Method for identifying, classifying and marking internal and external areas of photovoltaic tracker
By automatically identifying and classifying the inner and outer areas of photovoltaic trackers, the inefficiency and error caused by manual operation in existing technologies are solved, achieving efficient and accurate labeling of photovoltaic tracker areas.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-03-13
AI Technical Summary
The identification and labeling of the internal and external areas of existing photovoltaic trackers rely on manual work, which is cumbersome, time-consuming and labor-intensive, and prone to omissions in labeling and deviations in area identification, affecting the quality of design and construction.
Using an automated approach, the system automatically identifies and classifies the inner and outer regions of a photovoltaic tracker by acquiring user configuration information, preset array layout optimization strategies, and inner and outer region determination rules, and then performs visual annotation.
It improves the efficiency of batch processing of photovoltaic trackers, saves time and labor costs, avoids omissions in labeling and deviations in area identification, and provides reliable guarantees for subsequent design and construction.
Smart Images
Figure CN121661666A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of photovoltaic tracker design technology in engineering CAD, specifically a method for identifying, classifying, and labeling the internal and external regions of a photovoltaic tracker. Background Technology
[0002] In the design of photovoltaic (PV) trackers using AutoCAD software, accurate identification and standardized labeling of the inner and outer regions of the PV tracker are crucial for ensuring the effectiveness of the design. However, in existing technologies, the identification and labeling of these inner and outer regions are generally done manually. When faced with the need for batch processing of large-scale PV trackers, this manual operation is not only cumbersome and time-consuming, resulting in significantly low overall efficiency, but also prone to omissions in labeling and errors in region identification due to human factors, which adversely affects subsequent design verification and engineering construction. Summary of the Invention
[0003] To address the aforementioned shortcomings, this invention proposes a method for identifying, classifying, and labeling the inner and outer regions of photovoltaic trackers. The aim is to solve the problem that the identification and labeling of the inner and outer regions of existing photovoltaic trackers relies on manual labor. When facing the demand for large-scale photovoltaic trackers, the process is not only cumbersome and time-consuming, but also suffers from problems such as omissions in labeling and region identification due to human factors.
[0004] To achieve this objective, the present invention adopts the following technical solution: A method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker includes the following steps: Step S1: Obtain user configuration information, which includes the total number of photovoltaic trackers, the names of different specifications and types of photovoltaic trackers, the inner zone marking colors of different specifications and types of photovoltaic trackers, and the outer zone marking colors of different specifications and types of photovoltaic trackers. Step S2: Determine the target layer to which the target photovoltaic tracker array belongs in the current CAD drawing, obtain the closed polygon region in the target layer, and extract all target photovoltaic tracker arrays contained within the closed polygon region; Step S3: Based on the actual arrangement characteristics of all target photovoltaic tracker arrays, a preset array arrangement optimization strategy is used to normalize all target photovoltaic tracker arrays to obtain all effective photovoltaic tracker arrays; Step S4: Based on the preset inner and outer zone determination rules, identify the inner and outer zones corresponding to each valid photovoltaic tracker in all valid photovoltaic tracker arrays; Step S5: Set the classification attributes for the inner and outer zones of all valid photovoltaic trackers, and classify the inner and outer zones of all valid photovoltaic trackers according to their specifications and types; Step S6: Based on the inner and outer area labeling colors of photovoltaic trackers of different specifications and types, visually label the inner and outer areas of all valid photovoltaic trackers that have completed the classification.
[0005] Preferably, in step S3, the preset array layout optimization strategy is as follows: when the actual layout of the target photovoltaic tracker array is a regular layout, the target photovoltaic tracker array is normalized in an automatic mode; when the actual layout of the target photovoltaic tracker array is an irregular layout, the target photovoltaic tracker array is normalized in a manual mode; when the actual layout of the target photovoltaic tracker array is a specific layout, the target photovoltaic tracker array is normalized in a hybrid mode of automatic and manual.
[0006] Preferably, in step S4, the specific content of the preset inner and outer zone determination rules is as follows: Detect the neighbor association status of the current effective photovoltaic tracker in the four directions of up, down, left and right, and calculate the gap between the neighbor and the edge of the polygon region in the up and down direction (VertGap) or in the left and right direction (HorizGap), and calculate the overlap ratio between the neighbor and the effective photovoltaic tracker in the up and down direction (OverlapYRatio) or in the left and right direction (OverlapXRatio). Set the VertGap threshold (VertGapTol), the HorizGap threshold (HorizGapTol), and the overlap ratio threshold (minOverlapRatio); Determine whether the currently active photovoltaic tracker has neighbor associations in all four directions (up, down, left, and right), whether VertGap is less than VertGapTol or HorizGap is less than HorizGapTol, and whether OverlapYRatio or OverlapXRatio is greater than or equal to minOverlapRatio. If yes, the currently active photovoltaic tracker is determined to be in the inner zone; otherwise, the currently active photovoltaic tracker is determined to be in the outer zone.
[0007] Preferably, in step S5, the classification attributes of the inner and outer regions of all valid photovoltaic tracker arrays are set respectively, specifically including the following sub-steps: the classification attribute of valid photovoltaic trackers that meet the condition of "having neighbor associations in all four directions" is configured as inner region; the classification attribute of valid photovoltaic trackers that meet the condition of "not having neighbor associations in at least one of the four directions" is configured as outer region.
[0008] Preferably, step S5 further includes the following steps: configuring all valid photovoltaic trackers as inner zones by default; filtering out valid photovoltaic trackers with fewer than or equal to 1 neighbor and marking them as outer photovoltaic trackers; sorting all outer photovoltaic trackers by row, and for each row of outer photovoltaic trackers, determining whether there are photovoltaic trackers with larger specifications on their left and right edges; if so, modifying the classification attribute of the first three outer photovoltaic trackers closest to the one with larger specifications to inner zones; otherwise, keeping their original classification attributes unchanged.
[0009] Preferably, the method further includes the following steps: counting the number of inner zones, outer zones, and total number of valid photovoltaic trackers of each specification type, and generating a visualization table based on the statistical results.
[0010] The technical solution provided by this invention may include the following beneficial effects: This solution first extracts the target photovoltaic (PV) tracker array from the closed polygonal region of the target layer in the current CAD drawing, and then uses a preset array layout optimization strategy to obtain an effective PV tracker array. Next, it distinguishes the inner and outer regions of each effective PV tracker in the array according to preset inner and outer region determination rules, and performs visual annotation after classification by specifications, thus achieving automatic identification, classification, and annotation of PV trackers. Compared to the traditional manual operation mode, this solution adopts automated operation. When facing the batch processing needs of large-scale PV trackers, it can not only effectively save time and labor costs, thereby improving overall work efficiency, but also avoid annotation omissions and region identification errors caused by human factors, providing reliable assurance for subsequent design verification and engineering construction. Attached Figure Description
[0011] Figure 1 This is a flowchart illustrating the steps involved in identifying, classifying, and labeling the internal and external regions of a photovoltaic tracker. Detailed Implementation
[0012] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0013] A method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker includes the following steps: Step S1: Obtain user configuration information, which includes the total number of photovoltaic trackers, the names of different specifications and types of photovoltaic trackers, the inner zone marking colors of different specifications and types of photovoltaic trackers, and the outer zone marking colors of different specifications and types of photovoltaic trackers. Step S2: Determine the target layer to which the target photovoltaic tracker array belongs in the current CAD drawing, obtain the closed polygon region in the target layer, and extract all target photovoltaic tracker arrays contained within the closed polygon region; Step S3: Based on the actual arrangement characteristics of all target photovoltaic tracker arrays, a preset array arrangement optimization strategy is used to normalize all target photovoltaic tracker arrays to obtain all effective photovoltaic tracker arrays; Step S4: Based on the preset inner and outer zone determination rules, identify the inner and outer zones corresponding to each valid photovoltaic tracker in all valid photovoltaic tracker arrays; Step S5: Set the classification attributes for the inner and outer zones of all valid photovoltaic trackers, and classify the inner and outer zones of all valid photovoltaic trackers according to their specifications and types; Step S6: Based on the inner and outer area labeling colors of photovoltaic trackers of different specifications and types, visually label the inner and outer areas of all valid photovoltaic trackers that have completed the classification.
[0014] This solution provides a method for identifying, classifying, and labeling the internal and external regions of a photovoltaic tracker, such as... Figure 1As shown, the first step is to obtain user configuration information. This information includes the total number of photovoltaic (PV) trackers, the names of different PV tracker specifications, the inner zone labeling colors of different PV tracker specifications, and the outer zone labeling colors of different PV tracker specifications. Specifically, obtaining user configuration information provides a standardized data foundation for subsequent identification, classification, and labeling of the inner and outer zones of PV trackers. Further, in this embodiment, the total number of PV trackers is set to 3; the PV tracker specifications are set to 1×96, 1×56, and 1×28; the outer zone labeling color for 1×96 PV trackers is set to red, and the inner zone labeling color is set to orange; the outer zone labeling color for 1×56 PV trackers is set to dark blue, and the inner zone labeling color is set to light blue; the outer zone labeling color for 1×28 PV trackers is set to dark green, and the inner zone labeling color is set to light green. The second step is to determine the target layer to which the target photovoltaic tracker array belongs in the current CAD drawing, obtain a closed polygon region within that target layer, and extract all target photovoltaic tracker arrays contained within that closed polygon region. In this embodiment, by determining the target layer, irrelevant graphic interference can be quickly eliminated, the processing range can be narrowed, and the targeting of the target photovoltaic tracker array extraction can be improved. By obtaining the closed polygon region, the identification boundary of the inner and outer areas of the photovoltaic tracker can be effectively defined. By extracting all target photovoltaic tracker arrays within the closed polygon region, a reliable data foundation is provided for the subsequent identification of the inner and outer areas of the target photovoltaic tracker. The third step is to normalize all target photovoltaic tracker arrays based on their actual arrangement characteristics using a preset array arrangement optimization strategy, thereby obtaining all effective photovoltaic tracker arrays. In this embodiment, by using a preset array arrangement optimization strategy to normalize all target photovoltaic tracker arrays, the target photovoltaic tracker arrays become more regular, avoiding disordered or non-standard arrangements, and providing favorable conditions for the subsequent identification of the inner and outer areas of the target photovoltaic tracker. The fourth step is to identify the inner and outer areas of each valid photovoltaic tracker in all valid photovoltaic tracker arrays according to the preset inner and outer area determination rules. In this embodiment, the identification of the inner and outer areas of valid photovoltaic trackers is based on the preset inner and outer area determination rules to ensure that the regional identification standard of valid photovoltaic trackers is uniform and to avoid subjective differences in human judgment. The fifth step is to set the classification attributes of the inner and outer areas of all valid photovoltaic trackers, and to classify the inner and outer areas of all valid photovoltaic trackers according to the specifications and types of photovoltaic trackers. In this embodiment, by configuring classification attributes for the inner and outer areas of valid photovoltaic trackers, a data foundation is provided for the subsequent classification and visualization labeling of the inner and outer areas of valid photovoltaic trackers.By classifying the inner and outer zones of valid photovoltaic (PV) trackers according to their specifications, the regional characteristics of PV trackers of the same specifications become clearer. The sixth step involves visually labeling the inner and outer zones of all classified valid PV trackers using the corresponding inner and outer zone colors. In this embodiment, visually labeling the inner and outer zones of all classified valid PV trackers makes the inner and outer zones of different specifications intuitively distinguishable in CAD drawings, facilitating quick differentiation of zone types by subsequent designers. Further explanation: the visual labeling includes color and text labels. Color labels are used to quickly distinguish inner and outer zone attributes, while text labels supplement the specifications and zone categories of the PV trackers, thereby improving the completeness and readability of the labeling information.
[0015] This solution first extracts the target photovoltaic (PV) tracker array from the closed polygonal region of the target layer in the current CAD drawing, and then uses a preset array layout optimization strategy to obtain an effective PV tracker array. Next, it distinguishes the inner and outer regions of each effective PV tracker in the array according to preset inner and outer region determination rules, and performs visual annotation after classification by specifications, thus achieving automatic identification, classification, and annotation of PV trackers. Compared to the traditional manual operation mode, this solution adopts automated operation. When facing the batch processing needs of large-scale PV trackers, it can not only effectively save time and labor costs, thereby improving overall work efficiency, but also avoid annotation omissions and region identification errors caused by human factors, providing reliable assurance for subsequent design verification and engineering construction.
[0016] Preferably, in step S3, the preset array layout optimization strategy is as follows: when the actual layout of the target photovoltaic tracker array is a regular layout, the target photovoltaic tracker array is normalized in an automatic mode; when the actual layout of the target photovoltaic tracker array is an irregular layout, the target photovoltaic tracker array is normalized in a manual mode; when the actual layout of the target photovoltaic tracker array is a specific layout, the target photovoltaic tracker array is normalized in a hybrid mode of automatic and manual.
[0017] In this embodiment, an automatic mode is used for target photovoltaic tracker arrays with regular layouts. This mode relies on a preset algorithm to quickly complete the regularization process without manual intervention, significantly improving the processing efficiency of large-scale, regularly laid-out target photovoltaic tracker arrays. Further explanation: the preset algorithm is a minimum bounding rectangle fitting algorithm or a row and column clustering and alignment algorithm, etc. A manual mode is used for target photovoltaic tracker arrays with irregular layouts. This allows for flexible handling of irregular arrangement patterns, avoiding the limitations of automatic algorithms in adapting to complex layout scenarios and ensuring the accuracy of the regularization results. A hybrid automatic and manual mode is used for target photovoltaic tracker arrays with specific layouts. The automatic mode completes the basic regularization, and then manual intervention is used to optimize special areas, achieving a balance between efficiency and accuracy. In one embodiment, if the specific layout of the target photovoltaic tracker array is such that the core area is arranged with equal spacing of "3 rows × 20 columns," while the 3 columns of target photovoltaic trackers located at the edge of the polygonal area need to be gradually adjusted to conform to the boundary constraints of the area. For this layout, the row and column alignment and standard spacing of the core rule area are quickly completed in automatic mode. Then, the column spacing of the three target photovoltaic trackers at the edge of the polygon area is manually fine-tuned to ensure that they meet the boundary constraint adaptation requirements.
[0018] Preferably, in step S4, the specific content of the preset inner and outer zone determination rules is as follows: Detect the neighbor association status of the current effective photovoltaic tracker in the four directions of up, down, left and right, and calculate the gap between the neighbor and the edge of the polygon region in the up and down direction (VertGap) or in the left and right direction (HorizGap), and calculate the overlap ratio between the neighbor and the effective photovoltaic tracker in the up and down direction (OverlapYRatio) or in the left and right direction (OverlapXRatio). Set the VertGap threshold (VertGapTol), the HorizGap threshold (HorizGapTol), and the overlap ratio threshold (minOverlapRatio); Determine whether the currently active photovoltaic tracker has neighbor associations in all four directions (up, down, left, and right), whether VertGap is less than VertGapTol or HorizGap is less than HorizGapTol, and whether OverlapYRatio or OverlapXRatio is greater than or equal to minOverlapRatio. If yes, the currently active photovoltaic tracker is determined to be in the inner zone; otherwise, the currently active photovoltaic tracker is determined to be in the outer zone.
[0019] In this embodiment, VertGapTol is set to 1m, HorizGapTol is set to 3m, and minOverlapRatio is set to 30%. By simultaneously detecting the neighbor association status of photovoltaic trackers in the four directions (up, down, left, and right), and combining the two-dimensional judgment logic of gap parameters and overlap ratio parameters, the limitations of traditional single distance judgment or position judgment can be avoided. It can accurately identify inner photovoltaic trackers in dense areas of polygonal regions and outer photovoltaic trackers in edge, corner, or sparse areas, improving the adaptability and accuracy of partition judgment for complex application scenarios.
[0020] Preferably, in step S5, the classification attributes of the inner and outer regions of all valid photovoltaic tracker arrays are set respectively, specifically including the following sub-steps: valid photovoltaic trackers that meet the condition of "having neighbor associations in all four directions" are classified as inner region; valid photovoltaic trackers that meet the condition of "having no neighbor associations in at least one of the four directions" are classified as outer region. In this embodiment, by setting the conditions of "having neighbor associations in all four directions" and "having no neighbor associations in at least one direction," a clear and unambiguous judgment standard is established for the classification attributes of the inner and outer regions of photovoltaic trackers, ensuring the accuracy and consistency of subsequent classification of the inner and outer regions of photovoltaic trackers.
[0021] Preferably, step S5 further includes the following steps: configuring all valid photovoltaic trackers as inner zones by default; filtering out valid photovoltaic trackers with fewer than or equal to 1 neighbor and marking them as outer photovoltaic trackers; sorting all outer photovoltaic trackers by row, and for each row of outer photovoltaic trackers, determining whether there are photovoltaic trackers with specifications larger than the outer photovoltaic tracker on its left and right edges; if so, modifying the classification attribute of the first three outer photovoltaic trackers closest to the one with specifications larger than the outer photovoltaic tracker to inner zone; if not, keeping their original classification attribute unchanged.
[0022] In this embodiment, by first configuring all effective photovoltaic trackers as the inner area, and then locating the outer photovoltaic trackers in the edge area using the quantitative condition of "the number of neighbors is less than or equal to 1", and combining the determination of "the existence of large-scale photovoltaic trackers on the left and right edges of the outer photovoltaic trackers" and the rule of "correcting the classification attributes of the first three outer trackers", the technical pain point of "edge areas near large-scale photovoltaic trackers" in the array being mistakenly classified as outer areas is effectively solved, making the partitioning results more consistent with the physical characteristics of the actual array layout, and ensuring the accuracy and rationality of the classification.
[0023] Preferably, the method further includes the following steps: counting the number of inner zones, outer zones, and the total number of valid photovoltaic trackers of each specification type, and generating a visualization table based on the statistical results. In this embodiment, by automatically generating a visualization statistical table, designers can more intuitively and clearly obtain key information such as the zonal distribution ratio and total scale of photovoltaic trackers of each specification, thereby improving the efficiency of data reading and utilization.
[0024] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0025] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker, characterized in that: Includes the following steps: Step S1: Obtain user configuration information, which includes the total number of photovoltaic trackers, the names of different specifications and types of photovoltaic trackers, the inner zone marking colors of different specifications and types of photovoltaic trackers, and the outer zone marking colors of different specifications and types of photovoltaic trackers. Step S2: Determine the target layer to which the target photovoltaic tracker array belongs in the current CAD drawing, obtain the closed polygon region in the target layer, and extract all target photovoltaic tracker arrays contained within the closed polygon region; Step S3: Based on the actual arrangement characteristics of all target photovoltaic tracker arrays, a preset array arrangement optimization strategy is used to normalize all target photovoltaic tracker arrays to obtain all effective photovoltaic tracker arrays; Step S4: Based on the preset inner and outer zone determination rules, identify the inner and outer zones corresponding to each valid photovoltaic tracker in all valid photovoltaic tracker arrays; Step S5: Set the classification attributes for the inner and outer zones of all valid photovoltaic trackers, and classify the inner and outer zones of all valid photovoltaic trackers according to their specifications and types; Step S6: Based on the inner and outer area labeling colors of photovoltaic trackers of different specifications and types, visually label the inner and outer areas of all valid photovoltaic trackers that have completed the classification.
2. The method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker according to claim 1, characterized in that: In step S3, the specific details of the preset array layout optimization strategy are as follows: When the actual arrangement of the target photovoltaic tracker array is a regular layout, the automatic mode is used to normalize the target photovoltaic tracker array. When the actual arrangement of the target photovoltaic tracker array is irregular, the target photovoltaic tracker array is normalized using manual mode. When the actual arrangement of the target photovoltaic tracker array is a specific layout, a hybrid mode of automatic and manual processing is used to normalize the target photovoltaic tracker array.
3. The method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker according to claim 1, characterized in that: In step S4, the specific details of the preset inner and outer zone determination rules are as follows: Detect the neighbor association status of the current effective photovoltaic tracker in the four directions of up, down, left and right, and calculate the gap between the neighbor and the edge of the polygon region in the up and down direction (VertGap) or in the left and right direction (HorizGap), and calculate the overlap ratio between the neighbor and the effective photovoltaic tracker in the up and down direction (OverlapYRatio) or in the left and right direction (OverlapXRatio). Set the VertGap threshold (VertGapTol), the HorizGap threshold (HorizGapTol), and the overlap ratio threshold (minOverlapRatio); Determine whether the currently active photovoltaic tracker has neighbor associations in all four directions (up, down, left, and right), whether VertGap is less than VertGapTol or HorizGap is less than HorizGapTol, and whether OverlapYRatio or OverlapXRatio is greater than or equal to minOverlapRatio. If yes, the currently active photovoltaic tracker is determined to be in the inner zone; otherwise, the currently active photovoltaic tracker is determined to be in the outer zone.
4. The method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker according to claim 3, characterized in that: In step S5, the classification attributes of the inner and outer regions of all valid photovoltaic tracker arrays are set respectively, which specifically includes the following sub-steps: Valid photovoltaic trackers that meet the condition of "having neighbor associations in all four directions" are classified as inner zone. Valid photovoltaic trackers that meet the condition that "there is no neighbor association in at least one of the four directions (up, down, left, right)" will have their classification attribute set to "outer zone".
5. The method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker according to claim 3, characterized in that: Step S5 also includes the following steps: All active photovoltaic trackers are configured as inner zones by default; Filter out valid photovoltaic trackers with a neighbor number less than or equal to 1, and mark them as peripheral photovoltaic trackers; Sort all peripheral photovoltaic trackers by row. For each row of peripheral photovoltaic trackers, determine whether there are photovoltaic trackers with larger specifications on its left and right edges. If so, change the classification attribute of the three peripheral photovoltaic trackers closest to the one with larger specifications to the inner zone. If not, keep their original classification attribute unchanged.
6. The method for identifying, classifying, and labeling the inner and outer regions of a photovoltaic tracker according to claim 1, characterized in that: It also includes the following steps: The number of effective photovoltaic trackers in the inner zone, the number of effective photovoltaic trackers in the outer zone, and the total number of effective photovoltaic trackers of each specification type are counted, and a visualization table is generated based on the statistical results.