Ecological connectivity model construction method, apparatus and device, and readable storage medium

Abstract

The invention discloses an ecological connectivity model construction method and device, equipment and a readable storage medium, and belongs to the technical field of geographic information systems and ecological space recognition and analysis. Comprising the steps of firstly obtaining original ecological data of a target area, and performing consistency processing on the original ecological data to obtain initial ecological data of the target area; determining a core wild area in the target area based on the initial ecological data; creating a resistance surface of each wild core area; and finally, based on the resistance surface, constructing ecological corridors in different scenes in the target area to obtain an ecological connectivity model of the target area. The method achieves the effect of improving the accuracy of the ecological connectivity model.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information systems and ecological space identification and analysis, and more particularly to a method, apparatus, device, and readable storage medium for constructing an ecological connectivity model. Background Art

[0002] An ecological connectivity model is a mathematical or spatial analysis method used to evaluate and optimize the connectivity of ecological elements (such as species, genes, energy flow, etc.) between different habitats in a landscape. It can help researchers and decision-makers identify key ecological corridors, optimize the layout of protected areas, and formulate reasonable ecological restoration strategies.

[0003] However, the accuracy of the ecological connectivity model is affected by various factors such as the quality of ecological data, the complexity of species behavior, and human interference, resulting in insufficient accuracy of the current ecological connectivity model. Therefore, it is unable to accurately identify ecological corridors, thereby affecting the formulation of correct decisions. In summary, there is an urgent need for an ecological connectivity model with better accuracy. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, apparatus, device, and readable storage medium for constructing an ecological connectivity model. By using the initial ecological area, the core wilderness area in the target area is determined, and then an ecological connectivity model of the target area is created based on the resistance surface of the core wilderness area. Compared with the traditional method of constructing an ecological connectivity model only using the wilderness area, the core wilderness area further and more carefully divides the wilderness area. Therefore, the accuracy of the finally obtained ecological connectivity model is higher, achieving the effect of increasing the accuracy of the ecological connectivity model.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a method for constructing an ecological connectivity model, the method comprising:

[0007] Obtain the original ecological data of the target area, and perform consistency processing on the original ecological data to obtain the initial ecological data of the target area;

[0008] Based on the initial ecological data, determine the core wilderness area in the target area;

[0009] Create the resistance surface of each wilderness core area;

[0010] Based on the resistance surface, construct ecological corridors in different scenarios in the target area to obtain the ecological connectivity model of the target area.

[0011] In some embodiments, determining a core wilderness area in a target area based on initial ecological data includes:

[0012] Determining Boolean wilderness patches in the target area according to the initial ecological data;

[0013] Determining the area with the highest wilderness quality index in each Boolean wilderness patch according to the wilderness quality index of each Boolean wilderness patch;

[0014] Overlaying the area with the highest wilderness quality index with the bird habitat protection area, and taking the overlapping area as the core wilderness area.

[0015] In some embodiments, determining the area with the highest wilderness quality index in each Boolean wilderness patch according to the wilderness quality index of each Boolean wilderness patch includes:

[0016] Collecting wilderness quality indicators of Boolean wilderness patches; the wilderness quality indicators include biophysical naturalness indicators, population density indicators, distance from settlements indicators, distance from roads / railways indicators, settlement density indicators, and road / railway density indicators;

[0017] Normalizing the wilderness quality indicators according to the weights of each wilderness quality indicator;

[0018] Calculating the wilderness quality index of each Boolean wilderness patch according to the normalized wilderness quality indicators;

[0019] Selecting Boolean wilderness patches with a wilderness quality index higher than a preset threshold as the areas with the highest wilderness quality index.

[0020] In some embodiments, creating a resistance surface for each wilderness core area includes:

[0021] Normalizing the wilderness quality index of the core wilderness area;

[0022] Converting the normalized wilderness quality index into a resistance value to generate four resistance surfaces.

[0023] In some embodiments, based on the resistance surface, constructing ecological corridors in different scenarios in the target area to obtain an ecological connectivity model of the target area includes:

[0024] Using the minimum cost model, generating ecological corridors in different scenarios based on the area with the highest wilderness quality index and the resistance surface to obtain an ecological connectivity model of the target area.

[0025] In some embodiments, the method further includes:

[0026] Calculating the current density of the ecological corridor;

[0027] Identifying pinch points in the ecological corridor according to the current density.

[0028] In a second aspect, the present invention also provides an apparatus for constructing an ecological connectivity model, the apparatus comprising:

[0029] A data processing module, configured to obtain the original ecological data of the target area, perform consistency processing on the original ecological data, and obtain the initial ecological data of the target area;

[0030] A core determination module, configured to determine the core wilderness areas in the target area based on the initial ecological data;

[0031] A resistance creation module, configured to create a resistance surface for each wilderness core area;

[0032] A model construction module, configured to construct ecological corridors in different scenarios in the target area based on the resistance surface, and obtain the ecological connectivity model of the target area.

[0033] In a third aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the method for constructing an ecological connectivity model provided in the first aspect is implemented.

[0034] In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for constructing an ecological connectivity model provided in the first aspect is implemented.

[0035] In a fifth aspect, the present invention also provides a computer program product, comprising a computer program, and when the computer program is executed by a processor, the method for constructing an ecological connectivity model provided in the first aspect is implemented.

[0036] The beneficial effects of the present invention are as follows:

[0037] In the method for constructing an ecological connectivity model of the present invention, first, the original ecological data of the target area is obtained, and consistency processing is performed on the original ecological data to obtain the initial ecological data of the target area; then, based on the initial ecological data, the core wilderness areas in the target area are determined; then, a resistance surface for each wilderness core area is created; finally, based on the resistance surface, ecological corridors in different scenarios are constructed in the target area to obtain the ecological connectivity model of the target area. The above method determines the core wilderness areas in the target area through the initial ecological area, and then creates the ecological connectivity model of the target area according to the resistance surface of the core wilderness areas. Compared with the method of constructing an ecological connectivity model only using wilderness areas in the traditional technology, the core wilderness areas further and more finely divide the wilderness areas. Therefore, the accuracy of the finally obtained ecological connectivity model is higher, achieving the effect of increasing the accuracy of the ecological connectivity model.

[0038] The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and be implemented in accordance with the content of the description, the following describes in detail with reference to the preferred embodiments of the present invention and the accompanying drawings. Description of the Drawings

[0039] Figure 1 It is a schematic flowchart of a method for constructing an ecological connectivity model shown in an embodiment of the present invention;

[0040] Figure 2 It is a schematic flowchart of another method for constructing an ecological connectivity model shown in an embodiment of the present invention;

[0041] Figure 3 It is a schematic structural diagram of a device for constructing an ecological connectivity model shown in an embodiment of the present invention;

[0042] Figure 4 It is a schematic structural diagram of another device for constructing an ecological connectivity model shown in an embodiment of the present invention;

[0043] Figure 5 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. Detailed Description of the Embodiments

[0044] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0045] It should be noted that the references to "one embodiment", "embodiment", "example embodiment", etc. in this specification mean that the described embodiment may include specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. In addition, such expressions do not refer to the same embodiment. Further, when combining specific features, structures, or characteristics with an embodiment, it is within the knowledge of those skilled in the art to combine such features, structures, or characteristics with other embodiments even without explicit description.

[0046] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] In some embodiments, as Figure 1 shown, a method for constructing an ecological connectivity model is provided, including:

[0048] S101. Obtain the original ecological data of the target area, perform consistency processing on the original ecological data, and obtain the initial ecological data of the target area.

[0049] Among them, the original ecological data includes land use data, railway data, road data, settlement data, population density data, and nature reserve boundary data, etc.

[0050] Specifically, the original ecological data can be obtained by using sensors, or the original ecological data obtained by field investigation or drone investigation uploaded by staff can be obtained. For the original ecological data, cross-checks need to be carried out through superposition methods and visual comparison to ensure its consistency, and the initial ecological data is obtained after processing.

[0051] S102. Based on the initial ecological data, determine the core wilderness area in the target area.

[0052] Among them, the wilderness area is an area that contains natural land cover and has no roads and permanent human settlements.

[0053] Specifically, the initial ecological data can be input into the core wilderness area recognition model, and the wilderness area recognition model will divide the areas with large areas undamaged and retaining natural features as the core wilderness area.

[0054] Optionally, the method for determining the core wilderness area in the target area can also be: determine the Boolean wilderness patches in the target area according to the initial ecological data; determine the area with the highest wilderness quality index in each Boolean wilderness patch according to the wilderness quality index of each Boolean wilderness patch; superimpose the area with the highest wilderness quality index with the bird habitat protection area, and use the overlapping area as the core wilderness area.

[0055] Specifically, the initial ecological data can be input into the Boolean wilderness patch recognition model, and the wilderness patch recognition model can identify the Boolean wilderness patches in the target area. It should be noted that the Boolean wilderness patch recognition model is pre-set. Compared with the traditional determination of wilderness patches, the Boolean wilderness patch recognition model can define smaller areas as Boolean wilderness patches, because even the smallest wilderness patch may have ecological importance; then calculate the wilderness quality index of each Boolean wilderness patch, and use the top 10% of the Boolean wilderness patches with the highest wilderness index as the area with the highest wilderness quality index, and then select the bird habitat protection area from the target area, and use the area where the bird habitat protection area overlaps with the area with the highest wilderness quality index as the core wilderness area. That is to say, the wilderness area is divided into three types, which are the core wilderness area, the area with the highest wilderness quality index, and the Boolean wilderness patch in descending order of wilderness degree.

[0056] Optionally, the method for determining the area with the highest wilderness quality index may also be as follows: collect the wilderness quality indicators of Boolean wilderness patches; normalize the wilderness quality indicators according to the weights of each wilderness quality indicator; calculate the wilderness quality index of each Boolean wilderness patch based on the normalized wilderness quality indicators; select the Boolean wilderness patches with a wilderness quality index higher than the preset threshold as the area with the highest wilderness quality index.

[0057] Among them, the wilderness quality indicators include biophysical naturalness indicators, population density indicators, remoteness from settlements indicators, remoteness from roads / railways indicators, settlement density indicators, and road / railway density indicators; the biophysical naturalness indicators can reflect the degree of change of the ecosystem from its original state due to human transformation (such as settlements, deforestation, and agriculture); the population density indicators can effectively reflect the degree of human interference in the natural landscape; the remoteness from settlements indicators can reflect various negative impacts of settlements on the natural environment; the remoteness from roads / railways indicators can reflect the impact of roads / railways on the wilderness; the settlement density indicators reflect the obviousness of human habitation and are related to the perception and visual impact of the wilderness; the road / railway density indicators reflect the density of transportation infrastructure and related human artifacts, such as bridges, dams, transmission lines, etc.

[0058] Specifically, based on the land use type, the land naturalness can be quantified through existing expert scoring (1-10 points), and then the biophysical naturalness indicator (Biophysical Naturalness, abbreviated as BN) can be obtained;

[0059] Then, it is provided in the form of a 1-kilometer resolution grid, and the average value of each central pixel within a 3×3 km pixel moving window is calculated to obtain the population density indicator (Population Density, abbreviated as PD) to smooth the data and avoid edge effects.

[0060] Then, select the construction land data. First, convert the polygon data into point data, and then use the Euclidean distance analysis tool to calculate with the point data of the construction land as the source. The calculation formula refers to the following formula (1), and the obtained result is used as the remoteness from settlements indicator (Remoteness from Settlements, abbreviated as RS). The higher the raster value, the farther the raster is from the settlement.

[0061]

[0062] Where n = 3, Ri represents the Euclidean distance from the I-level settlement, and βi is the weight of different levels of settlements.

[0063] Then, using the road network data as the source, calculate using the Euclidean distance analysis tool, referring to formula (1), and the resulting value is used as the Remoteness from Roads / Railways (RR) indicator. The higher the value, the farther the raster is from the road.

[0064] Where n = 4, Ri represents the Euclidean distance from the railway and different levels of roads, and βi is the weight of different levels of roads and railways.

[0065] Then, based on kernel density analysis (search radius 20 km), combined with the weights of level 1, level 2, and level 3 settlements, calculate the Settlements Density (SD) indicator. The specific formula is as follows in formula (2).

[0066]

[0067] Where n = 3, Di is the settlement density, and βi is the weight of different settlement levels obtained from the expert survey.

[0068] Finally, use the 20-kilometer radius kernel density filter to calculate the roads / railways density of all railways and level 1, level 2, and level 3 roads. Using formula (2), calculate the Roads / Railways Density (RD) indicator.

[0069] Where n = 4, and βi is the weight of different levels of roads / railways obtained from the expert survey.

[0070] Then, using the expert survey method, determine a set of robust weights based on professional knowledge. Specifically, invite landscape experts who are familiar with the concept of wilderness and have experience in protected area research to participate in a survey. This survey aims to determine the weights required for the Wilderness Land Classification (WLC) model.

[0071] Before the survey was conducted, the meanings of the six wilderness indicators were explained in detail to ensure that each expert could accurately understand these indicators. Subsequently, experts were required to rate the importance of the following: (1) the relative importance of the six wilderness indicators; (2) the relative importance of different levels of settlements when calculating the distance to settlements and settlement density; (3) the relative importance of different levels of roads and railways when calculating the distance to roads / railways and roads / railways density.

[0072] After collecting the expert survey data, calculate the weights using formula (3). The final indicator weights are determined based on the average of the importance ratings given by the experts to reflect the comprehensive opinions of the expert group.

[0073]

[0074] Among them, Wi is the index weight, IRi is the importance rating of the i-th index, and n is the number of indices.

[0075] After obtaining all the wilderness quality indices, the Weighted Linear Combination (WLC) model is used. According to the weight distribution, each wilderness quality index is normalized to the range of 0-1 according to the following formula (4):

[0076]

[0077] Among them, NIi is the standardized index, and ai is the value of the i-th index.

[0078] Then, the Wilderness Quality Index (WQI) of each Boolean wilderness patch is calculated according to the following formula (5):

[0079]

[0080] Among them, n = 6, X is the standardized wilderness quality index, and wi is the weight of the i-th index obtained from the expert survey.

[0081] The calculated results need to be subjected to sensitivity analysis. The uncertainty of the wilderness continuum map mainly comes from the index weights. Therefore, it is necessary to conduct sensitivity analysis to show the impact of index weights on the results. By running the weighted linear combination model 25 times and using different weight sets, 25 wilderness continuum maps are created. By calculating the average value and standard deviation of the 25 result maps, the overall sensitivity and local sensitivity regions of the model are shown.

[0082] Finally, the top 10% of the Boolean wilderness patches with the highest wilderness quality index are selected as the areas with the highest wilderness quality index.

[0083] S103, create the resistance surface of each wilderness core area.

[0084] Optionally, the wilderness quality index of the core wilderness area can be normalized first; then the normalized wilderness quality index is converted into a resistance value to generate four resistance surfaces.

[0085] Specifically, the wilderness quality index of the core wilderness area is normalized according to the following formula (6);

[0086]

[0087] Among them, WQI is the original wilderness quality index extracted for the study area, and wqi-std refers to the standardized wilderness quality index.

[0088] By applying different transformation functions, the standardized WQI is converted into a resistance value (R) to generate four resistance surfaces; the first one uses the negative linear transformation of Eq, referring to formula (7);

[0089] R = 100 - 99×(wqi_std) (7)

[0090] The second, third, and fourth ones adopt the negative exponential transformation, referring to formula (8);

[0091]

[0092] Among them, R is the resistance value, the parameter c determines the curve shape of the function (here c = 1, c = 4, and c = 8 are used respectively), and wqi-std refers to the standardized wilderness quality index.

[0093] S104. Based on the resistance surface, ecological corridors in different scenarios are constructed in the target area to obtain the ecological connectivity model of the target area.

[0094] Among them, the ecological corridor refers to maintaining relatively low human impact between core wilderness areas, providing functional connectivity, so as to provide biophysical conditions for the survival of ecosystems and populations in the human-dominated landscape.

[0095] Specifically, the minimum cost model of Linkage Mapper is used to generate ecological corridors based on the core wilderness areas and the resistance surface. The minimum cost model determines the distribution of ecological corridors by calculating the minimum resistance path from one core wilderness area to another. Combining different core patches and resistance surfaces, 12 ecological networks are generated. By comparing the 12 ecological networks, the distribution differences of ecological corridors in different scenarios can be identified. These differences are mainly reflected in the path, length, and connectivity of the corridors. Among the generated ecological corridors, key nodes (pinch-points) are identified. These nodes are crucial for the connectivity of ecological corridors, and the identification of key nodes helps to determine the priority areas for protection and restoration.

[0096] Optionally, the current density of the ecological corridor can also be calculated; according to the current density, the pinch points in the ecological corridor can be identified.

[0097] Specifically, the PinchpointMapper tool in Linkage Mapper and Circuitscape are used for connectivity modeling. The current density of each pixel in the landscape is calculated through the circuit model, reflecting the potential paths and key nodes of species migration. The locations where the current density values are significantly higher than the surrounding areas are defined as pinch-points. These areas are the nodes where connectivity is most vulnerable or important. Record the geographical locations of the pinch-points, the main obstacles (such as roads and human activity areas), and the current density values.

[0098] For the method for constructing the ecological connectivity model in the above embodiments, first obtain the original ecological data of the target area, perform consistency processing on the original ecological data to obtain the initial ecological data of the target area; then based on the initial ecological data, determine the core wilderness areas in the target area; then create the resistance surfaces of each wilderness core area; finally, based on the resistance surfaces, construct ecological corridors in different scenarios in the target area to obtain the ecological connectivity model of the target area. Through the initial ecological area, the core wilderness areas in the target area are determined in the above method, and then the ecological connectivity model of the target area is created according to the resistance surfaces of the core wilderness areas. Compared with the method of constructing an ecological connectivity model only using wilderness areas in the traditional technology, the core wilderness areas are further and more detailedly divided compared with the wilderness areas. Therefore, the accuracy of the finally obtained ecological connectivity model is higher, achieving the effect of increasing the accuracy of the ecological connectivity model.

[0099] To more comprehensively demonstrate the present solution, an optional way of the method for constructing an ecological connectivity model is given in this embodiment, as Figure 2 shown:

[0100] S201, Obtain the original ecological data of the target area, perform consistency processing on the original ecological data to obtain the initial ecological data of the target area.

[0101] S202, Determine the Boolean wilderness patches in the target area according to the initial ecological data.

[0102] S203, Collect the wilderness quality indicators of the Boolean wilderness patches.

[0103] Among them, the wilderness quality indicators include biophysical naturalness indicators, population density indicators, distance from settlements indicators, distance from roads / railways indicators, settlement density indicators, and road / railway density indicators.

[0104] S204, Perform normalization processing on the wilderness quality indicators according to the weights of each wilderness quality indicator.

[0105] S205, Calculate the wilderness quality index of each Boolean wilderness patch according to the normalized wilderness quality indicators.

[0106] S206, select Boolean wild patches with a wildness quality index higher than a preset threshold as the area with the highest wildness quality index.

[0107] S207, overlay the area with the highest wildness quality index with the bird habitat reserve, and use the overlapping area as the core wild area.

[0108] S208, perform normalization processing on the wildness quality index of the core wild area.

[0109] S209, convert the normalized wildness quality index into a resistance value to generate four resistance surfaces.

[0110] S210, use the minimum cost model, based on the area with the highest wildness quality index and the resistance surfaces, to generate ecological corridors under different scenarios, and obtain the ecological connectivity model of the target area.

[0111] S211, calculate the current density of the ecological corridor.

[0112] S212, identify pinch points in the ecological corridor according to the current density.

[0113] For the specific processes of the above S201 - S212, reference can be made to the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be elaborated here.

[0114] Based on the same inventive concept, an embodiment of the present application also provides a construction device for an ecological connectivity model for implementing the construction method of the ecological connectivity model involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the construction device for the ecological connectivity model provided below can refer to the limitations on the construction method of the ecological connectivity model in the above text, and will not be elaborated here.

[0115] In one embodiment, as Figure 3 shown, a construction device for an ecological connectivity model is provided. The device includes:

[0116] A data processing module 30, configured to obtain the original ecological data of the target area, perform consistency processing on the original ecological data, and obtain the initial ecological data of the target area;

[0117] A core determination module 31, configured to determine the core wild area in the target area based on the initial ecological data;

[0118] A resistance creation module 32, configured to create resistance surfaces for each wild core area;

[0119] The model construction module 33 is used to construct ecological corridors in different scenarios in the target area based on the resistance surface, so as to obtain the ecological connectivity model of the target area.

[0120] In another embodiment, such as Figure 4 , the above Figure 3 The core determination module 31 includes:

[0121] The patch determination module 310 determines the Boolean wilderness patches in the target area according to the initial ecological data;

[0122] The area determination module 311 is used to determine the area with the highest wilderness quality index in each Boolean wilderness patch according to the wilderness quality index of each Boolean wilderness patch;

[0123] The core determination module 312 is used to superimpose the area with the highest wilderness quality index on the bird habitat protection area, and use the overlapping area as the core wilderness area.

[0124] In another embodiment, the above Figure 4 The area determination module 311 in is specifically used for: collecting the wilderness quality indicators of the Boolean wilderness patches; the wilderness quality indicators include biophysical naturalness indicators, population density indicators, distance from settlements indicators, distance from roads / railways indicators, settlement density indicators and road / railway density indicators; normalizing the wilderness quality indicators according to the weights of each wilderness quality indicator; calculating the wilderness quality index of each Boolean wilderness patch according to the normalized wilderness quality indicators; selecting the Boolean wilderness patches with a wilderness quality index higher than the preset threshold as the areas with the highest wilderness quality index.

[0125] In another embodiment, the above Figure 3 The resistance creation module 32 in is specifically used for: normalizing the wilderness quality index of the core wilderness area; converting the normalized wilderness quality index into a resistance value to generate four resistance surfaces.

[0126] In another embodiment, the above Figure 3 The model construction module 33 in is specifically used for:

[0127] Using the minimum cost model, based on the area with the highest wilderness quality index and the resistance surface, generate ecological corridors in different scenarios to obtain the ecological connectivity model of the target area; calculate the current density of the ecological corridor; identify the pinch points in the ecological corridor according to the current density.

[0128] The embodiments of the present application also provide an electronic device. In some embodiments, refer to Figure 5As shown, the electronic device 700 includes an input unit 710, a memory 720, a processor 730, and an output unit 740. The memory 720 stores program instructions that can be run on the processor 730. The processor 730 can execute the construction method and / or technical solution based on the ecological connectivity model in the foregoing embodiments by invoking the program instructions. The electronic device 700 can be a mobile terminal device such as a mobile phone or a computer.

[0129] In addition, an embodiment of the present application further provides a computer-readable storage medium for storing a computer program for executing the construction method of the ecological connectivity model. For example, computer program instructions, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. The program instructions for calling the method of the present application may be stored in a fixed or removable storage medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium and / or stored in a storage medium operating according to the program instructions.

[0130] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program code executable by the computing device. Thus, they can be stored in a storage device for execution by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them can be fabricated into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0131] The technical features of the above embodiments can be arbitrarily integrated. For the sake of brevity of description, not all possible integrations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the integration of these technical features, it should be considered as falling within the scope described in this specification.

[0132] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for constructing an ecological connectivity model, characterized in that The method includes: Obtaining the original ecological data of the target area, performing consistency processing on the original ecological data to obtain the initial ecological data of the target area; Based on the initial ecological data, determining the core wilderness areas in the target area; Creating resistance surfaces for each of the wilderness core areas; Based on the resistance surfaces, constructing ecological corridors in different scenarios in the target area to obtain the ecological connectivity model of the target area.

2. The method for constructing the ecological connectivity model according to claim 1, wherein Based on the initial ecological data, determining the core wilderness areas in the target area includes: Determining the Boolean wilderness patches in the target area according to the initial ecological data; According to the wilderness quality index of each of the Boolean wilderness patches, determining the area with the highest wilderness quality index in each Boolean wilderness patch; Overlaying the area with the highest wilderness quality index with the bird habitat protection area, and taking the overlapping area as the core wilderness area.

3. The method for constructing an ecological connectivity model according to claim 2, wherein According to the wilderness quality index of each of the Boolean wilderness patches, determining the area with the highest wilderness quality index in each Boolean wilderness patch includes: Collecting the wilderness quality indicators of the Boolean wilderness patches; the wilderness quality indicators include biophysical naturalness indicators, population density indicators, distance from settlements indicators, distance from roads / railways indicators, settlement density indicators, and road / railway density indicators; Normalizing the wilderness quality indicators according to the weights of each of the wilderness quality indicators; Calculating the wilderness quality index of each of the Boolean wilderness patches according to the normalized wilderness quality indicators; Selecting the Boolean wilderness patches with a wilderness quality index higher than a preset threshold as the areas with the highest wilderness quality index.

4. The method for constructing the ecological connectivity model according to claim 3, wherein, Creating resistance surfaces for each of the wilderness core areas includes: Normalizing the wilderness quality index of the core wilderness area; Converting the normalized wilderness quality index into a resistance value to generate four resistance surfaces.

5. The method for constructing the ecological connectivity model according to claim 4, wherein, Based on the resistance surfaces, constructing ecological corridors in different scenarios in the target area to obtain the ecological connectivity model of the target area includes: Using the minimum cost model, generating ecological corridors in different scenarios based on the area with the highest wilderness quality index and the resistance surfaces to obtain the ecological connectivity model of the target area.

6. The method for constructing the ecological connectivity model according to any one of claims 1-5, characterized in that, The method further includes: Calculating the current density of the ecological corridor; Identifying the pinch points in the ecological corridor according to the current density.

7. An apparatus for constructing an ecological connectivity model, characterized in that The device includes: A data processing module for obtaining the original ecological data of the target area, performing consistency processing on the original ecological data to obtain the initial ecological data of the target area; A core determination module for determining the core wilderness areas in the target area based on the initial ecological data; A resistance creation module for creating resistance surfaces for each of the wilderness core areas; A model construction module for constructing ecological corridors in different scenarios in the target area based on the resistance surfaces to obtain the ecological connectivity model of the target area.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for constructing the ecological connectivity model according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the method for constructing the ecological connectivity model according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for constructing the ecological connectivity model according to any one of claims 1 to 6.

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