogeneity of community structure; based on the high heterogeneity area, optimize and output the sampling station layout scheme to ensure that the high heterogeneity area is preferentially covered.

7. A simulation system for fish community survey sampling, which is characterized by using the method according to any one of claims 1 to 6 for simulating fish community survey sampling. The system comprises: an acquisition module for acquiring water dynamic environment data of a target sea area and fish historical ecological baseline data of a preset section; a processing module for determining the adaptive grid topology and fish species migration response coefficient of each monitoring unit in the target sea area according to the water dynamic environment data and the fish historical ecological baseline data; the processing module further identifies the fish resource aggregation core area of each monitoring unit based on the water dynamic environment data, the fish species migration response coefficient and the fish historical ecological baseline data; the processing module further constructs a fish multispecies coupled dynamics model in combination with the fish resource aggregation core area, the fish historical ecological baseline data and the water volume of each monitoring unit; the processing module further generates the community structure spatial distribution time sequence of each monitoring unit according to the adaptive grid topology, the fish multispecies coupled dynamics model and the preset continuous observation period; the processing module further constructs the ecosystem multi-dimensional state time sequence of the target sea area by fusing the community structure spatial distribution time sequence of all monitoring units; an execution module for generating the species diversity thermal field time sequence based on the ecosystem multi-dimensional state time sequence and the preset uncertainty quantification algorithm, and outputting the sampling station layout scheme which preferentially covers the high heterogeneity area of fish community.

8. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a code, and the processor is configured to acquire the code and execute the simulation method for fish community survey sampling according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the simulation method for fish community survey sampling according to any one of claims 1 to 6.

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