Collaborative optimization method of leisure agriculture and ecological resources based on social ecological network

Abstract

This invention discloses a method for synergistic optimization of leisure agriculture and ecological resources based on social-ecological networks. The method includes: acquiring multi-source data of the target study area and constructing a social-ecological network model; the social-ecological network model includes an ecological layer, a social layer, and an inter-layer network connecting the ecological and social layers; performing social-ecological dynamic simulation analysis using the social-ecological network model to obtain simulation analysis results; setting resource regulation parameters based on the simulation analysis results, and using these parameters to guide resource allocation and ecological regulation in leisure agriculture. This invention can effectively characterize the coupling relationship and evolution process of multiple individuals and multiple resources, improve the accuracy of resource utilization risk identification, and achieve synergistic optimization of ecological protection and leisure agriculture development.

Description

Technical Field

[0001] This invention relates to the field of ecological resources, specifically to a method for the synergistic optimization of leisure agriculture and ecological resources based on social ecological networks. Background Technology

[0002] With the continuous development of leisure agriculture and agritourism, the phenomenon of different individuals developing and utilizing ecological resources within the same geographical space is becoming increasingly common, and resource utilization exhibits characteristics of multiple superimposed and dynamic changes. In this process, ecological resources not only have their own carrying capacity limitations, but also have significant spatial correlation attributes. That is, changes in the intensity of resource utilization in a certain area may affect surrounding areas through ecological connectivity, thereby forming a cross-regional pressure transmission effect.

[0003] Currently, static evaluation methods based on single resource units or local areas are mostly used to analyze resource carrying capacity. These methods typically focus on resource intrinsic attributes or the utilization behavior of individual entities, making it difficult to simultaneously reflect the cumulative effects of resource utilization under conditions involving multiple entities, and also failing to reveal the spatial relationships between resources and regions. Furthermore, in leisure agriculture systems involving multiple entities, ecological resource units often correspond to the joint use of multiple individuals, and their development pressure is formed by the combined effects of the behaviors of multiple individuals. However, existing methods, when dealing with such coupling relationships between multiple entities and multiple resources, often rely on simple superposition or empirical judgment, lacking unified quantitative analysis methods, resulting in inaccurate identification of resource utilization risks.

[0004] Furthermore, existing technologies generally lack a unified modeling framework that can simultaneously describe the relationship between the spatial structure of ecological resources and individual behaviors, and also lack a technical path for conducting dynamic evolution analysis based on such models and guiding resource allocation and regulation in reverse, thus making it difficult to achieve refined management and collaborative optimization of ecological resources in leisure agriculture.

[0005] Therefore, it is necessary to provide a technical solution that can comprehensively characterize the spatial correlation characteristics of ecological resources and the utilization behavior of multiple individuals, and systematically analyze and regulate resource development pressure, so as to improve resource utilization efficiency and reduce ecological risks. Summary of the Invention

[0006] In view of this, the purpose of this invention is to overcome the deficiencies in the prior art and provide a method for the coordinated optimization of leisure agriculture and ecological resources based on social ecological networks. This method can effectively characterize the coupling relationship and evolution process of multiple individuals and multiple resources, improve the accuracy of resource utilization risk identification, and achieve coordinated optimization of ecological protection and leisure agriculture development.

[0007] The present invention provides a method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks, comprising:

[0008] Multi-source data of the target study area are acquired to construct a social-ecological network model; the social-ecological network model includes an ecological layer, a social layer, and an inter-layer network connecting the ecological layer and the social layer.

[0009] Social-ecological network models were used to conduct dynamic simulation analysis of social ecology, and the simulation analysis results were obtained.

[0010] Resource regulation parameters are set based on simulation analysis results, and these parameters are used to guide resource allocation and ecological regulation in leisure agriculture.

[0011] Furthermore, the ecosystem layer is constructed using the following method:

[0012] Based on land use data of the target study area, and through landscape type selection combined with morphological and spatial pattern analysis, areas exceeding [a certain size] were selected. The core area of ​​hectares serves as an ecological source;

[0013] Based on land use type, road distance, and vegetation cover index, a landscape resistance evaluation system is constructed to identify the minimum consumption path for material and energy flow between ecological source areas, which is then identified as ecological corridors to form a complete ecological connectivity network.

[0014] Furthermore, the social layer is constructed using the following method:

[0015] Based on field research and questionnaire data, and combined with the individual's business type, resource utilization scale, breadth of cooperation network, and frequency of development activities, individual nodes are classified into high-intensity development coefficients. and eco-friendly coefficient There are two types; the connections between individual nodes are established based on the actual interactions of cooperation, competition, and information exchange among individuals, forming a social network that reflects the characteristics of resource utilization behavior.

[0016] Furthermore, the inter-layer network is constructed using the following method:

[0017] Based on the individual's spatial location and activity range, combined with the spatial distribution and resource attributes of the ecological source area, a connection is established between the individual and the adjacent and suitable ecological source area, forming a connection between layers;

[0018] Based on an individual's development intensity, resource extraction intensity, and actual correlation with the surrounding environment, a differentiated weight is assigned to each connection between layers; the weight value is used to intuitively reflect the intensity of an individual's resource utilization of ecological resources.

[0019] Furthermore, when calculating the development pressure on a resource node, the resource utilization intensity of a single individual is not used directly. Instead, the overall development level is extracted based on the resource utilization behavior of all individuals connected to that resource node.

[0020] The overall development level is determined according to the following formula:

[0021] ;

[0022] in, Indicates the first Time, resource node The corresponding weighted average rate of rapid development; It is a social node For resource nodes Influence weight; Represents resource nodes A collection of directly connected social nodes; It is a social node In the The level of development at any given moment.

[0023] Furthermore, social-ecological network models are used to conduct dynamic simulation analysis of social ecology, specifically including:

[0024] Resources are connected via a network The tight coupling via the Laplace diffusion mechanism corresponds to the following kinetic equation:

[0025] ;

[0026] in, Resource diffusion rate; element Degree matrix Adjacency matrix of existing network The internal elements of the matrix obtained by the difference definition; express Time resource nodes Resource reserves; Represents social nodes Resource reserves; Represents a time variable;

[0027] The growth rate and maximum capacity of all resource nodes are set to a uniform value. This ignores the heterogeneity between nodes; for resource utilization strategies, the eco-friendly coefficient is set to... The high-intensity development coefficient is set to If a node adopts high-intensity resource utilization, its resource reserves will be reduced. It will converge to an empty state. If nodes adopt eco-friendly resource utilization methods, their resource reserves will... It will converge to ;

[0028] For each individual Set an independent wait time variable This variable describes the time point at which an individual's next strategy adjustment will occur, and its probability density function... It follows the following exponential distribution:

[0029] ;

[0030] in, The average interval of social renewal represents the expected waiting period between two behavioral learning or communication events for an individual. This parameter controls the temporal rhythm of the dissemination of social information among individuals.

[0031] Under this stochastic time protocol, the social learning process proceeds according to the following steps:

[0032] a. Trigger individual updates based on the minimum waiting time:

[0033] First, the resource stock is integrated forward based on the dynamic equation of the ecological subsystem until it reaches the minimum value among all individuals' next update times. :

[0034] ; Indicates the first The update time allocated to each node;

[0035] Corresponding individuals Those who are the first to qualify for renewal will have their strategies evaluated.

[0036] b. Strategy Imitation:

[0037] Activated individuals Randomly select an individual from their social neighborhood If both currently employ the same resource utilization strategy, no change will occur; if their resource utilization strategies differ, the difference in their fitness values ​​will be calculated. ; For individuals The fitness value in the current state; For individuals The fitness value in the current state; the fitness value is a comprehensive evaluation index used to measure the degree of influence of an individual on the ecological resource system in the current state; the fitness difference reflects the relative advantage of the neighbor's resource utilization strategy compared to its own strategy; whether an individual moves towards the neighbor's strategy is determined by the following smoothing probability function:

[0038] ;

[0039] This function ensures that: when the neighbor fitness value is higher, the imitation probability increases; when the neighbor fitness value is lower, the imitation probability decreases; and when the fitness difference is close to zero, the imitation behavior is a neutral random process.

[0040] c. Update the waiting time and proceed to the next loop:

[0041] After completing the strategy assessment and potential transitions, for the individual Another new waiting time is extracted from the distributed time scheduler: That is, for a certain individual in the network The waiting time for the next social update. Follow the average value The exponential distribution;

[0042] Then return to step a and repeat the above update process until the simulation ends.

[0043] Furthermore, the resource regulation parameters include the resource utilization strategy adjustment cycle and the resource diffusion rate.

[0044] The beneficial effects of this invention are as follows: This invention discloses a method for the coordinated optimization of leisure agriculture and ecological resources based on a social-ecological network. By acquiring multi-source data of the target area, a social-ecological network model is constructed, including an ecological layer, a social layer, and inter-layer relationships, achieving a unified expression of the spatial structure of ecological resources and the utilization behavior of multiple individuals. Based on this, dynamic simulation analysis of the social ecology is conducted to identify the resource pressure transmission paths and key influencing nodes under the superimposed effects of multiple individuals, and resource regulation parameters are set accordingly to dynamically optimize resource allocation and development intensity. This invention can effectively characterize the coupling relationship and evolution process between multiple individuals and multiple resources, improve the accuracy of resource utilization risk identification, achieve coordinated optimization of ecological protection and leisure agriculture development, and enhance the scientific and refined level of management. Attached Figure Description

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0046] Figure 1 This is a schematic diagram of the collaborative optimization method of the present invention. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings, as shown in the figures:

[0048] This embodiment discloses a method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks, including the following steps:

[0049] Multi-source data of the target study area are acquired to construct a social-ecological network model; the social-ecological network model includes an ecological layer, a social layer, and an inter-layer network connecting the ecological layer and the social layer.

[0050] Social-ecological network models were used to conduct dynamic simulation analysis of social ecology, and the simulation analysis results were obtained.

[0051] Resource regulation parameters are set based on simulation analysis results, and these parameters are used to guide resource allocation and ecological regulation in leisure agriculture. These resource regulation parameters include the resource utilization strategy adjustment cycle and the resource diffusion rate.

[0052] In this embodiment, the multi-source data of the target study area includes remote sensing image data, land use data and vegetation cover data, spatial distribution data of ecological resources, spatial location data of individuals and resource behavior data;

[0053] The ecological layer is the natural resource base supporting the development of leisure agriculture, and its core consists of ecological source areas and ecological corridors. The ecological layer is constructed using the following method:

[0054] Based on land use data of the target study area, landscape types were selected, such as prioritizing the preservation of ecologically valuable landscapes like woodlands, grasslands, wetlands, and water bodies. Combined with morphological and spatial pattern analysis, areas exceeding [a certain size] were selected. The core area of ​​hectares serves as an ecological source;

[0055] Based on land use type, road distance, and vegetation cover index, a landscape resistance evaluation system is constructed. Circuit theory and professional tools are used to identify the minimum consumption path for material and energy flow between ecological source areas, which is then identified as ecological corridors to form a complete ecological connectivity network.

[0056] The social layer serves as the carrier for the utilization of leisure agriculture resources, consisting of individual nodes and the social relationships between them. The social layer is constructed using the following method:

[0057] Based on field research and questionnaire data, and combined with the individual's business type, resource utilization scale, breadth of cooperation network, and frequency of development activities, individual nodes are classified into high-intensity development coefficients. and eco-friendly coefficient Two types; the connections between individual nodes are established based on the actual interactions of cooperation, competition, and information exchange among individuals, forming a social network that reflects the characteristics of resource utilization behavior. Among them, the high-intensity development coefficient Characterized by significant ecological impact, frequent development activities, and extensive cooperation networks; Eco-friendliness coefficient The representation emphasizes ecological protection and the adoption of sustainable development models. The "individual" refers to an abstract representation of a resource-using unit that interacts with ecological resources within the target study area, used to characterize the differences in resource use behavior at different spatial locations.

[0058] Interlayer networks serve as interactive bridges connecting the social and ecological layers, with the core function of connecting individuals with ecological resources. Interlayer networks are constructed using the following method:

[0059] Based on the individual's spatial location and activity range, combined with the spatial distribution and resource attributes of the ecological source area, a connection is established between the individual and the adjacent and suitable ecological source area, forming a connection between layers;

[0060] Based on an individual's development intensity, resource extraction intensity, and actual correlation with the surrounding environment, a differentiated weight is assigned to each connection between layers; the weight value is used to intuitively reflect the intensity of an individual's resource utilization of ecological resources.

[0061] Furthermore, an individual may depend on multiple resource units simultaneously, while the same resource unit may be shared by multiple individuals. This one-to-many and many-to-one correspondence means that resource pressure cannot be simply inferred from the behavior of a single individual, but must be comprehensively considered in light of the cumulative effects under shared circumstances.

[0062] Therefore, when calculating the development pressure on a resource node, this invention does not directly use the resource utilization intensity of a single individual, but rather extracts its comprehensive development level based on the resource utilization behavior of all individuals connected to that resource node. This comprehensive value reflects the magnitude of resource utilization pressure that the resource unit may face in reality: if multiple high-intensity development-oriented individuals jointly rely on the same resource, the overall development pressure increases significantly; if it is mainly used by eco-friendly individuals, the resource pressure is relatively low.

[0063] The overall development level is determined according to the following formula:

[0064] ;

[0065] in, Indicates the first Time, resource node The corresponding weighted average rate of rapid development; It is a social node For resource nodes Influence weight; Represents resource nodes A collection of directly connected social nodes; It is a social node In the The level of development at any given moment.

[0066] This calculation allows us to ignore the absolute differences in the stock of resource nodes and focus on comparing their relative consumption rates, that is, the rate of consumption caused by the weighted average level of development per unit of resource stock.

[0067] In this embodiment, a social-ecological network model is used to perform dynamic simulation analysis of social ecology, specifically including:

[0068] Resources are connected via a network The tight coupling via the Laplace diffusion mechanism corresponds to the following kinetic equation:

[0069] ;

[0070] in, Resource diffusion rate is an indicator of the speed at which a resource diffuses to a neighboring resource, reflecting the efficiency of resource spatial flow; element Degree matrix Adjacency matrix of existing network The internal elements of the matrix obtained by the difference definition; express Time resource nodes Resource reserves; Represents social nodes Resource reserves; Represents a time variable;

[0071] The growth rate and maximum capacity of all resource nodes are set to a uniform value. This ignores the heterogeneity between nodes; for resource utilization strategies, the eco-friendly coefficient is set to... The high-intensity development coefficient is set to If a node adopts high-intensity resource utilization, its resource reserves will be reduced. It will converge to an empty state. (Unstable); if nodes adopt eco-friendly resource utilization, their resource reserves... It will converge to (Stablize);

[0072] For each individual Set an independent wait time variable This variable describes the time point at which an individual's next strategy adjustment will occur, and its probability density function... It follows the following exponential distribution:

[0073] ;

[0074] in, The average interval of social renewal represents the expected waiting period between two behavioral learning or communication events for an individual. This parameter controls the temporal rhythm of the dissemination of social information among individuals.

[0075] Under this stochastic time protocol, the social learning process proceeds according to the following steps:

[0076] a. Trigger individual updates based on the minimum waiting time:

[0077] First, the resource stock is integrated forward based on the dynamic equation of the ecological subsystem until it reaches the minimum value among all individuals' next update times. :

[0078] ; Indicates the first The update time allocated to each node;

[0079] Corresponding individuals Those who are the first to qualify for renewal will have their strategies evaluated.

[0080] b. Strategy Imitation:

[0081] Activated individuals Randomly select an individual from their social neighborhood If both currently employ the same resource utilization strategy (e.g., both are high-intensity development or eco-friendly), then no change will occur; if their resource utilization strategies differ, then the difference in their fitness values ​​will be calculated. ; For individuals The fitness value in the current state; For individuals The fitness value in the current state; the fitness value is a comprehensive evaluation index used to measure the degree of influence of an individual on the ecological resource system in the current state; the fitness value can be a comprehensive evaluation index calculated by weighting parameters such as resource utilization intensity, ecological pressure index and spatial conflict degree, which will not be elaborated here.

[0082] The fitness difference reflects the relative advantage of the neighbor's resource utilization strategy compared to its own strategy; whether an individual moves towards the neighbor's strategy is determined by the following smoothing probability function:

[0083] ;

[0084] This function ensures that: when the neighbor fitness value is higher (e.g. When the fitness value of a neighbor is low, the probability of imitation increases; when the fitness value difference is close to zero, the imitation behavior is a neutral random process.

[0085] c. Update the waiting time and proceed to the next loop:

[0086] After completing the strategy assessment and potential transitions, for the individual Another new waiting time is extracted from the distributed time scheduler: That is, for a certain individual in the network The waiting time for the next social update. Follow the average value The exponential distribution; Indicates the first The update waiting time for each individual; This represents the average waiting time for social renewal;

[0087] Then return to step a and repeat the above update process until the simulation ends.

[0088] This invention can simultaneously characterize the rhythm of individual behavior adjustment and the spatial diffusion process of ecological resources within a unified technical framework, so that changes in resource stock are no longer regarded as the result of a single ecological or single management factor, but as a system evolution process formed by the combined effects of multiple time scales.

[0089] Meanwhile, it reveals that the stock of ecological resources exhibits differentiated evolution characteristics under different time scales, providing a technical basis for judging the transformation of the resource system between short-term consumption, medium-term low-level maintenance and long-term stability, thus providing an operable direction for optimizing the pace of resource utilization; moreover, it can provide a forward-looking judgment ability on the trend of resource evolution for the application scenarios of leisure agriculture and related multi-entity shared ecological resources, and improve the scientific nature and long-term effectiveness of resource management decisions.

[0090] To better understand the collaborative optimization method of the present invention, the following examples are provided for further illustration:

[0091] Social layer construction: Taking 297 large-scale leisure agriculture households as nodes, based on preprocessed social relationship data, 695 social connection edges are constructed, and the interaction relationship between individuals is represented in the form of an undirected network; based on the quantitative scoring results of resource utilization intensity, each node is labeled with the attributes of high-intensity development or eco-friendly, thus completing the construction of the social layer network.

[0092] Ecological layer construction: 140 core patches with sufficient area and complete ecological functions were selected as ecological source areas using landscape ecology methods; spatial analysis tools were used to calculate the landscape resistance between ecological source areas, and 299 minimum consumption paths were identified as ecological corridors to construct a complete ecological connectivity network.

[0093] Inter-layer network construction: Based on the spatial association between individuals and ecological resources, multiple pairs of inter-layer connections are constructed, totaling 488. The influence weight of each connection (three levels, 1-3) is determined according to the intensity of individual utilization and dependence on ecological resources. The weighted average development degree of each resource node is calculated through the comprehensive development level calculation formula, thus completing the quantitative construction of the inter-layer network.

[0094] Core resource control parameters: strategy adjustment cycle The value range is 1-14, set in integer increments; resource diffusion rate. The value range is [0, 1.0], covering all scenarios from no diffusion to strong diffusion.

[0095] Resource development intensity parameters: High-intensity development intensity coefficient =1.5, Eco-friendly Development Intensity Coefficient =0.5;

[0096] Network topology parameters: The social layer network contains 297 nodes and 695 edges; the ecological layer network contains 140 ecological source nodes and 299 ecological corridor edges; the inter-layer network contains 488 connecting edges.

[0097] Simulation control parameters: Each set of parameters is simulated 30 times, with the number of iterations set to 50, 100, 500, and 10,000 times respectively, to simulate the system evolution process at different time scales.

[0098] Multi-iteration simulation results and analysis:

[0099] 50 iterations (i.e., 50 updates):

[0100] In the initial stage of only 50 iterations, the average resource distribution of the system under different resource diffusion rates (social update time) is shown in Table 1:

[0101] Table 1

[0102]

[0103] At this stage, resource differences are not determined by strategy, but by the length of time the system has been in operation.

[0104] Small → Extremely short time between each social update → Short total system time → Resources hardly decreased, indicating a large group of users who update frequently ( (Small), although there is a lot of interaction, the total time for resource utilization is very short, and there is almost no chance for resources to decline;

[0105] Larger update intervals mean longer system lifespan (50 iterations for the same system) and greater pressure on resource consumption and utilization, indicating a larger user base with slower update speeds. (Large) is equivalent to not adjusting resource utilization strategies for a long time, continuously putting pressure on resources over a longer actual period of time, thus making resource decline more obvious.

[0106] From the rate of resource diffusion In terms of impact, this stage Its regulatory effect on resource distribution and strategic patterns is weak. The main reason is that in the short term, the spatial flow of resources, both domestically and internationally, has not yet formed a clear gradient, resulting in insufficient diffusion driving force; at the same time, resource stock remains at a high level, and resource constraints have not become the dominant factor restricting strategy diffusion.

[0107] After 100 iterations, the overall resource consumption decreased further, as shown in Table 2:

[0108] Table 2

[0109]

[0110] when When the impact is significant, individuals may fail to recognize the deterioration in resource carrying capacity due to delayed communication; market and ecological feedback may not be promptly transmitted to each individual; this leads to the prolonged application of incorrect resource utilization patterns, resulting in a deeper decline in resources.

[0111] and Smaller, large-scale groups are able to more quickly imitate more sustainable strategies from their surroundings due to their frequent updates, and are more agile in adjusting their resource utilization methods, thus experiencing smaller resource declines.

[0112] From the rate of resource diffusion In terms of impact, the rate of resource diffusion The smaller the area, the more restricted the flow of resources becomes, making it difficult for resources in the core area to spread to the surrounding areas. As a result, local stockpiles are concentrated, and areas with low development intensity accumulate more resources. The high diffusion rate accelerates the ecological dilution effect of high-pressure nodes on surrounding resource units, expanding the scope of resource consumption from local to overall, and further amplifying the downward trend of resources.

[0113] After 500 iterations, the resources exhibit a low steady state, as shown in Table 3:

[0114] Table 3

[0115]

[0116] When resources drop to a certain threshold, the system remains stuck at a low level of 0.2–0.5 for an extended period, making recovery difficult. This is because when resources are low, the system's natural recovery capacity is limited, while resource utilization continues to exert pressure. At the same time, social learning mechanisms retain strategy combinations that are detrimental to resources but still effective for short-term gains during long-term competition, causing the system to enter an irreversible exhaustion lock-in at both the strategy and resource levels.

[0117] From the rate of resource diffusion In terms of impact, this stage While diffusion still has some regulatory effect on resource distribution, the overall differences are minimal. On the one hand, low resource stock leads to a further narrowing of the spatial resource gradient, resulting in insufficient diffusion driving force; on the other hand, diffusion can only achieve a spatial redistribution of resources and cannot fundamentally increase the total regional resource volume, making it difficult to offset the cumulative effect of resource utilization pressure; simultaneously, high... Under certain conditions, the resource consumption pressure generated by localized high-intensity resource utilization is transmitted to the surrounding areas through diffusion, which in turn exacerbates the overall resource depletion.

[0118] When the number of iterations reaches an extremely long timescale of 10,000-20,000, the system enters a long-term equilibrium phase. The resource stock recovers significantly compared to all previous phases and gradually converges towards the median level of 0.5. The specific results are shown in Table 4.

[0119] Table 4

[0120]

[0121] This indicates that the system has entered a long-term ecological balance shaped by both natural recovery and resource utilization pressures. On extremely long timescales, high-intensity resource utilization strategies are gradually eliminated from social learning due to declining long-term fitness values; while eco-friendly strategies achieve a balance between fitness stability and resource sustainability, becoming the dominant behavior of the system and enabling resources to gradually recover from low to medium levels. (Diffusion coupling rate) This determines the spatial flow of resources, but has little impact on the final steady-state level; the resource stocks of each group eventually tend to converge. Overall, different... The value mainly affects the speed at which resources converge to the median level; the greater the diffusion rate, the longer it takes for the system to reach steady state.

[0122] Based on the dynamic simulation results of social and ecological multi-layer networks, it is recommended to implement differentiated management from two levels—short-term regulation and long-term structural optimization—to promote the sustainable development of leisure agriculture.

[0123] In the short term, the focus should be on shortening the adjustment cycle of social-level strategies. By strengthening information sharing and social learning mechanisms, individuals' responsiveness to resource changes and ecological feedback can be improved. Simultaneously, appropriate control measures should be implemented in areas of high-intensity resource use to prevent the concentrated accumulation of resource pressure in space. Based on this, the rate of ecological resource diffusion can be rationally guided. Spatial regulation can alleviate local resource overload, but avoid the diffusion effect that amplifies overall resource consumption.

[0124] In the long term, the core focus should be on optimizing the combination of resource utilization strategies. Through institutional incentive and constraint mechanisms, eco-friendly resource utilization should gradually replace unsustainable, high-intensity resource utilization patterns, thereby enhancing the overall stability of the system. The synergistic effect of social learning mechanisms and ecological restoration processes should promote the recovery and stabilization of resource stocks at a reasonable level, thus achieving a long-term balance between the development of leisure agriculture and ecological protection.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for collaborative optimization of leisure agriculture and ecological resources based on a social ecological network, characterized in that: include: Multi-source data of the target study area are acquired to construct a social-ecological network model; the social-ecological network model includes an ecological layer, a social layer, and an inter-layer network connecting the ecological layer and the social layer. Social-ecological network models were used to conduct dynamic simulation analysis of social ecology, and the simulation analysis results were obtained. Resource regulation parameters are set based on simulation analysis results, and these parameters are used to guide resource allocation and ecological regulation in leisure agriculture.

2. The method of claim 1, wherein the method is characterized by: The ecological layer is constructed using the following method: Based on the land use data of the target area, the core area with an area of more than 1000 ha was selected as the ecological source area through landscape type screening and spatial pattern analysis. Based on the land use data of the target area, the core area with an area of more than 1000 ha was selected as the ecological source area through landscape type screening and spatial pattern analysis. Based on land use type, road distance, and vegetation cover index, a landscape resistance evaluation system is constructed to identify the minimum consumption path for material and energy flow between ecological source areas, which is then identified as ecological corridors to form a complete ecological connectivity network. 3.The method of claim 1, wherein: The social layer is constructed using the following method: Based on field research and questionnaire data, and considering individual business types, resource utilization scale, breadth of cooperative networks, and frequency of development activities, individual nodes are categorized into high-intensity development coefficients. and eco-friendly coefficient There are two types; the connections between individual nodes are established based on the actual interactions of cooperation, competition, and information exchange among individuals, forming a social network that reflects the characteristics of resource utilization behavior.

4. The method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks according to claim 1, characterized in that: Construct the inter-layer network using the following method: Based on the individual's spatial location and activity range, combined with the spatial distribution and resource attributes of the ecological source area, a connection is established between the individual and the adjacent and suitable ecological source area, forming a connection between layers; Based on an individual's development intensity, resource extraction intensity, and actual correlation with the surrounding environment, a differentiated weight is assigned to each connection between layers; the weight value is used to intuitively reflect the intensity of an individual's resource utilization of ecological resources.

5. The method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks according to claim 4, characterized in that: When calculating the development pressure on a resource node, the resource utilization intensity of a single individual is not used directly. Instead, the overall development level is extracted based on the resource utilization behavior of all individuals connected to that resource node. The overall development level is determined according to the following formula: ； in, Indicates the first Time, resource node The corresponding weighted average rate of rapid development; It is a social node For resource nodes Influence weight; Represents resource nodes A collection of directly connected social nodes; It is a social node In the The level of development at any given moment.

6. The method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks according to claim 1, characterized in that: Using social-ecological network models to conduct dynamic simulation analysis of social ecology specifically includes: Resources are connected via a network The tight coupling via the Laplace diffusion mechanism corresponds to the following kinetic equation: ； in, Resource diffusion rate; element Degree matrix Adjacency matrix of existing network The internal elements of the matrix obtained by the difference definition; express Time resource nodes Resource reserves; Represents social nodes Resource reserves; Represents a time variable; The growth rate and maximum capacity of all resource nodes are set to a uniform value. This ignores the heterogeneity between nodes; for resource utilization strategies, the eco-friendly coefficient is set to... The high-intensity development coefficient is set to If a node adopts high-intensity resource utilization, its resource reserves will be reduced. It will converge to an empty state. If nodes adopt eco-friendly resource utilization methods, their resource reserves will... It will converge to ; For each individual Set an independent wait time variable This variable describes the time point at which an individual's next strategy adjustment will occur, and its probability density function... It follows the following exponential distribution: ； in, The average interval of social renewal represents the expected waiting period between two behavioral learning or communication events for an individual. This parameter controls the temporal rhythm of the dissemination of social information among individuals. Under this stochastic time protocol, the social learning process proceeds according to the following steps: a. Trigger individual updates based on the minimum waiting time: First, the resource stock is integrated forward based on the dynamic equation of the ecological subsystem until it reaches the minimum value among all individuals' next update times. : ; Indicates the first The update time allocated to each node; Corresponding individuals Those who are the first to qualify for renewal will have their strategies evaluated. b. Strategy Imitation: Activated individuals Randomly select an individual from their social neighborhood If both currently employ the same resource utilization strategy, no change will occur; if their resource utilization strategies differ, the difference in their fitness values ​​will be calculated. ; For individuals The fitness value in the current state; For individuals The fitness value in the current state; the fitness value is a comprehensive evaluation index used to measure the degree of influence of an individual on the ecological resource system in the current state; the fitness difference reflects the relative advantage of the neighbor's resource utilization strategy compared to its own strategy; whether an individual moves towards the neighbor's strategy is determined by the following smoothing probability function: ； This function ensures that: when the neighbor fitness value is higher, the imitation probability increases; when the neighbor fitness value is lower, the imitation probability decreases; and when the fitness difference is close to zero, the imitation behavior is a neutral random process. c. Update the waiting time and proceed to the next loop: After completing the strategy assessment and potential transitions, for the individual Another new waiting time is extracted from the distributed time scheduler: That is, for a certain individual in the network The waiting time for the next social update. Follow the average value The exponential distribution; Then return to step a and repeat the above update process until the simulation ends.

7. The method for synergistic optimization of leisure agriculture and ecological resources based on social ecological networks according to claim 1, characterized in that: The resource regulation parameters include the resource utilization strategy adjustment cycle and the resource diffusion rate.

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