Photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction

Abstract

The invention relates to the technical field of digital modeling of photovoltaic stations, in particular to a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction, which comprises the following steps: collecting high-precision three-dimensional laser point cloud data of a photovoltaic station, performing instance segmentation on the point cloud data by using a deep learning model, and obtaining a photovoltaic string reverse modeling result; the method comprises the following steps: accurately extracting a photovoltaic string, establishing a unique index, identifying geometric features and a contour point set of the photovoltaic string through vertex extraction, distance-based cluster search and an angle-based contour detection method, calculating an inclination angle and a direction angle of the photovoltaic string by using an interpolation fitting method, and calculating a central position of the photovoltaic string by using the contour point set. And carrying out detection and area calculation on the shielding or damage condition of the photovoltaic panel by adopting a sliding pane method, and finally finishing reverse modeling of the photovoltaic station. According to the method, the anti-interference capability and the modeling precision of data acquisition are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for intelligent management and operation and maintenance of a large-scale photovoltaic field station under various environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of digital modeling of photovoltaic stations, and in particular to a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction. Background Art

[0002] Photovoltaic power generation has developed rapidly as a clean and renewable energy form. With the large-scale construction of photovoltaic sites, the completion acceptance and operation and maintenance of photovoltaic sites have ushered in huge challenges. The efficiency and reliability of photovoltaic sites are affected by many factors, among which the actual position and angle parameters of photovoltaic strings are one of the keys to the efficiency and reliability of photovoltaic power stations.

[0003] During the completion acceptance stage, whether the actual spatial position and angle parameters of the photovoltaic strings deviate from the design is an important part of the project acceptance. In addition, in the context of the digital era, digital operation and maintenance based on the three-dimensional digital twin model is an inevitable trend of future development. The position and angle parameters of the photovoltaic strings are the core support of digital operation and maintenance. On this basis, the visualization, refinement, standardization and intelligent management of photovoltaic power stations can be realized, and the operation and maintenance level of photovoltaic power stations can be improved.

[0004] The existing technical means of using oblique photography to achieve reverse modeling of photovoltaic panels have the following problems:

[0005] 1. Easily affected by environmental factors, data quality is unstable.

[0006] The oblique photography method relies on high-quality image data, which is easily affected by geographical conditions (such as terrain undulations, building occlusions), lighting (such as shadows, reflections) and weather conditions (such as haze, rain and snow), resulting in image quality degradation and image deformation. Due to the influence of these external factors, the quality and accuracy of the generated 3D point cloud data are unstable, especially in long-distance shooting or complex terrain environments, the accuracy of the modeling results is low.

[0007] 2. The processing flow is complicated and consumes large computing resources.

[0008] The 3D reconstruction process of oblique photography technology involves multiple complex steps, such as feature point matching, spatial triangulation dense matching (aerial triangulation), sparse point cloud extraction and dense point cloud construction. Each step requires a lot of data processing and calculation, which is time-consuming and resource-intensive. At the same time, in order to correct image deformation and occlusion problems, additional correction operations are required, which further increases the computational burden and modeling time cost.

[0009] 3. The processing burden of non-target areas is heavy and the efficiency is low.

[0010] When generating a 3D model of a photovoltaic panel, the oblique photography method will capture a large amount of background information of non-photovoltaic areas (such as the ground, vegetation, buildings, etc.). The information of these non-target areas needs to be eliminated and corrected in the subsequent processing, which consumes a lot of computing resources and reduces the overall modeling efficiency.

[0011] The problems existing in the above-mentioned prior art limit the application of oblique photography method in reverse modeling of photovoltaic panels. Compared with laser point cloud technology, its efficiency and reliability are lower.

[0012] Therefore, studying the reverse modeling technology of photovoltaic stations can provide support for the completion acceptance, digital operation and maintenance, and post-evaluation of photovoltaic stations by determining the spatial position, angle, index, and contour of photovoltaic panels, which has great application value for the development of the photovoltaic power generation industry. Summary of the invention

[0013] In order to solve the above technical problems, the present invention provides a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction, comprising the following steps:

[0014] S1. Obtaining a three-dimensional laser point cloud of a photovoltaic station and design parameters of a photovoltaic string, and preprocessing the laser point cloud to obtain point cloud data, wherein the design parameters of the photovoltaic string include the width, length and diagonal length of the photovoltaic string;

[0015] S2. Based on the obtained point cloud data, the point cloud data is instance segmented based on the deep learning method, the photovoltaic string point cloud is extracted and a unique index is set for each photovoltaic string to establish the photovoltaic string index set PI T ;

[0016] S3. Based on the obtained photovoltaic string index set PI T Through the alternative vertex search method and the distance-based vertex cluster search method, the candidate vertex set Point of each photovoltaic string is extracted respectively. 0 And the feasible vertex set Point 1 , and obtain the contour straight line of the photovoltaic string through the angle judgment method, detect the integrity of the photovoltaic string, and establish the index set PI of the intact photovoltaic string respectively. E The index set PI of the blocked or damaged photovoltaic strings D ;

[0017] S4. Extract PI by sliding pane method E PV strings and PI that are blocked or damaged D Merge and calculate the blocked or damaged area of ​​the blocked or damaged photovoltaic strings;

[0018] S5. Contour extraction method based on feasible contour straight line processing to extract PI EThe contour point set C of all photovoltaic strings in E , and based on the principle that there is no laser point on one side of the line connecting adjacent edge points, PI is extracted D The set of all contour points without straight edges in D , the PI E With C E Uniquely associated by index, the PI D With C D Uniquely associated by index;

[0019] S6. According to the contour point set C E With C D , calculate the center position of the photovoltaic string, and calculate the inclination and direction angle of the photovoltaic string through the interpolation fitting method;

[0020] S7. Complete the reverse modeling of the photovoltaic station based on the obtained center position, angle and profile of the photovoltaic string.

[0021] Further, step S3 includes the following steps:

[0022] S301. PI by alternative vertex search method T Process the point cloud data in to obtain the candidate vertex set Point 0 , the Point 0 By index and PI T The only connection;

[0023] S302. PI is searched by vertex cluster search method based on distance T Process the point cloud data in to obtain the feasible vertex set Point 1 , the Point 1 By index and PI T The only connection;

[0024] S303. Determine whether the photovoltaic string is complete by using the contour line acquisition method based on angle judgment, and establish the damaged or blocked photovoltaic string index set PI D and the intact photovoltaic string index set PI E , get the right-angle vertex set Point 2 Associated with the vertex set Point', the PI D With PI E The sum of the number of photovoltaic strings is equal to PI T The sum of the number of photovoltaic strings in the Point 2 Uniquely associated with Point'.

[0025] Furthermore, the step S301 includes the following steps:

[0026] S301-1. Project the point cloud data onto the two-dimensional XY plane to obtain a two-dimensional point set:

[0027] P=(A x ,A y );

[0028] A x ={x1,x2…,x n};

[0029] A y ={y1,y2…,y n};

[0030] Where P is a set of plane points; A x A is the set of X-axis coordinate values ​​corresponding to the point; x is the set of Y-axis coordinate values ​​corresponding to the point; 1, 2, ... n is the index of the point;

[0031] S301-2. Obtain the maximum and minimum values ​​of the XY axis respectively, that is: max , x min ,y max ,y min , its calculation can be expressed as:

[0032] x max =max(A x )

[0033] x min =min(A x )

[0034] y max =max(A y )

[0035] y min =min(A y )

[0036] S301-3. Get x max , x min ,y max ,y min The index of the corresponding two-dimensional point set, and then obtain the corresponding three-dimensional point set P0;

[0037] S301-4. Pair the points in P0 with each other and calculate the distance d between the two points. The calculation can be expressed as:

[0038]

[0039] Where i and j represent the subscripts of the two paired points in P0, i≠j;

[0040] S301-5: Setting distance pre-threshold And take d greater than the distance threshold Points as candidate vertex set Point 0 , the distance threshold It is the diagonal length of the photovoltaic string design parameters.

[0041] Further, step S302 includes the following steps:

[0042] S302-1, according to the Point obtained in step 301 0 Create a collection to be tested And set the number of iterations n = 0, and set the distance threshold according to the laser point cloud scanning accuracy

[0043] Among them, the distance threshold Based on the laser point cloud resolution setting, the For PI T The candidate vertex set corresponding to the photovoltaic string with index q in the PV string;

[0044] S302-2. From Randomly select K data points as the initial cluster centers, calculate the mutual distance d′ between the K data points, and set the distance d′ less than the distance threshold The data points are merged into one cluster to obtain M initial clusters M≤K;

[0045] S302-3. Create a new set to be detected Calculate the centroids of the M clusters and calculate the distance d″ from other points in P1 to the centroids. Set d″ to a value less than the distance threshold. The data points are assigned to the corresponding clusters, and vice versa.

[0046] S302-4. Judgment Is it empty? If not, set n=n+1 and repeat S302-2 to S302-3; if it is empty, go to the next step;

[0047] S302-5. Obtain candidate vertex clusters A point is randomly selected in each cluster as a candidate vertex, which is the vertex set Point of the photovoltaic string. q =(p1,p2,…,p l ), where l and the candidate vertex cluster The number of

[0048] S302-6. Setting the threshold distance And remove the vertex set Point qFrom the remaining vertices, select the collinear vertices on the same straight line, and select the ones that satisfy the distance between adjacent vertices along the width direction of the photovoltaic string and the distance along the length direction of the photovoltaic string equal to the distance threshold respectively. The points of the PV string width and length in the The threshold distance Including the width and length of the photovoltaic string in the photovoltaic string design parameters;

[0049] Among them, the vertex set Point is eliminated q The collinear vertices on the same straight line in the vertex set Point q Is the cosine value of the straight line formed by any three vertices equal to 1 or -1, that is, is the absolute value of the cosine value

[0050] Further, step S303 includes the following steps:

[0051] S303-1. Based on the feasible vertices obtained in step S302 Create a set of right-angle vertices Associated vertex set Point′ q , and Point′ q The only connection;

[0052] S303-2. Take any vertex p b , take any two vertices p a 、p c Cosine value between the straight lines Its calculation can be expressed as:

[0053]

[0054] when When p is equal to 0, b Add to And record p b 、p a 、p c The index of p a 、p c is divided by p b Other vertices besides ;

[0055] S303-3. Verify the vertex set Is the number of midpoints equal to 4 and equal to If the number is the same, the string index is added to PI E Otherwise, it means that the PV string is blocked or damaged, and the PV string index is added to the set PI D .

[0056] Furthermore, the sliding pane method in step S4 includes the following steps:

[0057] S401. Set the window step length γ and density coefficient threshold a based on the 3D laser point cloud resolution of the photovoltaic station, and take PI E A photovoltaic string is selected and its point cloud is obtained, wherein the window step length γ is greater than the 3D laser point cloud resolution of the photovoltaic station, and the width and length of the photovoltaic string are integer multiples of the window step length γ;

[0058] S402. Select any vertex in the pane, slide along the width direction and the length direction of the photovoltaic string according to the step length γ to calculate the pane density coefficient ε, and compare it with the density coefficient threshold, identify all defective or blocked panes in the photovoltaic string, and record each pane coefficient of the pane until the entire photovoltaic string is detected. The number of slides is determined according to the length and width of the string. The specific density coefficient comparison model is:

[0059]

[0060] Wherein, v is the number of points in the pane; u is the ratio of the building pane area to the laser point cloud resolution; a is the density coefficient threshold, which is determined according to the 3D laser point cloud resolution of the photovoltaic station and the photovoltaic string design parameters; the density coefficient is used to evaluate the density of the point cloud data in each pane and determine whether the pane is defective or blocked;

[0061] S403. According to the list of window pane coefficients of the string, determine the number e of ε=1. If it is 0, it means that the string is intact. Otherwise, it is considered damaged or blocked, and the damaged or blocked area s is calculated. The calculation can be expressed as:

[0062] s=e×γ 2

[0063] Where s is the damaged or blocked area, e is the number of panes with ε=1, and γ is the pane step size.

[0064] Furthermore, the contour extraction method based on feasible contour straight line processing in step S5 includes the following steps:

[0065] S501. Set the search domain threshold β for PI E The set of right-angle vertices of the photovoltaic string with index q in Associated vertex set Point′ q , choose the kth right-angle vertex p k , for Point′ q The corresponding incident vertex p in a 、p c Treat them separately;

[0066] S502. PKP a The direction vector is the primitive vector, and the points in the cube area with the threshold β as the side length are traversed to determine the angle between the primitive vector and other vectors, and to determine whether the number of points in the cube is 0. If so, it is considered that the photovoltaic string is blocked or damaged, and the photovoltaic string index is added to the set PI D , and repeat S501; otherwise, execute S503;

[0067] S503. Set an angle threshold δ, calculate the angle between adjacent vectors, and if the adjacent angle is greater than the angle threshold δ, it is proved that the point is a contour point;

[0068] S504. Repeat S502 and S503 until all points in the small cube are traversed;

[0069] S505. Repeat S502-S504 until all small cubes are traversed to obtain the contour extraction result of the photovoltaic string.

[0070] Furthermore, the photovoltaic panel angle parameter estimation method based on interpolation fitting in step S6 includes the following steps:

[0071] S601. Construct a point set based on the point cloud data, and construct a photovoltaic string plane equation, which can be expressed as:

[0072] Ax+By+Cz+D=0

[0073] Where A, B, C are the values ​​of the plane normal vector a in the plane equation, and D is the position offset of the plane in the three-dimensional coordinate system;

[0074] S602. Take the z-axis unit vector b as the direction vector and calculate the inclination value of the photovoltaic string. The calculation can be expressed as:

[0075] θ i =arccos(a·b / ||a||×||b||)

[0076] S603. Take the north direction unit vector c of the photovoltaic string as the direction vector, and calculate the direction angle value of the photovoltaic string. The calculation can be expressed as:

[0077] θ d =arccos(a·c / ||a||×||c||).

[0078] The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction provided by the present invention has the following beneficial effects:

[0079] 1. High accuracy and stability: The application of laser point cloud technology ensures high accuracy and high reliability of data acquisition, and reduces the impact of external environmental factors on modeling quality.

[0080] 2. High efficiency and low resource consumption: Deep learning and intelligent point cloud processing methods simplify the data processing process, reduce the computational burden, and improve modeling efficiency.

[0081] 3. Fast and efficient detection: Automated photovoltaic module shading and damage detection and inclination and azimuth angle calculation methods support intelligent management of photovoltaic sites and improve overall power generation efficiency.

[0082] 4. Wide adaptability: This solution is suitable for photovoltaic sites of different scales and complex terrains. It can operate stably under a variety of environmental conditions and has better scalability and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 , a flow chart of the photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0084] Figure 2 , a schematic diagram of the occlusion or damage form of photovoltaic strings according to the photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0085] Figure 3 , a schematic diagram of an alternative vertex search method for the photovoltaic string inverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0086] Figure 4 , a schematic diagram of the results of the vertex cluster search method of the photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0087] Figure 5 , a schematic diagram of feasible vertex sets of the photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0088] Figure 6 , a schematic diagram of the sliding pane method of the photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0089] Figure 7 , a schematic diagram of a contour extraction method of a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction of the present invention.

[0090] Figure 8 , schematic diagram of photovoltaic string scanning in an embodiment of the present invention.

[0091] Fig. 9 , a schematic diagram of the photovoltaic string instance segmentation and unique indexing results in an embodiment of the present invention.

[0092] Fig.10 , schematic diagram of a two-dimensional point set of a photovoltaic string in an embodiment of the present invention.

[0093] Fig.11 , schematic diagram of a photovoltaic string candidate point set in an embodiment of the present invention.

[0094] Fig.12 , schematic diagram of feasible vertices of photovoltaic strings in an embodiment of the present invention.

[0095] Fig.13 , schematic diagram of the photovoltaic string contour point cloud in an embodiment of the present invention.

[0096] Fig.14 , a schematic diagram of a photovoltaic string contour point cloud with an index of 9 in an embodiment of the present invention.

[0097] Fig.15 , a schematic diagram of the center position point of a photovoltaic string with an index of 1 in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to enable those skilled in the art to better understand the content of the present invention and make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to further limit the present invention.

[0099] Embodiment 1:

[0100] like Figure 1 As shown, an embodiment of the present invention provides a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction, comprising the following steps:

[0101] S1. Use airborne LiDAR technology to obtain high-precision three-dimensional laser point clouds of photovoltaic sites, perform point cloud noise reduction, downsampling, and registration to obtain high-quality point cloud data; obtain the design parameters of photovoltaic strings in the completed design plan of the photovoltaic site, including length, width, and diagonal length.

[0102] Collect some photovoltaic string point clouds such as Figure 8 As shown in the figure, the design parameters of the photovoltaic strings in the completed design plan of the photovoltaic field are: a string has 3×26 photovoltaic modules, the module size is 2.382×1.134 (m), the photovoltaic string size is: 7.146×29.484 (m), and the diagonal length is 48.73m.

[0103] S2. Based on the photovoltaic field point cloud data, the PointNet++ model is used to extract the photovoltaic string point cloud, set a unique index for each photovoltaic string, and establish the photovoltaic string index set P0 T .

[0104] First, the PointNet++ model is used to perform instance segmentation to obtain each photovoltaic string; then a unique index is set for the photovoltaic string. Fig. 9 As shown, after instance segmentation, 12 photovoltaic strings are obtained.

[0105] PointNet++ is a deep learning model for 3D point cloud processing. It was proposed by the same team of the PointNet model to address the shortcomings of PointNet in processing local structures. PointNet++ introduces a module called "Set Abstraction". The main function of this module is to downsample the input point cloud and generate a new point cloud with multi-scale features. Specifically, the Set Abstraction module includes the following steps:

[0106] Sampling: Use the farthest point sampling strategy to select a set of key points from the input point cloud. This ensures that the selected key points are representative at different scales.

[0107] Grouping: With each key point as the center, the points near it are divided into small local areas. The size and shape of these local areas can be adjusted according to needs to capture features of different scales.

[0108] Feature extraction: Extract features from points in each local area, using a network structure such as PointNet. The extracted features contain the geometric information and context information of the local area.

[0109] Aggregation: Aggregate the features of each local area to obtain a multi-scale feature representation of each key point.

[0110] By repeating the above steps, PointNet++ can gradually expand the receptive field and realize multi-scale feature processing of point clouds.

[0111] The network structure of PointNet++ consists of multiple Set Abstraction layers, each of which contains a sampling layer, a grouping layer, and a PointNet layer. These layers work together to map the point cloud data from the original high-dimensional space to a low-dimensional feature space while retaining the hierarchical structure information of the point cloud. Compared with PointNet, PointNet++ has stronger robustness and higher accuracy when processing complex scenes. In addition, because PointNet++ adopts a hierarchical structure, the network can better process large-scale point cloud data and reduce computational costs. PointNet++ significantly improves the processing capabilities of point cloud data by introducing hierarchical feature learning and multi-scale grouping, especially in capturing local and global information.

[0112] S3. Using alternative vertex search method to PI T All photovoltaic string point clouds are processed to obtain the corresponding candidate vertex set Point 0 , the Point 0 By index and PI T The only connection.

[0113] The specific steps are:

[0114] S301. Obtain the three-dimensional laser point cloud of the photovoltaic string and project it onto the two-dimensional XY plane to obtain a two-dimensional point set; obtain the maximum and minimum values ​​of the maximum XY axis, that is: max , x min ,y max ,y min ; Get x max , x min ,y max ,y min The index of the corresponding two-dimensional point set, and then obtain the corresponding three-dimensional point set P0. Fig.10 As shown, a two-dimensional plane point set corresponding to the maximum and minimum values ​​of the photovoltaic string is obtained.

[0115] S302: Pair the points in P0 with each other and calculate the distance d, which can be expressed as:

[0116]

[0117] Where i and j represent the subscripts of the two paired points in P0, i≠j.

[0118] S303. Set the distance threshold according to the collected photovoltaic string design parameters is 48.73m, and d is greater than the distance threshold Point, get the candidate vertex set Point 0 . Get Fig.11 As shown, a set of candidate vertices is obtained after processing a photovoltaic string.

[0119] S4. Use distance-based vertex cluster search method to search PI T All photovoltaic string point clouds are processed to obtain the feasible vertex set Point 1 , the Point 1 By index and PI T The only connection.

[0120] The specific steps are:

[0121] S401. Based on the candidate vertex set Point 0 Create a collection to be tested And set the number of iterations n = 0, and set the distance threshold according to the laser point cloud scanning accuracy

[0122] Among them, the distance threshold Based on the laser point cloud resolution setting, the For PI T The candidate vertex set corresponding to the photovoltaic string with index q in the PV string;

[0123] S402. From Randomly select K data points as the initial cluster centers, calculate the mutual distance d′ between the K data points, and set the distance d′ less than the distance threshold The data points are merged into one cluster to obtain M initial clusters M≤K;

[0124] S403. Create a new set to be detected Calculate the centroids of the M clusters and calculate the distance d″ from other points in P1 to the centroids. Set d″ to a value less than the distance threshold. The data points are assigned to the corresponding clusters, and vice versa.

[0125] S404. Judgment Is it empty? If it is not empty, it means that there are still some items in the current set to be detected that do not meet the distance threshold. If there is a data point, n=n+1 is set, and steps S402 to S403 are repeated until When it is empty, go to the next step;

[0126] S405. Obtain candidate vertex clusters A point is randomly selected in each cluster as a candidate vertex, which is the vertex set Point of the photovoltaic string. q =(p1,p2,…,p l ), where l and the candidate vertex cluster The number of

[0127] S406. Setting threshold distance Include the width of the photovoltaic string 7.146m and the length of the photovoltaic string 29.484m, and remove the vertex set Point q The collinear vertices on the same straight line in the vertex set Point q Is the cosine value of the straight line formed by any three vertices equal to 1 or -1, that is, is the absolute value of the cosine value Select from the remaining vertices the distance between adjacent vertices along the width direction of the photovoltaic string and along the length direction of the photovoltaic string, which are equal to the distance thresholds respectively. The width and length of the photovoltaic string in Fig.12 As shown in the figure, the feasible vertex set obtained after processing a photovoltaic string is

[0128] S5. Use the contour line acquisition method based on angle judgment to determine whether the photovoltaic string is complete, and establish the damaged or blocked photovoltaic string index set PI D With other PV string index set PI E , get the right-angle vertex set Point 2 Associated with the vertex set Point'. D With PI E The sum of the number of elements in is equal to PI T The sum of the number of elements in Point 2 Uniquely associated with Point'.

[0129] The specific steps are:

[0130] S501. According to the feasible vertex set Create a set of right-angle vertices Associated vertex set Point′ q , and Point′ p The only connection;

[0131] S303-2. Take any vertex p b , take any two vertices p a 、p c Cosine value between the straight lines Its calculation can be expressed as:

[0132]

[0133] when When p is equal to 0, b Add to And record p b 、p a 、p c The index of p a 、p c is divided by p b Other vertices besides ;

[0134] S303-3. Verify the vertex set Is the number of midpoints equal to 4 and equal to If the number is the same, the string index is added to PI E Otherwise, it means that the PV string is blocked or damaged, and the PV string index is added to the set PI D .

[0135] After detection, there is one obstructed and damaged photovoltaic string in this embodiment, with an index of 9.

[0136] S6. For the photovoltaic string with index 9 that is blocked and damaged, extract PI by sliding pane method E PV strings and PI that are blocked or damaged D Merge and calculate the blocked or damaged area of ​​the blocked or damaged PV strings.

[0137] The specific steps are:

[0138] S601. Set the window step length γ and density coefficient threshold a based on the 3D laser point cloud resolution of the photovoltaic station, and take PI E A photovoltaic string is selected and its point cloud is obtained, wherein the window step length γ is greater than the 3D laser point cloud resolution of the photovoltaic station, and the width and length of the photovoltaic string are integer multiples of the window step length γ;

[0139] S602. Select any vertex in the pane, slide along the width direction and the length direction of the photovoltaic string according to the step length γ to calculate the pane density coefficient ε, and compare it with the density coefficient threshold, identify all defective or blocked panes in the photovoltaic string, and record each pane coefficient of the pane until the entire photovoltaic string is detected. The number of slides is determined according to the length and width of the photovoltaic string. The specific density coefficient comparison model is:

[0140]

[0141] Wherein, ε is the density coefficient, v is the number of points in the pane; u is the ratio of the building pane area to the laser point cloud resolution; a is the density coefficient threshold, which is determined according to the 3D laser point cloud resolution of the photovoltaic station and the photovoltaic string design parameters; the density coefficient is used to evaluate the density of the point cloud data in each pane and determine whether the pane is defective or blocked;

[0142] S603. According to the list of window pane coefficients of the string, determine the number e of ε=1. If it is 0, it means that the string is intact. Otherwise, it is considered damaged or blocked, and the damaged or blocked area s is calculated. The calculation can be expressed as:

[0143] s=e×γ 2

[0144] S6. Use the contour extraction method based on feasible contour straight line processing to extract PI E Each photovoltaic string contour point set C E , the PI E With C E Uniquely associated by index.

[0145] The specific steps are:

[0146] S601. Set the search domain threshold β for PI E The set of right-angle vertices of the photovoltaic string with index q in Associated vertex set Point′ q , choose the kth right-angle vertex p k , for Point′ q The corresponding incident vertex p in a 、p c Treat them separately;

[0147] S602. With edge p k p a The direction vector is the primitive vector, and the points in the cube area with the threshold β as the side length are traversed to determine the angle between the primitive vector and other vectors, and to determine whether the number of points in the cube is 0. If so, it is considered that the photovoltaic string is blocked or damaged, and the photovoltaic string index is added to the set PI D , and repeat S601; otherwise, execute S603;

[0148] S603. Set an angle threshold δ, calculate the angle between adjacent vectors, and if the adjacent angle is greater than the angle threshold δ, it is proved that the point is a contour point;

[0149] S604. Repeat S602 and S603 until all points in the small cube are traversed;

[0150] S605. Repeat S602-S604 until all small cubes are traversed and the following is obtained: Fig.13 As shown, the feasible domain to be searched of a photovoltaic string and the contour point cloud after the search.

[0151] S7. According to the principle that there is no laser point on one side of the line connecting adjacent edge points, D Extract the photovoltaic string contour point cloud set C from each photovoltaic string D , the PI D With C D Uniquely associated by index. Fig.14 As shown, the extraction results of the two-dimensional contour points and three-dimensional contour points of the photovoltaic string with index 9 that is blocked and damaged.

[0152] S8. Based on the PV string contour points C obtained in S6 and S7 E With C D By calculating the mean of the three-dimensional coordinates, the center position of the photovoltaic string is obtained (455803, 3073913, 70) as follows Fig.15 The center point of the photovoltaic string with index 1 is shown;

[0153] S9. Using the photovoltaic panel angle parameter estimation method based on interpolation fitting to calculate the inclination angle and direction angle of the photovoltaic string;

[0154] After calculation, the inclination angle and direction angle of the photovoltaic string in this example are shown in Table 1:

[0155] Table 1 PV string inclination and direction angle

[0156] String Index Direction inclination String Index Direction inclination 1 90.07 20.66 7 91.01 21.62 2 90.04 20.10 8 90.05 19.89 3 90.09 20.62 9 91.01 21.02 4 90.04 20.50 10 90.89 20.90 5 89.04 19.89 11 89.94 20.89 6 90.19 20.83 12 90.23 20.54

[0157] S10. Based on the spatial position, inclination, direction angle and contour of the photovoltaic strings, the reverse modeling of the photovoltaic station can be completed.

[0158] The present invention is explained from the viewpoints of purpose of use, efficiency, progress and novelty, and the practical progress it has is in line with the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings of this application are only preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to those of this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

[0159] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction, characterized in that: The following steps are involved: S1. Obtaining a three-dimensional laser point cloud of a photovoltaic station and design parameters of a photovoltaic string, and preprocessing the laser point cloud to obtain point cloud data, wherein the design parameters of the photovoltaic string include the width, length and diagonal length of the photovoltaic string; S2. Based on the obtained point cloud data, the point cloud data is instance segmented based on the deep learning method, the photovoltaic string point cloud is extracted and a unique index is set for each photovoltaic string to establish the photovoltaic string index set PI T ; S3. Based on the obtained photovoltaic string index set PI T Through the alternative vertex search method and the distance-based vertex cluster search method, the candidate vertex set Point of each photovoltaic string is extracted respectively. 0 And the feasible vertex set Point 1 , and obtain the contour straight line of the photovoltaic string through the angle judgment method, detect the integrity of the photovoltaic string, and establish the index set PI of the intact photovoltaic string respectively. E The index set PI of the blocked or damaged photovoltaic strings D ; S4. Extract PI by sliding pane method E PV strings and PI that are blocked or damaged D Merge and calculate the blocked or damaged area of ​​the blocked or damaged photovoltaic strings; S5. Contour extraction method based on feasible contour straight line processing to extract PI E The contour point set C of all photovoltaic strings in E , and based on the principle that there is no laser point on one side of the line connecting adjacent edge points, PI is extracted D The set C of all contour points without straight edges in D , the PI E With C E The PI is uniquely associated with the D With C D Uniquely associated by index; S6. According to the contour point set C E With C D , calculate the center position of the photovoltaic string, and calculate the inclination and direction angle of the photovoltaic string through the interpolation fitting method; S7. Complete the modeling of the photovoltaic station according to the obtained center position, angle and outline of the photovoltaic string.

2. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 1 is characterized in that: Step S3 includes the following steps: S301. PI by alternative vertex search method T Process the point cloud data in to obtain the candidate vertex set Point 0 , the Point 0 By index and PI T The only connection; S302. PI is searched by vertex cluster search method based on distance T Process the point cloud data in to obtain the feasible vertex set Point 1 , the Point 1 By index and PI T The only connection; S303. Determine whether the photovoltaic string is complete by using the contour line acquisition method based on angle judgment, and establish the damaged or blocked photovoltaic string index set PI D and the intact photovoltaic string index set PI E , get the right-angle vertex set Point 2 Associated with the vertex set Point', the PI D With PI E The sum of the number of photovoltaic strings is equal to PI T The sum of the number of photovoltaic strings in the Point 2 Uniquely associated with Point′.

3. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 2 is characterized in that: The step S301 includes the following steps: S301-1. Project the point cloud data onto the two-dimensional XY plane to obtain a two-dimensional point set: P=(A x ,A y ); A x ={x1,x2…,x n }; A y ={y1,y2…,y n }; Where P is a set of plane points; A x A is the set of X-axis coordinate values ​​corresponding to the point; x is the set of Y-axis coordinate values ​​corresponding to the point; 1, 2, ...n are the indexes of the points; S301-2. Obtain the maximum and minimum values ​​of the XY axis respectively, that is: max , x min ,y max ,y min , its calculation can be expressed as: x max =max(A x ) x min =min(A x ) and max =max(A y ) y min =min(A y ) S301-3. Get x max , x min ,y max ,y min The index of the corresponding two-dimensional point set, and then obtain the corresponding three-dimensional point set P0; S301-4. Pair the points in P0 with each other and calculate the distance d between the two points. The calculation can be expressed as: Where i and j represent the subscripts of the two paired points in P0, i≠j; S301-5: Take d greater than the distance threshold Points as candidate vertex set Point 0 , the distance threshold It is the diagonal length of the PV string in the PV string design parameters.

4. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 2 is characterized in that: Step S302 includes the following steps: S302-1. Based on the Point obtained in step 301 0 Create a collection to be tested Set the number of iterations n = 0 and set the distance threshold according to the laser point cloud scanning accuracy Said For PI T The candidate vertex set corresponding to the photovoltaic string with index q in the PV string; S302-2. From Randomly select K data points as the initial cluster centers, calculate the mutual distance d′ between the K data points, and set the distance d′ less than the distance threshold The data points are merged into one cluster to obtain M initial clusters Said M≤K; S302-3. Create a new set to be detected Calculate the centroids of the M clusters and calculate the distance d″ from other points in P1 to the centroids. Set d″ to a value less than the distance threshold. The data points are assigned to the corresponding clusters, and vice versa. S302-4. Judgment Is it empty? If not, set n=n+1 and repeat S302-2 to S302-3; if it is empty, go to the next step; S302-5. Obtain candidate vertex clusters A point is randomly selected in each cluster as a candidate vertex, which is the vertex set Point of the photovoltaic string. q =(p1, p2, ..., p l ), where l and the candidate vertex cluster The number of S302-6. Setting the threshold distance And remove the vertex set Point q From the remaining vertices, select the collinear vertices on the same straight line, and select the ones that satisfy the distance between adjacent vertices along the width direction of the photovoltaic string and the distance along the length direction of the photovoltaic string equal to the distance threshold respectively. The points of the PV string width and length in the figure are obtained. The threshold distance Including the width and length of the photovoltaic string in the photovoltaic string design parameters.

5. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 2 is characterized in that: Step S303 includes the following steps: S303-1. Based on the feasible vertices obtained in step S302 Create a set of right-angle vertices Associated vertex set Point′ q , and Point′ q The only connection; S303-2. Take any vertex p b , take any two vertices p a 、p c Cosine value between the straight lines Its calculation can be expressed as: when When p is equal to 0, b Add to And record p b 、p a 、p c The index of p a 、p c is divided by p b Other vertices besides ; S303-3. Verify the vertex set Is the number of midpoints equal to 4 and equal to If the number is the same, the string index is added to PI E Otherwise, it means that the PV string is blocked or damaged, and the PV string index is added to the set PI D .

6. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 1 is characterized in that: The sliding pane method in step S4 comprises the following steps: S401. Set the window step length γ and density coefficient threshold a based on the 3D laser point cloud resolution of the photovoltaic station, and take PI E A photovoltaic string is selected and its point cloud is obtained, wherein the window step length γ is greater than the 3D laser point cloud resolution of the photovoltaic station, and the width and length of the photovoltaic string are integer multiples of the window step length γ; S402. Select any vertex in the pane, slide along the width direction and the length direction of the photovoltaic string according to the step length γ to calculate the pane density coefficient ε, and compare it with the density coefficient threshold, identify all defective or blocked panes in the photovoltaic string, and record each pane coefficient of the pane until the entire photovoltaic string is detected. The number of slides is determined according to the length and width of the string. The specific density coefficient comparison model is: Wherein, ε is the density coefficient, v is the number of points in the pane; u is the ratio of the building pane area to the laser point cloud resolution; a is the density coefficient threshold, which is determined according to the 3D laser point cloud resolution of the photovoltaic station and the photovoltaic string design parameters; the density coefficient is used to evaluate the density of the point cloud data in each pane and determine whether the pane is defective or blocked; S403. According to the list of window pane coefficients of the string, determine the number e of ε=1. If it is 0, it means that the string is intact. Otherwise, it is considered damaged or blocked, and the damaged or blocked area s is calculated. The calculation can be expressed as: s=e×γ 2 Where s is the damaged or blocked area, e is the number of panes with ε=1, and γ is the pane step size.

7. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 1 is characterized in that: The contour extraction method based on feasible contour straight line processing in step S5 comprises the following steps: S501. Set the search domain threshold β for PI E The set of right-angle vertices of the photovoltaic string with index q in Associated vertex set Point′ q , choose the kth right-angle vertex p k , for Point′ q The corresponding incident vertex p in a 、p c Treat them separately; S502. With edge p k p a The direction vector is the primitive vector, and the points in the cube area with the threshold β as the side length are traversed to determine the angle between the primitive vector and other vectors, and to determine whether the number of points in the cube is 0. If so, it is considered that the photovoltaic string is blocked or damaged, and the photovoltaic string index is added to the set P1 D , and repeat S501; otherwise, execute S503; S503. Set an angle threshold δ, calculate the angle between adjacent vectors, and if the adjacent angle is greater than the angle threshold δ, it is proved that the point is a contour point; S504. Repeat S502 and S503 until all points in the small cube are traversed; S505. Repeat S502-S504 until all small cubes are traversed to obtain the contour extraction result of the photovoltaic string.

8. The photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction according to claim 1 is characterized in that: The photovoltaic panel angle parameter estimation method based on interpolation fitting in step S6 includes the following steps: S601. Construct a point set based on the point cloud data, and construct a photovoltaic string plane equation, which can be expressed as: Ax+By+Cz+D=0 Where A, B, C are the values ​​of the plane normal vector a in the plane equation, and D is the position offset of the plane in the three-dimensional coordinate system; S602. Take the z-axis unit vector b as the direction vector and calculate the inclination value of the photovoltaic string. The calculation can be expressed as: i i =arccos(a·b / ||a||×||b||) S603. Take the north direction unit vector c of the photovoltaic string as the direction vector, and calculate the direction angle value of the photovoltaic string. The calculation can be expressed as: i d =arccos(a·c / ||a||×||c||)。

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