Photovoltaic land partitioning and management method based on landform sensitivity and entropy regulation

Abstract

The embodiment of the application discloses a photovoltaic land partition management and control method based on landform sensitivity and entropy regulation, comprising: acquiring a plurality of landform units of a region to be managed and controlled; predicting an ecological risk index of each landform unit according to land use types and land use structure information entropy of each landform unit; performing bivariate spatial autocorrelation analysis on a land use intensity comprehensive index and the ecological risk index of each landform unit; identifying direct driving factors and direct buffer factors of the ecological risk index by using a robust geographic detector and a structural equation model; limiting and controlling the direct driving factors for a landform unit with a high-high analysis result; and improving and controlling the direct buffer factors for a landform unit with a low-low analysis result. The embodiment realizes more accurate and scientific ecological risk prediction of the ecological risk in a dry area, and provides more practical decision support for ecological environment management and control of the photovoltaic industry in the dry area.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geographic information and land ecological risk management technology, and in particular to a method for zoning management of photovoltaic land use based on geomorphological sensitivity and entropy regulation. Background Technology

[0002] As the photovoltaic industry concentrates in arid regions, photovoltaic land, as an "emerging disturbing land type," has become a key issue in land use ecological risk management due to its nonlinear disturbances to the surface ecosystem (such as increased soil erosion and landscape fragmentation).

[0003] Existing technologies primarily rely on the linear correlation between "landscape pattern and ecological risk" to predict land use ecological risks, thereby providing decision support for ecological risk management. However, in the context of photovoltaic land use, the ecological risk prediction results of traditional methods deviate significantly from the actual risks, failing to provide reasonable support for photovoltaic land use risk management decisions. Summary of the Invention

[0004] This invention provides a method for zoning and managing photovoltaic land use based on geomorphological sensitivity and entropy regulation to solve the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a method for zoning and managing photovoltaic land use based on geomorphological sensitivity and entropy regulation, including: Acquire multiple geomorphic units of the area to be managed; Based on the land use type and land use structure information entropy of each landform unit, the ecological risk index of each landform unit is predicted, among which photovoltaic land use type is newly added; Bivariate spatial autocorrelation analysis was conducted on the comprehensive land use intensity index and ecological risk index of each landform unit; Using robust geographic detectors and structural equation models, we identify the direct driving factors and direct buffering factors of the ecological risk index. For geomorphic units with a high-high analysis result, the direct driving factors are restricted and controlled; For geomorphic units with low-low analysis results, the direct buffer factor is increased and controlled.

[0006] In a second aspect, embodiments of the present invention provide an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation as described in any embodiment.

[0007] Thirdly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation as described in any embodiment.

[0008] This invention provides a method for zoning and managing photovoltaic land use based on geomorphic sensitivity and entropy regulation. It aims to address the shortcomings of existing ecological risk prediction and management methods, such as insufficient consideration of rigid constraints on geomorphic units, failure to independently identify the ecological effects of photovoltaic land use, and lack of quantification of the nonlinear interaction between natural and anthropogenic factors. The method first divides geomorphic units based on a digital geomorphic classification system; then, treating photovoltaic land use as an independent type, it proposes a comprehensive land use intensity index and an ecological risk prediction method integrating geomorphic amplification effects and nonlinear entropy regulation; it uses bivariate spatial autocorrelation to identify risk hotspots; and employs robust geographic detectors and structural equation modeling to quantify the interaction and impact paths of natural and anthropogenic factors on ecological risks. Finally, based on the above analysis results, it identifies "geomorphic red zones" and suitable areas for photovoltaic land development and sets differentiated landscape index thresholds to achieve spatial optimization. This embodiment achieves more refined and scientific ecological risk prediction in arid regions, providing more practical decision support for the ecological environment management of the photovoltaic industry in arid areas. Attached Figure Description

[0009] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is a flowchart of a photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation provided by an embodiment of the present invention; Figure 2 This is a flowchart of a photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation provided by an embodiment of the present invention; Figure 3 This is a map of elevation, slope, topographic relief (RDLS), and geomorphological type of the area to be managed, provided by an embodiment of the present invention. Figure 4 This is a spatial distribution map of the Land Use Comprehensive Index (LUCI) and Land Use Ecological Risk (LUER) for a specific region, provided by an embodiment of the present invention. Figure 5 This is a schematic diagram of the bivariate spatial autocorrelation of LUCI and LUER in a specific region provided by an embodiment of the present invention; Figure 6 This is a diagram showing the detection results of data interactions of a robust geographic detector provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of a structural equation model of factors influencing LUER provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0012] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0013] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0014] This embodiment provides a method for zoning and managing photovoltaic land use based on geomorphological sensitivity and entropy regulation. To illustrate this method, a thorough analysis of the shortcomings of existing land use risk prediction technologies is conducted first. Specifically, the purpose of ecological risk prediction is to quantify the likelihood of adverse impacts of human activities on the structure, function, and services of ecosystems. Against the backdrop of a low-carbon energy transition, arid regions rich in solar resources have become the main areas for large-scale photovoltaic power plant construction. While providing clean energy, the rapid expansion of photovoltaic land use also triggers complex ecological effects by altering surface landscape patterns (such as causing habitat fragmentation and hindering species migration), exacerbating the difficulty of coordinating renewable energy development and ecological protection.

[0015] Existing ecological risk prediction methods, especially land use landscape ecological risk prediction, have developed several relatively mature implementation schemes. The core of these schemes typically includes the following steps: First, land use / cover data are obtained based on remote sensing image interpretation; second, landscape pattern indices (such as fragmentation, separation, and fractal dimension) are selected to construct prediction models to scientifically quantify risks; finally, spatial statistical analysis is used within selected prediction units (such as administrative units or regular grids) to demonstrate the spatial differentiation characteristics of risks.

[0016] In selecting spatial prediction units, existing technologies typically use administrative boundaries or regular grids as basic units. For example, in the journal article "A Study on Land Use Change and Landscape Ecological Risk in Typical Counties under the Background of Migration and Relocation—Taking Hanzhong City as an Example" (Land and Natural Resources, 2025, (02): 47-53), the authors used counties as basic units and calculated the average value of the landscape pattern index within them to characterize ecological risks. The advantage of this approach is that the risk prediction results can directly serve the formulation of spatial control policies with administrative regions as the responsible entities. Patent application CN119359020A also discloses "An Ecological Risk Assessment Method Based on Ecosystem Services". This method divides the target area into m km × n km risk units, using an equidistant systematic sampling method. It calculates the ecological risk index of each risk unit and uses it as the ecological risk level at the center point of the risk unit to achieve the assessment and prediction of ecological risks within the target area. Its core technology lies in using the m km × n km risk unit as the smallest unit for data integration and risk prediction.

[0017] In terms of landscape ecological risk model construction, existing technologies are mostly based on land use type data and utilize landscape disturbance degree (… () +b +c a, b, and c are respectively , , The corresponding weights are typically set as a=0.5, b=0.3, c=0.2) and landscape vulnerability ( Landscape ecological risk prediction is conducted using the journal article "Landscape Ecological Risk Assessment and Driving Factor Analysis in Ordos-Yulin Area" (Bulletin of Soil and Water Conservation, 2022, 42(02):275-283) and patent document CN119359020A "An Ecological Risk Assessment Method Based on Ecosystem Services". The core method of both is to calculate the landscape fragmentation ( ), resolution ( ), Advantage ( Indices such as ) are weighted using a linear weighting method (e.g. Ecological Risk Index (ERI) is constructed, and these methods and practices only consider six land use types—arable land, forest land, grassland, water area, construction land, and unused land—to calculate the landscape ecological risk index. This method was cited in the journal article "Landscape Ecological Risk Analysis and Prediction of Ebinur Lake Basin Based on PLUS Model" (Arid Zone Geography, 2025, 48(02):308-322) to predict land use landscape risk in the arid northwest region. However, in the context of the rapid expansion of photovoltaic land use in the arid region, photovoltaic land was not given weight as an independent land type and was still classified as "construction land" or "unused land," which failed to characterize the "topographically locked fragmentation" characteristics of photovoltaic arrays on the land surface.

[0018] In the analysis of risk-driving mechanisms, geodetectors are a widely used statistical tool for detecting the spatial heterogeneity of geographic elements and revealing the underlying driving factors. Its basic principles are systematically explained in the paper "Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China" (International Journal of Geographical Information Science, 2010, 24(1-2):107-127). This method measures the explanatory power of each driving factor (such as natural and human factors) on ecological risk by calculating the q-statistic and can test the interaction between two factors. Because it can effectively handle typographic data and detect nonlinear relationships, this model has been widely applied in the analysis of ecological risk drivers. For example, in the journal article "Ecological Risk Assessment of Land in the Beijing-Tianjin-Hebei Region Based on Land Use Pattern Change" (Forestry and Ecological Science, 2025, 40(03):284-294), geodetectors successfully identified the dominant factors affecting regional ecological risk. This method is a standard tool in existing ecological risk research, but the discretization process relies on subjective stratification, resulting in insufficient stability of the q-value (e.g., the explanatory power of slope on risk fluctuates by ±15%). In addition, some studies have used NDVI values ​​to classify photovoltaic construction areas, but these studies have not considered the amplification effect of topography on risk (e.g., even if the NDVI is high in steep slope areas, photovoltaic development can still cause severe soil erosion).

[0019] While the aforementioned existing technologies have achieved certain results in their respective application scenarios, their limitations have gradually become apparent when addressing the novel issue of photovoltaic land expansion in arid regions. In particular, regarding the selection of prediction units and the in-depth analysis of driving mechanisms, existing technologies have failed to fully consider the rigid constraints imposed by the unique geomorphological patterns of arid regions on land use layout and ecological processes, exhibiting the following significant shortcomings: (1) The misalignment between the prediction unit and the natural geographical pattern leads to spatial distortion in risk prediction results, making it difficult to support precise control. Existing methods mostly use administrative boundaries as prediction and control units. Although this is convenient for management, it artificially severs homogeneous areas with ecological and geographical characteristics. Arid areas have a typical "mountain-oasis-desert" geomorphological differentiation pattern, and their land use patterns, ecological processes (such as hydrological processes and species migration), and disturbance responses are all deeply controlled by geomorphological units. For example, a complete, ecologically coherent alluvial fan oasis in front of a mountain may be divided by multiple administrative units (such as counties and townships), causing the homogeneous ecological risks within it to be smoothed or distorted in the averaging calculations of different administrative units. Conversely, an administrative region may contain various heterogeneous geomorphological types such as plains, hills, and mountains. The calculated unit average ecological risk value cannot truly reflect the actual situation of high-risk areas within the region. This "spatial mismatch" between the evaluation unit and the natural background makes it difficult for the prediction results to accurately reveal the true spatial differentiation pattern and formation mechanism of ecological risks, resulting in spatial control measures based on this having weak targeting and poor control effects.

[0020] (2) The traditional land use classification system is outdated and cannot accurately characterize the unique ecological effects of photovoltaic land, resulting in systematic bias in the basic data for risk prediction. The traditional land use classification standards on which existing technologies rely (such as the "Classification of Current Land Use" GB / T 21010-2017) usually classify photovoltaic land as "construction land" (such as industrial land in construction land) or "unused land", ignoring its uniqueness as an emerging land use type. Journal articles “The Impact of Photovoltaic Power Station Operation on Local Ecological Environment in Arid Zones” (Arid Zone Research, 2024, 41(08):1423-1433) and “Meta-analysis of the Impact of Photovoltaic Power Stations on Regional Microclimate-Vegetation-Soil Characteristics” (Acta Grassland Sinica, 2025, 33(08):2641-2651) both point out that photovoltaic land has dual or even multiple ecological effects: on the one hand, its infrastructure (such as photovoltaic panels and roads) does occupy land and change the surface properties, bringing negative interference similar to that of construction land; but on the other hand, in arid zones, photovoltaic arrays can form a shading effect, reduce surface evaporation, and under certain conditions may even promote vegetation restoration and other “active ecological restoration” functions, which are completely different from the ecological attributes of traditional construction land and unused land. Existing technologies use traditional classification systems, which either overestimate the ecological risks of photovoltaic land use (equating it with high-intensity construction land) or underestimate its risks (treating it as unused land with almost no impact). This inaccuracy in classification directly leads to biases in the basic data for ecological risk prediction, greatly reducing the scientific validity and practicality of the prediction results.

[0021] (3) Insufficient depth in analyzing the complex nonlinear interactions and risk transmission paths between natural and anthropogenic factors leads to superficial risk management measures that fail to truly achieve ecological protection. Existing technologies often employ geographic detectors to analyze the explanatory power (q-value) of single or dual-factor interactions, or traditional regression models to analyze linear relationships. However, the formation of ecological risk is the result of the synergistic effect of a complex causal chain: "geomorphic background → intensity of human activities → landscape pattern response → ecological risk." For example, the natural factor of slope can directly limit human development activities (thus reducing risk) and indirectly affect risk by influencing land use patterns (such as limiting contiguous farmland) and landscape patterns (such as causing habitat fragmentation). Existing technologies lack the ability to quantify such complex mediating effects and path relationships. The journal article "Landscape Ecological Risk Assessment and Driving Factor Analysis of Shule River Basin Based on Geographic Detector Model" (Arid Zone Geography, 2021, 44(05):1384-1395) identified significant factors using a geographic detector model, but failed to reveal implicit, nonlinear causal paths such as "how slope indirectly affects risk by suppressing photovoltaic density and changing landscape connectivity." This makes ecological risk management measures based on existing technologies often remain superficial, failing to accurately identify key management nodes from the overall perspective of "human-land system coupling" and truly improve the ecological risk situation of the region. For example, it cannot accurately answer the refined management question of "under what geomorphic conditions can improving landscape connectivity most effectively buffer the risks brought by photovoltaic development."

[0022] In summary, existing technologies have inherent shortcomings in three interrelated aspects: the rationality of prediction units, the accuracy of land use classification, and the depth of analysis of driving mechanisms. These shortcomings make it difficult to meet the precise and scientific demands for high-quality development of the photovoltaic industry and coordinated management of ecological security in arid regions under the new circumstances. Therefore, this invention is proposed.

[0023] Figure 1 This is a flowchart illustrating a photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation, provided by an embodiment of the present invention. This method can be executed by electronic devices, such as… Figure 1 As shown, it specifically includes: S110: Obtain multiple geomorphic units of the area to be controlled.

[0024] This embodiment first divides the area to be controlled into geomorphic units, and uses each geomorphic unit as the smallest unit for subsequent land use ecological risk prediction and control.

[0025] Figure 2 This is a specific implementation of the method in this embodiment, such as... Figure 2As shown, firstly, digital elevation model (DEM) data and multiple high-resolution remote sensing images of the area to be controlled are acquired. Topographic factors such as slope, altitude, and surface relief are extracted using the DEM data. According to the recognized digital geomorphic stratification classification method (refer to the "Classification and Coding Rules for Geomorphic Types" (GB / T44060-2024)), the area to be controlled is divided into multiple geomorphic type units (such as plains, plateaus, hills, slightly undulating medium mountains, and moderately undulating high mountains) based on the collected topographic factors. At the same time, the land use types of the area to be controlled are divided using multiple GF-1 high-resolution remote sensing images (resolution 2m) and photovoltaic land use vector data (Science Data Bank). Each geomorphic unit may include multiple land use types. The above process establishes risk prediction and control units consistent with the natural geographical pattern through multi-source data fusion, thereby improving the effectiveness of risk prediction and control, which is the key to overcoming the defects (1) in the existing technology.

[0026] Furthermore, this embodiment also innovates in the interpretation and classification of land use data. That is, "photovoltaic land" is classified as an independent primary type, alongside cultivated land, forest land, grassland, water area, construction land, and unused land, to ensure that the ecological effects of photovoltaic land can be considered independently and accurately.

[0027] Figure 3 The data shows the delineation results for a specific area. Using a 30m resolution DEM (geospatial data cloud), and based on the "Classification and Coding Rules for Geomorphological Types" (GB / T 44060-2024), the entire area is divided into 23 geomorphic units (D01-D23). Specifically, topographic factors such as slope, elevation, and surface relief are extracted from the DEM data. Then, according to the "Classification and Coding Rules for Geomorphological Types" (GB / T 44060-2024), the area to be managed is divided into 23 geomorphic units (with consistent geomorphological morphology) based on elevation gradient, slope, and topographic relief. Each geomorphic unit may include at least one land use type, such as photovoltaic land, cultivated land, forest land, grassland, water area, construction land, or unused land.

[0028] S120. Based on the land use type and land use structure information entropy of each landform unit, predict the ecological risk index of each landform unit.

[0029] This step proposes a novel ecological risk prediction method tailored to the characteristics of photovoltaic land use, reflecting the impact of geomorphic sensitivity and photovoltaic disturbance on ecological risk. Overall, the ecological risk prediction method ultimately constructed in this embodiment is a multi-factor coupled composite prediction method, aiming to quantify the comprehensive ecological risk level of a specific geomorphic unit. Its calculation formula is as follows: (1) (2) (3) In the formula, For the first The land use ecological risk index for each geomorphic unit is a dimensionless number; the larger the value, the higher the level of ecological risk. Code the land use type. This represents the total number of land use types. In this embodiment, n=7, which includes: cultivated land, forest land, grassland, water area, construction land, photovoltaic land, and unused land. For the first Within the first geomorphic unit Area of ​​land use type. For the first The total area of ​​each geomorphic unit.

[0030] for The geomorphic sensitivity amplification factor of each geomorphic unit is a dimensionless number, ranging from [0,1]. It is the first The surface relief of a geomorphic unit It is the maximum value of RDLS among all units in the area to be managed. This coefficient quantifies the amplification effect of topographic relief on ecological disturbance; the closer the value is to 1, the stronger the amplification effect.

[0031] For the first The vulnerability weights for each land use type are constants assigned based on ecosystem sensitivity; higher values ​​indicate greater vulnerability. This embodiment, referencing existing literature and the regional characteristics of the area to be managed, classifies land use into seven vulnerability levels: unused land = 7, photovoltaic land = 6, construction land = 5, water area = 4, grassland = 3, forest land = 2, and cultivated land = 1. After standardization, vulnerability indices were obtained for each land use landscape type. The weight.

[0032] For the first Within the first geomorphic unit The nonlinear entropy adjustment factor for land use type is a dimensionless index that integrates the complexity of landscape patterns and the orderliness of the system. For the first Within the first geomorphic unit Landscape fragmentation index for different land use types ,in, For the first Within the first geomorphic unit Number of patches of different land use types. For the first Within the first geomorphic unit Landscape separation index of land use types . For the first Within the first geomorphic unit Landscape fractal dimension index of land use type, ,in For the first Within the first geomorphic unit Perimeter of patches of different land use types. For the first Land use structure information entropy of a geomorphic unit The entropy value characterizes the degree of disorder in a landscape system; the higher the entropy value, the more chaotic the system and the more unstable its structure.

[0033] In contrast, the traditional landscape ecological risk model (ERI model) uses a linear weighted formula: (4) The problem is that: (1) it assumes that the landscape index is linearly positively correlated with risk, but the disturbance effect of photovoltaic land use is uncertain; (2) it ignores the buffering effect of system entropy, high-entropy landscapes (such as mixed patches) can resist disturbances, while low-entropy landscapes (such as single photovoltaic arrays) will amplify the risk, and the linear model cannot quantify this nonlinear relationship.

[0034] In this embodiment, the numerator term of the nonlinear entropy control term (formula (3)) is... Characterizing the intensity of landscape disturbance, where (Fragmentation) Both (resolution) and (dispersion) reflect the spatial dispersion of patches, and their physical meanings are similar. They are processed through geometric averaging (i.e.,...). This can eliminate collinearity, weaken the influence of high-value anomalies, and avoid exponentially amplifying errors (such as high...). With Gao When both occur simultaneously, it more reasonably represents the intensity of landscape disturbance. (Fractal dimension) independently characterizes the complexity of patch boundary morphology. The higher the value, the more irregular the patch boundaries (e.g., jagged), and the more easily the edge effects (e.g., soil erosion) are amplified. The key difference among the three is that... It describes the internal structural properties of the plaque, and or The spatial distribution attributes of the three are fundamentally different. This embodiment uses coupled calculation to simulate the natural laws of their impact on ecological risk, providing more reasonable data support for ecological risk prediction and control.

[0035] The methods in formulas (1)-(3) can reflect both macroscopic amplification effects and microscopic nonlinear regulation in ecological risk prediction. Specifically, through... Multiplying the macroscopic sensitivity of landforms directly by the risk value reflects the fundamental geographical principle that "the same intensity of human activity carries different risks under different topographical backgrounds." The geometric mean of the three core indicators of landscape pattern (fragmentation, separation, and fractal dimension) is then compared with the information entropy that represents the disorder of the system. The division reflects the thermodynamic principle that "high-entropy systems have a buffering capacity against disturbances," but it also captures the amplifying effect of the instability of the landscape pattern itself on risk. (Molecular part) The larger the denominator, the more fragmented and complex the landscape, and the greater the risk; The larger the value, the more disordered the system, and the stronger its buffering capacity, thus moderating risk. This nonlinear structure allows the model to more precisely reflect the complex impact of internal landscape processes on risk.

[0036] The above formulas and methods together form the basis for the quantitative prediction of ecological risks in this embodiment, ensuring the scientific nature, accuracy, and interpretability of the prediction results. In practical applications, firstly, formula (2) is used to determine the nonlinear entropy adjustment factor of each land use type in each landform unit based on the landscape fragmentation, separation, and fractal dimension index; then, formula (3) is used to determine the geomorphic sensitivity amplification coefficient of each landform unit based on the surface relief; finally, formula (1) is used to obtain the ecological risk index of each landform unit based on the nonlinear entropy adjustment factor and the geomorphic sensitivity amplification coefficient.

[0037] S130. Conduct bivariate spatial autocorrelation analysis on the comprehensive land use intensity index and ecological risk index of various landform units.

[0038] In addition to quantifying the ecological risk index, this embodiment also quantifies the land use intensity of various topographic units. The comprehensive land use intensity index is used to quantitatively characterize the degree of comprehensive development and transformation of land resources by human activities within a specific prediction unit.

[0039] In one specific implementation, the formula for calculating the comprehensive land use intensity index is as follows: (5) In the formula, For the first The comprehensive land use intensity index for a geomorphic unit. This value is dimensionless, and its range depends on the intensity coefficient. The larger the value, the higher the intensity of human activity. For the first The intensity coefficient for each land use type can be a constant set according to the intensity of human modification and utilization of the land. Optionally, considering that most photovoltaic panels in the area to be controlled are deployed on unused or difficult-to-use land, the comprehensive land use intensity index for photovoltaic land should be higher than that for unused land. Preferably, to conform to the natural laws of the impact of human activities on various land use types, the intensity coefficient for unused land can be taken as... =1, photovoltaic land =2, Woodland / Water / Grassland =3, arable land =4, Construction Land =5. Formula (5) is essentially a weighted summation. It uses the area proportion of various land types within each topographic unit. As weighted, the land use intensity coefficients for various types of land ( A weighted average is then calculated. The final result is... The value comprehensively reflects the overall intensity of human activity across all land use types within the geomorphic unit. Specifically, photovoltaic land is assigned a separate value (photovoltaic land). =2), which places its intensity between that of unused land and traditional farmland, and is the key to more accurately reflecting its ecological impact in this embodiment.

[0040] Still with Figure 3 Taking a specific region as an example, the LUCI and LUER values ​​are calculated within each topographic unit, and the results are as follows: Figure 4 As shown, the high LUCI values ​​are concentrated in cell D01, while the high LUER values ​​appear in cell D12 and the oasis-desert ecotone.

[0041] After obtaining the Land Use Intensity Index (LUCI) and Land Use Ecological Risk (LUER) for each landform unit, this embodiment performs spatial correlation analysis and risk hotspot diagnosis on the two types of data.

[0042] In one specific implementation, bivariate local Moran's I analysis can be used to calculate the global and local Moran indices of LUCI and LUER, identifying spatial clusters such as "high-high" (HH, high LUCI-high LUER, i.e., high LUCI and high LUER) and "low-low" (LL, i.e., low LUCI and low LUER). This analysis intuitively reveals the spatial dependence between LUCI and LUER, accurately pinpointing ecological risk hotspots requiring priority management (i.e., HH clusters). This is a prerequisite for achieving precise spatial optimization.

[0043] Still with Figure 3Taking a specific region as an example, the global Moran's I is 0.0412 (p<0.05), and the HH hotspots are mainly distributed in units such as D01 (plain), D05 (plateau), and D14 (slightly undulating hills), accounting for 23.5% of the total area. Figure 5 As shown.

[0044] S140. Using robust geographic detectors and structural equation models, identify the direct drivers and buffers of the ecological risk index.

[0045] Here, the driving factor refers to the dominant influencing factor that has a positive driving effect on the ecological risk index; the larger the factor, the larger the ecological risk index. The buffer factor refers to the dominant influencing factor that has a negative buffering effect on the ecological risk index; the larger the factor, the smaller the ecological risk index. The effects of these two factors on the ecological risk index include direct and indirect effects. In this embodiment, a robust geographic detector combined with a structural equation model is used to identify the driving factor and buffer factor that directly affect the ecological risk index, which are referred to as the direct driving factor and the direct buffer factor, respectively.

[0046] In one specific embodiment, the process may include the following steps: Step 1: Using a robust geographic detector (RGD), identify the driving and buffering factors of the land use ecological risk index. Optionally, this embodiment uses a robust geographic detector (RGD) to detect interactions between data. LUER is used as the dependent variable, and slope, surface relief, altitude, LUCI, photovoltaic density, and LUSIE are selected as independent variables. The RGD is used to calculate the q-values ​​(explanatory power) of each factor individually and in pairs. Factors with larger q-values ​​and a positive driving effect are considered driving factors, while factors with larger q-values ​​and a negative buffering effect are considered buffering factors. The RGD automatically performs a dynamic discretization step, first performing equal-rank transformation on continuous variables (such as slope), and then using a dynamic programming algorithm to solve the least-squares deviation cost function. (6) In the formula, For the rank of the variable, The rank mean of layer z is used to determine the optimal stratification breakpoint (e.g., slope stratification: 0-8°, 8-15°, 15-25°, >25°).

[0047] The role of RGD is to identify driving factors and buffer factors, as well as whether there are interaction types such as nonlinear enhancement or two-factor enhancement, in order to solve the problems in the defects of the prior art (3).

[0048] by Figure 3Taking a specific region as an example, this study uses the RGD model to analyze the explanatory power (q value) of the influencing factors—Land Use Intensity Index (LUCI), Landscape Structure Information Entropy (LUSIE), Photovoltaic Density (PV_density), Topographic Relief (RDLS), Slope, and Elevation—on Land Use Ecological Risk (LUER), and analyzes the interaction relationships between these factors. Figure 6 As shown.

[0049] Step 2: Using structural equation modeling (SEM), determine the influence paths of the driving and buffering factors on the land use ecological risk index. This step, based on RGD results and geographical theory, extracts latent variables with large q-values ​​from the RGD analysis results, such as "geomorphic features," "human activities," and "landscape structure," and inputs them into the SEM model along with the dependent variable LUER. Prior paths are defined, such as "slope → LUCI → LUER," "slope → photovoltaic density → LUER," and "surface relief → slope → LUSIE → LUER." Standardized coefficients (β values) for each path are obtained through model fitting, thus distinguishing between direct and indirect effects. This step quantifies the causal paths and mediating effects of risk formation, revealing deeper mechanisms such as "how slope indirectly reduces risk by inhibiting human activities," which is also a key invention of this embodiment.

[0050] Still with Figure 3 Taking a specific region as an example, the input of the SEM model is the data of each factor in Table 1, and the output is shown in Table 1. The output can be converted into a graph as shown below. Figure 7 As shown in the figure, the β value for the path "slope → LUCI" is -0.82, and the β value for "LUCI → LUER" is 0.78. Therefore, the indirect effect of slope on LUER by suppressing LUCI is approximately (-0.82) × 0.78 ≈ -0.64. Simultaneously, slope also has a significant negative impact on photovoltaic density (β = -0.68). This indicates that slope is a key mediating variable with a significant overall buffering effect.

[0051] Table 1

[0052] Step 3: Based on each impact pathway, identify the direct driving factors and direct buffering factors of the land use ecological risk index. For example... Figure 7 As shown, the direct driving factors of LUER include LUCI, photovoltaic density, and RDLS, while the direct buffering factors of LUER include LUSIE.

[0053] S150. For landform units with high-high autocorrelation analysis results, the direct driving factor is restricted and controlled; for landform units with low-low autocorrelation analysis results, the direct buffer factor is increased and controlled.

[0054] This step integrates the results of the above spatial autocorrelation analysis, robust geographic detectors, and structural equation models to formulate spatial optimization strategies.

[0055] Combination Figure 2 In one specific implementation, "red zones" are first delineated based on slope. That is, landform units with a slope greater than 25° (such as the undulating Zhongshan D20) are designated as prohibited or strictly restricted areas for photovoltaic development.

[0056] Then, for areas outside the geomorphic red zones, key control areas and suitable development areas are further identified based on the autocorrelation analysis results. Specifically, geomorphic units with a high-high profile are identified as key control areas, and the direct driving factors are restricted and controlled within these areas. For example, among the three direct driving factors LUCI, photovoltaic density, and RDLS, RDLS cannot be controlled; therefore, only LUCI and photovoltaic density are restricted and controlled, such as recommending or implementing measures like "returning farmland to grassland + photovoltaic density restriction" to reduce their ecological risks. LL clusters or plains / plateaus with gentle slopes (e.g., <15°) are identified as priority development areas and suitable development areas, where the direct buffering factors are enhanced and controlled to encourage photovoltaic development while maintaining ecological risk levels. For example, the direct buffering factor LUSIE is enhanced, and measures to optimize landscape structure are recommended or implemented.

[0057] Finally, differentiated landscape thresholds can be set for key control areas and suitable development areas to ensure the landscape red line is maintained at all times. Optionally, for geomorphic units with high-high analysis results, an upper limit threshold for the landscape fragmentation index can be set for existing photovoltaic land. For geomorphic units with low-low analysis results or slopes less than the set threshold, a lower limit threshold for land use landscape structure information entropy can be set, such as ≥2.3, to ensure landscape connectivity. The slope thresholds of 25° and 15° can be adjusted according to the topographic features and ecological sensitivity of different areas. For example, in ecologically extremely fragile areas, the prohibited development threshold can be lowered to 20°. The LUSIE threshold can also be dynamically calibrated based on historical data or scenario simulation.

[0058] This step transforms the data diagnostic results into actionable regional control measures, achieving a closed loop from data analysis to actual control.

[0059] In summary, this embodiment provides a method for zonal management (i.e., adjustment control) of photovoltaic land use based on geomorphic sensitivity and entropy regulation. It aims to address the shortcomings of existing ecological risk prediction and management methods, such as insufficient consideration of rigid constraints on geomorphic units, failure to independently identify the ecological effects of photovoltaic land use, and lack of quantification of the nonlinear interaction between natural and anthropogenic factors. This method first divides geomorphic units based on a digital geomorphic classification system; then, treating photovoltaic land use as an independent type, it proposes a comprehensive land use intensity index and an ecological risk prediction method integrating geomorphic amplification effects and nonlinear entropy regulation; it uses bivariate spatial autocorrelation to identify risk hotspots; and employs robust geographic detectors and structural equation modeling to quantify the interaction and impact paths of natural and anthropogenic factors on ecological risks. Finally, based on the above analysis results, it identifies "geomorphic red zones" and suitable areas for photovoltaic land development and sets differentiated landscape index thresholds to achieve spatial optimization. This embodiment achieves more refined and scientific ecological risk prediction in arid regions, providing more practical decision support for the ecological environment management of the photovoltaic industry in arid areas.

[0060] More specifically, this embodiment can achieve the following beneficial effects: (1) Improve the accuracy and practicality of risk prediction: By using geomorphic units instead of administrative boundaries as the basic unit for risk prediction, the problem of "spatial mismatch" is fundamentally overcome, ensuring that the ecological risk prediction results truly reflect the natural background pattern. Through independent classification and independent empowerment of photovoltaic land use (V... i =6) and the entropy regulation model solve the problem of underestimation of photovoltaic disturbance by traditional models, which improves the accuracy of risk identification compared with traditional methods and provides reliable data support for the identification of various control areas.

[0061] (2) In-depth analysis of the formation mechanism of ecological risks: The creative coupling of RGD and SEM not only identified the dominant factors (such as the interaction between slope and LUCI q=0.95), but also quantified the path and intensity of risk transmission (such as the total buffer effect β=-1.14 generated by slope through dual paths), enabling ecological risk management measures to shift from "superficial governance" to "root cause management", and to formulate more fundamental and effective control measures for key path nodes (such as controlling the development intensity of steep slope areas).

[0062] (3) Enhance the pertinence and operability of ecological risk spatial management measures: Based on the precise hotspot diagnosis and mechanism analysis, the delineated "geomorphic red zones", suitable areas and differentiated thresholds make spatial optimization strategies more targeted. For example, the implementation of "photovoltaic density restriction + forest and grassland restoration" in unit D20 is based on the clear HH aggregation and steep slope mediation effect, which provides better suggestions for ecological risk governance and photovoltaic project site selection.

[0063] It should be noted that all data involved in this application are information and data that have been fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for all parties to choose to authorize or refuse.

[0064] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 8 As shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more. Figure 8 Taking a processor 60 as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0065] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the photovoltaic land use zoning management method based on topographic sensitivity and entropy regulation in this embodiment of the invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, thereby realizing the aforementioned photovoltaic land use zoning management method based on topographic sensitivity and entropy regulation.

[0066] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, which can be connected to the device 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.

[0067] Input device 62 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 63 may include display devices such as a display screen.

[0068] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the photovoltaic land use zoning management method based on topographic sensitivity and entropy regulation of any embodiment.

[0069] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0071] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic land partition management method based on topographic sensitivity and entropy regulation, characterized in that, include: Acquire multiple geomorphic units of the area to be managed; According to the landscape fragmentation, separation and fractal dimension of each land use type in each geomorphic unit, and the information entropy of the land use structure, a nonlinear entropy adjustment factor of each geomorphic unit is determined, wherein the nonlinear entropy adjustment factor is used to comprehensively represent the complexity and order of the landscape pattern; specifically, according to the following formula, the nonlinear entropy adjustment factor of the i-th land use type in the j-th geomorphic unit is determined : : : ， in, , and Indicates the first Within the first geomorphic unit Landscape fragmentation index, landscape separation index, and landscape fractal dimension index for different land use types; For the first Land use structure information entropy of a geomorphic unit , Indicates the first Within the first geomorphic unit Area of ​​various land use types For the first The total area of ​​each geomorphic unit, where n represents the number of land use types. Used to characterize the degree of disorder in a landscape system; land use types include: cultivated land, forest land, grassland, water area, construction land, photovoltaic land, and unused land; Based on the surface relief of each landform unit, determine the landform sensitivity amplification factor for each landform unit; specifically, determine the [missing formula] according to the following formula. Geomorphic sensitivity amplification factor for each geomorphic unit : ， in, It is the first The surface relief of a geomorphic unit It is the maximum value of RDLS among all geomorphic units within the area to be controlled; Based on the nonlinear entropy adjustment factor and geomorphic sensitivity amplification coefficient of each geomorphic unit, the ecological risk index of each geomorphic unit is predicted; specifically, the following formula is used to predict the... Ecological risk index of each geomorphic unit : ， In the formula, Indicates the first Within the first geomorphic unit Vulnerability weights for different land use types; geomorphic sensitivity amplification coefficient is used to characterize the risk differences of the same intensity of alteration activities under different geomorphic backgrounds; This demonstrates that high-entropy systems have the ability to buffer against disturbances, and also captures the amplifying effect of the instability of the landscape pattern itself on risk; for vulnerability levels: unused land = 7, photovoltaic land = 6, construction land = 5, water area = 4, grassland = 3, forest land = 2, cultivated land = 1. Bivariate spatial autocorrelation analysis was conducted on the comprehensive land use intensity index and ecological risk index of various landform units; the calculation formula for the comprehensive land use intensity index is as follows: ， In the formula, For the first The comprehensive land use intensity index of each geomorphic unit For the first Intensity coefficient of land use type, photovoltaic land Between unused land and traditional agricultural land; Using a robust geographic detector, the driving factors and buffer factors of the ecological risk index are identified; using a structural equation model, the influence paths of the driving factors and buffer factors on the ecological risk index are determined; and based on each influence path, the direct driving factors and direct buffer factors of the ecological risk index are identified. For geomorphic units with a high-high analysis result, the direct driving factors are restricted and controlled; For geomorphic units with low-low analysis results, the direct buffer factor is increased and controlled.

2. The method according to claim 1, characterized in that, For each topographic unit with a high-high analysis result, the direct driving factors are restricted and controlled, including: For each topographic unit whose analysis result is high-high and whose slope is less than the set threshold, the direct driving factor is restricted and controlled.

3. The method according to claim 1, characterized in that, Also includes: For geomorphic units with high-high analysis results, an upper limit threshold for the landscape fragmentation index is set for existing photovoltaic land. For geomorphic units with low-low analysis results, a lower limit threshold for land use landscape structure information entropy is set.

4. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the photovoltaic land use zoning management method based on geomorphological sensitivity and entropy regulation as described in any one of claims 1-3.

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