Space block convergence method and device based on grouping combination, storage medium and electronic equipment

Abstract

The invention discloses a space block convergence method and device based on grouping combination, a storage medium and electronic equipment, and the method comprises the steps: dividing a target layout into a plurality of regions; performing horizontal scanning and vertical scanning on the pattern in each area by adopting a scanning line method, generating polygonal blocks, and forming a space block of each polygonal block; constructing a quadtree structure based on each space block; for any two adjacent areas, polygon blocks at the common boundary are extracted from the corresponding quadtree structures, and two boundary block sets are formed; performing grouping and merging processing on the two boundary block sets to generate a new boundary block set; forming a space block set by the new boundary block set and the non-boundary blocks in the two adjacent areas; optical rule detection is performed on each polygonal block in the set of spatial blocks. According to the scheme, the optical rule detection efficiency can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of photolithography technology, and specifically to a spatial block aggregation method, device, storage medium, and electronic device based on group merging. Background Art

[0002] As integrated circuit process nodes continue to advance toward nanometer scales like 7nm and 5nm, lithography exposure wavelengths (such as DUV 193nm ArF lasers) are significantly larger than the feature size of the mask layout, resulting in blurred and distorted pattern edges, directly impacting yield and even preventing mass production. To improve the reliability of high-end integrated circuit lithography, optical rule checking (ORC) has become a key acceleration step, using hierarchical spatial block division to quickly locate potential hotspots.

[0003] In traditional methods, the layout is first recursively divided into polygonal blocks by region, and then rule detection is performed based on the size, aspect ratio, and other factors of the polygonal blocks. To avoid boundary truncation effects, the overlap range of the regions is often artificially expanded, but this significantly reduces computational efficiency, especially in areas without patterns, requiring a larger overlap radius, which is difficult to meet engineering requirements. Summary of the Invention

[0004] The embodiments of the present application provide a spatial block aggregation method, device, storage medium, and electronic device based on group merging, which can improve the efficiency of ORC detection.

[0005] In a first aspect, an embodiment of the present application provides a spatial block aggregation method based on group merging, including: Divide the target territory into multiple areas; Scanning the pattern in each of the regions horizontally and vertically using a scanning line method to generate polygonal blocks, and forming a spatial block for each of the polygonal blocks; Constructing a quadtree structure based on each of the spatial blocks; For any two adjacent regions, polygonal blocks at common boundaries are extracted from the corresponding quadtree structures to form two boundary block sets; performing grouping and merging processing on the two boundary block sets to generate a new boundary block set; Combining the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; Optical rule detection is performed on each polygonal block in the set of spatial blocks.

[0006] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, performing group merging processing on the two boundary block sets to generate a new boundary block set includes: Sort each polygon block in the two boundary block sets according to the corresponding boundary coordinates to form two ordered linked lists; Performing coordinate merging processing on the two ordered linked lists to generate a target coordinate set; A grouping and merging process is performed on the two boundary block sets based on the target coordinate set to generate a new boundary block set.

[0007] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, performing group merging processing on the two boundary block sets based on the target coordinate set to generate a new boundary block set includes: Based on the target coordinate set, forming a plurality of line segments according to adjacent coordinate points; According to the overlap between each line segment and other line segments, a grouping and merging process is performed on the two boundary block sets to generate a new boundary block set.

[0008] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, group merging is performed on the two boundary block sets according to the overlap between each line segment and other line segments to generate a new boundary block set, including: For each of the line segments, when the line segment completely overlaps with another line segment, merging the corresponding two polygon blocks into a new polygon block; When the line segment partially overlaps with another line segment, merging the overlapping parts of the corresponding two polygonal blocks into a new polygonal block; When the line segment does not overlap with another line segment, no merging process is performed; The new polygonal blocks and the unmerged polygonal blocks are combined into a new boundary block set.

[0009] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, the pattern within each area is horizontally and vertically scanned using a scan line method to generate polygonal blocks, including: Traversing each row of the region by horizontal scanning lines, identifying and extracting all horizontally arranged pattern boundaries to form a plurality of horizontal boundaries; Traversing each column of the region by a vertical scanning line, identifying and extracting all vertically arranged pattern boundaries to form a plurality of vertical boundaries; A polygonal block is generated according to the horizontal boundary and the vertical boundary.

[0010] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, generating polygonal blocks according to the horizontal boundaries and the vertical boundaries includes: generating vertices of a polygonal block based on intersections of the horizontal boundary and the vertical boundary; A polygonal block is formed from the vertices.

[0011] In the spatial block aggregation method based on group merging provided in an embodiment of the present application, the spatial block forming each polygonal block includes: The minimum containing rectangle of each polygonal block is calculated as the spatial block of each polygonal block.

[0012] In a second aspect, an embodiment of the present application provides a spatial block aggregation device based on group merging, including: A division unit, used to divide the target layout into multiple areas; a scanning unit, configured to scan the pattern in each of the regions horizontally and vertically using a scanning line method to generate polygonal blocks, and form a spatial block for each of the polygonal blocks; A construction unit, configured to construct a quadtree structure based on each of the spatial blocks; An extraction unit, configured to extract polygonal blocks at common boundaries of any two adjacent regions from the corresponding quadtree structures, to form two boundary block sets; a merging unit, configured to perform group merging processing on the two boundary block sets to generate a new boundary block set; A combining unit, configured to combine the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; A detection unit is configured to perform optical rule detection on each polygonal block in the spatial block set.

[0013] In a third aspect, the present application provides a storage medium storing a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute any of the above-mentioned spatial block aggregation methods based on group merging.

[0014] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, the spatial block aggregation method based on group merging as described in any one of the above items is implemented.

[0015] In summary, the spatial block convergence method based on group merging provided in the embodiment of the present application includes dividing the target layout into multiple areas; using the scan line method to scan the pattern in each of the areas horizontally and vertically to generate polygonal blocks, and forming a spatial block for each of the polygonal blocks; constructing a quadtree structure based on each of the spatial blocks; for any two adjacent areas, extracting polygonal blocks at the common boundary from the corresponding quadtree structure to form two boundary block sets; performing group merging processing on the two boundary block sets to generate a new boundary block set; combining the new boundary block set with the non-boundary blocks in the two adjacent areas to form a spatial block set; and performing optical rule detection on each polygonal block in the spatial block set. This solution avoids the redundant calculations caused by the overlap of large areas in traditional methods, and greatly improves the efficiency of optical rule detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an application scenario of the spatial block aggregation method based on group merging provided in an embodiment of the present application.

[0018] Figure 2 It is a flow chart of a spatial block aggregation method based on group merging provided in an embodiment of the present application.

[0019] Figure 3 This is a schematic diagram of the distribution of boundary blocks of adjacent areas provided in an embodiment of the present application.

[0020] Figure 4 This is a schematic diagram of merging boundary blocks of adjacent areas provided in an embodiment of the present application.

[0021] Figure 5 It is a structural diagram of a spatial block aggregation device based on group merging provided in an embodiment of the present application.

[0022] Figure 6 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0024] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0025] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0026] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0027] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, terms such as "first" and "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] In traditional methods, the layout is first recursively divided into polygonal blocks by region, and then rule detection is performed based on the size, aspect ratio, and other factors of the polygonal blocks. To avoid boundary truncation effects, the overlap range of the regions is often artificially expanded, but this significantly reduces computational efficiency, especially in areas without patterns, requiring a larger overlap radius, which is difficult to meet engineering requirements.

[0029] Based on this, an embodiment of the present application provides a spatial block aggregation method, device, storage medium and electronic device based on group merging. Specifically, the spatial block aggregation device based on group merging can be integrated into an electronic device, which can be a server or a terminal; wherein the terminal can include a mobile phone, a wearable smart device, a tablet computer, a laptop computer, and a personal computer (PC), etc.; the server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.

[0030] For example, Figure 1 As shown, the electronic device can divide the target layout into multiple areas; use the scan line method to scan the pattern in each area horizontally and vertically to generate polygonal blocks, and form a spatial block for each polygonal block; build a quadtree structure based on each spatial block; for any two adjacent areas, extract the polygonal blocks at the common boundary from the corresponding quadtree structure to form two boundary block sets; perform grouping and merging processing on the two boundary block sets to generate a new boundary block set; combine the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; perform optical rule detection on each polygonal block in the spatial block set.

[0031] The following will describe the technical solutions of this application in detail through specific embodiments. It should be noted that the order of description of the following embodiments does not limit the priority order of the embodiments.

[0032] See also Figure 2 , Figure 2 : This is a flow chart of a spatial block aggregation method based on group merging provided by an embodiment of the present application. The specific flow of the spatial block aggregation method based on group merging can be as follows: 101. Divide the target territory into multiple areas.

[0033] In some embodiments, the target layout can be divided into multiple regions based on chip design criteria and optical parameters. For example, the target layout can be divided into several appropriately sized regions based on specific chip design requirements (e.g., pattern density, process node, etc.). Each region can contain similar design features, thereby reducing complexity and improving the efficiency of subsequent ORC.

[0034] For example, regions can be divided according to different lithography processes (such as the exposure wavelength used, optical correction technology, etc.). For different optical parameters, the division of design regions can be refined to ensure that potential optical defects can be accurately simulated and detected during the lithography process.

[0035] For example, when partitioning regions, the region size can be dynamically adjusted based on the geometric distribution of patterns in the target layout (e.g., layout complexity). The region size can be adaptively adjusted based on the density of patterns to optimize the ORC process and reduce computing resource consumption.

[0036] For example, different area partitioning strategies may be required for different process nodes (such as 7nm, 5nm, etc.). Smaller nodes may require finer area partitioning to ensure that every detail in the lithography process can be accurately simulated and inspected.

[0037] 102. Scan the pattern in each area horizontally and vertically using a scanning line method to generate polygonal blocks and form a spatial block for each polygonal block.

[0038] Specifically, a horizontal scan line can be used to traverse each row of the region to identify and extract all horizontally arranged pattern boundaries, forming a plurality of horizontal boundaries. A vertical scan line can be used to traverse each column of the region to identify and extract all vertically arranged pattern boundaries, forming a plurality of vertical boundaries. Then, polygonal blocks can be generated based on the horizontal and vertical boundaries. In some embodiments, vertices of the polygonal blocks can be generated based on the intersection of the horizontal and vertical boundaries, and the polygonal blocks can be further formed based on the vertices.

[0039] In some embodiments, a minimum enclosing rectangle of each polygonal block may be calculated as the spatial block of each polygonal block.

[0040] 103. Construct a quadtree structure based on each spatial block.

[0041] The quadtree structure is a tree-shaped data structure suitable for processing two-dimensional spatial data. It recursively divides the space into four sub-regions until the size of the region meets a certain threshold.

[0042] Specifically, each region can be used as the root node of a quadtree structure, and then the region can be continuously divided into four sub-regions using a binary search method until the side length of the sub-region is less than a preset threshold. In each leaf node of the quadtree structure, each polygon block can be filled into the corresponding leaf node according to the intersection relationship between its corresponding spatial block and the sub-region.

[0043] It's important to note that each polygon block can be repeatedly placed into multiple leaf nodes of the quadtree structure. The quadtree structure recursively divides the region into four sub-regions, enabling fine-grained spatial management. This partitioning method accurately maps polygon blocks to the leaf nodes of the quadtree structure, providing fast spatial query capabilities.

[0044] In subsequent operations (such as hotspot detection, rule checking, etc.), the quadtree structure can be used to quickly locate polygon blocks that intersect with a certain area, thereby greatly reducing the time complexity of the query.

[0045] Furthermore, the quadtree structure helps avoid traversing the entire region. When querying, only the leaf nodes of the relevant region need to be located, reducing redundant computations. Because each polygon block is placed in a corresponding leaf node based on the intersection of its spatial block and the subregion, repeated computations of irrelevant regions can be avoided, reducing wasted computing resources.

[0046] 104. For any two adjacent regions, polygonal blocks at the common boundary are extracted from the corresponding quadtree structures to form two boundary block sets.

[0047] The common boundary refers to the boundary where two areas touch or intersect.

[0048] In the specific implementation process, for each pair of adjacent regions, the common boundary between these adjacent regions can be identified by querying the corresponding quadtree structure. Then, polygon blocks located on the common boundary are extracted from the quadtree structure of each region. The geometry and position of these polygon blocks must intersect with the boundary. By searching for these qualified polygon blocks in the leaf nodes of the quadtree structure, polygon blocks belonging to the common boundary can be quickly located. Finally, all polygon blocks on the common boundary extracted from the two regions are combined into two boundary block sets.

[0049] 105. Perform grouping and merging processing on the two boundary block sets to generate a new boundary block set.

[0050] Specifically, each polygon block in the two boundary block sets can be sorted according to the corresponding boundary coordinates to form two ordered linked lists; the coordinates of the two ordered linked lists are merged to generate a target coordinate set; and based on the target coordinate set, the two boundary block sets are grouped and merged to generate a new boundary block set.

[0051] For example, assuming that the right boundary of region F overlaps with the left boundary of adjacent region N, boundary block sets Set1 and Set2 are extracted from the common boundary of these two regions respectively.

[0052] To facilitate subsequent merging operations, the two boundary block sets can be sorted. Specifically, Set 1 is sorted by the right boundary coordinate of each polygon block. Set 2 is sorted by the left boundary coordinate of each polygon block. The two sorted boundary block sets form two ordered linked lists. The right boundary coordinate is the rightmost coordinate of the polygon along the x-axis. The left boundary coordinate is the leftmost coordinate of the polygon along the x-axis.

[0053] Next, the coordinates in Set1 and Set2 are merged to generate the target coordinate set y_set. Specifically, the coordinates in the two ordered linked lists are sorted and duplicated to generate a unique target coordinate set y_set. The goal of this process is to extract all coordinate points that may intersect or overlap between the two boundary block sets. By merging all coordinates, an ordered target coordinate set y_set is ultimately obtained, which represents the intersection area of ​​the two boundary block sets.

[0054] Among them, the step of "performing grouping and merging processing on the two boundary block sets based on the target coordinate set to generate a new boundary block set" can be specifically: based on the target coordinate set, forming a number of line segments according to adjacent coordinate points; and performing grouping and merging processing on the two boundary block sets based on the overlap between each line segment and other line segments to generate a new boundary block set.

[0055] Each line segment represents the boundary of a boundary block, specifically the projection area of ​​the boundary block in the y-axis direction. The endpoints of each line segment are two consecutive coordinate points in y_set.

[0056] Specifically, for each line segment, when the line segment completely overlaps with another line segment, the corresponding two polygon blocks are merged into a new polygon block; when the line segment partially overlaps with another line segment, the overlapping parts of the corresponding two polygon blocks are merged into a new polygon block; when the line segment does not overlap with another line segment, no merging is performed; the new polygon block and the unmerged polygon blocks are combined into a new boundary block set.

[0057] like Figure 3-Figure 4 As shown in the figure, if the left endpoint of a line segment overlaps the right endpoint of another line segment, it means that the two line segments completely overlap, and the corresponding two polygon blocks can be merged into a new polygon block. For example, if the right boundary of a polygon block in Set1 completely overlaps the left boundary of a polygon block in Set2, then the two polygon blocks can be merged into a new polygon block.

[0058] If part of one line segment overlaps with part of another line segment, the polygon blocks in the overlapping area are merged into a new polygon block. If the right edge of a polygon block in Set1 partially overlaps the left edge of a polygon block in Set2, the overlapping portion can be merged into a new polygon block, while the non-overlapping portion remains as it is and is not merged, remaining as an independent polygon block.

[0059] If there is no overlap between two line segments, the pair is skipped and no merging is performed. In this case, the polygon blocks in Set1 and Set2 will not interfere with each other, so each polygon block can be retained.

[0060] It can be understood that the unmerged polygon blocks include polygon blocks without any remaining overlapping areas and non-overlapping parts of two partially overlapping polygon blocks.

[0061] It should be noted that after the merge operation is completed, the quadtree structure needs to be updated to facilitate subsequent query and processing. The new boundary block set will be incorporated into the quadtree structure, ensuring that subsequent calculations can quickly locate the new boundary block set and avoid redundant calculations.

[0062] 106. The new boundary block set and the non-boundary blocks in the two adjacent areas are combined into a spatial block set.

[0063] Among them, non-boundary blocks refer to polygonal blocks that exist inside the region and are not related to the common boundary. In other words, non-boundary blocks are usually blocks that are completely inside the region and do not involve overlapping parts of adjacent regions.

[0064] In a specific implementation, these non-boundary blocks can be extracted from the quadtree structure of the two regions to ensure that they do not belong to any common boundary.

[0065] 107. Perform optical rule detection on each polygon block in the spatial block set.

[0066] Specifically, non-boundary blocks within a region can be directly inspected for optical rules. New boundary blocks, however, require rule inspection based on factors such as size and aspect ratio. Blocks that do not conform to these rules are marked as hotspots (potential defect areas). This inspection method avoids unnecessary redundant calculations and only inspects areas that truly require inspection.

[0067] In summary, the spatial block convergence method based on group merging provided by the embodiment of the present application includes dividing the target layout into multiple areas; using the scan line method to scan the pattern in each area horizontally and vertically to generate polygonal blocks, and forming a spatial block for each polygonal block; constructing a quadtree structure based on each spatial block; for any two adjacent areas, extracting polygonal blocks at the common boundary from the corresponding quadtree structure to form two boundary block sets; performing group merging processing on the two boundary block sets to generate a new boundary block set; combining the new boundary block set with the non-boundary blocks in the two adjacent areas to form a spatial block set; performing optical rule detection on each polygonal block in the spatial block set. By adopting the group merging method, this solution avoids the computational burden brought by the overlap of large areas in the traditional method, thereby improving the overall computational efficiency. The application of the quadtree structure also makes the process of searching and updating polygonal blocks faster, thereby significantly shortening the ORC detection time. In other words, this solution avoids the redundant calculations brought by the overlap of large areas in the traditional method, greatly improving the efficiency of optical rule detection.

[0068] Furthermore, this solution's refined boundary block merging method avoids the loss of accuracy that comes with simply expanding the region. By precisely merging boundary blocks, the true distribution of the region can be more accurately restored, resulting in more precise detection results and fewer false positives and false negatives. By avoiding excessive region overlap, this solution significantly reduces unnecessary computational and storage resource consumption.

[0069] To facilitate better implementation of the spatial block aggregation method based on group merging provided in the embodiments of the present application, the embodiments of the present application also provide a spatial block aggregation device based on group merging. The meanings of the terms herein are the same as those in the spatial block aggregation method based on group merging described above. For specific implementation details, please refer to the description in the method embodiment.

[0070] See also Figure 5 , Figure 5 Schematic diagram of the structure of the spatial block aggregation device based on group merging provided by an embodiment of the present application. The spatial block aggregation device based on group merging may include a division unit 201, a scanning unit 202, a construction unit 203, an extraction unit 204, a merging unit 205, a combination unit 206 and a detection unit 207. A division unit 201 is used to divide the target layout into multiple areas; a scanning unit 202 for horizontally and vertically scanning the pattern in each region using a scanning line method to generate polygonal blocks and form a spatial block for each polygonal block; A construction unit 203, configured to construct a quadtree structure based on each spatial block; An extraction unit 204 is configured to extract polygonal blocks at common boundaries of any two adjacent regions from the corresponding quadtree structures to form two boundary block sets; A merging unit 205 is configured to perform group merging processing on two boundary block sets to generate a new boundary block set; A combining unit 206 is configured to combine the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; The detection unit 207 is configured to perform optical rule detection on each polygon block in the spatial block set.

[0071] The specific implementation of each of the above units can be found in the above-mentioned embodiment of the spatial block aggregation method based on group merging, and will not be described in detail here.

[0072] In summary, the spatial block aggregation device based on group merging provided in the embodiment of the present application can divide the target layout into multiple areas through the division unit 201; the scanning unit 202 uses the scanning line method to scan the pattern in each area horizontally and vertically to generate polygonal blocks, and form a spatial block for each polygonal block; the construction unit 203 constructs a quadtree structure based on each spatial block; the extraction unit 204 extracts the polygonal blocks at the common boundary from the corresponding quadtree structure for any two adjacent areas to form two boundary block sets; the merging unit 205 performs group merging processing on the two boundary block sets to generate a new boundary block set; the combination unit 206 combines the new boundary block set with the non-boundary blocks in the two adjacent areas to form a spatial block set; the detection unit 207 performs optical rule detection on each polygonal block in the spatial block set. This solution avoids the computational burden brought by the overlap of large areas in the traditional method by adopting the group merging method, thereby improving the overall computational efficiency. The application of the quadtree structure also makes the process of searching and updating polygonal blocks faster, thereby significantly shortening the ORC detection time. In other words, this solution avoids the redundant calculations caused by large-area overlap in traditional methods, greatly improving the efficiency of optical rule detection.

[0073] The embodiment of the present application further provides an electronic device, in which the spatial block aggregation device based on group merging according to the embodiment of the present application may be integrated, such as Figure 6 , which shows a schematic structural diagram of an electronic device involved in an embodiment of the present application, specifically: The electronic device may include one or more processing core processors 301 and one or more computer readable storage media memories 302 and other components. Those skilled in the art will understand that Figure 6 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. The processor 301 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing the software programs and / or this application stored in the memory 302, and calling the data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of storage media, user interface and application programs, etc., and the modem processor mainly handles wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0074] The memory 302 can be used to store software programs and the present application. The processor 301 executes various functional applications and data processing by running the software programs and the present application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store operating storage media, applications required for at least one function, etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0075] Although not shown, the electronic device may further include a display unit, an input unit, a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows: Divide the target territory into multiple areas; The pattern in each area is scanned horizontally and vertically using a scan line method to generate polygonal blocks, and a spatial block of each polygonal block is formed; Construct a quadtree structure based on each spatial block; For any two adjacent regions, polygonal blocks at the common boundary are extracted from the corresponding quadtree structure to form two boundary block sets; Performing grouping and merging processing on the two boundary block sets to generate a new boundary block set; The new boundary block set and the non-boundary blocks in the two adjacent areas form a spatial block set; Optical rule detection is performed on each polygonal block in the set of spatial blocks.

[0076] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0077] To this end, an embodiment of the present application provides a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the methods provided in the embodiments of the present application. For example, the instructions can execute the following steps: Divide the target territory into multiple areas; The pattern in each area is scanned horizontally and vertically using a scan line method to generate polygonal blocks, and a spatial block of each polygonal block is formed; Construct a quadtree structure based on each spatial block; For any two adjacent regions, polygonal blocks at the common boundary are extracted from the corresponding quadtree structure to form two boundary block sets; Performing grouping and merging processing on the two boundary block sets to generate a new boundary block set; The new boundary block set and the non-boundary blocks in the two adjacent areas form a spatial block set; Optical rule detection is performed on each polygonal block in the set of spatial blocks.

[0078] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0079] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0080] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by any method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0081] The above is a detailed introduction to the spatial block aggregation method, device, storage medium and electronic device based on group merging provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the core idea of ​​this application; at the same time, for technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A spatial block aggregation method based on group merging, characterized in that: include: Divide the target territory into multiple areas; Scanning the pattern in each of the regions horizontally and vertically using a scanning line method to generate polygonal blocks, and forming a spatial block for each of the polygonal blocks; Constructing a quadtree structure based on each of the spatial blocks; For any two adjacent regions, polygonal blocks at common boundaries are extracted from the corresponding quadtree structures to form two boundary block sets; performing grouping and merging processing on the two boundary block sets to generate a new boundary block set; Combining the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; Optical rule detection is performed on each polygonal block in the set of spatial blocks.

2. The spatial block aggregation method based on group merging according to claim 1, characterized in that: The performing grouping and merging processing on the two boundary block sets to generate a new boundary block set includes: Sort each polygon block in the two boundary block sets according to the corresponding boundary coordinates to form two ordered linked lists; Performing coordinate merging processing on the two ordered linked lists to generate a target coordinate set; A grouping and merging process is performed on the two boundary block sets based on the target coordinate set to generate a new boundary block set.

3. The spatial block aggregation method based on group merging according to claim 2, characterized in that: The performing grouping and merging processing on the two boundary block sets based on the target coordinate set to generate a new boundary block set includes: Based on the target coordinate set, forming a plurality of line segments according to adjacent coordinate points; According to the overlap between each line segment and other line segments, a grouping and merging process is performed on the two boundary block sets to generate a new boundary block set.

4. The spatial block aggregation method based on group merging according to claim 3, characterized in that: The step of performing grouping and merging processing on the two boundary block sets according to the overlap between each line segment and other line segments to generate a new boundary block set includes: For each of the line segments, when the line segment completely overlaps with another line segment, merging the corresponding two polygon blocks into a new polygon block; When the line segment partially overlaps with another line segment, merging the overlapping parts of the corresponding two polygonal blocks into a new polygonal block; When the line segment does not overlap with another line segment, no merging process is performed; The new polygonal blocks and the unmerged polygonal blocks are combined into a new boundary block set.

5. The spatial block aggregation method based on group merging according to claim 1, characterized in that: The method of using a scanning line method to perform horizontal and vertical scanning on the pattern in each area to generate a polygonal block includes: Traversing each row of the region by horizontal scanning lines, identifying and extracting all horizontally arranged pattern boundaries to form a plurality of horizontal boundaries; Traversing each column of the region by a vertical scanning line, identifying and extracting all vertically arranged pattern boundaries to form a plurality of vertical boundaries; A polygonal block is generated according to the horizontal boundary and the vertical boundary.

6. The spatial block aggregation method based on group merging according to claim 5, characterized in that: Generating a polygonal block according to the horizontal boundary and the vertical boundary includes: generating vertices of a polygonal block based on intersections of the horizontal boundary and the vertical boundary; A polygonal block is formed from the vertices.

7. The spatial block aggregation method based on group merging according to claim 1, characterized in that: The spatial blocks forming each of the polygonal blocks include: The minimum containing rectangle of each polygonal block is calculated as the spatial block of each polygonal block.

8. A spatial block aggregation device based on group merging, characterized in that: include: A division unit, used to divide the target layout into multiple areas; a scanning unit, configured to scan the pattern in each of the regions horizontally and vertically using a scanning line method to generate polygonal blocks, and form a spatial block for each of the polygonal blocks; A construction unit, configured to construct a quadtree structure based on each of the spatial blocks; An extraction unit, configured to extract polygonal blocks at common boundaries of any two adjacent regions from the corresponding quadtree structures, to form two boundary block sets; a merging unit, configured to perform group merging processing on the two boundary block sets to generate a new boundary block set; A combining unit, configured to combine the new boundary block set and the non-boundary blocks in the two adjacent areas into a spatial block set; A detection unit is configured to perform optical rule detection on each polygonal block in the spatial block set.

9. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the spatial block aggregation method based on group merging according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the spatial block aggregation method based on group merging according to any one of claims 1 to 7 is implemented.

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