Ecological security pattern construction system based on minimum cumulative resistance model

Abstract

The present application relates to the field of data processing technology, and relates to an ecological safety pattern construction system based on a minimum cumulative resistance model, which comprises an ecological data preprocessing and source extraction module, a resistance factor extraction module, a dynamic resistance surface construction module, a cumulative resistance evaluation module, a key ecological element identification module and a safety pattern generation module, and is characterized in that: the ecological source data in a target region is subjected to feature density statistics to obtain an ecological source distribution map; the ecological source distribution map is subjected to inhibition influence analysis to obtain resistance factors; the resistance factors are subjected to spatial data superposition to obtain a dynamic resistance surface; the ecological source distribution map is subjected to spatial diffusion resistance cumulative evaluation to obtain a cumulative resistance surface; the cumulative resistance surface is subjected to key ecological element analysis to obtain ecological corridors and ecological nodes; and the ecological safety pattern of the target region is constructed according to the ecological corridors and the ecological nodes; and the present application can improve the efficiency of the ecological safety pattern construction based on the minimum cumulative resistance model.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an ecological security pattern construction system based on the minimum cumulative resistance model. Background Technology

[0002] Existing technologies for constructing ecological security patterns lack efficient integration and preprocessing mechanisms when dealing with multi-source ecological data. This makes it difficult to accurately extract core information about ecological source areas, leading to biases in the depiction of their distribution and hindering the provision of reliable basic data support for subsequent pattern construction. Furthermore, traditional methods tend to identify resistance factors in a limited way, failing to fully correlate land use types and the inhibitory effects of human activities in the target area. This results in a lack of comprehensiveness and specificity in the selection of resistance factors, impacting the scientific rigor of ecological security pattern construction.

[0003] In the construction and cumulative resistance assessment stages, existing technologies mostly employ static processing methods, neglecting the dynamic impact of ecological temporal changes in the target area. They fail to dynamically adjust the weights of resistance factors according to ecological evolution patterns, resulting in a disconnect between the constructed resistance surface and the actual ecological condition. Furthermore, in the identification of key ecological elements, the analysis of the cumulative resistance surface is not in-depth enough, making it difficult to accurately screen effective ecological corridors and nodes. Consequently, the final ecological security pattern fails to meet the actual needs of regional ecological protection and sustainable development, and cannot effectively leverage the ecosystem's service functions and regulatory roles. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides an ecological security pattern construction system based on a minimum cumulative resistance model, characterized in that the system includes an ecological data preprocessing and source area extraction module, a resistance factor extraction module, a dynamic resistance surface construction module, a cumulative resistance assessment module, a key ecological element identification module, and a security pattern generation module, wherein: The ecological data preprocessing and source area extraction module is used to spatially register the ecological source data in the target area and perform feature density statistics on the registered data to obtain the ecological source area distribution map of the ecological source data. The resistance factor extraction module is used to analyze the inhibitory impact of the ecological source distribution map and obtain the resistance factor of the ecological source data. The dynamic resistance surface construction module is used to adjust the weights of resistance factors based on the ecological temporal changes of the target area, and to overlay the adjusted resistance factors with spatial data to obtain the dynamic resistance surface of the target area. The cumulative resistance assessment module is used to conduct a cumulative spatial diffusion resistance assessment on the source area distribution map based on the dynamic resistance surface and the minimum cumulative resistance model of the target area, and obtain the cumulative resistance surface of the target area. The key ecological element identification module is used to identify key ecological elements on the surface with accumulated resistance, thereby obtaining the ecological corridors and ecological nodes in the target area. The security pattern generation module is used to construct the ecological security pattern of the target area based on ecological corridors and ecological nodes.

[0005] In a preferred embodiment, when the ecological data preprocessing and source area extraction module performs spatial registration of ecological source data in the target area and performs feature density statistics on the registered data to obtain an ecological source area distribution map of the ecological source data, it is specifically used for: Integrate vegetation cover data and species distribution data of the target area into multi-source ecological data of the target area; Geometric correction is performed on multi-source ecological source data to obtain spatial registration data of multi-source ecological source data; Kernel density statistics were performed on vegetation cover data and species distribution data in spatial registration data to obtain vegetation density surface and species distribution density surface of multi-source ecological data. By fusing the vegetation density surface with the species distribution density surface, an ecological source distribution map of the target area is obtained.

[0006] In a preferred embodiment, when the resistance factor extraction module performs a suppression impact analysis on the ecological source distribution map to obtain the resistance factor of the ecological source data, it is specifically used for: Collect land use type data for the target area; Based on land use type data, the degree of inhibition between human activities and ecological security in the target area is assessed to obtain the disturbance intensity level of the target area. By coupling the ecological source area distribution map with the interference intensity level data, the interference impact in local spaces in the ecological source area distribution map can be identified. Based on the type and intensity of the disturbance, a resistance factor for the ecological source data is generated.

[0007] In a preferred embodiment, when the resistance factor extraction module couples the ecological source area distribution map with the disturbance intensity level data to identify the disturbance impact in local spaces within the ecological source area distribution map, it is specifically used for: The ecological source area space and the disturbance intensity level in the ecological source area distribution map are overlaid to obtain the overlay area of ​​the ecological source area distribution map. Based on the spatial location relationship of the ecological source area distribution map, the connectivity of the superimposed area is evaluated to obtain the mapping relationship between the spatial location relationship and the disturbance intensity level. Based on the mapping relationship, the disturbance impacts in local spaces of the ecological source area distribution map are determined.

[0008] In a preferred embodiment, when the dynamic resistance surface construction module performs weight adjustment on the resistance factors based on the ecological temporal changes of the target area, and performs spatial data superposition on the adjusted resistance factors to obtain the dynamic resistance surface of the target area, it is specifically used for: By comprehensively organizing and compiling the historical ecological time-series data of the above-mentioned target areas, the historical ecological impact data of the target areas are obtained. Based on the ecological temporal changes and historical ecological impact data of the target area, a spatiotemporal correlation analysis of the resistance factors is conducted to obtain the ecological evolution law of the target area. Based on the laws of ecological evolution, establish weight adjustment rules for resistance factors; Based on the weight adjustment rules, the resistance factors are assigned differentiated weights to obtain a series of weighted resistance factors for the target area. By superimposing and fusing spatial data with a series of weighted resistance factors, a dynamic resistance surface for the target area is obtained.

[0009] In a preferred embodiment, when the dynamic resistance surface construction module executes the weight adjustment rules for resistance factors based on ecological evolution laws, it is specifically used for: Based on the laws of ecological evolution, fluctuation detection is performed on the ecological security impact trend of resistance factors to obtain information on the changing trend of resistance factors; Based on the trends in annual rainfall and annual average temperature in historical ecological impact data, climate driving factors for the target region are constructed. By synergistically correlating historical ecological impact data with information on changing trends, the fluctuation mapping relationship of resistance factors is obtained; Based on the fluctuation mapping relationship, a weight adjustment correspondence table for climate driving factors and drag factors is determined; The weight adjustment correspondence table is used as the weight adjustment rule for the resistance factor.

[0010] In a preferred embodiment, when the cumulative resistance assessment module performs a spatial diffusion resistance cumulative assessment on the source area distribution map based on the minimum cumulative resistance model of the dynamic resistance surface and the target area to obtain the cumulative resistance surface of the target area, it is specifically used for: Input the dynamic resistance surface and the student origin distribution map into the minimum cumulative resistance model of the target area; Based on the distribution map of the place of origin, the spatial starting point for the ecological diffusion simulation of the target area is determined; Under the constraint of dynamic resistance surface, an anisotropic spatial diffusion simulation is performed on the spatial starting point, and the cumulative resistance value of the path traversed from the spatial starting point to the spatial node in the target region is statistically analyzed. The cumulative resistance values ​​under different paths are numerically compared, and the minimum cumulative resistance value is taken as the final resistance value of the spatial node. The final resistance values ​​of spatial nodes within the target area are collected to generate the cumulative resistance surface of the target area.

[0011] In a preferred embodiment, when the cumulative resistance assessment module performs the spatial starting point for determining the ecological diffusion simulation of the target area based on the biodiversity distribution map, it is specifically used for: Key ecological analysis was conducted on the high-quality ecological areas in the distribution map of birthplaces to obtain the core ecological source areas of the birthplace distribution map; Spatial cluster analysis was performed on the core area of ​​the ecological source to obtain the ecological source patches in the core area of ​​the ecological source. Based on the area, shape characteristics, and connectivity constraints of the ecological source patches, the location of the iconic pixels of the ecological source patches is generated. The location of representative pixels is determined as the spatial starting point for the ecological diffusion simulation of the target area.

[0012] In a preferred embodiment, when the key ecological element identification module performs key ecological element analysis on the cumulative resistance surface to obtain the ecological corridors and ecological nodes of the target area, it is specifically used for: The cumulative resistance surface is reclassified by grid, and multi-level resistance partitions of the target area are generated based on the range of the final resistance value. In a multi-level resistance zone, areas where the final resistance value is lower than a preset threshold are identified as potential low-resistance channels for the target area. Morphological refinement and connectivity analysis are performed on potential low-resistance channels to extract continuous low-resistance paths. Spatial correlation analysis was conducted between continuous low-resistance paths and ecological source patches in the ecological source distribution map to select paths connecting different ecological source patches as ecological corridors in the target area. Identify the intersection points of ecological corridors and the boundary points between ecological corridors and ecological source patches, and determine the boundary points and intersection points as ecological nodes in the target area.

[0013] In a preferred embodiment, when the security pattern generation module constructs the ecological security pattern of the target area based on ecological corridors and ecological nodes, it is specifically used for: The importance of ecological corridors is classified based on the final resistance value of the ecological corridor and the number of ecological source patches connected, thus generating classified ecological corridors. Ecological nodes are classified into different types, and the classified ecological nodes are generated based on their location on the ecological corridor and their spatial relationship with the ecological source patches. By spatially overlaying and topologically connecting the graded ecological corridors and classified ecological nodes, an ecological network for the target area is formed. Based on the ecological network, and combined with the administrative or natural geographical boundaries of the target area, the core protected areas and ecological buffer zones of the ecological security pattern are delineated. The core protected area, ecological buffer zone, graded ecological corridor and classified ecological node are integrated into a unified spatial data layer and output as an ecological security pattern map of the target area.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, through a professional ecological data preprocessing and source area extraction process, achieves efficient integration, accurate spatial registration, and feature density statistics of multi-source ecological data, significantly improving the accuracy of ecological source area distribution maps and providing high-quality basic data support for the construction of ecological security patterns. Simultaneously, relying on the coupled analysis of land use type data and disturbance intensity levels, it accurately extracts the resistance factors of ecological source data and dynamically adjusts the weights based on the ecological temporal changes in the target area, making the resistance surface construction more consistent with ecological evolution patterns and significantly enhancing the comprehensiveness and adaptability of resistance analysis.

[0015] 2. This invention employs anisotropic spatial diffusion simulation in cumulative resistance assessment, combined with precise positioning of the core ecological source area, ensuring the accuracy of cumulative resistance value calculation for spatial nodes. The generated cumulative resistance surface truly reflects the regional ecological diffusion resistance distribution. Through refined analysis methods such as raster reclassification and morphological refinement, precise identification of ecological corridors and ecological nodes is achieved. A complete ecological network is then formed through hierarchical classification and topological connections. The final constructed ecological security pattern covers key areas such as core protected areas and ecological buffer zones, possessing strong systematicity and practicality, and effectively supporting the precise implementation of regional ecological protection and sustainable development. Attached Figure Description

[0016] Figure 1 A system architecture diagram of an ecological security pattern construction system based on the minimum cumulative resistance model provided in an embodiment of the present invention; Figure 2 A detailed flowchart of dynamic weight adjustment and climate-driven coupling of an ecological security pattern construction system based on a minimum cumulative resistance model provided in an embodiment of the present invention; Figure 3 A detailed flowchart of the ecological corridor refinement and node identification of an ecological security pattern construction system based on the minimum cumulative resistance model provided in an embodiment of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] 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 belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0021] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0022] In practice, the server-side equipment deployed in the ecosystem security architecture construction system based on the minimum cumulative resistance model may consist of one or more devices. This system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node to provide the ecosystem security architecture construction system based on the minimum cumulative resistance model to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide the ecosystem security architecture construction system based on the minimum cumulative resistance model to various user terminals.

[0023] In terms of implementation, the ecological security framework construction system based on the minimum cumulative resistance model and the user terminal are mutually adaptable. That is, if the ecological security framework construction system based on the minimum cumulative resistance model is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the ecological security framework construction system based on the minimum cumulative resistance model is implemented as a website, then the user terminal is implemented as a webpage; or if the ecological security framework construction system based on the minimum cumulative resistance model is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0024] like Figure 1 The figure shown is a system architecture diagram of an ecological security pattern construction system based on the minimum cumulative resistance model provided in an embodiment of the present invention.

[0025] Figure 2 A flowchart detailing the dynamic weight adjustment and climate-driven coupling of an ecological security pattern construction system based on a minimum cumulative resistance model, provided in an embodiment of the present invention.

[0026] Figure 3 A detailed flowchart of the ecological corridor refinement and node identification of an ecological security pattern construction system based on the minimum cumulative resistance model provided in an embodiment of the present invention.

[0027] The ecological security pattern construction system 100 based on the minimum cumulative resistance model described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the ecological security pattern construction system 100 based on the minimum cumulative resistance model may include an ecological data preprocessing and source extraction module 101, a resistance factor extraction module 102, a dynamic resistance surface construction module 103, a cumulative resistance assessment module 104, a key ecological element identification module 105, and a security pattern generation module 106. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0028] In this embodiment of the invention, in the ecological security pattern construction system based on the minimum cumulative resistance model, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the ecological security pattern construction system based on the minimum cumulative resistance model provided by this embodiment of the invention, the applicability of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the ecological security pattern construction system based on the minimum cumulative resistance model. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0029] The following describes, with reference to specific embodiments, each component of the ecological security pattern construction system based on the minimum cumulative resistance model and its specific workflow: The ecological data preprocessing and source area extraction module 101 is used to spatially register the ecological source data in the target area and perform feature density statistics on the registered data to obtain the ecological source area distribution map of the ecological source data. In this embodiment of the invention, when the ecological data preprocessing and source area extraction module performs spatial registration of ecological source data in the target area and performs feature density statistics on the registered data to obtain the ecological source area distribution map of the ecological source data, it is specifically used for: Integrate vegetation cover data and species distribution data of the target area into multi-source ecological data of the target area; Geometric correction is performed on multi-source ecological source data to obtain spatial registration data of multi-source ecological source data; Kernel density statistics were performed on vegetation cover data and species distribution data in spatial registration data to obtain vegetation density surface and species distribution density surface of multi-source ecological data. By fusing the vegetation density surface with the species distribution density surface, an ecological source distribution map of the target area is obtained.

[0030] Vegetation cover data is typically presented in raster form, containing core information such as vegetation cover and vegetation type for each raster cell. Species distribution data is mostly in vector form, recording the coordinates of species locations or the polygonal distribution range. The integration process first uses the administrative coordinate system of the target area as a unified spatial reference benchmark. If the original spatial reference systems of the two types of data are inconsistent with this benchmark, the coordinate system of the data must be adjusted by comparing with the geographic coordinate benchmark point to ensure complete spatial reference consistency. Subsequently, the vector information in the species distribution data is associated with the raster cells of the vegetation cover data cell by cell. For species distribution location data, it is determined which vegetation cover raster cell each location falls into, and the corresponding species name, existence status, and other attribute information are added to the attribute table of the corresponding raster cell. For species distribution polygonal data, the overlap area ratio between each polygon and the vegetation cover raster cell is calculated, and the overlap ratio, species type, and other attribute information are mapped to the relevant raster cells. At the same time, the original key attributes such as vegetation cover and vegetation growth cycle in the vegetation cover data are retained to ensure that each grid cell contains both vegetation cover characteristics and species distribution information. This results in multi-source ecological data that integrates the core attributes of both types of data, has consistent spatial reference, and has no conflicting attribute information.

[0031] The core of geometric correction is to eliminate spatial location deviations caused by factors such as sensor accuracy limitations, terrain undulations, and data transmission errors during data acquisition, ensuring that the spatial locations of the two types of data in multi-source ecological data perfectly match the real geographical scene. First, at least 20 evenly distributed ground control points with clear geographical markers are selected within the target area, including road intersections, bridge endpoints, mountain peaks, and river confluences. The true geographical coordinates of each control point are obtained through on-site GPS measurements. Simultaneously, the pixel coordinates or vector vertex coordinates corresponding to each control point are determined in the vegetation cover data and species distribution data, respectively. Then, the differences between the true geographical coordinates of each control point and the coordinates in the data are compared one by one. For vegetation cover data, the row and column positions of each raster unit are adjusted according to the difference value to ensure that the geographical coordinates of the raster unit accurately correspond to the true location. For species distribution data, the spatial positions of polygons or points are corrected by modifying the coordinate values ​​of the vector element vertices to ensure that the vector graphics are consistent with the actual distribution range. During the calibration process, the calibration error of each control point is checked one by one. If the error of a single control point exceeds the preset range, the coordinate data of that control point is rechecked and calibrated again until the calibration errors of all control points are within a reasonable range. Finally, spatial registration data with accurate spatial position and complete spatial matching between the two types of data are obtained.

[0032] When performing kernel density statistics on vegetation cover data in spatially registered data, the size of the statistical spatial unit is first determined based on the ecological survey accuracy requirements of the target area. This ensures that the unit size can distinguish vegetation density differences between different areas while retaining sufficient detail. A fixed neighborhood is delineated around each statistical spatial unit, centered on the vegetation distribution pattern of the target area. This neighborhood must cover the surrounding vegetation areas that can influence the density characteristics of the central unit. Subsequently, the cover values ​​of all vegetation cover raster units within the neighborhood are extracted, summed, and then divided by the total number of raster units within the neighborhood to obtain the vegetation density value of the central statistical unit. This value directly reflects the vegetation density of the central unit and its surrounding areas. When performing kernel density statistics on species distribution data in spatial registration data, the same statistical spatial unit size as the vegetation cover data is used. The same neighborhood range is defined with each statistical unit as the center. If the species distribution data is point data, the total number of species points contained in the neighborhood range is counted. The total number is divided by the neighborhood area to obtain the number of species points in the unit area, which is used as the species distribution density value of the central unit. If the species distribution data is polygon data, the coverage area of ​​the species distribution polygon in the neighborhood range is calculated. The coverage area is divided by the total neighborhood area to obtain the species distribution ratio, which is used as the species distribution density value of the central unit. After completing the calculation of all statistical spatial units, continuous vegetation density surfaces and species distribution density surfaces are formed respectively.

[0033] The layer fusion process is based on vegetation density and species distribution density surfaces, with identical statistical spatial units for both types of surfaces to ensure precise spatial correspondence during fusion. First, each statistical spatial unit is used as the basic unit, and the vegetation density value on the vegetation density surface and the species distribution density value on the species distribution density surface are extracted simultaneously. Judgment criteria are set according to the core attribute requirements of the ecological source area: units where both vegetation density and species distribution density values ​​reach a preset high-density threshold are classified as core ecological source areas; units where vegetation density reaches the high-density threshold but species distribution density is close to it, or vice versa, are classified as potential ecological source areas; units where neither density value reaches the threshold is classified as non-ecological source areas. During the judgment process, both density data points for each unit are fully considered to ensure objective, accurate, and unbiased results. After determining the ecological source area level of all statistical spatial units, the units of different levels are continuously spliced ​​together according to their corresponding spatial locations to clearly define the distribution range and boundaries of core ecological source areas and potential ecological source areas. At the same time, the corresponding vegetation density value and species distribution density value are retained in the attribute information of each unit, and finally an ecological source area distribution map that can comprehensively and intuitively reflect the spatial distribution pattern and density characteristics of ecological source areas in the target area is formed.

[0034] The resistance factor extraction module 102 is used to perform inhibition effect analysis on the ecological source distribution map to obtain the resistance factor of the ecological source data. In this embodiment of the invention, when the resistance factor extraction module performs an inhibitory impact analysis on the distribution map of ecological source areas to obtain the resistance factor of the ecological source data, it is specifically used for: Collect land use type data for the target area; Based on land use type data, the degree of inhibition between human activities and ecological security in the target area is assessed to obtain the disturbance intensity level of the target area. By coupling the ecological source area distribution map with the interference intensity level data, the interference impact in local spaces in the ecological source area distribution map can be identified. Based on the type and intensity of the disturbance, a resistance factor for the ecological source data is generated.

[0035] The resistance factor extraction module, when coupling the ecological source area distribution map with the interference intensity level data to identify the interference impact in local spaces within the ecological source area distribution map, is specifically used for: The ecological source area space and the disturbance intensity level in the ecological source area distribution map are overlaid to obtain the overlay area of ​​the ecological source area distribution map. Based on the spatial location relationship of the ecological source area distribution map, the connectivity of the superimposed area is evaluated to obtain the mapping relationship between the spatial location relationship and the disturbance intensity level. Based on the mapping relationship, the disturbance impacts in local spaces of the ecological source area distribution map are determined.

[0036] When collecting land use type data for the target area, the first step is to define the data collection scope as the entire target area, ensuring data coverage of all land and key ecological areas at the land-water interface within the target area. The core content collected includes the land use types, specific distribution ranges, boundary outlines, and current land use status of various land types within the target area. Land use types need to be subdivided into core categories such as construction land, cultivated land, forest land, grassland, water area, and unused land, with each category corresponding to a clear description of its land use characteristics. The data collection method combines on-site surveys with verification of authoritative existing data. During on-site surveys, representative points of different geomorphic units within the target area are selected using stratified sampling. The actual land use type and current status are recorded on-site, and high-precision geographic coordinate positioning equipment is used to determine the accurate spatial location of each point. Authoritative existing data is selected from the latest land use status survey data released by the local natural resources department. The point data obtained from the on-site surveys are compared with the existing data region by region. Areas with discrepancies are re-verified on-site, and deviations in the existing data are corrected. At the same time, attribute information is supplemented for the collected data to ensure that each land use type includes key auxiliary information such as utilization intensity and years of use, ultimately forming land use type data for the target area that is comprehensive, clearly categorized, spatially accurate, and complete in attribute information.

[0037] When conducting correlation assessments based on land use type data, the core logic of the assessment is first clarified: land use type directly determines the intensity and mode of human activities, and the intensity of human activities is positively correlated with the degree of ecological security inhibition. First, the forms of human activities corresponding to each land use type are clearly defined: construction land corresponds to high-intensity human activities such as urban construction, industrial production, and transportation operations; arable land corresponds to medium-intensity human activities such as agricultural cultivation and pesticide and fertilizer application; forest land, grassland, and water areas correspond to low-intensity human activities primarily focused on ecological protection; and unused land corresponds to extremely low-intensity activities with minimal human intervention. Subsequently, the specific manifestations of the inhibitory effects of different human activities on ecological security are analyzed. High-intensity activities on construction land directly damage surface vegetation cover, alter soil structure, and block ecological corridors, strongly inhibiting the integrity and stability of the ecosystem; agricultural activities on arable land affect the surrounding ecological environment through soil disturbance and the release of chemical substances, resulting in a moderate degree of ecological security inhibition; low-intensity activities on ecological land such as forest land have minimal disturbance to the ecosystem and basically no inhibitory effect; and unused land has no significant inhibitory effect caused by human interference. Based on the differences in the degree of suppression mentioned above, the interference intensity of the target area is divided into four levels: high, medium, low, and extremely low. Among them, construction land corresponds to high-intensity interference, cultivated land corresponds to medium-intensity interference, forest land, grassland, and water area corresponds to low-intensity interference, and unused land corresponds to extremely low-intensity interference. This results in interference intensity level data that covers the entire target area and clearly defines the interference intensity level for each area.

[0038] When coupling the ecological source area distribution map with the disturbance intensity level data, the spatial reference system and grid cell size of both are first unified to ensure accurate spatial matching between the two types of data. The ecological source area distribution map contains the spatial distribution and boundary information of core and potential ecological source areas, while the disturbance intensity level data contains the spatial distribution information of disturbance intensity levels across the entire region. The coupling process uses the grid cells of the ecological source area distribution map as the basic unit, extracting the corresponding disturbance intensity level data cell by cell, and adding the disturbance intensity level attribute to the attribute table of the corresponding grid cell in the ecological source area distribution map to achieve attribute association between the two types of data. Based on this, the matching relationship between the disturbance intensity level and the ecological source area type of each local space within the ecological source area is analyzed in detail. For example, if a core ecological source area grid cell corresponds to a high-intensity disturbance level, it indicates that the local space is strongly disturbed by human activities; if a potential ecological source area grid cell corresponds to a medium-intensity disturbance level, it indicates that the local space has a certain degree of human disturbance; if an ecological source area grid cell corresponds to a low-intensity or very low-intensity disturbance level, it indicates that the local space is less affected by disturbance. At the same time, the spatial spread of the interference impact is clarified, and the potential impact of non-ecological source areas with high interference intensity levels on the local space of surrounding ecological source areas is analyzed. Finally, the type of interference impact, specific spatial location and impact range of each local space in the ecological source distribution map are accurately identified.

[0039] When generating resistance factors based on the type and intensity of disturbance impacts, the identified disturbance impacts are first categorized into four types: high-intensity anthropogenic disturbances dominated by construction land, agricultural activity disturbances dominated by cultivated land, low-intensity ecological protection disturbances, and extremely low-intensity natural disturbances. The first two types are the core disturbance types that significantly hinder ecological sources, while the latter two have negligible resistance effects. For each core disturbance type, the specific representation of the resistance factor is determined based on its intensity level. For high-intensity anthropogenic disturbances dominated by construction land, an "anthropogenic construction activity resistance factor" is generated. This factor clearly defines the spatial distribution range of the high-intensity disturbance and describes in detail the specific resistance manifestations of the disturbance on ecological sources, namely, hindering species migration within the ecological source, damaging vegetation growth environments, and reducing ecosystem connectivity. For agricultural activity disturbances dominated by cultivated land, an "agricultural cultivation activity resistance factor" is generated, corresponding to the spatial distribution range of medium-intensity disturbances, describing its resistance manifestations as affecting soil ecological functions, interfering with species habitats, and slowing down ecological processes. For low-intensity and extremely low-intensity disturbances, because their resistance effects on ecological sources are weak, no separate resistance factors are generated. The resistance factors in the final generated ecological source data must fully include core information such as factor type, corresponding disturbance intensity level, spatial distribution range, and specific resistance performance. This ensures that each resistance factor accurately corresponds to the disturbance impact borne by the ecological source area, providing clear resistance basis for subsequent ecological source protection and ecological corridor construction.

[0040] Before overlaying the ecological source area distribution map and the disturbance intensity level data, it is necessary to unify their spatial reference systems. The administrative coordinate system of the target area should be used as the benchmark. If the original spatial reference of either the ecological source area distribution map or the disturbance intensity level data is inconsistent with this benchmark, the data coordinates should be adjusted by comparing them with landmark geographic coordinate points within the target area to ensure accurate spatial correspondence between the two types of data. The ecological source area distribution map includes vector areas and corresponding attribute information of core ecological source areas and potential ecological source areas. The disturbance intensity level data is vector area data covering the entire target area, with each area labeled with four disturbance intensity levels: high, medium, low, and extremely low. During the layer overlay process, based on the vector area of ​​the ecological source area distribution map, the vector area of ​​the interference intensity level data is covered region by region to the corresponding spatial range of the ecological source area distribution map. The overlapping areas are fused, retaining the type attribute of the ecological source area and the level attribute of the interference intensity level data. The interference intensity level area outside the ecological source area is not included in the fusion range, and only the overlay result within the spatial range of the ecological source area is retained. Finally, an overlay area of ​​the ecological source area distribution map is formed that simultaneously carries the ecological source area attribute and the corresponding interference intensity level attribute, and the spatial range is completely consistent with the ecological source area distribution map.

[0041] When assessing the connectivity of superimposed areas based on the spatial relationships of ecological source area distribution maps, the core dimensions for judging spatial relationships are first clarified as the adjacency status and the existence of connectivity paths between superimposed areas. First, the boundary information of each ecological source area within the superimposed area is examined one by one to determine whether two adjacent ecological source areas share a boundary. If a shared boundary exists, they are considered directly adjacent. If no shared boundary exists, it is checked whether there are continuous low-interference-intensity areas between the two areas as potential connectivity channels. The validity of the channel is confirmed by on-site verification of the surface cover in the channel area. If a valid connectivity channel exists, it is considered indirectly connected; otherwise, it is considered disconnected. After clarifying the spatial relationships of each superimposed surface region, the interference intensity level corresponding to each surface region is extracted, and a one-to-one correspondence is established. For example, when two directly adjacent superimposed surface regions correspond to high-intensity interference and medium-intensity interference respectively, a mapping relationship of "directly adjacent - high intensity - medium intensity" is formed; when two indirectly connected superimposed surface regions both correspond to low-intensity interference, a mapping relationship of "indirectly connected - low intensity - low intensity" is formed; when non-connected superimposed surface regions correspond to extremely low-intensity interference and high-intensity interference respectively, a mapping relationship of "non-connected - extremely low intensity - high intensity" is formed. Finally, the complete mapping relationship between spatial position and interference intensity level is obtained.

[0042] When determining the local spatial disturbance impacts in the ecological source area distribution map based on mapping relationships, the mapping relationships are first classified and sorted to clarify the disturbance logic corresponding to different mapping relationships. For "directly adjacent" mapping relationships, if there is a combination of high and low disturbance intensity levels in adjacent areas, it is determined that the high-intensity area has a diffuse disturbance impact on the local space of the adjacent low-intensity ecological source area, and the disturbance impact is manifested as the infiltration of human activities in the high-intensity area into the low-intensity ecological source area; if both adjacent areas have high disturbance intensity levels, it is determined that the local space has a superimposed disturbance impact, and the disturbance impact is manifested as the enhanced destructive effect of the dual high-intensity human activities on the ecological source area. For "indirectly connected" mapping relationships, if the connecting channel has a low disturbance intensity level, the high-intensity area has a conductive disturbance impact on the local space of the distant ecological source area through the channel, and the disturbance impact is manifested as the ecological pressure in the high-intensity area being gradually transmitted along the channel; if the connecting channel has a medium disturbance intensity level, it is determined that the disturbance impact is weakened during the transmission process, forming an attenuated disturbance impact. For "disconnected" mapping relationships, high-intensity areas have no direct interference impact on the local spaces of other ecological source areas, only creating localized concentrated interference impacts within their own local spaces; while low or extremely low-intensity areas have no significant anthropogenic interference impacts on their local spaces. Based on this logic, the mapping relationship type corresponding to each local space in the overlay areas is matched one by one to clarify the type of interference impact for each local space. At the same time, the source area and scope of the interference impact are marked, ultimately determining the interference impact of all local spaces in the ecological source area distribution map.

[0043] The dynamic resistance surface construction module 103 is used to adjust the weight of resistance factors based on the ecological temporal changes of the target area, and to overlay the adjusted resistance factors with spatial data to obtain the dynamic resistance surface of the target area. In this embodiment of the invention, when the dynamic resistance surface construction module performs weight adjustment on the resistance factor based on the ecological temporal changes of the target area, and performs spatial data superposition on the adjusted resistance factor to obtain the dynamic resistance surface of the target area, it is specifically used for: By comprehensively organizing and compiling the historical ecological time-series data of the above-mentioned target areas, the historical ecological impact data of the target areas are obtained. Based on the ecological temporal changes and historical ecological impact data of the target area, a spatiotemporal correlation analysis of the resistance factors is conducted to obtain the ecological evolution law of the target area. Based on the laws of ecological evolution, establish weight adjustment rules for resistance factors; Based on the weight adjustment rules, the resistance factors are assigned differentiated weights to obtain a series of weighted resistance factors for the target area. By superimposing and fusing spatial data with a series of weighted resistance factors, a dynamic resistance surface for the target area is obtained.

[0044] When the dynamic resistance surface construction module executes the weight adjustment rules for resistance factors established based on ecological evolution laws, it is specifically used for: Based on the laws of ecological evolution, fluctuation detection is performed on the ecological security impact trend of resistance factors to obtain information on the changing trend of resistance factors; Based on the trends in annual rainfall and annual average temperature in historical ecological impact data, climate driving factors for the target region are constructed. By synergistically correlating historical ecological impact data with information on changing trends, the fluctuation mapping relationship of resistance factors is obtained; Based on the fluctuation mapping relationship, a weight adjustment correspondence table for climate driving factors and drag factors is determined; The weight adjustment correspondence table is used as the weight adjustment rule for the resistance factor.

[0045] When comprehensively organizing historical ecological time-series data for the target area, the core scope of the data is first defined, encompassing nearly 30 years of vegetation cover, species distribution, land use type, and corresponding disturbance intensity records. All data must be divided into time-series stages on an annual basis to ensure continuity and completeness over time. A unified spatial reference system is established, using the administrative coordinate system of the target area as a benchmark. Coordinate calibration is performed on time-series data from different sources and periods. By comparing the coordinate information of fixed geographic landmarks within the target area, the spatial location of the data is adjusted to ensure complete consistency in the spatial range and accuracy of all time-series data. Subsequently, the data is categorized and sorted by annual time series, extracting core ecological indicators for each year, including vegetation cover area, distribution range of dominant species, proportion of major land use types, and corresponding disturbance intensity levels. Invalid information, such as missing or ambiguous records, is removed. Valid data is then linked by year to establish an index, clarifying the correspondence between ecological indicators and disturbance factors in different years. This ultimately forms historical ecological impact data for the target area, covering complete time-series stages, with a unified spatial reference, complete indicator information, and clear correlations.

[0046] When conducting spatiotemporal correlation analysis based on the ecological temporal changes and historical ecological impact data of the target area, the changes in ecological indicators for each year are first extracted from the historical ecological impact data to clarify the core characteristics of ecological temporal changes, including the increasing or decreasing trend of vegetation cover area, the migration direction of species distribution range, and the conversion pattern of land use types. These ecological temporal change characteristics are then linked to the corresponding time stages and spatial locations, and then the types of resistance factors and disturbance intensity data of each spatial location at the corresponding time stage are correlated one by one. By analyzing the impact results of different resistance factors on ecological indicators at different time stages, the changing trends of the ecosystem under the influence of resistance factors are summarized. For example, under the continuous influence of human construction activities, the vegetation cover area decreases year by year and the species distribution range gradually shrinks in a certain area; or under the influence of agricultural cultivation activities, the vegetation cover of the ecological source area around cultivated land shows a periodic change with the cultivation season. Finally, the ecological evolution law of the target area reflecting the correlation between temporal evolution, spatial distribution, and the influence of resistance factors is extracted.

[0047] When establishing weight adjustment rules for resistance factors based on ecological evolution laws, the core basis for weight adjustment is first to clarify that the influence and duration of different resistance factors on ecological temporal changes are crucial. Key conclusions are extracted from ecological evolution laws to distinguish between core resistance factors that dominate ecological changes and secondary resistance factors with smaller impacts. For example, if the laws show that the destructive effects of human construction activities on the ecosystem are long-lasting, wide-ranging, and cause irreversible ecological changes, then these are identified as dominant resistance factors. Agricultural activities, on the other hand, have seasonal and partially recoverable impacts and are therefore identified as secondary resistance factors. Based on this, the core logic for weight adjustment is set as follows: dominant resistance factors are assigned higher weights, and secondary resistance factors are assigned lower weights. Furthermore, the weights change over time in accordance with ecological evolution laws; for instance, the weight of dominant resistance factors gradually increases as their duration of influence lengthens, while the weight of secondary resistance factors is adjusted in stages according to the seasonal fluctuations in their impact. At the same time, the applicable scenarios and time points for weight adjustment are clarified. For example, in the stage of accelerated ecological degradation, the weight adjustment range of the dominant resistance factor is increased, and finally a set of weight adjustment rules for resistance factors that are completely in line with the laws of ecological evolution and clarify the weight assignment standards and adjustment logic of various resistance factors are formed.

[0048] When assigning differentiated weights to resistance factors based on weight adjustment rules, a comprehensive review of the types, spatial distribution, and corresponding time stages of all resistance factors within the target area is first conducted. Each resistance factor is then compared against the weight adjustment rules to determine its base weight. Dominant resistance factors are assigned a high base weight, while secondary resistance factors are assigned a low base weight. Next, based on the time characteristics of resistance factors in ecological evolution, the base weights are adjusted time-dimensionally. For example, human construction activity resistance factors are assigned a high base weight in the initial stage of their effect, gradually increasing the weight value as the effect continues; agricultural cultivation activity resistance factors are assigned a base weight during the crop growing season and a lower weight value during the non-growing season. During the weight assignment process, the weight information of each resistance factor is bound to its corresponding spatial location and time stage, ensuring that each resistance factor has a clear and unique weight value under different spatiotemporal conditions. This ultimately results in a series of weighted resistance factors covering all spatiotemporal scenarios, with each resistance factor possessing a precise weight attribute for the target area.

[0049] When spatially overlaying and fusing serialized weighted drag factors, the spatial grid cell size and spatial reference system of all serialized weighted drag factors are first unified to ensure precise spatial alignment of weighted drag factors of different types and time stages. Overlaying and fusing are carried out sequentially according to time stages. Within each time stage, using the spatial grid of the target area as the basic unit, the weight values ​​of all serialized weighted drag factors within that unit are extracted. The comprehensive drag value within the unit is determined by summing and integrating the weight values ​​of all drag factors within the unit; the sum is used as the comprehensive drag representation value of that grid unit. Simultaneously, the corresponding drag factor type, individual weight value, and time stage information are retained in the attribute information of each grid unit to ensure the traceability of the overlay results. After completing the unit-by-unit overlay and fusion of all time stages, static drag surface data for each time stage is obtained. The static drag surfaces of all time stages are then concatenated and integrated in chronological order to form a continuous dataset that dynamically reflects the distribution and intensity changes of drag at different time stages. Finally, a dynamic drag surface of the target area is obtained, which can intuitively present the spatiotemporal dynamic evolution characteristics of drag in the target area.

[0050] When conducting fluctuation detection based on ecological evolution patterns, the ecological security impact records corresponding to each resistance factor are first extracted from these patterns. These records contain changes in the core state indicators of the ecosystem under the influence of resistance factors at different time stages. The ecological security impact records corresponding to the same resistance factor are arranged chronologically to form a continuous temporal impact sequence. By comparing the ecological security impact states of adjacent time stages, it is determined whether the influence of resistance factors on ecological security is increasing, decreasing, or stable. The time points and durations of changes in the impact state are also recorded. For impact sequences exhibiting periodic characteristics, the start and end times of the cycle and the trajectory of impact intensity changes within each cycle are clearly defined. This fully captures the temporal fluctuation characteristics of the resistance factor's impact on ecological security, ultimately obtaining information on the changing trends of resistance factors, including the type of influence trend, the time points of change, the amplitude of fluctuations, and the duration of the changes.

[0051] Annual rainfall and average annual temperature data for the target area over the past 30 years were extracted from historical ecological impact data and organized chronologically to form time-series series of annual rainfall and average annual temperature. The differences in rainfall and average temperature between adjacent years were calculated annually. The sign of the difference indicates the direction of annual variation in rainfall and average temperature, while the magnitude of the difference indicates the magnitude of the variation, thus revealing trends in annual rainfall and average annual temperature. These two types of trend information were correlated and integrated to identify the combined characteristics of their changes within the same time period. For example, a period of sustained increase in rainfall and a steady rise in average temperature, or a period of periodic fluctuation in rainfall and a stable average temperature. These integrated trend combinations were used as core characteristics to construct climate driving factors for the target area that reflect the core features of its temporal climate changes.

[0052] First, historical ecological impact data is segmented chronologically into several stages that correspond to the time nodes of the trend information, ensuring that the ecological state of the historical ecological impact data corresponds to the impact trend of the trend information within each stage. Specific values ​​of ecological state indicators are extracted from the historical ecological impact data for each stage, along with the trend information of resistance factors within the corresponding stage, establishing a one-to-one correspondence between ecological state indicators and resistance factor trends within the same stage. By analyzing the correlation between the differences in resistance factor trends and the differences in ecological state indicator changes across different stages, the response pattern of ecological state indicators when resistance factors exhibit different trends is clarified. For example, when the resistance factor's influence trend is increasing, the ecological state indicator values ​​show a decreasing trend; when the resistance factor's influence trend is stable, the ecological state indicator values ​​remain stable. Ultimately, a fluctuation mapping relationship of resistance factors is formed, reflecting the correspondence between the resistance factor's trend and ecological state fluctuations.

[0053] First, the ecological state fluctuation characteristics corresponding to the changing trends of different resistance factors are extracted from the fluctuation mapping relationship. Then, combined with the different trend combinations of climate driving factors, the moderating effect of climate driving factors on the correlation between the changing trends of resistance factors and ecological state fluctuations is analyzed. For example, when the climate driving factor is "continuously increasing rainfall + steadily rising average temperature", the ecological state decline corresponding to the strengthening trend of the same resistance factor is greater; when the climate driving factor is "stable rainfall + unchanged average temperature", the ecological state decline corresponding to the strengthening trend of the resistance factor is relatively smaller. Based on this moderating effect, the adjustment direction and adjustment magnitude of the weights of each resistance factor under different combinations of climate driving factors are determined. For example, under a certain combination of climate driving factors, the weight of the human construction activity resistance factor needs to be increased, while the weight of the agricultural cultivation activity resistance factor needs to be slightly decreased. The different combination types of climate driving factors, the corresponding resistance factor types, the weight adjustment direction, and the adjustment magnitude are systematically organized into a clear tabular form, finally obtaining a correspondence table of weight adjustments between climate driving factors and resistance factors.

[0054] The core components of the weight adjustment correspondence table are clearly defined, including the combination type of climate driving factors, the specific type of resistance factor, the direction of weight adjustment, the magnitude of weight adjustment, and the applicable time period. These elements fully cover the core logic and execution standards for resistance factor weight adjustment. Using this correspondence table as the core basis for resistance factor weight adjustment, subsequent weight assignment only requires referring to the current combination type of climate driving factors in the target area and querying the table for the corresponding resistance factor's adjustment direction and magnitude. The weight adjustment operation can then be directly executed without needing to formulate additional adjustment logic. Therefore, the weight adjustment correspondence table is directly used as the rule for resistance factor weight adjustment.

[0055] The cumulative resistance assessment module 104 is used to conduct a cumulative spatial diffusion resistance assessment on the source area distribution map based on the dynamic resistance surface and the minimum cumulative resistance model of the target area, and obtain the cumulative resistance surface of the target area. In this embodiment of the invention, when the cumulative resistance assessment module executes a minimum cumulative resistance model based on the dynamic resistance surface and the target area to perform a cumulative spatial diffusion resistance assessment on the source area distribution map and obtain the cumulative resistance surface of the target area, it is specifically used for: Input the dynamic resistance surface and the student origin distribution map into the minimum cumulative resistance model of the target area; Based on the distribution map of the place of origin, the spatial starting point for the ecological diffusion simulation of the target area is determined; Under the constraint of dynamic resistance surface, an anisotropic spatial diffusion simulation is performed on the spatial starting point, and the cumulative resistance value of the path traversed from the spatial starting point to the spatial node in the target region is statistically analyzed. The cumulative resistance values ​​under different paths are numerically compared, and the minimum cumulative resistance value is taken as the final resistance value of the spatial node. The final resistance values ​​of spatial nodes within the target area are collected to generate the cumulative resistance surface of the target area.

[0056] When performing the cumulative resistance assessment module to determine the spatial starting point of the ecological diffusion simulation of the target area based on the biodiversity distribution map, it is specifically used for: Key ecological analysis was conducted on the high-quality ecological areas in the distribution map of birthplaces to obtain the core ecological source areas of the birthplace distribution map; Spatial cluster analysis was performed on the core area of ​​the ecological source to obtain the ecological source patches in the core area of ​​the ecological source. Based on the area, shape characteristics, and connectivity constraints of the ecological source patches, the location of the iconic pixels of the ecological source patches is generated. The location of representative pixels is determined as the spatial starting point for the ecological diffusion simulation of the target area.

[0057] The dynamic resistance surface contains the resistance values ​​of grid cells and their corresponding spatial coordinates for different time periods within the target area. The origin distribution map contains vector areas and spatial location data for core and potential origin areas. Before input, the spatial reference systems of both must be unified, using the administrative coordinate system of the target area as a benchmark. By comparing the coordinates of fixed geographic landmarks within the target area, the spatial positions of the two types of data are adjusted to ensure precise spatial alignment between the grid cells and the origin area. Simultaneously, according to the data format requirements of the minimum cumulative resistance model, the resistance value attributes of the dynamic resistance surface and the type attributes of the origin distribution map are organized, transforming the spatial coordinates and attribute information of both types of data into a structured data format recognizable by the model. Subsequently, the organized dynamic resistance surface data and origin distribution map data are completely imported into the minimum cumulative resistance model of the target area, ensuring that the model accurately reads the spatial relationships and core attribute information of the two types of data, thus completing the input operation.

[0058] The core biodiversity areas marked on the biodiversity distribution map exhibit strong ecological stability and outstanding diffusion capacity, serving as the core sources of ecological diffusion. Potential biodiversity areas can serve as supplementary diffusion sources. When determining the spatial starting point, the vector area boundaries of all core biodiversity areas on the biodiversity distribution map are first extracted. By identifying the geometric center of each area and key diffusion nodes on its boundaries, these centers and key nodes are used as core spatial starting points. Then, the geometric centers of the vector areas of potential biodiversity areas are extracted as supplementary spatial starting points. The spatial coordinates of all extracted starting points are verified to ensure that the coordinate information of each starting point is completely consistent with the spatial reference system of the biodiversity distribution map. Duplicate starting points that are too close are eliminated. Finally, all spatial starting points for ecological diffusion simulation that possess ecological diffusion initiation capacity, are spatially evenly distributed, and cover the entire biodiversity area are identified.

[0059] Anisotropic spatial diffusion simulations must fully incorporate the spatiotemporal dynamic characteristics of dynamic resistance surfaces. During the simulation, the diffusion process is carried out simultaneously in all directions from each spatial starting point, covering 360 degrees on the horizontal plane to ensure no diffusion direction is missed. The diffusion process strictly adheres to the constraints of the dynamic resistance surface. Upon reaching an adjacent spatial node, the resistance value corresponding to that node is extracted and added to the cumulative resistance value from the starting point to the previous node, yielding the cumulative resistance value from the starting point to the current node. Simultaneously, the trajectory of this path and all nodes traversed are recorded. This diffusion process is repeated for each spatial starting point until the diffusion range covers all spatial nodes in the target area. After completing the diffusion simulation for all starting points, the cumulative resistance values ​​for each spatial node corresponding to different starting points and different paths are statistically analyzed, ensuring that the cumulative resistance values ​​of all reachable paths for each spatial node are completely recorded.

[0060] For each spatial node within the target area, the cumulative resistance values ​​of all paths traversed by that node are first aggregated to form a set of cumulative resistance values ​​for that node. All values ​​in the set are compared one by one, sorted by numerical value, and the smallest cumulative resistance value is selected from the sorted results; this value is the final resistance value for that spatial node. During the comparison process, the calculation trajectory of each cumulative resistance value must be verified to confirm that the corresponding path is truly reachable, and that the accumulated resistance values ​​are complete without omissions or duplications. This ensures that the selected minimum cumulative resistance value accurately reflects the minimum cost of diffusion from the source area to that node. Only one unique final resistance value is retained for each spatial node, and other non-minimum cumulative resistance values ​​are discarded.

[0061] First, the final resistance value of each spatial node is bound to its corresponding spatial coordinates, ensuring that each coordinate point has a clearly defined final resistance value attribute. Using the spatial extent of the target area as the boundary, the spatial nodes bound with final resistance values ​​are continuously stitched together according to the grid cell arrangement order. During the stitching process, the spatial positions of adjacent nodes are seamlessly connected, and the resistance values ​​transition naturally. For blank areas without direct node coverage, continuous interpolation is performed using the final resistance values ​​of adjacent nodes to ensure that the entire target area is covered by resistance values, forming a complete and continuous surface region. Simultaneously, the original data of the final resistance value of each spatial node and its corresponding source path information are retained in the surface region attributes. Finally, a cumulative resistance surface of the target area is generated that can completely and intuitively reflect the distribution of the minimum cumulative resistance required for diffusion from the source area to various spatial locations within the target area.

[0062] The core criteria for determining high-quality ecological areas in the biogeographic origin distribution map are vegetation cover integrity, species habitat suitability, and ecosystem stability. In the key ecological analysis process, core ecological attribute data for all areas in the biogeographic origin distribution map are first extracted, including vegetation cover type, vegetation growth status, dominant species distribution density, and ecological corridor connectivity. The authenticity of the distribution map data is verified through field surveys. The integrity of vegetation cover in each area is checked to confirm the absence of significant vegetation fragmentation. Species habitat suitability is assessed, selecting areas with high frequency of dominant species activity and concentrated breeding sites. Ecosystem stability is assessed, retaining areas with minimal human disturbance and intact ecological structure. Areas that simultaneously meet the criteria of complete vegetation cover, suitable species habitat, and stable ecosystem are designated as high-quality ecological areas. Then, by analyzing the spatial relationships among these areas, isolated and fragmented areas with excessively small areas are eliminated, and spatially continuous high-quality areas are integrated, ultimately forming the core ecological source areas of the biogeographic origin distribution map with clear boundaries and excellent ecological attributes.

[0063] Spatial clustering analysis is based on the spatial continuity of the core ecological source areas. The core logic of clustering is to group spatially adjacent core ecological source areas without significant barriers into the same cluster unit. First, the spatial boundary coordinates of the core ecological source areas are analyzed. Each adjacent core area is compared to see if it shares a boundary or is connected by a continuous low-resistance region. If two core areas share a boundary, they are directly considered spatially connected and classified into the same cluster. If no shared boundary exists, the area between them is checked to see if it is an extension of the core ecological source area, without significant spatial barriers such as artificial structures or wide rivers, and if the ecological attributes of this connecting area are consistent with the core area. In such cases, all three are integrated into the same cluster unit. After a thorough comparison of all core ecological source areas, each independent cluster unit forms a complete spatial patch, ultimately yielding the ecological source patches of the core ecological source areas.

[0064] The determination of area constraints requires that the ecological source patches possess sufficient ecological dispersal support capacity. By measuring the actual coverage of each ecological source patch, patches whose area meets the basic requirements for ecological dispersal are retained, while fragmented patches that are too small to support species dispersal are eliminated. Shape characteristic analysis focuses on the regularity of patch outlines. By observing the continuity of patch boundaries, patches with extremely fragmented boundaries, irregular shapes, and multiple internal breaks are excluded, while patches with continuous boundaries and relatively regular shapes are retained. Connectivity constraints require verifying the connectivity within the patches to ensure there are no significant ecological barriers. By analyzing the vegetation distribution and species activity paths within the patches, it is confirmed that different areas within the patches can be interconnected through natural corridors. Based on the above constraints, when generating the location of the landmark pixel, the geometric center of each patch is first determined. By outlining the boundary of the patch, the coordinates of the center point that can divide the patch area are found. The pixel corresponding to the center point is the initial landmark pixel. Then, the ecological attributes of the pixel are checked to ensure that it is located in an area with dense vegetation cover and frequent species activity. If the ecological attributes of the geometric center pixel are not good, it is adjusted to the nearest high-quality ecological attribute pixel in the surrounding area. Finally, the location of the landmark pixel of the ecological source patch is determined.

[0065] First, the spatial validity of the generated landmark pixel locations is verified to confirm that each pixel is located within its corresponding ecological source patch, and that the ecological environment at the pixel's location can support the initiation of the ecological diffusion process without significant ecological resistance. After verification, the spatial coordinates of these landmark pixel locations are directly used as the starting point for the ecological diffusion simulation. Each landmark pixel location corresponds to an independent spatial starting point, ensuring that all ecological source patches have a corresponding spatial starting point coverage, and that the starting point accurately represents the core ecological diffusion area of ​​each patch. Finally, these representative pixel locations that have passed validity verification are officially determined as the spatial starting point for the ecological diffusion simulation of the target area, providing a precise starting location basis for subsequent spatial diffusion simulations.

[0066] The key ecological element identification module 105 is used to identify key ecological elements on the cumulative resistance surface to obtain the ecological corridors and ecological nodes of the target area. In this embodiment of the invention, when the key ecological element identification module performs key ecological element analysis on the cumulative resistance surface to obtain the ecological corridors and ecological nodes of the target area, it is specifically used for: The cumulative resistance surface is reclassified by grid, and multi-level resistance partitions of the target area are generated based on the range of the final resistance value. In a multi-level resistance zone, areas where the final resistance value is lower than a preset threshold are identified as potential low-resistance channels for the target area. Morphological refinement and connectivity analysis are performed on potential low-resistance channels to extract continuous low-resistance paths. Spatial correlation analysis was conducted between continuous low-resistance paths and ecological source patches in the ecological source distribution map to select paths connecting different ecological source patches as ecological corridors in the target area. Identify the intersection points of ecological corridors and the boundary points between ecological corridors and ecological source patches, and determine the boundary points and intersection points as ecological nodes in the target area.

[0067] The cumulative resistance surface is presented in raster data form, with each raster cell carrying a unique final resistance value. The raster reclassification process first clarifies the spatial reference system and raster cell size, ensuring that the spatial location of all rasters accurately corresponds to the actual geographic coordinates of the target area. Then, based on the range of final resistance values, level intervals are divided. The level division is based on the resistance carrying capacity for ecological diffusion, setting intervals from low to high, with each interval corresponding to a resistance level. Each raster cell in the cumulative resistance surface is individually assessed, and its final resistance value is assigned to the corresponding level interval. Simultaneously, each level is assigned a unique identifier to ensure that raster cells of different resistance levels can be clearly distinguished. After completing the level classification of all raster cells, raster cells of the same level are continuously integrated according to their spatial location, forming areas with clear boundaries and well-defined level attributes. This ultimately generates a multi-level resistance partitioning of the target area, covering the entire target region and containing multiple resistance level areas.

[0068] The preset threshold is determined based on the minimum resistance tolerance range for natural species dispersal between ecological source patches, ensuring that areas below this threshold can provide smooth pathways for species dispersal. In multi-level resistance zoning, the final resistance value range corresponding to each resistance level is checked for each area to determine whether the resistance value of that level is completely below the preset threshold. For areas with resistance levels completely below the preset threshold, they are directly included in the candidate range for potential low-resistance pathways. For areas where some grid cells have resistance values ​​below the threshold and some above the threshold, cells with resistance values ​​below the preset threshold are extracted grid by grid. These scattered cells are integrated according to spatial adjacency, and isolated, fragmented grids that cannot form continuous pathways are eliminated. Through the above screening and integration, potential low-resistance pathways are finally formed in the target area, consisting of continuous grid cells, where all resistance values ​​meet the requirement of being below the preset threshold.

[0069] The morphological refinement process focuses on extracting the core framework of potential low-resistance channels. First, it analyzes the grid distribution pattern of these channels, identifying redundant grid units at the channel edges. These redundant units are typically scattered extensions of the channel edges and do not affect the core connectivity function. These redundant grid units are then removed region by region, retaining only the core grid units that maintain channel connectivity. This narrows the width of the potential low-resistance channel to reflect its core orientation while ensuring its continuity and integrity are not compromised. During connectivity analysis, the grid continuity of the refined channel is checked segment by segment. If grid breaks are found, it is verified whether the grid units on either side of the break belong to the same resistance level and are spatially close. The ecological attributes of the broken area are also confirmed to meet the requirements for low-resistance channels. If so, the break is connected by adding adjacent low-resistance grid units. After refinement and connectivity restoration, continuous low-resistance paths of potential low-resistance channels with continuous grids, clear orientation, and no breaks are extracted.

[0070] First, a unified spatial reference system was established between the continuous low-resistance pathways and the ecological source area distribution map to ensure precise spatial alignment. The ecological source area distribution map contains multiple independent ecological source area patches, each with clearly defined boundary coordinates and attribute information. The continuous low-resistance pathways were then spatially overlaid with the ecological source area distribution map, and the spatial positions of each path's two ends were examined to determine if the path could spatially intersect with two or more different ecological source area patches. For paths connecting different ecological source area patches, it was further verified whether the resistance value remained at a low level throughout the path, ensuring that the path could provide a stable channel for species dispersal between different ecological source area patches. Pathways that only connected within a single ecological source area patch and could not achieve cross-patch connectivity were eliminated. Finally, paths that effectively connected different ecological source area patches and possessed complete connectivity were selected and identified as ecological corridors for the target area.

[0071] First, a comprehensive analysis of the spatial orientation and distribution trajectory of all ecological corridors was conducted, with each intersection point being checked. When two or more ecological corridors converge at the same spatial location, this intersection point is identified as the convergence point, and its precise geographic coordinates are recorded. Next, the ecological corridors and ecological source area distribution maps were spatially overlaid, focusing on the boundary areas between ecological corridors and ecological source area patches. The edge grids of the ecological corridors were compared segment by segment with the boundary grids of the ecological source area patches. When a corridor grid and a patch boundary grid form spatial contact, this contact point is identified as the boundary point between the ecological corridor and the ecological source area patch, and its precise geographic coordinates are also recorded. All identified intersection and boundary points were then spatially deduplicated, removing duplicate points with completely overlapping coordinates. Finally, these deduplicated boundary and intersection points were combined with the convergence point to determine the ecological nodes of the target area.

[0072] The security pattern generation module 106 is used to construct the ecological security pattern of the target area based on ecological corridors and ecological nodes.

[0073] In this embodiment of the invention, when the security pattern generation module constructs the ecological security pattern of the target area based on ecological corridors and ecological nodes, it is specifically used for: The importance of ecological corridors is classified based on the final resistance value of the ecological corridor and the number of ecological source patches connected, thus generating classified ecological corridors. Ecological nodes are classified into different types, and the classified ecological nodes are generated based on their location on the ecological corridor and their spatial relationship with the ecological source patches. By spatially overlaying and topologically connecting the graded ecological corridors and classified ecological nodes, an ecological network for the target area is formed. Based on the ecological network, and combined with the administrative or natural geographical boundaries of the target area, the core protected areas and ecological buffer zones of the ecological security pattern are delineated. The core protected area, ecological buffer zone, graded ecological corridor and classified ecological node are integrated into a unified spatial data layer and output as an ecological security pattern map of the target area.

[0074] The final resistance value corresponding to each ecological corridor is extracted. This value is directly taken from the final resistance value of the corridor grid unit in the cumulative resistance surface. At the same time, the number of ecological source patches connected by each ecological corridor is counted to clarify the cross-patch connectivity of a single corridor. The importance classification is based on the core logic of "the lower the resistance value and the more connected patches, the higher the importance". First, the final resistance value is divided into two intervals: low and medium. Then, the number of connected patches is divided into two levels: two or more and one, forming a combined classification standard. Ecological corridors with a final resistance value in the low interval and two or more connected patches are identified as Level 1 important ecological corridors; those with a final resistance value in the low interval but one connected patch, or those with a final resistance value in the medium interval and two or more connected patches, are identified as Level 2 important ecological corridors; and those with a final resistance value in the medium interval and one connected patch are identified as Level 3 important ecological corridors. Each level of ecological corridor is assigned a unique level identifier attribute while retaining its original spatial coordinates and resistance value information, ultimately generating a graded ecological corridor with clear level attributes and spatial scope.

[0075] Each ecological node's spatial location was verified individually to determine whether it was situated at the intersection of ecological corridors or at the boundary between an ecological corridor and an ecological source patch. The spatial distance and connectivity between the node and the source patch were also recorded. Node classification was based on their functional positioning. Nodes located at the intersection of two or more ecological corridors were classified as corridor hub nodes, responsible for the transfer and distribution of ecological flows between multiple corridors. Nodes located at the boundary between ecological corridors and source patches, directly connecting the source area and the corridor, were classified as source area entrance / exit nodes, serving as crucial starting channels for the outward diffusion of ecological flows from the source area. Nodes neither located at corridor intersections nor directly connected to the source area were excluded from the classified ecological node scope due to their lack of core ecological functions. Both types of nodes were assigned type identifiers, labeled with their corresponding corridor names or ecological source patch names, ultimately generating a classified set of ecological nodes with clearly defined functional types and complete attribute information.

[0076] First, a unified spatial reference system is established for the graded ecological corridors and categorized ecological nodes to ensure precise alignment of their spatial coordinates and avoid spatial deviations during overlay. During spatial overlay, the spatial data of the categorized ecological nodes are layered onto the graded ecological corridor spatial layer, ensuring each node is precisely located at its corresponding corridor position or the boundary between the corridor and the source area. During topological connection, it is verified that each corridor hub node forms a complete connection with all intersecting ecological corridors, ensuring uninterrupted spatial connectivity between nodes and corridors. It is also verified that each source area entrance / exit node simultaneously connects with both the ecological source area patch boundary and the ecological corridor, ensuring that ecological flow can smoothly enter the corridor from the source area through the nodes. For areas lacking complete connectivity, the connection is repaired by adjusting node coordinates or supplementing corridor grid cells, ultimately forming an ecological network for the target area with precise node-corridor connections and complete connectivity.

[0077] Based on the core components of the ecological network, the delineation of the core protection zone prioritizes covering the complete extent of the ecological source patches, while also incorporating primary important ecological corridors to ensure that the ecological functions of these sources of ecological flow and highly important corridors remain undisturbed. The boundary of the core protection zone is defined using the outermost boundary of the ecological source patches and the outer edge of the primary ecological corridor as benchmarks, extending outward to natural geographical barriers or areas free from human interference, forming a closed protective boundary. The ecological buffer zone is delineated starting from the boundary of the core protection zone, extending outward to cover secondary and tertiary important ecological corridors and all classified ecological nodes. The outer boundary of the buffer zone is determined in conjunction with the administrative or natural geographical boundaries of the target area, ensuring that the buffer zone completely encompasses the core protection zone and low- to medium-level corridors and nodes, forming a layered protection for the core protection zone. The boundary coordinates of the core protection zone and the ecological buffer zone are clearly defined, and their respective protection attribute identifiers are assigned, completing the delineation of both types of areas.

[0078] First, the spatial data of the core protected area, ecological buffer zone, graded ecological corridors, and classified ecological nodes were converted into a unified vector data format to ensure consistent data structure. Simultaneously, the spatial reference system of all data was re-verified to ensure no coordinate deviation. The four types of data were then sequentially overlaid onto the same spatial layer, assigning differentiated visual attributes to different types of elements. For example, the core protected area was filled with dark green, the ecological buffer zone with light green, different widths of green lines for different levels of ecological corridors, and different styles of markers for different types of ecological nodes. Core information for each type of element was added to the layer attribute table, including the protection scope of the core protected area, the functional positioning of the ecological buffer zone, the grade and resistance value of the ecological corridor, and the type and connecting objects of the ecological nodes. Finally, the integrated spatial data layer was output as a visualized ecological security pattern map of the target area, ensuring clear spatial relationships between various elements, traceable attribute information, and a complete presentation of the ecological security protection system of the target area.

[0079] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0080] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0081] 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.

Claims

1. A system for constructing an ecological security pattern based on a minimum cumulative resistance model, characterized in that, The system includes an ecological data preprocessing and source extraction module, a resistance factor extraction module, a dynamic resistance surface construction module, a cumulative resistance assessment module, a key ecological element identification module, and a security pattern generation module, wherein: The ecological data preprocessing and source area extraction module is used to spatially register the ecological source data in the target area and perform feature density statistics on the registered data to obtain the ecological source area distribution map of the ecological source data. The resistance factor extraction module is used to analyze the inhibitory impact of the ecological source distribution map and obtain the resistance factor of the ecological source data. The dynamic resistance surface construction module is used to adjust the weights of resistance factors based on the ecological temporal changes of the target area, and to overlay the adjusted resistance factors with spatial data to obtain the dynamic resistance surface of the target area. The cumulative resistance assessment module is used to conduct a cumulative spatial diffusion resistance assessment on the source area distribution map based on the dynamic resistance surface and the minimum cumulative resistance model of the target area, and obtain the cumulative resistance surface of the target area. The key ecological element identification module is used to identify key ecological elements on the surface with accumulated resistance, thereby obtaining the ecological corridors and ecological nodes in the target area. The security pattern generation module is used to construct the ecological security pattern of the target area based on ecological corridors and ecological nodes.

2. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, The ecological data preprocessing and source area extraction module, when performing spatial registration of ecological source data in the target area and performing feature density statistics on the registered data to obtain the ecological source area distribution map of the ecological source data, is specifically used for: Integrate vegetation cover data and species distribution data of the target area into multi-source ecological data of the target area; Geometric correction is performed on multi-source ecological source data to obtain spatial registration data of multi-source ecological source data; Kernel density statistics were performed on vegetation cover data and species distribution data in spatial registration data to obtain vegetation density surface and species distribution density surface of multi-source ecological data. By fusing the vegetation density surface with the species distribution density surface, an ecological source distribution map of the target area is obtained.

3. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, When performing suppression effect analysis on the ecological source area distribution map to obtain the resistance factor of the ecological source data, the resistance factor extraction module is specifically used for: Collect land use type data for the target area; Based on land use type data, the degree of inhibition between human activities and ecological security in the target area is assessed to obtain the disturbance intensity level of the target area. By coupling the ecological source area distribution map with the interference intensity level data, the interference impact in local spaces in the ecological source area distribution map can be identified. Based on the type and intensity of the disturbance, a resistance factor for the ecological source data is generated.

4. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 3, characterized in that, The resistance factor extraction module, when coupling the ecological source area distribution map with the interference intensity level data to identify the interference impact in local spaces within the ecological source area distribution map, is specifically used for: The ecological source area space and the disturbance intensity level in the ecological source area distribution map are overlaid to obtain the overlay area of ​​the ecological source area distribution map. Based on the spatial location relationship of the ecological source area distribution map, the connectivity of the superimposed area is evaluated to obtain the mapping relationship between the spatial location relationship and the disturbance intensity level. Based on the mapping relationship, the disturbance impacts in local spaces of the ecological source area distribution map are determined.

5. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, When the dynamic resistance surface construction module performs weight adjustment on the resistance factors based on the ecological temporal changes of the target area, and then performs spatial data superposition on the adjusted resistance factors to obtain the dynamic resistance surface of the target area, it is specifically used for: By comprehensively organizing and compiling the historical ecological time-series data of the above-mentioned target areas, the historical ecological impact data of the target areas are obtained. Based on the ecological temporal changes and historical ecological impact data of the target area, a spatiotemporal correlation analysis of the resistance factors is conducted to obtain the ecological evolution law of the target area. Based on the laws of ecological evolution, establish weight adjustment rules for resistance factors; Based on the weight adjustment rules, the resistance factors are assigned differentiated weights to obtain a series of weighted resistance factors for the target area. By superimposing and fusing spatial data with a series of weighted resistance factors, a dynamic resistance surface for the target area is obtained.

6. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 5, characterized in that, When the dynamic resistance surface construction module executes the weight adjustment rules for resistance factors established based on ecological evolution laws, it is specifically used for: Based on the laws of ecological evolution, fluctuation detection is performed on the ecological security impact trend of resistance factors to obtain information on the changing trend of resistance factors; Based on the trends in annual rainfall and annual average temperature in historical ecological impact data, climate driving factors for the target region are constructed. By synergistically correlating historical ecological impact data with information on changing trends, the fluctuation mapping relationship of resistance factors is obtained; Based on the fluctuation mapping relationship, a weight adjustment correspondence table for climate driving factors and drag factors is determined; The weight adjustment correspondence table is used as the weight adjustment rule for the resistance factor.

7. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, The cumulative resistance assessment module, when executing a minimum cumulative resistance model based on the dynamic resistance surface and the target area to perform a cumulative spatial diffusion resistance assessment on the source area distribution map and obtain the cumulative resistance surface of the target area, is specifically used for: Input the dynamic resistance surface and the student origin distribution map into the minimum cumulative resistance model of the target area; Based on the distribution map of the place of origin, the spatial starting point for the ecological diffusion simulation of the target area is determined; Under the constraint of dynamic resistance surface, an anisotropic spatial diffusion simulation is performed on the spatial starting point, and the cumulative resistance value of the path traversed from the spatial starting point to the spatial node in the target region is statistically analyzed. The cumulative resistance values ​​under different paths are numerically compared, and the minimum cumulative resistance value is taken as the final resistance value of the spatial node. The final resistance values ​​of spatial nodes within the target area are collected to generate the cumulative resistance surface of the target area.

8. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 7, characterized in that, When performing the cumulative resistance assessment module to determine the spatial starting point of the ecological diffusion simulation of the target area based on the biodiversity distribution map, it is specifically used for: Key ecological analysis was conducted on the high-quality ecological areas in the distribution map of birthplaces to obtain the core ecological source areas of the birthplace distribution map; Spatial cluster analysis was performed on the core area of ​​the ecological source to obtain the ecological source patches in the core area of ​​the ecological source. Based on the area, shape characteristics, and connectivity constraints of the ecological source patches, the location of the iconic pixels of the ecological source patches is generated. The location of representative pixels is determined as the spatial starting point for the ecological diffusion simulation of the target area.

9. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, When the key ecological element identification module performs key ecological element analysis on the cumulative resistance surface to obtain the ecological corridors and ecological nodes of the target area, it is specifically used for: The cumulative resistance surface is reclassified by grid, and multi-level resistance partitions of the target area are generated based on the range of the final resistance value. In a multi-level resistance zone, areas where the final resistance value is lower than a preset threshold are identified as potential low-resistance channels for the target area. Morphological refinement and connectivity analysis are performed on potential low-resistance channels to extract continuous low-resistance paths. Spatial correlation analysis was conducted between continuous low-resistance paths and ecological source patches in the ecological source distribution map to select paths connecting different ecological source patches as ecological corridors in the target area. Identify the intersection points of ecological corridors and the boundary points between ecological corridors and ecological source patches, and determine the boundary points and intersection points as ecological nodes in the target area.

10. The ecological security pattern construction system based on the minimum cumulative resistance model as described in claim 1, characterized in that, When the security pattern generation module constructs the ecological security pattern of the target area based on ecological corridors and ecological nodes, it is specifically used for: The importance of ecological corridors is classified based on the final resistance value of the ecological corridor and the number of ecological source patches connected, thus generating classified ecological corridors. Ecological nodes are classified into different types, and the classified ecological nodes are generated based on their location on the ecological corridor and their spatial relationship with the ecological source patches. By spatially overlaying and topologically connecting the graded ecological corridors and classified ecological nodes, an ecological network for the target area is formed. Based on the ecological network, and combined with the administrative or natural geographical boundaries of the target area, the core protected areas and ecological buffer zones of the ecological security pattern are delineated. The core protected area, ecological buffer zone, graded ecological corridor and classified ecological node are integrated into a unified spatial data layer and output as an ecological security pattern map of the target area.

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