A protection priority area identification method and related device considering protection cost constraints and decision preferences

Abstract

The application discloses a protection priority area identification method considering protection cost constraints and decision preferences and related equipment, and the method comprises the following steps: constructing a multi-source heterogeneous basic database; generating a value index grid layer and a cost index grid layer, and then determining the corresponding benchmark weight coefficient; introducing an ordered weighted average model to determine the decision preference pedigree; then determining the effective decision preference parameter interval and generating a candidate scheme set; constructing a multi-dimensional protection performance characteristic matrix to screen the candidate scheme set, and determining the optimal decision preference parameter interval; extracting the priority area space entity from the optimal decision preference interval to determine the protection priority area identification result. The embodiment of the application can efficiently and accurately realize the filtering of the candidate scheme and the definition of the robust decision interval, output the protection space layout scheme with theoretical optimality and management feasibility, and can be widely applied to the technical field of geographic information data processing.

Description

Technical Field

[0001] This application relates to the field of geographic information data processing technology, and in particular to a method and related equipment for identifying protection priority areas that takes into account protection cost constraints and decision-making preferences. Background Technology

[0002] Establishing nature reserves is considered an important nature-based solution for mitigating biodiversity loss, maintaining ecosystem service provision, and addressing global climate change. How to scientifically and efficiently identify and delineate Conservation Priority Areas (CPAs) has become a core issue in the field of Systematic Conservation Planning (SCP).

[0003] Existing technologies for identifying priority protection zones mainly rely on spatial overlay analysis and heuristic optimization algorithms (such as Marxan and Zonation). Although some progress has been made in theory and practice, existing technical solutions still reveal many systemic defects when facing the complex needs of modern ecological space governance. First, the ecological conservation value assessment system suffers from dimensional limitations. Existing technologies typically treat ecosystem services and biodiversity as independent research subjects. Some methods focus on spatial identification based on regulatory services such as water conservation and soil retention, easily overlooking the habitat requirements of rare and endangered species. Other methods, based on species distribution models (SDMs), struggle to comprehensively reflect the fundamental supporting functions of macro-ecosystems in maintaining regional water and soil balance and carbon cycling. Because high-value areas of ecosystem services and species diversity hotspots often exhibit inconsistencies or even misalignments in spatial distribution, existing technologies lack a unified system for assessing conservation value that integrates macro-ecological function dimensions and micro-species habitat dimensions.

[0004] Second, the measurement of implementation costs and realistic constraints is either lacking or oversimplified. The effectiveness of conservation planning depends not only on the ecological value endowment of the identified area but also on the realistic feasibility of implementing conservation actions. Current technologies, when identifying priority conservation areas, often treat spatial planning as a process of finding the optimal solution under ideal conditions, failing to fully quantify the socio-economic obstacles faced by conservation actions. Although some studies have introduced land use type or land price as single cost proxy variables, they have failed to establish a systematic constraint measurement framework encompassing opportunity costs (human activity disturbance), management costs (topography and accessibility), ecological restoration costs (baseline degradation status), and social coordination costs (community industrial dependence). This results in conservation solutions often facing extremely high financial burdens and social coordination resistance during the implementation phase.

[0005] Third, there is insufficient parameterization mechanism for expressing decision-making preferences. In actual spatial governance decisions, different decision-makers exhibit significant differences in their trade-offs between "high protection value" and "low protection cost." Some decisions lean towards risk preference (preferring to protect extremely high-value areas and tolerating high costs), while others lean towards risk aversion (preferring to obtain a stable protection base within a limited cost budget). Existing mainstream identification algorithms are mostly static global optimization models, typically using fixed weights or a few preset scenarios for evaluation. This makes it difficult to embed the risk attitudes of decision-makers into the evaluation model, thus failing to form a continuous simulation capability for different decision preference scenarios. Consequently, the solutions output by the decision support system lack flexibility and are unable to support multi-stakeholder consultation and adaptive management.

[0006] Fourth, multi-scenario simulation methods lack wide-area parameter search and interval robustness identification. In research on decision preferences, even when using multi-criteria evaluation models such as Ordered Weighted Averaging (OWA), existing techniques generally employ a static comparison strategy that pre-sets a small number of discrete parameter values ​​based on subjective experience (e.g., setting 3 to 5 scenarios to represent different preferences). This method fails to perform a wide-area search within a continuous parameter space, cannot reveal the continuous dynamic characteristics of weight structure evolution and spatial pattern migration, and is prone to getting trapped in local optima, failing to define the range of optimal decision parameters with universality and robustness.

[0007] Fifth, there is a lack of algorithms for dimensionality reduction and non-dominated solution set extraction of high-dimensional data oriented towards multi-dimensional performance objectives. Multi-scenario decision-making inevitably generates a massive number of candidate solutions, and how to evaluate and select these solutions is another major technical bottleneck. Existing technologies mostly rely on linear evaluation using a single efficiency indicator. However, high-quality protection solutions need to achieve a balance between macro-level protection effectiveness, unit cost efficiency, and the degree of coordination among multiple ecological elements. Existing technologies mostly rely on single indicators or linear scoring for evaluation, making it difficult to simultaneously consider macro-level protection effectiveness, unit cost efficiency, and the level of coordination among multiple ecological elements. Therefore, there is a lack of a technical process capable of reducing candidate solutions to a multi-dimensional performance matrix and further extracting non-dominated optimal results.

[0008] Therefore, there is an urgent need to provide a protection priority area identification method and related equipment that takes into account protection cost constraints and decision preferences, so as to solve the technical problems existing in the prior art, such as fragmented protection value evaluation, insufficient characterization of cost constraints, discontinuous expression of decision preferences, and insufficient robustness in optimal solution selection. Summary of the Invention

[0009] The main objective of this application is to propose a method and related equipment for identifying protection priority areas that takes into account ecological protection value, protection implementation cost constraints, and the preferences of decision-makers.

[0010] To achieve the above objectives, one aspect of this application proposes a method for identifying protection priority areas that takes into account protection cost constraints and decision-making preferences, including: Acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification; Based on the multi-source heterogeneous basic database, spatial measurements of ecosystem services and biodiversity in the study area are performed, and value index raster layers corresponding to each raster unit are generated. Based on the aforementioned multi-source heterogeneous basic database, a multi-dimensional protection cost constraint evaluation system is constructed, including opportunity cost, management cost, recovery cost, and collaboration cost, and a cost index raster layer corresponding to each raster unit is generated. The value index and the cost index are standardized, and the benchmark weight coefficients corresponding to the value index and the cost index are determined. An ordered weighted average model is introduced, and the decision preference parameter is used as the adjustment variable of the ordered weighted average model. The ranking weights of each evaluation index are generated according to the decision preference parameter, and the ranking weights are fused with the benchmark weight coefficient to obtain the comprehensive priority result of each grid cell under different decision preference scenarios, thus forming a decision preference spectrum. The decision preference parameters are continuously searched within a preset parameter range, and the effective decision preference parameter range is determined based on the spatial pattern changes, score distribution patterns, and parameter disturbance response degrees corresponding to the comprehensive priority results under different parameter values. Discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the priority region grid set based on the comprehensive priority result corresponding to each sampled parameter to generate a candidate solution set; The protection effectiveness, protection efficiency, and contribution performance indicators of protection elements for each candidate scheme are calculated separately, and a multidimensional protection performance feature matrix of the candidate schemes is constructed. Based on the multidimensional protection performance feature matrix, the candidate scheme set is clustered and screened, and the Pareto non-dominated solution is extracted from the screened candidate schemes to determine the corresponding optimal decision preference parameter range. Based on the candidate schemes corresponding to the optimal decision preference parameter range, the spatial entities of the protection priority area are extracted, and the final protection priority area identification result is output.

[0011] In some embodiments, the step of spatially measuring ecosystem services and biodiversity in the study area based on the multi-source heterogeneous basic database and generating a raster layer of value indicators corresponding to each raster unit includes: Based on the aforementioned multi-source heterogeneous basic database, the material quantification of ecosystem services in the study area is carried out, specifically including: using the water yield equation to calculate water conservation, using the modified general soil loss equation to calculate soil retention, and using the carbon storage calculation model and the empirical equation based on wind erosion climate factors to characterize carbon sequestration services and windbreak and sand fixation services, respectively. Based on the aforementioned multi-source heterogeneous basic database, spatial simulation of biodiversity dimensions is conducted in the study area. Specifically, this includes: conducting Spearman correlation tests on pre-selected environmental factors to complete habitat suitability assessment; defining trunk roads, industrial and mining construction land, and high-intensity grazing grasslands as stress sources; setting habitat suitability parameters and anti-interference sensitivity indices for various types of land use; introducing a half-saturated constant function; and outputting a continuous raster map representing habitat health status to complete habitat quality assessment.

[0012] In some embodiments, the step of constructing a multi-dimensional protection cost constraint evaluation system based on the multi-source heterogeneous basic database, including opportunity cost, management cost, recovery cost, and collaboration cost, and generating a cost index raster layer corresponding to each raster unit, includes: By overlaying population density grid, nighttime light intensity grid and agricultural and pastoral activity intensity grid with equal weights and normalizing them, a cost resistance surface reflecting the heat of regional economic development is formed, thus constructing an opportunity cost representation. Using the main highway network as the cost source, the terrain relief index and land cover barrier coefficient are set as the passage resistance. The minimum cost distance algorithm in GIS is used to generate a raster and construct a representation of management cost. The temporal degradation slope of the normalized vegetation index was used to assess the ecological fragility of the land surface and to construct a characterization of restoration costs. Based on the statistical proportion of the population employed in the primary industry to the total population, a collaborative cost representation is constructed; The cost indicators for the study area are determined based on the opportunity cost representation, the management cost representation, the recovery cost representation, and the synergy cost representation.

[0013] In some embodiments, the introduction of an ordered weighted average model involves using decision preference parameters as adjustment variables for the ordered weighted average model, generating ranking weights for each evaluation index based on the decision preference parameters, and fusing the ranking weights with the benchmark weight coefficients to obtain the comprehensive priority results of each grid cell under different decision preference scenarios, forming a decision preference spectrum, including: An ordered weighted average model is introduced, and the human decision preference parameter is set as a preference variable that controls the distribution shape of the order weights; Generate the ordinal weight of each evaluation index in the sorting state based on the decision preference parameters; The benchmark weight coefficient and the ordinal weight are fused to form a decision preference spectrum corresponding to different risk attitudes; Based on the aforementioned decision preference spectrum, the comprehensive priority result of each grid cell under different decision preference scenarios is calculated.

[0014] In some embodiments, the step of calculating the protection effectiveness, protection efficiency, and protection element contribution performance indicators for each candidate scheme, and constructing a multidimensional protection performance feature matrix for the candidate schemes, includes: Calculate the percentage of the sum of standardized ecological values ​​within the coverage area of ​​candidate schemes to the total value of the entire region, construct a performance indicator of protection effectiveness, and use it to assess the overall achievement of macro-protection goals; Calculate the ratio of the performance index of the protection effectiveness to the total cost within the coverage of the candidate solutions, and construct a performance index of protection efficiency to evaluate the protection effectiveness corresponding to the unit protection cost; The enhancement coefficient of the average distribution density of various independent ecosystem services within the priority area corresponding to the candidate scheme relative to the baseline average value of the whole area is calculated to construct a performance index of the contribution of protection elements, which is used to evaluate the goodness of the protection plan. Based on the protection effectiveness performance index, the protection efficiency performance index, and the protection element contribution performance index, the multidimensional protection performance feature matrix is ​​constructed.

[0015] In some embodiments, generating the ordinal weights of each evaluation index in the ranking state based on the decision preference parameters includes: Obtain the values ​​of decision preference parameters corresponding to the decision-maker's risk attitude; Obtain the total number of indicators that measure the value and cost of participation in the evaluation; The evaluation indicators are sorted according to the attribute values ​​in the unit comprehensive evaluation, and then the ranking weight of the corresponding order is calculated based on the value of the decision preference parameter and the total number of indicators.

[0016] In some embodiments, the calculation formula for measuring water conservation using the water production equation is as follows: ,in, For grid Water conservation capacity For grid Annual precipitation, For grid The actual annual evaporation; The calculation formula for soil retention using the modified general soil loss equation is as follows: ,in, For grid Soil retention capacity As the erosivity factor of rainfall, As a soil erodibility factor, and These are slope length and slope factor, respectively. As a vegetation cover management factor, Factors related to soil and water conservation measures.

[0017] Another aspect of this application embodiment provides a protection priority area identification device that takes into account protection cost constraints and decision-making preferences, including: The first module is used to acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification. The second module is used to perform spatial measurement of ecosystem services and biodiversity in the study area based on the multi-source heterogeneous basic database, and generate value index raster layers corresponding to each raster unit. The third module is used to construct a multi-dimensional protection cost constraint evaluation system based on the multi-source heterogeneous basic database, including opportunity cost, management cost, recovery cost and collaboration cost, and generate cost index raster layers corresponding to each raster unit. The fourth module is used to standardize the value indicators and the cost indicators, and to determine the benchmark weight coefficients corresponding to the value indicators and the cost indicators. The fifth module is used to introduce an ordered weighted average model, using decision preference parameters as adjustment variables for the ordered weighted average model. Based on the decision preference parameters, the module generates the ranking weights of each evaluation index and integrates the ranking weights with the benchmark weight coefficients to obtain the comprehensive priority results of each grid cell under different decision preference scenarios, thus forming a decision preference spectrum. The sixth module is used to continuously search the decision preference parameters within a preset parameter range, and determine the effective decision preference parameter range based on the spatial pattern changes, score distribution patterns and parameter disturbance response degrees corresponding to the comprehensive priority results under different parameter values. The seventh module is used to discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the priority area grid set according to the comprehensive priority result corresponding to each sampled parameter to generate a candidate solution set; The eighth module is used to calculate the protection effectiveness, protection efficiency, and protection element contribution performance indicators of each candidate scheme, and to construct a multidimensional protection performance feature matrix of the candidate schemes. The ninth module is used to perform clustering and screening of the candidate scheme set based on the multidimensional protection performance feature matrix, and to extract Pareto non-dominated solutions from the screened candidate schemes to determine the corresponding optimal decision preference parameter range. The tenth module is used to extract the spatial entities of the protection priority area based on the candidate schemes corresponding to the optimal decision preference parameter range, and output the final protection priority area identification result.

[0018] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0019] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0020] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0021] The embodiments of this application include at least the following beneficial effects: 1. By embedding decision preference parameters into an ordered weighted average model and adjusting the order weight distribution with the decision preference parameters, a comprehensive calculation of value indicators and cost indicators can be achieved within a unified grid evaluation framework. This can form a decision preference spectrum that reflects different risk attitudes, thereby improving the method's ability to express the decision-making logic of different governance entities and the continuity and interpretability of the protection priority area identification results.

[0022] 2. By continuously searching the preference parameters based on the aforementioned decision preference spectrum, the spatial pattern of the overall priority layer under different decision preference scenarios can be revealed, thereby avoiding the problem of subjective comparison based on only a few discrete scenarios.

[0023] 3. By generating a set of candidate solutions within the effective decision preference parameter range, and further combining cluster screening with Pareto non-dominated solution extraction, the robustness and objectivity of the optimal solution identification process can be improved.

[0024] 4. By outputting the spatial entities of the protection priority area corresponding to the optimal decision preference parameter range, a data foundation can be directly provided for spatial layout optimization and protection gap identification in the geographic information system. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2This is a flowchart of the overall steps provided in the embodiments of this application; Figure 3 This is a spatial distribution measurement map of the multi-dimensional comprehensive protection value and four-dimensional protection cost of the object area provided in the embodiments of this application; Figure 4 This application provides a sample of the spatial distribution of protection priority area pixels and hotspot migration under different decision risk preference parameters after introducing the OWA model. Figure 5 This is a quantitative evaluation comparison chart of the multidimensional protection effectiveness of various candidate schemes generated based on different decision preferences, provided in the embodiments of this application. Figure 6 This is a comparison and analysis diagram of the optimal decision preference interval selection process based on K-means clustering hierarchical and Pareto front extraction provided in the embodiments of this application, and the protection effectiveness of schemes inside and outside the interval; Figure 7 This application provides a spatial distribution pattern of multi-level protection priority zone schemes extracted within the locked optimal decision preference interval, as well as a spatial gap identification map of the existing protected area system. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0027] It is understood that the terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0030] In this application, "value indicators" refer to raster evaluation indicators generated from the ecosystem service dimension and the biodiversity dimension; "cost indicators" refer to raster evaluation indicators generated from opportunity cost, management cost, restoration cost, and synergy cost; "comprehensive priority result" refers to the comprehensive raster evaluation result obtained after standardizing, aligning the direction of value indicators and cost indicators, and integrating them with OWA; and "candidate schemes" refer to the set of priority area raster schemes or their corresponding spatial entity schemes extracted based on the comprehensive priority results under different decision preference parameters.

[0031] Before providing a detailed description of the embodiments of this application, some related technologies involved in the embodiments of this application will be described first, as follows: Existing technologies for identifying priority protection zones mainly rely on spatial overlay analysis and heuristic optimization algorithms (such as Marxan and Zonation). Although some progress has been made in theory and practice, existing technical solutions still reveal many systemic defects when facing the complex needs of modern ecological space governance. First, the ecological conservation value assessment system suffers from dimensional limitations. Existing technologies typically treat ecosystem services and biodiversity as independent research subjects. Some methods focus on spatial identification based on regulatory services such as water conservation and soil retention, easily overlooking the habitat requirements of rare and endangered species. Other methods, based on species distribution models (SDMs), struggle to comprehensively reflect the fundamental supporting functions of macro-ecosystems in maintaining regional water and soil balance and carbon cycling. High-value areas of ecosystem services and species diversity hotspots often exhibit spatial inconsistencies or even misalignments, lacking a comprehensive assessment technology system that deeply and non-linearly integrates macro-functional dimensions with micro-species dimensions.

[0032] Second, the measurement of implementation costs and realistic constraints is either lacking or oversimplified. The effectiveness of conservation planning depends not only on the ecological value endowment of the identified area but also on the realistic feasibility of implementing conservation actions. Current technologies, when identifying priority conservation areas, often treat spatial planning as a process of finding the optimal solution under ideal conditions, failing to fully quantify the socio-economic obstacles faced by conservation actions. Although some studies have introduced land use type or land price as single cost proxy variables, they have failed to establish a systematic constraint measurement framework encompassing opportunity costs (human activity disturbance), management costs (topography and accessibility), ecological restoration costs (baseline degradation status), and social coordination costs (community industrial dependence). This results in conservation solutions often facing extremely high financial burdens and social coordination resistance during the implementation phase.

[0033] Third, there is insufficient parameterization mechanism for expressing decision-making preferences. In actual spatial governance decisions, different decision-makers exhibit significant differences in their trade-offs between "high protection value" and "low protection cost." Some decisions lean towards risk preference (preferring to protect extremely high-value areas and tolerating high costs), while others lean towards risk aversion (preferring to obtain a stable protection base within a limited cost budget). Existing mainstream identification algorithms are mostly static global optimization models, unable to dynamically parameterize the continuous risk attitudes of decision-makers. This results in the lack of flexibility in the solutions output by the decision support system, making it difficult to support multi-stakeholder consultation and adaptive management.

[0034] Fourth, multi-scenario simulation methods lack wide-area parameter search and interval robustness identification. In research on decision preferences, even when using multi-criteria evaluation models such as Ordered Weighted Averaging (OWA), existing techniques generally employ a static comparison strategy that pre-sets a small number of discrete parameter values ​​based on subjective experience (e.g., setting 3 to 5 scenarios to represent different preferences). This method fails to perform a wide-area search within a continuous parameter space, cannot reveal the continuous dynamic characteristics of weight structure evolution and spatial pattern migration, and is prone to getting trapped in local optima, failing to define the range of optimal decision parameters with universality and robustness.

[0035] Fifth, there is a lack of algorithms for dimensionality reduction and non-dominated solution set extraction of high-dimensional data oriented towards multi-dimensional performance objectives. Multi-scenario decision-making inevitably generates a massive number of candidate solutions, and how to evaluate and screen these solutions is another major technical bottleneck. Existing technologies mostly rely on linear evaluation using a single efficiency indicator. However, high-quality protection solutions need to achieve a balance between macro-level protection effectiveness, unit cost efficiency, and the degree of coordination among multiple ecological elements. Existing technologies lack a joint screening mechanism that integrates cluster analysis and multi-objective Pareto front optimization, failing to effectively eliminate suboptimal solutions from the high-dimensional evaluation matrix, resulting in a lack of rigorous mathematical support for the final solution selection.

[0036] To address the shortcomings of existing technologies in protection priority zone identification, such as limited evaluation dimensions, incomplete cost constraints, lack of decision preferences, subjective scenario simulation, and inadequate multi-objective optimization mechanisms, this application aims to provide a protection priority zone identification method that integrates multi-source data, assesses value / cost, models OWA preferences, forms a preference spectrum, searches continuous parameters, generates candidate solutions, screens multi-dimensional performance, and identifies the optimal interval. This method uses an ordered weighted average model as a decision modeling tool that integrates value and cost, embedding decision preference parameters within the model to adjust the order weight distribution of evaluation indicators, thereby forming a decision preference spectrum covering different risk attitudes. Furthermore, by performing a continuous parameter search on the decision preference spectrum, candidate protection priority zone solutions under different decision preference scenarios are generated, providing input for subsequent identification of the optimal decision preference interval.

[0037] The method and related equipment for identifying protection priority areas, taking into account protection cost constraints and decision-making preferences, provided in this application, relate to technical fields such as territorial spatial ecological protection planning, optimized layout of nature reserve systems, multi-objective spatial decision support systems, and geographic information spatial analysis. The method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms, but is not limited to the above forms.

[0038] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0039] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0040] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0041] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0042] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0043] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc. It can also be a vehicle-mounted terminal of the various device types described above, but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment does not impose any limitations.

[0044] Exemplary based on Figure 1 The implementation environment shown in this application embodiment provides a protection priority area identification method that takes into account protection cost constraints and decision preferences. The following description uses the application of this protection priority area identification method that takes into account protection cost constraints and decision preferences in server 101 as an example. It can be understood that this method can also be applied to terminal 102.

[0045] Reference Figure 2 , Figure 2 The flowchart illustrates a protection priority zone identification method for servers, considering protection cost constraints and decision preferences, provided in this application embodiment. The executing entity of this method can be any of the aforementioned computer devices (including servers or terminals). (Refer to...) Figure 2 The method may include the following steps: S210. Acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification. Specifically, the system acquires spatial baseline data for the target study area, including but not limited to land use / land cover (LULC) data, digital elevation models (DEMs), meteorological monitoring raster data, soil physicochemical property data, geographic distribution coordinates of target plant and animal species, population spatial density data, nighttime light remote sensing indices, traffic network vector data, and socioeconomic statistics. By unifying the geographic coordinate system and projection system, and performing resampling, rasterization, spatial clipping, and spatial extent alignment on data from different sources, a standard research database with consistent spatial resolution, spatial extent, and topological relationships is constructed. All types of data in the multi-source heterogeneous basic database are unified to the same spatial reference system and a unified analysis raster, enabling comparable calculations of subsequent value indicators, cost indicators, and comprehensive priority results at the raster cell scale.

[0046] S220. Based on the multi-source heterogeneous basic database, spatial measurement of ecosystem services and biodiversity in the study area is performed, and value index raster layers corresponding to each raster unit are generated. Specifically, this application measures ecosystem services and biodiversity in the study area from two dimensions: (1) Ecosystem service dimension: The surface water conservation capacity is calculated using the water balance equation and water production model; the soil retention capacity is calculated based on the modified general soil loss equation (RUSLE) and factors such as rainfall erosion, soil erodibility, slope length and slope; the total carbon sequestration of vegetation layer and soil layer is calculated using carbon storage measurement method; and the wind erosion model parameterization method is introduced to calculate the windbreak and sand fixation capacity.

[0047] (2) Biodiversity dimension: Using the distribution points of threatened species and the screened environmental covariates as input variables, the maximum entropy distribution model (MaxEnt) is used to predict the probability distribution of potential suitable habitats for species; a habitat quality assessment model is adopted, based on the land cover type and the preset distance decay function of human threat sources, to assess the degree of habitat degradation and support capacity in the region.

[0048] This application's embodiments map ecosystem service measurement results and biodiversity measurement results into a raster layer of value indicators at the raster unit scale, forming a comprehensive conservation value basis for identifying priority conservation areas.

[0049] S230. Based on the aforementioned multi-source heterogeneous basic database, construct a multi-dimensional protection cost constraint evaluation system that includes opportunity cost, management cost, recovery cost, and collaboration cost, and generate cost index raster layers corresponding to each raster unit. Specifically, the embodiments of this application construct a cost constraint evaluation system covering multiple dimensions such as social, economic, and managerial factors: (1) Opportunity cost representation: The spatial distribution surface of human disturbance intensity (HDI) is constructed by weighting and superimposing population density, nighttime light intensity and agricultural and pastoral development intensity; (2) Management cost representation: Taking the main traffic artery as the source point and the terrain undulation and surface cover as the resistance coefficient, the minimum cumulative cost path algorithm is used to construct the management accessibility cost (MAC) distribution surface; (3) Restore cost characterization: Based on the regional ecological vulnerability and degradation index baseline, an ecological restoration cost (ERC) distribution surface is constructed; (4) Coordination cost representation: Spatial interpolation is performed using data on the proportion of primary industry employment in administrative units to construct the distribution surface of community economic coordination cost (CECC).

[0050] Specifically, the aforementioned cost dimensions collectively characterize the potential for development opportunities to be forsaken, management difficulties, ecological restoration investments, and community collaboration resistance that conservation actions may face during the implementation phase. After rasterization and standardization, each cost dimension forms a cost indicator raster layer for subsequent comprehensive priority calculations.

[0051] S240. Standardize the value index and the cost index, and determine the benchmark weight coefficients corresponding to the value index and the cost index. Specifically, this application employs the range standardization method to unify the aforementioned value and cost indicators into the dimensionless interval [0, 1]. A multi-criteria decision matrix is ​​constructed based on the Analytic Hierarchy Process (AHP), and the benchmark weight coefficient vectors for each protection value indicator and protection cost indicator are determined through expert evaluation and the consensus ratio (CR) test. These benchmark weight coefficients characterize the importance of each evaluation indicator and, in the subsequent ordered weighted average model, participate in the comprehensive priority calculation along with the ordinal weights generated from decision preference parameters.

[0052] In some embodiments, the benchmark weight coefficients may also be obtained by using entropy weighting, combined weighting, or other weighting methods that can express the importance of indicators.

[0053] S250. Introduce an ordered weighted average model, embed decision preference parameters into the ordered weighted average model as adjustment variables, adjust the distribution of the order weights after ranking each evaluation index, and integrate the adjusted order weights with the benchmark weight coefficients to form a decision preference spectrum that reflects different risk attitudes. Specifically, this application does not directly use a fixed weight superposition method to linearly synthesize the protection value index and the protection cost index, but instead introduces an ordered weighted average (OWA) model as a decision modeling tool for multi-index fusion.

[0054] In the OWA model, the pre-determined baseline weights are used to characterize the importance of each evaluation indicator itself, while the ordinal weights derived from the decision preference parameters are used to characterize the degree of emphasis that the decision-making subject places on different ordinal attribute values ​​under different risk attitudes.

[0055] Before calculating the overall priority, the value indicators and cost indicators are first aligned in direction. Value indicators are used in the calculation in a positive direction, while cost indicators are converted into evaluation quantities in the same direction as the protection priority before being used in the calculation. Then, the standardized evaluation indicator values ​​corresponding to each grid cell are sorted by numerical value. Next, the decision preference parameters are input into the OWA model to generate ordinal weights corresponding to the ranking positions. Finally, the ordinal weights are fused with the baseline weight coefficients to achieve joint integration of value indicators and cost indicators under the same evaluation framework, thereby obtaining the overall priority results of each grid cell under different decision preference scenarios.

[0056] This allows for the formation of a decision preference spectrum that covers different decision-making logics such as risk appetite, risk neutrality, and risk aversion.

[0057] The so-called decision preference spectrum is essentially a set of continuous or quasi-continuous evaluations composed of a series of OWA fusion rules corresponding to different decision preference parameter values.

[0058] S260. Based on the decision preference spectrum, perform a continuous search on the decision preference parameters within a preset range, calculate the comprehensive priority result of each grid cell under different decision preference scenarios, and determine the effective decision preference parameter range based on the change characteristics of the comprehensive priority result. Specifically, after forming the decision preference spectrum, this embodiment of the application performs a continuous search on the decision preference parameters within a preset parameter range.

[0059] During the continuous search process, each set of parameter values ​​corresponds to a specific preference scenario in the decision preference spectrum; for each preference scenario, the system calculates the comprehensive priority result for each grid cell in the study area based on the corresponding OWA fusion rule.

[0060] As decision preference parameters change, the distribution of rank weights changes accordingly, which in turn leads to a change in the way protection value and protection cost are integrated, resulting in a set of comprehensive priority layers with spatial differences.

[0061] By comparing and analyzing the spatial pattern changes, hotspot migration characteristics, score distribution patterns, and parameter disturbance response levels of this set of comprehensive priority layers, the range of parameters that have a significant response to parameter changes and whose spatial pattern evolution is continuous can be identified.

[0062] Based on this, the effective decision preference parameter range is determined.

[0063] The effective decision preference parameter range is used to narrow the parameter search range when generating subsequent candidate solutions, so as to avoid including parameter segments that have too weak an impact on the spatial results or exhibit excessive polarization in the subsequent optimization process.

[0064] S270. Discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the protection priority area spatial scheme based on the comprehensive priority result under the corresponding parameters to generate a candidate scheme set. Specifically, after determining the effective decision preference parameter range, this embodiment of the application performs discrete sampling of the decision preference parameters within the range according to a preset sampling step size. Each discrete sampling point corresponds to a specific decision preference scenario in the decision preference spectrum, and generates a comprehensive priority space result layer. For each comprehensive priority space result layer, the system sorts according to the comprehensive priority value of each grid cell, and identifies the corresponding primary protection priority zone, secondary protection priority zone, or other level protection space cell based on a preset protection ratio threshold or hierarchical extraction rule. The protection priority zone space schemes corresponding to different discrete sampling parameters together constitute a candidate scheme set.

[0065] Each candidate scheme corresponds to a set of priority area grids extracted from the comprehensive priority ranking results, and can be further transformed into a protected priority area spatial entity.

[0066] Therefore, the candidate scheme set is essentially a discretized expression of the protection space configuration results under different preference scenarios within the effective decision preference parameter range.

[0067] S280. Calculate the protection effectiveness, protection efficiency, and protection element contribution performance indicators for each candidate scheme, and construct a multidimensional protection performance feature matrix for the candidate schemes. Specifically, this application's embodiments construct a scheme performance evaluation system covering three core dimensions: calculating the protection effectiveness (CEI, representing the proportion of total ecological value contribution of the scheme within the corresponding priority area), protection efficiency (CER, representing the protection effectiveness corresponding to unit protection cost), and protection element contribution (CCF, representing the comprehensive improvement rate of the scheme on the average value of each individual ecological indicator across the entire area). Based on this calculation... The performance indicators of each candidate solution are used to generate a multidimensional performance feature matrix.

[0068] S290. Determine the optimal decision preference interval based on the multidimensional protection performance characteristic matrix; Specifically, in this embodiment, after normalizing the multidimensional protection performance feature matrix, a K-means unsupervised clustering algorithm is used for stratification. The optimal number of clusters is determined by combining the sum of squared errors within each cluster and the silhouette coefficient. The clustering results are compared by mean, and clusters with better overall performance are extracted. The lower bound or lower limit threshold of each index (CEI, CER, and CCF) within the cluster is calculated, and based on this, dimensionality reduction screening is performed on all candidate schemes to obtain a subset of high-performance candidate schemes.

[0069] S300. Extract Pareto non-dominated solutions from the subset of high-performance candidate solutions, and determine the optimal decision preference parameter interval based on the decision preference parameter distribution corresponding to the non-dominated solutions; extract the spatial entities of the protection priority area based on the candidate solutions corresponding to the optimal decision preference parameter interval, and output the final protection priority area identification result.

[0070] Specifically, in this embodiment, the aforementioned subset of high-performing candidate solutions is mapped to a three-dimensional objective optimization space composed of CEI, CER, and CCF. The Pareto front non-dominated decision rule is applied to extract the optimal solution set that is not strictly dominated by any other solution. Furthermore, the preference parameter α corresponding to the optimal solution set is traced backward to analyze its continuous distribution characteristics on the parameter axis, selecting a continuous real-valued closed interval with continuous parameter distribution and stable performance. This serves as the final optimal decision preference interval output.

[0071] Finally, this application's embodiments extract the spatial entities of the primary and secondary protection priority zones corresponding to the optimal decision preference interval, and perform spatial topological difference operations with the existing national parks, nature reserves, and other protected areas in the target region. This quantitatively identifies various protection blind spots not covered by the existing protection system, clarifies the constraint mechanisms for gap formation, and provides spatial data support for subsequent expansion of the protection network.

[0072] The following section uses a specific application scenario as an example to explain in detail the specific implementation process of the method in this application: It should be noted that the specific regional environment and the specific parameter value range mentioned in the following embodiments (such as...) The 51 proposed solutions, specific species types, and software algorithm platforms are all optimal implementations provided to demonstrate the feasibility of this method. Without departing from the spirit of this application and the basic logic of the claims, any equivalent substitutions or parameter range adjustments made by those skilled in the art based on specific project needs are within the scope of protection of this application.

[0073] Example: Systematic identification of priority protection areas on the Qinghai-Tibet Plateau: This embodiment takes the Qinghai-Tibet Plateau, which is vast in area, has complex topography and extremely rich biodiversity, but also faces severe pressure from human grazing and development, as the research object, and fully reproduces the protection priority area identification mechanism of this application.

[0074] Step 1: Standardized integration of heterogeneous spatial data.

[0075] To support high-precision spatial modeling, this system acquired a comprehensive data system for the region. This application acquired baseline ecological and environmental data, meteorological observation data, topographic and geomorphological data, soil physicochemical property data, distribution point data of flora and fauna species, socio-economic statistical data, and data characterizing human activity pressures covering the study area. Using projection transformation, spatial correction, resampling, and mask clipping techniques, all heterogeneous multi-source data were unified to the same spatial reference frame and a set raster resolution, constructing a standardized composite research database. This research database includes land use / cover raster data, elevation data, slope and aspect data, temperature and precipitation data, soil erodibility data, coordinates of threatened flora and fauna distribution points (POIs), nighttime light index data, road network distribution vector data, residential point (POI) data, and socio-economic and industrial structure statistical data for the target area.

[0076] Specifically: In terms of basic surface parameters, a digital elevation model (DEM) with a resolution of 30m is introduced, and slope and topographic relief raster are calculated based on it; an annual land use / land cover remote sensing monitoring dataset is introduced.

[0077] Regarding meteorological and soil parameters, we obtained raster climate surfaces such as precipitation, temperature, and potential evapotranspiration released by the National Meteorological Data Platform, and called up soil texture classification and soil organic carbon content parameters from the World Soil Database (HWSD).

[0078] Regarding biodiversity parameters, the system connects to the GBIF global species database and extracts coordinate records of several representative birds and mammals unique to this region, classified as Critically Endangered (CR), Endangered (EN), and Vulnerable (VU).

[0079] Regarding parameters of human economic activity, the data integrates the latest NPP-VIIRS nighttime light remote sensing data, 1km precision population grid data, OpenStreetMap road network vector data, and industrial structure proportions from statistical yearbooks.

[0080] All the above data were forcibly converted into a unified Albers iso-area projection system within the geographic information platform, and then standardized to 1km using a bilinear interpolation resampling algorithm. A standard 1km analysis grid, after masking and outlier removal, forms the basic research database.

[0081] Step 2: Spatial simulation of the dual value dimensions of ecology and habitat.

[0082] This step aims to thoroughly quantify the natural background endowments of the study area.

[0083] 2.1 Material Quantification of Key Ecosystem Services: For ecosystem service assessment, four key ecological functions were selected: water conservation, soil retention, carbon sequestration, and windbreak and sand fixation. Using eco-hydrological assessment models and biogeochemical models, the material quantities of each service were calculated and represented in a raster format, specifically including: Water conservation (WC) is calculated using a water yield equation, which involves subtracting actual evapotranspiration from the precipitation in a cell, and then applying nonlinear correction based on the maximum soil water-holding depth and topographic moisture index to output the surface water conservation potential surface. The assessment and calculation formula for water conservation (WC) is shown in formula (1):

[0084] In the formula, For grid Water conservation capacity For grid Annual precipitation, For grid The actual annual evaporation; Soil conservation (SR) is calculated using the modified generalized soil loss equation (RUSLE). Rainfall erosivity factor is interpolated using multi-year monthly average precipitation data, soil erodibility factor is estimated using the EPIC model, and slope length and steepness factor are extracted and calculated from a high-precision DEM. The difference between potential erosion and actual erosion after surface cover protection is calculated to generate a soil conservation efficiency distribution map. The soil conservation (SR) assessment is based on the modified generalized soil loss equation, and the calculation formula is shown in formula (2):

[0085] In the formula, For grid Soil retention capacity As the erosivity factor of rainfall, As a soil erodibility factor, and These are slope length and slope factor, respectively. As a vegetation cover management factor, Factors related to soil and water conservation measures.

[0086] Carbon sequestration service (CS) and windbreak and sand fixation (SP) were parametrically extrapolated using the Carbon module of the InVEST model and empirical equations based on wind erosion climate factors, respectively, generating value layers with significant spatial heterogeneity.

[0087] 2.2 Spatial Simulation of Biodiversity Support Functions: For biodiversity assessment, the suitability of target species habitats and regional habitat quality are considered holistically. A maximum entropy species distribution model is used to simulate the probability of potential suitable habitat distribution for protected species based on environmental variables. Simultaneously, a habitat evaluation model is used to comprehensively consider the distance attenuation effect of various human activity threats, assessing the degradation status of surface habitats and the biological carrying capacity of ecosystems, referencing... Figure 3 Specifically, it includes: During the habitat suitability (HS) assessment, Spearman correlation tests were conducted on 59 pre-selected environmental factors (covering seasonal temperature variation, lowest temperature of the coldest month, altitude, vegetation index, etc.) to eliminate those with extremely strong collinearity (correlation coefficients). After considering the characteristic variables, 32 items were retained. Using the MaxEnt distribution probability model, 75% of the species coordinates were randomly selected for machine learning training, and 25% were retained for generalization testing. After iterative calculation, the area under the receiver operating characteristic curve (AUC) of the model exceeded 0.80, confirming the scientific validity of the high probability distribution area definition.

[0088] In this embodiment, the AUC value is greater than 0.80, indicating that the obtained high-fitness distribution results have good discriminative ability.

[0089] In habitat quality (HQ) assessment, the InVEST habitat module is used to define trunk roads, industrial and mining construction land, and high-intensity grazing pastures as stress sources. Habitat suitability parameters and anti-interference sensitivity indices are set for various types of land use. A half-saturation constant function is introduced to output a continuous raster map representing the health status of the habitat.

[0090] Biodiversity assessment, including habitat quality assessment, is mainly based on the InVEST model, and its calculation formula is shown in formula (3):

[0091] in, Land use type Middle grid Habitat quality; Land use type Habitat suitability; For habitat degradation, threat source weights and distance decay functions were considered holistically. It is a half-saturation constant, which takes the value of half the maximum value of habitat degradation; This is a scaling constant, typically taken as 2.5.

[0092] Step 3: Quantitative measurement of multidimensional protection costs.

[0093] Conservation efforts cannot be divorced from socio-economic realities. They must be based on the overall current situation of the target area and the difficulties of implementation, while also taking into account... Figure 3 This application constructs a four-dimensional protection cost system encompassing opportunity cost, management cost, ecological restoration cost, and social collaboration cost. It selects human disturbance intensity, management accessibility cost, ecosystem degradation and restoration cost, and community economic resource dependence as corresponding cost proxy indicators. The above indicator factors are then graded, weighted, and rasterized to obtain the constraint resistance surfaces for each individual cost. This embodiment constructs four types of resistance layers: 3.1 Opportunity Cost Characterization: Human Interference Intensity (HDI) By overlaying population density grids, nighttime light intensity grids, and agricultural and pastoral activity intensity grids with equal weights and normalizing them, a cost resistance surface reflecting the intensity of regional economic development is formed. High-value areas represent areas where protecting the region requires relinquishing significant economic development opportunities.

[0094] 3.2 Management Cost Representation: Management Accessibility Cost (MAC) Based on the actual traffic logic of daily patrols and law enforcement, and taking the main highway network as the cost source, the terrain relief index and land cover barrier coefficient are set as the passage resistance. The minimum cost distance algorithm in GIS is used to generate a raster, and high value areas reflect that the area is extremely difficult to reach and supply personnel due to geographical isolation.

[0095] 3.3 Restore Cost Characterization: Ecological Restoration Cost (ERC) The naturalized vegetation index (NDVI) is used to assess the ecological fragility of the land surface. Severely degraded areas with a sharp decline in the index are given a higher restoration cost estimate, which represents the scale of upfront economic investment required to rebuild a stable ecosystem.

[0096] 3.4. Characterization of Coordination Costs: Community Economic Coordination Costs (CECC) The statistical proportion of the population employed in the primary industry to the total population of each county-level administrative division is extracted and transformed into a continuous raster through spatial kriging interpolation. This represents the dependence of the region's residents' livelihoods on traditional agricultural and pastoral resources. The higher the value, the higher the social negotiation and alternative livelihood compensation costs faced in implementing strict ecological closure policies.

[0097] Step 4: Range Standardization and AHP Expert Matrix Determination. All individual indicators of protection value and protection cost obtained in Steps 2 and 3 are subjected to range standardization to eliminate dimensional differences between multi-source data. A multi-criteria evaluation hierarchy is established, and a judgment matrix is ​​constructed using the analytic hierarchy process. The benchmark weight coefficients of each individual indicator within the protection value criterion layer and the protection cost criterion layer are determined through consistency checks.

[0098] Specifically, this application utilizes the range normalization formula. This compresses all the aforementioned value and cost indicators of different dimensions and orders of magnitude into the floating-point range of [0, 1].

[0099] Subsequently, an Analytic Hierarchy Process (AHP) decision model was constructed. Experts in ecological security, spatial planning, and resource economics were invited to construct pairwise comparison judgment matrices. The maximum eigenvalues ​​and their corresponding eigenvectors for the value criterion layer and the cost criterion layer were calculated respectively. The consistency ratio (CR) was then used to determine the model. After rigorous evaluation, the baseline objective weight vectors of each indicator are extracted, laying the foundation for subsequent OWA integration.

[0100] The baseline weight is not the final comprehensive weight, but rather participates in the comprehensive priority calculation together with the ordinal weight generated by parameter α in step 5.

[0101] Step 5: Decision preference modeling, preference spectrum formation and continuous parameter search based on OWA model.

[0102] Traditional methods often employ algebraic overlay algorithms with fixed weights, which fail to reflect the dynamic changes in the decision-making body's trade-off between "high protection value" and "low protection cost".

[0103] Therefore, this application introduces the ordered weighted average (OWA) model as a modeling tool for the comprehensive evaluation of protection value and protection cost, and embeds the decision preference parameter α into the OWA model as a core variable for adjusting the distribution of order weights.

[0104] The OWA model in this embodiment of the application has a core algorithm that includes the nonlinear aggregation of criterion benchmark weights and positional weights. The formula for calculating the comprehensive evaluation value is shown in formula (4), where the positional weights are... The function expression is shown in formula (5):

[0105]

[0106] In the formula, For grid cells Comprehensive protection priority value; The total number of value and cost indicators used in the evaluation; For grid cells After standardization, the evaluation indicators are arranged in descending order of their numerical values. Each attribute value; For positional order Associated ordinal weights; Decision preference parameters that reflect the risk attitude of decision-makers.

[0107] In this model, the baseline weights determined in step 4 are used to characterize the importance of each value indicator and cost indicator itself; while the ordinal weights corresponding to parameter α are used to characterize the degree of emphasis that the decision-making subject places on higher or lower attribute values ​​after ranking under different risk attitudes.

[0108] By changing the value of parameter α, a series of different OWA fusion rules can be obtained; these fusion rules together constitute a decision preference spectrum covering different risk attitudes.

[0109] Specifically, when the parameter α is small, the OWA model assigns higher ranking weights to attribute values ​​that rank higher, and the corresponding decision scenarios tend to emphasize high-yield attributes, exhibiting a relatively risk-seeking decision logic. When the parameter α is large, the OWA model pays more attention to attribute values ​​that rank lower, and the corresponding decision scenarios emphasize bottom-line constraints and control of adverse conditions, exhibiting a relatively risk-averse decision logic.

[0110] Therefore, the continuous change of parameter α can be regarded as a parameterized expression of the continuous transition of decision preference from risk preference to risk aversion.

[0111] Based on this, the embodiments of this application perform an automated iterative search for α within a preset continuous parameter range.

[0112] For each parameter value, the overall priority of each grid cell in the study area is calculated according to the corresponding OWA fusion rule, thereby obtaining a set of overall priority space results that correspond one-to-one with the decision preference spectrum.

[0113] The research results indicate that: Figure 4 As shown, when α approaches zero, the ordinal weights may concentrate on the attribute values ​​of higher ordinal positions, and the algorithm exhibits a strong risk preference attribute. The generated protected patches may be characterized by relatively discrete high-value segments. When α is large, the ordinal weights may emphasize the attribute values ​​of lower ordinal positions more, and the model exhibits a strong risk aversion attribute. The overall priority space pattern changes more gradually.

[0114] By analyzing the changes in spatial hotspot migration, score distribution, and parameter disturbance response of the comprehensive priority layer under different parameter values, we can further identify effective decision preference parameter ranges that can effectively reflect the dynamic trade-off between value and cost.

[0115] In this embodiment, by analyzing the spatial centroid variation rate and the changes in the overall priority pattern under parameter perturbation, the effective decision preference parameter range is determined to be α∈[0.2,10].

[0116] It should be noted that this interval is only used to illustrate that the method of this application can identify the effective decision preference parameter interval in a continuous parameter space, and does not constitute a limitation on this application.

[0117] Step 6: Discrete sampling and candidate solution generation based on the effective decision preference parameter range.

[0118] After determining the effective decision preference parameter range, the system performs discrete sampling of parameter α within the range according to a preset step size Δα, so as to extract several representative decision preference scenarios from the continuous decision preference spectrum.

[0119] Each discrete sampling parameter corresponds to a set of defined OWA fusion rules, and a comprehensive priority space result layer is generated accordingly.

[0120] Subsequently, the system sorts the raster cells in each comprehensive priority spatial result layer according to the comprehensive priority value, and extracts the corresponding protection priority area spatial cells according to the preset protection area ratio threshold or protection level division rules.

[0121] Each discrete sampling parameter corresponds to a set of priority area grids, and the corresponding protection priority area spatial entity can be generated through grid-to-surface or region connectivity extraction.

[0122] In this embodiment, the top 30% of pixels in terms of total area are defined as "Level 1 Priority Protection Area", and pixels whose area percentage is between 30% and 50% are defined as "Level 2 Priority Protection Area".

[0123] The set of candidate schemes is composed of the spatial schemes of the protection priority area corresponding to each discrete sampling parameter, which provides input for subsequent multi-dimensional protection performance evaluation and optimal decision preference interval identification.

[0124] In this embodiment, Δα=0.2 is taken, which yields 51 candidate schemes corresponding to parameters α=0.2, 0.4, 0.6...10.0.

[0125] It should be noted that the sampling step size and the number of candidate schemes can be set according to the scale of the study area, the computational accuracy requirements, and the results of model sensitivity analysis.

[0126] Step 7: Construct a multidimensional solution performance measurement space (CEI-CER-CCF).

[0127] refer to Figure 5 For the 51 alternative solutions generated above, a performance verification system with three orthogonal dimensions is established: Dimension A: Conservation Effectiveness (CEI), which is the percentage of the sum of standardized ecological values ​​within the coverage area of ​​candidate solutions to the total value of the entire region; Dimension B: Protection Efficiency (CER), which is the ratio of the protection effectiveness to the total cost within the coverage of the candidate solution; Dimension C: Conservation Factor Contribution (CCF), which is the enhancement coefficient of the average distribution density of various independent ecosystem services within the coverage area of ​​the candidate scheme relative to the baseline average value of the entire region.

[0128] The system generated a high-dimensional data performance matrix with 51 rows and 3 columns.

[0129] Specifically, the multidimensional protection performance evaluation system for candidate decision-making schemes is reflected by three indicators: protection effectiveness (CEI), protection efficiency (CER), and protection element contribution (CCF). The calculation formulas are shown in formulas (6), (7), and (8), respectively:

[0130]

[0131]

[0132] In the formula, formula (6) The proportion of comprehensive ecological value covered by the characterization scheme. For grid cells The comprehensive protection value score, Indicates the identified protection priority zone range. Indicates the entire study area; Formula (7) This characterizes the protection effectiveness corresponding to the unit protection cost. For grid cells within the priority area The standardized comprehensive protection cost value; Formula (8) The characterization scheme's ability to comprehensively coordinate various individual ecological elements. The total number of protection elements (including various ecosystem services and habitat indicators). For the first The average value of class elements within the protection priority area, This is the mean of the element across the entire region.

[0133] Step 8: Unsupervised dimensionality reduction and initial threshold screening based on K-means.

[0134] refer to Figure 6 Since the number of 51 schemes is still too large and they interfere with each other, the system calls the K-means unsupervised clustering engine to perform spatial clustering on the three-dimensional performance matrix standardized by Z-score.

[0135] The algorithm automatically calculates the sum of squared errors (SSE) for different K values ​​from 1 to 10, generates elbow curves, and combines them with the local maximum profile coefficient to accurately determine the optimal number of clusters. .

[0136] Among the five generated clusters, by comparing the coordinate vectors of the cluster centers of each cluster, the system automatically identified a "high-performance cluster" that leads in overall average values ​​across three-dimensional indicators.

[0137] Using the minimum envelope rule, the system directly extracts the lower bound values ​​of each of the three dimensions from the elite cluster (for example, the numerical threshold obtained in this verification is: ).

[0138] After eliminating suboptimal solutions from the 51 selected options that performed poorly in any dimension, only 13 candidate solutions with excellent overall performance were retained (corresponding to parameter ranges that converged significantly to...). ).

[0139] It should be noted that the dimensionality reduction of the high-performance candidate set described in step 8 includes the following steps: using the Z-score standardization method to eliminate the magnitude differences of performance indicators in each dimension; during the K-means clustering iteration process, calculating the sum of squared errors (SSE) within the cluster under different K values ​​to plot the elbow curve, and determining the optimal number of classifications K by combining the maximum inflection point of the silhouette coefficient; after determining the cluster with the highest overall mean score, using the minimum envelope method to determine the lower bound hard screening thresholds of CEI, CER, and CCF within that cluster.

[0140] Step 9: Pareto multi-objective front stripping and establishment of continuous optimal decision interval.

[0141] refer to Figure 6 For the remaining 13 high-value solutions, the system introduces Pareto multi-objective optimization theory. It should be noted that the Pareto front multi-objective non-dominated solution identification described in step 9 involves finding solutions that satisfy the following conditions from the candidate solution set filtered in step 8: there is no other solution whose values ​​in the three dimensions of CEI, CER, and CCF are not inferior to this solution, and at least one dimension is strictly superior to this solution; the set of all candidate solutions satisfying this condition constitutes the Pareto front set; by analyzing the gradient of the difference in parameter α between solutions in this set, the largest continuous sub-interval where the parameter difference is less than the set tolerance is identified as the optimal decision interval.

[0142] The algorithm performs non-dominance determination in three-dimensional space: if solution A is not inferior to solution B in any of CEI, CER, and CCF, and is strictly superior to solution B in at least one of them, then solution A dominates solution B.

[0143] The system automatically eliminates all dominated compromise solutions and extracts the Pareto front non-dominated solution set that is completely embedded in the outermost part of the three-dimensional surface.

[0144] The innovation of this embodiment lies not only in discovering the frontier solution, but also in its corresponding original input parameters. Perform reverse tracing and continuity calculation.

[0145] refer to Figure 7 Statistical tests clearly confirm that in these non-dominated solutions, the corresponding parameters Between The solution set within the range exhibits high topological connectivity and stable numerical distribution on the number axis. That is, when the parameters are slightly perturbed within this closed interval, the protection performance index does not experience a precipitous oscillation, demonstrating extremely strong robustness.

[0146] thus, This refers to the globally optimal continuous decision preference interval for this region.

[0147] This range is only the result obtained in this embodiment and is used to illustrate that the method of this application can output the optimal decision preference range, and is not a limitation on the parameter range of the method of this application.

[0148] In addition, this application also includes a step of performing a post-validation of the extracted optimal continuous decision interval; the validation process includes: using the Mann-Whitney U nonparametric statistical test method to compare the significant difference level of the schemes within the optimal continuous decision interval and the schemes outside the interval in terms of core protection performance; applying a preset proportion of floating disturbance to the hard screening threshold extracted in step 8, and calculating and verifying the change range of the optimal continuous interval to ensure the robustness of the model screening algorithm.

[0149] The post-implementation rationality verification step can be used as a preferred implementation method to improve the reliability of the interpretation of the optimal decision preference interval.

[0150] Step 10: Verification of gaps in the nature reserve system.

[0151] Extract the Conservation Priority Area (CPA) map generated within the optimal decision preference range, and perform GIS Intersect and Erase topology operations with the legally designated boundaries of national parks, nature reserves, and natural parks within the study area.

[0152] The study identified two key gaps. The first is a high-value, high-socioeconomic-constraint gap, where some extremely high-priority protection zones are located in densely populated, agriculturally and pastorally developed river valleys. These areas experience extremely high levels of human disturbance and community coordination costs, revealing a subjective avoidance phenomenon in historical planning due to the need to mitigate high opportunity costs. For this, it is recommended to avoid establishing traditional strict protected areas and instead introduce "Other Effective Regional Conservation Mechanisms (OECMs)," specifically addressing these gaps by establishing restricted development zones around protected areas, promoting community co-management, and establishing cross-regional ecological redemption mechanisms. The second gap is a high-value, high-management-barrier gap, identifying numerous high-value priority zones in remote, extremely rugged terrain. These areas have extremely low socioeconomic costs, but exponentially increasing management accessibility costs, leading to hidden blind spots in historical planning due to patrol difficulties. For this, it is recommended to designate these areas as "strictly nature-controlled no-go zones" and abandon costly manual ground patrols, instead relying on non-contact methods such as integrated air-ground remote sensing monitoring and drone inspections to achieve low-cost and effective management.

[0153] It should be noted that the existing protection network gap identification and adaptive management strategy generation described in step 10 involves spatially subtracting the extracted final priority area pattern from the nature reserve vector; by quantitatively revealing the high-cost protection blind spots caused by property rights constraints and the accessibility service blind spots caused by terrain barriers, zoning governance strategies are generated to support the expansion planning of nature reserves.

[0154] In summary, this application essentially provides an integrated algorithm framework for identifying priority protection zones. Through a technical process of "OWA model introduction—decision preference spectrum formation—continuous parameter search—candidate scheme generation—cluster screening—Pareto non-dominated solution extraction," it offers a technical path to address the multi-objective equilibrium and human preference uncertainty issues in systematic conservation planning. Notably, this application does not separately superimpose cost constraints after generating the spatial entities of priority zones; instead, it incorporates both value and cost indicators into the comprehensive priority calculation, adjusting their ranking weight distribution through decision preference parameters, thereby achieving a joint value-cost trade-off at the comprehensive priority level. Furthermore, this research allows planning entities with different interests (such as environmental protection departments and financial departments) to conduct scientific parameter fine-tuning and negotiation within the optimal range to achieve a "Pareto optimal compromise" that maximizes ecological benefits and controls socio-economic costs, significantly reducing the practical obstacles to the implementation of conservation policies. Therefore, it can provide irreplaceable high-precision underlying decision support for the integration and optimization of the nature reserve system, the refined assessment of ecological red lines, and the site selection of major ecological protection and restoration projects.

[0155] Compared with the prior art, the method proposed in the embodiments of this application has the following significant technical advancements and beneficial effects: 1. It achieves deep parameter coupling between value assessment and cost constraints, improving the feasibility of the plan. Breaking through the traditional planning paradigm based on one-way ecological drive, this application systematically quantifies the four-dimensional costs of human activity interference, management accessibility, ecological restoration, and community coordination. This makes the delineation of priority areas not only ecologically superior but also practically operable in terms of economics and management, significantly reducing institutional friction in the later planning implementation.

[0156] 2. It overcomes the subjective nature of parameter setting and achieves objective and robust wide-area optimization. Unlike existing multi-scenario analyses that rely on subjective experience to set discrete parameters, the originality of this application lies in introducing a convergence mechanism based on continuous wide-area parameter search. This is achieved through "wide-area frequency sweep..." Main interval definition "High-density discreteness" fully encompasses all potential states of decision-making trade-offs, ensuring the rigor and scientific objectivity of the optimization process.

[0157] 3. A joint screening algorithm engine based on "clustering dimensionality reduction + multi-objective non-dominated extraction" was constructed. This application cascades the K-means clustering algorithm in machine learning with Pareto optimization theory in systems engineering, effectively solving the problem of difficulty in selecting from a massive number of solutions in a multi-dimensional evaluation space. The algorithm logic can automatically eliminate suboptimal solutions and unstable local extrema, accurately lock the Pareto optimal surface with strict multi-objective equilibrium, and has extremely high computational efficiency and theoretical rigor.

[0158] 4. This application innovatively outputs a continuous decision preference interval, enhancing the flexibility of decision support. The final result is not merely a few static spatial distribution maps, but a decision preference interval with continuous mathematical meaning. This range provides spatial planning managers with a decision-making buffer zone that is resistant to interference, allowing for flexible adjustment of parameters during actual multi-party consultations, which greatly improves the adaptability and practical value of the spatial decision support system.

[0159] Another aspect of this application embodiment provides a protection priority area identification device that takes into account protection cost constraints and decision-making preferences, including: The first module is used to acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification. The second module is used to perform spatial measurement of ecosystem services and biodiversity in the study area based on the multi-source heterogeneous basic database, and generate value index raster layers corresponding to each raster unit. The third module is used to construct a multi-dimensional protection cost constraint evaluation system based on the multi-source heterogeneous basic database, including opportunity cost, management cost, recovery cost and collaboration cost, and generate cost index raster layers corresponding to each raster unit. The fourth module is used to standardize the value indicators and the cost indicators, and to determine the benchmark weight coefficients corresponding to the value indicators and the cost indicators. The fifth module is used to introduce an ordered weighted average model, using decision preference parameters as adjustment variables for the ordered weighted average model. Based on the decision preference parameters, the module generates the ranking weights of each evaluation index and integrates the ranking weights with the benchmark weight coefficients to obtain the comprehensive priority results of each grid cell under different decision preference scenarios, thus forming a decision preference spectrum. The sixth module is used to continuously search the decision preference parameters within a preset parameter range, and determine the effective decision preference parameter range based on the spatial pattern changes, score distribution patterns and parameter disturbance response degrees corresponding to the comprehensive priority results under different parameter values. The seventh module is used to discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the priority area grid set according to the comprehensive priority result corresponding to each sampled parameter to generate a candidate solution set; The eighth module is used to calculate the protection effectiveness, protection efficiency, and protection element contribution performance indicators of each candidate scheme, and to construct a multidimensional protection performance feature matrix of the candidate schemes. The ninth module is used to perform clustering and screening of the candidate scheme set based on the multidimensional protection performance feature matrix, and to extract Pareto non-dominated solutions from the screened candidate schemes to determine the corresponding optimal decision preference parameter range. The tenth module is used to extract the spatial entities of the protection priority area based on the candidate schemes corresponding to the optimal decision preference parameter range, and output the final protection priority area identification result.

[0160] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0161] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned protection priority area identification method that takes into account protection cost constraints and decision preferences. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0162] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0163] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described protection priority area identification method that takes into account protection cost constraints and decision preferences.

[0164] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0165] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0167] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0168] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0170] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0171] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for identifying protection priority areas that takes into account protection cost constraints and decision-making preferences, characterized in that, include: Acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification; Based on the multi-source heterogeneous basic database, spatial measurements of ecosystem services and biodiversity in the study area are performed, and value index raster layers corresponding to each raster unit are generated. Based on the aforementioned multi-source heterogeneous basic database, a multi-dimensional protection cost constraint evaluation system is constructed, including opportunity cost, management cost, recovery cost, and collaboration cost, and a cost index raster layer corresponding to each raster unit is generated. The value index and the cost index are standardized, and the benchmark weight coefficients corresponding to the value index and the cost index are determined. An ordered weighted average model is introduced, and the decision preference parameter is used as the adjustment variable of the ordered weighted average model. The ranking weights of each evaluation index are generated according to the decision preference parameter, and the ranking weights are fused with the benchmark weight coefficient to obtain the comprehensive priority result of each grid cell under different decision preference scenarios, thus forming a decision preference spectrum. The decision preference parameters are continuously searched within a preset parameter range, and the effective decision preference parameter range is determined based on the spatial pattern changes, score distribution patterns, and parameter disturbance response degrees corresponding to the comprehensive priority results under different parameter values. Discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the priority region grid set based on the comprehensive priority result corresponding to each sampled parameter to generate a candidate solution set; The protection effectiveness, protection efficiency, and contribution performance indicators of protection elements for each candidate scheme are calculated separately, and a multidimensional protection performance feature matrix of the candidate schemes is constructed. Based on the multidimensional protection performance feature matrix, the candidate scheme set is clustered and screened, and the Pareto non-dominated solution is extracted from the screened candidate schemes to determine the corresponding optimal decision preference parameter range. Based on the candidate schemes corresponding to the optimal decision preference parameter range, the spatial entities of the protection priority area are extracted, and the final protection priority area identification result is output.

2. The protection priority area identification method according to claim 1, taking into account protection cost constraints and decision-making preferences, is characterized in that, The step involves spatially measuring ecosystem services and biodiversity in the study area based on the multi-source heterogeneous basic database, generating a raster layer of value indicators corresponding to each raster unit, including: Based on the aforementioned multi-source heterogeneous basic database, the material quantification of ecosystem services in the study area is carried out, specifically including: using the water production equation to calculate water conservation, using the modified general soil loss equation to calculate soil retention, and using the carbon storage calculation model and the empirical equation based on wind erosion climate factors to characterize carbon sequestration services and windbreak and sand fixation services, respectively. Based on the aforementioned multi-source heterogeneous basic database, spatial simulation of biodiversity dimensions is conducted in the study area. Specifically, this includes: conducting Spearman correlation tests on pre-selected environmental factors to complete habitat suitability assessment; defining trunk roads, industrial and mining construction land, and high-intensity grazing grasslands as stress sources; setting habitat suitability parameters and anti-interference sensitivity indices for various types of land use; introducing a half-saturated constant function; and outputting a continuous raster map representing habitat health status to complete habitat quality assessment.

3. The protection priority area identification method according to claim 1, taking into account protection cost constraints and decision-making preferences, is characterized in that... Based on the aforementioned multi-source heterogeneous basic database, a multi-dimensional protection cost constraint evaluation system is constructed, including opportunity cost, management cost, recovery cost, and collaboration cost. This generates a cost indicator raster layer corresponding to each raster unit, including: By overlaying population density grid, nighttime light intensity grid and agricultural and pastoral activity intensity grid with equal weights and normalizing them, a cost resistance surface reflecting the heat of regional economic development is formed, thus constructing an opportunity cost representation. Using the main highway network as the cost source, the terrain relief index and land cover barrier coefficient are set as the passage resistance. The minimum cost distance algorithm in GIS is used to generate a raster and construct a representation of management cost. The temporal degradation slope of the normalized vegetation index was used to assess the ecological fragility of the land surface and to construct a characterization of restoration costs. Based on the statistical proportion of the population employed in the primary industry to the total population, a collaborative cost representation is constructed; The cost indicators for the study area are determined based on the opportunity cost representation, the management cost representation, the recovery cost representation, and the synergy cost representation.

4. The protection priority area identification method according to claim 1, taking into account protection cost constraints and decision-making preferences, is characterized in that... The introduced ordered weighted average model uses decision preference parameters as adjustment variables. Based on these parameters, it generates ranking weights for each evaluation index and integrates these ranking weights with the baseline weight coefficients to obtain the comprehensive priority results for each grid cell under different decision preference scenarios, forming a decision preference spectrum, including: An ordered weighted average model is introduced, and the decision preference parameter is set as a preference variable that controls the distribution shape of the order weights; Generate the ordinal weight of each evaluation index in the sorting state based on the decision preference parameters; The benchmark weight coefficient and the ordinal weight are fused to form a decision preference spectrum corresponding to different risk attitudes; Based on the aforementioned decision preference spectrum, the comprehensive priority result of each grid cell under different decision preference scenarios is calculated.

5. The protection priority area identification method according to claim 1, taking into account protection cost constraints and decision-making preferences, is characterized in that... The process of calculating the protection effectiveness, protection efficiency, and contribution performance indicators of protection elements for each candidate scheme, and constructing a multidimensional protection performance feature matrix for the candidate schemes, includes: Calculate the percentage of the sum of standardized ecological values ​​within the coverage area of ​​candidate schemes to the total value of the entire region, construct a performance indicator of protection effectiveness, and use it to assess the overall achievement of macro-protection goals; Calculate the ratio of the performance index of the protection effectiveness to the total cost within the coverage of the candidate solutions, and construct a performance index of protection efficiency to evaluate the protection effectiveness corresponding to the unit protection cost; The enhancement coefficient of the average distribution density of various independent ecosystem services within the priority area corresponding to the candidate scheme relative to the baseline average value of the whole area is calculated to construct a performance index of the contribution of protection elements, which is used to evaluate the goodness of the protection plan. Based on the protection effectiveness performance index, the protection efficiency performance index, and the protection element contribution performance index, the multidimensional protection performance feature matrix is ​​constructed.

6. The protection priority area identification method according to claim 4, taking into account protection cost constraints and decision-making preferences, is characterized in that... The step of generating the ordinal weights of each evaluation index in the ranking state based on the decision preference parameters includes: Obtain the values ​​of decision preference parameters corresponding to the decision-maker's risk attitude; Obtain the total number of indicators that measure the value and cost of participation in the evaluation; The evaluation indicators are sorted according to the attribute values ​​in the unit comprehensive evaluation, and then the ranking weight of the corresponding order is calculated based on the value of the decision preference parameter and the total number of indicators.

7. The protection priority area identification method according to claim 2, taking into account protection cost constraints and decision-making preferences, is characterized in that... The calculation formula for measuring water conservation using the water production equation is as follows: ,in, For grid Water conservation capacity For grid Annual precipitation, For grid The actual annual evaporation; The calculation formula for soil retention using the modified general soil loss equation is as follows: ,in, For grid Soil retention capacity As the erosivity factor of rainfall, As a soil erodibility factor, and These are slope length and slope factor, respectively. As a vegetation cover management factor, Factors related to soil and water conservation measures.

8. A protection priority area identification device that takes into account protection cost constraints and decision-making preferences, characterized in that, include: The first module is used to acquire multi-source heterogeneous spatial data of the study area and perform unified spatial reference processing on the multi-source heterogeneous spatial data to construct a multi-source heterogeneous basic database for spatial identification. The second module is used to perform spatial measurement of ecosystem services and biodiversity in the study area based on the multi-source heterogeneous basic database, and generate value index raster layers corresponding to each raster unit. The third module is used to construct a multi-dimensional protection cost constraint evaluation system based on the multi-source heterogeneous basic database, including opportunity cost, management cost, recovery cost and collaboration cost, and generate cost index raster layers corresponding to each raster unit. The fourth module is used to standardize the value indicators and the cost indicators, and to determine the benchmark weight coefficients corresponding to the value indicators and the cost indicators. The fifth module is used to introduce an ordered weighted average model, using decision preference parameters as adjustment variables for the ordered weighted average model. Based on the decision preference parameters, the module generates the ranking weights of each evaluation index and integrates the ranking weights with the benchmark weight coefficients to obtain the comprehensive priority results of each grid cell under different decision preference scenarios, thus forming a decision preference spectrum. The sixth module is used to continuously search the decision preference parameters within a preset parameter range, and determine the effective decision preference parameter range based on the spatial pattern changes, score distribution patterns and parameter disturbance response degrees corresponding to the comprehensive priority results under different parameter values. The seventh module is used to discretely sample the decision preference parameters within the effective decision preference parameter range, and extract the priority area grid set according to the comprehensive priority result corresponding to each sampled parameter to generate a candidate solution set; The eighth module is used to calculate the protection effectiveness, protection efficiency, and protection element contribution performance indicators of each candidate scheme, and to construct a multidimensional protection performance feature matrix of the candidate schemes. The ninth module is used to perform clustering and screening of the candidate scheme set based on the multidimensional protection performance feature matrix, and to extract Pareto non-dominated solutions from the screened candidate schemes to determine the corresponding optimal decision preference parameter range. The tenth module is used to extract the spatial entities of the protection priority area based on the candidate schemes corresponding to the optimal decision preference parameter range, and output the final protection priority area identification result.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

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 as described in any one of claims 1 to 7.

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