A photovoltaic area camera positioning and deploying method, device, equipment and medium

Abstract

The present application relates to photovoltaic power station monitoring technical field, disclose a kind of photovoltaic area camera positioning deployment method, device, equipment and medium, the present application determines the deployment parameter of camera in each position in photovoltaic area according to scene parameter, and with avoiding component reflection interference and support shelter as constraint condition, candidate point is selected from each position according to deployment parameter;According to the operation state of photovoltaic area, candidate point is clustered and divided, to obtain monitoring area, and camera is deployed in monitoring area;For the blind area outside monitoring area, realize coverage by the movement of camera deployed on preset path;The intensity of illumination and component temperature of photovoltaic area are obtained, the working mode of camera is adjusted according to intensity of illumination, and camera is triggered to shoot according to component temperature.The present application effectively improves monitoring precision, reduces construction and operation cost, solves the technical problems of poor view angle adaptation, equipment redundancy and insufficient working condition linkage in the prior art.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant monitoring technology, specifically to a method, device, equipment, and medium for positioning and deploying cameras in photovoltaic areas. Background Technology

[0002] With the large-scale and intensive development of photovoltaic power plants, the need for monitoring large photovoltaic areas (covering various terrains such as plains and mountains) is becoming increasingly urgent. The rationality of camera positioning and deployment directly affects the monitoring coverage, equipment investment costs, and operation and maintenance efficiency. Currently, existing photovoltaic area camera positioning technologies are mainly based on terrain features (such as DEM digital elevation models) or simple area division (grids, diagonal point placement) for point selection. The core objective is only to achieve a basic balance between "coverage range" and "number of devices," without fully considering the specific characteristics of the photovoltaic area itself for targeted design.

[0003] The existing technology has the following limitations: (1) The impact of core parameters such as the tilt angle of the photovoltaic module array and the height of the bracket on the camera's viewing angle was not considered. The deployment method with a fixed height and fixed downward angle is prone to problems such as blind spots and glare caused by module reflection and bracket obstruction, resulting in insufficient monitoring accuracy. (2) The site optimization relies on static algorithms and does not link with the operating conditions of the photovoltaic power station (such as hot spots on the modules and changes in day and night illumination), so dynamic adaptation cannot be achieved; (3) Redundant equipment deployment, often achieved by adding fixed bullet cameras and thermal imaging equipment to fill blind spots and monitor multiple scenes, resulting in a high number of cameras and increased construction and maintenance costs; (4) The existing applications of 360-degree PTZ cameras are mostly general deployments, which do not fully explore their advantages of full coverage and adjustable cruise. They are not deeply adapted to the characteristics of photovoltaic modules and operating conditions, and cannot give full play to the equipment's efficiency. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for positioning and deploying cameras in photovoltaic areas, in order to solve the problems in existing photovoltaic area camera deployments, such as poor viewing angle adaptation due to ignoring component tilt angle and bracket height, numerous blind spots due to reflective obstruction, high equipment usage due to static redundancy, and rigid monitoring strategies due to the inability to link changes in light intensity and component operating conditions, requiring additional deployment of dedicated equipment.

[0005] In a first aspect, the present invention provides a method for positioning and deploying cameras in a photovoltaic area, comprising: acquiring scene parameters of the photovoltaic area, the scene parameters including at least the tilt angle of the photovoltaic modules and the height of the support frame; determining the deployment parameters of cameras at various locations in the photovoltaic area based on the scene parameters, and selecting candidate locations from each location based on the deployment parameters, with constraints of avoiding interference from module reflections and shading by the support frame; clustering the candidate locations according to the operating status of the photovoltaic area to obtain a monitoring area, and deploying cameras in the monitoring area; for blind spots outside the monitoring area, achieving coverage by moving the deployed cameras along a preset path; acquiring the light intensity and module temperature of the photovoltaic area, adjusting the working mode of the cameras according to the light intensity, and triggering the cameras to take pictures according to the module temperature.

[0006] This invention collects scene parameters such as the tilt angle of photovoltaic modules and the height of the support frame, and determines the deployment parameters of the cameras accordingly. This allows the camera's viewing angle to adaptively match the actual posture of the photovoltaic modules, fundamentally avoiding monitoring blind spots and glare problems caused by module reflection interference and support frame obstruction, thus improving monitoring accuracy. By using reflection and obstruction avoidance as constraints to select candidate locations and combining them with operational status for clustering, precise camera deployment and optimized usage are achieved, avoiding equipment redundancy in traditional deployment methods. Furthermore, the movement of deployed cameras along preset paths achieves coverage of blind spots outside the monitored area, eliminating the need for additional fixed blind spot compensation equipment, reducing equipment usage and construction costs. Simultaneously, by acquiring light intensity and module temperature to adjust the camera's working mode and trigger shooting, the cameras can dynamically adapt to changes in day and night light and module operating conditions, taking into account multiple needs of module inspection and perimeter security, without the need for separate deployment of dedicated equipment such as thermal imaging. This invention effectively improves monitoring accuracy, reduces construction and maintenance costs, and solves the technical problems of poor viewing angle adaptation, equipment redundancy, and insufficient operational status linkage in existing technologies.

[0007] In one optional implementation, the camera is a PTZ camera. The PTZ camera features remotely adjustable tilt angle, 360-degree continuous rotation, and multi-preset position cruise capabilities. This implementation integrates the PTZ camera with the photovoltaic area scene depth, enabling it to flexibly adjust the tilt angle and installation height according to the tilt angle of different photovoltaic modules and the height of the support bracket, achieving adaptive matching with the photovoltaic module's attitude. Simultaneously, utilizing the PTZ camera's 360-degree coverage without blind spots and multi-preset position cruise capabilities, a single PTZ camera can cover multiple directions and areas through time-based rotation, eliminating the need for additional fixed bullet cameras or thermal imaging equipment. This achieves multi-purpose functionality, minimizing equipment usage and reducing construction and maintenance costs.

[0008] In one optional implementation, the scene parameters also include component spacing, inverter / substation location, and / or site DEM digital elevation data. Component spacing is used in calculating shading range and dividing the monitoring unit grid; in densely populated areas, the camera installation height is automatically adjusted and the cluster unit area is reduced, while in sparse areas, the unit area is expanded, achieving refined placement and avoiding minor blind spots between rows and columns. Inverter / substation locations are marked as primary high-priority monitoring core points, ensuring high-value equipment is at the center of the PTZ camera coverage and achieving focused inspection and dual coverage. DEM elevation data is used to select high points with an altitude greater than 2m above the surrounding component areas as candidate locations, ensuring basic camera coverage. Through the integrated use of these multi-dimensional scene parameters, this implementation achieves precise adaptation of camera deployment to the actual terrain, equipment distribution, and array arrangement of the photovoltaic area, further improving the integrity of monitoring coverage and the rationality of equipment deployment.

[0009] In one optional implementation, the deployment parameters include at least the camera's installation height, working tilt angle, and coverage radius. The installation height determines the actual height of the PTZ camera's pole, ensuring it avoids obstruction from front-row supports and components, and guarantees unobstructed line-of-sight coverage of the distant array. The working tilt angle determines the optimal viewing angle of the PTZ camera lens, avoiding glare interference from specular reflections on component surfaces, ensuring image clarity and detail recognition accuracy. The coverage radius determines the effective monitoring range of a single PTZ camera, providing a quantitative basis for subsequent candidate point spacing control and cluster unit area division. Through the combined use of these deployment parameters, this implementation achieves joint quantitative control of camera installation height, tilt angle, and coverage range, ensuring that each PTZ camera operates in its assigned monitoring area in an optimal, non-reflective, and unobstructed configuration. This avoids blind spots and insufficient accuracy issues caused by experience-based deployment, further guaranteeing full-area coverage and reducing redundant equipment usage.

[0010] In one optional implementation, deployment parameters for cameras at various locations within the photovoltaic area are determined based on scene parameters. With constraints such as avoiding component reflection interference and bracket obstruction, candidate locations are selected from each location based on these deployment parameters. This includes: determining the installation height and working angle of cameras at each location within the photovoltaic area based on pre-built mapping relationships between photovoltaic module tilt angle and camera working angle, and between bracket height and camera installation height; and selecting candidate locations from each location with constraints such as the working angle being greater than the critical angle of component reflection and the installation height being greater than the safe height corresponding to the bracket obstruction angle. By combining the above mapping relationships and constraints, this implementation method achieves precise quantitative matching between camera deployment parameters and the actual geometric characteristics of photovoltaic modules. Specifically, the camera's working tilt angle is adaptively adjusted to follow the module's tilt angle, and the installation height is adaptively adjusted to follow the bracket height. This allows the camera to avoid glare interference caused by module reflection and blind spots caused by front bracket obstruction from the source. It ensures that each selected candidate location meets the physical compliance conditions of no reflection and no obstruction, providing a complete set of compliant locations for subsequent clustering deployment. This ensures that the finally installed cameras all operate at the optimal viewing angle.

[0011] In one optional implementation, candidate points are clustered and divided according to the operating status of the photovoltaic area to obtain monitoring areas, and cameras are deployed in the monitoring areas. This includes: classifying the photovoltaic area by operating priority to obtain areas with at least two priority levels; using a clustering algorithm to cluster the candidate points, merging adjacent candidate points of the same priority level into one monitoring unit; determining the monitoring area based on each monitoring unit; and deploying cameras in each monitoring area. In this implementation, by classifying the photovoltaic area by operating priority and using a clustering algorithm to merge adjacent candidate points of the same priority level into monitoring units and deploying cameras, the camera deployment density is precisely matched to the actual monitoring needs of each area. High-priority areas achieve focused coverage due to the dense clustering of monitoring units, while low-priority areas avoid over-deployment due to the sparse clustering of monitoring units. While ensuring the reliability of monitoring in key areas, this significantly reduces the number of cameras used in unnecessary areas at the system level, lowering construction and maintenance costs.

[0012] In one optional implementation, a clustering algorithm is used to cluster candidate points, including: using the location coordinates and operational priority of candidate points as clustering features, clustering candidate points using the K-means clustering algorithm, and selecting the cluster center or a high point within the cluster area that meets the coverage conditions as the camera installation location for the monitoring unit. Through this method, this implementation utilizes the K-means clustering algorithm to use location coordinates and operational priority as joint clustering features, achieving optimized grouping of candidate points. This prioritizes the aggregation of points in high-priority areas and selects the optimal installation location, while low-priority areas are clustered and merged or naturally sparsified. This ensures dual coverage in key areas while minimizing equipment usage in unnecessary areas. Furthermore, prioritizing the selection of cluster centers or high points that meet the coverage conditions as installation locations fully utilizes terrain advantages to improve single-point coverage efficiency and further reduces redundant equipment deployment.

[0013] Secondly, the present invention provides a camera positioning and deployment device for a photovoltaic area, comprising: a scene parameter acquisition module for acquiring scene parameters of the photovoltaic area, the scene parameters including at least the tilt angle of the photovoltaic modules and the height of the support frame; a candidate point selection module for determining the deployment parameters of cameras at various locations in the photovoltaic area based on the scene parameters, and selecting candidate points from each location based on the deployment parameters, with constraints of avoiding component reflection interference and support frame shading; a clustering deployment module for clustering the candidate points according to the operating status of the photovoltaic area to obtain a monitoring area, and deploying cameras in the monitoring area; a blind spot coverage module for covering blind spots outside the monitoring area by moving the deployed cameras along a preset path; and a working condition linkage module for acquiring the light intensity and module temperature of the photovoltaic area, adjusting the working mode of the cameras according to the light intensity, and triggering the cameras to take pictures according to the module temperature.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic area camera positioning and deployment method described in the first aspect or any corresponding embodiment thereof.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic area camera positioning and deployment method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the first step in the deployment method for positioning cameras in a photovoltaic area according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the photovoltaic area camera positioning and deployment method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a photovoltaic area camera positioning and deployment device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] With the large-scale development of photovoltaic power plants, existing camera deployment methods are mainly based on terrain or regional division, failing to consider the impact of core parameters such as photovoltaic module tilt angle and support height on the camera's viewing angle. This leads to frequent occurrences of reflections, glare, and blind spots due to support obstruction, resulting in insufficient monitoring accuracy. Furthermore, traditional deployment methods rely on static algorithms and often compensate for blind spots by adding fixed bullet cameras and thermal imaging equipment, leading to high equipment usage, increased construction and maintenance costs, and an inability to dynamically adapt to changes in lighting conditions and module operating conditions. Therefore, this invention provides a method, device, equipment, and medium for positioning and deploying cameras in photovoltaic areas to solve the technical problems of poor viewing angle adaptation, equipment redundancy, and insufficient operational condition linkage in existing technologies.

[0022] According to an embodiment of the present invention, a method for positioning and deploying a camera in a photovoltaic area is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] This embodiment provides a method for positioning and deploying cameras in photovoltaic areas. Figure 1 This is a flowchart of a photovoltaic area camera positioning and deployment method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain the scene parameters of the photovoltaic area. The scene parameters include at least the tilt angle of the photovoltaic modules and the height of the support frame.

[0024] The photovoltaic zone refers to a centralized photovoltaic power generation area consisting of photovoltaic module arrays, combiner boxes, inverters, transformer substations, and other equipment, covering various terrains such as plains, gentle slopes, and mountains.

[0025] Scene parameters refer to quantitative data used to characterize the physical features and spatial layout of the photovoltaic area. Among them, the photovoltaic module tilt angle, measured in degrees, refers to the angle between the photovoltaic module panel and the horizontal plane. Its value affects the angle of sunlight incidence and the direction of reflected light propagation, and is one of the core input parameters for determining the camera's working tilt angle in this invention. The bracket height, measured in meters, refers to the vertical height of the top of the photovoltaic module bracket relative to the ground. Its value affects the shading range of the front-row modules on the rear-row modules, and is one of the core input parameters for determining the camera's installation height in this invention. By collecting these scene parameters, a data foundation is provided for subsequently determining the camera deployment parameters, ensuring that the camera's viewing angle matches the actual spatial orientation of the photovoltaic modules, thereby avoiding reflection interference and bracket shading problems caused by missing parameters at the source.

[0026] In addition, the above parameters were collected separately for each location in the photovoltaic area. Different locations may have different tilt angles and heights due to terrain undulations and differences in module arrangement, in order to ensure the accuracy of subsequent deployment parameters at each location.

[0027] Step S102: Based on the scene parameters, determine the deployment parameters of the cameras at each location in the photovoltaic area, and select candidate locations from each location based on the deployment parameters, taking into account the constraints of avoiding component reflection interference and bracket shading.

[0028] Deployment parameters refer to the quantifiable technical indicators that need to be determined when a camera is installed in a specific location. These include at least the camera's installation height, working angle, and coverage radius, and serve as the direct basis for subsequent selection of candidate locations and actual installation and deployment.

[0029] In this step, the deployment parameters for cameras at various locations within the photovoltaic area refer to the calculation of the corresponding deployment parameters for each pre-determined potential installation location within the photovoltaic area. Since terrain height, module arrangement, tilt angle, and other scene parameters may vary at different locations within the photovoltaic area, the optimal deployment parameters for each location may also differ.

[0030] In this step, the constraints of avoiding component reflection interference and bracket obstruction are used. This means that the critical angle of component reflection and the bracket obstruction angle are used as the judgment criteria. The critical angle of component reflection is determined by the tilt angle of the photovoltaic module and is used to define the lower limit of the working downward angle of the camera, so that the camera will not produce glare due to sunlight reflected from the component mirror at the determined working downward angle. The bracket obstruction angle is determined by the camera installation height and the bracket height together and is used to define the lower limit of the camera installation height, so that the camera will not be blocked by the front bracket or the upper edge of the component at the determined installation height.

[0031] By imposing the above constraints, we ensure that the deployment parameters at the final selected candidate locations meet the physical compliance requirements of no glare and no obstruction.

[0032] In this step, the filtering process involves traversing all locations within the photovoltaic area, performing the aforementioned constraint checks on each location, retaining the locations that pass the checks as candidate locations, and removing the locations that fail the checks, thereby forming a candidate location pool for use as input in subsequent clustering deployment steps.

[0033] Step S103: Cluster the candidate points according to the operating status of the photovoltaic area to obtain the monitoring area, and deploy cameras in the monitoring area.

[0034] Among them, the operating status refers to the importance of monitoring each area in the photovoltaic zone during power generation and the risk level of the equipment, including but not limited to factors that affect the monitoring priority, such as the probability of hot spots on the modules, the concentration of equipment, and the frequency of personnel or vehicle entry.

[0035] Prioritizing the operation of photovoltaic areas refers to dividing the photovoltaic area into at least two priority levels based on the above-mentioned operating status. For example, areas with high monitoring importance, such as inverter clusters and areas with high incidence of hot spots in modules, are classified as high-priority areas, while areas with low monitoring importance, such as remote wastelands, are classified as low-priority areas. This is used to determine the different requirements of camera coverage density and response speed for different areas.

[0036] Clustering refers to using clustering algorithms to group candidate points that are geographically adjacent and belong to the same priority level into the same cluster, based on the location coordinates and operational priority of the candidate points. The aim is to optimize the grouping of discrete candidate points according to their spatial location and operational priority, ensuring that points within the same monitoring unit have similar spatial locations and the same level of monitoring importance.

[0037] A monitoring area refers to the spatial range covered by each monitoring unit obtained after clustering candidate points. Typically, one monitoring unit corresponds to one monitoring area. A monitoring unit contains multiple candidate points, but not all of them are equipped with cameras. Instead, the optimal location (such as the cluster center or a high point) is selected from the candidate points within the unit for a single camera deployment. Through clustering, high-priority areas have a higher camera deployment density due to the denser candidate points and the larger number of monitoring units formed after clustering and merging. Conversely, low-priority areas have a lower camera deployment density due to the sparser candidate points and fewer monitoring units. Furthermore, after clustering and merging, each monitoring unit deploys only one camera, avoiding equipment redundancy caused by installing cameras at multiple candidate points within the same area.

[0038] Deploying cameras refers to installing them at specific locations, such as cluster centers or high points within a cluster that meet coverage requirements. This step, through clustering, ensures that the camera deployment density precisely matches the actual monitoring needs of each area, guaranteeing reliable coverage in key areas while reducing the amount of equipment used in unnecessary areas.

[0039] Step S104: For blind spots outside the monitoring area, coverage is achieved by moving the deployed cameras along a preset path.

[0040] In this step, the blind spots outside the monitored area refer to the boundary gaps between monitoring units formed after clustering in step S103, low-priority areas not included in any monitoring unit (such as corner wastelands), and local areas that cannot be covered by the camera's field of view due to factors such as terrain undulations and component obstruction. These blind spots were not included in the fixed monitoring range during the clustering deployment phase because the camera installation positions were fixed or the clustering did not cover them; they are the remaining non-covered areas after fixed deployment.

[0041] The movement of deployed cameras along a preset path refers to the periodic movement of the cameras along a pre-set spatial trajectory and time sequence. This movement is not limited to uniform continuous rotation, but also includes intermittent jumps between multiple preset spatial directions according to a set time sequence, achieving sequential scanning and observation of different directions in a time-division manner. Through the above movement, areas not covered during the fixed deployment phase are periodically included in the observation range in the time dimension.

[0042] This step leverages the inherent mobility of the cameras already deployed within the monitored area. By moving them along preset paths, the cameras can cover blind spots missed during the fixed deployment phase, without requiring additional hardware. The aim is to avoid the traditional methods of adding fixed cameras or dedicated blind spot coverage equipment, thereby minimizing the total amount of equipment used while ensuring full coverage.

[0043] Step S105: Obtain the light intensity and module temperature of the photovoltaic area, adjust the working mode of the camera according to the light intensity, and trigger the camera to take pictures according to the module temperature.

[0044] Among them, light intensity refers to the visible light irradiance in the photovoltaic area environment, and the unit is lux. Its value changes with time (such as day and night, sunshine and rain), which directly affects the imaging quality and detail recognition ability of the camera in the visible light band.

[0045] Module temperature refers to the surface temperature of a photovoltaic module, measured in degrees Celsius (°C). Its value varies with solar radiation intensity and the module's operating status. When a hot spot fault occurs inside the module, the temperature of the fault area will be significantly higher than that of the surrounding normal area. Therefore, module temperature is an important physical quantity characterizing whether the module's operating condition is abnormal.

[0046] Adjusting the camera's operating mode based on light intensity means using light intensity as a criterion. In sufficient light, the camera operates in high-definition mode to acquire high-resolution images of component surface details, facilitating the identification of minor defects such as hot spots and damage. In low-light conditions, it switches to infrared mode to continue acquiring monitoring footage in low-light or no-light environments using infrared imaging principles. The purpose is to automatically adapt the camera's operating mode to changes in day and night lighting conditions, ensuring effective acquisition of monitoring footage around the clock.

[0047] Triggering camera imaging based on component temperature means that when a component temperature becomes abnormal, the corresponding camera in that area is automatically triggered to capture images of the abnormal location. Abnormal component temperatures typically indicate equipment malfunctions such as hot spots in that area, requiring timely acquisition of image data for remote confirmation and handling by maintenance personnel. The purpose is to automatically switch the camera from routine inspection mode to abnormal response mode, enabling targeted imaging of the abnormal area without manual intervention from maintenance personnel. This also avoids the need for a separate thermal imaging device for temperature detection, achieving multi-purpose functionality with a single device.

[0048] The photovoltaic area camera positioning and deployment method provided in this embodiment collects scene parameters such as the tilt angle of photovoltaic modules and the height of the support frame, and determines the deployment parameters of the cameras accordingly. This allows the camera's viewing angle to adaptively match the actual posture of the photovoltaic modules, fundamentally avoiding monitoring blind spots and glare problems caused by module reflection interference and support frame obstruction, thus improving monitoring accuracy. By using reflection and obstruction avoidance as constraints to select candidate locations and combining them with operating status for clustering, precise camera deployment and optimized usage are achieved, avoiding equipment redundancy in traditional deployment methods. Furthermore, the movement of deployed cameras along preset paths achieves coverage of blind spots outside the monitoring area, eliminating the need for additional fixed blind spot compensation equipment, reducing equipment usage and construction costs. Simultaneously, by acquiring light intensity and module temperature to adjust the camera's working mode and trigger shooting, the cameras can dynamically adapt to changes in day and night light intensity and module operating conditions, taking into account multiple needs of module inspection and perimeter security, without the need to deploy dedicated equipment such as thermal imaging. This invention effectively improves monitoring accuracy, reduces construction and maintenance costs, and solves the technical problems of poor perspective adaptation, equipment redundancy, and insufficient operational linkage in the prior art.

[0049] This embodiment provides a method for positioning and deploying cameras in photovoltaic areas. Figure 2 This is a flowchart of a photovoltaic area camera positioning and deployment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the scene parameters of the photovoltaic area. The scene parameters include at least the tilt angle of the photovoltaic modules and the height of the support frame.

[0050] In one alternative implementation, the scenario parameters also include component spacing, inverter / substation location, and / or site DEM digital elevation data.

[0051] In this embodiment, scene parameters of the photovoltaic area are collected. These parameters include the tilt angle of the photovoltaic modules, the height of the support structure, the spacing between modules, the location of the inverter / substation, and the site's DEM (Digital Elevation Model) data, which are integrated to form a photovoltaic area scene database. Specifically, the tilt angle error of the module array is no greater than 0.5°, the positioning accuracy of the inverter / substation is no greater than 1m, the resolution of the DEM data is no greater than 5m, and the data update frequency is no greater than 24 hours. Collecting these scene parameters provides a data foundation for subsequently determining the camera deployment parameters, ensuring that the camera's viewing angle matches the actual spatial orientation of the photovoltaic modules.

[0052] Step S202: Based on the scene parameters, determine the deployment parameters of the cameras at each location in the photovoltaic area, and select candidate locations from each location based on the deployment parameters, taking into account the constraints of avoiding component reflection interference and bracket shading.

[0053] In one alternative implementation, the camera is a PTZ camera.

[0054] In one alternative implementation, the deployment parameters include at least the camera's installation height, working angle, and coverage radius.

[0055] In this embodiment, a field-of-view adaptation model is pre-constructed. This model is based on the principles of optical reflection and three-dimensional geometric vision, aiming for glare-free, unobstructed, and 360-degree full-field coverage. The photovoltaic module tilt angle and support height are used as core input parameters. Optical simulation is used to calculate the critical angle of module reflection and the support occlusion range. A linkage mapping relationship is established between the photovoltaic module tilt angle and the camera's working downward angle, as well as between the support height and the camera's installation height. The adjustable ranges of each mapping relationship are also defined.

[0056] The construction process of the above field-of-view adaptation model is as follows: (a) Construction of scene sample dataset.

[0057] In this embodiment, a scenario sample dataset is first constructed. Samples of photovoltaic sites under various operating conditions are collected, including different component tilt angles (15°~35°), different support heights (2.0~3.5m), different terrains (plains / mountains), and different light intensities (0~100000 lux), to establish a multi-dimensional scenario sample library covering more than 200 sets of real photovoltaic engineering conditions.

[0058] Specifically, the samples are categorized according to five core dimensions: (1) Module tilt angle samples: sampled at gradients of 15°, 20°, 25°, 30° and 35° with a step size of 5°, covering all conventional photovoltaic tilt angles in the industry; (2) Support height samples: samples were collected at gradients of 2.0m, 2.5m, 3.0m and 3.5m to cover the mainstream support heights of ground photovoltaic systems; (3) Array density samples: distinguish between dense arrays (component spacing 1.2m×3.0m), conventional arrays (1.35m×3.5m), and sparse arrays (1.5m×4.0m); (4) Topographic samples: plains, gentle slopes, and mountainous terrain (DEM elevation difference 0~8m); (5) Lighting environment samples: strong light (60,000~100,000 lux), normal light (5,000~60,000 lux), weak light / night (0~5,000 lux).

[0059] Sample collection employed a combination of on-site measurements and simulation supplementation. For on-site measurements, multiple operational photovoltaic power plants were selected, and fixed-point measurements were conducted at different tilt angles, support heights, array spacings, and terrain areas. The actual shading angle, actual critical reflection angle, effective field of view, and blind zone area at different heights and tilt angles were collected for each operating condition. For simulation supplementation, for extreme operating conditions such as large tilt angles and mountainous terrain with significant elevation differences, supplementary sampling was conducted using an optical simulation platform to ensure uniform sample distribution, ultimately resulting in over 200 sets of valid, independent operating condition samples.

[0060] (ii) Sample standardization, cleaning and labeling.

[0061] After collection, the samples are cleaned and labeled in a standardized manner.

[0062] First, a unified initial screening was conducted on both the field-measured samples and the simulation-completed samples to remove invalid samples, including equipment failure sampling data, data affected by sudden changes in light intensity, extreme anomalies caused by shading, and samples exceeding the normal operating conditions of the photovoltaic industry. Then, the 3σ statistical discriminant rule was used to detect anomalies in various parameters, removing discrete outliers in parameters such as tilt angle, support height, shading angle, reflection angle, and blind zone area that deviated from the sample mean by more than three standard deviations, thus avoiding extreme noise data interfering with the model fitting accuracy.

[0063] Secondly, consistency calibration was performed on multi-source samples acquired from different collection devices, at different times, and in different locations to unify data acquisition accuracy and unit standards. The angular accuracy of all samples was uniformly maintained to 0.1°, the height and distance accuracy to 0.01m, and the light intensity accuracy to 100 lux. Simultaneously, deviation calibration was performed between simulated and measured samples. Using actual measured data as a benchmark, systematic errors in the simulation model were corrected to ensure that the two types of sample data are from the same source, at the same scale, and can be fused for computation.

[0064] Next, the input feature parameters are subjected to min-max normalization. The normalized feature set includes six core parameters: photovoltaic module tilt angle, support height, module lateral spacing, module longitudinal spacing, site elevation difference, and ambient light intensity. For each type of feature parameter, the entire valid sample dataset is traversed, and the global maximum and minimum values ​​of the parameter are calculated for each dimension, recording the extreme value ranges of each dimension (e.g., the extreme value range of module tilt angle is 15°~35°, and the extreme value range of support height is 2.0m~3.5m). A linear normalization algorithm is used to compress and transform the original data. The normalization calculation formula is: X new =(XX min ) / (X max -X min ), where X is the original sampled value of the parameter, X max X represents the global maximum value of this dimension of features. min X is the global minimum value of this dimension of features. new To normalize the output values, all dimensional parameters are mapped to the 0-1 range. For the very few boundary samples that equal extreme values, the normalized output value range is forcibly constrained to [0.00, 1.00], retaining two decimal places. Simultaneously, a division-by-zero protection mechanism is implemented to prevent calculation errors caused by the lack of fluctuation in data across a single dimensional sample, ensuring stable calculation across the entire dataset. After normalization, all input feature dimensions are unified and numerical ranges are consistent, eliminating the problem of high numerical parameters dominating model fitting and low numerical features failing.

[0065] Then, the cleaned effective samples were labeled with multi-dimensional structured labels. Each sample was simultaneously bound to three types of label systems: (1) site condition label, including component tilt angle level, support height level, array density level, and terrain type; (2) optical feature label, including measured occlusion critical angle, reflection critical angle, and effective field of view; (3) monitoring effect label, including blind area, screen reflection interference degree, and maximum visible distance.

[0066] Finally, the labeled samples were evenly grouped according to the principle of uniform distribution of working conditions, ensuring a balanced proportion of samples with varying tilt angles, support heights, array density, and lighting intensity, thus avoiding sample tilting issues. All valid samples were divided into training, validation, and test sets in a 7:2:1 ratio. The training set was used for fitting the linkage mapping function and solving model parameters, the validation set was used for iterative hyperparameter tuning, and the test set was used for verifying the final model's generalization ability. The cleaned, normalized, labeled, and segmented structured samples were uniformly stored in a database, with each sample forming a complete data chain of "working condition input—optical features—optimal viewing angle parameters—monitoring effect evaluation".

[0067] (III) Optical geometric modeling.

[0068] Based on the component tilt angle, support height, component array spacing, and DEM elevation data stored in the sample library, the real photovoltaic array layout scene is reproduced in the three-dimensional simulation space. The spatial positional relationship of support columns, component panels, array row spacing, and site elevation difference is accurately reproduced. The installation point of the PTZ camera is defined as the spatial observation viewpoint, and the entire photovoltaic array is defined as the monitoring target surface. A three-dimensional through-view geometric architecture of viewpoint and target surface is constructed.

[0069] Based on the principle of three-dimensional geometric line-of-sight, taking the PTZ camera's viewpoint as the origin of light emission, multiple limiting lines of sight are constructed by traversing the outermost support vertices and the upper edges of the modules of the photovoltaic array step by step towards the far end. The angle between the limiting line of sight and the horizontal line of sight is then calculated, and this angle is defined as the support shading critical angle under the current operating condition. The support shading critical angle θ z The calculation formula is: θ z =arctan[(HH z ) / L]; Where, θ z H represents the critical angle of obstruction by the bracket, signifying the minimum downward angle at which the camera is just not obstructed by the top edge of the component; H is the installation height of the PTZ camera; H z L represents the top height of the photovoltaic support; L represents the maximum horizontal monitoring distance from the PTZ camera to the remote photovoltaic array.

[0070] Based on the principle of optical specular reflection, the photovoltaic glass module is equivalent to a specular reflecting surface. The geometric relationship between incident and reflected rays is established according to the module's tilt angle parameters. Using the typical parallel incident angle of sunlight during the day as a benchmark, the propagation path of reflected light after sunlight strikes the module surface is calculated. The limiting incident angle at which the reflected ray can directly hit the lens of a PTZ camera is then determined, and the critical reflection angle of the module is calculated in reverse. The critical reflection angle θ is then used for calculation. g The calculation formula is: θ g =2α-90°; Where α is the tilt angle of the photovoltaic module.

[0071] By combining the critical angles of bracket obstruction and reflection, the effective working field of view of the PTZ camera is solved under dual constraints: using the critical angle of obstruction as the lower threshold and the critical angle of reflection as the upper threshold, a clean angle range with neither bracket obstruction nor component reflection interference is selected, which is the effective monitoring field of view of the PTZ camera under the current working conditions. At the same time, considering different lighting conditions, the reflection diffusion deviation under strong light environment is corrected to complete the accurate calibration of the effective field of view.

[0072] (iv) Fitting of linkage relationship.

[0073] With the core constraints of unobstructed view, no reflective interference, and maximum coverage of a single device, we traversed the effective field-of-view data of the entire sample and obtained two sets of linearly linked mapping functions for the bracket height and the PTZ camera installation height, and the component tilt angle and the PTZ camera monitoring downward angle, through least squares fitting. At the same time, considering the stray light and diffuse reflection interference characteristics of the photovoltaic field, we introduced a reflective compensation correction coefficient to offset the systematic deviation between the actual engineering and the ideal optical simulation. The specific calculation formula is as follows: H j =H z +Δh0+k1 H z ; Among them, H j The optimal installation height for the PTZ camera; H z Here, Δh0 represents the top height of the photovoltaic support frame; Δh0 is the basic safety ultra-high constant; and k1 is the linear linkage coefficient between the support frame height and the PTZ camera height, ranging from 0.8 to 1.2. This formula achieves adaptive linkage between the support frame height and the PTZ camera pole height. The higher the support frame, the greater the risk of array shading, and the model automatically matches a higher PTZ camera installation height.

[0074] θ=α k2+θ0+Δθ; Where θ is the optimal working tilt angle of the PTZ camera; k2 is the module tilt angle linkage proportional coefficient, ranging from 0.35 to 0.45; α is the tilt angle of the photovoltaic module; θ0 is the basic tilt angle offset constant, ranging from 5° to 8°; and Δθ is the reflection compensation correction angle, ranging from 3° to 7°. The reflection compensation angle is dynamically fine-tuned according to the light intensity and the module tilt angle. The larger the tilt angle and the stronger the light, the slightly higher the compensation angle is, which is used to counteract the glare interference from diffuse reflection from the specular surface.

[0075] Based on the above two sets of quantitative linkage formulas, a two-dimensional adaptive linkage mapping kernel for photovoltaic scenarios is constructed to achieve accurate quantitative matching between scenario parameters and equipment deployment parameters.

[0076] (v) Iterative correction of engineering errors.

[0077] To eliminate discrepancies between theoretical modeling and engineering practice, a multi-round simulation comparison and on-site measurement iterative correction method was used to complete model optimization training. Each iteration quantitatively verified the occlusion rate, reflective interference level, and blind spot area of ​​the monitored area. The iteration convergence conditions were set as follows: occlusion area ratio not exceeding 3%, reflective interference ratio not exceeding 5%, and single-point blind spot area not exceeding 5㎡. The maximum number of iterations did not exceed 50. After model iteration convergence, the optimal parameter library, standardized mapping formula, and adjustable step size interval were solidified, ultimately forming a standardized model system that can be directly used for PTZ camera adaptive parameter matching and intelligent deployment.

[0078] In one optional implementation, the input parameters of the field-of-view adaptation model constructed according to the above steps include five categories: photovoltaic module tilt angle α (15°~35°), photovoltaic support top height H, etc. z (2.0~3.5m), component lateral and longitudinal spacing, site DEM elevation data, and real-time environmental parameters (light intensity, component surface temperature). The output parameters of the field-of-view adaptation model include three categories: optimal installation height H of the PTZ camera. j (4.5~13m), optimal working tilt angle θ of the PTZ camera (12°~28°), and maximum effective coverage radius R of a single PTZ camera. max (150~220m). The above three types of output parameters are directly used for point placement, pole height determination, and automatic adjustment of PTZ camera angle. When calculating the occlusion range of the support, the smaller the component spacing and the higher the array density, the greater the risk of far-end occlusion. The model automatically fine-tunes the minimum installation height of the camera according to the component spacing to ensure unobstructed line of sight even under dense array conditions.

[0079] In this embodiment, the mapping relationships between the photovoltaic module tilt angle and the camera's working tilt angle, and between the bracket height and the camera's installation height, are core mapping rules embedded within the field-of-view adaptation model, i.e., the model kernel. These mapping relationships are neither intermediate products nor final output parameters, but rather the basis for generating the final deployment parameters. The operating logic of the field-of-view adaptation model is as follows: after inputting scene parameters such as the photovoltaic module tilt angle and bracket height, it calls the built-in linkage mapping formula to output the camera's installation height, working tilt angle, and coverage radius.

[0080] After the field-of-view adaptation model is built, it is deployed in the control terminal or edge computing device of the photovoltaic area. When in use, the scene parameters such as the tilt angle of the photovoltaic module and the height of the support frame collected in step S201 are input into the field-of-view adaptation model. The field-of-view adaptation model outputs the installation height and working tilt angle of the camera at each location according to the pre-fitted mapping relationship. At the same time, the critical angle of component reflection and the support frame shading angle calculated during the model construction process are used as constraints to verify the output installation height and working tilt angle.

[0081] Specifically, step S202 above includes: Step S2021: Based on the pre-constructed mapping relationship between the tilt angle of the photovoltaic module and the working angle of the camera, and the mapping relationship between the bracket height and the camera installation height, determine the installation height and working angle of the camera at each location in the photovoltaic area.

[0082] Step S2022: Using the working tilt angle being greater than the critical angle of component reflection and the installation height being greater than the safe height corresponding to the bracket shading angle as constraints, candidate locations are selected from each position.

[0083] In one optional implementation, when screening candidate locations, priority is given to selecting relatively high points in the photovoltaic area, with an altitude greater than 2m higher than the surrounding module area; the spacing between candidate locations is controlled between 180m and 220m, the coverage radius of a single location is greater than 150m, and the identification accuracy is greater than 0.1m.

[0084] Step S203: Cluster the candidate points according to the operating status of the photovoltaic area to obtain the monitoring area, and deploy cameras in the monitoring area.

[0085] Specifically, step S203 above includes: Step S2031: Classify the photovoltaic area by operation priority to obtain areas with at least two priority levels.

[0086] In this embodiment, the photovoltaic area is classified into operational priorities. The first-level area comprises inverter clusters and areas with high rates of hot spots on modules, where the hot spot detection threshold is a module surface temperature not lower than 60°C. The second-level area is the entire photovoltaic module array. The third-level area is peripheral wasteland, accounting for no more than 10% of the total photovoltaic area. The classification accuracy is no greater than 10 square meters. Through this classification, a priority label library for the photovoltaic area is established, providing a basis for subsequent clustering and segmentation.

[0087] Step S2032: Clustering algorithm is used to cluster the candidate points, and adjacent candidate points belonging to the same priority level are merged into a monitoring unit. The monitoring area is determined according to each monitoring unit.

[0088] In the above process, a clustering algorithm is used to cluster the candidate points, including: using the location coordinates and running priority of the candidate points as clustering features, using the K-means clustering algorithm to cluster the candidate points, and using the cluster center or the highest point in the cluster area that meets the coverage conditions as the installation location of the camera of the monitoring unit.

[0089] For example, the K-means dynamic clustering algorithm is used to cluster the regions at each level, with no more than 50 clustering iterations and a clustering error of no more than 3%. Adjacent high-priority regions are merged into one monitoring unit, with a unit area of ​​no more than 40,000 m². 2 After clustering, each monitoring unit becomes a monitoring area. This clustering approach ensures that the camera deployment density is precisely matched to the actual monitoring needs of each area.

[0090] In one optional implementation, when clustering candidate points using a clustering algorithm, the area of ​​the clustering units is adjusted according to the component spacing: the clustering unit area is reduced in dense array areas and expanded in sparse array areas to achieve refined point placement. After clustering, the integrity of the coverage of a single PTZ camera within each monitoring unit is checked based on the component spacing to avoid minor blind spots between rows and columns. When implementing priority grading, the inverter / substation location is marked as a first-level high-priority monitoring core point. When clustering candidate points using the K-means clustering algorithm, the inverter / substation location is prioritized as the cluster center to ensure that high-value equipment is located in the central coverage area of ​​the monitoring unit.

[0091] Step S2033: Deploy cameras in each monitoring area.

[0092] In this embodiment, cameras are deployed at the center or highest point of each monitoring area. The cameras are 360° PTZ cameras with at least 4 megapixels and an infrared range of at least 200 meters. This deployment ensures that the cameras within each monitoring area achieve 360° full coverage.

[0093] Step S204: For blind spots outside the monitoring area, coverage is achieved by moving the deployed cameras along a preset path.

[0094] In one alternative implementation, when setting the cruise preset position, the direction of the inverter / substation location is preferentially set as the preset position to achieve key inspection and dual coverage of high-value equipment areas.

[0095] Step S205: Obtain the light intensity and module temperature of the photovoltaic area, adjust the working mode of the camera according to the light intensity, and trigger the camera to take pictures according to the module temperature.

[0096] In one optional implementation, when the abnormal surface temperature of the component triggers the camera to take pictures, the concentrated area of ​​inverter / substation equipment is taken as the key monitoring area, and the camera in the corresponding area is preferentially triggered to turn and zoom in for pictures.

[0097] In this embodiment, a light sensor and a temperature sensor are integrated into the camera. The light sensor has a measurement range of 0 to 100,000 lux and an accuracy of ±5%; the temperature sensor has a measurement range of -40°C to 120°C and an accuracy of ±0.5°C. These sensors collect real-time data on day and night light intensity and component surface temperature, and transmit this data to the control terminal.

[0098] In this embodiment, the control terminal performs the following linkage control based on the light intensity: when the light intensity is not less than 5000 lux, it is determined that the daytime light is sufficient, and the camera is controlled to switch to high-definition mode with a resolution of not less than 1080P. At the same time, the field of view is narrowed to a horizontal field of view of 45° to 60° to focus on the identification of component details (including hot spots, damage, etc.) with an identification accuracy of not less than 0.5mm; when the light intensity is less than 5000 lux, it is determined that the nighttime light is insufficient, and the camera is controlled to switch to infrared mode with an infrared wavelength of 8 to 14μm. At the same time, the field of view is expanded to a horizontal field of view of 90° to 120° to focus on covering the perimeter and equipment security with an identification distance of not less than 150m.

[0099] In this embodiment, the control terminal also performs the following linkage control based on the component surface temperature: when the component surface temperature is not lower than 60°C and the temperature difference with the surrounding components is not lower than 20°C, it is determined to be a suspected hot spot abnormality. The control terminal automatically triggers the camera in the corresponding area to turn to the abnormal position at a turning speed of not less than 30° / s and magnifies the shooting details with a magnification of 10 to 30 times.

[0100] Through the above-mentioned lighting-operating condition dual linkage mechanism, this invention realizes the automatic switching between three working modes of a single PTZ camera: "high-definition inspection of component details during the day, infrared coverage of perimeter security at night, and directional shooting triggered by abnormal temperature". It takes into account multiple needs such as component inspection, equipment security, and perimeter protection, without the need to deploy a separate thermal imaging camera or a fixed camera, realizing "one machine for multiple uses" and reducing the duplication of equipment deployment.

[0101] In another optional implementation, based on the above scheme, the angle and height of the cameras at all locations are adjusted, and tests are conducted simulating different working conditions.

[0102] In this embodiment, after completing the camera deployment and linkage mechanism configuration, the angle and height of the cameras at all locations are adjusted. Based on the field-of-view adaptation model, it is verified whether there is glare interference or bracket obstruction in the actual monitoring images of each camera location. Specifically, the pitch angle and installation height of each camera are adjusted to ensure that the glare interference level is no greater than 5% and the obstruction area is no greater than 3%, ensuring that the coverage of each monitoring area meets the standards. The coverage standard is: full coverage of no less than 99.5%, and dual coverage of the primary area reaching 100%.

[0103] In this embodiment, after completing the angle and height adjustments, the system was tested under simulated different operating conditions. These conditions included: strong light (illuminance not less than 80,000 lux), nighttime conditions (illuminance not more than 100 lux), and abnormal component conditions (component temperature not less than 60°C). Under these conditions, the response speed of the AI ​​adaptive blind spot compensation module and the light-condition dual-linkage mechanism were tested to ensure a response speed of no more than 1 second. Based on the test results, the cruise cycle and field of view adjustment parameters were optimized, adjusting the cruise cycle to 15-30 seconds per revolution, the field of view adjustment range to 45°-120°, and the depression angle adjustment step to 0.1°.

[0104] Through the above debugging and testing, the system is guaranteed to operate stably under various working conditions, achieving the preset coverage and response speed targets, and finally completing the camera positioning deployment.

[0105] The photovoltaic area camera positioning and deployment method provided in this embodiment has the following beneficial effects: (1) Deep adaptation of PTZ camera to photovoltaic module scene, avoiding reflection and blind spots from the source. This invention establishes a linkage mapping relationship between the tilt angle of the photovoltaic module and the working tilt angle of the PTZ camera, and between the height of the bracket and the installation height of the PTZ camera, by using the tilt angle of the photovoltaic module and the height of the bracket as the core input parameters. This allows the tilt angle, installation height and field of view of the PTZ camera to be adaptively adjusted according to the actual spatial posture of the photovoltaic module, avoiding glare interference caused by module reflection and blind spots caused by front bracket obstruction. This solves the problem of viewing angle deviation caused by ignoring the characteristics of photovoltaic modules in the prior art. At the same time, by utilizing the 360-degree coverage characteristic of the PTZ camera, a single PTZ camera can achieve scanning coverage of the entire surrounding horizontal direction, without the need to deploy multiple fixed devices in a single area, realizing "single-machine full-area adaptation and blind spot coverage".

[0106] (2) Cooperative optimization of PTZ cameras with dynamic clustering and AI adaptive blind spot filling minimizes the number of cameras required. This invention breaks through the existing static grid division and fixed algorithm optimization deployment mode. It deploys PTZ cameras as the core equipment and performs dynamic clustering based on the operation priority of the photovoltaic area. High-priority areas are densely clustered to achieve key coverage, while low-priority areas are sparsely clustered to avoid over-deployment. On this basis, taking advantage of the PTZ camera's 360-degree cruise and precise preset position control, AI adaptive blind spot filling is used to perform time-round patrol coverage of low-priority areas and overlapping blind spots of monitoring units. There is no need to deploy additional fixed bullet cameras for blind spot filling. Under the premise of ensuring full coverage, the total number of cameras is minimized, solving the problems of "redundant deployment and uneven coverage" in the existing technology.

[0107] (3) Dual mechanism of light and working condition linkage for PTZ camera to achieve "one camera for multiple uses". This invention integrates light and temperature sensors at the PTZ camera end, and links day and night light intensity with component operating conditions to dynamically adjust the working mode of the PTZ camera - when there is sufficient light during the day, switch to high-definition mode to focus on component details (such as hot spots, damage), and switch to infrared mode to cover the perimeter and equipment security when there is insufficient light at night. When the surface temperature of the component is abnormal, the corresponding PTZ camera is automatically triggered to turn and zoom in for shooting. A single PTZ camera can take care of multiple needs such as component inspection, equipment security and perimeter protection. There is no need to deploy thermal imaging bullet cameras or fixed bullet cameras separately, avoiding repeated deployment of equipment and realizing "one camera to cover multiple scenarios and one camera to replace multiple cameras".

[0108] This embodiment also provides a photovoltaic area camera positioning and deployment device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] This embodiment provides a camera positioning and deployment device for photovoltaic areas, such as... Figure 3 As shown, it includes: The scene parameter acquisition module 301 is used to acquire scene parameters of the photovoltaic area. The scene parameters include at least the tilt angle of the photovoltaic module and the height of the support frame. The candidate point selection module 302 is used to determine the deployment parameters of the cameras at each location in the photovoltaic area based on the scene parameters, and select candidate points from each location based on the deployment parameters, with the constraints of avoiding component reflection interference and bracket shading. Clustering deployment module 303 is used to cluster candidate points according to the operating status of the photovoltaic area to obtain the monitoring area, and deploy cameras in the monitoring area. The blind spot coverage module 304 is used to cover blind spots outside the monitoring area by moving the deployed cameras along a preset path; The operating condition linkage module 305 is used to acquire the light intensity and module temperature of the photovoltaic area, adjust the working mode of the camera according to the light intensity, and trigger the camera to take pictures according to the module temperature.

[0110] In some optional implementations, the candidate site filtering module 302 includes: The camera deployment parameter determination unit is used to determine the installation height and working angle of the camera at each location in the photovoltaic area based on the pre-built mapping relationship between the tilt angle of the photovoltaic module and the working angle of the camera, and the mapping relationship between the bracket height and the installation height of the camera. The candidate point selection unit is used to select candidate points from various locations based on constraints such as the working tilt angle being greater than the critical angle of component reflection and the installation height being greater than the safe height corresponding to the bracket shading angle.

[0111] In some alternative implementations, the clustering deployment module 303 includes: Priority division unit is used to classify the operation priority of photovoltaic areas, resulting in areas with at least two priority levels.

[0112] The monitoring area determination unit is used to cluster candidate points using a clustering algorithm, merge adjacent candidate points of the same priority level into one monitoring unit, and determine the monitoring area based on each monitoring unit.

[0113] The camera deployment unit is used to deploy cameras in various monitoring areas.

[0114] The photovoltaic area camera positioning and deployment device provided in this embodiment of the invention can execute the photovoltaic area camera positioning and deployment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0115] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0116] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0117] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0118] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the photovoltaic area camera positioning deployment method of the embodiments of the present invention.

[0119] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0120] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic area camera positioning and deployment method shown in the above embodiments is implemented.

[0121] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0122] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for positioning and deploying cameras in a photovoltaic area, characterized in that, The method includes: Obtain scene parameters for the photovoltaic area, including at least the tilt angle of the photovoltaic modules and the height of the support structure; Based on the scene parameters, the deployment parameters of the cameras at each location in the photovoltaic area are determined, and candidate locations are selected from each location based on the deployment parameters, with the constraints of avoiding component reflection interference and bracket obstruction. The candidate locations are clustered and divided according to the operating status of the photovoltaic area to obtain the monitoring area, and cameras are deployed in the monitoring area. For blind spots outside the monitored area, coverage is achieved by moving the deployed cameras along a preset path; The system acquires the light intensity and module temperature of the photovoltaic area, adjusts the camera's working mode based on the light intensity, and triggers the camera to take pictures based on the module temperature.

2. The method for positioning and deploying cameras in photovoltaic areas according to claim 1, characterized in that, The camera in question is a PTZ camera.

3. The method for positioning and deploying cameras in photovoltaic areas according to claim 1, characterized in that, The scenario parameters also include component spacing, inverter / substation location and / or site DEM digital elevation data.

4. The method for positioning and deploying cameras in photovoltaic areas according to claim 1, characterized in that, The deployment parameters include at least the camera's installation height, working angle, and coverage radius.

5. The method for positioning and deploying cameras in photovoltaic areas according to claim 1, characterized in that, The process involves determining the deployment parameters of cameras at various locations within the photovoltaic area based on the scene parameters, and selecting candidate locations from each location based on the deployment parameters, with constraints including avoiding interference from component reflections and bracket obstruction. Based on the pre-established mapping relationship between the tilt angle of the photovoltaic module and the working downward angle of the camera, and the mapping relationship between the height of the bracket and the installation height of the camera, the installation height and working downward angle of the camera at each location in the photovoltaic area are determined. Candidate locations are selected from each position, with the working angle being greater than the critical angle of component reflection and the installation height being greater than the safe height corresponding to the bracket blocking angle as constraints.

6. The method for positioning and deploying cameras in photovoltaic areas according to claim 1, characterized in that, The step of clustering the candidate locations based on the operating status of the photovoltaic zone to obtain a monitoring area, and deploying cameras in the monitoring area, includes: The photovoltaic zone is classified into operational priorities, resulting in areas with at least two priority levels. Clustering algorithms are used to cluster the candidate points, and adjacent candidate points belonging to the same priority level are merged into one monitoring unit. The monitoring area is determined based on each monitoring unit. Deploy cameras in each monitored area.

7. The method for positioning and deploying cameras in photovoltaic areas according to claim 6, characterized in that, The step of using a clustering algorithm to cluster the candidate points includes: Using the location coordinates of the candidate points and the running priority as clustering features, the candidate points are clustered using the K-means clustering algorithm. The cluster center or the highest point within the cluster area that meets the coverage conditions is used as the camera installation location of the monitoring unit.

8. A camera positioning and deployment device for photovoltaic areas, characterized in that, The device includes: A scene parameter acquisition module is used to acquire scene parameters of the photovoltaic area, the scene parameters including at least the tilt angle of the photovoltaic module and the height of the support frame; The candidate location filtering module is used to determine the deployment parameters of cameras at each location in the photovoltaic area based on the scene parameters, and to filter candidate locations from each location based on the deployment parameters, with the constraints of avoiding component reflection interference and bracket shading. The clustering deployment module is used to cluster the candidate points according to the operating status of the photovoltaic area to obtain the monitoring area, and to deploy cameras in the monitoring area. The blind spot coverage module is used to cover blind spots outside the monitoring area by moving the deployed cameras along a preset path; The operating condition linkage module is used to acquire the light intensity and component temperature of the photovoltaic area, adjust the working mode of the camera according to the light intensity, and trigger the camera to take pictures according to the component temperature.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic area camera positioning and deployment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic area camera positioning and deployment method according to any one of claims 1 to 7.

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