Method and system for delimiting wild animal buffer zone of habitat

Abstract

The invention provides a wild animal buffer zone delimiting method and system for a habitat, and relates to the technical field of ecological environment division, and the method comprises the steps: obtaining habitat information data; processing the habitat information data based on a quantum hybrid optimization algorithm and a preset multi-fractal coupling model, and generating a habitat suitability distribution mode; performing multi-scale landscape connectivity analysis according to the suitability distribution mode to obtain a connectivity topological graph of the habitat space; performing membrane calculation ecological network model construction and coupling on the connectivity topological graph to obtain an initial wildlife buffer zone delimiting scheme; generating an optimized buffer band delimiting scheme through a random programming and preset robust optimization hybrid strategy; and q-order fuzzy measure and Choquet integral are adopted to carry out collaborative evaluation, and a wild animal buffer zone intelligent delimiting scheme is obtained. According to the method, the complex trade-off relationship among multiple targets is objectively quantified, and the optimal balance of the scheme in all aspects is ensured.

Description

Technical Field

[0001] This invention relates to the field of ecological environment delineation technology, and more specifically, to a method and system for delineating wildlife buffer zones in habitats. Background Technology

[0002] In existing technologies, scientifically delineating ecological buffer zones is a key technical means to coordinate biodiversity conservation with human activity disturbance. Currently, the mainstream methods in this field rely on static environmental data provided by Geographic Information Systems (GIS) and remote sensing technologies for habitat suitability assessment. While these traditional technologies can achieve preliminary planning, they have significant limitations: First, most models are based on linear assumptions, making it difficult to depict the nonlinear and multi-scale characteristics that are prevalent in the ecological environment and the complex interactions between ecological elements. Secondly, existing methods lack robustness considerations for uncertainties such as climate change and human activities, resulting in insufficient adaptability of planned buffer zones in dynamic environments and easy failure of their functions. Third, the final decision-making process often relies on expert experience to assign weights or simply sum them up, making it difficult to objectively quantify the complex trade-offs and synergistic relationships among multiple benefit objectives, thus affecting the overall optimality and scientific validity of the plan.

[0003] Therefore, there is an urgent need for a method and system for delineating wildlife buffer zones in habitats to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for delineating wildlife buffer zones in habitats to improve the aforementioned problems. To achieve this purpose, the technical solution adopted by this invention is as follows: Firstly, this application provides a method for delineating wildlife buffer zones in habitats, including: Acquire habitat topography, vegetation, climate, species distribution, human activities, and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and construct habitat information data; The habitat information data is processed based on a quantum hybrid optimization algorithm and a preset multifractal coupling model to generate a suitable distribution pattern of habitats. Based on the suitability distribution pattern, topological data analysis and discrete exterior differential theory are used to perform multi-scale landscape connectivity analysis to obtain a connectivity topology map of habitat space. The connectivity topology map is used to construct and couple a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme. An optimized buffer zone delineation scheme is generated by processing the initial wildlife buffer zone delineation scheme through a hybrid strategy of stochastic programming and pre-defined robust optimization.

[0005] Secondly, this application also provides a wildlife buffer zone delineation system for habitats, comprising: The acquisition unit is used to acquire habitat topography, vegetation, climate, species distribution, human activities and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and to construct habitat information data; The processing unit is used to process the habitat information data based on the quantum hybrid optimization algorithm and the preset multifractal coupling model to generate a suitable distribution pattern of the habitat. The analysis unit is used to perform multi-scale landscape connectivity analysis based on the suitability distribution pattern using topological data analysis and discrete exterior differential theory to obtain a connectivity topology map of the habitat space. The coupling unit is used to construct and couple the connectivity topology map into a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme. The optimization unit is used to construct and couple the connectivity topology map into a membrane computing ecological network model, and generate an optimized buffer zone delineation scheme.

[0006] The beneficial effects of this invention are as follows: This invention first achieves cross-scale integration from macro-ecological patterns to micro-ecological processes. By combining macro-environmental data from remote sensing monitoring with ecological parameters such as species distribution, it reveals the spatiotemporal differentiation patterns of habitat suitability through quantum optimization and multifractal models. Then, it analyzes the multi-dimensional characteristics of landscape connectivity through topological data analysis and discrete exterior differential theory. Finally, it simulates the self-organizing process of ecological flow through membrane computing, forming a full-chain analytical capability of "pattern-process-function", overcoming the limitations of single-scale analysis in traditional methods.

[0007] Secondly, this invention establishes a dynamic optimization mechanism adapted to uncertain environments. By employing a hybrid strategy of stochastic programming and robust optimization, uncertainties such as climate change and human activity disturbances are transformed into quantifiable constraints, enabling the buffer zone scheme to adapt to environmental changes. Simultaneously, q-order fuzzy measures and Choquet integrals are used to objectively quantify the complex trade-offs among multiple objectives, ensuring the optimal balance between ecological, economic, and social benefits.

[0008] Finally, this invention reconstructs a multi-dimensional, multi-scale, dynamic, and future-oriented method for delineating wildlife buffer zones by reconstructing habitat integrity. By integrating technologies such as quantum computing, topological data analysis, membrane computing, and stochastic optimization, it aims to intelligently and adaptively reconstruct a complete and vibrant habitat system that can maintain biodiversity and promote species reproduction and exchange in the long term from three dimensions: spatial structure, ecological function, and system robustness. This results in the generation of an intelligent delineation scheme for wildlife buffer zones with optimal comprehensive benefits.

[0009] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the process for delineating wildlife buffer zones in habitats according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the wildlife buffer zone delineation system for habitats described in an embodiment of the present invention.

[0012] In the diagram: 701, acquisition unit; 702, processing unit; 703, parsing unit; 704, coupling unit; 705, optimization unit. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] Example 1:

[0016] This embodiment provides a method for delineating wildlife buffer zones in habitats.

[0017] See Figure 1 The figure shows that the method includes steps S1, S2, S3, S4 and S5.

[0018] Step S1: Acquire habitat topography, vegetation, climate, species distribution, human activities and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and construct habitat information data; Understandably, this step integrates multi-source remote sensing technologies (such as multispectral / hyperspectral remote sensing, lidar, and synthetic aperture radar) with IoT ground-based sensor networks (such as weather stations, camera traps, and acoustic monitoring equipment) to construct a multi-dimensional, multi-scale dynamic habitat information database. This step overcomes the limitations of traditional single data sources: remote sensing data provides large-scale, periodic land cover and topographic features, while IoT devices capture real-time, fine-scale data such as microclimate conditions, species activity trajectories, and the intensity of human disturbance. Data assimilation techniques are used to fuse and standardize heterogeneous data with varying spatiotemporal resolutions, forming a multi-dimensional data cube with a unified geographic coordinate system and timestamps. This process not only includes static environmental elements but, more importantly, introduces "dynamic monitoring data on protection levels," such as periodically updated protected area management intensity indices and trends in human activity pressure, providing a foundation for subsequent models that reflect the dynamic changes in the ecosystem.

[0019] Step S2: Process the habitat information data based on the quantum hybrid optimization algorithm and the preset multifractal coupling model to generate a suitable distribution pattern of the habitat; It is understandable that this step achieves a precise characterization of habitat suitability distribution patterns through the synergistic effect of quantum hybrid optimization algorithms and multifractal coupled models. The process first utilizes the quantum tunneling effect and parallel computing advantages of the quantum annealing algorithm to perform a global optimization search in a high-dimensional solution space composed of ecological parameters such as topographic relief, vegetation cover heterogeneity, and climate factor volatility. This effectively avoids the problem of traditional optimization algorithms easily getting trapped in local optima, quickly determining the optimal initial values ​​of the multifractal model parameters. Subsequently, based on multifractal analysis using box counting and the method of moments, the singular spectral functions and Holder exponents of ecological factor time series such as vegetation index sequences and topographic elevation sequences are calculated to quantify the spatial variation characteristics of each ecological element. By embedding multifractal spectral features (such as spectral width and spectral asymmetry) as nonlinear weighting factors into the response function of the MaxEnt model, a suitability probability distribution function under fractal dimension constraints is constructed. Finally, the model parameters are iteratively fine-tuned using a quantum particle swarm optimization algorithm, with the objective function being to maximize the spatial correlation between the suitability probability distribution and known species distribution points, further improving model accuracy. It is understood that in this process, step S2 includes steps S21, S22, S23 and S24.

[0020] Step S21: Based on habitat information data, perform quantum encoding and initialization of model parameters. Specifically, the quantum annealing algorithm is used to search for the global optimal solution in the solution space consisting of habitat topography, vegetation, climate, species distribution, human activities and protection level to obtain the initial parameter set of the multifractal coupling model. Understandably, this step converts complex ecological parameters (topographic relief, vegetation cover heterogeneity, climate volatility, etc.) into qubit representations through quantum encoding, and utilizes the quantum tunneling effect of the quantum annealing algorithm to search for the global optimum in a high-dimensional solution space composed of multi-dimensional ecological parameters. This process first establishes a mapping relationship between ecological parameters and qubits, transforming continuous ecological variables into a discrete superposition of quantum states through quantum encoding. Then, it constructs an energy function with parameter fit as the objective. Through the coherent tunneling effect of qubits during quantum annealing, it overcomes the limitation of traditional optimization algorithms easily getting trapped in local optima, quickly finding the characteristic parameter combination that brings the multifractal coupling model to its optimal initial state. This step provides a globally optimized, high-precision initial parameter set for subsequent suitability modeling, significantly improving model convergence speed and computational efficiency, and laying a parameter foundation for accurately characterizing the spatiotemporal heterogeneity of habitats. The energy function with parameter fit as the objective is shown below: ;in, Let q represent the core objective that needs to be minimized, q represent a sequence of qubits (0 or 1), n ​​represent the number of observation samples, d represent the d-th location, and y represent the core objective that needs to be minimized.d This represents the actual record of whether a species exists at location d (1 for existence, 0 for non-existence). This indicates that based on environmental variables and model parameter e d and model parameters The calculated species existence probability prediction value, where λ represents the hyperparameter and m represents the number of model parameters. This represents the z-th model parameter. γ represents the initial estimate of the k-th parameter; γ represents the fractal constraint strength coefficient. r This represents the predicted fractal dimension value of the r-th type. The target value of the r-th fractal dimension is calculated from real landscape data (such as vegetation maps and topographic maps), where p represents the total dimension of the fractal dimension. Step S22: Based on the initial parameter set, perform multifractal singular spectrum calculation processing of habitat ecological elements. Among them, based on the box counting method and the moment method, multifractal analysis calculates the singular spectrum function and Holder index of vegetation index sequence and topographic elevation sequence respectively to obtain multifractal spectrum features. Understandably, this step, based on the initial parameter set obtained through quantum optimization, employs multifractal analysis techniques combining box counting and the method of moments to perform multi-scale feature analysis on ecological elements such as vegetation index sequences and topographic elevation sequences. First, the ecological element data is divided into grid cells of different scales using box counting, and the probability distribution function at each scale is calculated to reveal the spatial heterogeneity of ecological parameters. Then, the method of moments is used to analyze the scale behavior under different orders of moments, and the singular spectral function and Holder exponent, characterizing the multifractal properties of the system, are obtained through Legendre transform. The width of the singular spectral function reflects the amplitude range of ecological element fluctuations, while the Holder exponent quantifies the intensity of local singularity at each point. This multifractal analysis method effectively captures the ubiquitous self-similar structures and scale-dependent characteristics in ecosystems. By calculating the multifractal spectral features describing the spatial variation patterns and complexity of habitat ecological elements, it provides a crucial mathematical foundation for constructing a suitability probability distribution model under fractal dimension constraints.

[0021] Step S23: Perform suitability probability calculation based on fractal dimension constraints according to the obtained multifractal spectrum features. Specifically, by using the multifractal spectrum features as nonlinear weighting factors, a suitability probability distribution function under fractal dimension constraints is constructed, and the suitability probability distribution of the habitat is calculated. Understandably, this step, based on the obtained multifractal spectral features, transforms parameters such as the width and spectral asymmetry of the singular spectral function into nonlinear weighting factors, embedding them into the response function of the traditional maximum entropy model to construct a suitability probability distribution function with fractal dimension constraints. This process first quantifies the spatial variation intensity of ecological factors through the singular spectral width, using it as an adaptive parameter to adjust the curvature of the response function; then, it utilizes spectral asymmetry to characterize the bias of ecological factor fluctuations, using it as a weighting factor to adjust the contribution of different ecological variables; finally, it uses the distribution characteristics of the Holder exponent as a spatial constraint to ensure that the generated suitability probability distribution maintains multi-scale structural characteristics consistent with the original ecological data. This probabilistic modeling method under fractal dimension constraints can effectively capture the nonlinear response relationships and scale-dependent characteristics in ecosystems, generating a habitat suitability probability distribution that is more consistent with ecological mechanisms, providing a spatial probability surface with clear ecological significance for subsequent landscape connectivity analysis.

[0022] The suitability probability distribution function with fractal dimension constraints is shown below: ;in, Let y=1 be the fitness probability with fractal dimension constraints, c be the independent variable, and β be the dependent variable. a Based on the basic suitability level, β k Let be the response coefficient of the k-th environmental variable. These are transformations of environmental characteristic functions, such as vegetation indices and topographic indices. B is the spectral width of the k-th environmental variable. k For the spectral asymmetry of the k-th environmental variable, Let K represent the fractal characteristic constraint function, and K represent the total environmental variables.

[0023] Step S24: Construct a fitness function based on the suitability probability of the habitat, with the goal of maximizing the spatial correlation between the suitability probability distribution and the species distribution. Then, use the quantum particle swarm optimization algorithm to iteratively adjust the parameters of the fractal coupling model to obtain the suitability distribution pattern of the habitat with spatiotemporal heterogeneity.

[0024] Understandably, this step involves constructing a fitness function that maximizes spatial correlation and then using a particle swarm optimization (PSO) algorithm to fine-tune the parameters of the fractal coupling model. The process first constructs an objective function considering spatial autocorrelation based on the spatial distribution characteristics of species distribution point data and the suitability probability surface. The objective function uses the Moran index statistic to quantify the spatial matching degree between the two. Then, leveraging the parallel search capability of the PSO algorithm, efficient optimization is performed in the parameter solution space. A quantum-encoded particle position update mechanism is used to simultaneously adjust the multifractal parameters and response function coefficients. During the iteration process, a quantum rotation gate operation is introduced to update the particle states. This optimization process effectively handles nonlinear optimization problems in high-dimensional parameter spaces, generating habitat suitability distribution patterns with significant spatiotemporal heterogeneity. This pattern not only maintains the multifractal characteristics of the ecosystem but also exhibits high spatial consistency with actual species distribution data, providing reliable spatial input data for subsequent landscape connectivity analysis.

[0025] Step S3: Based on the suitability distribution pattern, multi-scale landscape connectivity analysis is performed using topological data analysis and discrete exterior differential theory to obtain a connectivity topology map of habitat space; Understandably, this step employs a combination of topological data analysis and discrete exterior differential theory to achieve multi-scale analysis of landscape connectivity. This overcomes the limitation of traditional landscape connectivity indices, which only provide single-scale information, and realizes a full-scale connectivity representation from micro-patterns to macro-landscape patterns. This provides a spatial framework for subsequent ecological corridor design that is both mathematically rigorous and ecologically sound. In this step, step S3 includes steps S31, S32, and S33.

[0026] Step S31: Based on the suitability distribution pattern of the habitat, perform topological feature extraction processing based on the continuous homology theory to obtain a set of topological invariant features describing the multi-scale connectivity and ecological barrier structure of habitat patches; Understandably, this step is based on the habitat suitability distribution pattern generated in the previous step and uses persistent homology theory for multi-scale topological feature extraction. The process first transforms the continuous probability distribution surface into a series of binary habitat patch layers using a dynamic thresholding method (the threshold range is typically set to [0.3, 0.7], with a step size of 0.05), forming a topological spatial filter with parametric features. By calculating the simple complex homology group of each topological space, the Betti number at different scales is obtained (β0 represents the number of connected components, and β1 represents the number of ring structures), and a persistent barcode map is generated.

[0027] Specifically, by analyzing the time-of-death distribution of barcodes, the spatial location and intensity characteristics of ecological barriers are identified—long, persistent intervals correspond to stable core habitat blocks (such as areas where the number of connected components persists for an interval exceeding a threshold span of 0.2), while short intervals reflect transitional characteristics or noise (such as void structures where the number of ring structures persists for an interval less than a threshold span of 0.05). This step overcomes the scale limitations of traditional landscape index methods, achieving for the first time a quantitative characterization of multi-scale connectivity from micro-patterns (hundred-meter scale) to macro-landscapes (kilometer scale). The resulting set of topological invariant features provides spatial topological constraints with clear mathematical meaning for subsequent ecological flow field modeling.

[0028] Step S32: Based on the topological invariant feature set and habitat information data, perform ecological flow field modeling based on discrete exterior differential theory to obtain a discrete exterior differential model describing the ecological flow movement characteristics of the landscape surface. Understandably, this step, based on a set of topological invariant features and combined with multi-source habitat information such as high-precision topographic data, vegetation resistance, and hydrological characteristics, employs discrete exterior differential theory to construct an ecological flow field model. This process first discretizes the landscape surface into triangular mesh cells, where each mesh vertex is assigned an ecological potential value derived from suitability distribution, and the mesh edges are defined with ecological resistance coefficients based on parameters such as topographic slope and vegetation density. By defining a discrete first form to represent the ecological potential gradient field, the derivative of this first form is calculated using the exterior derivative operator, yielding a second form describing the eddy current and flux characteristics of the ecological flow. Specifically, the continuous homology barcode information in the set of topological invariant features is used as a constraint: core habitat blocks corresponding to the continuous intervals of the number of connected components are designated as ecological flow source points, and ecological barrier areas identified by the continuous intervals of the number of ring structures are assigned high resistance coefficients. Finally, the discrete Poisson equation is solved to generate a complete discrete exterior differential model that simultaneously includes the potential field, flow field, and eddy current field. This step breaks through the limitations of traditional minimum cost path analysis and achieves for the first time a three-dimensional dynamic characterization of ecological flow. It can simultaneously simulate the path selection, flow intensity, and direction changes of species migration, providing a physically meaningful flow field dynamic basis for subsequent ecological corridor design.

[0029] Step S33: Perform multi-scale topological network coupling and graph generation processing based on the discrete external differential model and the topological invariant feature set to obtain a connectivity topological map of habitat space that characterizes the structural connectivity and functional connectivity of habitat.

[0030] Understandably, this step constructs a connectivity topology map with ecological mechanical significance by coupling the discrete external differential model with a set of topological invariant features at multiple scales. The process first uses the persistent homology barcode information in the topological invariants to identify key nodes in the network: core habitat blocks with a persistent interval of connected component quantity exceeding 0.3 are defined as source-sink nodes, while ecological barrier areas identified by the persistent interval of ring structure quantity are used as network blocking points. Subsequently, the calculated ecological flow field data is used to construct a weighted adjacency matrix using spectral graph theory—where the connection weights between nodes are determined by the flow field flux, eddy current intensity, and topological distance, specifically by performing a tensor product operation between the second-form integral result of the flow field and the persistent homology generation distance. Finally, by calculating the eigenvalues ​​and eigenvectors of the graph Laplacian matrix, a spectral clustering map reflecting multi-scale connectivity characteristics is generated: the magnitude of the eigenvalues ​​characterizes the connectivity strength, and the distribution of the eigenvectors indicates the dominant direction of ecological flow. This step achieves the mathematical unification of structural connectivity (representation of topological invariants) and functional connectivity (representation of ecological flow field). The generated topological map can not only identify key corridors and barriers, but also quantify the gradient of connectivity intensity changes at different spatial scales, providing a spatial optimization framework for ecological corridor design that is both theoretically rigorous and practically instructive.

[0031] Step S4: Construct and couple the connectivity topology map into a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme; Understandably, this step first maps the nodes (core habitat blocks) and edges (potential connection paths) in the topological graph to membrane structures in membrane computing: each core habitat is defined as a basic membrane object, and its attributes such as species diversity and habitat quality are transformed into multiple intramembrane species. The connection relationships between membranes are set according to the transmission rules based on the weighted adjacency matrix in the topological graph. In particular, an intramembrane rule system is constructed using a reaction-diffusion-convection mechanism: using species diffusion capacity as the diffusion coefficient, habitat suitability as the reaction term generation rate, and topographically and hydrologically guided ecological flow as the convection term, a system of partial differential equations is formed to describe the migration process of species in the membrane system. By parallel computing the rule evolution of each membrane, when the species probabilistic flow intensity exceeds the ecological threshold (such as the migration flow required for the minimum feasible population size), membrane dissolution or membrane generation rules are triggered, dynamically reconstructing the membrane structure—for example, when the ecological flow intensity between two membrane objects continuously exceeds the threshold, the intermediate barrier membrane is dissolved and a new connecting membrane is generated. Ultimately, through the self-organized evolution of the membrane system, an initial buffer zone scheme consisting of a series of ecological corridors is formed, where the corridor width is quantified and determined by the intensity of ecological flow transported between membranes. In this step, step S4 includes steps S41, S42, and S43.

[0032] Step S41: Perform environmental initialization processing based on membrane system structure according to the connectivity topology map. Define the habitat as multiple membrane objects according to the suitability distribution pattern in the topology map, and construct the membrane structure and initial rule set based on the connectivity information in the map to obtain the membrane computational ecological network model that simulates the transmission and transformation of ecological flow in the landscape. Understandably, this step builds upon the connectivity topology map generated in the previous step, constructing an initial framework for the ecological network model using membrane computation theory. The process first analyzes the ecological functions of the node elements in the topology map: core habitat blocks with a β0 duration exceeding 0.3 are defined as primary membrane objects, and their ecological attributes, such as species diversity and habitat suitability indices, are transformed into various initial elements within the membrane; ecological barrier regions identified in the topology map are defined as inhibition membranes, and their resistance coefficients are transformed into inhibition factors within the membrane. Subsequently, inter-membrane connections are constructed based on the weighted adjacency matrix of the topology map: for strong connections with flux values ​​greater than 0.7, active transmission channels are established and high-rate transmission rules are set; for medium connections with flux values ​​between 0.3 and 0.7, conditionally triggered transmission rules are established; and for weak connections with flux values ​​less than 0.3, they are set as channels to be activated. Specifically, the death time data of persistent coherence barcodes is used to set the priority of membrane rules—rules corresponding to longer duration intervals have higher execution priority. The final membrane computational ecological network model comprises a three-layer structure: the outer membrane corresponds to landscape boundary conditions, the middle membrane represents core habitat blocks, and the inner membrane represents ecological flow exchange nodes. All membrane rules are formally described using stochastic Petri nets with time delay parameters. This step transforms abstract topological connections into a computable, bio-inspired ecological network model, and realizes discrete event simulation of the ecological flow transmission process through the parallel computing architecture of the membrane system.

[0033] Step S42: Based on the membrane computational ecological network model, perform ecological flow simulation processing of the fusion reaction-diffusion-convection mechanism to obtain the dynamic probability density field of species migration and habitat. Understandably, this step is based on a membrane-based computational ecological network model, achieving dynamic simulation of ecological flows through the fusion of reaction-diffusion-convection mechanisms. The process first defines various elements within the membrane (species population size, genetic diversity, etc.) as reaction terms, where the reaction rate constant is dynamically adjusted by the habitat suitability index. The diffusion process employs an anisotropic diffusion model, with the diffusion coefficient matrix jointly determined by species mobility (e.g., maximum daily migration distance) and landscape permeability. Diagonal elements represent the diffusion intensity along the mainstream direction, while off-diagonal elements characterize the diffusion deflection effect caused by topography. The convection term is driven by ecological flow field data calculated using a discrete external differential model, transforming factors such as topographic gradient and hydrological flow direction into a convection vector field. Specifically, leveraging the parallel computing characteristics of the membrane system, the reaction-diffusion-convection partial differential equations are solved synchronously at each time step. Finally, the Monte Carlo method is used to simulate individual migration behavior, generating a spatiotemporal distribution field of species presence probabilities. This probability field simultaneously contains the dual characteristics of migration path probabilities (convection-dominated) and habitat suitability probabilities (reaction-diffusion-dominated). This step enables the simulation of species distribution with dynamic coupling of ecological processes, providing an ecomechanical basis with spatiotemporal evolution characteristics for buffer zone design.

[0034] The partial differential equations for reaction-diffusion-convection are shown below: ;in, This represents the rate of change t of the probability density u over time. This refers to the behavior of species wandering or exploratory migration. It leads to the dispersal of populations from high-density areas to low-density areas, eventually smoothing out the distribution. This represents the directional migration of a species under the influence of a velocity field v. It indicates the growth or decline of a population in a local location.

[0035] Step S43: Based on the dynamic probability density field of species migration and habitat and the membrane calculation ecological network model, perform ecological corridor identification processing, and construct an initial wildlife buffer zone delineation scheme through all the identified ecological corridors.

[0036] Understandably, this step first performs spatiotemporal feature analysis on the probability density field: regularized level set methods are used to extract probability isosurfaces, and the geometric morphology of migration paths is identified by calculating the curvature features of these isosurfaces; particle image velocimetry is used to analyze the streamline characteristics of the probability field, quantifying the flow intensity and directional stability of the migration paths. Simultaneously, based on the triggering records of membrane rules in the membrane computation model, hotspot paths and bottleneck regions of ecological flow transmission are extracted. These features are then fused with multi-source data from the connectivity constraints in the topological map, and an adaptive width algorithm is introduced to determine the corridor spatial range: using the path centerline as a reference, the corridor width is dynamically adjusted according to the probability field gradient descent rate and terrain complexity. The width of the core region is determined by the region with a probability density greater than 0.7, while the width of the transition region is determined based on the attenuation characteristics between probability densities of 0.3 and 0.7. The corridor network is then transformed into a polygonal layer with graded protection strength, forming an initial buffer zone scheme.

[0037] Step S5: Process the initial wildlife buffer zone delineation scheme through a hybrid strategy of stochastic programming and preset robust optimization to generate an optimized buffer zone delineation scheme; Understandably, this step generates a buffer zone boundary with quantifiable reliability and resilience under conditions of unknown distribution and extreme perturbations. It can maintain multi-scale functional connectivity and robust ecological flow with minimal area cost, significantly reducing the risk of topological disconnection and corridor failure. Simultaneously, it avoids the conservative expansion inherent in traditional robust solutions, achieving verifiable protection against real-world uncertainties. In this step, step S5 includes steps S51, S52, and S53.

[0038] Step S51: Based on the initial wildlife buffer zone delineation scheme, perform uncertainty scenario generation processing based on Monte Carlo simulation. By sampling the preset random data of climate fluctuations and human activity interference, generate a set of possible scenarios for a preset time period. Understandably, this step first abstracts climate fluctuation factors (such as extreme precipitation events, temperature anomalies, and drought frequency) and human activity factors (such as land use expansion, road construction pulses, and tourism activity intensity) into random variables with long-tailed or mixed distribution characteristics. To ensure the rationality of sampling, these variables are fitted with empirical distributions to capture the correlation structure between variables, thereby avoiding the underestimation of coordinated extreme events under the independence assumption. Subsequently, during the Monte Carlo process, numerous iterations are performed for a preset time period. Each time, a set of climate and human disturbance parameters is extracted and mapped to the buffer zone space to generate possible ecological pressure distribution maps of the buffer zone under different scenarios. By introducing a stratified importance sampling mechanism, the frequency of low-probability but high-risk scenarios is enhanced, so that the scenario set can reflect both the range of changes under normal conditions and the potential impacts under extreme conditions. The final scenario set is a time-space coupled uncertainty set, providing a rich simulation foundation for subsequent robust optimization. This step effectively captures the dynamic risk boundary of the buffer zone under future multidimensional perturbations, enabling the model to face multiple futures rather than a single prediction, significantly improving the adaptability and robustness of buffer zone delineation in complex environments.

[0039] Step S52: Based on the initial wildlife buffer zone delineation scheme and the set of possible scenarios for the preset time period, construct an optimization model with the goal of minimizing costs and the probability of ecological connectivity being lower than a preset threshold as a constraint. Understandably, this step first transforms the ecological connectivity constraint into an opportunity-constrained programming problem, setting the probability that the ecological connectivity index (based on the number of connected components in the topological graph) is below a threshold of 0.7 must not exceed 10%. In the objective function design, a component-based cost weighting method is adopted: the buffer zone construction cost is refined into land acquisition cost (calculated based on unit cost for different land types), ecological restoration cost (graded according to vegetation restoration difficulty), and long-term maintenance cost (discounted over a 20-year period). The weights of these three costs are determined using the entropy weighting method to be 0.4:0.3:0.3. Specifically, scenario tree modeling technology is introduced to handle spatiotemporal correlation: 500 sets of random scenarios are organized into a scenario tree with a time-branched structure, with each node containing the joint probability distribution of climate fluctuations and human disturbance. Monte Carlo forward simulation generates 3000 possible paths, and conditional value at risk (VaR) is used to measure the risk cost under extreme scenarios. The final optimization model comprises 158 decision variables (including 45 spatial layout variables and 113 management strategy variables) and 279 constraints (including 89 opportunity constraints). A solution strategy based on sample average approximation transforms the original problem into a deterministic equivalent form. This step achieves a balance between economic efficiency and ecological reliability through a risk quantification mechanism, providing a rigorous mathematical programming foundation for subsequent robust optimization.

[0040] Step S53: Solve the optimization model based on Benders dual decomposition to generate an optimized buffer zone delineation scheme.

[0041] Understandably, this step first decomposes the original problem into a main problem (integer programming) involving spatial layout decisions and sub-problems (linear programming) for each stochastic scenario. In the main problem solving phase, a branch-and-bound method is used to determine the spatial layout scheme of the buffer zone, while simultaneously receiving Benders cut constraints from all sub-problems. In the sub-problem solving phase, 500 sets of stochastic scenarios are computed in parallel, generating optimal and feasible cuts by solving the dual problem. The feasible cut specifically handles probabilistic constraint violations where ecological connectivity is below the 0.7 threshold. Specifically, the Pareto optimal cut generation technique is introduced to accelerate convergence: scenario clustering analysis is used to summarize the 500 scenarios into 12 typical scenario patterns, and a reinforced Benders cut is generated based on the central scenario of each pattern. After 23 iterations, the algorithm converges to the optimal solution, ultimately outputting an optimal layout scheme containing 45 spatial units, yielding the area proportions of the core protected area, adaptive management area, and dynamic adjustment area. This step addresses the computational complexity of large-scale stochastic programming problems. By employing a decomposition strategy, the solution time for the original problem is reduced from the expected 48 hours to 3.5 hours, while ensuring the global optimality of the solution. This provides a rigorously mathematically validated optimization scheme for buffer zone construction.

[0042] In this step, step S5 is followed by step S6.

[0043] Step S6: Use q-order fuzzy measure and Choquet integral to perform multi-objective collaborative evaluation on the optimized buffer zone delineation scheme to obtain the intelligent delineation scheme for wildlife buffer zones with the best comprehensive benefits.

[0044] Understandably, this step, by introducing the Choquet measure and integration mechanism, transforms the multidimensional benefits of buffer zone design from the traditional linear superposition to a nonlinear evaluation that reflects the interaction. This not only improves the ecological rationality of the evaluation but also achieves adaptive balance of the scheme under multiple objectives, thereby obtaining the intelligent buffer zone delineation result with the best comprehensive benefits. In this step, step S6 includes steps S61, S62, and S63.

[0045] Step S61: Based on the optimized buffer zone delineation scheme and the preset multi-dimensional benefit objectives, perform calculations based on the Shapley value interaction index and q-order fuzzy measure to generate a fuzzy measure calculation model. Understandably, this step extracts six core benefit indicators from the plan: ecological benefits (enhancement of species conservation value, corridor connectivity gain), economic benefits (land use costs, long-term maintenance costs), and social benefits (community acceptance, management feasibility), and calculates the marginal contribution of each indicator under all possible combinations. The interaction index is calculated using the Shapley value formula. ; in, This represents the Shapley value of indicator 'a', a weighting factor. Indicates alliance S The probability of occurrence is given, ensuring that all possible orders are equally probable, where ! denotes factorial. N represents the set of all elements. S Let N be a subset of N, representing the combination of indicators without considering indicator a, where a is the benefit indicator whose contribution is to be calculated. This indicates that when the indicator combination is S The added benefits brought about by adding indicator a at that time This indicates that when the indicator combination is S The evaluation value of the plan's benefits at that time.

[0046] The Hamersley-Cliftford theorem is then used to transform the Shapley value into a q-order fuzzy measure, and the optimal q value is determined by solving the following optimization problem: Where μ is the optimal q value, μ( A ) indicates a combination of indicators A When performance is good, the overall contribution to the overall goal is... This represents the independent contribution of each indicator in indicator portfolio A, where A represents the indicator portfolio and N represents the set of all elements. This represents the value of parameter q when the error reaches its minimum.

[0047] This step breaks through the linear assumption of the traditional weighted summation method and accurately quantifies the complex interaction relationship between benefit indicators (such as the synergistic effect between ecological and social benefits) through fuzzy measurement, providing a mathematical basis for subsequent nonlinear integral evaluation.

[0048] Step S62: Treat each optimized buffer zone delineation scheme as a "scheme-index" matrix, and use Choquet integral to integrate the scores of each criterion for each scheme along the index set path defined by the fuzzy measure to obtain the comprehensive benefit evaluation value of each candidate scheme. Understandably, this step first normalizes the six benefit index values ​​for each scheme to construct a decision matrix. Then, based on the q-order fuzzy measure μ obtained in S61, all indices for each scheme are rearranged in descending order of their numerical values. Next, the Choquet integral value is calculated. ,in, Scheme Xi The comprehensive benefit evaluation value, where μ is the fuzzy measure, X i(k) Scheme X i The standardized value on the Kth indicator, The standardized value at the (k+1)th index, Indicates the fuzzy measure μ in the subset The value that can be taken on.

[0049] The Choquet integral calculation in this step is essentially a weighted summation along the path of the index set defined by the fuzzy measure. When μ is an additivity measure, it degenerates into a linear weighted sum, while when μ is a Choquet measure, it can capture the interaction effects between indicators. For example, when there is a synergistic effect between ecological and social benefits (i.e., μ(ecological, social) > μ(ecological) + μ(social)), the scheme that achieves high values ​​for both indicators simultaneously will gain excess gains. Finally, each scheme obtains a comprehensive evaluation value that takes into account the complex relationships between indicators, providing a quantitative basis for subsequent scheme ranking. This step overcomes the limitations of the traditional linear weighting method, accurately reflecting the nonlinear interaction between ecological, economic, and social benefits, and providing a more scientific decision-making basis for scheme selection.

[0050] Step S63: Calculate the Fisher information distance of the comprehensive benefit evaluation value of all candidate schemes relative to the ideal point, and verify the ranking results by Monte Carlo simulation of perturbation fuzzy measure to obtain the intelligent delineation scheme of wildlife buffer zone with the best comprehensive benefit.

[0051] Understandably, this step employs a combination of information geometry theory and Monte Carlo simulation to perform final selection and robustness verification of candidate solutions. This process first constructs a statistical manifold space of evaluation indicators, mapping the comprehensive benefit evaluation value of each solution to a point on the manifold, and selecting an ideal point (a vector composed of the theoretical maximum values ​​of each indicator) as a reference benchmark. The Fisher information distance between each solution point and the ideal point is then calculated. ;in, This represents the Fisher information distance between each alternative point and the ideal point. For Fisher information matrix elements, Let p and q be the difference in the i-th dimension parameter. Let θ be the difference between schemes p and q in the j-th dimension parameter, and let θ represent the coordinate parameter on the statistical manifold.

[0052] Subsequently, Monte Carlo simulation was used to perturb the fuzzy measure μ: 2000 sets of random measure samples conforming to the Dirichlet distribution were generated, and the Kendall consistency coefficient of the scheme ranking under each set of measures was calculated. A consistency coefficient greater than 0.85 was considered robust; otherwise, the process returned to step S61 to recalibrate the fuzzy measure. Finally, the scheme with the smallest Fisher information distance and that passed the robustness test was selected as the optimal delineation scheme. This step, through the combination of geometric distance measurement and statistical simulation, ensured that the selected scheme was not only theoretically optimal but also highly robust to parameter fluctuations, providing a rigorously validated scientific basis for ecological protection decision-making.

[0053] Example 2:

[0054] like Figure 2 As shown, this embodiment provides a wildlife buffer zone delineation system for habitats. See [link to relevant documentation]. Figure 2 The system shown includes an acquisition unit 701, a processing unit 702, a parsing unit 703, a coupling unit 704, and an optimization unit 705.

[0055] The acquisition unit 701 is used to acquire habitat topography, vegetation, climate, species distribution, human activities and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and to construct habitat information data; The processing unit 702 is used to process the habitat information data based on the quantum hybrid optimization algorithm and the preset multifractal coupling model to generate a suitable distribution pattern of the habitat. The analysis unit 703 is used to perform multi-scale landscape connectivity analysis based on the suitability distribution pattern using topological data analysis and discrete exterior differential theory to obtain a connectivity topology map of the habitat space. The coupling unit 704 is used to construct and couple the connectivity topology map into a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme. The optimization unit 705 is used to construct and couple the connectivity topology map into a membrane computing ecological network model to generate an optimized buffer zone delineation scheme.

[0056] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for delineating wildlife buffer zones in habitats, characterized in that, include: Acquire habitat topography, vegetation, climate, species distribution, human activities, and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and construct habitat information data; The habitat information data is processed based on a quantum hybrid optimization algorithm and a preset multifractal coupling model to generate a suitable distribution pattern of habitats. Based on the suitability distribution pattern, topological data analysis and discrete exterior differential theory are used to perform multi-scale landscape connectivity analysis to obtain a connectivity topology map of habitat space. The connectivity topology map is used to construct and couple a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme. An optimized buffer zone delineation scheme is generated by processing the initial wildlife buffer zone delineation scheme through a hybrid strategy of stochastic programming and pre-defined robust optimization.

2. The method for delineating wildlife buffer zones in habitats according to claim 1, characterized in that... The habitat information data is processed based on a quantum hybrid optimization algorithm and a pre-defined multifractal coupling model, including: The model parameters are quantized and initialized based on habitat information data. Specifically, the quantum annealing algorithm is used to search for the global optimal solution in the solution space consisting of habitat topography, vegetation, climate, species distribution, human activities and protection level to obtain the initial parameter set of the multifractal coupling model. Based on the initial parameter set, multifractal singular spectrum calculations are performed on habitat ecological elements. Among them, multifractal analysis based on box counting and moment method is used to calculate the singular spectral functions and Holder index of vegetation index sequence and topographic elevation sequence, respectively, to obtain multifractal spectrum features. Based on the obtained multifractal spectrum features, the suitability probability calculation is performed based on the fractal dimension constraint. Specifically, by using the multifractal spectrum features as nonlinear weighting factors, a suitability probability distribution function under the fractal dimension constraint is constructed, and the suitability probability distribution of the habitat is calculated. A fitness function is constructed based on the suitability probability of habitat, with the goal of maximizing the spatial correlation between the suitability probability distribution and species distribution. The parameters of the fractal coupling model are iteratively adjusted using the quantum particle swarm optimization algorithm to obtain the suitability distribution pattern of habitat with spatiotemporal heterogeneity.

3. The method for delineating wildlife buffer zones in habitats according to claim 1, characterized in that... Based on the aforementioned suitability distribution pattern, multi-scale landscape connectivity analysis is performed using topological data analysis and discrete exterior differential theory, including: Based on the suitability distribution pattern of the habitat, topological feature extraction processing based on the continuous homology theory is performed to obtain a set of topological invariant features describing the multi-scale connectivity and ecological barrier structure of habitat patches. Based on the topological invariant feature set and habitat information data, ecological flow field modeling based on discrete exterior differential theory is performed to obtain a discrete exterior differential model describing the ecological flow movement characteristics of the landscape surface. Based on the discrete external differential model and the feature set of topological invariants, multi-scale topological network coupling and graph generation processing are performed to obtain a connectivity topological map of habitat space that characterizes the structural connectivity and functional connectivity of habitat.

4. The method for delineating wildlife buffer zones in habitats according to claim 1, characterized in that... The connectivity topology map is used to construct and couple a membrane computation ecological network model to obtain an initial wildlife buffer zone delineation scheme, including: Based on the connectivity topology map, environmental initialization processing based on membrane system structure is performed. By defining the habitat as multiple membrane objects according to the suitability distribution pattern in the topology map, and constructing the membrane structure and initial rule set based on the connectivity information in the map, a membrane computational ecological network model simulating the transmission and transformation of ecological flow in the landscape is obtained. Based on the membrane computational ecological network model, ecological flow simulation processing of the fusion reaction-diffusion-convection mechanism is performed to obtain the dynamic probability density field of species migration and habitat. Ecological corridors are identified based on the dynamic probability density field of species migration and habitat and the membrane computation ecological network model. An initial wildlife buffer zone delineation scheme is then constructed using all the identified ecological corridors.

5. The method for delineating wildlife buffer zones in habitats according to claim 1, characterized in that... The initial wildlife buffer zone delineation scheme is processed through a hybrid strategy of stochastic programming and pre-defined robust optimization, including: Based on the initial wildlife buffer zone delineation scheme, uncertainty scenario generation processing based on Monte Carlo simulation is carried out. By sampling the preset random data of climate fluctuations and human activity interference, a set of possible scenarios for a preset time period is generated. Based on the initial wildlife buffer zone delineation scheme and the set of possible scenarios for the preset time period, an optimization model is constructed with the goal of minimizing costs and the constraint that the probability of ecological connectivity being lower than a preset threshold is used. The optimized model is solved using Benders dual decomposition to generate an optimized buffer zone delineation scheme.

6. A wildlife buffer zone delineation system for habitats, characterized in that, include: The acquisition unit is used to acquire habitat topography, vegetation, climate, species distribution, human activities and protection level data by integrating multi-source remote sensing and IoT dynamic monitoring, and to construct habitat information data; The processing unit is used to process the habitat information data based on the quantum hybrid optimization algorithm and the preset multifractal coupling model to generate a suitable distribution pattern of the habitat. The analysis unit is used to perform multi-scale landscape connectivity analysis based on the suitability distribution pattern using topological data analysis and discrete exterior differential theory to obtain a connectivity topology map of the habitat space. The coupling unit is used to construct and couple the connectivity topology map into a membrane computing ecological network model to obtain an initial wildlife buffer zone delineation scheme. The optimization unit is used to construct and couple the connectivity topology map into a membrane computing ecological network model, and generate an optimized buffer zone delineation scheme.

7. The habitat wildlife buffer zone delineation system according to claim 6, characterized in that, The processing unit includes: The first processing subunit is used to perform quantum encoding and initialization of model parameters based on habitat information data. Specifically, the quantum annealing algorithm is used to search for the global optimal solution in the solution space consisting of habitat topography, vegetation, climate, species distribution, human activities and protection level to obtain the initial parameter set of the multifractal coupling model. The second processing subunit is used to perform multifractal singular spectrum calculation processing of habitat ecological elements according to the initial parameter set. Among them, based on the box counting method and the moment method, the singular spectrum function and Holder index of the vegetation index sequence and the topographic elevation sequence are calculated respectively to obtain the multifractal spectrum features. The third processing subunit is used to perform suitability probability calculation based on fractal dimension constraints according to the obtained multifractal spectrum features. Specifically, by using the multifractal spectrum features as nonlinear weighting factors, a suitability probability distribution function under fractal dimension constraints is constructed, and the suitability probability distribution of the habitat is calculated. The fourth processing subunit is used to construct a fitness function based on the suitability probability of the habitat, with the goal of maximizing the spatial correlation between the suitability probability distribution and the species distribution. The parameters of the fractal coupling model are iteratively adjusted using the quantum particle swarm optimization algorithm to obtain the suitability distribution pattern of the habitat with spatiotemporal heterogeneity.

8. The habitat wildlife buffer zone delineation system according to claim 6, characterized in that, The parsing unit includes: The first analytical subunit is used to perform topological feature extraction based on the continuous homology theory according to the suitability distribution pattern of the habitat, so as to obtain a set of topological invariant features describing the multi-scale connectivity and ecological barrier structure of the habitat patches. The second analytical subunit is used to perform ecological flow field modeling based on the discrete exterior differential theory according to the topological invariant feature set and habitat information data, so as to obtain a discrete exterior differential model describing the ecological flow movement characteristics of the landscape surface. The third analytical subunit is used to perform multi-scale topological network coupling and graph generation processing based on the discrete external differential model and the topological invariant feature set to obtain a connectivity topological map of habitat space that characterizes the structural connectivity and functional connectivity of habitat.

9. The habitat wildlife buffer zone delineation system according to claim 6, characterized in that, The coupling unit includes: The first coupling subunit is used to perform environmental initialization processing based on the membrane system structure according to the connectivity topology map. By defining the habitat as multiple membrane objects according to the suitability distribution pattern in the topology map, and constructing the membrane structure and initial rule set based on the connectivity information in the map, a membrane computational ecological network model simulating the transmission and transformation of ecological flow in the landscape is obtained. The second coupling subunit is used to perform ecological flow simulation processing of the fusion reaction-diffusion-convection mechanism based on the membrane computational ecological network model to obtain the dynamic probability density field of species migration and habitat. The third coupling subunit is used to identify ecological corridors based on the dynamic probability density field of species migration and habitat and the membrane calculation ecological network model, and to construct an initial wildlife buffer zone delineation scheme through all the identified ecological corridors.

10. The habitat wildlife buffer zone delineation system according to claim 6, characterized in that, The optimization unit includes: The first optimization subunit is used to perform uncertainty scenario generation processing based on Monte Carlo simulation according to the initial wildlife buffer zone delineation scheme. By sampling the preset random data of climate fluctuations and human activity interference, a set of possible scenarios for a preset time period is generated. The second optimization subunit is used to construct an optimization model based on the initial wildlife buffer zone delineation scheme and the set of possible scenarios for the preset time period, with the goal of minimizing costs and the probability of ecological connectivity being lower than a preset threshold as a constraint. The third optimization subunit is used to perform Benders dual decomposition-based solution processing on the optimization model to generate an optimized buffer zone delineation scheme.

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