Regulation and control simulation method of species population ecosystem

Abstract

The invention provides a regulation and control simulation method for a population ecosystem, and the method comprises the steps: obtaining historical data of the population ecosystem, and constructing an initial space population model based on the historical data; calculating a Sobol index of each variable in the initial space population model, and screening out a key regulation variable from the initial space population model based on the Sobol index of each variable; and obtaining observation data of the population ecosystem, dynamically adjusting the key regulation and control variables based on the observation data to obtain a target space population model, and realizing simulation of the population ecosystem. By adopting the method, accurate regulation and control and simulation of the population ecosystem are realized, and the accuracy of regulation and control simulation of the population ecosystem is improved.

Description

Technical Field

[0001] The present invention relates to the field of data-driven ecological regulation, and in particular to a regulation simulation method for a population ecosystem. Background Art

[0002] Population ecosystem simulation involves constructing mathematical models to quantify interactions between populations (such as predator-prey relationships), spatial density distribution, and dynamic evolution. The core function of population ecosystem simulation is to provide a quantitative basis for decision-making in ecological conservation and species management. Therefore, population ecosystem simulation is essential for ecological conservation and management.

[0003] Existing population ecosystem regulation simulation technologies are typically based on static parameter models (such as the classic Lotka-Volterra equation) and a single data source (such as population density statistics). However, studies have found that static parameter models are unable to respond in real time to dynamic factors such as environmental resource fluctuations and changes in the intensity of interactions between species. For example, they are unable to quantify the inhibitory effect of prey refugia formed by the concealment of their habitats on predation efficiency, or the reduction in predation success rates of predators due to the defensive behavior of prey groups. As a result, the model's long-term predictions deviate from the actual ecological trajectory due to the fixed parameters, making it impossible to achieve accurate regulation simulation of population ecosystems, reducing the accuracy of population ecosystem regulation simulations. The limitations of a single data source make it impossible for the model to integrate multi-dimensional observational information. It relies solely on population density statistics, resulting in a single model input dimension, which in turn reduces the accuracy of population ecosystem regulation simulations. Summary of the Invention

[0004] In view of this, the object of the present invention is to provide a method for regulating and simulating a population ecosystem, so as to achieve accurate regulation and simulation of the population ecosystem and improve the accuracy of the regulation and simulation of the population ecosystem.

[0005] The present invention provides a method for simulating the regulation of a population ecosystem, the method comprising:

[0006] Acquiring historical data of a population ecosystem, and constructing an initial spatial population model based on the historical data;

[0007] Calculating the Sobol index of each variable in the initial spatial population model, and screening key regulatory variables from the initial spatial population model based on the Sobol index of each variable;

[0008] Observation data of the population ecosystem is obtained, and the key regulatory variables are dynamically adjusted based on the observation data to obtain a target space population model, thereby achieving simulation of the population ecosystem.

[0009] Optionally, the historical data includes spatial density distribution of prey / predators and population density of prey / predators, and constructing an initial spatial population model based on the historical data includes:

[0010] constructing an initial prey spatial density distribution equation and an initial predator spatial density distribution equation according to the historical data;

[0011] Determining a target prey space density distribution equation based on the prey's half-saturation coefficient and the initial prey space density distribution equation;

[0012] Determining a target predator spatial density distribution equation based on the prey's anti-predator rate and the initial predator spatial density distribution equation;

[0013] The initial spatial population model is constructed based on the target prey spatial density distribution equation and the target predator spatial density distribution equation.

[0014] Optionally, calculating the Sobol index of each variable in the initial spatial population model includes:

[0015] Defining an output target, wherein the output target is system stability or outbreak risk;

[0016] Determining the total variance of the initial spatial population model based on the output target;

[0017] The total variance is decomposed to obtain the Sobol index of each variable, wherein the Sobol index of each variable represents the independent contribution of the variable to the output target.

[0018] Optionally, the key regulatory variables are screened from the initial spatial population model based on the Sobol index of each variable, including:

[0019] According to the Sobol index of each variable, several optimal control variables are selected;

[0020] The total Sobol effect index is determined based on the Sobol index of each preferred control variable;

[0021] When the total Sobol effect index meets a preset threshold, each preferred control variable is determined as a key control variable.

[0022] Optionally, dynamically adjusting the key control parameters based on the observation data to obtain a target space population model includes:

[0023] Construct parameter sets based on each key regulatory variable;

[0024] Obtaining predicted values ​​of prey and predator density distributions by solving the initial spatial population model according to the parameter set;

[0025] Calculating the matching probability between the observed data and the predicted value through an error model to form a local likelihood function;

[0026] According to the local likelihood function, the parameter set is updated in combination with the Hamiltonian Monte Carlo HMC sampling method;

[0027] The key control parameters are adjusted according to the updated parameter set and the dynamic adjustment target.

[0028] Optionally, the method further includes:

[0029] After each acquisition of observation data of the population ecosystem, a step of dynamically adjusting the key control variables based on the observation data to obtain a target spatial population model is performed;

[0030] The target spatial population model obtained by the dynamic adjustment is used to simulate the population ecosystem to obtain the simulation result;

[0031] The simulation result is compared with the observation data obtained next time, and the accuracy of the simulation result is verified based on the comparison result.

[0032] Optionally, the method further includes:

[0033] Predicting the population distribution state of the population ecosystem based on the target spatial population model;

[0034] The management strategy of the population ecosystem is dynamically adjusted according to the prediction result of the population distribution status.

[0035] The technical solutions provided by this application include but are not limited to the following beneficial effects:

[0036] This application first obtains historical data of the population ecosystem and constructs an initial spatial population model based on the historical data. According to information such as past population distribution and quantity, a preliminary virtual ecosystem model can be built to enable the model to simulate the survival, predation and diffusion processes of prey and predators, taking into account that they may have hiding places or counterattack behaviors, so that the simulation is closer to the real situation.

[0037] Then, the Sobol index of each variable in the initial spatial population model is calculated, and the key regulatory variables are screened out from the initial spatial population model based on the Sobol index of each variable. By calculating the importance of each influencing factor, the key factors that have the greatest impact on ecosystem stability or risk can be found, such as which factors will directly lead to fluctuations in prey populations or predator outbreaks. In this way, subsequent adjustments can focus on these key factors to avoid blind adjustments.

[0038] Finally, the observation data of the population ecosystem is obtained, and the key regulatory variables are dynamically adjusted based on the observation data to obtain the target spatial population model, thereby realizing the simulation of the population ecosystem. The key factors in the model can be adjusted in real time using the latest observation data to make the model more in line with the current actual ecological conditions, thereby more accurately simulating the changes in the ecosystem and providing a more reliable reference for protection and management.

[0039] Using the above method, the initial spatial population model is constructed by obtaining historical data, the Sobol index is calculated to screen key regulatory variables, and then the variables are dynamically adjusted using observation data to obtain the target model, thereby achieving accurate and efficient regulation and simulation of the population ecosystem and improving the accuracy of population ecosystem regulation simulation.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flow chart of a population ecosystem regulation simulation method provided by the first embodiment of the present invention is shown;

[0043] Figure 2 A flowchart of a method for constructing an initial spatial population model provided by the first embodiment of the present invention is shown;

[0044] Figure 3 A flow chart showing a method for determining the Sobol index of a variable provided in the first embodiment of the present invention is shown;

[0045] Figure 4 A flowchart of a method for determining key control variables provided by the first embodiment of the present invention is shown;

[0046] Figure 5 A flow chart of a method for determining a target space population model provided by the first embodiment of the present invention is shown;

[0047] Figure 6 A flowchart of a simulation result verification method provided by the first embodiment of the present invention is shown;

[0048] Figure 7A flow chart of an ecosystem management strategy adjustment method provided by the first embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0050] Example 1

[0051] To facilitate understanding of this application, Figure 1 The flowchart of the method for simulating the regulation of a population ecosystem provided by the first embodiment of the present invention is shown to provide a detailed description of the first embodiment of the present application.

[0052] See also Figure 1 As shown, Figure 1 A flow chart of a method for simulating the regulation of a population ecosystem provided by the first embodiment of the present invention is shown, wherein the method comprises steps S101 to S104:

[0053] S101: Acquire historical data of a population ecosystem, and construct an initial spatial population model based on the historical data.

[0054] Specifically, the team first obtains raw data on various populations within a designated spatial ecosystem, including population density, environmental resources, living habits, food chain relationships, and topographic maps. Key indicators such as birth rate, mortality rate, competitive intensity, and predation efficiency are then identified. Based on these data and indicators, an initial spatial population model is constructed. This model uses reaction-diffusion equations to describe the spatial density distribution and dynamics of prey and predators, while also accounting for the effects of resource limitations and refuge mechanisms. The prey equation introduces an additive term to reflect the impact of limited resources on their survival and reproduction (strong or weak effects), and uses a Type II functional response function and refuge coefficient to reflect the protection of prey populations by refuges. The predator equation uses a bilinear form to account for the effect of prey anti-predation behavior on predator density, ultimately forming an initial model that incorporates the interactions between biological processes, resource limitations, and refuge mechanisms.

[0055] S102: Calculating the Sobol index of each variable in the initial spatial population model, and screening out key regulatory variables from the initial spatial population model based on the Sobol index of each variable.

[0056] Specifically, taking system stability or outbreak risk as the output target, define the model function Y = f(X), where X is the input variable, and calculate the Sobol index of each variable through variance decomposition. Among them, the first-order index measures the independent contribution of a single variable to the output, and the total effect index measures the combined contribution of the variable and its interaction terms with other variables. Specifically, the first-order index is calculated by fixing the mean of other variables, and the total effect index is calculated by conditional variance. First, the parameters with greater influence are selected as the preferred control variables based on the first-order index, and then the total effect index is calculated for the preferred variables. When the total effect index is greater than the preset threshold, it is determined as a key control factor and used as the key parameter for subsequent dynamic adjustment.

[0057] S103: Obtain observation data of the population ecosystem, dynamically adjust the key control variables based on the observation data to obtain a target space population model, and simulate the population ecosystem.

[0058] Specifically, observation data are collected through a variety of means: using drones equipped with multispectral cameras to obtain high-resolution images, combining spectral characteristics and terrain data to identify activity hotspots of prey and predators, deploying cameras to count the frequency of species occurrence and generate density heat maps; marking adult individuals with collars or biorecorders to record their activity range, shelters and other behavioral data, and using readers to calculate the survival rate and shelter rate of juveniles; determining food composition through fecal sequencing and stomach content analysis, and calculating the habitat utilization index based on trajectory data.

[0059] After integrating multi-source data and cleaning and standardizing them, Bayesian inference is used to dynamically adjust key parameters: first, a normal distribution prior distribution is set for the key parameters, and a predicted value is generated based on the current parameter solution model. Assuming that the observation error obeys a Gaussian distribution or a Poisson distribution, a dynamic likelihood function is constructed; by sequentially fusing real-time data, the posterior distribution at the previous moment is used as the current prior, and the posterior distribution is updated in combination with the likelihood function (Hamiltonian Monte Carlo sampling is used to improve computational efficiency); finally, the optimal parameter set is generated based on the statistical characteristics of the posterior distribution, such as the mean and confidence interval, and injected into the model to form a target space population model, thereby realizing a dynamic simulation of the population ecosystem.

[0060] In an alternative embodiment, see Figure 2 As shown, Figure 2A flowchart of a method for constructing an initial spatial population model provided by a first embodiment of the present invention is shown, wherein the historical data includes the spatial density distribution of prey / predators and the population density of prey / predators. Constructing the initial spatial population model based on the historical data includes steps S201 to S204:

[0061] S201: constructing an initial prey spatial density distribution equation and an initial predator spatial density distribution equation according to the historical data.

[0062] Specifically, using information such as the spatial density distribution of prey and predators and population density in historical data, we construct the initial prey spatial density distribution equation and the initial predator spatial density distribution equation to describe basic biological processes. Among them, the initial prey spatial density distribution equation describes the natural growth of prey, the loss of population due to predation by predators, and spatial diffusion, taking into account the intrinsic growth rate of prey, the intensity of intraspecific competition (environmental carrying capacity), the predation rate and diffusion capacity of predators; the initial predator spatial density distribution equation describes the growth, natural death and spatial diffusion of predators depending on prey, involving predation conversion efficiency, natural mortality rate and diffusion coefficient. These two equations preliminarily simulate the predator-prey relationship and the spatial distribution of populations through reaction-diffusion form.

[0063] More specifically, we first obtain historical data on each population of the ecosystem within a specified spatial range, including population density, environmental resources, living habits, and food chain relationships and topographic maps between species, and identify key indicators, including population birth rate, mortality rate, competition intensity, and predation efficiency. Then, based on the collected data and historical data of the population, we construct a basic spatial population model to describe the biological processes between populations and the interaction and density distribution of resource limitations and refuge mechanisms. The biological processes of the spatial population model focus on the dynamic changes of prey and predators in the population ecosystem, and describe the spatial density distribution of the population through the reaction-diffusion equation, specifically including:

[0064] Initial prey spatial density distribution equation:

[0065]

[0066] Among them, x(x,y) represents the spatial density distribution of prey, x represents the density of prey population, y is the density of predator population, a represents the intrinsic growth rate of prey, b1 represents the competition coefficient within the prey population, c represents the predator predation rate, d1 represents the diffusion coefficient of prey, and Laplace operator It describes the diffusion behavior of prey population density in space, Ω represents the area where the ecosystem population is located, and t represents time.

[0067] Initial predator spatial density distribution equation:

[0068]

[0069] Among them, y(x, y) represents the spatial density distribution of predators, x represents the density of the prey population, y is the density of the predator population, d represents the natural mortality rate of predators, b2 represents the competition coefficient within the predator population, β represents the predation conversion efficiency, d2 represents the diffusion coefficient of predators, c represents the predation rate of predators, and the Laplace operator describes the diffusion behavior of the predator population density in space, Ω represents the area where the ecosystem population is located, and t represents time.

[0070] S202: Determine the target prey spatial density distribution equation according to the half-saturation coefficient of the prey and the initial prey spatial density distribution equation.

[0071] Specifically, to reflect the inhibition of the prey refuge mechanism (such as habitat shelter) on the predation process, the half-saturation coefficient of the prey is introduced into the initial prey spatial density distribution equation, and the predation efficiency is adjusted through a non-linear function. The half-saturation coefficient reflects the characteristic that it is difficult for the prey to be predated due to the protection of the refuge at low density - the larger this coefficient, the stronger the protection effect of the prey at low population density. The modified equation more realistically simulates the ecological strategy of the prey using the refuge to maintain the population size by depicting the phenomenon that "the predation efficiency decreases significantly when the prey density is low".

[0072] More specifically, consider the impact of resource limitation on biological processes: when environmental resources are limited, the prey population is often more affected. When the population density is low, the individual survival and reproduction success rate shows a downward trend. The impact of resource limitation on the prey in biological processes can be considered by introducing the Allee effect, and the prey spatial density distribution equation considering the impact of resource limitation on biological processes is obtained:

[0073]

[0074] Among them, represents the additive Allee term. When ah < n, it shows a strong Allee effect. When 0 < n < ah, it shows a weak Allee effect. h represents the half-saturation constant, and n is the maximum Allee effect intensity.

[0075] Consider the impact of refuge mechanisms on biological processes: In complex ecosystems, not all prey can be caught by predators because prey typically possess a variety of refuge mechanisms that provide them with varying degrees of protection. For example, almost all prey populations have habitats or refuges that effectively protect them and help maintain high biomass levels within the ecosystem. In addition to using refuges to escape predators, prey also exhibit anti-predator behavior, which is a form of self-defense for species that primarily acts against predators.

[0076] The spatial density distribution equation of prey is considered to take into account the effect of refuge: refuge has a direct impact on the maintenance of prey population, by introducing the Holling-II type functional response function Considering the impact of the refuge on the prey spatial density distribution, the target prey spatial density distribution equation considering the impact of the refuge is obtained:

[0077]

[0078] Among them, m represents the evacuation coefficient, represents the Holling-II functional response function, and b represents the half-saturation coefficient of the prey.

[0079] S203: Determine a target predator spatial density distribution equation based on the prey's anti-predator rate and the initial predator spatial density distribution equation.

[0080] Specifically, to address the inhibitory effects of prey defenses (such as group counterattacks) on predators, the initial predator equation incorporates the prey's counterpredation rate, quantifying the intensity of prey counterpredation behavior through a bilinear term. The counterpredation rate represents the negative feedback effect on predators per unit prey density (such as increased predator mortality or decreased reproduction). This extends the model from a "one-way predation" model to a "two-way interaction between prey and predator," more accurately describing the complex dynamic relationships between species in an ecosystem.

[0081] More specifically, the predator's spatial density distribution equation takes into account the impact of prey anti-predation behavior: the prey's anti-predation behavior is mainly considered in a bilinear form based on the state variables x (prey) and y (predator) to obtain the target predator spatial density distribution equation:

[0082]

[0083] Where η represents the prey's counter-predation rate on the predator.

[0084] S204: Constructing the initial spatial population model based on the target prey spatial density distribution equation and the target predator spatial density distribution equation.

[0085] Specifically, the corrected prey and predator equations are联立 to form an initial spatial population model that integrates biological processes (predation, competition, diffusion) and ecological mechanisms (refuge protection, anti-predation behavior). This model calibrates key parameters (such as growth rate, predation rate, refuge coefficient, etc.) through historical data, and can simulate the density distribution and dynamic interactions of prey and predators in space, providing a basic framework for subsequent parameter sensitivity analysis (screening key regulatory variables) and Bayesian inference dynamic calibration, and achieving a preliminary quantitative simulation of the predation-prey process and stability in the population ecosystem. The initial spatial population model is as follows:

[0086]

[0087] where x(x,y) represents the spatial density distribution of prey, y(x,y) represents the spatial density distribution of predators, a represents the intrinsic growth rate of prey, b1 and b2 respectively represent the competition coefficients within the prey and predator populations, c represents the predation rate of predators, b represents the half-saturation coefficient of prey, and the additive Allee term is: When ah < n, it shows a strong Allee effect, and when 0 < n < ah, it shows a weak Allee effect. m represents the refuge coefficient, d represents the natural mortality rate of predators, β represents the predation conversion efficiency, η represents the anti-predation rate of prey against predators, d1 represents the diffusion coefficient of prey, d2 represents the diffusion coefficient of predators, and the Laplace operator describes the diffusion behavior of population density in space, and Ω represents the region where the ecosystem population is located.

[0088] In an optional embodiment, refer to Figure 3 as shown Figure 3 shows a flowchart of a method for determining the Sobol index of a variable provided in Embodiment 1 of the present invention. Among them, calculating the Sobol index of each variable in the initial spatial population model includes steps S301 to S303:

[0089] S301: Define the output objective, where the output objective is system stability or outbreak risk.

[0090] Specifically, first clarify the evaluation direction of the model, and set the stability of the ecosystem (such as the fluctuation range of population density, species coexistence state) or outbreak risk (such as the possibility of prey being on the verge of extinction due to over-reproduction of predators) as the output objective of quantitative analysis. This objective needs to be reflected by specific indicators output by the model, such as the spatio-temporal distribution uniformity of prey and predator densities, the fluctuation threshold of population growth rate, etc. By defining the output objective, a clear analysis dimension is provided for subsequent parameter impact assessment, ensuring that the sensitivity analysis focuses on the core needs of ecosystem management (such as preventing species outbreaks or maintaining community stability).

[0091] More specifically, we first define the model and input variables: Assume there is a function f(x1, x2, ... x d ), where x1, x2, … x d represents the input variable, f is the output target, and d is the number of input variables. Here, system stability or outbreak risk is used as the output target. The Sobol index evaluates each input variable x i Or the effect of their combination on f. Then construct the Sobol index: the first-order Sobol index S i Represents the input variable x i The total Sobol effect index STi represents the independent contribution to the output of the input variable x i and the contribution of all its interaction terms to the output;

[0092] S302: Determine the total variance of the initial spatial population model based on the output target.

[0093] Specifically, after defining the output target, the total variance of the output results of the initial spatial population model is calculated. The total variance reflects the overall uncertainty of the model output (such as the system stability index or the outbreak risk value), covering the combined impact of all input parameters and their interactions on the output. For example, if the output target is a "predator outbreak risk index", the total variance can be calculated by the fluctuation range of this index in multiple simulations. This step provides a benchmark for the subsequent variance decomposition, and the total variance can be used to quantify the contribution of each parameter to the output result.

[0094] More specifically, the Sobol index is calculated by variance decomposition:

[0095] Assume that the output of the model is Y = f(x1, x2, ... x d ), its total variance Var(Y) can be expressed as:

[0096] Var(Y)=Var(f(x1,x2,…x d ))=E[(f(x1,x2,…x d )-E[f]) 2 ];

[0097] Among them, E represents the expectation operator, E[f] represents the function f(x1,x2,…x d ) expectations.

[0098] S303: Decomposing the total variance to obtain a Sobol index of each variable, wherein the Sobol index of each variable represents the independent contribution of the variable to the output target.

[0099] Specifically, the total variance is distributed to each input parameter through the variance decomposition technique to obtain the first-order Sobol index of each variable, which measures the independent contribution of a single parameter to the output target (that is, the influence of the interaction between the parameter and other parameters is not considered). Specifically, by fixing the mean of other parameters, only the target parameter is allowed to vary within its value range, and the proportion of the variance of the output result to the total variance at this time is calculated, which is the first-order Sobol index of the parameter. The larger the index value, the stronger the independent impact of the parameter on the stability of the system or the risk of outbreak. For example, if the Sobol index of the predation rate is 0.4, it means that 40% of the variation in the system output can be explained by changes in the predation rate alone. This step helps identify the key parameters that play a dominant role in the dynamics of the ecosystem and provides a targeted basis for subsequent regulatory strategies.

[0100] More specifically, the calculated total variance is decomposed to obtain the Sobal index of each input variable, where the first-order Sobal index S i It is calculated by fixing the means of other variables: Total Sobol effect index Among them, Var(ff i ) means that x is removed from the total variance i The residual variance after individual contributions; Var i (f) means that when other input variables are fixed at their mean values, only the i-th variable x i The output variance caused by Var(f) represents the total variance of the model output; f i Is fixed x i Output in case of .

[0101] In an alternative embodiment, see Figure 4 As shown, Figure 4 A flowchart of a method for determining key regulatory variables provided by the first embodiment of the present invention is shown, wherein the key regulatory variables are screened from the initial spatial population model based on the Sobol index of each variable, including steps S401 to S403:

[0102] S401: Select several preferred control variables based on the Sobol index of each variable.

[0103] Specifically, the first-order Sobol index of all input variables in the initial spatial population model is calculated. This index measures the independent contribution of a single variable to system stability or outbreak risk. By comparing the first-order index values ​​of each variable, variables with larger index values ​​are selected as preferred control variables. For example, if the first-order exponents of predation rate, refuge coefficient, and prey intrinsic growth rate rank high (such as all greater than 0.2), they are included in the preferred variable set as candidate parameters for further analysis. This step quantifies the influence of a single parameter, eliminates redundant variables that contribute little to the system output, and narrows the range of control parameters.

[0104] S402: Determine a total Sobol effect index based on the Sobol index of each preferred control variable.

[0105] Specifically, for the selected preferred control variables, their total Sobol effect index is further calculated. The total effect index not only includes the independent contribution of the variable, but also covers the interactive effects of the variable with all other variables. For example, the predation rate may have a synergistic effect with the refuge coefficient - when the refuge coefficient is high, the impact of changes in the predation rate on system stability may be amplified or weakened. Through the variance decomposition method, the comprehensive effect of each preferred variable after considering all interaction terms is calculated to obtain the total effect index, thereby comprehensively evaluating the complex impact of the parameters on the system.

[0106] S403: When the total Sobol effect index meets a preset threshold, each preferred control variable is determined as a key control variable.

[0107] Specifically, a preset threshold (such as 0.1) is set to determine whether the preferred variable is a key regulatory factor. If the total effect index of a preferred variable exceeds the threshold, it is determined that the variable has a dominant influence on the stability of the system or the risk of outbreak, and it is identified as a key regulatory variable. For example, if the total effect index of the predation rate is 0.3 (greater than the threshold of 0.1), it means that its independent effect and interaction with other variables jointly explain 30% of the variation in the system output, and it must be used as the core parameter for subsequent dynamic regulation. Conversely, variables that do not reach the threshold are regarded as secondary parameters and are not included in the key regulation range for the time being. This process ensures that the regulatory strategy focuses on the key factors that play a decisive role in the dynamics of the ecosystem by quantifying the comprehensive impact of the parameters.

[0108] In an alternative embodiment, see Figure 5 As shown, Figure 5 A flow chart of a method for determining a target space population model provided by the first embodiment of the present invention is shown, wherein the target space population model is obtained by dynamically adjusting the key control parameters based on the observation data, including steps S501 to S505:

[0109] S501: Construct a parameter set based on each key control variable.

[0110] Specifically, the parameters that have a significant impact on system stability or outbreak risk (such as predation rate, refuge coefficient, prey anti-predation rate, etc.) are extracted from the results of the global parameter sensitivity analysis to form a set of key control parameters to be adjusted. The parameters that show a greater impact after the sensitivity analysis are used to construct the parameter set θ = {θ1, θ2, ...θ n}, and choose the normal distribution θ i ~N(μ,σ 2 ) as the prior distribution, where μ represents the mean, which is determined based on historical data or preliminary model calibration results; θ i represents the i-th parameter to be estimated; N represents the normal distribution, n is the number of parameters; σ 2 Represents the variance, reflecting the uncertainty of the initial parameters. At the same time, based on the ecological significance and the range of observation data, constraints m1∈(0,1) are imposed on the parameters (such as non-negative predation rate and refuge coefficient between 0 and 1) to ensure that the parameter set is within a reasonable biological range.

[0111] S502: According to the parameter set, the predicted values ​​of prey and predator density distribution are obtained by solving the initial spatial population model.

[0112] Specifically, the parameters in the current parameter set are substituted into the initial spatial population model (i.e., the target prey / predator equation including the refuge mechanism and anti-predation behavior), and the partial differential equation is solved by numerical methods (such as the finite difference method) to generate the predicted density distribution value x of prey and predator in space. model (t,Ω,θ),y model (t,Ω,θ), θ represents the parameter to be estimated, t is time, Ω is the sample space, x model ,y model Represents the model's predicted values ​​for prey and predators, respectively. For example, based on the current predation rate and refuge coefficient, this can simulate the density distribution of prey in a habitat hotspot at the next moment, or predict the population spread of predators due to anti-predation behavior. These predicted values ​​serve as the model's theoretical estimate of the ecosystem state and are used for comparison with actual observational data.

[0113] S503: Calculate the matching probability between the observed data and the predicted value through the error model to form a local likelihood function.

[0114] Specifically, according to the type of observation data, a suitable error model is selected to quantify the difference between the predicted value and the measured data: Continuous data (such as population density heat maps monitored by drones): Assume that the error follows a Gaussian distribution, the mean is the predicted value, and the variance reflects environmental noise or measurement error. Discrete data (such as the number of individuals counted by the mark-recapture method): Use Poisson distribution or negative binomial distribution to characterize the counting error. The conditional probability of the observation data under the current parameters is calculated through the above distribution to form a local likelihood function. For example, for single-time-step observation data, the likelihood function is the joint probability density of the observation errors at each spatial point; for multi-time series data, the likelihood value at each moment is multiplied to capture time dependence.

[0115] More specifically, it is assumed that the deviations between the observed data and the model predictions follow a specific probability distribution—for example, for continuous density data, a Gaussian distribution is often used to characterize the error, with a mean equal to the model prediction and a variance reflecting environmental noise or measurement uncertainty: t =N~(x model (t,θ),σ 2 ), where D t is the error at time t, D t Obeying normal distribution N,x model (t,θ) is the predicted value of the density distribution of prey in space, representing the mean of the normal distribution, σ 2 is the variance, t is the time, and for discrete count data (such as the number of individuals in the mark-recapture method), the Poisson distribution or negative binomial distribution may be selected. The dynamic likelihood function needs to further integrate the time series characteristics: for a single time step, the likelihood function can be defined as the joint probability density of the observation errors at each spatial point, and the independent and identically distributed assumption is used to simplify the calculation, that is: In the multi-time sequential scenario, it is necessary to accumulate the likelihood values ​​at each moment, or introduce an autoregressive structure to capture the time dependency, that is, Among them, x obs,i represents the i-th observation data, L represents the likelihood function, x model,i represents the predicted value of the i-th model, x obs,i -x model,i represents the difference between the i-th observed data and the model predicted value, represents the i-th variance, T represents the total time, n represents the number of parameters, D 1：T Represents the error of the parameter θ at the previous T time points.

[0116] S504: updating the parameter set according to the local likelihood function in combination with Hamiltonian Monte Carlo (HMC) sampling method.

[0117] Specifically, the prior distribution is combined with the likelihood function using Bayes' theorem, and Hamiltonian Monte Carlo (HMC) sampling is used to efficiently explore the parameter space and update the posterior distribution of the parameters. First, the initial state is determined: the posterior distribution at the previous moment is used as the current prior. New parameters are then proposed: a candidate parameter set is generated through HMC sampling, and Hamiltonian dynamics is used to avoid the inefficiency of random walks and improve sampling efficiency. Next, an acceptance-rejection decision is made: the acceptance rate of the candidate parameters is calculated, and the new parameter set is accepted with probability, otherwise the original parameters are retained, gradually approaching the high-probability region of the posterior distribution. Through this process, real-time observation data is sequentially incorporated into parameter inference, gradually correcting for parameter uncertainty.

[0118] More specifically, when the system obtains observation data of a new time window (such as prey density observation data x obs (t) and the observed predator density data y obs (t), first assume θ based on the current parameters t-1 Generate model prediction value x model (t;θ t-1 ), then the matching probability between the observed data and the predicted value is calculated through the error model (Gaussian likelihood), forming the local likelihood function L(θ) at that moment (time t) t |D t ). Then, Bayes’ theorem is used to combine this likelihood with the posterior probability P(θ t-1 |D 1:t-1 ) and normalized, θ t-1 represents the parameter at time t-1, D 1:t-1 Represents the error of the parameter at the previous t-1 moment, and obtains the updated posterior distribution probability P(θ t |D 1:t )=L(θ t |D t )·P(θ t |D 1:t-1 ). Then, combined with Hamiltonian Monte Carlo (HMC) sampling or variational inference, a new parameter set θ~q(θ|θ t-1 ), calculate the acceptance rate of the new parameters Accept the parameter set θ with probability α, otherwise keep θ t-1 , where q(θ t-1 |θ) represents the parameter value at time t, q(θ|θ t-1 ) represents the parameter value at time t-1, L(θ) represents the prior probability under parameter θ, P(θ) represents the posterior probability under parameter θ, L(θ t-1 ) represents the parameter θ t-1 The prior probability under t-1 ) represents the parameter θ t-1 The posterior probability under .

[0119] S505: Adjust the key control parameters according to the updated parameter set and the dynamic adjustment target.

[0120] Specifically, based on the updated posterior distribution of parameters, the optimal control parameters are generated and strategies are formulated. First, statistical feature extraction is performed: the posterior mean (point estimate) or 95% confidence interval of the parameters is calculated (such as the mean of the refuge coefficient is 0.6, and the confidence interval is 0.4-0.8). Then risk-sensitive decisions are made: if stability is pursued, the posterior median is selected as the adjustment benchmark to reduce the impact of extreme values; if the risk of outbreak is avoided, the lower limit of the confidence interval is adopted (such as setting a conservative threshold of no more than 0.3 for predation rate). Then the model is injected and verified: the adjusted parameters are input into the initial model, the future population density distribution is predicted, and control instructions are generated (such as adjusting the scope of the protected area or the fishing quota). A closed loop is formed through real-time monitoring feedback (such as restarting calibration when the error exceeds 15%) to ensure that the model continues to adapt to the dynamic changes of the ecosystem.

[0121] More specifically, the statistical characteristics of key parameters are extracted from the updated posterior distribution. For example, the posterior mean of the protection rate m2 is calculated As a point estimate, or by determining its 95% confidence interval [θ t,0.025 ,θ t,0.975 ] to characterize the uncertainty range, with the 0.025 and 0.975 quantiles corresponding to the lower and upper bounds of the interval, respectively. Decision rules are designed to accommodate different risk preferences: if stability is prioritized, the posterior median is used as the benchmark for parameter adjustment to avoid interference from extreme values; if the risk of ecological collapse (e.g., predator outbreaks) needs to be mitigated, the lower bound of the confidence interval is chosen (e.g., setting a conservative threshold for the predation rate c). The adjusted parameters are input into a spatial population model to predict future prey and predator density distributions. These parameters are then used to generate actionable control instructions (e.g., the intensity of work to maintain ecosystem stability) or to dynamically adjust fishing quotas. During implementation, the effects of regulation are monitored in real time through a network of sensor devices. If the observed data deviates from the predictions by more than a preset tolerance (e.g., population density error >15%), a feedback loop is triggered—recalibrating model parameters, optimizing prior distributions, or adjusting the weighting of decision rules (e.g., prioritizing ecological protection objectives). This process forms a closed loop of "evaluation-correction-revalidation," ensuring continuous model adaptation in a dynamic environment, ultimately achieving stable and efficient ecosystem management.

[0122] Before executing step S501, the observation data of the population ecosystem is obtained, including the following methods:

[0123] Drone and multispectral imaging collection: Use drones equipped with multispectral cameras to obtain high-resolution images, combine spectral characteristics to identify prey and predators in vegetation cover, and then identify animal activity hotspots based on terrain data; deploy cameras within the activity range of hotspots, count the frequency of species occurrence per unit time, and combine spatial interpolation to generate density heat maps.

[0124] Individual tagging and tracking collection: Use GPS collars or biorecorders to tag adult individuals and record their activity range, shelters, reproductive behavior, and death signals; deploy RFID readers at nests or fixed stations to count the survival rate and shelter rate of juveniles.

[0125] Resource utilization overlap analysis: Fecal DNA sequencing and stomach content analysis were performed based on species diets to determine the diet composition of prey and predators; GPS trajectory data were then used to calculate the habitat utilization index to quantify spatial competition.

[0126] In an alternative embodiment, see Figure 6 As shown, Figure 6 A flowchart of a simulation result verification method provided by the first embodiment of the present invention is shown, wherein the method further includes steps S601 to S603:

[0127] S601: After each acquisition of observation data of the population ecosystem, a step of dynamically adjusting the key control variables based on the observation data to obtain a target spatial population model is performed.

[0128] Specifically, whenever new observation data is obtained, such as the prey density distribution x monitored by drones obs (t) and predator density distribution y obs (t), a round of Bayesian sequential inference is initiated, dynamically calibrating model parameters using a sequential posterior update method. First, new data is integrated with historical data, and data uncertainty is quantified using an error model. Then, the posterior distribution of model parameters at the previous time step is used as the current prior, combined with the new data to construct a dynamic likelihood function. Hamiltonian Monte Carlo (HMC) sampling is used to update the parameter posterior distribution, resulting in a target spatial population model adapted to the latest observations. This process forms a real-time closed loop from "data acquisition to parameter calibration," ensuring that model parameters are continuously optimized as the ecosystem dynamically adapts.

[0129] S602: Utilizing the target spatial population model obtained through the dynamic adjustment, the population ecosystem is simulated to obtain the simulation result.

[0130] Specifically, the updated target space population model parameter θ t+1By substituting these parameters (such as calibrated predation rates and refuge coefficients) into the model and numerically solving the reaction-diffusion equation, the model simulates ecological indicators such as the spatial density distribution of prey and predators and population growth rates over the next time period. For example, based on the newly calibrated parameters, the model can predict the spread of predators within a habitat over the next month, or the density fluctuations of prey due to resource constraints. These simulation results reflect theoretical predictions based on the current state of the ecosystem, providing a forward-looking basis for management decisions.

[0131] S603: Compare the simulation result with the observation data obtained next time, and verify the accuracy of the simulation result based on the comparison result.

[0132] Specifically, the simulation results are compared with newly acquired independent observation data (such as the number of populations surveyed in the field and environmental parameters recorded by sensors), and the prediction accuracy of the model is evaluated by calculating error indicators (such as root mean square error and absolute percentage error). If the deviation is within the preset tolerance (such as the population density error ≤ 15% mentioned in the document), the model is considered valid; if the deviation exceeds the threshold, the feedback correction mechanism is triggered: recheck the data quality, adjust the prior distribution or optimize the decision rule weights, and execute the parameter calibration process again until the simulation results match the observed data. This process forms an adaptive cycle of "simulation-verification-correction" to ensure that the model continues to reliably reflect changes in population ecosystems in a dynamic environment.

[0133] In an alternative embodiment, see Figure 7 As shown, Figure 7 A flowchart of an ecosystem management strategy adjustment method provided by the first embodiment of the present invention is shown, wherein the method further includes steps S701 to S702:

[0134] S701: Predicting the population distribution status of the population ecosystem based on the target spatial population model.

[0135] Specifically, a dynamically calibrated target space population model (i.e., a model that updates parameters by integrating the latest observational data) is used to perform a forward-looking simulation of the future state of the population ecosystem. Specifically, by solving the reaction-diffusion equation that includes biological processes, resource limitations, and refuge mechanisms, key indicators such as the spatiotemporal density distribution of prey and predators, population growth rate, and species coexistence status are predicted. For example: predicting the density recovery trend of prey in a specific area due to refuge protection; simulating changes in the diffusion path of predators due to intensified anti-hunting behavior; and evaluating the impact of environmental resource fluctuations (such as vegetation reduction caused by drought) on the risk of population outbreaks. This prediction process is based on the posterior distribution of model parameters and can generate probabilistic results that include uncertainty (such as a 95% confidence interval for population density), providing a risk quantification basis for management decisions.

[0136] S702: Dynamically adjust the management strategy of the population ecosystem according to the prediction result of the population distribution status.

[0137] Specifically, based on the prediction results of S701 and combined with ecological protection goals (such as maintaining species diversity and preventing and controlling invasive species outbreaks), management strategies are dynamically optimized through the following processes: (1) Risk assessment: Compare the prediction results with ecological safety thresholds (such as the minimum survival density of prey and the upper limit of predator control) to identify high-risk areas or trends (such as the predator density in a certain area exceeds the threshold by 20%). (2) Strategy generation: If the population is predicted to be on the verge of collapse (such as the prey density is lower than the critical value), trigger protection measures (such as expanding the scope of refuges and artificially replenishing food resources); if the risk of predator outbreaks is predicted to be high, formulate intervention plans (such as adjusting fishing quotas and introducing natural enemies for regulation). (3) Parameter feedback: Convert management strategies into model parameter adjustment instructions (such as increasing the refuge coefficient to enhance prey protection), and re-inject them into the target model to simulate the strategy effect, forming a closed loop of "predation-decision-simulation verification". (4) Dynamic iteration: The effectiveness of strategy implementation is evaluated through real-time monitoring data. If the deviation between the actual population response and the prediction exceeds the preset tolerance (such as density error >15%), the parameter calibration and strategy optimization process is restarted to ensure the adaptability and effectiveness of management measures.

[0138] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0139] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. However, such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for simulating the regulation of a population ecosystem, characterized in that: The method comprises: Acquiring historical data of a population ecosystem, and constructing an initial spatial population model based on the historical data; Calculating the Sobol index of each variable in the initial spatial population model, and screening key regulatory variables from the initial spatial population model based on the Sobol index of each variable; Observation data of the population ecosystem is obtained, and the key regulatory variables are dynamically adjusted based on the observation data to obtain a target space population model, thereby achieving simulation of the population ecosystem.

2. The method according to claim 1, characterized in that The historical data includes spatial density distribution of prey / predators and population density of prey / predators. The initial spatial population model is constructed based on the historical data, including: constructing an initial prey spatial density distribution equation and an initial predator spatial density distribution equation according to the historical data; Determining a target prey space density distribution equation based on the prey's half-saturation coefficient and the initial prey space density distribution equation; Determining a target predator spatial density distribution equation based on the prey's anti-predator rate and the initial predator spatial density distribution equation; The initial spatial population model is constructed based on the target prey spatial density distribution equation and the target predator spatial density distribution equation.

3. The method according to claim 1, characterized in that The calculating of the Sobol index of each variable in the initial spatial population model includes: Defining an output target, wherein the output target is system stability or outbreak risk; Determining the total variance of the initial spatial population model based on the output target; The total variance is decomposed to obtain the Sobol index of each variable, wherein the Sobol index of each variable represents the independent contribution of the variable to the output target.

4. The method according to claim 1, wherein The Sobol index based on each variable is used to screen key regulatory variables from the initial spatial population model, including: According to the Sobol index of each variable, several optimal control variables are selected; The total Sobol effect index is determined based on the Sobol index of each preferred control variable; When the total Sobol effect index meets a preset threshold, each preferred control variable is determined as a key control variable.

5. The method according to claim 1, wherein The dynamically adjusting the key control parameters based on the observation data to obtain the target space population model includes: Construct parameter sets based on each key regulatory variable; Obtaining predicted values ​​of prey and predator density distributions by solving the initial spatial population model according to the parameter set; Calculating the matching probability between the observed data and the predicted value through an error model to form a local likelihood function; According to the local likelihood function, the parameter set is updated in combination with the Hamiltonian Monte Carlo HMC sampling method; The key control parameters are adjusted according to the updated parameter set and the dynamic adjustment target.

6. The method according to claim 1, characterized in that The method further comprises: After each acquisition of observation data of the population ecosystem, a step of dynamically adjusting the key control variables based on the observation data to obtain a target spatial population model is performed; The target spatial population model obtained by the dynamic adjustment is used to simulate the population ecosystem to obtain the simulation result; The simulation result is compared with the observation data obtained next time, and the accuracy of the simulation result is verified based on the comparison result.

7. The method according to claim 1, characterized in that The method further comprises: Predicting the population distribution state of the population ecosystem based on the target spatial population model; The management strategy of the population ecosystem is dynamically adjusted according to the prediction result of the population distribution status.

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