Landscape multifunctionality and fairness optimization method and device, equipment and storage medium

Abstract

The invention provides a landscape multifunctionality and fairness optimization method and device, equipment and a storage medium. Relates to the technical field of land space planning. The method comprises the following steps: acquiring land utilization type data, and generating an unconstrained sample set based on continuous uniform distribution based on a random sample generator; generating a constraint sample set based on discrete uniform distribution according to the unconstrained sample set on the basis of a set land utilization constraint condition and landscape proportion data; based on the constraint sample set, performing land utilization conversion quantity simulation according to continuous multivariable probability distribution and random weight to obtain a plurality of landscape combinations; calculating the multifunctionality and fairness of each landscape combination; and determining an optimal landscape scene according to the multifunctionality and fairness of each landscape combination. According to the method, the optimal landscape combination is found through scene setting, the multifunctionality of ecological system service is improved, and the phenomena of unevenness of ecological benefits in group distribution and the like are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of land space planning, and in particular to a method, device, equipment and storage medium for optimizing landscape multifunctionality and fairness. Background Art

[0002] With increasing pressure on land resources, landscape management strategies must simultaneously deliver multiple benefits to numerous stakeholder groups. How to establish quantitative and spatial landscape management strategies that link stakeholder groups remains an urgent issue to improve the social welfare of ecosystem services. Currently, research on multifunctionality and equity still faces problems such as incomplete consideration of ecosystem types, difficulty in generalizing and applying measured ecosystem supply results, and lack of spatial quantitative expression. The present invention proposes a landscape optimization method based on a random sample generator. By combining the supply of various ecosystem services with the ecosystem service priority information of multiple stakeholder groups, and by constructing a landscape combination simulation tool, this paper explores how different landscape combinations affect the supply and demand benefits of ecosystem services (multifunctionality) and the fairness of the distribution of such benefits among different stakeholder groups (fairness), and thereby determines the optimal landscape combination schemes under several scenarios, providing a case reference for landscape planning in global reservoir impact areas, mountain-urban transition zones, and rapidly urbanizing basins.

[0003] Currently, the primary method for assessing the value of ecosystem services based on land use is the value equivalent method. This method, based on the value equivalent per unit area, is simple, widely applicable, and can be used at various scales to explore the spatiotemporal dynamics of land use and ecosystem service values. The concept of a "holistic" ecosystem has a long history in ecology, influencing the development of ecosystem research towards a comprehensive, integrated, and multidisciplinary approach. In the early 21st century, the concept of "ecosystem multifunctionality" emerged, defining the ability of an ecosystem to simultaneously provide multiple functions and services. This concept has gradually evolved into two research areas: the multifunctionality of ecosystem functions and the multifunctionality of ecosystem services. Multifunctionality of ecosystem services is defined as the simultaneous provision of multiple ecosystem services relative to human needs. It combines biophysical indicators of ecosystem service provision with demand measures from multiple stakeholder groups, improving the comprehensiveness and accuracy of assessments. The multifunctionality approach, based on standardized priority scores, overcomes the difficulties of integrating material and non-material values within a single indicator, thereby quantifying the overall impact of changes in ecosystem service provision on various stakeholder groups.

[0004] Equity research is considered a challenge in landscape management strategy studies. Current research primarily explores landscape equity from two perspectives: 1. Unequal access to resources stemming from uneven landscape allocation. This focus primarily on environmental accessibility, such as using the Lorenz curve and Gini coefficient of per capita green space area and services to examine the spatial equity of urban green space ecosystem services. While this type of research offers some guidance on distributive equity within specific ecosystem types, it often focuses on a single ecosystem type, lacks a holistic perspective, or focuses on maximizing a single value, ignoring the subjective and differentiated needs of different groups. Summary of the Invention

[0005] The present application provides a method, device, equipment and storage medium for optimizing landscape multifunctionality and fairness to solve the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for optimizing landscape multifunctionality and fairness, comprising:

[0007] Obtain land use type data and generate an unconstrained sample set based on continuous uniform distribution based on a random sample generator;

[0008] Based on the set land use constraints and landscape proportion data, generating a constrained sample set based on discrete uniform distribution according to the unconstrained sample set; wherein the land use constraints include a fixed number and / or range restriction of land use types;

[0009] Based on the constrained sample set, a quantitative simulation of land use conversion is performed according to a continuous multivariate probability distribution and random weights to obtain multiple landscape combinations;

[0010] Calculate the multifunctionality and equity of each landscape combination;

[0011] Determine the optimal landscape scenario based on the multifunctionality and fairness of each landscape combination.

[0012] In one possible design, land use type data is obtained and, based on a random sample generator, an unconstrained sample set based on a continuous uniform distribution is generated, including:

[0013] Create a data generation function;

[0014] Using the data generation function, receiving the number of data sets, the number of samples of each land type, the current land use ratio, and a random seed;

[0015] When it is determined that a random seed is provided, the data generation function is looped multiple times, and an independent data set is created each time, wherein the independent data set includes an ID column and all land use types;

[0016] Based on the independent data set, a land use type is selected, uniformly distributed random numbers are generated for the land use type, and proportional data of other land use types are generated, and normalization is performed according to the number of remaining types to obtain a data sample;

[0017] Assign a unique ID to each data sample, merge all data samples, round the final result, insert the current land use ratio in the first row, adjust the data in the ID column, and obtain an unconstrained sample set based on continuous uniform distribution.

[0018] In a possible design, based on the set land use constraints and landscape proportion data, a constrained sample set based on discrete uniform distribution is generated from the unconstrained sample set, including:

[0019] The land use type and current land use ratio are obtained from the unconstrained sample set, and the constraints of land use are set as follows: paddy fields and dry land have fixed numbers, and forest land and grassland have range restrictions;

[0020] Creating a first data frame, filling the first data frame with fixed values of various land use types, performing uniform sampling on the land use types with limited ranges, and using the generated values as fixed values of the land use types with limited ranges;

[0021] According to the set sum threshold, the sum of fixed values is calculated, and the adjustable land use types are fine-tuned. A certain adjustable land use type is randomly selected to increase or decrease the value so that the sum of fixed values is within the set sum threshold.

[0022] The fixed values of land use types are converted into proportional values and merged with the current landscape proportional data to obtain a constrained sample set based on discrete uniform distribution.

[0023] In one possible design, based on the constrained sample set, a quantitative simulation of land use conversion is performed according to a continuous multivariate probability distribution and random weights to obtain multiple landscape combinations, including:

[0024] Obtaining a data file containing land use types and current landscape proportions from the constrained sample set;

[0025] Creating a second data frame, and directly filling the land use type data with a fixed number into the second data frame;

[0026] Calculate the expected total proportion of forestland and grassland, dynamically generate forestland data and grassland data, and adjust the proportion of forestland data from 0 to the remaining proportion during the dynamic generation process, where the remaining proportion is the expected total proportion of forestland and grassland minus the actual total proportion of forestland and grassland;

[0027] Different random weight combinations are dynamically generated to randomly distribute the dynamically generated forestland data and grassland data among different land use types, thereby obtaining multiple different landscape combinations.

[0028] In one possible design, the multifunctionality and equity of each landscape combination are calculated as follows:

[0029] Calculate the total value of ecosystem services using the following formula:

[0030] ESV=∑A k D k

[0031] Where ESV is the total ecosystem service value A k is the area of the kth land use type; D k is the ecological value coefficient of the kth land use type;

[0032] Based on the calculated total ecosystem service value, the multifunctionality of the landscape combination is calculated using the following formula:

[0033] mf j =∑(supply i ×Stakeholder priority ij )

[0034]

[0035] In the formula, mf j represents the multifunctionality of the jth interest group; supply i represents the total value of ecosystem services of type i; stakeholder priority ij represents the priority of the jth interest group for the i-th ecosystem service, and MF represents the multifunctionality of the landscape combination;

[0036] The fairness of the landscape combination is calculated by the following formula:

[0037]

[0038] In the formula, G represents the fairness of the landscape combination, n represents the number of stakeholder groups, and mf′ j Represents a sorted sequence of multi-function values.

[0039] In one possible design, the optimal landscape scenario is determined based on the multifunctionality and equity of each landscape combination, including;

[0040] Select multiple landscape combinations with the highest comprehensive scores of multifunctionality and equity, and average the multiple landscape combinations to determine the optimal multifunctionality and equity scenario;

[0041] The top multiple landscape combinations with increased soil conservation services were taken as the optimal soil conservation scenario;

[0042] The top multiple landscape combinations with increased hydrological regulation services were taken as the optimal hydrological regulation scenarios.

[0043] In a second aspect, the present application provides a landscape multifunctionality and fairness optimization device, comprising:

[0044] A first sample generation module is configured to obtain land use type data and generate an unconstrained sample set based on a continuous uniform distribution based on a random sample generator;

[0045] a second sample generation module configured to generate a constrained sample set based on a discrete uniform distribution according to the unconstrained sample set based on set land use constraints and landscape proportion data; wherein the land use constraints include a fixed number and / or range restriction of land use types;

[0046] A numerical simulation module is configured to perform a quantitative simulation of land use conversion based on the constrained sample set according to a continuous multivariate probability distribution and random weights to obtain a plurality of landscape combinations;

[0047] a numerical calculation module configured to calculate the multifunctionality and equity of each landscape combination;

[0048] The landscape optimization module is configured to determine the optimal landscape scenario based on the multifunctionality and fairness of each landscape combination.

[0049] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the landscape multifunctionality and fairness optimization method described in the first aspect and various possible designs of the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions. When a processor executes the computer-executable instructions, the landscape multifunctionality and fairness optimization method described in the first aspect and various possible designs of the first aspect is implemented.

[0051] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the landscape multifunctionality and fairness optimization method described in the first aspect and various possible designs of the first aspect.

[0052] The beneficial effects of this application are:

[0053] Given the infinite number of ideal landscape combinations, this application designed a landscape optimization scheme. This scheme simulates optimal landscape combinations based on a random sampling model and applies it to landscape optimization aimed at achieving both multifunctionality and equity. On the one hand, by quantifying ecosystem service provision and combining it with social surveys to reveal the general demands of different interest groups for different ecosystem services, the multifunctionality and equity of different landscape combinations were assessed. On the other hand, through scenario-based design, the optimal landscape combination was discovered, achieving enhanced ecosystem service multifunctionality and reducing inequality in the distribution of ecological benefits across groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] Figure 1 A flow chart of a method for optimizing landscape multifunctionality and fairness provided in an embodiment of the present application;

[0056] Figure 2 A flowchart of the implementation of the landscape multifunctionality and fairness optimization method provided in the embodiment of the present application in a specific scenario;

[0057] Figure 3 A schematic diagram of an example area to which the landscape multifunctionality and fairness optimization method provided in an embodiment of the present application is applied;

[0058] Figure 4 The service value assessment diagram of the Three Gorges Reservoir ecosystem provided in the embodiment of this application;

[0059] Figure 5 The baseline multifunctionality map of the Three Gorges Reservoir area provided in the examples of this application, where (a) the spatiotemporal pattern of ESV supply capacity per unit area of each ecosystem type; (b) the actual capacity of each ecosystem type to provide each service;

[0060] Figure 6 A simulation diagram of the number of landscapes in the Three Gorges Reservoir area provided in an embodiment of the present application, wherein: a: a conceptual representation of multifunctionality and fairness, where blue areas indicate higher multifunctionality and higher fairness relative to the baseline landscape; the size of the stakeholder icons indicates the horizontal multifunctionality of each stakeholder group; the horizontal axis indicates the change in multifunctionality relative to the baseline landscape, and the vertical axis indicates the change in fairness; b: the change in multifunctionality and fairness of an unrestricted landscape combination; c: the change in multifunctionality and fairness of a landscape combination under constraints; d: three optimal landscape scenarios;

[0061] Figure 7A schematic diagram of the structure of a landscape multifunctionality and fairness optimization device provided in an embodiment of the present application;

[0062] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0063] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0064] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0065] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data, user data or medical image data involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0066] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0067] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0068] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0069] The present application embodiment provides a method for optimizing landscape multifunctionality and fairness. Figure 1 As shown in Figure 2, the landscape multifunctionality and equity optimization method includes the following steps:

[0070] S100: obtaining land use type data, and generating an unconstrained sample set based on a continuous uniform distribution based on a random sample generator;

[0071] S200: Based on the set land use constraint conditions and landscape proportion data, generating a constrained sample set based on discrete uniform distribution according to the unconstrained sample set; wherein the land use constraint conditions include a fixed number and / or range restriction of land use types;

[0072] S300: Based on the constrained sample set, a land use conversion quantity simulation is performed according to a continuous multivariate probability distribution and a random weight to obtain a plurality of landscape combinations;

[0073] S400: Calculate the multifunctionality and equity of each landscape combination;

[0074] S500: Determine the optimal landscape scenario based on the multifunctionality and fairness of each landscape combination.

[0075] In some embodiments, step S100 is implemented by the following steps:

[0076] S101: Create a data generation function;

[0077] S102: Receive the number of data sets, the number of samples of each land type, the current land use ratio, and a random seed using the data generation function;

[0078] S103: When it is determined that a random seed is provided, looping the data generation function multiple times, creating an independent data set each time, wherein the independent data set includes an ID column and all land use types;

[0079] S104: Based on the independent data set, select a land use type, generate uniformly distributed random numbers for the land use type and generate proportion data of other land use types, and perform normalization according to the number of remaining types to obtain a data sample;

[0080] S105: Assign a unique ID to each data sample, merge all data samples, round the final result, insert the current land use ratio in the first row, adjust the data in the ID column, and obtain an unconstrained sample set based on continuous uniform distribution.

[0081] In some embodiments, step S200 is implemented by the following steps:

[0082] S201: Obtaining land use types and current land use ratios from the unconstrained sample set, and setting land use constraints as follows: paddy fields and dry land have fixed quantities, and woodlands and grasslands have range restrictions;

[0083] S202: creating a first data frame, filling the first data frame with fixed values of various land use types, performing uniform sampling on the land use types within the restricted range, and using the generated values as fixed values of the land use types within the restricted range;

[0084] S203: Calculate the sum of the fixed values according to the set sum threshold, fine-tune the adjustable land use type, and randomly select an adjustable land use type to increase or decrease the value so that the sum of the fixed values is within the set sum threshold;

[0085] S204: Convert the fixed value of the land use type into a proportional value, and merge it with the current landscape proportional data to obtain a constrained sample set based on discrete uniform distribution.

[0086] In some embodiments, step S300 is implemented by the following steps:

[0087] S301: Acquire a data file containing land use types and current landscape proportions from the constraint sample set;

[0088] S302: creating a second data frame, and directly filling a fixed number of land use type data into the second data frame;

[0089] S303: Calculating the expected total proportion of forestland and grassland, dynamically generating forestland data and grassland data, and adjusting the proportion of forestland data from 0 to a remaining proportion during the dynamic generation process, where the remaining proportion is the expected total proportion of forestland and grassland minus the actual total proportion of forestland and grassland;

[0090] S304: Dynamically generate different random weight combinations to randomly distribute the dynamically generated woodland data and grassland data among different land use types, thereby obtaining a plurality of different landscape combinations.

[0091] In some embodiments, step S400 is implemented by the following steps:

[0092] S401: Calculate the total value of ecosystem services using the following formula:

[0093] ESV=∑A k D k

[0094] Where ESV is the total value of ecosystem services; A k is the area of the kth land use type; D k is the ecological value coefficient of the kth land use type;

[0095] S402: Based on the calculated total ecosystem service value, calculate the multifunctionality of the landscape combination using the following formula:

[0096] mf j =∑(supply i ×Stakeholder priority ij )

[0097]

[0098] In the formula, mf j represents the multifunctionality of the jth interest group; supply i represents the total value of ecosystem services of type i; stakenholder priority ij represents the priority of the jth interest group for the i-th ecosystem service, and MF represents the multifunctionality of the landscape combination;

[0099] S403: Calculate the fairness of the landscape combination using the following formula:

[0100]

[0101] In the formula, G represents the fairness of the landscape combination, n represents the number of stakeholder groups, and mf′ j Represents a sorted sequence of multi-function values.

[0102] In some embodiments, the optimal landscape scenario is determined based on the multifunctionality and fairness of each landscape combination as follows: multiple landscape combinations with the highest comprehensive scores of multifunctionality and fairness are selected, and the optimal multifunctionality and fairness scenario is determined after averaging the multiple landscape combinations; the top multiple landscape combinations with increased soil conservation services are used as the optimal soil conservation scenario; and the top multiple landscape combinations with increased hydrological regulation services are used as the optimal hydrological regulation scenario.

[0103] Example 2:

[0104] Based on the landscape multifunctionality and fairness optimization method provided in Example 1, this embodiment provides a specific application example of the landscape multifunctionality and fairness optimization method.

[0105] like Figure 2 As shown in FIG, it is a flowchart of the implementation of the landscape multifunctionality and fairness optimization method in a specific scenario. This embodiment first gives an example area where the landscape multifunctionality and fairness optimization method is applied. The schematic diagram of the example area is as follows Figure 3As shown, the gorge reservoir area, a distinct geographical unit, refers to the reservoir inundation zone directly affected by the Three Gorges Project and backwaters (106°20′-110°30′, 29°-31°50′N). It encompasses Chongqing City and 26 counties in Hubei Province, covering a total area of approximately 58,000 square kilometers. Located on a sloped terrain characterized by alternating second and third terraces in China's topography, it features alternating high mountains and valleys, a dense network of rivers, and an average elevation of 1,500 meters. Forest cover accounts for approximately 48.2%, dominated by forested land with a canopy density exceeding 30%, primarily distributed in mountainous areas above 1,000 meters. Cultivated land accounts for 36.5% of the reservoir area, primarily located in hilly areas and river valleys below 800 meters. The humid subtropical monsoon climate, with an average annual temperature of approximately 16°C and precipitation of approximately 1,100 mm, and the extensive forest landscape make the Three Gorges Reservoir area a biodiverse region.

[0106] The data used in this example include land use data, socioeconomic statistics, survey data, and land use simulation driving force data. The specific sources are as follows: (1) The land use data for 2000, 2010, and 2020 are from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https: / / www.resdc.cn / ), with a resolution of 30m. Based on the supply of ecosystem services in the Three Gorges Reservoir area, the secondary land categories were appropriately adjusted and merged. (2) Socioeconomic data are used for equivalence table correction and are from the China Statistical Yearbook - National Bureau of Statistics (https: / / www.stats.gov.cn / sj / ndsj / ), including the Chongqing Statistical Yearbook, the Hubei Statistical Yearbook, the China County and City Socioeconomic Statistical Yearbook, and relevant statistical reports from various government departments. (3) Survey data are the priorities of different interest groups in the Three Gorges Reservoir area for different ecosystem services, which are used to calculate the multifunctionality and fairness of ecosystem services. They are derived from 303 questionnaires in the Three Gorges Reservoir area, including actual surveys, online questionnaires, and telephone interviews. (4) Driving force data are used to simulate land use scenarios based on multifunctionality and fairness, including 20 driving factors such as natural location, transportation location, and socioeconomic location. The transportation location data are mainly derived from OpenStreetMap (https: / / openmaptiles.org / ).

[0107] The landscape multifunctionality and fairness optimization method is specifically implemented by the following five steps, which are steps 1 to 5, corresponding to steps S100 to S500 in Example 1, respectively.

[0108] Step 1: Generate an unconstrained sample set based on continuous uniform distribution.

[0109] Define a set of land-use types and create the generate_random_data function to generate random data. The function accepts the number of datasets, the number of samples for each land-use type, the current land-use proportion, and a random seed. During data generation, the function first checks whether the random seed is provided to ensure reproducible results. The function then loops num_datasets times, creating a new DataFrame each time containing an ID column and all land-use types. Next, a land-use type is selected and uniformly distributed random numbers are generated for that type using np.random.uniform(0,1,num_samples_per_class). Proportions for the remaining land-use types are also generated using np.random.uniform and normalized by the number of remaining types to ensure reasonable values. Each data sample is then assigned a unique ID, and the selected land-use type data is sorted in ascending order to introduce some structure. After all datasets are merged, the final result is rounded, and current_proportions (current land-use proportions) is inserted in the first row for subsequent analysis. Finally, the order of the ID column is adjusted, and the final dataset is returned.

[0110] Step 2: Generate a constrained sample set based on discrete uniform distribution.

[0111] Land-use types and their current proportions are read from an Excel file (i.e., the unconstrained sample set based on a continuous uniform distribution obtained in Step 1) and land-use constraints are defined. Some land-use types have fixed quantities (e.g., paddy fields, dry land), some have range restrictions (e.g., forest land), and others have no specific restrictions. During the data initialization phase, a 6000-row data frame is created and directly populated with fixed-value land-use types. For range-restricted types, np.random.randint(min, max+1) is used for uniform sampling, ensuring that the generated values fall within the specified range. Subsequently, to ensure the total area of each sample, the code calculates the sum of the fixed values and fine-tunes the adjustable land-use types by randomly selecting a category and increasing or decreasing the value until the total amount is met. Finally, the code converts the data into proportions, merges them with the current landscape proportion data, and stores them in Excel. Overall, this method ensures data randomness while strictly controlling the total area through an adjustment mechanism, ensuring that the generated data conforms to the land-use constraints.

[0112] Step 3: Simulation of land use conversion quantity based on Dirichlet distribution and random weights.

[0113] First, a data file containing land-use types and the current proportion of the landscape is read. The parameters for each land-use type are then defined, primarily including whether it is a fixed value (fixed), whether it is "forest land" (type=1), and whether it is "grassland" (type=0). These parameters determine how the land-use type values are generated. Next, all land-use type data with fixed values (such as "paddy field" and "dry land") are directly populated into the data frame. Next, the total proportion of "forest land" and "grassland" is calculated and generated using np.linspace(). The "forest land" data increases from 0 to the remaining proportion, while the "grassland" proportion is the remaining proportion. The code then uses the np.random.dirichlet() method to generate different random weights and, based on these weights, randomly distributes the "forest land" and "grassland" values across different land-use types (such as "forest land," "sparse woodland," and "high-cover grassland"). The random weights ensure that the distribution of each dataset is diverse while preserving the overall proportion. Finally, the code rounds all generated data to four decimal places, adds the current landscape scale to the final data table, reorders the columns, adds an incrementing ID column, and saves the data to a new Excel file.

[0114] Step 4: Calculate the multifunctionality and fairness of the landscape combination.

[0115] The value equivalent method is used to evaluate the supply of ecosystem services, and the equivalent coefficient is appropriately modified based on the actual situation of the study area. The ecosystem service value coefficient method is used to calculate the ecosystem service value, and the formula is:

[0116] ESV=∑A k D k

[0117] ESV is the total value of ecosystem services, unit 10 8 Yuan; A k is the area of the kth land use type, unit: hm 2 ;D k is the ecological value coefficient of the kth land use type.

[0118] Ecosystem service multifunctionality quantifies the simultaneous provision of multiple ecosystem services relative to human demands. Because the multifunctionality score incorporates measures of the provision of all priority services, it allows for an assessment of the aggregate impacts of changes in ecosystem services across stakeholder groups, as well as the equitable distribution of this provision across society.

[0119] In this example, the multifunctionality of the example region at the reservoir level is calculated as the average of the multifunctionality values of all groups, and the fairness is calculated as the negative Gini index of the multifunctionality values of all groups (from -1, maximum unfairness, to 0, complete fairness), and the formula is:

[0120] mf j =∑(supply i ×Stakeholder priority ij )#(2)

[0121] Among them, mf j : Multifunctionality of the jth interest group; supply i : Total value of ecosystem service of type i; stakeholder priority ij : The priority of the jth interest group for the i-th ecosystem service.

[0122]

[0123] MF: Multifunctionality, which is equal to the average of the multifunctionality scores of each stakeholder group.

[0124]

[0125] G: fairness, calculated as the negative Gini index; n: number of stakeholder groups; _j^': Sorted sequence of multifunctional values.

[0126] In the code, the data of two Excel files are first read, and the land use service types and their normalized priorities are extracted. Next, it uses pandas to process the data and weights each row in data_2 using the column data of data_1 to generate weighted data and calculate the total score of each row. Then, the code calculates the Gini coefficient for each scenario, which is used to measure the fairness of land use service distribution. For each scenario, it also calculates community_MF (average total score), MF_differ (relative change percentage) and gini_differ (Gini coefficient change). Finally, the results are simplified through groupby, retaining only one row of data for each id group to avoid redundancy, and the complete results and the simplified results are saved in Excel files separately. This allows users to analyze the changes in the fairness of land use services under different scenario settings based on the calculation results and make further decisions or research.

[0127] Step 5: Setting the optimal landscape scenario.

[0128] To gain a more detailed understanding of how land-use strategies impact stakeholder groups, several specific land-use scenarios were developed, based on the ecological functional zoning of the study area. These scenarios provide a reference for developing land-use strategies to achieve specific goals. The 25 landscape combinations with the highest combined multifunctionality and equity scores were selected and averaged to form the Optimal Multifunctionality and Equity Scenario (OMES). The Optimal Soil Conservation Scenario (OSCS) represents the top 25 combinations with increased soil conservation services, and the Optimal Hydrological Regulation Scenario (OSCS) represents the top 25 combinations with increased hydrological regulation services.

[0129] Regarding the quantification of ecosystem service value and the needs of various groups, such as Figure 4 As shown, from 2000 to 2020, the Three Gorges Reservoir area exhibited a typical "dual-core driving and gradient differentiation" landscape pattern: over 26% dryland and over 48% forestland constituted the matrix landscape. Different land use types underwent changes during the study period, primarily characterized by an average annual expansion rate of 13.3% for construction land. After the Three Gorges Project completed its impoundment in 2008, the area of water increased by 53.7%. The areas of woodland, shrubland, and other wooded high-cover grassland showed an overall fluctuating growth trend, while the areas of paddy fields, dryland, sparse woodland, medium-cover grassland, low-cover grassland, and wetlands continued to decline. The spatial distribution of ecosystem service capacity per unit area across various land types reveals that water areas > wetlands > forested land. The eastern mountainous region has significantly higher capacity than the western region, with high-capacity areas concentrated in the mountainous regions of Yiling District, Zigui County, Xingshan County, Badong County, and other districts and counties. From the perspective of actual value supply and composition of each category, the ecosystem service value of forest land in the reservoir area is the highest, and the climate regulation service value contributes the most to it; followed by the ecosystem service value supply of water area, and the hydrological regulation service value contributes the most. Figure 5 As shown, differences in service perceptions among different groups, coupled with the actual supply of ecosystem services in the Three Gorges Reservoir area, lead to inequalities in multifunctionality among these groups. To achieve the sustainable supply and utilization of ecosystem services, the needs of different interest groups need to be considered in management and planning. Policymakers and planners should take measures to enhance public understanding of ecosystem services, optimize the structure of service supply, and promote communication and coordination among stakeholders.

[0130] Regarding the multifunctionality and fairness under different landscape combinations, such as Figure 6As shown, when land conversion restrictions are not considered, an analysis of 24,000 landscape combinations reveals that only 3.5% can break the "multifunctionality-equity" trade-off, indicating that the baseline landscape is close to a local optimum. In theoretical optimization, adjusting the proportion of forest land can increase multifunctionality by 150% and equity by 10%. Adjusting the scale of dryland can improve equity by 20%, but at the expense of 50% multifunctionality. The expansion of construction land and paddy fields leads to a 50% decrease in multifunctionality and an 80% decrease in equity, creating a "double suppression effect." 74.3% of the degraded combinations are concentrated in the fourth quadrant, highlighting the imbalance between the supply of watershed / wetland services and the demand for their benefits. Based on planning constraints (fixing the existing areas of paddy fields, drylands, and water bodies, setting construction land at 1.3 times, and fluctuating other land types by ±50%), the optimized paths among the 6,000 combinations screened revealed three key characteristics: 1) conversion of high-cover grassland increased multifunctionality by 0.46% and equity by 0.07%; 2) expansion of sparse woodland increased multifunctionality by 2.42% and equity by 1.04%, reflecting its ecological service advantages; and 3) a reduction in the proportion of forested land weakened system performance, reflecting the critical role of stand structure. Scenario simulations showed that the Optimal Multifunctional Equity Scenario (OMES) (multifunctionality +4.77%, equity +1.71%) and the Optimal Soil Conservation Scenario (OSCS) had high landscape overlap, confirming the synergy between ecological priorities and livelihood needs. The Optimal Hydrological Regulation Scenario (OHCS) exhibited a more discrete landscape configuration (multifunctionality +4.39%, equity +1.35%), reflecting the spatially specific requirements of flood storage and regulation. These three scenarios collectively confirmed that systematic optimization of forestland structure and enhancement of grassland cover are the core pathways to achieving the reservoir region's multidimensional goals.

[0131] Example 3:

[0132] Figure 7 This is a schematic diagram of the structure of the landscape multifunctionality and fairness optimization device provided in the embodiment of the present application. This embodiment of the present application also provides a landscape multifunctionality and fairness optimization device. Figure 7 As shown, the landscape multifunctionality and equity optimization device includes:

[0133] The first sample generation module 701 is configured to obtain land use type data and generate an unconstrained sample set based on a continuous uniform distribution based on a random sample generator;

[0134] The second sample generation module 702 is configured to generate a constrained sample set based on a discrete uniform distribution according to the unconstrained sample set based on set land use constraints and landscape proportion data; wherein the land use constraints include a fixed number and / or range restriction of land use types;

[0135] The numerical simulation module 703 is configured to perform a numerical simulation of land use conversion based on the constrained sample set according to a continuous multivariate probability distribution and random weights to obtain a plurality of landscape combinations;

[0136] The numerical calculation module 704 is configured to calculate the multifunctionality and fairness of each landscape combination;

[0137] The landscape optimization module 705 is configured to determine the optimal landscape scenario according to the multifunctionality and fairness of each landscape combination.

[0138] The landscape multifunctionality and fairness optimization device provided in the embodiment of the present application can be used to implement the technical solution of the landscape multifunctionality and fairness optimization method in the above embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0139] Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 8 As shown, the electronic device may include: a processor 801 and a memory 802 , wherein the processor 801 and the memory 802 may communicate with each other; illustratively, the processor 801 and the memory 802 communicate with each other via a communication bus 803 .

[0140] The processor 801 executes the computer-executable instructions stored in the memory 802, so that the processor 801 performs the solution in the above embodiment. The processor 801 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0141] The communication bus 803 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but this does not mean that there is only one bus or one type of bus. The transceiver is used to implement communication between the database access device and other computers (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory.

[0142] The electronic device provided in the embodiment of the present application may be the terminal device of the above embodiment.

[0143] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the technical solution of the landscape multifunctionality and fairness optimization method of the above embodiment.

[0144] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, the technical solution of the landscape multifunctionality and fairness optimization method in the above embodiment can be implemented.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0146] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to implement the solution of this embodiment based on actual needs.

[0147] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more modules may be integrated into a single unit. The above-mentioned modules may be implemented in the form of hardware or hardware plus software functional units.

[0148] The above-mentioned integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0149] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASICs). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0150] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0151] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0152] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0153] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0154] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing landscape multifunctionality and fairness, characterized in that: include: Obtain land use type data and generate an unconstrained sample set based on continuous uniform distribution based on a random sample generator; Based on the set land use constraints and landscape proportion data, generating a constrained sample set based on discrete uniform distribution according to the unconstrained sample set; wherein the land use constraints include a fixed number and / or range restriction of land use types; Based on the constrained sample set, a quantitative simulation of land use conversion is performed according to a continuous multivariate probability distribution and random weights to obtain multiple landscape combinations; Calculate the multifunctionality and equity of each landscape combination; Determine the optimal landscape scenario based on the multifunctionality and fairness of each landscape combination.

2. The method according to claim 1, characterized in that Obtain land use type data and generate an unconstrained sample set based on a continuous uniform distribution based on a random sample generator, including: Create a data generation function; Using the data generation function, receiving the number of data sets, the number of samples of each land type, the current land use ratio, and a random seed; When it is determined that a random seed is provided, the data generation function is looped multiple times, and an independent data set is created each time, wherein the independent data set includes an ID column and all land use types; Based on the independent data set, a land use type is selected, uniformly distributed random numbers are generated for the land use type, and proportional data of other land use types are generated, and normalization is performed according to the number of remaining types to obtain a data sample; Assign a unique ID to each data sample, merge all data samples, round the final result, insert the current land use ratio in the first row, adjust the data in the ID column, and obtain an unconstrained sample set based on continuous uniform distribution.

3. The method according to claim 1, characterized in that Based on the set land use constraint conditions and landscape proportion data, a constrained sample set based on discrete uniform distribution is generated according to the unconstrained sample set, including: The land use type and current land use ratio are obtained from the unconstrained sample set, and the constraints of land use are set as follows: paddy fields and dry land have fixed numbers, and forest land and grassland have range restrictions; Creating a first data frame, filling the first data frame with fixed values of various land use types, performing uniform sampling on the land use types with limited ranges, and using the generated values as fixed values of the land use types with limited ranges; According to the set sum threshold, the sum of fixed values is calculated, and the adjustable land use types are fine-tuned. A certain adjustable land use type is randomly selected to increase or decrease the value so that the sum of fixed values is within the set sum threshold. The fixed values of land use types are converted into proportional values and merged with the current landscape proportional data to obtain a constrained sample set based on discrete uniform distribution.

4. The method according to claim 1, wherein Based on the constrained sample set, a quantitative simulation of land use conversion is performed according to continuous multivariate probability distribution and random weights to obtain multiple landscape combinations, including: Obtaining a data file containing land use types and current landscape proportions from the constrained sample set; Creating a second data frame, and directly filling the land use type data with a fixed number into the second data frame; Calculate the expected total proportion of forestland and grassland, dynamically generate forestland data and grassland data, and adjust the proportion of forestland data from 0 to the remaining proportion during the dynamic generation process, where the remaining proportion is the expected total proportion of forestland and grassland minus the actual total proportion of forestland and grassland; Different random weight combinations are dynamically generated to randomly distribute dynamically generated forestland data and grassland data among different land use types, thereby obtaining multiple different landscape combinations.

5. The method according to claim 1, wherein The multifunctionality and equity of each landscape combination are calculated as follows: Calculate the total value of ecosystem services using the following formula: ESV=∑A k D k Where, ESV is the total value of ecosystem services; A k is the area of the kth land use type; D k is the ecological value coefficient of the kth land use type; Based on the calculated total ecosystem service value, the multifunctionality of the landscape combination is calculated using the following formula: mf j =∑(sypply i ×stakeholder priority ij ) In the formula, mf j represents the multifunctionality of the jth interest group; supply i represents the total value of ecosystem services of type i; stakeholder priority ij represents the priority of the jth interest group for the i-th ecosystem service, and MF represents the multifunctionality of the landscape combination; The fairness of the landscape combination is calculated by the following formula: In the formula, G represents the fairness of the landscape combination, n represents the number of stakeholder groups, and mf′ j Represents a sorted sequence of multi-function values.

6. The method according to claim 1, characterized in that Determine the optimal landscape scenario based on the multifunctionality and equity of each landscape combination, including; Select multiple landscape combinations with the highest comprehensive scores of multifunctionality and equity, and average the multiple landscape combinations to determine the optimal multifunctionality and equity scenario; The top multiple landscape combinations with increased soil conservation services were taken as the optimal soil conservation scenario; The top multiple landscape combinations with increased hydrological regulation services were taken as the optimal hydrological regulation scenarios.

7. A landscape multifunctionality and fairness optimization device, characterized in that: include: A first sample generation module is configured to obtain land use type data and generate an unconstrained sample set based on a continuous uniform distribution based on a random sample generator; a second sample generation module configured to generate a constrained sample set based on a discrete uniform distribution according to the unconstrained sample set based on set land use constraints and landscape proportion data; wherein the land use constraints include a fixed number and / or range restriction of land use types; A numerical simulation module is configured to perform a quantitative simulation of land use conversion based on the constrained sample set according to a continuous multivariate probability distribution and random weights to obtain a plurality of landscape combinations; a numerical calculation module configured to calculate the multifunctionality and equity of each landscape combination; The landscape optimization module is configured to determine the optimal landscape scenario based on the multifunctionality and fairness of each landscape combination.

8. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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