A method and device for determining a photovoltaic ecological restoration scheme, and a medium

Abstract

This application provides a method, apparatus, equipment, and medium for determining a photovoltaic ecological restoration scheme, relating to the field of photovoltaic ecological restoration technology. The method includes: acquiring the spatial geometric parameters and environmental parameters of a photovoltaic array; simulating soil and water transport in the micro-water and soil environment surrounding the photovoltaic array based on the spatial geometric parameters and environmental parameters, obtaining a water stress waveform in the area surrounding the photovoltaic array; dividing the area surrounding the photovoltaic array into multiple restoration zones based on the water stress waveform; and using a stress spectrum coupling algorithm to match the water stress waveform of each restoration zone with the physiological response spectra of different plants in a configured ecological restoration plant library to obtain a photovoltaic ecological restoration scheme. This application accurately quantifies the heterogeneity of the micro-water and soil environment of photovoltaic sites, achieving differentiated zoning and precise vegetation adaptation, effectively improving the adaptability and long-term effectiveness of photovoltaic site ecological restoration.

Description

Technical Field

[0001] This application relates to the field of photovoltaic ecological restoration technology, and more specifically, to a method, apparatus, equipment, and medium for determining a photovoltaic ecological restoration scheme. Background Technology

[0002] With the large-scale advancement of the new energy industry, the construction scale of ground-mounted photovoltaic power stations continues to expand. Ecological restoration and stable vegetation cultivation of the land under the panels and between the arrays have become essential aspects of comprehensive photovoltaic site management. Balancing power generation benefits with regional ecological restoration has become the mainstream trend in the industry.

[0003] Existing photovoltaic ecological restoration solutions rely solely on regional macro-climate and basic soil properties, combined with conventional plant stress resistance indicators for general seed selection, and use experience to divide simple planting areas to complete the homogeneous or extensive zoning of photovoltaic sites. This results in a mismatch between plants and the actual microenvironment of the planting area, leading to poor ecological restoration results. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment and medium for determining a photovoltaic ecological restoration scheme, which solves the above-mentioned problems existing in the prior art. It can accurately quantify the heterogeneity of micro-water and soil environment of photovoltaic sites, realize differentiated zoning and precise vegetation matching, and effectively improve the adaptability and long-term effectiveness of photovoltaic site ecological restoration.

[0005] Firstly, a method for determining a photovoltaic ecological restoration scheme is provided, which may include: Obtain the spatial geometric parameters and environmental parameters of the photovoltaic array; Based on the spatial geometric parameters and the environmental parameters, the micro-water and soil environment around the photovoltaic array is simulated to obtain the water stress waveform of the area around the photovoltaic array. Based on the moisture stress waveform, the area surrounding the photovoltaic array is divided into multiple repair zones; A stress spectrum coupling algorithm was used to match the water stress waveforms of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library to obtain a photovoltaic ecological restoration scheme.

[0006] In an optional implementation, before simulating soil and water transport in the micro-water and soil environment surrounding the photovoltaic array based on the spatial geometric parameters and the environmental parameters, the method further includes: For any row of photovoltaic modules in a photovoltaic array, construct a coupled model of surface runoff motion of the photovoltaic modules in that row based on the spatial geometric parameters of the photovoltaic modules. Environmental parameters are input into the coupled model of surface runoff motion of the photovoltaic modules in this row to simulate the runoff process within a precipitation infiltration response cycle, and the runoff simulation results of the photovoltaic modules in this row within a precipitation infiltration response cycle are obtained.

[0007] In an optional implementation, based on the spatial geometric parameters and the environmental parameters, a water and soil transport simulation is performed on the micro-water and soil environment surrounding the photovoltaic array, including: Based on the confluence simulation results and spatial geometric parameters of each row of photovoltaic modules in one precipitation infiltration response cycle, the plate edge infiltration boundary conditions of the pre-constructed soil hydrodynamic model around the photovoltaic array are determined. Using the soil hydrodynamic model based on the environmental parameters, the drip transport process of a precipitation infiltration response cycle is simulated to determine the soil and water characteristics under the plate during a precipitation infiltration response cycle. Based on spatial geometric parameters and environmental parameters, the shielding effect is calculated and the inter-plate soil and water characteristics are determined for a precipitation infiltration response cycle. By coupling the soil and water characteristics under the plate and the soil and water characteristics between the plates, the water stress waveform of the area surrounding the photovoltaic array in one precipitation infiltration response cycle is obtained.

[0008] In an optional implementation, the soil and water characteristics under the slab include the cumulative duration of soil saturation at different time points within a precipitation infiltration response cycle for each spatial point under the slab; the soil and water characteristics between the slabs include the cumulative duration of drought at different time points within a precipitation infiltration response cycle for each transverse strip between the slabs. By coupling the soil and water characteristics under the panels and the soil and water characteristics between the panels, the water stress waveform of the area surrounding the photovoltaic array is obtained, including: The regional grid of the area surrounding the photovoltaic array is obtained by splicing together the duration of soil saturation at each spatial point under the plate and the duration of rain shadow and drought in each transverse strip between the plates according to their spatial location within a precipitation infiltration response cycle. The soil and water characteristics corresponding to each grid unit within a precipitation infiltration response cycle are used as the water stress waveform of each grid unit within a precipitation infiltration response cycle. Based on the water stress waveform of each grid cell in one precipitation infiltration response cycle, the water stress waveform of the area surrounding the array in one precipitation infiltration response cycle is obtained.

[0009] In an optional implementation, the area surrounding the photovoltaic array is divided into multiple repair zones based on the moisture stress waveform, including: Calculate the similarity of the water stress waveforms for each grid cell; A waveform-based clustering algorithm was used to cluster different grid cells according to the similarity of the water stress waveforms of each grid cell, resulting in multiple remediation areas.

[0010] In an optional implementation, before employing a stress spectrum coupling algorithm to match the water stress waveforms of each remediation zone with the physiological response spectra of different plants in a configured ecological remediation plant library, the method further includes: Acquire the spatial boundaries and target water stress waveforms of each remediation area; The area of ​​each restoration zone is determined based on its spatial boundaries. Based on the target water stress waveform of each remediation area, the area type of each remediation area is determined.

[0011] In an optional implementation, a stress spectrum coupling algorithm is used to match the water stress waveforms of each remediation area with the physiological response spectra of different plants in a configured ecological remediation plant library, including: For any restoration area, calculate the matching degree between the target water stress waveform of the restoration area and the waveforms of different plants in the configured ecological restoration plant library; Based on the matching degree between the water stress waveform of the restoration area and the waveforms of different plants, multiple plant varieties with high waveform matching degree with the water stress waveform of the restoration area and meeting the configured constraints are selected as the target plant varieties of the restoration area. For any target plant variety, the target planting density of the target plant variety is determined based on the waveform matching degree between the target plant variety and the target water stress waveform of the restoration area, as well as the area type of the restoration area and the target water stress waveform. The product of the target planting density of the target plant species and the area of ​​the restoration area is taken as the total number of the target plant species planted in the restoration area. Based on the target plant species, target planting density, and target planting quantity for each restoration area, a photovoltaic ecological restoration plan is generated.

[0012] Secondly, a device for determining a photovoltaic ecological restoration scheme is provided, the device may include: The acquisition unit is used to acquire the spatial geometric parameters and environmental parameters of the photovoltaic array. The simulation unit is used to simulate soil and water transport in the micro-water and soil environment around the photovoltaic array based on the spatial geometric parameters and the environmental parameters, and to obtain the water stress waveform in the area around the photovoltaic array. The division unit is used to divide the area surrounding the photovoltaic array into multiple repair zones based on the moisture stress waveform; The matching unit is used to match the water stress waveform of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library using a stress spectrum coupling algorithm to obtain a photovoltaic ecological restoration scheme.

[0013] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0014] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0015] This application utilizes the spatial geometric parameters and environmental parameters of photovoltaic arrays to conduct refined simulations of soil and water transport, quantifying and generating differentiated water stress waveforms to replace traditional macroscopic experience-based judgment methods. This accurately characterizes the dynamic differences in the micro-soil and water environment of different areas of the photovoltaic site, solving the problems of extensive zoning and one-sided environmental characterization in existing technologies. Through a stress spectrum coupling algorithm, it achieves precise matching between regional water stress characteristics and plant physiological response characteristics, breaking the limitations of homogeneous vegetation configuration, realizing customized ecological restoration layout in different zones, effectively improving the adaptability of plants to habitats, and ensuring the long-term and stable implementation of ecological restoration of photovoltaic sites. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This application provides an architecture diagram of a system for determining a photovoltaic ecological restoration scheme. Figure 2 A flowchart illustrating a method for determining a photovoltaic ecological restoration scheme provided in an embodiment of this application; Figure 3 A schematic diagram of a device for determining a photovoltaic ecological restoration scheme provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] The method for determining the photovoltaic ecological restoration scheme provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include a server and multiple data acquisition components. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal may be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods; this application does not limit the connection.

[0020] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0021] Figure 2 This is a flowchart illustrating a business data processing method provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Obtain the spatial geometric parameters and environmental parameters of the photovoltaic array.

[0022] The photovoltaic array comprises at least one row of photovoltaic modules. The spatial geometric parameters of the photovoltaic array may include: the start and end coordinates of each row of photovoltaic modules, the module tilt angle, the number of modules, the continuous length of the modules, the minimum height above ground of the modules, the spacing between adjacent rows of modules, the arrangement direction, and trench parameters, as well as the width of a single photovoltaic module. The module tilt angle is the angle between each row of photovoltaic modules and the horizontal plane. The continuous length of the modules is the total length of the photovoltaic modules continuously arranged on the same support along the module arrangement direction. The minimum height above ground of the modules is the vertical distance between the lowest point of the module (usually the lower edge) and the ground surface directly below. The trench parameters are the centerline coordinates, width, and depth of the reserved drainage ditch or edge clearing channel between two adjacent rows of photovoltaic modules. The number of modules is the number of photovoltaic modules in a row. The spacing between adjacent rows of modules is the projected distance on the horizontal plane between the lower edge of the previous row of modules and the upper edge of the next row of modules. The arrangement direction is the main axis direction (azimuth) of the module arrangement. Environmental parameters may include: soil porosity, regional potential evapotranspiration rate, surface slope direction and gradient, soil texture classification, field water holding capacity, initial soil moisture content, and soil saturated hydraulic conductivity. Rate, wilting coefficient, rainfall return period time-series rainfall sequence, and wind field parameters; the rainfall return period time-series rainfall sequence is a rainfall intensity sequence with ten-minute or one-hour intervals, with a total duration of twenty-four hours, corresponding to the ten-year standard return period of the photovoltaic array area; the initial soil moisture content is the volumetric water content of the root layer (ten to thirty centimeters underground) before the start of the rainy season or before the occurrence of a rainfall event; the soil saturated hydraulic conductivity is the flux of the soil under a unit water potential gradient under saturated conditions; the surface slope direction and gradient are the surface water in the area below and between the photovoltaic panels. The direction (azimuth) and elevation difference per meter of flow at different locations; the percentage content of soil texture as sand, silt, and clay; field capacity is the percentage of water volume remaining in the soil after gravity water is drained; the wilting coefficient is the soil volumetric water content when plants are permanently wilted; the wilting coefficient is determined in the laboratory using the pot method (soil samples are taken after representative plants have wilted to determine the water content), or obtained by referring to tables based on empirical values ​​of soil texture; the wind field parameters for the rainfall return period are the average wind speed and direction at a height of two meters above the ground during the standard return period of rainfall.

[0023] In practice, the spatial geometric parameters of the photovoltaic array can be obtained from the design drawings or three-dimensional building information model of the photovoltaic array; data can also be collected from the photovoltaic array through data acquisition components; and environmental parameters can be obtained by combining meteorological data of the area where the photovoltaic array is located with laboratory measurements.

[0024] Step S220: Based on spatial geometric parameters and environmental parameters, simulate soil and water transport in the micro-water and soil environment around the photovoltaic array to obtain the water stress waveform in the area around the photovoltaic array.

[0025] In specific implementation, for any row of photovoltaic modules in the photovoltaic array, a coupled model of surface runoff motion is constructed based on the spatial geometric parameters of that row of photovoltaic modules. Specifically, for any row of photovoltaic modules, a surface geometric model is constructed based on the start and end coordinates, tilt angle, number of modules, continuous length of modules, and width of a single photovoltaic module. A surface water flow kinematic model is constructed based on the tilt angle of the modules. The surface slope value is calculated based on the tilt angle. Based on the configured physical laws of surface water flow motion and the kinematic equations of shallow water flow, a surface water flow kinematic model is constructed using the surface slope value and the Manning roughness coefficient of the photovoltaic module surface as parameters. This model includes calculation rules for the flow velocity and direction of water on the surface. Specifically, the calculation rules are as follows: along the length of the surface... The functional relationship between the direction, unit width flow rate, and water layer thickness is determined by the Manning formula, where the flow coefficient is calculated by dividing the square root of the slope value of the plate by the Manning roughness coefficient. The plate surface water flow kinematic model is used to characterize the motion logic of the water flow on the plate surface. The plate surface water flow kinematic model is fused with the plate surface geometric model to obtain the plate surface runoff motion coupling model of the row of photovoltaic modules. The spatial constraint relationship (i.e., the coordinates and boundary attributes corresponding to the boundary division standard) in the plate surface two-dimensional geometric model is associated and matched with the water flow velocity and flow direction calculation rules in the plate surface water flow kinematic model. That is, a zero flow inflow condition is applied at the upper boundary position of the geometric model, a free outflow condition is applied at the lower boundary position, and the parameterized flow velocity and flow direction calculation rules are applied in the internal region of the plate surface to complete the coupling integration of the two models. Environmental parameters are input into the coupled model of surface runoff motion of the photovoltaic modules to simulate the runoff process of one precipitation infiltration response cycle, obtaining the runoff simulation results of the photovoltaic modules in one precipitation infiltration response cycle. One precipitation infiltration response cycle is the process from the start of rainfall to the completion of subsequent water recession. The runoff simulation results may include: the outflow sequence at the lower edge of the module surface, the spatial coordinate trajectory of the drip line at the lower edge, the instantaneous flow sequence of the drip line, and the cumulative outflow volume. Each sequence includes the corresponding outflow volume at each time point of the precipitation infiltration response cycle. Instantaneous flow rate; specifically, the rainfall recurrence interval time-series rainfall sequence is used as the driving input to the surface runoff motion coupling model of each row of photovoltaic modules. The initial condition is that the water layer thickness at all points on the panel surface is zero at the start of rainfall. Boundary conditions are defined in each panel surface runoff motion coupling model: zero flow at the upper edge and free outflow at the lower edge. This drives each panel surface runoff motion coupling model to perform a confluence simulation. The simulation process involves calling the calculation rules in the panel surface water flow kinematics model and, within the spatial range defined by the geometric model, solving the water flow between the rainfall input and the panel surface runoff output time-by-time. The system calculates the dynamic changes in water layer thickness at various points on the photovoltaic panel surface and the time sequence of outflow at the lower edge of the panel. Based on the spatial geometric parameters of the row of photovoltaic modules, it calculates the spatial coordinate trajectory of the drip line along the lower edge of the photovoltaic modules. Specifically, based on the coordinates of the starting endpoint, tilt angle, continuous length, and minimum ground clearance of the photovoltaic modules, the spatial coordinate trajectory of the drip line along the lower edge of the photovoltaic modules is calculated. Along the continuous length of the modules, starting from the starting point coordinates in the start and end coordinates, the three-dimensional spatial coordinates of each point on the lower edge of the photovoltaic modules are calculated sequentially at fixed intervals. Based on the coordinates of the starting endpoint (taking the lowest point at one end of the lower edge of the module as the starting point), tilt angle, and continuous length of the modules, the three-dimensional coordinates of each point are calculated sequentially at fixed intervals along the lower edge direction. The horizontal projection coordinates of each point are obtained by superimposing the horizontal displacement of the starting point's horizontal coordinates, where the horizontal displacement is equal to the step size multiplied by the cosine of the tilt angle of the module. The vertical height of each point is obtained by adding the vertical lift to the vertical height of the starting point, where the vertical lift is equal to the step size multiplied by the sine of the tilt angle of the module. Connect all point coordinates in sequence to generate the three-dimensional spatial coordinate trajectory of the drip line; extract the flow rate value corresponding to each calculation moment from the outflow sequence at the lower edge of the photovoltaic panel of the row of modules to generate the instantaneous flow rate sequence and cumulative outflow volume of the drip line; specifically, integrate the instantaneous flow rate sequence of the drip line over time (i.e., multiply and sum the flow rate value at each moment with the time step) to obtain the total water volume of the drips during the entire rainfall process from the beginning to the end of the rainfall, which is used as the cumulative outflow volume.

[0026] Based on the runoff simulation results and environmental parameters of each row of photovoltaic modules in a precipitation infiltration response cycle, the edge infiltration boundary conditions of the pre-constructed soil hydrodynamic model around the photovoltaic array are determined. Specifically, the instantaneous flow sequence of the drip line is converted into the edge infiltration boundary conditions: the instantaneous flow at the drip line location is used as the upper boundary infiltration flux applied to the surface soil directly below that location; based on the minimum ground clearance of the modules in the spatial geometry parameters, the vertical distance between the drip line and the ground surface is determined (to determine whether the dripping water infiltrates in a free-fall manner), and the infiltration flux corresponding to each time point within a precipitation infiltration response cycle is used as the edge infiltration boundary condition data. Using the aforementioned soil hydrodynamic model based on the environmental parameters, the drip transport process of a precipitation infiltration response cycle is simulated to determine the soil and water characteristics under the plate during a precipitation infiltration response cycle. Specifically, taking the plate edge infiltration boundary conditions as the infiltration driver and soil saturated hydraulic conductivity and soil porosity as hydraulic conduction parameters, a soil hydrodynamic infiltration model (constructed based on Darcy's law and Richards' equation) is used to simulate the vertical and horizontal transport process of drip water in the soil under the plate during a precipitation infiltration response cycle. This yields the length, depth, and width ranges of the preferential flow wetting body under the plate, as well as the cumulative soil saturation time sequence at each time point within the precipitation infiltration response cycle (i.e., the soil water content from the start of the precipitation infiltration response cycle to the current time). The cumulative number of hours when the soil moisture content exceeds field capacity is recorded. This sequence increases monotonically over time, with the endpoint being the total saturation duration, which serves as the soil and water characteristics under the plate. Specifically, the soil directly below and around the drip line is discretized into a two-dimensional or three-dimensional computational region. Using the initial soil moisture content as the starting condition, the redistribution of infiltration water in the soil is solved hourly, and the advancing position of the wetting front is calculated. Based on the simulation results, the three-dimensional spatial range of the preferential flow wetting body formed in the soil under the plate is determined (including the maximum extension length along the component length direction, vertical infiltration depth, and lateral diffusion width). The continuous duration of soil moisture content exceeding field capacity within the wetting body is recorded cumulatively at each time point within the cycle, resulting in the cumulative soil saturation duration sequence for that region, which serves as the soil and water characteristics under the plate. Based on spatial geometric and environmental parameters, the shading effect is calculated, and the soil and water characteristics between the slabs are determined over a precipitation infiltration response cycle. Specifically, a raindrop trajectory model is established based on the spacing between adjacent slab rows, slab tilt angle, minimum slab height above ground, slab continuous length, rainfall return period, and wind field parameters. The minimum slab height above ground is added to the sine of the slab continuous length multiplied by the sine of the slab tilt angle to obtain the upper edge height. This upper edge height is then input into the raindrop trajectory model to calculate the horizontal offset distance of the raindrop from the slab upper edge to the surface between the slabs. Based on the slab tilt angle, slab continuous length, and minimum slab height above ground, the horizontal projection positions of the slab upper and lower edges are determined. The calculated horizontal offset distance is superimposed on the horizontal projection point of the slab upper edge to obtain the maximum horizontal arrival point of the raindrop. The continuous interval from the horizontal projection point of the lower edge of the front row of slabs to this maximum horizontal arrival point is considered as the rain shadow shading area formed by the front row of slabs on the surface between the slabs. The area between the slabs is divided into multiple transverse strips perpendicular to the slab row direction, utilizing the raindrop falling... The trajectory model determines whether each horizontal strip is within the rain shadow shading area and determines the rainfall shading coefficient of each horizontal strip: if the center of any horizontal strip falls completely within the rain shadow shading area, the rainfall shading coefficient of that strip is 0; if it is completely outside the shading area, the shading coefficient is 1; if there is partial overlap, the shading coefficient is calculated according to the overlap length ratio (the value is between 0 and 1). The product of the rainfall intensity at each time point in the rainfall recurrence interval time series and the shading coefficient of each horizontal strip is used as the effective rainfall intensity series of each horizontal strip. The effective rainfall intensity sequence, initial soil moisture content, wilting coefficient, field capacity, soil saturated hydraulic conductivity, and regional potential evapotranspiration rate of each horizontal strip are input into a pre-selected dynamic soil moisture simulation model to simulate the change process of soil moisture content within a precipitation infiltration response cycle. The model outputs the cumulative drought duration sequence at each time point within the cycle (i.e., the cumulative number of hours from the beginning of the cycle to the current time when the soil moisture content is lower than the wilting coefficient). This sequence is monotonically increasing, with the endpoint being the total drought duration, which serves as the interplate soil and water characteristics. By coupling the soil and water characteristics under the photovoltaic array and the soil and water characteristics between the photovoltaic array, the water stress waveform of the area surrounding the photovoltaic array within a precipitation infiltration response cycle is obtained. Specifically, the soil and water characteristics under the photovoltaic array and the soil and water characteristics between the photovoltaic array are spliced ​​together according to their spatial location to form a continuous grid covering the entire area surrounding the photovoltaic array. For each grid cell, the corresponding cumulative soil saturation duration sequence and cumulative drought duration sequence are extracted according to whether it belongs to the under-array area or the inter-array area. This forms a spatially differentiated temporal waveform with each time within the precipitation infiltration response cycle as the horizontal axis and the cumulative stress duration (cumulative soil saturation duration and cumulative drought duration) at the corresponding time as the vertical axis. This waveform is the water stress waveform of the area.

[0027] Step S230: Based on the moisture stress waveform, divide the area around the photovoltaic array into multiple repair areas.

[0028] In practice, the similarity of the water stress waveform of each grid cell is calculated. A waveform-based clustering algorithm is employed to cluster different grid cells based on the similarity of their water stress waveforms, resulting in multiple remediation regions and their corresponding spatial boundaries and water stress waveforms. The waveform-based clustering algorithm can be a combination of dynamic time-bending distance and hierarchical clustering. Specifically, adjacent grid cells with similar water stress waveforms are merged into the same hydraulically homogeneous region, and the spatial boundary, target water stress waveform, and statistical differences between the original waveform and the centroid waveform at each location within the homogeneous region are output.

[0029] In one embodiment of this application, after dividing the area surrounding the photovoltaic array into multiple repair areas, the method may further include: Based on the target water stress waveforms of each remediation area, the area type of each remediation area is determined. The area type can include: waterlogged area, saturated area, wet area, arid area, and alternating wet area. Specifically, for any remediation area, the mean or median of the waveforms of all grid cells within that area is taken as the target water stress waveform. This waveform is a time series, containing the change of cumulative stress duration (saturation cumulative duration or drought cumulative duration) over time within a precipitation infiltration response cycle. Features of the remediation area are extracted from the target water stress waveform. These features can include: target cumulative saturation duration and target cumulative drought duration. The target cumulative saturation duration is the target water stress for the remediation area. The endpoint value of the cumulative saturation duration sequence in the waveform represents the total duration during which the soil exceeds field capacity. The target cumulative drought duration is the endpoint value of the cumulative drought duration sequence in the target water stress waveform for the remediation area, representing the total duration during which the soil is below the wilting coefficient. Based on the characteristics of the remediation area and the preset water type discrimination rules, the type of the remediation area is determined: if the target cumulative saturation duration accounts for more than 80% of the total duration of the cycle, it is identified as a waterlogged or saturated area; if the target cumulative drought duration accounts for more than 80% of the total duration of the cycle, it is identified as a drought area; if both proportions are between 30% and 70% and the waveform shows obvious fluctuations, it is identified as a wet-dry alternating area; if both proportions are low, it is identified as a wet area. Specific thresholds can be adjusted according to local soil and climate conditions.

[0030] Step S240: Using the stress spectrum coupling algorithm, the water stress waveform of each restoration area is matched with the physiological response spectra of different plants in the configured ecological restoration plant library to obtain the photovoltaic ecological restoration scheme.

[0031] Among them, the ecological restoration plant bank can include the physiological response waveforms of different plants at different time points within a precipitation infiltration response cycle and their auxiliary ecological attributes; the physiological response waveforms of different plants can include: saturation tolerance cumulative curve, drought tolerance cumulative curve and dry-wet alternation recovery cumulative curve.

[0032] A photovoltaic ecological restoration plan may include the spatial boundaries of different restoration areas, the target plant species allocated to each restoration area, the planting density (plants per square meter) of each target plant species, the total number of plants, and the arrangement order of different species within the area.

[0033] In practice, for any restoration area, the matching degree between the target water stress waveform of the restoration area and the waveforms of different plants in the configured ecological restoration plant bank is calculated. Specifically, the target water stress waveform of the restoration area is elastically aligned and matched with the physiological response waveforms of different plants in the plant bank: the target water stress waveform of the restoration area and the physiological response waveform of the plants are matched one by one at different time points within a precipitation infiltration response cycle to obtain the waveforms corresponding to the phenological conditions; the difference between the ordinates of the waveforms at different time points is calculated, and all differences are accumulated. Based on the accumulated result, a matching degree coefficient between 0 and 1 is obtained; during matching, the plant response curve is allowed to stretch and contract moderately on the time axis to find the best fit, and the comprehensive matching degree coefficient between each restoration area and each plant is obtained. This coefficient reflects the suitability of the plant under the water stress environment in the area. Based on the matching degree between the water stress waveform of the restoration area and the waveforms of different plants, multiple plant varieties with high waveform matching degree with the water stress waveform of the restoration area and meeting the configured constraints are screened to obtain the target plant varieties of the restoration area. Among them, the constraints may include mutual exclusion constraints and diversity constraints. The mutual exclusion constraint is that the same plant variety cannot be assigned to two restoration areas where the difference in water stress waveform exceeds a set threshold at the same time. The diversity constraint may include: setting a minimum number of plant varieties (e.g., at least two) and a maximum number of varieties (e.g., no more than five) for each restoration area in advance to ensure ecological diversity; and setting an upper limit on the total number of times the same plant variety can be used throughout the entire power plant area to avoid the excessive dominance of a single species. Specifically, according to the waveform matching degree coefficient from high to low, the currently available plant varieties that meet the mutual exclusion and diversity constraints are assigned to each restoration area in turn. After assignment, the plant variety is temporarily removed from the candidate list of other mutually exclusive areas and the iteration is repeated until all restoration areas meet the minimum number of varieties requirement or no new varieties can be assigned.

[0034] For any given target plant species, the initial planting density is determined based on the waveform matching degree between the target plant species and the target water stress waveform of the remediation area, as well as the regional type of the remediation area. Specifically, the higher the matching degree coefficient, the higher the base density; when the matching degree coefficient is moderate, the base density is appropriately reduced. Specific values ​​can be obtained from a pre-set comparison table of different waveform matching degrees and different planting densities. Based on the target water stress waveform of the remediation area, the initial planting density of the target plant species is adjusted to obtain the target planting density of the target plant species. Specifically, the endpoint value in the target water stress waveform of the remediation area is used as the total saturation duration and total drought duration. The proportions of the total saturation duration and total drought duration to the total duration of precipitation infiltration response are calculated. For each plant species, the waterlogging tolerance level (divided into strong, medium, and weak levels) and drought tolerance level (also divided into strong, medium, and weak levels) are obtained. If the proportion of the total saturation duration is greater than 60%, the density is adjusted according to the following rules: for varieties with weak waterlogging tolerance, the density is multiplied by 0.7; for varieties with medium waterlogging tolerance, the density is multiplied by 0.9; and for varieties with strong waterlogging tolerance, the density remains unchanged. If the proportion of the total saturation duration is between 30% and 60%, only the density of varieties with weak waterlogging tolerance is multiplied by 0.9, while the density of other varieties remains unchanged. If the proportion of the total saturation duration is less than 30%, the density is not adjusted based on the saturation duration. If the total drought duration accounts for more than 60%, the density is adjusted according to the following rules: for varieties with weak drought resistance, the density is multiplied by 0.7; for varieties with moderate drought resistance, the density is multiplied by 0.9; and for varieties with strong drought resistance, the density remains unchanged. If the total drought duration accounts for between 30% and 60%, only the density of varieties with weak drought resistance is multiplied by 0.9, while the density of other varieties remains unchanged. If the total drought duration accounts for less than 30%, the density is not adjusted due to the drought duration. If both saturation and drought adjustment conditions are met, the two adjustment coefficients are multiplied together and applied to the base density (for example, first multiply by 0.7 for saturation, then multiply by 0.8 for drought, resulting in a combined coefficient of 0.56).

[0035] Based on the spatial boundaries of the restoration area, determine the area of ​​the restoration area; the product of the target planting density of the target plant species and the area of ​​the restoration area is taken as the total number of the target plant species planted in the restoration area. Based on the target plant species, target planting density, and target planting quantity for each restoration area, a photovoltaic ecological restoration plan is generated.

[0036] In another embodiment of this application, the photovoltaic ecological restoration scheme may further include: the arrangement order of different target plant varieties; the method for determining the arrangement order of different target plant varieties may include: For any given restoration area, obtain the water flooding tolerance level of each target plant species in that restoration area; Obtain the surface slope direction of the restoration area, and determine the dominant water flow direction in the restoration area based on the surface slope direction; Based on the dominant direction of water flow, determine the arrangement scheme of different target plant varieties; specifically, if there is no significant slope (slope less than 0.2%), it is marked as having no dominant water flow direction; if there is a dominant water flow direction, divide the restoration area into upstream and downstream sections along the water flow direction; place the target plant varieties with the highest water-tolerance level in the upstream section, and the target plant varieties with the lowest water-tolerance level in the downstream section; place the target plant varieties with medium water-tolerance level in the middle section; if there is no dominant water flow direction, adopt an equilateral triangle staggered mixed planting method, with all varieties evenly distributed, the plant spacing between adjacent plants being equal, the row spacing being the same as the plant spacing, and adjacent rows being staggered by half a plant spacing.

[0037] In one embodiment of this application, the method for constructing an ecological restoration plant bank may include: For each candidate plant, multiple water treatments were set up in a controlled environment (such as a greenhouse or grower): including a saturation stress group (soil continuously saturated), a drought stress group (soil moisture content dropped below the wilting coefficient and maintained), a wet-dry alternation group (saturation and drought alternating cycles), and a normal water control group. All treatments simulated the duration of one precipitation infiltration response cycle (consistent with the aforementioned cycle), and plant physiological indicators were recorded at the same time sampling intervals. During the experimental period, key physiological indicators for each plant were measured at fixed time intervals (e.g., hourly or daily), including: relative leaf water content, root activity, malondialdehyde content, proline accumulation, chlorophyll fluorescence parameters (or simply photosynthetic efficiency), and plant morphology (e.g., wilting, yellowing, lodging). The indicators measured at each time point were combined into a stress response intensity value, reflecting the degree of water stress experienced by the plant at that moment. For each plant species and each water treatment type, a plant physiological response waveform was plotted with time within the experimental period on the horizontal axis and stress response intensity at each time point on the vertical axis. Then, three characteristic curves matching the water stress waveform were extracted from this waveform: a saturation tolerance cumulative curve (i.e., the cumulative time process from which the plant's response intensity reaches the tolerance threshold under saturation stress); a drought tolerance cumulative curve; and a wet-dry alternation recovery cumulative curve. All of these curves represent monotonically increasing or fluctuating time series, with their endpoint values ​​representing the plant's final tolerance capacity under that stress type. The system stored all of these response curves for all plants in an ecological restoration plant database.

[0038] For each candidate plant, its basic ecological attributes, such as root depth, growth type (herbaceous, shrub, tree), shade tolerance level, and suitable soil type, are recorded and stored in association with the physiological response spectrum.

[0039] Corresponding to the above method, this application also provides a device for determining a photovoltaic ecological restoration scheme, such as... Figure 3 As shown, the device includes: Acquisition unit 310 is used to acquire the spatial geometric parameters and environmental parameters of the photovoltaic array; The simulation unit 320 is used to simulate soil and water transport in the micro-water and soil environment of the area surrounding the photovoltaic array based on the spatial geometric parameters and the environmental parameters, and to obtain the water stress waveform of the area surrounding the photovoltaic array. The dividing unit 330 is used to divide the area surrounding the photovoltaic array into multiple repair areas according to the moisture stress waveform; Matching unit 340 is used to match the water stress waveform of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library using a stress spectrum coupling algorithm to obtain a photovoltaic ecological restoration scheme.

[0040] The functions of each functional unit in the photovoltaic ecological restoration scheme determination device provided in the above embodiments of this application can be realized through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the photovoltaic ecological restoration scheme determination device provided in the embodiments of this application will not be repeated here.

[0041] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0042] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Obtain the spatial geometric parameters and environmental parameters of the photovoltaic array; Based on the spatial geometric parameters and the environmental parameters, the micro-water and soil environment around the photovoltaic array is simulated to obtain the water stress waveform of the area around the photovoltaic array. Based on the moisture stress waveform, the area surrounding the photovoltaic array is divided into multiple repair zones; A stress spectrum coupling algorithm was used to match the water stress waveforms of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library to obtain a photovoltaic ecological restoration scheme.

[0043] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0044] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0045] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0046] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0047] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0048] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the method for determining the photovoltaic ecological restoration scheme described in any of the above embodiments.

[0049] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for determining the photovoltaic ecological restoration scheme described in any of the above embodiments.

[0050] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0054] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0055] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for determining a photovoltaic ecological restoration scheme, characterized in that, The method includes: Obtain the spatial geometric parameters and environmental parameters of the photovoltaic array; Based on the spatial geometric parameters and the environmental parameters, the micro-water and soil environment around the photovoltaic array is simulated to obtain the water stress waveform of the area around the photovoltaic array. Based on the moisture stress waveform, the area surrounding the photovoltaic array is divided into multiple repair zones; A stress spectrum coupling algorithm was used to match the water stress waveforms of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library to obtain a photovoltaic ecological restoration scheme.

2. The method as described in claim 1, characterized in that, Before simulating soil and water transport in the micro-water and soil environment surrounding the photovoltaic array based on the spatial geometric parameters and the environmental parameters, the method further includes: For any row of photovoltaic modules in a photovoltaic array, construct a coupled model of surface runoff motion of the photovoltaic modules in that row based on the spatial geometric parameters of the photovoltaic modules. Environmental parameters are input into the coupled model of surface runoff motion of the photovoltaic modules in this row to simulate the runoff process within a precipitation infiltration response cycle, and the runoff simulation results of the photovoltaic modules in this row within a precipitation infiltration response cycle are obtained.

3. The method as described in claim 2, characterized in that, Based on the spatial geometric parameters and the environmental parameters, a water and soil transport simulation is performed on the micro-water and soil environment surrounding the photovoltaic array, including: Based on the confluence simulation results and spatial geometric parameters of each row of photovoltaic modules in one precipitation infiltration response cycle, the plate edge infiltration boundary conditions of the pre-constructed soil hydrodynamic model around the photovoltaic array are determined. Using the soil hydrodynamic model based on the environmental parameters, the drip transport process of a precipitation infiltration response cycle is simulated to determine the soil and water characteristics under the plate during a precipitation infiltration response cycle. Based on spatial geometric parameters and environmental parameters, the shielding effect is calculated and the inter-plate soil and water characteristics are determined for a precipitation infiltration response cycle. By coupling the soil and water characteristics under the plate and the soil and water characteristics between the plates, the water stress waveform of the area surrounding the photovoltaic array in one precipitation infiltration response cycle is obtained.

4. The method as described in claim 3, characterized in that, The soil and water characteristics under the slab include the cumulative duration of soil saturation at different time points within a precipitation infiltration response cycle for each spatial point under the slab; the soil and water characteristics between the slabs include the cumulative duration of drought at different time points within each transverse strip between the slabs within a precipitation infiltration response cycle. By coupling the soil and water characteristics under the panels and the soil and water characteristics between the panels, the water stress waveform of the area surrounding the photovoltaic array is obtained, including: The regional grid of the area surrounding the photovoltaic array is obtained by splicing together the duration of soil saturation at each spatial point under the plate and the duration of rain shadow and drought in each transverse strip between the plates according to their spatial location within a precipitation infiltration response cycle. The soil and water characteristics corresponding to each grid unit within a precipitation infiltration response cycle are used as the water stress waveform of each grid unit within a precipitation infiltration response cycle. Based on the water stress waveform of each grid cell in one precipitation infiltration response cycle, the water stress waveform of the area surrounding the array in one precipitation infiltration response cycle is obtained.

5. The method as described in claim 4, characterized in that, Based on the moisture stress waveform, the area surrounding the photovoltaic array was divided into multiple repair zones, including: Calculate the similarity of the water stress waveforms for each grid cell; A waveform-based clustering algorithm was used to cluster different grid cells according to the similarity of the water stress waveforms of each grid cell, resulting in multiple remediation areas.

6. The method as described in claim 1, characterized in that, Before employing a stress spectrum coupling algorithm to match the water stress waveforms of each remediation area with the physiological response spectra of different plants in the configured ecological remediation plant library, this method also includes: Acquire the spatial boundaries and target water stress waveforms of each remediation area; The area of ​​each restoration zone is determined based on its spatial boundaries. Based on the target water stress waveform of each remediation area, the area type of each remediation area is determined.

7. The method as described in claim 6, characterized in that, A stress spectrum coupling algorithm was used to match the water stress waveforms of each remediation area with the physiological response spectra of different plants in a configured ecological restoration plant library, including: For any restoration area, calculate the matching degree between the target water stress waveform of the restoration area and the waveforms of different plants in the configured ecological restoration plant library; Based on the matching degree between the water stress waveform of the restoration area and the waveforms of different plants, multiple plant varieties with high waveform matching degree with the water stress waveform of the restoration area and meeting the configured constraints are selected as the target plant varieties of the restoration area. For any target plant variety, the target planting density of the target plant variety is determined based on the waveform matching degree between the target plant variety and the target water stress waveform of the restoration area, as well as the area type of the restoration area and the target water stress waveform. The product of the target planting density of the target plant species and the area of ​​the restoration area is taken as the total number of the target plant species planted in the restoration area. Based on the target plant species, target planting density, and target planting quantity for each restoration area, a photovoltaic ecological restoration plan is generated.

8. A device for determining a photovoltaic ecological restoration scheme, characterized in that, The device includes: The acquisition unit is used to acquire the spatial geometric parameters and environmental parameters of the photovoltaic array. The simulation unit is used to simulate soil and water transport in the micro-water and soil environment around the photovoltaic array based on the spatial geometric parameters and the environmental parameters, and to obtain the water stress waveform in the area around the photovoltaic array. The division unit is used to divide the area surrounding the photovoltaic array into multiple repair zones based on the moisture stress waveform; The matching unit is used to match the water stress waveform of each restoration area with the physiological response spectra of different plants in the configured ecological restoration plant library using a stress spectrum coupling algorithm to obtain a photovoltaic ecological restoration scheme.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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