Solar-based intelligent protection system for a leveling point

Abstract

This invention belongs to the field of leveling point protection technology, specifically disclosing a solar-powered intelligent leveling point protection system. The system includes: a leveling point deployment module, a leveling point adjustment module, a priority analysis module, and a level determination module. First, preliminary leveling points are dynamically set and gridded based on the photovoltaic area and the number of critical infrastructures. Then, based on sunlight conditions, the points are screened and their coordinates are fine-tuned to generate a sunlight-adapted set. Next, the location correlation, load influence, and cross-sectional proximity of all leveling points in the set are analyzed to generate an ordered leveling point set. Finally, by analyzing the environmental risk value and the maximum monitoring demand of its associated critical infrastructure, the maximum value is taken as the protection level decision value, thereby matching the corresponding level of protection device. Through deployment planning, sunlight adaptation, multi-factor priority ranking, and graded protection, the rationality of leveling point deployment is improved.

Description

Technical Field

[0001] This invention belongs to the field of leveling point protection technology and relates to a solar-powered intelligent leveling point protection system. Background Technology

[0002] As large-scale ground-mounted facilities, photovoltaic power plants place high demands on ground settlement monitoring due to the long-term stable operation of key infrastructure such as photovoltaic arrays, transformer substations, and booster stations. Leveling points serve as the benchmark for settlement monitoring, and the scientific placement of these points and the effectiveness of protective measures directly affect the accuracy of monitoring data and the reliability of the network.

[0003] Currently, the layout of benchmarks in engineering projects is mostly based on standard experience and manual on-site surveys. In scenarios with special needs, such as photovoltaic power stations, this method is difficult to systematically take into account multiple factors such as the sunshine conditions of benchmarks, regional load distribution, geological risks, and the importance of monitoring, which may lead to shortcomings in the overall optimization and cost-effectiveness of the layout scheme.

[0004] Currently, existing technologies for leveling points mainly focus on their physical protection structures after construction. For example, Chinese invention patent CN205642384U discloses a high-precision leveling control point device. This device, by adding a concrete cushion layer, a frustum-shaped concrete pile protection device, and a protective cover plate, aims to reduce the deformation or damage of leveling points caused by external factors, thereby ensuring measurement accuracy and reducing maintenance costs.

[0005] However, existing technologies based on physical structure protection have the following obvious limitations:

[0006] First, this method is a passive and generalized protection of benchmarks at predetermined locations. It cannot solve the planning and site selection decision-making problems in the early stage of benchmark deployment. It relies on manual experience for deployment, which leads to uneven network coverage, insufficient monitoring of key areas, or poor sunlight conditions at benchmarks, thus affecting the overall effectiveness of the monitoring network.

[0007] Second, this method lacks the ability to make hierarchical decisions and cannot match different levels of protection measures to different benchmarks based on the specific environmental risks of the benchmarks and the differences in the importance of the infrastructure they monitor. This results in insufficient protection for high-risk, high-value benchmarks or excessive protection for low-risk benchmarks, leading to resource misallocation and cost waste.

[0008] Therefore, in response to the settlement monitoring needs of photovoltaic power plants, there is an urgent need for an integrated system that can perform global intelligent optimization during the design phase and realize the decision-making process from level point deployment to level point hierarchical protection. Summary of the Invention

[0009] In view of this, in order to solve the problems mentioned in the background technology, a solar-based intelligent protection system for leveling points is proposed.

[0010] The objective of this invention can be achieved through the following technical solution: This invention provides a solar-based intelligent protection system for leveling points, comprising: a leveling point deployment module, which divides the photovoltaic power station area into various photovoltaic areas, sets the number of leveling points to be deployed based on the area of ​​the photovoltaic area and the number of key infrastructures, and generates preliminary leveling points at equal intervals according to a preset grid based on the number of deployments.

[0011] The leveling point adjustment module adjusts the coordinates of leveling points that do not meet the preset threshold within a preset radius fine-tuning area according to the solar intensity and sunshine duration of each preliminary leveling point within a preset time period, so as to generate the final leveling point set.

[0012] The priority analysis module, based on the spatial distribution density characteristics of all benchmarks in the final benchmark set, the load correlation characteristics with key infrastructure, and the proximity characteristics with geological and topographic sections, performs location correlation, load influence, and section proximity analysis to determine the layout priority of each benchmark and sort them to generate an ordered benchmark set.

[0013] The level determination module analyzes the environmental risk factors and monitoring requirements of all level points in the ordered set of level points to determine the protection device level of each level point.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention achieves accurate identification of the differences in monitoring needs of different photovoltaic areas by dynamically calculating the number of grid points in combination with the area of ​​photovoltaic areas and the number of key infrastructures during the leveling point deployment stage, thereby generating a preliminary grid that is more in line with actual needs, and improving the rationality and coverage balance of the entire leveling point network deployment from the source.

[0015] (2) The present invention filters and fine-tunes the coordinates of the preliminary leveling point according to the solar intensity and duration within a preset time period, ensuring that the final leveling point meets the continuous power supply requirements of the solar monitoring equipment, thereby solving the potential problem of insufficient energy for some monitoring equipment caused by the traditional method ignoring solar conditions.

[0016] (3) This invention achieves a quantitative assessment of the urgency of setting up benchmarks by comprehensively calculating the spatial distribution density deviation of benchmarks, the load influence value of adjacent key infrastructure, and the proximity to geological and topographic sections, and performing a multi-factor weighted fusion priority ranking.

[0017] (4) By analyzing the human and natural environmental risk values ​​of each benchmark and the maximum demand value of the key infrastructure monitored thereon when determining the protection level, the present invention matches the corresponding level of protection device, thereby achieving the optimal allocation of protection resources and ensuring that high-risk benchmarks are adequately protected while controlling the overall cost.

[0018] (5) The present invention calculates the environmental risk value and the maximum monitoring requirement of each benchmark separately, and takes the maximum value of the two as the protection level decision value, and then matches the corresponding level of protection device, thereby realizing the key protection of high-risk benchmarks and the economic protection of low-risk benchmarks. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0021] Figure 2 This is a schematic diagram showing the connection steps of the final leveling point set generation process in this invention.

[0022] Figure 3 This is a schematic diagram showing the connection steps of the ordered leveling point set generation method of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention provides a solar-powered intelligent protection system for leveling points. First, the number of leveling points in each area is dynamically set based on the photovoltaic region area and the number of critical infrastructures. Preliminary leveling points are then generated at equal intervals according to a preset grid. Next, based on the solar radiation intensity and duration of each leveling point, coordinate optimization is performed on leveling points that do not meet threshold requirements within their surrounding fine-tuning areas, ultimately outputting a final set of leveling points. On this basis, three analyses are performed on all leveling points in this set: location correlation, load impact, and cross-sectional proximity. A weighted fusion calculation is then used to obtain a comprehensive priority score for each leveling point, thereby generating an ordered set of leveling points. Finally, by analyzing the human and natural environmental risks of each leveling point and the monitoring needs of the associated critical infrastructures, a matching protection device level is determined for each leveling point.

[0025] Please see Figure 1 As shown, the present invention provides a solar-powered intelligent protection system for leveling points, which includes: a leveling point layout module, a leveling point adjustment module, a priority analysis module, and a level determination module.

[0026] In the above, the leveling point adjustment module is connected to the leveling point layout module and the priority analysis module, respectively. The priority analysis module is also connected to the grade determination module.

[0027] The leveling point deployment module divides the photovoltaic power station area into various photovoltaic zones, sets the number of leveling points based on the area of ​​each photovoltaic zone and the number of key infrastructure facilities, and generates preliminary leveling points at equal intervals according to a preset grid based on the deployed points. The specific implementation includes the following steps:

[0028] First, perform the area division operation. As an optional implementation method, the entire photovoltaic power station area can be divided into several photovoltaic zones based on information such as the photovoltaic power station site planning map or operation and management zoning.

[0029] Secondly, for each photovoltaic area, the number of leveling points is dynamically set based on its area and the number of key infrastructure (such as transformer substations, booster stations, and main support arrays) within the area. One specific implementation method is to calculate the baseline number of leveling points for the photovoltaic area based on its area and a preset baseline leveling density per unit area. For example, the preset baseline leveling density per unit area can be based on the average leveling density that has been proven feasible in practice in photovoltaic power plant projects of similar scale and geological conditions.

[0030] The total number of critical infrastructures within the photovoltaic (PV) area is statistically analyzed. Based on this statistical analysis and the total number of critical infrastructures, a photovoltaic (PV) deployment density adjustment coefficient is calculated. The specific calculation formula is as follows: In the formula, This is the adjustment factor for the deployment density of photovoltaic areas. This refers to the number of critical infrastructure facilities within the photovoltaic area. This refers to the total number of all critical infrastructure components in the entire photovoltaic power plant. For example, a preset weighting coefficient greater than 0. This is used to adjust the intensity of the impact of infrastructure factors on the number of deployments.

[0031] The number of leveling points for each photovoltaic area is calculated by multiplying the deployment density adjustment coefficient by the baseline deployment number. This method allows the deployment density to be adaptively adjusted according to the area characteristics and infrastructure density of different areas. Compared with a uniform deployment scheme with a fixed density, it can effectively avoid insufficient or excessive deployment of leveling points due to uneven resource allocation.

[0032] Finally, based on the calculated number of photovoltaic arrays in each region, preliminary leveling point coordinates are generated in the corresponding photovoltaic region according to a preset equidistant grid, forming an initial leveling point set covering the entire power station.

[0033] Please see Figure 2 As shown, the leveling point adjustment module, based on the solar intensity and duration of each preliminary leveling point within a preset time period, adjusts the coordinates of leveling points that do not meet a preset threshold within a fine-tuning area of ​​a preset radius until the threshold is met, thereby generating a final set of leveling points. The core function of the leveling point adjustment module is to ensure that all candidate leveling points can meet the solar radiation requirements for the continuous and stable operation of the solar monitoring equipment. The specific implementation includes the following steps:

[0034] Q1. Calculate the average solar intensity of each preliminary leveling point within a preset time period.

[0035] Q2. Compare the average solar intensity and sunshine duration of each preliminary benchmark with their preset solar intensity threshold and sunshine duration threshold, respectively.

[0036] Furthermore, the preset solar intensity threshold and sunshine duration threshold are the minimum illumination requirements to ensure the continuous and stable operation of the solar power supply equipment at the leveling point. Their values ​​can be determined based on the energy balance principle, specifically: based on the average daily power consumption of the selected solar monitoring equipment, the capacity of the supporting energy storage battery, and the number of days of continuous operation without sunlight required by the system, combined with the conversion efficiency of the photovoltaic panels, the minimum average daily solar energy input required to ensure the system's energy self-sufficiency is calculated. Then, based on typical meteorological data of the photovoltaic power station location, the aforementioned minimum average daily solar energy input is converted into solar intensity threshold and sunshine duration threshold.

[0037] Q3. When the average solar intensity is greater than or equal to the preset solar intensity threshold and the sunshine duration is greater than the preset sunshine duration threshold, its location coordinates are used as the final coordinates.

[0038] Q4. If either the average daily sunshine intensity or the sunshine duration does not meet its corresponding threshold, the preliminary benchmark is determined to be a benchmark to be adjusted.

[0039] Q5. For the level point to be adjusted, use its position coordinates as the center and perform a grid search within the preset fine-tuning radius. If one or more new coordinates that simultaneously satisfy the solar intensity threshold and the solar duration threshold are found, then select the new coordinates with the best comprehensive lighting conditions as its final coordinates.

[0040] Q6. If no new coordinates that meet the threshold are found, mark them as insufficient lighting level points and use their position coordinates as the final coordinates.

[0041] Q7. For all leveling points, construct a final leveling point set containing the final coordinates of all leveling points and their corresponding illumination status attributes. The illumination status attributes are determined based on the judgment results of steps Q3 to Q6, specifically whether the illumination is suitable or insufficient.

[0042] It should be noted that the final set of benchmarks includes the final coordinates of all preliminary benchmarks after processing. For benchmarks marked as insufficiently lit, their final coordinates are their location coordinates. These benchmarks will be evaluated along with other benchmarks in the subsequent deployment priority analysis and protection level determination process. However, because they cannot meet the basic lighting conditions for solar power supply, such benchmarks should be specially marked in the final system output report or engineering implementation plan, and a prompt should be given indicating that non-solar power supply methods or priority power supply schemes should be adopted.

[0043] For example, the gridded search within the preset fine-tuning radius specifically includes the following process:

[0044] Q5-1. Define the search grid: Establish a two-dimensional planar search area centered on the coordinates of the leveling point to be adjusted, with a preset fine-tuning radius as the boundary. Divide this area into a regular square grid with a side length of a preset step size (e.g., 0.5 meters or 1 meter). All intersections of the grid constitute the set of candidate new coordinates to be evaluated. The step size can be determined based on both positioning accuracy requirements and computational efficiency, for example, set to 0.5 meters or 1 meter.

[0045] Q5-2. Evaluate the illumination conditions of candidate points: For each candidate new coordinate, call the illumination analysis steps Q1-Q2 to calculate its average solar intensity and duration within a preset time period.

[0046] Q5-3. Screening and Selection: Traverse all candidate points and mark the candidate points that simultaneously satisfy the conditions of average solar intensity being greater than or equal to the solar intensity threshold and average solar duration being greater than or equal to the solar duration threshold as valid adjustment points.

[0047] If there are multiple effective adjustment points, the ratio of average solar intensity to solar intensity threshold is taken as the solar intensity ratio, and the ratio of average sunshine duration to sunshine duration threshold is taken as the sunshine duration ratio. Then, the average of the solar intensity ratio and the sunshine duration ratio is calculated as the illumination adaptation coefficient of each effective adjustment point, and the effective adjustment point with the highest illumination adaptation coefficient is selected as the new coordinate.

[0048] If no valid adjustment point exists, proceed to step Q6 and mark it as an under-illuminated level point.

[0049] The priority analysis module, based on the spatial distribution density characteristics of all benchmarks in the final benchmark set, the load correlation characteristics with key infrastructure, and the proximity characteristics with geological and topographic sections, performs location correlation, load influence, and section proximity analysis to determine the layout priority of each benchmark and sort them to generate an ordered benchmark set.

[0050] For example, the location correlation analysis includes: using the ratio of the number of level points in the photovoltaic region to which the level point belongs to the area of ​​the region as the expected local density of each level point.

[0051] Centered on the benchmark, count the actual number of benchmarks within the preset evaluation radius and calculate the actual local density of benchmarks.

[0052] The relative deviation between the actual density and the expected density is calculated as the density deviation coefficient for each leveling point. A smaller density deviation coefficient indicates that the actual distribution around the leveling point more closely matches the expected density for the area, and the distribution is more reasonable. A larger density deviation coefficient indicates a greater deviation between the actual distribution and the intended distribution; an actual local density much larger than the expected local density indicates over-clustering, while an actual local density much smaller than the expected local density indicates over-sparseness.

[0053] For example, the load impact analysis includes: searching for all critical infrastructure within a preset evaluation radius for the leveling points in the final set of leveling points.

[0054] Based on the distances of each critical infrastructure site to the benchmark, and in conjunction with a pre-set assessment radius, the load impact values ​​of each critical infrastructure site are calculated. The contribution of distance to the load impact value follows the distance decay principle: the impact value of a single load source on the benchmark is positively correlated with the foundation load value of that load source and negatively correlated with the distance between the load source and the benchmark. The closer the distance, the greater the contribution of the impact value.

[0055] The load impact values ​​of all critical infrastructure are summed to obtain the load impact values ​​of each leveling point.

[0056] It should be noted that the load impact of a single critical infrastructure on a specific leveling point can be calculated using the formula described above: In the formula: This represents the load influence value. This is the base load value for critical infrastructure. This value can be preset based on the type of facility, design load, or historical monitoring data (for example, a higher value is assigned to a step-up substation, and a lower value is assigned to a regular transformer substation). The actual distance from the infrastructure to the benchmark; This is the preset evaluation radius. The distance exceeds... The facilities were considered to have a negligible impact. This represents the distance decay function, which is defined in the interval [missing information]. Take a value that satisfies: when hour, That is, the one with the greatest impact; when hour, That is, the effect is zero; the function value follows It increases and monotonically decreases. The distance decay function... One exemplary implementation is a linear decay function: .

[0057] For example, the cross-sectional proximity analysis includes: obtaining the vertical distance between each benchmark and each adjacent cross-sectional feature from the proximity features of all benchmarks and geological topographic cross-sections in the final benchmark set; and calculating the average of the vertical distances of each adjacent cross-sectional feature to obtain the baseline vertical distance. The cross-sectional feature lines include, but are not limited to: abrupt changes in terrain slope, excavation and filling boundary lines of the site, and boundaries between different geological units.

[0058] The minimum vertical distance from the characteristics of each adjacent cross-section is obtained, and its ratio to the baseline vertical distance is used as the cross-section risk proximity. This ratio amplifies local risk signals: a small risk proximity value indicates that the minimum vertical distance is significantly less than the baseline vertical distance, meaning that a cross-section is abnormally close to the benchmark while other cross-sections are relatively far away, suggesting a potential risk source at that benchmark, and its deployment and protection should be prioritized accordingly. Conversely, a risk proximity value close to or greater than 1 indicates that the distance distribution between the benchmark and each cross-section is relatively uniform, and the geological risk is relatively balanced or low. This normalization process not only eliminates the influence of the absolute distance dimension but also enhances the sensitivity of identifying local high-risk situations.

[0059] Please see Figure 3As shown, exemplarily, generating an ordered set of leveling points includes: obtaining the density deviation coefficient, load influence value, and cross-sectional risk proximity of the leveling points in the final set of leveling points.

[0060] The comprehensive priority score of each level point is corrected based on the illumination status attribute of each level point in the final level point set, resulting in the final priority score of each level point. Specifically, the correction coefficient is 1 for level points with illumination status attribute of "illuminated," and the correction coefficient for level points with illumination status attribute of "insufficient illumination" is a preset priority reduction coefficient, and the value range is between 0 and 1.

[0061] All benchmarks are sorted in descending order of their final priority scores, and an ordered set of benchmarks is generated based on the sorting results.

[0062] It should be added that the comprehensive priority score is achieved through weighted fusion calculation, and the calculation formula is as follows: In the formula For comprehensive priority scoring, , , These are the density deviation coefficient, load influence value, and cross-sectional risk proximity, respectively. , , These are the corresponding weighting coefficients, used to quantify the influence of different indicators on the priority of benchmark deployment.

[0063] By using weighted fusion calculation to calculate the comprehensive priority score, the different contributions of density uniformity, load risk and geological risk to the deployment priority can be accurately reflected by the weight allocation, and the relative importance of each indicator can be reflected. At the same time, the calculation realizes the effective integration of information from three independent evaluation dimensions, providing a unified and comparable decision basis for deployment order.

[0064] The weighting coefficients can be determined using the Analytic Hierarchy Process (AHP). One implementation method includes: constructing a judgment matrix for density deviation coefficient, load influence value, and cross-sectional risk proximity based on AHP criteria; assigning values ​​to the relative importance of each indicator using a 1-9 scaling method; subsequently calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix; performing a consistency check; and assigning values ​​to the normalized eigenvectors respectively. , , And satisfy .

[0065] The level determination module analyzes the environmental risk factors and monitoring requirement factors of all level points in the ordered set of level points to determine the protection device level of each level point. The specific implementation includes the following two aspects of analysis and final decision-making:

[0066] Environmental risk factor analysis: Calculating the environmental risk value of the benchmark. On one hand, based on environmental risk factors, the duration of pedestrian and vehicular traffic at the benchmark within a preset time period is statistically analyzed to calculate the degree of human activity disturbance. On the other hand, the degree of natural environmental disturbance at the benchmark is assessed. Finally, the degree of human activity disturbance and the degree of natural environmental disturbance are integrated to obtain the environmental risk value of the benchmark. The specific implementation includes the following steps:

[0067] The passage time of people and vehicles at all level points in the ordered set of level points is obtained from the environmental risk factors within a preset time period, and the ratio of the passage time of people and vehicles to the preset time period is used as the degree of human activity interference.

[0068] The proximity relationships between all level points in the ordered set of level points and various natural environmental risk sources are obtained from environmental risk factors, and the degree of natural environmental disturbance is calculated.

[0069] The environmental risk value of all leveling points in the ordered leveling point set is obtained by averaging the human activity disturbance degree and the natural environment disturbance degree.

[0070] It should be added that the natural environmental disturbance degree is used to quantify the potential natural hazard risks faced by the benchmark due to its geographical location. Its calculation is based on a multi-factor risk assessment framework, which considers at least the following three categories of natural hazard factors: hydrogeological conditions (such as whether it is located in a waterlogged low-lying area), slope stability (such as the distance to the nearest slope and the slope), and adverse geological effects (such as whether it is located in a rockfall or landslide affected area).

[0071] For each benchmark, the aforementioned factors are assessed and assigned corresponding risk sub-values. For example, the following principle can be used for quantification: for hydrogeological conditions, if the benchmark is located in a waterlogged area or its elevation is lower than the surrounding catchment threshold, a higher risk sub-value is assigned; otherwise, a lower value is assigned.

[0072] For slope stability, the risk component value is calculated based on the distance from the benchmark to the slope and its slope, using a pre-set distance-slope risk correlation table or attenuation function. The closer the distance and the greater the slope, the higher the risk component value.

[0073] For adverse geological effects, if the benchmark is located within the known or determined influence range, a high-risk sub-value is assigned.

[0074] After obtaining the values ​​of each risk component, the maximum value is selected as the degree of natural environmental disturbance. The greater the degree of natural environmental disturbance, the higher the comprehensive natural hazard risk faced by the benchmark, and the greater the weight it needs to be considered in the subsequent determination of the protection level.

[0075] The monitoring demand factor analysis aims to quantify the importance of the monitoring tasks to be undertaken by each benchmark. By pre-setting basic importance weights for different types of infrastructure (such as booster stations, transformer substations, and photovoltaic arrays) and introducing an influence coefficient that decreases with distance, the comprehensive influence of each facility on the benchmark is calculated. Finally, the facility with the largest comprehensive influence is selected as the maximum monitoring demand for that benchmark. This ensures that the allocation of monitoring resources accurately targets the most important facility in the area that most needs monitoring at that benchmark, thus prioritizing the monitoring reliability of the most critical areas within a limited number of benchmarks. The specific implementation includes the following steps:

[0076] W1. Determine the basic importance weight: Based on all critical infrastructure within the pre-set assessment radius of each benchmark, match each critical infrastructure with the basic importance weight corresponding to each critical infrastructure type to obtain the basic importance weight of each critical infrastructure.

[0077] W2. Calculate the distance impact coefficient: Based on the distance between each critical infrastructure and the benchmark and the preset assessment radius, calculate the distance impact coefficient for each critical infrastructure. Specifically, subtract the ratio of distance to preset assessment radius from the value of 1 to obtain the distance impact coefficient for each critical infrastructure. The closer the distance, the closer the distance impact coefficient is to 1, and the greater the impact.

[0078] W3. Calculate the overall importance weight: Multiply the distance impact coefficient of each critical infrastructure with its basic importance weight to obtain the importance weight of each critical infrastructure.

[0079] W4. Determine the maximum monitoring demand: Select the maximum value from the importance weights of each critical infrastructure as the maximum monitoring demand of the benchmark.

[0080] It should be added that the basic importance weights corresponding to the key infrastructure types are used to quantify the relative importance of different types of facilities in the settlement monitoring network. These weights are primarily based on the facility's functional criticality, structural sensitivity to settlement, and its irreplaceable role in the operation of the photovoltaic power plant system.

[0081] For example, based on engineering experience and industry consensus, critical infrastructure can be pre-defined into several importance levels, and a baseline weight value can be assigned to each level. For example:

[0082] Core hub facilities (such as substations and main control buildings): Because they are the nodes for the convergence and control of power for the entire station, structural settlement may cause system-level risks, and are therefore assigned the highest weight (e.g., 1.0).

[0083] Key conversion facilities (such as box-type transformers and inverter clusters): affect the grid connection of local power generation units and are given a medium weight (e.g., 0.7).

[0084] Array support facilities (such as photovoltaic array foundations and tracking bracket shafts): affect the flatness and efficiency of the photovoltaic panels, and are assigned a weight to the foundation (e.g., 0.5).

[0085] For example, determining the protection level of each benchmark includes: obtaining the environmental risk value and maximum monitoring requirement of each benchmark, and then selecting the maximum value from the environmental risk value and maximum monitoring requirement as the protection level decision value of each benchmark.

[0086] The protection level decision value of each leveling point is matched with the protection level decision value range corresponding to each protection device level to obtain the protection device level of each leveling point.

[0087] It should be added that the protection level decision value ranges corresponding to each protection device level constitute the decision-making basis for mapping the quantitative assessment results to specific engineering measures. The division of this range aims to ensure that the intensity of protection resource allocation is positively correlated with the actual risk level faced by the benchmark and its monitoring value.

[0088] The delineation of the intervals can be based on engineering experience regarding protection capabilities: First, it is necessary to clarify the typical risk level or monitoring reliability level that each level of protection device (such as foundation piles of different specifications or protective covers) can effectively withstand in its design. Through engineering experience, experimental data, or simulation analysis, the approximate range of protection level decision values ​​applicable to each device is determined, and then the interval boundaries are set based on this. For example, if a Level 1 protection device is designed to cope with the most severe environments and the most important monitoring tasks, then the lower limit of its decision index interval can be set to a higher value.

[0089] In a specific implementation scenario, it is assumed that the theoretical range of the protection level decision value, after normalization, is as follows: Furthermore, a three-tiered protection system is planned. This can be exemplarily defined based on the above logic:

[0090] Level 1 protection device: Suitable for the highest risk or monitoring level, with a decision index range of [missing information]. This level corresponds to the highest protection specifications (such as deep buried extended foundation, high-strength alloy protective cover and integrated abnormal alarm function).

[0091] Secondary protection device: suitable for leveling points with moderate risk or monitoring needs, with a decision index range of [missing information]. This level corresponds to standard protection specifications (such as conventional concrete piles or cast iron protective covers with locks).

[0092] Level 3 protection device: Suitable for leveling points with relatively low risk or monitoring needs, with a decision indicator range of [missing information]. This level corresponds to economical basic protection (such as precast concrete marker posts or simple ground markings).

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0094] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0097] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solar-powered intelligent protection system for leveling points, characterized in that: The system includes: The leveling point deployment module divides the photovoltaic power station area into various photovoltaic areas, sets the number of leveling points based on the area of ​​the photovoltaic area and the number of key infrastructures, and generates preliminary leveling points at equal intervals according to the preset grid based on the number of leveling points. The leveling point adjustment module adjusts the coordinates of leveling points that do not meet the preset threshold within a preset radius fine-tuning area according to the solar intensity and sunshine duration of each preliminary leveling point within a preset time period, so as to generate the final leveling point set. The priority analysis module, based on the spatial distribution density characteristics of all benchmarks in the final benchmark set, the load correlation characteristics with key infrastructure, and the proximity characteristics with geological and topographic sections, performs location correlation, load influence, and section proximity analysis to determine the layout priority of each benchmark and sort them to generate an ordered benchmark set. The level determination module analyzes the environmental risk factors and monitoring requirements of all level points in the ordered set of level points to determine the protection device level of each level point.

2. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The number of benchmarks to be set includes: Based on the area of ​​the photovoltaic region and the preset benchmark deployment density per unit area, calculate the benchmark deployment number of the photovoltaic region; The total number of critical infrastructures within the photovoltaic area is counted, and then the deployment density adjustment coefficient of the photovoltaic area is calculated based on the number of critical infrastructures and the total number of critical infrastructures. The number of leveling points in each photovoltaic area is calculated by multiplying the deployment density adjustment coefficient with the baseline deployment number.

3. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The generation of the final set of leveling points includes: The average solar intensity of each preliminary leveling point is calculated by averaging the solar intensity of each preliminary leveling point within a preset time period. The average solar radiation intensity and duration of each preliminary benchmark are compared with their preset solar radiation intensity threshold and solar radiation duration threshold, respectively. When the average solar intensity is greater than or equal to the preset solar intensity threshold and the sunshine duration is greater than the preset sunshine duration threshold, its location coordinates are used as the final coordinates. If either the average solar radiation intensity or the duration of sunshine does not meet its corresponding threshold, the preliminary benchmark is determined to be a benchmark to be adjusted. For the level point to be adjusted, a grid search is performed within a preset fine-tuning radius, centered on its position coordinates. If one or more new coordinates that simultaneously satisfy the solar intensity threshold and the solar duration threshold are found, the new coordinates with the best comprehensive lighting conditions are selected as its final coordinates. If no new coordinates that meet the threshold are found, they are marked as insufficiently lit level points, and their position coordinates are used as the final coordinates. For all leveling points, construct a final leveling point set containing the final coordinates of all leveling points and their corresponding illumination state attributes.

4. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The location correlation analysis includes: The ratio of the number of leveling points in the photovoltaic region to which each leveling point belongs to the area of ​​that region is used as the expected local density of each leveling point. Using the benchmark as the center, count the actual number of benchmarks within the preset evaluation radius and calculate the actual local density of benchmarks. The relative deviation between the actual density and the expected density is calculated and used as the density deviation coefficient for each leveling point.

5. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The load impact analysis includes: For the level points in the final set of level points, search for all critical infrastructure within their preset evaluation radius; Based on the distance of each key infrastructure to the leveling point and combined with the preset assessment radius, the load impact value of each key infrastructure is calculated. The load impact values ​​of all critical infrastructure are summed to obtain the load impact values ​​of each leveling point.

6. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The cross-sectional proximity analysis includes: The vertical distance between a benchmark and its neighboring geological and topographic features is obtained from all benchmarks in the final benchmark set. The average vertical distance of each neighboring feature is calculated to obtain the benchmark vertical distance. The minimum value is obtained from the vertical distances of each adjacent cross-section feature, and the ratio of this minimum value to the baseline vertical distance is used as the cross-section risk proximity.

7. The solar-powered intelligent protection system for leveling points according to claim 3, characterized in that: The generation of the ordered set of leveling points includes: Obtain the density deviation coefficient, load influence value, and cross-sectional risk proximity of the level points in the final set of level points; For each benchmark, the density deviation coefficient, load influence value and cross-sectional risk proximity are weighted and integrated to obtain the comprehensive priority score of each benchmark. Based on the illumination status attributes of each level point in the final level point set, the comprehensive priority score of each level point is corrected to obtain the final priority score of each level point. All benchmarks are sorted in descending order of their final priority scores, and an ordered set of benchmarks is generated based on the sorting results.

8. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The analysis of environmental risk factors for all leveling points in the ordered set of leveling points includes: The passage time of people and vehicles at all level points in the ordered set of level points is obtained from environmental risk factors within a preset time period, and the ratio of the passage time of people and vehicles to the preset time period is used as the degree of human activity interference. The proximity relationships between all leveling points in the ordered set of leveling points and various natural environmental risk sources are obtained from environmental risk factors, and the degree of natural environmental disturbance is calculated. The environmental risk value of all leveling points in the ordered leveling point set is obtained by averaging the human activity disturbance degree and the natural environment disturbance degree.

9. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The analysis of monitoring requirements for all leveling points in the ordered set of leveling points includes: Based on all critical infrastructure within the pre-set assessment radius of each benchmark, the basic importance weight of each critical infrastructure is matched with the basic importance weight corresponding to each type of critical infrastructure to obtain the basic importance weight of each critical infrastructure. Based on the distance between each key infrastructure and the benchmark and the preset assessment radius, the distance influence coefficient of each key infrastructure is calculated; The importance weight of each critical infrastructure is obtained by multiplying its distance impact coefficient with its basic importance weight. The maximum value among the importance weights of each critical infrastructure is selected as the maximum monitoring requirement for the benchmark.

10. The solar-powered intelligent protection system for leveling points according to claim 1, characterized in that: The determination of the protection device level for each leveling point includes: The environmental risk value and maximum monitoring requirement of each benchmark are obtained, and then the maximum value is selected from the environmental risk value and maximum monitoring requirement as the protection level decision value of each benchmark. The protection level decision value of each leveling point is matched with the protection level decision value range corresponding to each protection device level to obtain the protection device level of each leveling point.

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