Natural reserve function connectivity evaluation system and method based on biological ecological habit research

Abstract

The invention belongs to the technical field of ecological protection, and particularly relates to a natural reserve function connectivity evaluation system and method based on biological ecological habit research, specifically, species activity data is acquired in real time through a GPS tracking module, an infrared camera and a voiceprint acquisition device, and a dynamic resistance surface model is constructed in combination with multi-source geographic space data; and the visual angle parameter of the infrared camera is innovatively introduced as a correction factor of the human interference intensity, so that the dynamic adjustment of the resistance value is realized. And calculating a corridor connectivity index by adopting a minimum cumulative resistance model and a circuit theoretical model, and finally generating a visual evaluation report. The method overcomes the defect that a traditional static evaluation method cannot reflect the real-time influence of human activities, and significantly improves the accuracy and timeliness of ecological corridor planning. Particularly, through intelligent linkage of a gear switching mechanism of the stepped guide rail and the visual angle of the infrared camera, collaborative optimization of self-adaptive adjustment of the monitoring equipment and dynamic correction of the resistance surface is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological protection, and particularly relates to a natural reserve functional connectivity assessment system and method based on the research of biological ecological habits. Background Art

[0002] At present, the assessment of natural reserve functional connectivity mainly relies on static geographical data (such as land use types, road distributions) and species distribution models. Common methods include the Minimum Cumulative Resistance Model (MCR) and the Circuit Theory Model, which generate resistance surfaces by integrating environmental factors such as vegetation cover and terrain slope, and then simulate species migration paths. In the prior art, species activity data are mostly collected through GPS tracking or fixed-point cameras, and human disturbance factors are usually indirectly estimated using fixed buffers (such as within 500 m of roads) or historical statistical data.

[0003] The existing methods have significant deficiencies: First, the static resistance surface cannot reflect the real-time impact of human activities, resulting in the disconnection between corridor planning and actual ecological needs; Second, the traditional human disturbance assessment relies on manually demarcating buffers or historical data, ignoring the real-time signals collected by devices (such as the correlation between camera perspective changes and human activities), causing calculation deviations in resistance values; In addition, the existing systems lack a dynamic coupling mechanism for multi-source data and the resistance surface model, making it difficult to achieve accurate spatio-temporal dynamic assessment. These problems lead to a lag in reserve management decisions and a reduction in the effectiveness of ecological corridors. There is an urgent need for an assessment system that integrates real-time biological monitoring and dynamic resistance surface correction. Summary of the Invention

[0004] The purpose of the present invention is to provide a natural reserve functional connectivity assessment system and method based on the research of biological ecological habits, so as to solve the problem that the traditional natural reserve connectivity assessment method in the prior art cannot dynamically integrate species real-time activity data and human disturbance factors, resulting in deviations between the calculated resistance surface and the actual situation.

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

[0006] In the first aspect, the present invention proposes a natural reserve functional connectivity assessment system based on the research of biological ecological habits, including:

[0007] A biological monitoring module, including a GPS tracking module worn by species, infrared cameras deployed in the reserve, and a voiceprint acquisition module; the biological monitoring module is used to collect species spatio-temporal behavior data in the target area and form a species activity hotspot table;

[0008] A dynamic resistance surface generation module, which is used to access multi-source geospatial data of the target area and generate a normalized environmental factor raster, combine the species activity hotspot table and the species parameter library, and use the spatio-temporal dynamic weight algorithm to calculate the resistance surface and generate a dynamic resistance surface raster;

[0009] A connectivity calculation module, which is used to process the dynamic resistance surface raster based on the minimum cumulative resistance model and the circuit theory model to obtain the output corridor effectiveness index;

[0010] A visualization and decision-making module, which is used to form a corridor connectivity report according to the corridor effectiveness index and conduct a visual display.

[0011] Furthermore, the spatio-temporal behavior data of the species in the target area includes:

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

[0013] Species activity images and timestamps captured by infrared cameras;

[0014] Species vocalization characteristics and occurrence frequencies obtained by the vocalization fingerprint acquisition module.

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

[0016] A spatial registration sub-module, which is used to unify remote sensing images, road network data, and terrain data to the WGS84 coordinate system and output the registered raster dataset {G1, G2,..., G n}, G n is the nth environmental factor raster layer;

[0017] An index fusion sub-module, which is used to calculate and output the environmental factor raster layer E k (x, y) with the expression:

[0018]

[0019] where (x, y) is the planar coordinate of the raster pixel; max(G k ) and min(G k ) are the minimum and maximum values of the kth raster layer respectively;

[0020] A resistance surface calculation sub-module, which generates a dynamic resistance surface raster R(x, y) according to the environmental factor raster layer E k (x, y) with the expression:

[0021]

[0022] where a kis the weight of the k-th environmental factor, β is the influence coefficient of the species activity hotspot, P is the species activity hotspot table, P i is the i-th hotspot coordinate, δ is the spatial decay radius, and its value is positively correlated with the species activity range. ||(x,y)-p i || is the Euclidean distance between the pixel and the hotspot P i of.

[0023] Furthermore, the connectivity calculation module includes:

[0024] A resistance path analysis sub-module for calculating the minimum cost path of species migration through the minimum cumulative resistance model;

[0025] A circuit theory sub-module for simulating the corridor connectivity probability based on the current density distribution and generating a corridor effectiveness index.

[0026] Furthermore, the biological monitoring module further includes a mounting base, a stepped guide rail provided on the mounting surface of the mounting base, and a signal receiving sub-module signal-connected to the GPS tracking module. The signal receiving sub-module adapts to the signal capture of the GPS tracking module at different distances by adjusting the sliding radius between the stepped guide rails:

[0027] The stepped guide rail includes at least 3 sliding radius gears;

[0028] Each gear corresponds to a different Bluetooth signal strength reception threshold for triggering the perspective adjustment of the infrared camera according to the proximity of the GPS tracking module.

[0029] Furthermore, when the received signal of the signal receiving sub-module reaches the current gear threshold, the infrared camera automatically switches to a preset perspective. The relationship between the gear threshold and the preset perspective of the infrared camera is:

[0030] The first gear threshold triggers the narrow perspective mode with a 30° focal length;

[0031] The second gear threshold triggers the standard perspective mode with a 60° focal length;

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

[0033] Furthermore, when the dynamic resistance surface generation module generates the resistance surface grid, it dynamically corrects the resistance value of the grid unit corresponding to the real-time perspective parameter of the infrared camera, where:

[0034] When the infrared camera is in the narrow perspective mode, multiply the resistance value of the grid unit within the monitoring range of the camera by the first coefficient;

[0035] When the infrared camera is in the wide-angle mode, multiply the resistance value of the grid unit within the monitoring range of the camera by the second coefficient;

[0036] The specific values of the first coefficient and the second coefficient are obtained by calibrating the frequency of human activities in the historical data of the infrared camera.

[0037] Furthermore, when the visual decision-making module displays the corridor connectivity, it superimposes and displays the real-time signal gear information of the signal receiving sub-module and the triggered infrared camera view mode.

[0038] Second, the present invention proposes a method for evaluating the functional connectivity of a nature reserve based on the research of biological ecological habits, which is implemented based on the functional connectivity evaluation system of the nature reserve described in any one of the above. The method includes the following steps:

[0039] S1: Obtain the movement trajectory, activity images and vocalization feature data of the target species through the GPS tracking module worn by the species, the infrared cameras deployed in the reserve, and the vocalization collection module. At the same time, integrate remote sensing images, terrain data and road network information to construct a multi-source database;

[0040] S2: Process the collected species activity data through spatio-temporal analysis to generate a species distribution hot spot table, and perform coordinate unification and normalization processing on the multi-source geospatial data; Combine the dynamic resistance surface generation module to construct a dynamic resistance surface model based on the ecological habits of the species and environmental factors, and correct the resistance value according to the infrared camera view parameters for the intensity of human activities;

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

[0042] S4: Display the corridor connectivity report through a three-dimensional visualization platform in the visual decision-making module.

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

[0044] 1. By dynamically integrating the real-time activity data of species and multi-source environmental factors, the present invention significantly improves the accuracy and timeliness of the evaluation of the functional connectivity of nature reserves. By using multi-modal monitoring means such as GPS tracking, infrared cameras and vocalization collection, it realizes the comprehensive collection of species spatio-temporal behavior data. At the same time, it innovatively uses the infrared camera view parameters as a dynamic correction factor for the intensity of human interference, overcoming the defect that the traditional static resistance surface model cannot reflect the real-time changes of human activities. Through the gear switching mechanism of the stepped guide rail and the intelligent linkage of the camera view, it realizes the coordinated optimization of the adaptive adjustment of the monitoring equipment and data collection.

[0045] 2. Through the fusion calculation of the minimum cumulative resistance model and the circuit theory model, the present invention realizes the multi-dimensional quantitative evaluation of the ecological corridor connectivity. The dynamic resistance surface generation module can automatically update the weights of environmental factors according to real-time monitoring data, and generate high-precision resistance surface grids in combination with the human disturbance correction factor. The visualization decision-making module provides an interactive three-dimensional display interface, intuitively presenting the corridor connectivity heat map and the operating status of equipment, and providing scientific decision-making support for the managers of the protected area. This system effectively solves the problems of lagging evaluation results and insufficient consideration of human disturbance factors in traditional methods, and greatly improves the management efficiency of the protected area and the ecological protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0047] Figure 2 is a schematic structural diagram of the biological monitoring module in the present invention.

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

[0049] The following further describes the present application in detail with reference to the drawings. It is necessary to point out here that the following detailed embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0050] As Figure 1As shown in the figure, this embodiment proposes a natural reserve functional connectivity evaluation system based on the research of biological ecological habits, including: a biological monitoring module 100, a dynamic resistance surface generation module 200, a connectivity calculation module 300, and a visualization and 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 the spatio-temporal behavior data of species in the target area and form a species activity hot spot table. The spatio-temporal behavior data of species in the target area includes: the species movement trajectory data recorded by the GPS tracking module 20; the species activity images and timestamps captured by the infrared camera 50; the species voiceprint features and occurrence frequencies obtained by the voiceprint acquisition module 40; the dynamic resistance surface generation module 200 is used to access multi-source geospatial data in the target area and generate a normalized environmental factor raster, combine the species activity hot spot table and the species parameter library, and use the spatio-temporal dynamic weight algorithm to calculate the resistance surface to generate a dynamic resistance surface raster; the connectivity calculation module 300 is used to process the dynamic resistance surface raster based on the minimum cumulative resistance model and the circuit theory model to obtain the output corridor effectiveness index; the visualization and decision-making module 400 is used to form a corridor connectivity report according to the corridor effectiveness index and perform visualization display.

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

[0052] A spatial registration sub-module 210, which is used to unify remote sensing images, road network data, and terrain data to the WGS84 coordinate system and output the registered raster dataset {G1, G2,..., G n}, G n is the nth environmental factor raster layer;

[0053] An index fusion sub-module 220, which is used to calculate and output the environmental factor raster layer E k (x, y) containing vegetation index, road density, and terrain slope, and the expression is:

[0054]

[0055] where (x, y) is the plane coordinate of the raster cell; max(G k ) and min(G k ) are the minimum and maximum values of the kth raster layer respectively;

[0056] A resistance surface calculation sub-module 230, which generates a dynamic resistance surface raster R(x, y) according to the environmental factor raster layer E k (x, y), and the expression is:

[0057]

[0058] where a k is the weight of the k-th environmental factor, β is the influence coefficient of the species activity hotspot, P is the species activity hotspot table, P i is the i-th hotspot coordinate, δ is the spatial decay radius, and its value is positively correlated with the species activity range. ||(x,y)-p i || is the Euclidean distance between the pixel and the hotspot P i of.

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

[0060] Specifically, the working process of the connectivity calculation module 300 deeply integrates ecological principles and landscape genetics methods. First, the resistance path analysis sub-module (310) is based on the corrected resistance raster output by the dynamic resistance surface generation module 200, and uses the Dijkstra algorithm (a classic algorithm in the prior art for calculating the shortest path between nodes in a graph) to calculate the minimum cumulative resistance path (MCR) between source habitats. The traditional static environmental factors are replaced with the dynamic resistance values containing human disturbance correction generated by this system, and GPS hotspot data is introduced as a weighted node for path calculation, so that the generated ecological corridor is more in line with the actual migration preferences of species. Subsequently, the circuit theory sub-module 320 converts the resistance surface into a conductive surface matrix, calculates the current density values of each grid unit through parallel circuit simulation, dynamically adjusts the "node voltage" (habitat quality index) according to the human activity frequency monitored by the infrared camera, and fuses the species communication hotspots identified by the voiceprint data as "short-circuit nodes". Finally, the generated corridor effectiveness index reflects both structural connectivity and functional connectivity.

[0061] Combined with Figure 2 As shown, the biological monitoring module further includes a mounting base 10, a stepped guide rail 80 provided on the mounting surface of the mounting base 10, and a signal receiving sub-module 30 signal-connected to the GPS tracking module 20. The signal receiving sub-module 30 adapts to the signal capture of the GPS tracking module 20 at different distances by adjusting the sliding radius between it and the stepped guide rail 80: the stepped guide rail 80 includes at least 3 sliding radius gears; each gear corresponds to a different signal strength reception threshold, which is used to trigger the perspective adjustment of the infrared camera according to the proximity of the GPS tracking module.

[0062] In addition, combined with Figure 2As shown, a first driving motor 60 is further provided on the installation base 10, which is used to drive the signal receiving sub-module 30 to rotate when adjusting the sliding radius between the adjustment and the stepped guide rail 80, so as to realize gear jumping; a second driving motor 70 is also provided, which is used to drive the infrared camera 50 to rotate relative to the installation base 10, aiming to dynamically adjust the camera shooting angle according to the GPS real-time distance and azimuth detected by the signal receiving sub-module 30, so that the narrow-angle mode accurately tracks the close-range target individuals, and the wide-angle mode covers the long-distance group activities, thereby improving the effective capture rate of species behavior data.

[0063] In a specific embodiment, when the received signal of the signal receiving sub-module 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:

[0064] The first gear threshold triggers the narrow-angle mode, with a 30° focal length;

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

[0066] The third gear 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 corresponding to the real-time viewing angle parameter of the infrared camera 50. Among them: when the infrared camera 50 is in the narrow-angle mode, the resistance value of the grid unit within the monitoring range of the camera is multiplied by the first coefficient; when the infrared camera 50 is in the wide-angle mode, the resistance value of the grid unit within the monitoring range of the camera is multiplied by the second coefficient; the specific values of the first coefficient and the second coefficient are calibrated through the frequency of human activities in the historical data of the infrared camera 50.

[0068] Exemplarily, if the first coefficient is 1.3 and the second coefficient is 0.7, the resistance value R″(x, y) is adjusted according to the viewing angle mode of the infrared camera:

[0069]

[0070] It can be understood that when the GPS tracking module 20 worn by the species enters the monitoring range, the signal receiving sub-module 30 real-time analyzes the RSSI value (Received Signal Strength Indication), drives the guide rail motor to lift to the matching gear, and at the same time triggers the linkage switching of the viewing angle of the infrared camera 50 - such as switching to the high-gear narrow angle (30°) to accurately capture the close-range target, and the low-gear wide angle (120°) to cover the long-distance large range.

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

[0072] In a specific embodiment, when the visualization decision module displays the corridor connectivity, it superimposes and displays the real-time signal gear information of the signal receiving sub-module and the triggered infrared camera view mode.

[0073] This embodiment also proposes a method for evaluating the functional connectivity of a nature reserve based on the research of biological ecological habits, which is implemented based on the above-mentioned nature reserve functional connectivity evaluation system. The method includes the following steps:

[0074] S1: Obtain the movement trajectory, activity images and vocalization feature data of the target species through the GPS tracking module worn by the species, the infrared cameras deployed in the reserve and the acoustic fingerprint acquisition module. At the same time, integrate remote sensing images, terrain data and road network information to construct a multi-source database;

[0075] S2: Process the collected species activity data through spatio-temporal analysis to generate a species distribution hotspot table, and perform coordinate unification and normalization processing on the multi-source geospatial data; combine the dynamic resistance surface generation module to construct a dynamic resistance surface model based on species ecological habits and environmental factors, and correct the resistance value according to the infrared camera view parameters for human activity intensity;

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

[0077] S4: Display the corridor connectivity report through a three-dimensional visualization platform in the visualization decision module.

[0078] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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 the present application.

Claims

1. A natural reserve functional connectivity assessment system based on the study of biological ecological habits, characterized in that, Comprising: A biological monitoring module (100), including a GPS tracking module (20) worn by a species, an infrared camera (50) deployed in a protected area, and a voiceprint acquisition module (40); the biological monitoring module (100) is used to collect spatio-temporal behavior data of species in a target area and form a species activity hotspot table; A dynamic resistance surface generation module (200), configured to access multi-source geospatial data of a target area and generate a normalized environmental factor raster, combine the species activity hotspot table and a species parameter library, and use a spatio-temporal dynamic weight algorithm to perform resistance surface calculation to generate a dynamic resistance surface raster; A connectivity calculation module (300), configured to process the dynamic resistance surface raster based on a minimum cumulative resistance model and a circuit theory model to obtain an output corridor effectiveness index; A visualization decision module (400), configured to form a corridor connectivity report according to the corridor effectiveness index and perform visualization display.

2. The natural reserve functional connectivity assessment system based on the research of biological ecological habits according to claim 1, wherein The spatio-temporal behavior data of the species in the target area includes: Species movement trajectory data recorded by the GPS tracking module (20); Species activity images and timestamps captured by the infrared camera (50); Species voiceprint features and occurrence frequencies obtained by the voiceprint acquisition module (40).

3. The natural reserve functional connectivity assessment system based on the study of biological ecological habits according to claim 1, wherein The dynamic resistance surface generation module (200) includes: Spatial registration sub-module (210), which is used to unify remote sensing images, road network data, and terrain data into the WGS84 coordinate system, and output the registered raster dataset {G1, G2,..., G n}, where G n is the nth environmental factor raster layer; An index fusion sub-module (220) for calculating and outputting an environmental factor raster layer E including a vegetation index, a road density, and a terrain slope, where E(x, y) has the following expression: k (x, y), and the expression is: where (x, y) are the planar coordinates of the grid cell; max(G k ) and min(G k ) are the minimum and maximum values of the k-th grid layer, respectively; Resistance surface calculation sub-module (230), which generates a dynamic resistance surface grid R(x, y) according to the environmental factor grid layer E k (x, y), and the expression is: Among them, a k is the weight of the k-th environmental factor, β is the influence coefficient of the species activity hotspot, P is the species activity hotspot table, P i is the i-th hotspot coordinate, δ is the spatial decay radius, and its value is positively correlated with the species activity range. ||(x, y) - p i || is the Euclidean distance between the pixel and the hotspot P i .

4. The natural reserve functional connectivity assessment system based on the study of biological ecological habits according to claim 1, characterized in that, The connectivity calculation module (300) includes: A resistance path analysis sub-module (310), configured to calculate the minimum cost path of species migration through a minimum cumulative resistance model; A circuit theory sub-module (320), configured to simulate the corridor connectivity probability based on the current density distribution and generate a corridor effectiveness index.

5. The natural reserve functional connectivity assessment system based on the study of biological ecological habits according to claim 1, characterized in that, The biological monitoring module (100) further includes a mounting base (10), a stepped guide rail (80) provided on the mounting surface of the mounting base (10), and a signal receiving sub-module (30) signal-connected to the GPS tracking module (20). The signal receiving sub-module (30) adapts to GPS tracking module (20) signal capture at different distances by adjusting the sliding radius between it and the stepped guide rail (80): The stepped guide rail (80) includes at least 3 sliding radius gears; Each gear corresponds to a different Bluetooth signal strength reception threshold, and is used to trigger the perspective adjustment of the infrared camera (50) according to the proximity of the GPS tracking module (20).

6. The natural reserve functional connectivity assessment system based on the study of biological ecological habits according to claim 5, wherein, When the received signal of the signal receiving sub-module (30) reaches the current gear threshold, the infrared camera (50) automatically switches to a preset perspective. The relationship between the gear threshold and the preset perspective of the infrared camera (50) is: The first gear threshold triggers the narrow perspective mode, with a 30° focal length; The second gear threshold triggers the standard perspective mode, with a 60° focal length; The third gear threshold triggers the wide angle mode, with a 120° focal length.

7. The natural reserve functional connectivity assessment system based on the study of biological ecological habits according to claim 6, characterized in that When the dynamic resistance surface generation module (200) generates a resistance surface raster, it dynamically corrects the resistance value of the grid cell corresponding to the real-time perspective parameter of the infrared camera (50), where: When the infrared camera (50) is in the narrow perspective mode, multiply the resistance value of the grid cells within the monitoring range of this camera by a first coefficient; When the infrared camera (50) is in the wide angle mode, multiply the resistance value of the grid cells within the monitoring range of this camera by a second coefficient; The specific values of the first coefficient and the second coefficient are obtained by calibrating the frequency of human activities in the historical data of the infrared camera.

8. The natural reserve functional connectivity assessment system based on the research of biological ecological habits according to claim 7, characterized in that, When the visual decision-making module (400) displays the corridor connectivity, the real-time signal gear information of the signal receiving sub-module (30) and the triggered infrared camera view mode are superimposed and displayed.

9. A method for evaluating the functional connectivity of a nature reserve based on the study of the biological ecological habits, characterized in that, Implemented based on the natural reserve functional connectivity evaluation system according to any one of claims 1-8, the method includes the following steps: S1: Obtain the movement trajectories, activity images, and vocalization feature data of the target species through the GPS tracking module (20) worn by the species, the infrared cameras (50) deployed in the reserve, and the acoustic fingerprint acquisition module (40). At the same time, integrate remote sensing images, terrain data, and road network information to construct a multi-source database; S2: Process the collected species activity data through spatio-temporal analysis to generate a species distribution hotspot table, and perform coordinate unification and normalization processing on the multi-source geospatial data; combine with the dynamic resistance surface generation module (200), construct a dynamic resistance surface model based on the ecological habits of the species and environmental factors, and correct the resistance value according to the human activity intensity in combination with the infrared camera view parameters; S3: Calculate the ecological corridor effectiveness index by using the minimum cumulative resistance algorithm and the circuit theory model through the connectivity calculation module (300) to generate an optimal migration path network; S4: Display the corridor connectivity report through a 3D visualization platform in the visual decision-making module (400).

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