Ecological network key node repair method and system for connectivity improvement

Abstract

The application provides an ecological network key node repair method and system for connectivity improvement, and relates to the technical field of ecological restoration. The method comprises the following steps: screening potential ecological function nodes based on physical habitat quality, calculating multi-dimensional environmental background carrying capacity values of the nodes by fusing multi-dimensional environmental monitoring data, and generating first or second repair strategies at the node level according to the values; identifying inter-node implicit connectivity obstacles, calculating ecological flux transmission resistance values and path impedance multiples containing sound, light and heat correlation attributes; and generating a third repair strategy for preferentially performing spatial structure reconstruction or a fourth repair strategy for performing regional collaborative control based on the coupling relationship between the node repair strategy and the path impedance multiple. The application can quantize the implicit physical field barrier, accurately locate and repair the 'pseudo connectivity' region, and realize the functional improvement of the ecological network.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, specifically to a method and system for repairing key nodes in an ecological network aimed at improving connectivity. Background Technology

[0002] In modern landscapes characterized by high levels of human activity, habitat fragmentation has become the primary threat to biodiversity. Constructing ecological networks capable of maintaining gene exchange and migration is the core task of ecological restoration. However, existing ecological network planning and restoration technologies are mainly based on visible light remote sensing imagery and land cover data, focusing on restoring explicit "structural connectivity" such as vegetation continuity. This single-dimensional physical spatial restoration often fails to achieve true "functional connectivity," primarily because it neglects the implicit ecological barrier mechanisms existing in multidimensional environmental fields. First, with the expansion of transportation networks and industrial facilities, the continuous generation of high-intensity low-frequency anthropogenic noise covers the key communication frequency bands used by birds, amphibians and other species for courtship and early warning. This "soundscape masking effect" causes even corridors with intact vegetation structures to become de facto "acoustic dead zones" because organisms cannot communicate effectively, forcing species to avoid them. Second, the high-intensity artificial light sources widely used in the urbanization process, especially LED lighting facilities rich in short-wave blue light, create an unnatural light radiation gradient at night. This change in the light environment not only disrupts the diurnal rhythm of nocturnal organisms, but also forms an insurmountable "light fence" for insects and small mammals that are phototactic or photophobic, cutting off key nighttime migration routes. Second, the neglected microclimate thermodynamic barrier, due to the urban heat island effect caused by the heat absorption of a large number of impermeable underlying surfaces (such as asphalt and concrete), will generate strong vertical thermal updrafts and turbulent shear forces in local areas. This aerodynamically unstable flow field constitutes a physically impassable "aerodynamic wall" for small pollinating insects (such as bees and butterflies) that rely on stable airflow for flight, causing them to be unable to fly through even though they have a path.

[0003] Existing technologies lack quantitative consideration and monitoring methods for the aforementioned implicit physical field barriers such as sound, light, and heat, leading to a large number of ecological restoration projects falling into the predicament of "pseudo-connectivity." That is, ecological corridors built with huge investments cannot play an actual biological flow transport function due to the existence of these implicit barriers. Although target species have corridors, they cannot actually pass through them. Long-term population isolation will lead to inbreeding, loss of genetic diversity, and even local extinction. Corridors that have not been repaired due to implicit light and heat stress may evolve into "ecological traps," attracting organisms to gather but increasing their mortality due to high-intensity disturbance. Microclimate barriers block the paths of pollinating insects, which will cause plant reproduction failure and the collapse of the bottom of the food chain. Finally, a large amount of funds are invested in ineffective areas, resulting in waste of resources, and incorrect assessments can mislead regional planning and delay the rescue of endangered species. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for repairing key nodes in an ecological network to improve connectivity, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for restoring key nodes in ecological networks to improve connectivity, applied to the planning of biological migration corridors in fragmented habitats, includes the following steps: Step S1: Construct a set of potential ecological function nodes and determine node restoration strategies; acquire multispectral remote sensing images and environmental monitoring data of the target area, and screen potential ecological function nodes based on physical habitat quality values; map sound scene monitoring data, nighttime light radiation data, and surface thermodynamic flow field data to potential ecological function nodes, and calculate the multidimensional environmental background carrying capacity value of each node; compare the multidimensional environmental background carrying capacity value with the preset carrying capacity threshold to generate a first restoration strategy or a second restoration strategy for each node; Step S2: Construct a multidimensional ecological resistance network and calculate path impedance; based on the sensory physiological limits of the target species, identify latent connectivity barriers between potential ecological function node pairs, and calculate the associated attribute values ​​of acoustic masking blockage, light environment intrusion blocking, and thermal turbulence barrier; calculate the ecological flux transmission resistance value of the path between each node pair based on the associated attribute values, and determine the path impedance ratio based on the ratio of the ecological flux transmission resistance value to the basic geographical distance; Step S3: Generate a comprehensive repair strategy; based on the node repair strategy generated in step S1 and the path impedance ratio calculated in step S2, determine the coupling state between the node and the path, and generate a third or fourth repair strategy.

[0006] Further, in step S1, the soundscape monitoring data stream is collected by a distributed acoustic sensor array deployed inside potential ecological function nodes, and the background noise energy spectrum of a specific frequency band is extracted through band-pass filtering; the night light radiation spectrum data is obtained by inverting the ground illuminance from night light remote sensing satellite images and combining the proportion of the blue light band corrected by a ground spectrometer; the surface thermodynamic flow field data is obtained by combining the surface temperature inversion data and the elevation model and simulating the near-surface wind field and the intensity of thermal convection using computational fluid dynamics (CFD) software.

[0007] Further, a high-resolution multispectral remote sensing image of the target area is obtained. Based on the projection coordinate system, it is spatially divided into a regular grid matrix through resampling technology, and each grid cell is set as the xth grid. The vegetation normalized difference index (NDVI) and the modified normalized difference water index (MNDWI) are calculated and obtained through the following formulas: where is the reflectance of the near-infrared band with a central wavelength of 842 nm; is the reflectance of the red light band with a central wavelength of 665 nm; is the reflectance of the green light band with a central wavelength of 560 nm; is the reflectance of the short-wave infrared band with a central wavelength of 1610 nm; If MNDWI > 0, it is determined as a water-covered area; it is used for subsequent extraction of waterfront corridor nodes. If NDVI > 0.6, it is determined as a high-coverage vegetation-covered area; If 0.4 < NDVI ≤ 0.6, it is determined as a medium-coverage vegetation-covered area; If 0.2 < NDVI ≤ 0.4, it is determined as a low-coverage vegetation-covered area; If NDVI ≤ 0.2 and NDWI ≤ 0, it is determined as a non-permeable surface-covered area; According to the sensitivity of threat sources to surface cover types and the distance attenuation function, the habitat quality value of each grid cell is calculated; the lowest survival suitability threshold is set, and continuous pixel patches with habitat quality values higher than the threshold are retained; the geometric area of the continuous pixel patches is calculated, and patches with an area smaller than the preset minimum population habitat threshold are excluded, and the remaining patches are marked as potential ecological function nodes.

[0008] Further, in step S1, before calculating the multi-dimensional environmental background carrying capacity values of each node, it is necessary to convert the environmental monitoring data into a dimensionless implicit stress index, which specifically includes: collecting data using a distributed acoustic sensor, extracting the background noise energy spectrum in a specific frequency band, and calculating the acoustic stress index by combining the biological comfort background noise benchmark and the avoidance threshold; constructing the light intrusion stress index by retrieving the ground illuminance from night light remote sensing images and combining the energy proportion of the blue light band; constructing the thermal turbulence stress index by simulating the near-surface vertical heat convection velocity using computational fluid dynamics and combining the ultimate wind resistance of the target species; the multi-dimensional environmental background carrying capacity value is obtained by calculating the initial physical carrying capacity value of the node and subtracting the environmental loss value composed of the weighted sum of the acoustic stress index, the light intrusion stress index, and the thermal turbulence stress index.

[0009] Further, the calculation method of the initial physical carrying capacity value includes: performing morphological spatial pattern analysis on potential ecological function nodes, and dividing the node area into a core area and a bridging area; For the core area, extract the vegetation vertical structure complexity and the water source distance as physical carrying factors; For the bridging area, extract the corridor width and the edge effect intensity as structural carrying factors; perform standardization processing on the physical carrying factors and the structural carrying factors, and uniformly map them to the positive numerical interval; Construct the average acoustic stress index, the average light intrusion stress index, and the average thermal turbulence stress index of all grids in the k-th node of the potential ecological function node, calculate the comprehensive implicit stress index, and correct the initial physical carrying capacity value to obtain the multi-dimensional environmental background carrying capacity value of the k-th node.

[0010] Further, generate the first repair strategy or the second repair strategy for each node, and the specific logic is as follows: set the first threshold T1 and the second threshold T2, where T1 < T2; if the multi-dimensional environmental background carrying capacity value is less than T1, it is determined that the node is in an ecological collapse state, and generate the first repair strategy, which includes physical expansion parameters for guiding the increase of the node patch area or the removal of hardened ground; if the multi-dimensional environmental background carrying capacity value is greater than or equal to T1 and less than T2, it is determined that the node is in an environmentally damaged state, and generate the second repair strategy, which includes environmental load reduction parameters for guiding the reduction of the noise level or the light intensity in the node area.

[0011] Further, in step S2, the potential ecological function nodes are given associated attributes, and the specific classification logic is as follows: Acoustic masking blockage association: When the background noise frequency between node A and node B mainly concentrates within the communication frequency range of the target species and the signal-to-noise ratio is lower than the effective communication threshold, it is determined that there is acoustic masking blockage, and assign its acoustic conduction loss coefficient; Light environment intrusion blocking association: When the nighttime spectral composition between node C and node D contains high-intensity short-wavelength blue light and the photon flux density exceeds the phototaxis / avoidance response threshold of the target nocturnal insect, it is determined that there is light environment intrusion blocking and a light barrier repulsion coefficient is assigned to it; Thermal turbulence barrier association: When the speed of the thermal updraft caused by the hardened ground between node E and node F exceeds the maximum wind resistance speed of the target small flying organism, a thermal turbulence barrier is determined to exist and is assigned an aerodynamic drag coefficient.

[0012] Furthermore, by combining the acoustic transmission loss coefficient, the light barrier repulsion coefficient, and the aerodynamic drag coefficient, the ecological flux transmission resistance value of the path between node p and node q is calculated, and the path impedance ratio is obtained.

[0013] Furthermore, a third repair strategy is generated, specifically satisfying the following conditions: when the path impedance ratio is greater than or equal to the preset blocking threshold T4, and any node connected to the path is determined to require the execution of the first repair strategy, the third repair strategy is generated; the third repair strategy includes spatial structure reconstruction parameters, marks the physical expansion parameters in the first repair strategy as pending activation, and outputs instructions to construct ecological corridors or underground passages, prioritizing the restoration of path connectivity; A fourth remediation strategy is generated, specifically meeting the following conditions: when the path impedance ratio is greater than or equal to the high impedance threshold T3 and less than the blocking threshold T4, and any node connected to the path is determined to require the second remediation strategy, the fourth remediation strategy is generated; the fourth remediation strategy includes regional collaborative management parameters, which are used to output dynamic adjustment instructions for interference sources along the path. The instructions include reducing the traffic noise flux of roads around the path during peak biological migration periods, or adjusting the spectral composition of lighting facilities around the path to reduce the proportion of blue light.

[0014] A system for repairing critical nodes in an ecological network to improve connectivity includes: The multi-source sensing data acquisition module is used to acquire sound scene data through a distributed acoustic sensor array, obtain multispectral and night light image data through a remote sensing interface, and obtain surface thermodynamic flow field data by combining computational fluid dynamics simulation. The node screening and carrying capacity analysis module is used to construct a set of potential ecological function nodes from the collected data based on the physical habitat quality value, and to calculate the multi-dimensional environmental background carrying capacity value of each node by integrating multi-source sensing data, thereby generating a first or second restoration strategy at the node level. The resistance network construction and analysis module is used to identify hidden connectivity barriers between potential ecological function nodes and calculate the ecological flux transmission resistance value and path impedance ratio, which include acoustic, optical, and thermal correlation attributes. The integrated strategy generation module is used to generate a third repair strategy when the path is determined to be in a hidden open circuit state, and a fourth repair strategy when the path is determined to be in a high impedance access state, based on the coupling relationship between the node repair strategy and the path impedance ratio.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention overcomes the limitations of traditional methods that rely solely on geographic vegetation structure to assess connectivity by integrating multi-dimensional physical field data, including sound masking, light intrusion, and thermal turbulence barriers. The system effectively identifies "hidden disconnections" in physical spaces that are impassable to organisms due to high-frequency noise or nighttime blue light, eliminating "pseudo-connectivity" blind spots in ecological restoration and ensuring the actual unimpeded flow of gene exchange pathways for target species.

[0016] This invention achieves precise quantitative characterization of node carrying capacity and path resistance by constructing an environmental sensitivity correction model and an ecological impedance ratio algorithm. The method transforms heterogeneous environmental monitoring data into a unified dimensionless stress index, accurately calculating the physiological cost ratio of environmental pressure on biological migration. This provides a scientific basis for the transition of ecological network planning from "qualitative planning" to "quantitative calculation," and enhances the reliability of restoration data.

[0017] This invention automatically generates tiered and customized governance solutions by logically coupling node repair strategies with path impedance states, significantly optimizing resource allocation. For high-impedance paths, it employs soft adjustments such as "regional collaborative management" (e.g., noise reduction, dimming), while for broken-circuit nodes, it utilizes hard engineering methods such as "spatial structural reconstruction" (e.g., building covered bridges). This achieves precise matching between low-cost environmental control and key civil engineering projects, greatly improving the input-output ratio of ecological restoration projects. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the mapping relationship between the core technical process and physical scene in addressing habitat fragmentation in this invention. Figure 2 This is a schematic diagram of the overall system flow of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Example 1: Please see Figures 1-2 This invention provides a technical solution: a method for repairing key nodes in ecological networks to improve connectivity, applied to the planning of biological migration corridors in fragmented habitats, the specific steps of which include: Step S1: Construct a set of potential ecological function nodes and determine node restoration strategies; acquire multispectral remote sensing images and environmental monitoring data of the target area, and screen potential ecological function nodes based on physical habitat quality values; map sound scene monitoring data, nighttime light radiation data, and surface thermodynamic flow field data to potential ecological function nodes, and calculate the multidimensional environmental background carrying capacity value of each node; compare the multidimensional environmental background carrying capacity value with the preset carrying capacity threshold to generate a first restoration strategy or a second restoration strategy for each node; Step S2: Construct a multidimensional ecological resistance network and calculate path impedance; based on the sensory physiological limits of the target species, identify latent connectivity barriers between potential ecological function node pairs, and calculate the associated attribute values ​​of acoustic masking blockage, light environment intrusion blocking, and thermal turbulence barrier; calculate the ecological flux transmission resistance value of the path between each node pair based on the associated attribute values, and determine the path impedance ratio based on the ratio of the ecological flux transmission resistance value to the basic geographical distance; Step S3: Generate a comprehensive repair strategy; based on the node repair strategy generated in step S1 and the path impedance ratio calculated in step S2, determine the coupling state between the node and the path, and generate a third or fourth repair strategy.

[0022] Figure 1This diagram illustrates the core technical process and physical scene mapping relationship of this invention in addressing habitat fragmentation. The isometric scene diagram at the bottom of the figure visually represents the physical objects and environmental fields in steps S1 to S2. The figure shows potential ecological function nodes (vegetation patches on both sides) cut off by the road network (representing the source of obstruction). In particular, three types of "hidden connectivity barriers" are visualized through special line symbols: concentric circles around roads and vehicles represent acoustic masking, conical dashed lines below streetlights represent light environment intrusion blocking, and wavy lines above hardened ground represent thermal turbulence barriers. The diagram illustrates the connecting structure spanning the road. When the path impedance ratio exceeds the threshold, the third restoration strategy (i.e., spatial structure reconstruction measures such as constructing ecological corridors) generated in step S3 is applied. "Potential node identification and carrying capacity calculation" corresponds to step S1, which involves acquiring remote sensing and environmental monitoring data, screening nodes, and calculating multi-dimensional environmental background carrying capacity values. "Multi-dimensional resistance network construction" corresponds to the first half of step S2, which involves identifying acoustic, optical, and thermal hidden barriers and calculating associated attribute values. "Path impedance coupling analysis" corresponds to the second half of step S2, which involves calculating ecological flux transmission resistance values ​​and path impedance ratios based on associated attributes. "Comprehensive restoration strategy generation" corresponds to step S3, which involves outputting a graded governance scheme (such as physical expansion or collaborative management) based on the coupling state of node strategies and path impedance.

[0023] Example 2: S1. Based on the physical habitat quality value, the target area is initially screened to construct a set of potential restoration nodes; high-resolution multispectral remote sensing images of the target area are acquired, land cover types are interpreted, and vegetation normalization index and water index are calculated; the calculation results are input into a preset habitat quality value assessment model to generate a distribution map of physical habitat quality values ​​across the entire region; a threshold judgment and screening procedure is executed: the pixel values ​​in the distribution map of physical habitat quality values ​​across the entire region are compared with the preset minimum survival suitability threshold, and continuous pixel patches with values ​​higher than the threshold are retained; the geometric area of ​​continuous pixel patches is further calculated, and patches with an area smaller than the minimum population habitat threshold are removed, and the remaining patches are marked as potential ecological function nodes; sound scene monitoring data stream, nighttime light radiation spectrum data, and surface thermodynamic flow field data of the target area are collected and fused, and mapped onto potential ecological function nodes, and the multidimensional environmental background carrying capacity value of each node is calculated; the multidimensional environmental background carrying capacity value is compared with the preset first threshold and second threshold respectively, and a first restoration strategy or a second restoration strategy for the node is generated based on the comparison results; In step S1, the acoustic monitoring data stream is collected by a distributed acoustic sensor array deployed within potential ecological function nodes, and the background noise energy spectrum of a specific frequency band is extracted by bandpass filtering; the nighttime light radiation spectrum data is obtained by inverting the ground illuminance from nighttime light remote sensing satellite imagery, and combined with the blue light band ratio corrected by the ground spectrometer; the surface thermodynamic flow field data is obtained by combining the surface temperature inversion data with the elevation model, and using computational fluid dynamics (CFD) software to simulate the near-surface wind field and thermal convection intensity.

[0024] The actual application scenario of this embodiment is the "Green Vein Reconnection" project in a rapidly urbanizing coastal new area. This area covers approximately 50 square kilometers, bordered by a primary forest reserve (source area) to the north and a wetland park (drainage area) to the south, fragmented by a high-density urban road network, commercial complexes, and industrial parks. Although the planning department has preserved several green patches and planted trees, long-term monitoring has revealed minimal interaction between target species (such as local indicator species: red-whiskered bulbuls, small nocturnal ferret badgers, and pollinating bees) between the northern and southern patches, indicating severe habitat fragmentation. Traditional restoration methods focus solely on vegetation cover, neglecting the interference of road noise on bird communication, the obstruction of nocturnal mammals by nighttime commercial lights, and the blockage of insect flight by hot air currents from hardened surfaces.

[0025] The purpose of this embodiment is to use the method of the present invention to identify key nodes that appear to be connected but actually have hidden barriers of "sound, light, and heat", and to formulate precise repair strategies.

[0026] S1. This step aims to accurately identify "potential ecological function nodes" from the complex urban land cover that possess the basic conditions for biological survival but may have deficiencies at the level of implicit physical fields (sound, light, and heat). This process is not a simple green space extraction, but a comprehensive preliminary screening of physical suitability and environmental carrying capacity.

[0027] Specifically, it includes the following steps: S11. Acquire the target area, taking a coastal new area as an example, using multispectral remote sensing imagery of the target area. The multispectral remote sensing imagery of the target area contains 13 spectral bands, with a maximum spatial resolution of 10m. The acquired multispectral remote sensing imagery of the target area is spatially divided into a regular grid matrix based on a projected coordinate system using resampling technology. Each grid cell, hereinafter referred to as a raster, has a physical size of 10m × 10m. To distinguish between vegetation and water bodies, the Normalized Difference Vegetation Index (NDVI) and the Improved Difference Water Index (MNDWI) are extracted using band calculations. In this invention, their calculation results serve as the basic input variables for subsequent habitat quality value models. The vegetation normalized index and water index are calculated using the following formulas: Among them, the central wavelength of the reflectance in the near-infrared band is 842 nm; is the reflectance in the red light band, and the central wavelength is 665 nm. is the reflectance in the green light band, and the central wavelength is 560 nm; is the reflectance in the short-wave infrared band, and the central wavelength is 1610 nm; If MNDWI > 0, it is determined as a water body coverage area, and the code 50 is assigned for subsequent extraction of waterfront corridor nodes; If NDVI > 0.6, it is determined as a high-coverage vegetation coverage area (such as broad-leaved forest), and the code 10 is assigned; If 0.4 < NDVI ≤ 0.6, it is determined as a medium-coverage vegetation coverage area (such as shrub forest), and the code 20 is assigned; If 0.2 < NDVI ≤ 0.4, it is determined as a low-coverage vegetation coverage area (such as urban lawn), and the code 30 is assigned; If NDVI ≤ 0.2 and NDWI ≤ 0, it is determined as a non-permeable surface coverage area (such as construction land or road), and the code 40 is assigned.

[0028] S12. Combining the NDVI and MNDWI index characteristics calculated in S11, each grid cell x is assigned a unique land use / land cover (LULC) type code. Based on the physical habitat quality value assessment sub-step of the InVEST model, in this embodiment, the InVEST model is used to form a two-dimensional matrix of M×N, where any grid x (coordinate) corresponds to a specific LULC type to generate a global habitat quality value map. The land use / land cover (LULC) type is divided into categories such as forest land, grassland, water body, construction land, and road. Set "habitat suitability" and sensitivity to threat sources for each LULC, as shown in Table 1.

[0029] S121. Calculate the total threat level borne by each grid x , the impact of threat sources (such as roads, factories) on the habitat decays with distance, and the formula is as follows: Among them, j is the index of the land use / land cover type; r is the threat source type index, referring to the category of disturbance sources that cause habitat degradation. In this embodiment, R threat sources are defined. For example: r = 1: arterial road (generating noise and segmentation); r = 2: industrial land (generating pollution and heat island); r = 3: urban construction land (generating light pollution and human flow). R is the total number of threat sources; y is the threat source grid, referring to each specific grid point position belonging to the r-th type of threat source within the global range; Y rThis represents the total number of grid cells representing the r-th type of threat source. That is, the total number of pixels in the entire map belonging to this threat type (for example, 5000 pixels in the entire map are marked as "road"). This indicates that the outer loop iterates through all types of threat sources (e.g., calculates roads first, then factories), and the inner loop iterates through the position of each specific pixel under that type (calculating and accumulating the impact of each road segment on the raster x); w r The weights of threat sources are indicated by setting the weight of industrial land at 1.0 (most destructive), urban roads at 0.7 (second most destructive), and rural roads at 0.3. A weighting factor is used to standardize the relative weights of each threat source, ensuring that the calculation results do not fluctuate drastically in terms of dimensions as the number of threat sources increases or decreases. This refers to the threat intensity value at grid y, specifically a binary variable: if grid y is a threat source, then r y =1; otherwise 0; The spatial decay exponent represents the decrease in the influence of a threat source as distance increases. Using an exponential decay model, the distance decay exponent of a threat source located at location y on the target habitat at location x is calculated as follows: in, The straight-line distance between grid x and threat grid y. This is the maximum influence distance at which the influence of the r-th type of threat source decays to a negligible level. A constant of 2.99 is the decay coefficient. This constant is chosen based on mathematical properties to ensure that when the distance d... xy =dmax r At that time, the threat's influence decays to approximately 5% of its initial value (exp( 2.99)≈0.05, meaning that the threat beyond this distance can be considered as 0.

[0030] β x This is the accessibility level coefficient, indicating whether grid x is legally or physically protected from direct attacks by threat sources. Its value ranges from [0,1]; when β... x =1 indicates a completely open area where threats can fully take effect (e.g., ordinary green space); β x =0 indicates a strictly protected area (such as a fenced core protected area), where threats cannot reach it. D xj Forced to 0. S jr This represents the habitat sensitivity coefficient, specifically indicating the susceptibility of grid x (of land cover type j) to the r-th type of threat source. It is retrieved from Table 1. An example is: Case 1: When calculating the susceptibility of broadleaf forest (j=10) to road (r=r... oadWhen the threat of road disturbance is calculated, the value is 0.80; the physical meaning is that broadleaf forests are very sensitive to road disturbance, and the noise and segmentation effect generated by the road will cause 80% of the effective damage. Case 2: When calculating the threat of urban lawns (j=30) to roads (r=r) oad When considering the threat posed by road disturbances, the table yields a value of 0.20. This physically means the lawn is an artificial habitat, insensitive to road disturbances, and only suffers damage with a 20% weight. Case 3: When calculating the threat posed by a broadleaf forest (j=10) to an industrial area (r=ind), the table yields a value of 0.90. The numerical meaning of Sjr ​​∈ [0,1]. A larger value (closer to 1) indicates a more vulnerable habitat type and more severe damage from this type of threat source; a smaller value (closer to 0) indicates a stronger resistance / tolerance to this threat. Formula D xj The physical meaning is that the total threat level of each grid x is equal to the threat source pixel y in the entire area, based on its threat source weight w. r After the distance decay index i rxy After conversion, multiply by the target plot's own habitat sensitivity coefficient S. jr and accessibility level coefficient β x The final total stress value is obtained by summing these values. A higher value indicates greater environmental pressure around the site, and a higher habitat quality score. xj The lower it will be; S121. After calculating the total threat level D for each grid cell... xj Then, the habitat suitability H of the LULC type to which the raster belongs is considered. j The final habitat quality value Q is generated by retrieving the value from Table 1 and using the half-saturation function of the InVEST model. xj The formula is as follows: Where k represents the half-saturation constant, and is taken as the value of all grid cells D in the entire domain. xj Half the average value. Its function is to adjust the sensitivity curve of the function so that the model can distinguish subtle differences in habitat quality values ​​even at low threat levels; z is a scaling factor with a value of 2.5, used to control the nonlinearity of habitat quality values ​​in response to threat levels.

[0031] Through steps S121 to S122, this invention does not merely distinguish between "woodland" and "factory" on a map, but rather constructs a deep ecological assessment model. For example, comparing two "broadleaf forests": Plot A1 is located deep in the mountains, far from roads and factories. xy It's very large, causing i rxy ≈0, and therefore D xj ≈0. Final Q xj ≈H j ×(1 0) = 1.0 (high-quality habitat). Plot B1 is adjacent to the industrial park. Its d xy Very small, and w ind =1.0、S ind =0.90 are all extremely high, leading to D xj The value is very large. Ultimately, Q... xj It will be severely lowered (e.g., dropped to 0.4), even though it will still appear green on the remote sensing map.

[0032] S13, Threshold Determination and Geometric Screening Sub-step: This step "cuts out" physical patches with restoration value from the continuous habitat quality value map and sets a minimum survival suitability threshold T. hab =0.6. The biological significance of this threshold is that areas below 0.6 are typically severely fragmented urban greenbelts or highly disturbed edge areas, which cannot support the establishment of nests by target species (such as small mammals); Binarize the habitat quality value distribution map: Perform connected component labeling on the binarized image and set a minimum population habitat threshold area A. min =5000m 2 (i.e., 50 pixels), this threshold is calculated based on the minimum home range radius of the target species (such as the ferret badger), and is less than the minimum population habitat threshold A. min Patches with excessive "edge effect" are considered ecological dead zones and do not have the potential to serve as nodes. The remaining patches are marked as potential ecological function nodes, as shown in Table 2.

[0033] Table 2: Example Table of Patch Screening Results S14. Collect and integrate the soundscape monitoring data stream, nighttime light radiation spectrum data and surface thermodynamic flow field data of the target area, map them to potential ecological function nodes, and calculate the multidimensional environmental background carrying capacity value of each node. This step aims to uniformly map heterogeneous acoustic, optical, and thermal monitoring data onto a 10m×10m grid system of "potential ecological function nodes" determined by S13, and to convert physical quantities of different dimensions into dimensionless stress indices, in preparation for the final calculation of carrying capacity. This includes the following sub-steps: S141. Within the areas of the preserved potential ecological function nodes (such as PTemp01, PTemp03, PTemp04, etc.), deploy acoustic sensors at 200m intervals. Perform bandpass filtering (2kHz-6kHz, corresponding to the sensitive frequency band of the target species, Red-whiskered Bulbul) on the collected audio, extract the average sound pressure level (Leq) of the background noise, and use inverse distance weighted interpolation to interpolate the Leq values ​​of discrete sampling points to generate a continuous sound scene raster map, align it with the grid matrix in S11, and convert the decibel values ​​into the sound stress index P. sound,x : Among them, L eq,x L is the interpolated noise value at grid x; min The biological comfort background noise standard is set at 35 dB; L max The threshold for complete biological avoidance is set to 85 dB. If L eq,x <L min Then P sound,x =0; if L eq,x >L max Then P sound,x =1; S142. Acquire nighttime light remote sensing images (invert ground illuminance) and combine them with ground spectrometer data (blue light percentage). Resample the remote sensing data to 10m resolution using bilinear interpolation. Considering the coupling between light intensity and spectral composition, construct the light intrusion stress index P. light,x : Among them, E v,x E represents the nighttime illuminance at grid x. thre The light pollution threshold (set to 10 Lux), R blue,x The energy percentage (0-1) is the blue light band (440-490nm); α is the weighting coefficient (0.6, indicating that light intensity has the main influence). The calculation results are truncated to the [0,1] interval. S143. Using surface temperature inversion data combined with CFD simulation, the vertical thermal convection velocity near the surface (at a height of 1.5m) was obtained; after rasterizing and matching the simulation results, the thermal turbulence stress index P was constructed. heat,x : Among them, Vz x V represents the vertical hot air velocity (m / s) at grid x. limit The maximum wind speed (set to 1.5 m / s) required to maintain the flight attitude of a target insect (such as a bee). The exponential square represents the non-linear, rapid increase in flight drag as wind speed increases.

[0034] The method for obtaining the multidimensional environmental baseline carrying capacity value in step S1 includes: performing morphological spatial pattern analysis (MSPA) on the screened potential ecological function nodes to distinguish the core area, bridging area and porous area; for core area nodes, extracting their vegetation vertical structure complexity and water source distance as physical carrying capacity factors; for bridging area nodes, extracting their corridor width and edge effect intensity as structural carrying capacity factors; combining the physical carrying capacity factors and structural carrying capacity factors, calculating the comprehensive score using the entropy weight method, and performing normalization processing to obtain the initial physical carrying capacity value, which represents the theoretical maximum ecological carrying capacity of the node without considering hidden barriers.

[0035] S15. This step aims to comprehensively evaluate the actual ecological carrying capacity of "potential ecological function nodes" under the combined effects of natural physical attributes (such as vegetation structure and distance to water sources) and implicit environmental fields (sound, light, and heat), thereby obtaining a multi-dimensional environmental baseline carrying capacity value that conforms to the actual living environment, denoted as Cbase; the specific calculation process includes the following sub-steps: S151. Node Functional Zoning and Factor Extraction Sub-steps based on Morphological Spatial Pattern Analysis (MSPA): Using Guidos-Toolbox software, morphological spatial pattern analysis was performed on the potential ecological function nodes (binarized images) selected in S13. The edge width parameter was set to 30m (based on the target species' sensitivity to edge effects), and the potential ecological function nodes were internally divided into three types, including: The core area, specifically the internal region more than 30m from the edge of the node, is the main habitat of organisms. The bridging zone is specifically a narrow strip of land connecting two different core zones, i.e., a corridor functional area. Porous areas, specifically cavities within the core area (such as clearings in a forest). For different types of nodes, key evaluation indicators are extracted, including: For nodes in the core area, physical carrying capacity factors are extracted, including: vegetation vertical structure complexity V. struc and the distance of the water source to D water ; Factor 1, vegetation vertical structure complexity V struc The (positive indicator) is obtained by calculating the vertical distribution variance of vegetation echoes based on LiDAR point cloud data. A larger variance indicates richer vegetation layers (tree-shrub-grass) and higher biodiversity potential; Factor 2, distance from water source D. water The negative indicator is obtained by calculating the Euclidean distance (in meters) from the centroid of a node to the nearest permanent body of water (such as a river or lake). The closer the distance, the higher the accessibility to water sources, which is more conducive to the survival of organisms. For the bridging zone nodes, the structural bearing capacity factor is extracted, including: corridor width W. corrand edge effect intensity E edge : Factor 3, corridor width W corr The positive indicator is obtained by extracting the average width (in meters) of the bridging zone skeleton line. A larger width indicates safer biological migration. Factor 4, edge effect intensity E. edge The negative indicator is obtained by calculating the ratio of the edge area to the core area. The smaller the ratio, the more intact the core area is and the less affected it is by external interference.

[0036] S152, Initial Physical Structure Score Calculation Sub-step Based on Entropy Weight Method. This step only scores the physical and structural attributes of the nodes themselves, temporarily disregarding the effects of sound, light, and heat. Define k as the index of the potential ecological function node, k=1,2,...,K, where K is the total number of nodes. Define m as the index of the evaluation factor. To unify the calculation logic, the above four factors are integrated to construct an evaluation matrix. The element a in the matrix... km This represents the original measurement value of the k-th node on the m-th factor; to eliminate inconsistencies in units (such as "meter" and "variance") and differences in dimensions among the factors, the original measurement value a needs to be... km The standardized value Z is obtained by standardization. km ; The standardized processing logic can be divided into the following two cases: Scenario 1: For "positive indicators" (the higher the value, the better), when the evaluation factor m is "vegetation vertical structure complexity V" struc "or "corridor width W" corr When using the formula "", the forward standardization formula is adopted: Where, max(A) m ) and min(A m ) represent the maximum and minimum values ​​of all nodes on the m-th factor, respectively; Scenario 2: For "negative indicators" (the smaller the value, the better), when the evaluation factor m is "distance from water source D" water "or "edge effect intensity E" edge When “”, the negative standardization formula is used: Using the two formulas above, all factor values ​​Z km All are mapped to the [0,1] interval and have a unified directionality (i.e., the larger the value, the better the ecological conditions). S153, where the initial physical bearing capacity value C based on the entropy weight method. phy The weights of each factor are objectively determined using information entropy theory to avoid subjective assignment bias. The information entropy H of the m-th evaluation factor is calculated. m : Calculate the weight ω of the m-th evaluation factor m : The initial physical bearing capacity C of each node k is calculated by weighted summation. phy,k : Initial physical bearing capacity value C phy,k The theoretical maximum ecological carrying capacity of a node without considering hidden barriers.

[0037] S154. Final bearing capacity calculation by integrating implicit environmental stress data. This step aggregates the grid-level acoustic, optical, and thermal data obtained in S14 and calculates the initial physical bearing capacity value. Make corrections; The raster-level stress index (P) generated by S14 is used to... sound,x ,P light,x ,P heat,x Map the data to node k. Calculate the average acoustic stress index for all rasters within the coverage area of ​​node k. Average light intrusion stress index and mean thermal turbulence stress index : Based on the mean sound stress index Average light intrusion stress index and mean thermal turbulence stress index Calculate the comprehensive latent stress index : The weights of 0.4 / 0.4 / 0.2 are based on the target species characteristics in Example 1; Calculation of multidimensional environmental background bearing capacity : The formula represents the background carrying capacity value of a multidimensional environment. It is based on the "hard" physical conditions of the nodes, minus the damage caused by "soft" environmental stress. The constant 0.8 is the environmental sensitivity correction coefficient, which means that even if the physical habitat is very good, if the acoustic, light and thermal environment is extremely harsh, its effective carrying capacity will be greatly reduced, thereby accurately identifying the key nodes that need physical field repair.

[0038] A first threshold and a second threshold are preset. The multidimensional environmental background carrying capacity value of the potential ecological function node k is compared with the preset first threshold and the second threshold, respectively. Based on the comparison results, a first remediation strategy or a second remediation strategy for the node is generated, including: like If the value is less than the first threshold T1, the node is determined to be in an ecological collapse state, and a first restoration strategy is generated. The first restoration strategy includes physical expansion parameters to guide increasing the node patch area or removing hardened ground. If the value is greater than or equal to the first threshold T1 and less than the second threshold T2, the node is determined to be in an environmentally damaged state, and a second repair strategy is generated. The second repair strategy includes environmental de-burden parameters, which are used to guide the reduction of noise level or light intensity in the node area.

[0039] The first threshold, T1 = 0.3, is the critical line for ecological collapse. Below this value, even with favorable physical conditions, the environmental damage is too great, necessitating physical expansion or fundamental reconstruction. The second threshold, T2 = 0.6, is the critical line for ecological health. Values ​​between T1 and T2 indicate that the physical foundation is still acceptable, requiring only reduction of environmental pressure (noise reduction, light avoidance). Specific multi-dimensional environmental background carrying capacity threshold determination and restoration strategies are shown in Table 3.

[0040] Table 3: Threshold Determination and Repair Strategies for Multidimensional Environmental Background Carrying Capacity The technical principle of this embodiment lies in constructing a dual ecological assessment system that integrates explicit physical space and implicit environmental physical fields, aiming to solve the problem of biological flow being "visible but impassable" in complex habitats. The system first utilizes high-resolution multispectral remote sensing interpretation and the InVEST model to calculate habitat quality values ​​based on the sensitivity of land cover type (LULC) to threat sources and distance attenuation patterns, thus completing the initial spatial screening of potential nodes. Then, it innovatively introduces multidimensional heterogeneous physical field data such as soundscape sound pressure level, nighttime blue light radiation ratio, and near-surface thermal turbulence velocity, combining MSPA morphological analysis and entropy weighting to calculate the initial physical carrying capacity of nodes under ideal conditions. The core algorithm involves constructing an environmental sensitivity correction model (Cbase calculation formula), transforming the physical quantities of sound, light, and heat into dimensionless stress indices to correct the theoretical carrying capacity of nodes through "environmental depreciation." By setting dual thresholds for ecological collapse (T1) and environmental damage (T2), the algorithm logic can accurately distinguish whether a node has a "hard injury" caused by insufficient physical area or a "soft injury" caused by environmental noise, light, and heat stress, thereby achieving an evaluation leap from single structural connectivity to multi-dimensional functional connectivity.

[0041] The beneficial effects of this embodiment are mainly reflected in effectively overcoming the defects of "pseudo-connectivity" and resource misallocation caused by neglecting implicit barriers in existing technologies. First, this solution breaks through the limitation of traditional ecological restoration relying solely on vegetation coverage. By quantifying acoustic masking effects, light environment intrusion blocking, and thermal turbulence barriers, it accurately identifies "ecological trap" nodes that, although the vegetation structure is intact, are affected by noise interference with bird communication, nighttime light blocking animal paths, or hot air currents cutting off insect flight, thus avoiding the investment of construction funds in ineffective areas. Second, it achieves precise classification and customization of restoration strategies. Based on the calculated multi-dimensional environmental background carrying capacity value, the system automatically matches "physical expansion" or "environmental burden reduction" strategies. This not only prevents unnecessary large-scale civil engineering expansion for "environmentally damaged" nodes that only require noise reduction treatment, but also ensures that "ecologically collapsed" nodes undergo fundamental spatial reconstruction, improving the actual success rate of gene exchange of target species in fragmented habitats and the input-output ratio of ecological restoration projects.

[0042] Example 2: In step S2, association attributes are assigned to potential ecological function node pairs, specifically based on the following classification logic: Acoustic masking and hindrance correlation: When the background noise frequency between node A and node B is mainly concentrated in the target species' communication frequency range (such as the bird song frequency band 2kHz-6kHz) and the signal-to-noise ratio is lower than the effective communication threshold, it is determined that there is acoustic masking and hindrance, and an acoustic transmission loss coefficient is assigned to it. Light environment intrusion blocking association: When the nighttime spectral composition between node C and node D contains high-intensity short-wavelength blue light and the photon flux density exceeds the phototaxis / avoidance response threshold of the target nocturnal insect, it is determined that there is light environment intrusion blocking and a light barrier repulsion coefficient is assigned to it; Thermal turbulence barrier association: When the speed of the thermal updraft caused by the hardened ground between node E and node F exceeds the maximum wind resistance speed of the target small flying organism (such as a bee), it is determined that a thermal turbulence barrier exists and is assigned an aerodynamic drag coefficient.

[0043] This embodiment aims to solve the problem of "identifying nodes but not knowing whether the pathways between them are open." Traditional methods calculate connectivity solely based on geographical distance, while this embodiment uses physical field parameterization to identify "hidden disconnections," such as birds being unable to hear their companions or insects being trapped by lights or dispersed by hot air currents. S2. Parameterize the implicit connectivity barriers between potential ecological function nodes and construct a multidimensional ecological resistance network. In this embodiment, the set of potential ecological function nodes selected in S1 is defined as the vertex set V in graph theory. nodes The core of this step lies in constructing the edge set and calculating the ecological flux transmission resistance value on each edge.

[0044] The specific implementation steps are as follows: S21. Initial Screening and Spatial Topology Construction of Potential Paths: Based on the node spatial distribution output from S1, a potential connection network between nodes is constructed using the Delaunay triangulation algorithm. Any two adjacent potential ecological function nodes are defined as node p and node q. A maximum biological migration distance threshold L is set. max =2000m (based on the maximum dispersal capacity of the target species, small mammals). If the Euclidean distance between node p and node q > Lmax, then the connection is determined to be physically unreachable and is removed; otherwise, the connection path is retained and denoted as path λ. pq .

[0045] S22. Assignment and calculation of acoustic masking and hindrance correlation attributes, for path λ pq The soundscape monitoring data of the area traversed by the path was collected.

[0046] The decision logic is to set the effective communication frequency window for the target species (such as the red-whiskered bulbul) to Freq. win =[2kHz, 6kHz], minimum signal-to-noise ratio threshold is SNR min =10dB. Calculate path λ pq The middle area is in Freq win Average background noise sound pressure level N in the frequency band avg,pq Construct the acoustic transmission loss coefficient R acous,pq : Where, N limit The species' tolerance limit (set to 60 dB); γ sound Let be the acoustic damping constant (value 1.5). This formula indicates that as noise increases, the resistance of organisms along this path increases exponentially. S23. Assignment and calculation of light environment intrusion blocking related attributes; For path λ pq Analyze nighttime light environment data. Focus on short-wavelength blue light (wavelength <500nm) that has a lethal attraction or repulsion effect on nocturnal insects. Set a photon flux density threshold Φ. thre =0.5μmol / (m2 s).

[0047] Get path λ pq The photon flux density Φ pq and the ratio of blue light energy in the spectrum blue Construct the light barrier repulsion coefficient R opt,pq : The physical meaning of the formula is that the higher the blue light content and the stronger the light intensity, the greater the drag coefficient, which means that insects are very likely to get lost on this path or be "captured" by the light and unable to reach the next node.

[0048] S24. Assignment and calculation of thermal turbulence barrier related attributes, for path λ pq Using CFD simulations, the thermal flow field of the path-crossing area (mainly above paved roads or industrial areas) was analyzed, showing that pollinating insects such as bees travel in vertical updrafts with velocities exceeding those of Wind. crit At a speed of 1.0 m / s, the flight attitude cannot be maintained, and the path λ is extracted. pq Maximum vertical airflow velocity Wind max,pq Construct the aerodynamic drag coefficient R therm,pq : The physical meaning of the formula is that the cubic relationship characterizes the nonlinear and rapid increase of fluid resistance and velocity. Once the critical wind speed is exceeded, the resistance will approach infinity (i.e., path breakage).

[0049] S25, Comprehensive acoustic transmission loss coefficient R acous,pq Light barrier repulsion coefficient R opt,pq and aerodynamic drag coefficient R therm,pq Calculate the path λ between node p and node q. pq Ecological flux transport resistance value : Among them, L pq θ1, θ2, and θ3 are the basic geographic distance between nodes p and q; θ1, θ2, and θ3 are weighting factors (in this embodiment, a balanced weight of 0.33 is used). This formula maps the dimensionless barrier effect generated by the environmental physical field to an equivalent geographic distance. Here, Lpq is the basic geographic distance (in meters). The ecological flux transport resistance value of the path λpq between nodes p and q is calculated. Convert to impedance ratio R atioZ Specifically, this refers to the basic geographical distance between node p and node q, as well as the ecological flux transmission resistance value. The ratio. Impedance ratio R atioZ The physical meaning of this indicator is: how many times greater is the physiological cost that an organism has to pay to travel this path compared to when only geographical distance is considered.

[0050] In system call step S1, the generated policy states (first repair policy / second repair policy / no policy) for nodes p and q are combined with the R of path λpq. atioZThe following logic is executed: a third repair strategy is generated, which specifically meets the following conditions: when the path impedance ratio is greater than or equal to the preset blocking threshold T4, and any node connected to the path is determined to require the execution of the first repair strategy, the third repair strategy is generated; the third repair strategy includes spatial structure reconstruction parameters, marks the physical expansion parameters in the first repair strategy as pending activation, and outputs instructions to build ecological corridors or underground passages, prioritizing the restoration of path connectivity; A fourth remediation strategy is generated, specifically meeting the following conditions: when the path impedance ratio is greater than or equal to the high impedance threshold T3 and less than the blocking threshold T4, and any node connected to the path is determined to require the second remediation strategy, the fourth remediation strategy is generated; the fourth remediation strategy includes regional collaborative management parameters, which are used to output dynamic adjustment instructions for interference sources along the path. The instructions include reducing the traffic noise flux of roads around the path during peak biological migration periods, or adjusting the spectral composition of lighting facilities around the path to reduce the proportion of blue light.

[0051] The third threshold (high impedance threshold T3) is set to T3 = 2.0. When the impedance ratio R... atioZ Greater than or equal to T3 and less than T4 (i.e., 2.0 ≤ R) atioZ When the impedance ratio (RatioZ) is less than 4.0, it means that environmental resistance makes it more than twice as difficult for organisms to cross the path, and the path is in a "high-impedance accessibility state". At this time, organisms can barely pass through, but will suffer significant physiological stress, generating a fourth repair strategy (regional collaborative control type); the fourth threshold (blockage threshold T4) is set to T4=4.0. When the impedance ratio (RatioZ) is greater than or equal to T4 (i.e., RatioZ≥4.0), it means that the environmental resistance is extremely high (e.g., strong blue light barrier or high-intensity noise), which causes organisms to have a very high probability of dying or being forced to turn back when attempting to cross, and the path is in a "hidden disconnection state" (i.e., physically connected, but ecologically broken), generating a third repair strategy (spatial structure reconstruction type); as shown in Table 4 below.

[0052] Table 4: Multidimensional Ecological Resistance Network Path Impedance Analysis and Integrated Restoration Strategies The technical principle of this embodiment lies in constructing an ecological resistance network model based on graph theory and multiphysics coupling. The system utilizes the Delaunay triangulation algorithm to construct potential topological connections between nodes. Then, breaking through the limitations of traditional methods that only measure connectivity by geographical distance, it parametrically models acoustic masking effects, light intrusion, and thermal turbulence barriers. By introducing acoustic damping constants, light barrier repulsion coefficients, and aerodynamic drag coefficients, the algorithm transforms heterogeneous environmental physical field data into dimensionless "ecological flux transport resistance values," and further calculates the "impedance ratio (R)" characterizing the physiological cost of biological migration. atioZThe indicator quantitatively reflects the degree to which the effective distance of a path is "artificially increased" due to environmental stress. By setting a high impedance threshold (T3=2.0) and a blocking threshold (T4=4.0), the model can accurately determine whether the path is in a "difficult to traverse" or "fractured" state. Combined with the physical repair needs of nodes, it triggers "hard engineering reconstruction" (third strategy) or "soft environment management" (fourth strategy) through logical coupling, so as to achieve a precise match between restoration measures and the degree of ecological damage.

[0053] The beneficial effects of this embodiment are mainly reflected in addressing the pain point that existing technologies cannot identify and repair "hidden ecological disconnections." First, it enhances the actual functionality of ecological corridors by identifying and repairing hidden barriers formed by high-frequency noise, nighttime blue light, or hot air currents, avoiding the construction of ineffective corridors that "appear connected but are impassable to organisms," and ensuring unobstructed gene exchange pathways for target species (such as birds and nocturnal insects). Second, it achieves an optimal balance between restoration costs and ecological benefits. The system automatically distinguishes between "high impedance" and "disconnection" states. For areas requiring only environmental management (fourth restoration strategy), expensive civil engineering is avoided, and functionality can be restored simply by adjusting street light spectra or traffic speed limits. For areas requiring structural reconstruction (third restoration strategy), resources are concentrated on constructing covered bridges or passageways. This tiered governance model reduces the total project cost while maximizing biodiversity conservation effectiveness.

[0054] A system for repairing critical nodes in an ecological network to improve connectivity includes: The multi-source sensing data acquisition module is used to acquire sound scene data through a distributed acoustic sensor array, obtain multispectral and night light image data through a remote sensing interface, and obtain surface thermodynamic flow field data by combining computational fluid dynamics simulation. The node screening and carrying capacity analysis module is used to construct a set of potential ecological function nodes from the collected data based on the physical habitat quality value, and to calculate the multi-dimensional environmental background carrying capacity value of each node by integrating multi-source sensing data, thereby generating a first or second restoration strategy at the node level. The resistance network construction and analysis module is used to identify hidden connectivity barriers between potential ecological function nodes and calculate the ecological flux transmission resistance value and path impedance ratio, which include acoustic, optical, and thermal correlation attributes. The integrated strategy generation module is used to generate a third repair strategy when the path is determined to be in a hidden open circuit state, and a fourth repair strategy when the path is determined to be in a high impedance access state, based on the coupling relationship between the node repair strategy and the path impedance ratio.

[0055] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for repairing key nodes in an ecological network to improve connectivity, characterized in that, Applied to the planning of biological migration corridors in fragmented habitats, the specific steps include: Step S1: Construct a set of potential ecological function nodes and determine node restoration strategies; Obtain multi-spectral remote sensing images and environmental monitoring data of the target area, and screen out potential ecological function nodes based on the physical habitat quality value; Map the soundscape monitoring data, night light radiation data, and surface thermodynamic flow field data to the potential ecological function nodes, and calculate the multi-dimensional environmental background carrying capacity value of each node; Compare the multi-dimensional environmental background carrying capacity value with the preset carrying capacity threshold to generate the first restoration strategy or the second restoration strategy for each node. Step S2: Construct a multi-dimensional ecological resistance network and calculate path impedance; Based on the sensory physiological limits of the target species, identify the hidden connectivity obstacles between potential ecological function nodes, and calculate the associated attribute values of acoustic masking blockage, light environment intrusion blocking, and thermal turbulence barrier; Calculate the ecological flux transmission resistance value of the path between each pair of nodes according to the associated attribute value, and determine the path impedance magnification factor based on the ratio of the ecological flux transmission resistance value to the basic geographical distance. Step S3: Generate a comprehensive restoration strategy; Based on the node restoration strategy generated in Step S1 and the path impedance magnification factor calculated in Step S2, determine the coupling state of the nodes and the paths, and generate the third restoration strategy or the fourth restoration strategy.

2. The method for repairing key nodes in an ecological network to improve connectivity as described in claim 1, characterized in that: In Step S1, the soundscape monitoring data stream is collected by a distributed acoustic sensor array deployed inside the potential ecological function nodes, and the background noise energy spectrum of a specific frequency band is extracted through band-pass filtering; The night light radiation spectrum data is obtained by inverting the ground illuminance from night light remote sensing satellite images and combining the blue light band ratio corrected by a ground spectrometer; The surface thermodynamic flow field data is obtained by combining the surface temperature inversion data and the elevation model and simulating the near-surface wind field and heat convection intensity using computational fluid dynamics (CFD) software.

3. The method for repairing key nodes in an ecological network to improve connectivity as described in claim 1, characterized in that: Obtain the high-resolution multi-spectral remote sensing image of the target area, and divide it into a regular grid matrix in space based on the projection coordinate system, and set each grid cell as the x-th grid. The vegetation normalized difference index (NDVI) and the modified normalized difference water index (MNDWI) are calculated and obtained through the following formulas: in, The center wavelength of the near-infrared reflectance is 842nm; The reflectivity is in the red light band, with a center wavelength of 665nm. The reflectivity is for the green light band, with a center wavelength of 560nm. The reflectance is for the short-wave infrared band, with a center wavelength of 1610 nm. If MNDWI > 0, it is determined as a water-covered area; used for subsequent extraction of waterfront corridor nodes. If NDVI > 0.6, it is determined as a high-coverage vegetation-covered area. If 0.4 < NDVI ≤ 0.6, it is determined as a medium-coverage vegetation-covered area. If 0.2 < NDVI ≤ 0.4, it is determined as a low-coverage vegetation-covered area. If NDVI ≤ 0.2 and NDWI ≤ 0, it is determined as a non-permeable surface-covered area. According to the sensitivity of the threat source to the surface cover type and the distance attenuation function, calculate the habitat quality value of each grid cell; Set the lowest survival suitability threshold, and retain the continuous pixel patches with habitat quality values higher than the threshold; Calculate the geometric area of the continuous pixel patches,剔除 patches with an area smaller than the preset minimum population habitat threshold, and mark the remaining patches as potential ecological function nodes.

4. The method for repairing key nodes in an ecological network to improve connectivity as described in claim 1, characterized in that: In step S1, before calculating the multi-dimensional environmental background carrying capacity values of each node, it is necessary to convert the environmental monitoring data into dimensionless implicit stress indices, which specifically include: collecting data using distributed acoustic sensors, extracting the background noise energy spectrum of specific frequency bands, and calculating the acoustic stress index by combining the biological comfort background noise benchmark and the avoidance threshold; retrieving the ground illuminance using night light remote sensing images and constructing the light intrusion stress index by combining the energy proportion of the blue light band; simulating the near-surface vertical thermal convection velocity using computational fluid dynamics and constructing the thermal turbulence stress index by combining the maximum wind resistance of the target species; the multi-dimensional environmental background carrying capacity value is obtained by calculating the initial physical carrying capacity value of the node and subtracting the environmental loss value composed of the weighted sum of the acoustic stress index, the light intrusion stress index, and the thermal turbulence stress index.

5. The method for repairing key nodes in an ecological network for improving connectivity as described in claim 1, characterized in that: The calculation method of the initial physical carrying capacity value includes: performing morphological spatial pattern analysis on potential ecological function nodes and dividing the node area into a core area and a bridging area; For the core area, extracting the vegetation vertical structure complexity and the water source distance as physical carrying factors; For the bridging area, extracting the corridor width and the edge effect intensity as structural carrying factors; standardizing the physical carrying factors and the structural carrying factors and uniformly mapping them to the positive numerical interval; Construct the average acoustic stress index, the average light intrusion stress index, and the average thermal turbulence stress index of all grids in the k-th node of the potential ecological function node, calculate the comprehensive implicit stress index, and correct the initial physical carrying capacity value to obtain the multi-dimensional environmental background carrying capacity value of the k-th node.

6. The method for repairing key nodes in an ecological network for improving connectivity as described in claim 1, characterized in that: Generate the first repair strategy or the second repair strategy for each node. The specific logic is as follows: set the first threshold T1 and the second threshold T2, where T1 < T2; if the multi-dimensional environmental background carrying capacity value is less than T1, it is determined that the node is in an ecological collapse state, and the first repair strategy is generated. The first repair strategy includes physical expansion parameters, which are used to guide the increase of the node patch area or the removal of hardened ground; If the multi-dimensional environmental background carrying capacity value is greater than or equal to T1 and less than T2, it is determined that the node is in an environmentally damaged state, and the second repair strategy is generated. The second repair strategy includes environmental load reduction parameters, which are used to guide the reduction of the noise level or the light intensity in the node area.

7. The method for repairing key nodes in an ecological network for improving connectivity as described in claim 1, characterized in that: In step S2, the potential ecological function nodes are assigned associated attributes, and the specific classification logic is executed as follows: Acoustic masking and blocking association: When the background noise frequency between node A and node B mainly concentrates within the communication frequency range of the target species and the signal-to-noise ratio is lower than the effective communication threshold, it is determined that there is acoustic masking and blocking, and an acoustic conduction loss coefficient is assigned to it; Light environment intrusion blocking association: When the night spectral composition between node C and node D contains high-intensity short-wave blue light and the photon flux density exceeds the light-seeking / light-avoiding reaction threshold of the target nocturnal insects, it is determined that there is light environment intrusion blocking, and a light barrier rejection coefficient is assigned to it; Thermal turbulence barrier association: When the thermal updraft velocity caused by the hardened ground surface between node E and node F exceeds the maximum flight wind resistance of the target small flying organisms, it is determined that there is a thermal turbulence barrier, and an aerodynamic resistance coefficient is assigned to it.

8. The method for repairing key nodes in an ecological network for improving connectivity as described in claim 7, characterized in that: By combining the acoustic transmission loss coefficient, the light barrier repulsion coefficient, and the aerodynamic drag coefficient, the ecological flux transmission resistance value of the path between node p and node q is calculated, and the path impedance ratio is obtained.

9. The method for repairing key nodes in an ecological network for improving connectivity as described in claim 8, characterized in that: A third repair strategy is generated, specifically satisfying the following conditions: when the path impedance ratio is greater than or equal to the preset blocking threshold T4, and any node connected to the path is determined to require the execution of the first repair strategy, the third repair strategy is generated. The third restoration strategy includes spatial structure reconstruction parameters, marks the physical expansion parameters in the first restoration strategy as pending activation, and outputs instructions to build ecological corridors or underground passages, prioritizing the restoration of path connectivity; A fourth remediation strategy is generated, specifically meeting the following conditions: when the path impedance ratio is greater than or equal to the high impedance threshold T3 and less than the blocking threshold T4, and any node connected to the path is determined to require the second remediation strategy, the fourth remediation strategy is generated; the fourth remediation strategy includes regional collaborative management parameters, which are used to output dynamic adjustment instructions for interference sources along the path. The instructions include reducing the traffic noise flux of roads around the path during peak biological migration periods, or adjusting the spectral composition of lighting facilities around the path to reduce the proportion of blue light.

10. A critical node repair system for ecological networks aimed at improving connectivity, applied to the critical node repair method for ecological networks aimed at improving connectivity as described in any one of claims 1-9, characterized in that: include: The multi-source sensing data acquisition module is used to acquire sound scene data through a distributed acoustic sensor array, obtain multispectral and night light image data through a remote sensing interface, and obtain surface thermodynamic flow field data by combining computational fluid dynamics simulation. The node screening and carrying capacity analysis module is used to construct a set of potential ecological function nodes from the collected data based on the physical habitat quality value, and to calculate the multi-dimensional environmental background carrying capacity value of each node by integrating multi-source sensing data, thereby generating a first or second restoration strategy at the node level. The resistance network construction and analysis module is used to identify hidden connectivity barriers between potential ecological function nodes and calculate the ecological flux transmission resistance value and path impedance ratio, which include acoustic, optical, and thermal correlation attributes. The integrated strategy generation module is used to generate a third repair strategy when the path is determined to be in a hidden open circuit state, and a fourth repair strategy when the path is determined to be in a high impedance access state, based on the coupling relationship between the node repair strategy and the path impedance ratio.

Images