Optimization system for array arrangement of photovoltaic power generation cells

Abstract

The application discloses a photovoltaic power generation cell layer array arrangement optimization system and particularly relates to the technical field of photovoltaic power generation, comprising a site determination module, an arrangement layout module, a parameter collection module, a parameter processing module and an optimization design module.The application classifies the target site into flat sites, uniform slope sites and complex sites through elevation data modeling and terrain analysis before arrangement design; adapts exclusive arrangement point layout schemes, full-dimension parameter collection strategies and nonlinear evaluation models to different site types, accurately calculates photovoltaic power generation coefficients and failure risk coefficients; intelligently optimizes and selects based on the arrangement value index of multi-parameter fusion, generates the optimal arrangement space, realizes accurate fitting of arrangement design and site terrain and illumination characteristics, significantly reduces arrangement failure and measurement deviation caused by site differences, provides a scientific basis for photovoltaic power station design decision, and adapts to the core needs of cost reduction and efficiency increase in the industry.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, more particularly, the present application relates to a photovoltaic power generation cell layer array arrangement optimization system. BACKGROUND

[0002] Early photovoltaic arrays adopt a one-size-fits-all design with fixed inclination and equal spacing, only considering basic non-shading, completely ignoring dynamic light and component differences. With the decline of photovoltaic subsidies, the industry is shifting towards cost reduction and efficiency improvement. As the core of system design, array arrangement directly affects land utilization rate, cable loss and operation and maintenance cost. By optimizing the cell layer array arrangement, land utilization rate can be improved, and cable loss can be reduced, becoming a key lever to control power generation cost.

[0003] The core of the traditional cell layer array arrangement system is static planning based on empirical formula and macro data, relying entirely on manual calculation and basic rules, without dynamic optimization or complex scenario adaptation. Specifically, data collection: collect the latitude, longitude and annual average sunshine duration of the project location; determine the inclination: determine according to the local latitude, calculated by the industry general empirical formula; calculate the component spacing: calculated by the empirical formula; array layout and circuit connection: laid out in a grid shape according to rows and columns, the number of rows and columns are simply calculated according to the total installed capacity and single component power; planning verification: engineers check whether there is obvious shading and whether the circuit can be connected by manually drawing or simply measuring on site.

[0004] However, in actual use, it still has some disadvantages, first, the traditional photovoltaic power generation cell layer array arrangement is mainly based on empirical formula and fixed parameter design, relying on macro static data and manual planning, but the array power generation efficiency is affected by many dynamic factors such as solar trajectory change, terrain shading, component performance difference, etc. The monitoring range and design dimension of the existing system are relatively narrow, which cannot provide sufficient data support for efficient power generation, and the power generation efficiency and land utilization rate still need to be improved; secondly, due to the limitation of experience, when engineers find power loss or shading problems caused by arrangement, they need to check the inclination adaptation, spacing redundancy, etc. Due to insufficient monitoring indicators and optimization tools, the problem checking and adjustment process may consume a lot of time, which is not conducive to the overall income of the power station, and the arrangement design and optimization efficiency still needs to be further improved. SUMMARY

[0005] Therefore, the embodiments of the present application provide a photovoltaic power generation cell layer array arrangement optimization system, which calculates the photovoltaic power generation coefficient and fault risk coefficient of different site types by collecting basic parameters and site parameters, and then selects the arrangement area to generate the optimal arrangement space, reduces the design and operation and maintenance cost, and effectively solves the problems raised in the background art.

[0006] To achieve the above object, the present application provides the following technical solutions:

[0007] The site determination module determines the target site type according to the battery layer position, wherein the site type includes a flat site, a uniform slope site and a complex site.

[0008] The arrangement module arranges a plurality of arrangement points according to the site type, numbers the arrangement points in a preset order, and extracts the arrangement region corresponding to the site according to the numbering order, and marks them as 1, 2, …, n, respectively.

[0009] The parameter acquisition module acquires the basic parameters of the arrangement region corresponding to the target site, and acquires the site parameters for different site types.

[0010] The parameter processing module aggregates the basic indicators into a photovoltaic power generation coefficient and a fault risk coefficient based on the basic parameters and the site parameters of different sites through a nonlinear evaluation model.

[0011] The optimization design module constructs an arrangement value index by taking the photovoltaic power generation coefficient and the fault risk coefficient as input, and arranges the arrangement comprehensive index corresponding to each arrangement region in descending order, thereby generating an optimal arrangement space.

[0012] The technical effects and advantages of the present application are as follows:

[0013] 1. The present application preliminarily evaluates the topographic features of the target site before arrangement design to determine the site type, and then flexibly collects and processes parameters according to the site type in actual arrangement design, designs a special arrangement point arrangement scheme for different site types, so that the arrangement design can better fit the topographic state and illumination characteristics of the target site, and can maximize the reliability and accuracy of the subsequent power generation efficiency improvement and risk control benchmark, thereby significantly reducing the arrangement failure problem caused by site differences in optimization design, reducing power generation loss and fault risk, and improving the overall benefit of photovoltaic array arrangement.

[0014] 2. The present application further strengthens the data-driven and automated processing of the whole process after completing the initial arrangement optimization through basic parameters and site parameters, and then uses the precise quantitative evaluation results (photovoltaic power generation coefficient, fault risk coefficient) to perform arrangement value iterative optimization, which can provide the most practical arrangement data through full-dimensional parameter acquisition and model calculation, ensure the timeliness and accuracy of the optimization scheme, and on the other hand, the optimal space is selected through the sorting results of the subsequent arrangement value index, which can not only ensure the focus on high-value areas to improve power generation efficiency, but also ensure the construction feasibility through space integration and boundary correction. BRIEF DESCRIPTION OF DRAWINGS

[0015] Fig. 1 The present application is a schematic diagram of the overall structure.

[0016] Fig. 2 A flow chart for obtaining the degree of influence of the parameters of the application.

[0017] Fig. 3 A flow chart for generating the optimal arrangement space of the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0019] As shown in the photovoltaic power generation cell layer array arrangement optimization system, the system comprises a site determination module, an arrangement and layout module, a parameter acquisition module, a parameter processing module and an optimization design module. Figs. 1-3 The specific embodiments of the application include the following:

[0020] The site determination module determines the target site type according to the cell layer position, wherein the site type comprises a flat site, a uniform slope site and a complex site.

[0021] In the embodiment, it is specifically noted that the target site type is determined as follows:

[0022] The geographic data of the cell layer position is collected, including but not limited to site elevation data and terrain contour data, thereby generating an elevation model.

[0023] It is necessary to supplement that the geographic data of the cell layer position can be obtained by unmanned aerial vehicle aerial photography or satellite remote sensing. When collecting, the aerial photography equipment with a precision of ≥1 cm / px is specifically selected, the flight height is controlled at 50-80 meters, the site is fully covered, the initial elevation point cloud is generated, the terrain contour data generates a site orthographic image by unmanned aerial vehicle aerial photography, the site boundary, natural terrain turning points (such as ridges, gentle slope starting points), artificial obstacles (such as fences, ditches) positions are marked, and the elevation model generated according to the elevation data, terrain contour data and the like can intuitively display the height change of the site terrain.

[0024] The average slope and the slope change rate are calculated by a terrain analysis algorithm on the elevation model, wherein the slope represents the inclination degree of a point of the site relative to the horizontal plane, and in the elevation model, it is represented as the change rate of the elevation value of each pixel relative to its adjacent pixels.

[0025]

[0026] ​It should be noted that the average slope is specifically calculated by traversing all pixels in the elevation model, for a pixel, its slope value S1 is calculated by the formula: wherein and respectively represent the change rate of elevation in the east-west direction x and the north-south direction y, and represents the natural constant, and then after obtaining the slope values of all pixels in the entire elevation model, the slope values of all pixels are arithmetically averaged, i.e. average slope = Σ (slope value of each pixel) / total number of pixels;

[0027] The slope change rate reflects the ups and downs of the slope in the field and the complexity of the terrain, the higher the value, the more broken and irregular the terrain, the specific calculation method is to obtain the slope deviation by subtracting the average pixel from the slope value of each pixel, square the deviations, then sum and divide by the total number of pixels to obtain the variance of the slope value, and then take the square root of the variance to obtain the slope change rate.

[0028] It should be explained that the above calculation process is completely automatic and does not require human intervention, the user only needs to input the elevation data, and the system can output the average slope and the slope change rate.

[0029] The calculated average slope and slope change rate are compared with the preset threshold value to automatically classify the site type, the site type includes flat site, uniform slope site and complex site, if the average slope is less than 5% and the slope change rate is less than 3%, it is determined as a flat site, if the average slope is greater than or equal to 5% and the slope change rate is less than 8%, it is determined as a uniform slope site, if the average slope is greater than or equal to 5% and the slope change rate is greater than or equal to 8%, it is determined as a complex site.

[0030] The arrangement module: according to the site type, arrange a plurality of arrangement points, and number the arrangement points in a preset order, and then extract the arrangement area corresponding to the site according to the numbering order, and mark them as 1, 2, …, n respectively.

[0031] In this embodiment, it should be specifically pointed out that the arrangement points are arranged as follows:

[0032] If the site type is a flat site, the entire rectangular or approximately rectangular site is divided into regular square grids according to the site area, and an arrangement point is arranged at each grid intersection, the advantage of this method is that the points are evenly distributed, the data coverage is comprehensive, and the flatness model of the site can be efficiently constructed, and the boundary can be accurately defined, which is suitable for flat terrain without significant elevation changes, and then a rectangular coordinate system is established for numbering, for example, the southwest corner of the site is taken as the coordinate origin to establish a plane rectangular coordinate system, and the arrangement points are numbered in the order of eastward (X axis) first and northward (Y axis) second;

[0033] If the site type is a same-slope site, a contour method is used to generate a contour map, and arrangement points are arranged along key contours, where the key contours are usually every certain elevation interval, such as 5 meters or 10 meters, and when the arrangement points are arranged, the arrangement points need to be arranged densely on the contours at the top of the slope, the foot of the slope, and the middle of the slope surface. This method can accurately capture the overall trend and uniformity of the slope, ensure the accuracy of subsequent array arrangement based on the contour (such as arranging the array along the contour to keep the support flat), and number according to the contour elevation. For example, the elevation of the contour is numbered as the main order from high to low, and within the same contour, the order is numbered from west to east or from one end point to the other end point.

[0034] If the site type is a complex site, a terrain feature point method is used to arrange feature points in the site, including ridge lines, valley lines, slope tops, slope feet, and slope mutation points. The ridge line is the highest edge line of the mountain range, the valley line is the lowest water collection line of the terrain, and it is the area with the most serious shelter, the slope top is the highest point of the local area, the slope foot is the lowest point of the local area, and the slope mutation point is the turning point of the terrain from gentle to steep or from steep to gentle. This arrangement method can accurately outline the complex terrain structure with the least control points, and provide the most critical data for subsequent search for photovoltaic enrichment areas and avoidance of serious shelter. The arrangement points are numbered according to the terrain features. For example, the arrangement priority is set as first-level ridge line, second-level slope top, third-level slope mutation point, fourth-level valley line, and fifth-level slope foot, and the system numbers all the feature points in this priority order.

[0035] It needs to be further explained that the arrangement area is extracted according to the following steps:

[0036] Taking each arrangement point of the target site as the center and a preset distance as the radius, an arrangement circle is constructed, where the preset distance can be the average distance between adjacent control points or the design distance value. Then, geometric intersection operation is performed on the arrangement circles corresponding to adjacent arrangement points, and finally the intersection operation result of each adjacent arrangement circle is defined as the effective arrangement area of the photovoltaic cell layer array.

[0037] It needs to be explained that this arrangement method can accurately adapt to the site terrain and boundary constraints based on the spatial distribution of the arrangement points, and ensure that the arrangement area covers the effective illumination range and avoids overlapping with the site obstacles or invalid areas.

[0038] Parameter acquisition module: basic parameters of the arrangement area corresponding to the target site are acquired, and site parameters are acquired for different site types.

[0039] In this embodiment, it needs to be specifically pointed out that the parameter acquisition module includes a basic parameter acquisition unit, a differentiated site parameter acquisition unit, and a data transmission unit; the basic parameter acquisition unit acquires solar irradiance, solar elevation angle, and solar azimuth angle in real time based on a silicon-based light sensor in a central unobstructed area of the site, and acquires the temperature of the component backboard in real time through a temperature and humidity sensor attached to the backboard; the solar irradiance, solar elevation angle, and solar azimuth angle take the central latitude and longitude of the site as the reference datum, and the acquisition frequency is set to once every 10 minutes.

[0040] The differentiated site parameter acquisition unit adapts to the requirements of flat sites, uniform slope sites, and complex sites through targeted equipment: for flat sites, 1080P low-altitude cameras deployed at the edges of the site to obtain image data of dynamic obstructions, thereby obtaining the dynamic obstruction area, wherein the dynamic obstructions include but are not limited to clouds, tree swaying, while measuring the height of the fixed obstructions around the site and the fixed obstruction area of the site edge, wherein the fixed obstructions include buildings and trees, the low-altitude camera takes the arrangement point at the edge of the site as the installation datum, the lens faces the obstruction direction, and continuous shooting is performed;

[0041] For uniform slope sites, the height difference and slope angle of adjacent slope layers are collected by laser radar, and the soil bearing values of different slope sections are measured by a soil bearing capacity detector; the laser radar is arranged at the edge of each slope layer unit, and the scanning direction is perpendicular to the contour line; the soil bearing capacity is collected at 3 points on the upper 1 / 3, middle 1 / 3, and lower 1 / 3 of the slope, and the bearing value is recorded;

[0042] For complex sites, micro-terrain partition data is obtained by a high-precision unmanned aerial vehicle with dynamic positioning, and soil moisture content is collected by a soil moisture detector; the unmanned aerial vehicle establishes a three-dimensional coordinate system with the highest elevation point of the site as the datum, divides the site into "steep slope area, gentle slope area, and depression area" micro-terrain units, and sets partition collection points at the center of each unit; micro-terrain partition data includes partition sunshine duration, partition obstruction angle, and partition area; the soil moisture detector selects 2 measurement points in each micro-terrain unit, and collects data at 8:00 and 18:00 every day;

[0043] The data transmission unit transmits the collected basic parameters such as solar irradiance, component current and voltage, and the differentiated site parameters of flat sites, uniform slope sites, and complex sites to the site parameter preprocessing module through a wireless network, and uses SSL protocol encryption in the transmission process to ensure data integrity.

[0044] It needs to be explained that the solar irradiance and the solar elevation angle are the core basis for calculating the light receiving efficiency of the component, and the data accuracy directly determines the prediction accuracy of the power generation. Abnormal irradiance fluctuation usually means cloud cover or sensor failure, which needs to be calibrated in time to avoid optimization deviation; the component backboard temperature is a key indicator for evaluating the risk of hot spot. When the temperature exceeds 85℃, the power attenuation rate of the component will increase by more than 10%, or even cause the component to burn out. Through real-time monitoring, hot spot failure can be warned in advance; the shading data of the flat site can simulate the shadow projection range at different times to avoid the array layout falling into the fixed shadow area; the image data and horizontal distance of the flat site directly determine the optimization direction of the adaptive spacing. If the fixed shading height measurement deviation is 0.5m, it may cause the winter solstice shadow coverage calculation deviation and cause shading loss; if there is a large deviation in the height difference of the slope layer of the unified slope site, the annual power generation of the low slope layer may be reduced, and the insufficient soil bearing capacity will cause the foundation settlement of the support and increase the risk of failure; due to the complexity of the complex site, if the low-lying area and steep slope area are not distinguished, unified arrangement will cause water accumulation in the low-lying area and the inclination of the component in the steep slope area is not suitable; therefore, by selecting the above basic parameters and differentiated site parameters, the core needs of the two optimization goals of photovoltaic power generation and fault risk can be fully covered, and accurate data support is provided for subsequent modeling and algorithm optimization.

[0045] Parameter processing module: based on the basic parameters and site parameters of different sites, the basic indicators are aggregated into photovoltaic power generation coefficients and fault risk coefficients through a nonlinear evaluation model.

[0046] In this embodiment, it needs to be specifically pointed out that the parameter processing module judges the influence degree of each parameter based on the parameters of different sites, performs normalization processing, and performs weighted calculation, thereby constructing a nonlinear evaluation model to obtain photovoltaic power generation coefficients and fault risk coefficients corresponding to different site types, which are specifically represented as follows:

[0047] For flat sites, the photovoltaic power generation coefficient C1 is specifically represented as follows:

[0048] ,

[0049] Where S d represents the dynamic shading area, H s represents the solar elevation angle, S g represents the fixed shading area, S t represents the total area of the site, I f represents the solar irradiance, I 标 represents the standard solar irradiance, which can be specifically 1000W / m 2 , e represents the natural constant, w c11 , w c12 , w c13 and w c14respectively represent the influence degree of dynamic shading area, solar altitude angle, fixed shading area and solar irradiance respectively;

[0050] The fault risk coefficient Q1 is specifically represented as follows:

[0051] ,

[0052] wherein T b represents the component backboard temperature, Sv represents the total area of the component in the site, w q11 and w q12 respectively represent the influence degree of the component backboard temperature and the shading area respectively;

[0053] The photovoltaic power generation coefficient C2 for the same slope site is:

[0054] ,

[0055] wherein θ p represents the slope angle, A s represents the deviation angle between the solar azimuth angle and the slope, Hg represents the average slope layer height difference, L represents the slope length, t z represents the shading time, t 标 represents the total sunshine time of the site, w c21 , w c22 , w c23 and w c24 respectively represent the influence degree of the slope angle, the deviation angle, the average slope layer height difference and the shading time respectively;

[0056] The fault risk coefficient Q2 is specifically represented as follows:

[0057] ,

[0058] wherein σ represents the actual soil bearing value, σs represents the preset soil bearing value, Fz represents the actual force of the photovoltaic support, Fm represents the limit force of the photovoltaic support, w q21 , w q22 and w q23 respectively represent the influence degree of the soil bearing, the slope angle and the force of the photovoltaic support respectively;

[0059] The photovoltaic power generation coefficient C3 for the complex site is:

[0060] ,

[0061] wherein m represents the total number of micro-terrain partitions, j represents the micro-terrain partition number, t zj represents the partition sunshine time, t 标 represents the total sunshine time of the site, α j represents the partition shading angle, Sr represents the partition shading area, w c31 and w c32 respectively represent the influence degree corresponding to the partition sunshine duration and shading angle;

[0062] The fault risk coefficient Q3 is specifically represented as follows:

[0063] ,

[0064] wherein w zj represents the partition soil water content, w 标 represents the water content warning value, w q31 and w q32 respectively represent the influence degree corresponding to the soil water content and soil bearing.

[0065] It should be pointed out that the photovoltaic power generation coefficient reflects the efficiency of the array receiving light and converting into electric energy, and the fault risk coefficient reflects the probability of the array failure caused by terrain, shading and environment. When processing parameters, the photovoltaic power generation coefficient and the fault risk coefficient of different site types are calculated respectively through each parameter, thereby avoiding analysis deviation caused by different sites.

[0066] It should be further pointed out that the influence degree of each parameter is determined according to the parameter priority, which is specifically as follows:

[0067] According to the parameter priority corresponding to the flat site, the uniform slope site and the complex site, the random forest algorithm is used to calculate the contribution degree of each parameter to the target coefficient. The contribution degree ratio is the initial value of the influence degree. Exemplarily, the flat site C1: through simulation, the If contribution degree is 35%, the Hs contribution degree is 25%, the Sd contribution degree is 20%, and the Sg contribution degree is 20%. The initial influence degree is set to 0.35, 0.25, 0.2 and 0.2.

[0068] It should be supplemented that the parameter priority is analyzed as follows: flat site: solar irradiance is the core of light energy input, with the highest priority, solar altitude angle directly affects the component light receiving angle, with the second priority, dynamic shading area and fixed shading area indirectly affect power generation by reducing effective light, with relatively low priority; high component backboard temperature will cause hot spot failure, directly leading to component damage, with the highest priority; component total area affects heat distribution (the larger the area, the slower the heat dissipation), with the second priority;

[0069] Uniform slope site: slope angle, solar azimuth angle and slope deviation angle directly determine the daily effective light duration, with the highest priority, average slope layer height difference and shading duration only affect local light receiving, with the second priority; actual soil bearing value determines the stability of the support foundation, which is the core of avoiding support collapse, with the highest priority, photovoltaic support actual stress affects structure safety, with the second priority;

[0070] Complex site: The partition sunshine duration directly determines the power generation of each partition, with the highest priority; the partition shielding angle affects the local light utilization rate, with the second priority; the partition soil water content exceeding the warning value will cause the module water accumulation and the support corrosion, with the highest priority, and the soil bearing value decreases with the increase of soil water content, which is greatly affected by the water content, so the priority is second.

[0071] The initial influence degree is normalized to ensure that the sum of the influence degrees of all parameters is 1, and the calculation logic of the nonlinear model is adapted, and the normalization calculation formula is specifically represented as:

[0072] ,

[0073] wherein wp represents the normalized influence degree of parameter p, w p，初始 represents the initial influence degree of parameter p, and c is the total number of parameters.

[0074] Finally, the normalized influence degree is substituted into the nonlinear evaluation model of the corresponding site, and thus the photovoltaic power generation coefficient and the failure risk coefficient of each site are calculated.

[0075] Optimization design module: the photovoltaic power generation coefficient and the failure risk coefficient are taken as inputs to construct the arrangement value index, and the arrangement comprehensive indexes of each arrangement region are arranged in descending order, and thus the optimal arrangement space is generated.

[0076] In this embodiment, it needs to be specifically explained that the optimal arrangement space generated by the optimization design module is as follows:

[0077] The photovoltaic power generation coefficient and the failure risk coefficient corresponding to the site type to which the arrangement region belongs are combined with the arrangement point distribution density to calculate the arrangement value index of each arrangement region, and the specific representation is:

[0078] F i =C i 1，2，3 ×Q i 1，2，3 ×D / D max , wherein Fi represents the arrangement value index corresponding to the i-th arrangement region, C i 1，2，3 represents the photovoltaic power generation coefficient corresponding to the i-th arrangement region, Q i 1，2，3 represents the failure risk coefficient corresponding to the i-th arrangement region, and D represents the average arrangement point distribution density, wherein the distribution density is the number of arrangement points in unit area, and D max represents the maximum arrangement point distribution density.

[0079] All effective arrangement regions are sorted in descending order based on the arrangement value index to form a region value priority list;

[0080] Based on the sorted region list, the top 70% of regions are selected as optimized arrangement regions, and the selected optimized arrangement regions are spatially integrated to remove overlapping parts between regions, and the polygon fitting method is used to correct the boundaries of irregular edge regions;

[0081] The filtered optimized arrangement regions and related analysis results are fitted into a structured optimization report and transmitted to the user terminal.

[0082] It should be noted that the related analysis results include region basic information: total area of the optimized arrangement region, spatial coordinate range (based on arrangement point GPS data), and region number;

[0083] Comprehensive index analysis: F value distribution interval and average F value of the optimized arrangement region, and F value difference compared with non-selected regions (for example, the average F value of the optimized region is 0.78, and the average F value of the non-selected region is 0.42);

[0084] Core parameter index: average C value and average Q value corresponding to the optimized arrangement region, which quantifies the optimization effect (for example, the average C value of the optimized region is 0.85, which is 18% higher than the average level of the whole region; the average Q value is 0.92, and the fault risk is reduced by 20%);

[0085] Arrangement suggestion: for the optimized region, the basic arrangement parameters such as component arrangement density and recommended inclination angle are supplemented.

[0086] Secondly: in the drawings of the disclosed embodiments, only the structures related to the disclosed embodiments are involved, other structures can be referred to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other;

[0087] Finally: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A photovoltaic power generation cell array layout optimization system, characterized in that, include: Site determination module: Determines the target site type based on the location of the battery layer, including flat sites, sites with uniform slope, and complex sites. Layout module: Layout several layout points according to the site type, and number the layout points in a preset order. Then, extract the layout area corresponding to the site according to the numbering order and mark them as 1, 2, ..., n respectively. Parameter acquisition module: Collects basic parameters of the layout area corresponding to the target site, and collects site parameters for different site types; Parameter processing module: Based on the basic parameters and site parameters of different sites, the module aggregates the basic indicators into photovoltaic power generation coefficient and fault risk coefficient through a nonlinear evaluation model; The nonlinear evaluation model is a weighted nonlinear function constructed for flat sites, sites with uniform slope aspect, and complex sites, specifically including: For flat sites, the formula for calculating the photovoltaic power generation coefficient C1 is: ； The formula for calculating the failure risk coefficient Q1 is: ； For sites with the same slope aspect, the formula for calculating the photovoltaic power generation coefficient C2 is: ； The formula for calculating the failure risk coefficient Q2 is: ； For complex sites, the formula for calculating the photovoltaic power generation factor C3 is: ； The formula for calculating the failure risk coefficient Q3 is: ； An optimization design module: taking photovoltaic power generation coefficient and fault risk coefficient as input to construct arrangement value index F i , and the calculation formula is: i =C i 1，2，3 ×Q i 1，2，3 ×D / D max , wherein C i 1，2，3 is the photovoltaic power generation coefficient corresponding to the i th arrangement area, Q i 1，2，3 is the corresponding fault risk coefficient, D is the average arrangement point distribution density, and D max is the maximum arrangement point distribution density; and the arrangement comprehensive indexes corresponding to each arrangement area are arranged in descending order, thereby generating an optimal arrangement space.

2. The photovoltaic power generating cell layer array arrangement optimization system according to claim 1, characterized by: The target site type is determined as follows: Collect geographic data on the location of the battery layer, including but not limited to site elevation data and terrain contour data, and generate an elevation model from it; The average slope and slope change rate are calculated for the elevation model using terrain analysis algorithms. The slope represents the degree of inclination of a point on the site relative to the horizontal plane, and in the elevation model, it is represented as the rate of change of the elevation value of each cell relative to its neighboring cells. The calculated average slope and slope change rate are compared with preset thresholds to automatically classify the site type, which includes flat site, uniform slope site, and complex site. If the average slope is less than 5% and the slope change rate is less than 3%, it is determined to be a flat site. If the average slope is greater than or equal to 5% and the slope change rate is less than 8%, it is determined to be a uniform slope site. If the average slope is greater than or equal to 5% and the slope change rate is greater than or equal to 8%, it is determined to be a complex site.

3. The photovoltaic power generating cell layer array arrangement optimization system according to claim 1, characterized by: The specific layout of the arrangement points is as follows: If the site type is flat, a regular grid is used to divide the entire rectangular or approximately rectangular site into regular square grids according to the site area. A layout point is set at the intersection of each grid, and then a rectangular coordinate system is established for numbering. If the site type is a site with the same slope aspect, the contour line method is used to generate a contour map, and the points are arranged along the key contour lines and numbered according to the contour line elevation. If the site type is complex, the topographic feature point method is used to lay out the feature points in the site. The feature points include ridge lines, valley lines, slope tops, slope toes, and places where the slope changes abruptly. The layout points are numbered according to the topographic features.

4. The photovoltaic power generating cell layer array arrangement optimization system according to claim 1, characterized by: The specific steps for extracting the layout area are as follows: Using the pre-deployed layout points at the target site as centers, a layout circle is constructed with a preset distance as the radius. The preset distance is either the average spacing between adjacent control points or the design spacing value. Subsequently, a geometric intersection operation is performed on the layout circles corresponding to adjacent layout points. Finally, the effective layout area of ​​the photovoltaic cell array is defined by the intersection operation result of each adjacent layout circle.

5. The photovoltaic power generating cell layer array arrangement optimization system according to claim 1, characterized by: The parameter acquisition module includes a basic parameter acquisition unit, a differentiated site parameter acquisition unit, and a data transmission unit. The basic parameter acquisition unit collects solar irradiance, solar altitude angle, and solar azimuth angle in real time based on a silicon-based light sensor in the unobstructed area at the center of the site, and collects the temperature of the component back panel in real time through a temperature and humidity sensor attached to the back panel of the component. The differentiated site parameter acquisition unit adapts to the needs of flat sites, sites with uniform slope, and complex sites through targeted equipment: For flat sites, it acquires image data of dynamic obstructions by deploying 1080P low-altitude cameras in the sensitive area of ​​site edge shading, thereby obtaining the dynamic shading area, and at the same time measures the height of fixed obstructions around the site and the fixed shading area at the edge of the site. For sites with uniform slope aspect, the height difference and slope aspect angle of adjacent slope layers are collected by lidar, and the soil bearing capacity is measured by a soil bearing capacity tester. For complex sites, a high-precision UAV with dynamic positioning is used to acquire micro-topographic zoning data, and a soil moisture meter is used to collect soil moisture content. The UAV establishes a three-dimensional coordinate system based on the highest elevation point of the site, and divides the site into micro-topographic units. Each unit has a zoning collection point at its center. The micro-topographic zoning data includes the zoning sunshine duration, zoning shading angle, and zoning area.

6. The photovoltaic power generating cell layer array arrangement optimization system according to claim 1, characterized by: The parameter processing module determines the influence of each parameter based on the parameters of different sites, performs normalization processing, and performs weighted calculations to construct a nonlinear evaluation model, thereby obtaining the photovoltaic power generation coefficient and fault risk coefficient corresponding to different site types.

7. The photovoltaic power generating cell layer array arrangement optimization system according to claim 6, characterized by: The degree of influence of each parameter is determined according to its priority, as follows: Based on the parameter priorities corresponding to flat sites, sites with uniform slope aspect, and complex sites, the random forest algorithm is used to calculate the contribution of each parameter to the target coefficient, and the contribution ratio is the initial value of the influence. The initial impact level is normalized, and the normalization calculation formula is as follows: ， where w p represents the normalized parameter p corresponding to the degree of influence, w p，初始 represents the initial degree of influence of the parameter p, and c is the total number of parameters; Finally, the normalized impact level is substituted into the nonlinear evaluation model of the corresponding site to calculate the photovoltaic power generation coefficient and failure risk coefficient of each site.

8. The photovoltaic power generation cell array layout optimization system according to claim 1, characterized in that: The optimization design module generates the optimal layout space through the following process: The photovoltaic power generation coefficient and fault risk coefficient corresponding to the site type of the layout area are combined with the distribution density of the layout points to calculate the layout value index of each layout area, which is specifically expressed as follows: F i =C i 1，2，3 ×Q i 1，2，3 ×D / D max , wherein F represents the arrangement value index corresponding to the i th arrangement area, C i 1，2，3 represents the photovoltaic power generation coefficient corresponding to the i th arrangement area corresponding to the flat site, the uniform slope site or the complex site, Q i 1，2，3 represents the fault risk coefficient corresponding to the i th arrangement area corresponding to the flat site, the uniform slope site or the complex site, and D represents the average arrangement point distribution density, wherein the distribution density is the number of arrangement points in a unit area, D max represents the maximum arrangement point distribution density. All effective layout areas are sorted in descending order based on the layout value index to form a regional value priority list. Based on the sorted list of regions, the top 70% of regions are selected as optimized layout regions. The selected optimized layout regions are then spatially integrated to remove overlapping parts between regions. A polygon fitting method is used to correct the boundaries of irregular regions. The selected optimized layout areas and related analysis results are fitted into a structured optimization report and transmitted to the user terminal.

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