A nature reserve functional connectivity evaluation system and method based on biological ecological habit research

Abstract

This invention belongs to the field of ecological protection technology, specifically relating to a system and method for assessing the functional connectivity of nature reserves based on the study of biological ecological habits. Specifically, it acquires real-time species activity data through a GPS tracking module, infrared camera, and acoustic signature acquisition device. A dynamic resistance surface model is constructed by combining multi-source geospatial data, and innovatively, the infrared camera's perspective parameter is introduced as a correction factor for the intensity of human interference, enabling dynamic adjustment of resistance values. The corridor connectivity index is calculated using a minimum cumulative resistance model and a circuit theory model, ultimately generating a visual assessment report. This invention overcomes the shortcomings of traditional static assessment methods that cannot reflect the real-time impact of human activities, significantly improving the accuracy and timeliness of ecological corridor planning. In particular, through the intelligent linkage between the stepped guide rail's gear switching mechanism and the infrared camera's perspective, synergistic optimization of adaptive adjustment of monitoring equipment and dynamic correction of the resistance surface is achieved.

Description

Technical Field

[0001] This invention belongs to the field of ecological protection technology, specifically relating to a system and method for assessing the functional connectivity of nature reserves based on the study of biological ecological habits. Background Technology

[0002] Currently, the assessment of functional connectivity in nature reserves mainly relies on static geographic data (such as land use type and road distribution) and species distribution models. Common methods include minimum cumulative resistance (MCR) models and circuit theory models, which generate resistance surfaces by integrating environmental factors such as vegetation cover and topographic slope, thereby simulating species migration paths. In existing technologies, species activity data are mostly collected through GPS tracking or fixed-point cameras, while human disturbance factors are usually estimated indirectly using fixed buffer zones (such as a 500m radius around a road) or historical statistical data.

[0003] Existing methods have significant shortcomings: First, static resistance surfaces cannot reflect the real-time impact of human activities, leading to a disconnect between corridor planning and actual ecological needs. Second, traditional human disturbance assessments rely on manually defined buffer zones or historical data, ignoring real-time signals collected by equipment (such as the correlation between changes in camera perspective and human activities), resulting in biased resistance value calculations. Furthermore, existing systems lack a dynamic coupling mechanism between multi-source data and resistance surface models, making accurate spatiotemporal dynamic assessments difficult. These problems lead to delayed management decisions in protected areas and reduced effectiveness of ecological corridors, necessitating an assessment system that integrates real-time biological monitoring and dynamic resistance surface correction. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for assessing the functional connectivity of nature reserves based on the study of biological ecological habits, in order to solve the problem that the existing traditional methods for assessing the connectivity of nature reserves cannot dynamically integrate real-time species activity data and human interference factors, resulting in deviations between the calculated resistance surface and the actual situation.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] Firstly, this invention proposes a functional connectivity assessment system for nature reserves based on research into biological ecological habits, comprising:

[0007] The biological monitoring module includes a GPS tracking module worn by the species, infrared cameras deployed in the protected area, and a voiceprint acquisition module; the biological monitoring module is used to collect spatiotemporal behavioral data of species in the target area and form a species activity hotspot table.

[0008] The dynamic resistance surface generation module is used to access multi-source geospatial data of the target area and generate a normalized environmental factor raster. Combined with the species activity hotspot table and species parameter library, the resistance surface is calculated using a spatiotemporal dynamic weighting algorithm to generate a dynamic resistance surface raster.

[0009] The connectivity calculation module is used to process the dynamic resistance surface grid based on the minimum cumulative resistance model and the circuit theory model to obtain the output corridor effectiveness index.

[0010] The visualization decision-making module is used to generate a corridor connectivity report based on the corridor effectiveness index and to display it visually.

[0011] Furthermore, the spatiotemporal behavioral data of species in the target area includes:

[0012] Species movement trajectory data recorded by the GPS tracking module;

[0013] Infrared camera images of species activity and timestamps;

[0014] The voiceprint characteristics and frequency of occurrence of species acquired by the voiceprint acquisition module.

[0015] Furthermore, the dynamic resistance surface generation module includes:

[0016] The spatial registration submodule is used to unify remote sensing imagery, road network data, and terrain data into the WGS84 coordinate system, and outputs the registered raster dataset {G1, G2, ..., G...}. n}, G n This is the nth environmental factor raster layer;

[0017] The index fusion submodule is used to calculate and output an environmental factor raster layer E containing vegetation index, road density, and terrain slope. k (x, y), the expression is:

[0018]

[0019] Where (x,y) are the planar coordinates of the raster cell; max(G k ), min(G k ) are the minimum and maximum values ​​of the k-th raster layer, respectively;

[0020] The resistance surface calculation submodule calculates the resistance surface based on the environmental factor grid layer E. k The dynamic drag surface grid R(x,y) is generated from (x,y) using the following expression:

[0021]

[0022] Among them, a kLet β be the weight of the k-th environmental factor, β be the influence coefficient of species activity hotspots, and P be the species activity hotspot table. i Let ||(x,y)-p| represent the coordinates of the i-th hotspot, and δ be the spatial decay radius, whose value is positively correlated with the species' activity range. i ||For pixels and hotspots P i The Euclidean distance.

[0023] Furthermore, the connectivity calculation module includes:

[0024] The resistance path analysis submodule is used to calculate the minimum cost path for species migration using the minimum cumulative resistance model.

[0025] The circuit theory submodule is used to simulate corridor connectivity probability based on current density distribution and generate corridor effectiveness index.

[0026] Furthermore, the biological monitoring module also includes a mounting base, a stepped guide rail disposed on the mounting surface of the mounting base, and a signal receiving submodule connected to the GPS tracking module. The signal receiving submodule adjusts its sliding radius along the stepped guide rail to adapt to GPS tracking module signal capture at different distances.

[0027] The stepped guide rail includes at least three sliding radius settings;

[0028] Each setting corresponds to a different Bluetooth signal strength reception threshold, which is used to trigger the infrared camera's viewing angle adjustment in stages based on the proximity of the GPS tracking module.

[0029] Furthermore, when the received signal of the signal receiving submodule reaches the current level threshold, the infrared camera automatically switches to a preset viewing angle. The relationship between the level threshold and the preset viewing angle of the infrared camera is as follows:

[0030] The first threshold triggers the narrow angle mode, with a focal length of 30°.

[0031] The second threshold triggers the standard viewing angle mode, with a focal length of 60°.

[0032] The third threshold triggers the wide-angle mode with a 120° focal length.

[0033] Furthermore, when generating the drag surface grid, the dynamic drag surface generation module dynamically corrects the drag value of the corresponding grid cell based on the real-time viewing angle parameters of the infrared camera, wherein:

[0034] When the infrared camera is in narrow field of view mode, the resistance value of the grid cell within the camera's monitoring range is multiplied by a first coefficient;

[0035] When the infrared camera is in wide-angle mode, the resistance value of the grid cell within the camera's monitoring range is multiplied by a second coefficient;

[0036] The specific values ​​of the first and second coefficients are obtained by calibrating the frequency of human activities in historical data from infrared cameras.

[0037] Furthermore, when displaying corridor connectivity, the visualization decision module overlays the real-time signal level information of the signal receiving submodule and the triggered infrared camera view mode.

[0038] Secondly, this invention proposes a method for assessing the functional connectivity of nature reserves based on research into biological ecological habits. This method is implemented using the functional connectivity assessment system for nature reserves described above, and includes the following steps:

[0039] S1: The target species' movement trajectory, activity images, and vocal characteristics data are obtained through GPS tracking modules worn by the species, infrared cameras deployed in the reserve, and acoustic signature acquisition modules. At the same time, remote sensing images, topographic data, and road network information are integrated to construct a multi-source database.

[0040] S2: The collected species activity data is processed through spatiotemporal analysis to generate a species distribution hotspot table, and the multi-source geospatial data is processed for coordinate unification and normalization; combined with the dynamic resistance surface generation module, a dynamic resistance surface model is constructed based on species ecological habits and environmental factors, and the resistance value is corrected for human activity intensity by combining infrared camera perspective parameters.

[0041] S3: The connectivity calculation module uses the minimum cumulative resistance algorithm and circuit theory model to calculate the ecological corridor effectiveness index and generate the optimal migration path network.

[0042] S4: Display corridor connectivity reports through a 3D visualization platform in the visualization decision module.

[0043] The beneficial effects of this invention are as follows:

[0044] 1. This invention significantly improves the accuracy and timeliness of functional connectivity assessment in nature reserves by dynamically integrating real-time species activity data with multi-source environmental factors. Employing multimodal monitoring methods such as GPS tracking, infrared cameras, and acoustic signature acquisition, it achieves comprehensive collection of species spatiotemporal behavioral data. Simultaneously, it innovatively uses infrared camera viewing angle parameters as a dynamic correction factor for human interference intensity, overcoming the limitation of traditional static resistance surface models in reflecting real-time changes in human activity. Through a stepped guide rail gear-switching mechanism and intelligent linkage with the camera viewing angle, it achieves adaptive adjustment of monitoring equipment and coordinated optimization of data acquisition.

[0045] 2. This invention achieves a multi-dimensional quantitative assessment of ecological corridor connectivity through the fusion of the minimum cumulative resistance model and circuit theory model. The dynamic resistance surface generation module automatically updates environmental factor weights based on real-time monitoring data and generates a high-precision resistance surface grid by combining human interference correction factors. The visualization decision-making module provides an interactive 3D display interface, intuitively presenting a heat map of corridor connectivity and equipment operating status, providing scientific decision support for protected area managers. This system effectively solves the problems of lagging assessment results and insufficient consideration of human interference factors in traditional methods, significantly improving the management efficiency and ecological protection effectiveness of protected areas. Attached Figure Description

[0046] Figure 1 This is a system block diagram of the present invention.

[0047] Figure 2 This is a schematic diagram of the structure of the biological monitoring module in this invention.

[0048] In the diagram: 10, mounting base; 20, GPS tracking module; 30, signal receiving submodule; 40, voiceprint acquisition module; 50, infrared camera; 60, first drive motor; 70, second drive motor; 80, stepped guide rail; 100, biological monitoring module; 200, dynamic resistance surface generation module; 210, spatial registration submodule; 220, index fusion submodule; 230, resistance surface calculation submodule; 300, connectivity calculation module; 310, resistance path analysis submodule; 320, circuit theory submodule; 400, visualization decision-making module. Detailed Implementation

[0049] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0050] like Figure 1As shown, this embodiment proposes a functional connectivity assessment system for nature reserves based on biological ecological behavior research, including: a biological monitoring module 100, a dynamic resistance surface generation module 200, a connectivity calculation module 300, and a visualization decision-making module 400. The biological monitoring module 100 includes a GPS tracking module 20 (a positioning chip capable of emitting GPS positioning signals) worn by species, an infrared camera 50 deployed in the reserve, and a voiceprint acquisition module 40. The biological monitoring module 100 is used to collect spatiotemporal behavioral data of species in the target area and form a species activity hotspot table. The spatiotemporal behavioral data of species in the target area includes: the number of species movement trajectories recorded by the GPS tracking module 20. The data includes: species activity images and timestamps captured by infrared camera 50; species voiceprint features and frequency of occurrence acquired by voiceprint acquisition module 40; a dynamic resistance surface generation module 200 for accessing multi-source geospatial data of the target area and generating a normalized environmental factor raster, combining a species activity hotspot table and a species parameter library, and using a spatiotemporal dynamic weighting algorithm to calculate the resistance surface and generate a dynamic resistance surface raster; a connectivity calculation module 300 for processing the dynamic resistance surface raster based on a minimum cumulative resistance model and a circuit theory model to obtain an output corridor effectiveness index; and a visualization decision module 400 for generating a corridor connectivity report based on the corridor effectiveness index and displaying it visually.

[0051] In one specific embodiment, the dynamic resistance surface generation module 200 includes:

[0052] Spatial registration submodule 210 is used to unify remote sensing imagery, road network data, and terrain data into the WGS84 coordinate system, and output the registered raster dataset {G1, G2, ..., G...} n}, G n This is the nth environmental factor raster layer;

[0053] The index fusion submodule 220 is used to calculate and output an environmental factor raster layer E containing vegetation index, road density, and terrain slope. k (x, y), the expression is:

[0054]

[0055] Where (x,y) are the planar coordinates of the raster cell; max(G k ), min(G k ) are the minimum and maximum values ​​of the k-th raster layer, respectively;

[0056] The resistance surface calculation submodule 230 calculates the resistance surface based on the environmental factor grid layer E. k The dynamic drag surface grid R(x,y) is generated from (x,y) using the following expression:

[0057]

[0058] Among them, a k Let β be the weight of the k-th environmental factor, β be the influence coefficient of species activity hotspots, and P be the species activity hotspot table. i Let ||(x,y)-p| represent the coordinates of the i-th hotspot, and δ be the spatial decay radius, whose value is positively correlated with the species' activity range. i ||For pixels and hotspots P i The Euclidean distance.

[0059] In one specific embodiment, the connectivity calculation module 300 includes a resistance path analysis submodule 310 and a circuit theory submodule 320. The resistance path analysis submodule 310 is used to calculate the minimum cost path for species migration using a minimum cumulative resistance model. The circuit theory submodule 320 is used to simulate corridor connectivity probability based on current density distribution and generate a corridor effectiveness index.

[0060] In practical implementation, the connectivity calculation module 300 deeply integrates ecological principles and landscape genetics methods. First, the resistance path analysis submodule (310) calculates the minimum cumulative resistance path (MCR) between source habitats based on the corrected resistance grid output by the dynamic resistance surface generation module 200 using the Dijkstra algorithm (a classic algorithm used in the prior art to calculate the shortest path between nodes in a graph). It replaces traditional static environmental factors with dynamic resistance values ​​generated by the system that include human interference corrections, and introduces GPS hotspot data as weighted nodes for path calculation, making the generated ecological corridors more consistent with the actual migration preferences of species. Subsequently, the circuit theory submodule 320 converts the resistance surface into a conductive surface matrix, simulates and calculates the current density value of each grid cell through parallel circuits, dynamically adjusts the "node voltage" (habitat quality index) according to the frequency of human activities monitored by infrared cameras, and integrates species communication hotspots identified by voiceprint data as "short-circuit nodes." The final generated corridor effectiveness index reflects both structural connectivity and functional connectivity.

[0061] Combination Figure 2 As shown, the biological monitoring module also includes a mounting base 10, a stepped guide rail 80 disposed on the mounting surface of the mounting base 10, and a signal receiving submodule 30 connected to the GPS tracking module 20. The signal receiving submodule 30 adjusts the sliding radius between itself and the stepped guide rail 80 to adapt to the signal capture of the GPS tracking module 20 at different distances. The stepped guide rail 80 includes at least three sliding radius settings. Each setting corresponds to a different signal strength reception threshold, which is used to trigger the viewing angle adjustment of the infrared camera in stages according to the proximity of the GPS tracking module.

[0062] In addition, combined Figure 2As shown, the mounting base 10 is also equipped with a first drive motor 60, which drives the signal receiving submodule 30 to rotate when adjusting the sliding radius between the submodule and the stepped guide rail 80, thereby realizing gear switching; a second drive motor 70 is also provided, which drives the infrared camera 50 to rotate relative to the mounting base 10. The purpose is to dynamically adjust the camera shooting angle according to the real-time distance and orientation of the GPS detected by the signal receiving submodule 30, so that the narrow-angle mode can accurately track close-range target individuals, and the wide-angle mode can cover distant group activities, thereby improving the effective capture rate of species behavior data.

[0063] In one specific embodiment, when the received signal of the signal receiving submodule 30 reaches the current level threshold, the infrared camera 50 automatically switches to a preset viewing angle. The relationship between the level threshold and the preset viewing angle of the infrared camera 50 is as follows:

[0064] The first threshold triggers the narrow angle mode, with a focal length of 30°.

[0065] The second threshold triggers the standard viewing angle mode, with a focal length of 60°.

[0066] The third threshold triggers the wide-angle mode with a 120° focal length.

[0067] When the dynamic resistance surface generation module generates the resistance surface grid, it dynamically corrects the resistance value of the grid unit based on the real-time viewing angle parameters of the infrared camera 50. Specifically: when the infrared camera 50 is in narrow viewing angle mode, the resistance value of the grid unit within the camera's monitoring range is multiplied by a first coefficient; when the infrared camera 50 is in wide viewing angle mode, the resistance value of the grid unit within the camera's monitoring range is multiplied by a second coefficient. The specific values ​​of the first and second coefficients are obtained by calibrating the frequency of human activities in the historical data of the infrared camera 50.

[0068] For example, if the first coefficient is 1.3 and the second coefficient is 0.7, the drag value R″(x,y) is adjusted according to the infrared camera's viewing angle mode:

[0069]

[0070] Understandably, when the GPS tracking module 20 worn by the species enters the monitoring range, the signal receiving submodule 30 analyzes the RSSI value (Received Signal Strength Indicator) in real time, drives the guide rail motor to lift and lower to the matching gear, and triggers the linkage switching of the infrared camera 50's viewing angle—such as switching to the high gear narrow viewing angle (30°) to accurately capture close targets, and the low gear wide angle (120°) to cover a large area at a distance.

[0071] It should be noted that the infrared camera 50 in this application needs to be configured with a multi-mode automatic switching lens (narrow angle, standard angle, wide angle), with a narrow angle of 30° for high-precision individual identification and a wide angle of 120° for large-scale group monitoring; the voiceprint acquisition configuration 40 deploys an omnidirectional microphone array, equipped with a wind noise reduction module and a voiceprint classification algorithm, which can simultaneously record the voiceprint characteristics of species.

[0072] In one specific embodiment, when displaying corridor connectivity, the visualization decision module overlays the real-time signal level information of the signal receiving submodule and the triggered infrared camera view mode.

[0073] This embodiment also proposes a method for assessing the functional connectivity of nature reserves based on research on biological ecological habits. The method is implemented using the aforementioned system for assessing the functional connectivity of nature reserves and includes the following steps:

[0074] S1: The target species' movement trajectory, activity images, and vocal characteristics data are obtained through GPS tracking modules worn by the species, infrared cameras deployed in the reserve, and acoustic signature acquisition modules. At the same time, remote sensing images, topographic data, and road network information are integrated to construct a multi-source database.

[0075] S2: The collected species activity data is processed through spatiotemporal analysis to generate a species distribution hotspot table, and the multi-source geospatial data is processed for coordinate unification and normalization; combined with the dynamic resistance surface generation module, a dynamic resistance surface model is constructed based on species ecological habits and environmental factors, and the resistance value is corrected for human activity intensity by combining infrared camera perspective parameters.

[0076] S3: The connectivity calculation module uses the minimum cumulative resistance algorithm and circuit theory model to calculate the ecological corridor effectiveness index and generate the optimal migration path network.

[0077] S4: Display corridor connectivity reports through a 3D visualization platform in the visualization decision module.

[0078] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A system for assessing the functional connectivity of nature reserves based on research into biological ecological habits, characterized in that, include: The biological monitoring module (100) includes a GPS tracking module (20) worn by the species, an infrared camera (50) deployed in the protected area, and a voiceprint acquisition module (40); the biological monitoring module (100) is used to collect spatiotemporal behavior data of species in the target area and form a species activity hotspot table; The dynamic resistance surface generation module (200) is used to access multi-source geospatial data of the target area and generate a normalized environmental factor raster. Combined with the species activity hotspot table and species parameter library, the resistance surface is calculated using a spatiotemporal dynamic weighting algorithm to generate a dynamic resistance surface raster. The connectivity calculation module (300) is used to process the dynamic resistance surface grid based on the minimum cumulative resistance model and the circuit theory model to obtain the output corridor effectiveness index; The visualization decision module (400) is used to generate a corridor connectivity report based on the corridor effectiveness index and to visualize it. The biological monitoring module (100) also includes a mounting base (10), a stepped guide rail (80) on the mounting surface of the mounting base (10), and a signal receiving submodule (30) connected to the GPS tracking module (20). The signal receiving submodule (30) adjusts its sliding radius along the stepped guide rail (80) to accommodate signals from the GPS tracking module (20) at different distances. The stepped guide rail (80) includes at least three sliding radius settings; Each gear corresponds to a different Bluetooth signal strength reception threshold, which is used to trigger the viewing angle adjustment of the infrared camera (50) according to the proximity of the GPS tracking module (20); When the received signal of the signal receiving submodule (30) reaches the current gear threshold, the infrared camera (50) automatically switches to the preset viewing angle. The relationship between the gear threshold and the preset viewing angle of the infrared camera (50) is as follows: The first threshold triggers the narrow angle mode, with a focal length of 30°. The second threshold triggers the standard viewing angle mode, with a focal length of 60°. The third threshold triggers the wide-angle mode, with a 120° focal length. When generating the resistance surface grid, the dynamic resistance surface generation module (200) dynamically corrects the resistance value of the corresponding grid cell based on the real-time viewing angle parameters of the infrared camera (50), wherein: When the infrared camera (50) is in narrow field of view mode, the resistance value of the grid cell within the camera's monitoring range is multiplied by a first coefficient; When the infrared camera (50) is in wide-angle mode, the grid cell resistance value within the camera's monitoring range is multiplied by a second coefficient; The specific values ​​of the first and second coefficients are obtained by calibrating the frequency of human activities in historical data from infrared cameras.

2. The functional connectivity assessment system for nature reserves based on biological ecological habits research according to claim 1, characterized in that, The spatiotemporal behavioral data of species in the target area include: Species movement trajectory data recorded by GPS tracking module (20); Images of species activity and timestamps captured by an infrared camera (50); The voiceprint characteristics and frequency of occurrence of the species obtained by the voiceprint acquisition module (40).

3. The functional connectivity assessment system for nature reserves based on biological ecological habits research according to claim 1, characterized in that, The dynamic resistance surface generation module (200) includes: The spatial registration submodule (210) is used to unify remote sensing imagery, road network data, and terrain data into the WGS84 coordinate system and output the registered raster dataset. G n This is the nth environmental factor raster layer; The index fusion submodule (220) is used to calculate and output an environmental factor raster layer containing vegetation index, road density, and terrain slope. The expression is: ； Where (x,y) are the planar coordinates of the raster cell; These are the minimum and maximum values ​​of the k-th raster layer, respectively; The resistance surface calculation submodule (230) calculates the resistance surface based on the environmental factor grid layer E. k The dynamic drag surface grid R(x,y) is generated from (x,y) using the following expression: ； Among them, a k Let β be the weight of the k-th environmental factor, β be the influence coefficient of species activity hotspots, and P be the species activity hotspot table. i Let i be the coordinates of the i-th hotspot. This is the spatial decay radius, and its value is positively correlated with the species' range of activity. For pixels and hotspots P i The Euclidean distance.

4. The functional connectivity assessment system for nature reserves based on biological ecological habits research according to claim 1, characterized in that, The connectivity calculation module (300) includes: The resistance path analysis submodule (310) is used to calculate the minimum cost path for species migration using the minimum cumulative resistance model; The circuit theory submodule (320) is used to simulate the corridor connectivity probability based on the current density distribution and generate the corridor effectiveness index.

5. The functional connectivity assessment system for nature reserves based on biological ecological habits research according to claim 1, characterized in that, When displaying corridor connectivity, the visualization decision module (400) overlays the real-time signal level information of the signal receiving submodule (30) and the triggered infrared camera view mode.

6. A method for assessing the functional connectivity of nature reserves based on research on biological ecological habits, characterized in that, Based on the nature reserve functional connectivity assessment system according to any one of claims 1-5, the method includes the following steps: S1: The target species’ movement trajectory, activity images and vocal characteristics data are obtained by using the GPS tracking module (20) worn by the species, the infrared camera (50) deployed in the reserve and the voiceprint acquisition module (40), and remote sensing images, terrain data and road network information are integrated to build a multi-source database. S2: The collected species activity data are processed through spatiotemporal analysis to generate a species distribution hotspot table, and the multi-source geospatial data are processed for coordinate unification and normalization; combined with the dynamic resistance surface generation module (200), a dynamic resistance surface model is constructed based on species ecological habits and environmental factors, and the resistance value is corrected for human activity intensity by combining infrared camera perspective parameters. S3: The connectivity calculation module (300) uses the minimum cumulative resistance algorithm and circuit theory model to calculate the ecological corridor effectiveness index and generate the optimal migration path network; S4: Display the corridor connectivity report in the visualization decision module (400) using a 3D visualization platform.

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