Efficient slicing-free method, device and system for mass framing image data

Abstract

The invention relates to an efficient slicing-free method, device and system for mass framing image data. The method mainly comprises the steps of performing first grouping based on a coordinate system of framing images, performing second grouping based on the number of closed graphs after the first grouping, and performing third grouping based on the number of files after the second grouping; establishing a virtual grid file after virtual combination for the framing image data after third grouping, and performing thinning sampling processing to generate a thinning grid file; taking the rarefied grid file as a simplified data set, and taking the framing image file before grouping as an original data set; when rendering is carried out, if the rendering level is lower than a preset level, retrieval and rendering output are carried out through the simplified data set; and if the rendering level is higher than the preset level, performing retrieval and rendering output through the original data set. According to the invention, the rendering efficiency at a low level can be greatly improved, and the rendering performance during low-level rendering is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of framed image data processing, and in particular to a method, device and system for efficiently processing massive framed image data without slicing. Background Art

[0002] Imagery data is one of the primary data outputs of satellite remote sensing and drone aerial photography. Publishing imagery data as spatial services is a common way to share it. To enable applications to smoothly access and display image data, imagery data is typically sliced ​​and converted into image tiles organized into a pyramid structure. However, due to the high precision and large volume of image data, traditional slicing typically takes days, weeks, or even months. As applications increasingly demand the timeliness of image data and require frequent updates, the traditional pre-slicing service publishing process is increasingly unable to meet actual usage needs.

[0003] Based on actual usage needs, more and more methods for tile-free rendering of framed image data have been proposed. However, in actual use scenarios, when these methods are used for tile-free rendering of massive framed image data exceeding terabytes (for example, the image data of a province), at low rendering levels, the large number of image data files involved in the rendering can lead to long rendering times and poor performance.

[0004] In view of this, how to overcome the defects of existing technologies and how to improve rendering efficiency and rendering performance at low rendering levels when facing slice-free rendering of massive segmented image data have become important technical issues that need to be urgently solved in the industry. Summary of the Invention

[0005] In response to the defects or improvement needs in the existing technology: How to improve rendering efficiency and rendering performance when the rendering level is low when facing the non-slicing rendering of massive framed image data. The present invention provides a method, device and system for efficient non-slicing of massive framed image data, which pre-processes the massive framed image data by grouping and merging framed images and data thinning, and then uses different data sets for rendering according to different rendering levels, thereby greatly improving the rendering efficiency at low levels and ensuring the rendering performance when performing low-level rendering, thereby achieving the purpose of meeting generation and use; at the same time, it can also ensure the rendering effect when performing high-level rendering.

[0006] The present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for efficiently processing massive framed image data without slicing, comprising:

[0008] The first grouping is performed based on the coordinate system of the framed image, the second grouping is performed based on the number of closed graphics after the first grouping, and the third grouping is performed based on the number of files after the second grouping.

[0009] A virtual raster file is created for the frame image data after the third grouping, and a thinning sampling process is performed to generate a thinned raster file; the thinned raster file is used as a simplified dataset, and the frame image file before grouping is used as the original dataset;

[0010] When rendering, if the rendering level is lower than the preset level, the simplified dataset is used for retrieval and rendering output; if the rendering level is higher than the preset level, the original dataset is used for retrieval and rendering output.

[0011] In some embodiments, the first grouping based on the coordinate system of the framed images specifically includes:

[0012] Get the coordinate system information of all framed images;

[0013] The first grouping operation is performed based on the coordinate system of the framed image, and the framed image data with the same coordinate system are divided into the same group.

[0014] In some embodiments, performing a second grouping based on the number of closed graphics after the first grouping specifically includes:

[0015] Find the union of the outer rectangles of each group of image data after the first grouping;

[0016] A second grouping operation is performed on the frame image data according to the number of closed graphics in the union, and the frame image data in the same closed graphics are divided into the same group.

[0017] In some embodiments, performing a third grouping based on the number of files after the second grouping specifically includes:

[0018] Determine the preset total number of groups and the first preset number of files;

[0019] According to the result of the second grouping, if the number of framed images in a closed figure is greater than the first preset file number, the framed image data in the corresponding closed figure are grouped for a third time according to the preset total group number, so that the number of framed images in each group is less than the first preset file number.

[0020] In some embodiments, the step of creating a virtual raster file after virtual merging based on the framed image data after the third grouping and performing a thinning sampling process to generate the thinned raster file specifically includes:

[0021] For the frame image data after the third grouping, the frame image data of each group are virtually merged to generate virtual raster files, and all virtual raster files are tiled;

[0022] All virtual raster files are subjected to thinning sampling processing using a bilinear interpolation algorithm, and thinned raster files are generated.

[0023] In some embodiments, when rendering, if the rendering level is lower than a preset level, searching and rendering outputting through a simplified data set; if the rendering level is higher than the preset level, searching and rendering outputting through an original data set specifically includes:

[0024] Read the bounding box range and coordinate system of all files in the simplified dataset and the original dataset respectively, specify a unified coordinate system for different coordinate systems, perform virtual projection based on the unified coordinate system, and create spatial indexes for the simplified dataset and the original dataset respectively;

[0025] When rendering a tile with a specified row and column number, calculate the border of the tile to be rendered;

[0026] If the rendering level is lower than the preset level, the spatial index of the simplified dataset is directly searched based on the calculated border of the tile to be rendered, and the hit data is read for rendering output;

[0027] If the rendering level is higher than the preset level, the spatial index of the original dataset is searched based on the calculated border of the tile to be rendered, and the number of hit files is determined to determine whether to read the hit data in the original dataset for rendering output.

[0028] In some embodiments, if the rendering level is higher than a preset level, searching the spatial index of the original dataset based on the calculated bounding box of the tile to be rendered, and determining whether to read the hit data in the original dataset for rendering output according to the number of hit files specifically includes:

[0029] Determining a second preset file quantity;

[0030] If the number of hit files is less than the second preset number of files after searching the spatial index of the original dataset, the hit data in the original dataset is directly read for rendering and output;

[0031] If the number of hit files is greater than the second preset number of files after searching the spatial index of the original dataset, the spatial index of the simplified dataset is searched again, and the hit data is read for rendering and output.

[0032] In a second aspect, the present invention further provides a device for efficiently processing massive framed image data without slicing, the device comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for efficiently and slicing massive framed image data as described in the first aspect.

[0034] In a third aspect, the present invention further provides a system for efficiently processing massive framed image data without slicing, applying the method for efficiently processing massive framed image data without slicing as described in the first aspect. The system includes an image grouping module, a data set establishment module, and a retrieval and rendering module, wherein:

[0035] The image grouping module is used to: perform a first grouping based on the coordinate system of the framed image, perform a second grouping based on the number of closed graphics after the first grouping, and perform a third grouping based on the number of files after the second grouping;

[0036] The data set establishment module is used to: establish a virtual raster file after virtual merging for the frame image data after the third grouping, and perform a thinning sampling process to generate a thinned raster file; use the thinned raster file as a simplified data set, and use the frame image file before grouping as the original data set;

[0037] The retrieval and rendering module is used to: when rendering, if the rendering level is lower than the preset level, perform retrieval and rendering output through the simplified data set; if the rendering level is higher than the preset level, perform retrieval and rendering output through the original data set.

[0038] In a fourth aspect, the present invention further provides a non-volatile computer storage medium storing computer executable instructions, which are executed by one or more processors to complete the method for efficient and slicing-free processing of massive framed image data as described in the first aspect.

[0039] Compared with the existing technology, the present invention provides a method, device and system for efficiently processing massive framed image data without slicing. The beneficial effects of the present invention are as follows: massive framed image data are preprocessed in a certain manner through grouping and merging of framed images and data thinning, and then different data sets are used for rendering according to different rendering levels, thereby greatly improving the rendering efficiency at low levels and ensuring the rendering performance during low-level rendering, thereby achieving the purpose of generation and use; at the same time, it can also ensure the rendering effect during high-level rendering. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0041] Figure 1 This is a flow chart of a method for efficiently slicing massive framed image data provided by Example 1 of the present invention;

[0042] Figure 2 A specific flow chart of the first grouping provided in Example 1 of the present invention;

[0043] Figure 3 A specific flow chart of the second grouping provided in Example 1 of the present invention;

[0044] Figure 4 A specific flow chart of the third grouping provided in Example 1 of the present invention;

[0045] Figure 5 A specific flow chart of the thinning sampling process provided in Example 1 of the present invention;

[0046] Figure 6 A specific flow chart of searching and rendering output according to rendering level provided in Example 1 of the present invention;

[0047] Figure 7 A specific flow chart of searching and rendering output according to the number of hit files provided in Example 1 of the present invention;

[0048] Figure 8 A schematic diagram of a module for a system for efficiently processing massive framed image data without slicing, provided in Example 1 of the present invention;

[0049] Figure 9 A schematic diagram of data partitioning provided in Example 2 of the present invention;

[0050] Figure 10 This is a schematic diagram of the results after the first grouping provided by Example 2 of the present invention;

[0051] Figure 11 This is a schematic diagram of the results after the second grouping provided in Example 2 of the present invention;

[0052] Figure 12 This is a schematic diagram of the results after the third grouping provided in Example 2 of the present invention;

[0053] Figure 13 This is a schematic diagram of the virtual merging result provided in Example 2 of the present invention;

[0054] Figure 14 Schematic diagram of simplified data and original data provided in Example 2 of the present invention;

[0055] Figure 15 A schematic diagram of a rendering area provided in Example 2 of the present invention;

[0056] Figure 16 This is a schematic diagram of the structure of a device for efficiently processing massive framed image data without slicing, provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention. These all fall within the scope of protection of the present invention. It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other and are all within the scope of protection of this application. In addition, although the functional modules may be divided in the device schematic diagram and the logical order may be shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flow chart.

[0058] Unless otherwise defined, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which this invention belongs. The terms used in this specification and in the description of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. Furthermore, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0059] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as meaning open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" and the like are intended to indicate that the specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0060] In order to make the purpose, technical solutions and advantages of this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only intended to illustrate this application and are not intended to limit this application. The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0061] Example 1:

[0062] like Figure 1 As shown, an embodiment of the present invention provides a method for efficiently processing massive framed image data without slicing, and the method includes the following steps.

[0063] Step 101: Perform a first grouping based on the framed image's coordinate system, a second grouping based on the number of closed figures after the first grouping, and a third grouping based on the number of files after the second grouping. Grouping by coordinate system ensures that the framed image data in each group belongs to the same coordinate system. Grouping by the number of closed figures ensures that the framed image data in each group belongs to the same closed figure. Grouping by the number of files ensures that the number of files in each group is not excessive. This grouping process reduces the complexity of subsequent retrieval and rendering, improving their efficiency.

[0064] Step 102: Create a virtual raster file for the image data after the third grouping, and perform a thinning sampling process to generate a thinned raster file. This process virtually merges the data of each group and uses thinning sampling to reduce the data volume and simplify the data, thereby reducing the complexity of subsequent retrieval and rendering and improving the efficiency of retrieval and rendering.

[0065] Step 103: Use the thinned raster file as the simplified dataset and the ungrouped framed image file as the original dataset. This step separates the simplified dataset from the original dataset so that you can select the corresponding dataset for retrieval and rendering based on your needs, balancing efficiency and quality.

[0066] Step 104: During rendering, if the rendering level is lower than the preset level, the simplified dataset is used for search and rendering output; if the rendering level is higher than the preset level, the original dataset is used for search and rendering output. In this step, when the rendering level is low, efficiency is more important, so the simplified dataset is used for search and rendering output to achieve improved rendering efficiency. When the rendering level is high, quality is more important, so the original dataset is used for search and rendering output to ensure rendering quality.

[0067] Based on the above steps, the embodiment of the present invention performs certain preprocessing on massive framed image data through two aspects: framed image grouping and merging, and data thinning. Then, different data sets are used for rendering according to different rendering levels, thereby greatly improving the rendering efficiency at low levels and ensuring the rendering performance during low-level rendering, thereby achieving the purpose of meeting generation and use; at the same time, it can also ensure the rendering effect during high-level rendering.

[0068] refer to Figure 2 As shown, in some embodiments, the first grouping based on the coordinate system of the framed images specifically includes the following steps.

[0069] Step 201: Obtain the four boundaries and coordinate system information of all the framed images. The four boundaries refer to the boundaries in the four directions of east, south, west and north. The outer rectangular frame representing the corresponding framed image can be determined based on the four boundaries.

[0070] Step 202: Perform a first grouping operation based on the coordinate system of the framed images, and group the framed image data of the same coordinate system into the same group.

[0071] refer to Figure 3 As shown, in some embodiments, the second grouping based on the number of closed figures after the first grouping specifically includes the following steps.

[0072] Step 301: Calculate the union of the outer rectangles of each group of framed image data after the first grouping. The four boundaries of each framed image can form an outer rectangular frame range, and the union refers to the large frame range synthesized by these rectangular frames.

[0073] Step 302: A second grouping operation is performed on the framed image data based on the number of closed figures in the union. The framed image data within the same closed figure are grouped together. Because not all data in the same group is necessarily connected based on the coordinate system, some data may be connected here and some there, resulting in a distributed distribution. Therefore, data that can be connected to form a closed figure is further subdivided into groups.

[0074] refer to Figure 4 As shown, in some embodiments, performing the third grouping based on the number of files after the second grouping specifically includes the following steps.

[0075] Step 401: Determine the preset total number of groups and the first preset number of files. Generally speaking, the total number of groups should not be too large, and the first preset number of files should not be too large. The specific number can be determined according to needs.

[0076] Step 402: Based on the results of the second grouping, if the number of framed images in a closed figure is greater than the first preset number of files, the framed image data in the corresponding closed figure are grouped a third time based on the preset total number of groups, so that the number of framed images in each group is less than the first preset number of files. Specifically, during the third grouping, the number of groups required can be determined based on the total number of groups. Then, the corresponding closed figure with a larger number of images is split horizontally and / or vertically to ensure that the number of files in each partition is not excessive, that is, less than the first preset number of files.

[0077] refer to Figure 5 As shown, in some embodiments, the virtual raster file after virtual merging is established for the frame image data after the third grouping, and thinning sampling is performed to generate the thinned raster file, which specifically includes the following steps.

[0078] Step 501: For the framed image data after the third grouping, the framed image data of each group is virtually merged to generate a virtual raster file, and all virtual raster files are tiled. This step performs virtual merging to facilitate subsequent rapid retrieval and rendering.

[0079] Step 502: Thinning the sampling of all virtual raster files using a bilinear interpolation algorithm to generate thinned raster files. This step can reduce the amount of data by 5-10 times through thinning, further facilitating subsequent rapid retrieval and rendering.

[0080] refer to Figure 6 As shown, in some embodiments, when rendering, if the rendering level is lower than the preset level, the retrieval and rendering output are performed through the simplified data set; if the rendering level is higher than the preset level, the retrieval and rendering output are performed through the original data set, which specifically includes the following steps.

[0081] Step 601: Read the bounding boxes and coordinate systems of all files in the simplified and original datasets, assign a unified coordinate system to each of the different coordinate systems, and perform virtual projection based on the unified coordinate system to create spatial indexes for each of the simplified and original datasets. This step addresses the issue of different coordinate systems by assigning a unified coordinate system and then performing virtual projection to obtain a usable spatial index.

[0082] Step 602: When rendering a tile with a specified row and column number, calculate the border of the tile to be rendered. This allows the subsequent use of the level priority and the number of hit files to select the dataset to be rendered, i.e., to select data from the simplified dataset or the original dataset for rendering output.

[0083] Step 603: If the rendering level is lower than the preset level, a search is performed directly in the spatial index of the simplified dataset based on the calculated bounding box of the tile to be rendered, and the resulting data is read for rendering output. The preset level in this step can be pre-set, for example, to level 6. If the level is lower than level 6, a search is performed directly in the spatial index of the simplified dataset, and the resulting data is read for rendering output. This step can greatly improve rendering efficiency at low levels, ensuring rendering performance during low-level rendering, thereby achieving the desired rendering performance.

[0084] Step 604: If the rendering level is higher than the preset level, a search is performed in the spatial index of the original dataset based on the calculated border of the tile to be rendered, and a determination is made based on the number of hit files whether to read the hit data in the original dataset for rendering output. For example, if the level is higher than level 6, a search is performed in the spatial index of the original dataset. Furthermore, it is also possible to determine whether to read the hit data in the original dataset for rendering output or to perform a search in the spatial index of the simplified dataset for rendering output based on the number of hit files after the search in the spatial index of the original dataset; if the number of hit files is small, the former is selected to improve rendering quality, and if the number of hit files is large, the latter is selected to improve rendering efficiency.

[0085] refer to Figure 7 As shown, in some embodiments, if the rendering level is higher than the preset level, a search is performed in the spatial index of the original data set based on the calculated border of the tile to be rendered, and whether to read the hit data in the original data set for rendering output is determined according to the number of hit files, which specifically includes the following steps.

[0086] Step 701: Determine a second preset file number. The second preset file number is an indicator for distinguishing the number of hit files. If it is greater than the second preset file number, the number of hit files is large, and if it is less than the second preset file number, the number of hit files is small.

[0087] Step 702: If the number of files found after searching the spatial index of the original dataset is less than a second preset number of files, the data found in the original dataset is directly read for rendering. For example, if the second preset number of files is set to 30, and the number of files found in the search is less than 30, the data found in the original dataset is directly read for rendering. This step ensures high-level rendering quality even when the number of files is not too large.

[0088] Step 703: If the number of files found after searching the spatial index of the original dataset exceeds a second preset number, a new search is performed in the spatial index of the simplified dataset, and the resulting data is read for rendering and output. For example, if the number of files found during the search exceeds 30, the search is performed again in the simplified dataset, and the data from the resulting data is read for rendering and output. This step ensures high-level rendering efficiency when the number of files is too large.

[0089] In summary, the embodiments of the present invention provide a method for efficiently processing massive framed image data without slicing. The method preprocesses the massive framed image data by grouping and merging the framed images and performing data thinning. Different data sets are then used for rendering at different rendering levels, thereby greatly improving the rendering efficiency at low levels and ensuring the rendering performance at low levels, thereby achieving the purpose of satisfying generation and use. At the same time, the method can also take into account both the rendering effect and the rendering efficiency at high levels based on the number of hit files.

[0090] Based on the above method, this embodiment also provides a system for efficiently slicing massive frame image data. Figure 8 As shown, the system includes an image grouping module, a data set establishment module, and a retrieval and rendering module, wherein: the image grouping module is used to: perform a first grouping based on the coordinate system of the framed image, perform a second grouping based on the number of closed graphics after the first grouping, and perform a third grouping based on the number of files after the second grouping; the data set establishment module is used to: create a virtual raster file after virtual merging for the framed image data after the third grouping, and perform a thinning sampling process to generate a thinned raster file; use the thinned raster file as a simplified data set, and use the framed image file before grouping as the original data set; the retrieval and rendering module is used to: when rendering, if the rendering level is lower than a preset level, perform retrieval and rendering output through the simplified data set; if the rendering level is higher than the preset level, perform retrieval and rendering output through the original data set. The specific implementation process of each module can be referred to the above method steps, and will not be repeated here.

[0091] Example 2:

[0092] Based on the method for efficiently processing massive framed image data without slicing provided in the above-mentioned embodiment 1, embodiment 2 of the present invention provides a specific example for illustration.

[0093] Examples of the above scheme are as follows:

[0094] 1. Reference Figure 9 As shown, Figure 9 Each small grid represents a frame image data, assuming that the data has two coordinate systems.

[0095] 2. After the data is grouped for the first time based on the coordinate system, the result graph after grouping is obtained. Figure 10 As shown in the figure, the data in area A and area B are in the same coordinate system.

[0096] 3. Figure 10 After the grouping is completed, there are two areas in area A and two areas in area B. Since the two areas of A and B do not share the same edge, that is, they are not continuous in space, we need to group them again based on space. The grouping results are as follows: Figure 11 shown. Figure 11 After grouping, there are four areas: A, B, C, and D. From the picture, we can see that all four areas are closed.

[0097] 4. Assuming that the number of frame image data in each area does not exceed 100, Figure 11 As can be seen from the above, the number of areas A and B exceeds 100, so the partition is partitioned for the third time based on the number, and the partition results are as follows Figure 12 shown.

[0098] 5. Since the data in each partition is continuous, the data can be virtually merged based on the spatial position and the size of each partition image to generate a virtual merge file. Figure 13 shown.

[0099] The following is an example of the generated file:

[0100]

[0101]

[0102] As shown in the file example, the new size after merging is calculated. At the same time, SrcRect in each frame image records the original size information, and DstRect records the position information based on the mapping on the merged image.

[0103] The above six areas are merged in sequence to generate six tiled virtual files, A.rtv, B.rtv, C.rtv, D.rtv, E.rtv, and F.rtv.

[0104] 6. Perform thin sampling on the six virtual files respectively to generate the merged frame image data A, B, C, D, E, and F.

[0105] 7. Load the two data sets separately and generate simplified data and original data. Figure 14 As shown, the simplified data and the original data are consistent in spatial position.

[0106] 8. When rendering, after calculating the rendering area, if it does not exceed the specific rendering level, you can directly hit and render by simplifying the data to improve the rendering speed. After exceeding the specific rendering level, you can select the corresponding file for rendering according to the number of files hit by the layer and area, such as Figure 15 As shown, after exceeding a specific rendering level, rendering area 1 hits 2 regional files of simplified data and 30 files of original data. In order to improve rendering speed, simplified data can be used directly for rendering output to reduce the number of files involved in rendering. Rendering area 2 hits 1 regional file when simplified data is used and 6 files when original data is used. In this way, higher-definition rendering results can be provided directly through original data while ensuring rendering speed.

[0107] Example 3:

[0108] Based on the method for efficiently processing massive framed image data without slicing provided in the above embodiment 1, the present invention also provides a device for efficiently processing massive framed image data without slicing that can be used to implement the above method and system, such as Figure 16 FIG. 1 is a schematic diagram of the device architecture of an embodiment of the present invention. The device for efficiently processing massive image data without slicing in this embodiment includes one or more processors 21 and a memory 22. Figure 16 A processor 21 is taken as an example.

[0109] The processor 21 and the memory 22 may be connected via a bus or other means. Figure 16 The bus connection is taken as an example.

[0110] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as the method for efficiently processing massive amounts of framed image data without slicing in Example 1. The processor 21 executes the non-volatile software programs, instructions, and modules stored in the memory 22 to execute various functional applications and data processing of the apparatus for efficiently processing massive amounts of framed image data without slicing, thereby implementing the method for efficiently processing massive amounts of framed image data without slicing in Example 1.

[0111] The memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 22 may optionally include a memory remotely located relative to the processor 21, and such remote memory may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0112] The program instructions / modules are stored in the memory 22. When executed by one or more processors 21, the method for efficiently slicing massive framed image data in the above embodiment 1 is executed. For example, the method described above is executed. Figure 1-Figure 7 The steps shown.

[0113] The above-mentioned product can execute the method provided in the embodiment of this application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of this application.

[0114] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0115] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or of course by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as described above. For the sake of simplicity, they are not provided in detail. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficiently processing massive amounts of framed image data without slicing, characterized in that: include: The first grouping is performed based on the coordinate system of the framed image, the second grouping is performed based on the number of closed graphics after the first grouping, and the third grouping is performed based on the number of files after the second grouping. A virtual raster file is created for the frame image data after the third grouping, and a thinning sampling process is performed to generate a thinned raster file; the thinned raster file is used as a simplified dataset, and the frame image file before grouping is used as the original dataset; When rendering, if the rendering level is lower than the preset level, the simplified dataset is used for retrieval and rendering output; if the rendering level is higher than the preset level, the original dataset is used for retrieval and rendering output.

2. The method for efficiently processing massive framed image data without slicing according to claim 1, characterized in that: The first grouping based on the coordinate system of the framed images specifically includes: Get the coordinate system information of all framed images; The first grouping operation is performed based on the coordinate system of the framed image, and the framed image data with the same coordinate system are divided into the same group.

3. The method for efficiently processing massive framed image data without slicing according to claim 2, characterized in that: The second grouping based on the number of closed figures after the first grouping specifically includes: Find the union of the outer rectangles of each group of image data after the first grouping; A second grouping operation is performed on the frame image data according to the number of closed graphics in the union, and the frame image data in the same closed graphics are divided into the same group.

4. The method for efficiently processing massive framed image data without slicing according to claim 3, characterized in that: The third grouping based on the number of files after the second grouping specifically includes: Determine the preset total number of groups and the first preset number of files; According to the result of the second grouping, if the number of framed images in a closed figure is greater than the first preset file number, the framed image data in the corresponding closed figure are grouped for a third time according to the preset total group number, so that the number of framed images in each group is less than the first preset file number.

5. The method for efficiently processing massive framed image data without slicing according to claim 1, characterized in that: The step of establishing a virtual merged virtual raster file for the frame image data after the third grouping and performing a thinning sampling process to generate the thinned raster file specifically includes: For the frame image data after the third grouping, the frame image data of each group are virtually merged to generate virtual raster files, and all virtual raster files are tiled; All virtual raster files are subjected to thinning sampling processing using a bilinear interpolation algorithm, and thinned raster files are generated.

6. The method for efficiently processing massive framed image data without slicing according to claim 1, characterized in that: When rendering, if the rendering level is lower than the preset level, the simplified data set is used for retrieval and rendering output; If the rendering level is higher than the preset level, the retrieval and rendering output through the original dataset specifically includes: Read the bounding box range and coordinate system of all files in the simplified dataset and the original dataset respectively, specify a unified coordinate system for different coordinate systems, perform virtual projection based on the unified coordinate system, and create spatial indexes for the simplified dataset and the original dataset respectively; When rendering a tile with a specified row and column number, calculate the border of the tile to be rendered; If the rendering level is lower than the preset level, the spatial index of the simplified dataset is directly searched based on the calculated border of the tile to be rendered, and the hit data is read for rendering output; If the rendering level is higher than the preset level, the spatial index of the original dataset is searched based on the calculated border of the tile to be rendered, and the number of hit files is determined to determine whether to read the hit data in the original dataset for rendering output.

7. The method for efficiently processing massive framed image data without slicing according to claim 6, characterized in that: If the rendering level is higher than the preset level, searching the spatial index of the original dataset based on the calculated border of the tile to be rendered, and determining whether to read the hit data in the original dataset for rendering output according to the number of hit files specifically includes: Determining a second preset file quantity; If the number of hit files is less than the second preset number of files after searching the spatial index of the original dataset, the hit data in the original dataset is directly read for rendering and output; If the number of hit files is greater than the second preset number of files after searching the spatial index of the original dataset, the spatial index of the simplified dataset is searched again, and the hit data is read for rendering and output.

8. A device for efficiently processing massive amounts of framed image data without slicing, characterized in that: The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for efficiently and slicing massive framed image data as described in any one of claims 1 to 7.

9. A system for efficiently processing massive framed image data without slicing, using the method for efficiently processing massive framed image data without slicing as claimed in any one of claims 1 to 7, characterized in that: The system includes an image grouping module, a data set building module, and a retrieval rendering module, wherein: The image grouping module is used to: perform a first grouping based on the coordinate system of the framed image, perform a second grouping based on the number of closed graphics after the first grouping, and perform a third grouping based on the number of files after the second grouping; The data set establishment module is used to: establish a virtual raster file after virtual merging for the frame image data after the third grouping, and perform a thinning sampling process to generate a thinned raster file; use the thinned raster file as a simplified data set, and use the frame image file before grouping as the original data set; The retrieval and rendering module is used to: when rendering, if the rendering level is lower than the preset level, perform retrieval and rendering output through the simplified data set; if the rendering level is higher than the preset level, perform retrieval and rendering output through the original data set.

10. A non-volatile computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which are executed by one or more processors to implement the method for efficiently processing massive framed image data without slicing as described in any one of claims 1 to 7.

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