Spatial data automatic processing and slicing method, device, equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses a method, device, equipment and storage medium for automatic processing and slicing of spatial data. The method obtains spatial data of a target area, uses orthophoto data of unmanned aerial vehicles, several groups of satellite remote sensing image data and vector data of different resolutions, generates a spatial data processing set, builds a spatial data slice pyramid according to an initial deployment strategy, and then obtains an optimal adjustment strategy for the next structural adjustment cycle according to the spatial data slice resolution requirement of each demand user in the previous structural adjustment cycle, while considering the adjustment amount of the corresponding slice level resolution used by each demand user and the slice level adjustment number of the server spatial data slice deployment, realizes the update of the slice deployment structure, improves the automation and intelligence of spatial data processing efficiency, improves the rationality of spatial data slice deployment and update, and alleviates the contradiction between the adaptability of the deployment structure and the server data deployment adjustment.

Description

Technical Field

[0001] The present invention relates to the field of spatial data processing technology, and in particular to a method, device, equipment and storage medium for automatic processing and slicing of spatial data. Background Art

[0002] In recent years, with the rapid development of high-altitude satellites, low-altitude drones and other applications in different industries, spatial data has played a pivotal role in production and life. Spatial data processing technology has provided technical support for the widespread application of geographic information systems, making the processing and display of large-scale spatial data more efficient, and providing a strong guarantee for the application of geographic information systems in urban planning, resource management, environmental monitoring and other fields. At the same time, it also puts forward higher requirements for the accuracy and timeliness of data analysis and the use of results.

[0003] The traditional spatial data analysis and processing process has the following defects: (1) The professional technical requirements for multi-data type and format conversion are high. In the traditional data processing process, it is required to be highly familiar with and understand the principles of spatial data formats, including data formats, geographic coordinate systems, projection coordinate systems, etc., and it also requires the ability to operate and use professional software; (2) The data processing process is cumbersome and has a high error rate. In the spatial data processing process, it takes dozens of steps to complete a set of data to the actual application size. An error in a single link in the process will lead to incorrect data results, resulting in repeated rework and lack of timeliness; (3) Integration and deployment are difficult. Data processed by the data process needs to be manually deployed for service integration and application. There will be a large amount of data copying and data standardization deployment technology in the process. At the same time, when deploying data, as spatial data is used, the deployment structure needs to be continuously adapted and adjusted according to user needs. The continuous adjustment of data deployment puts considerable pressure on the server's storage and data processing. There is a contradiction between the adaptability of the deployment structure and the server data deployment adjustment.

[0004] Therefore, how to improve the automation and intelligence of spatial data processing efficiency, improve the rationality of spatial data slice deployment and update, and alleviate the contradiction between the adaptability of the deployment structure and the server data deployment adjustment is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for automatic processing and slicing of spatial data to solve the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides a method for automatic processing and slicing of spatial data, comprising the following steps:

[0007] Acquire spatial data of the target area; wherein the spatial data includes drone orthophoto data, several sets of satellite remote sensing image data of different resolutions, and vector data;

[0008] Processing the UAV orthophoto data to obtain a first image data set of the target area;

[0009] Using a plurality of sets of satellite remote sensing image data with different resolutions to correct and process the first image data set, to obtain a second image data set of the target area, and using vector data to perform coordinate transformation on the second image data set, to obtain a spatial data processing set of the target area;

[0010] Based on the spatial data processing set, construct a spatial data slice pyramid of the target area;

[0011] The spatial data slice demand information of the previous structure adjustment period is obtained, and the slice deployment structure of the spatial data slice pyramid of the next structure adjustment period is adjusted according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

[0012] Optionally, the step of processing the drone orthophoto data to obtain a first image data set of the target area specifically includes:

[0013] Obtaining orthophotos collected by a drone, and extracting POS information from the orthophotos; wherein the POS information includes the latitude and longitude, altitude, internal orientation elements, and external orientation elements of the photos;

[0014] Set the target coordinate system, obtain the parameters of the drone camera and add the elevation data set of the drone flight, generate the drone flight collection path, perform the fusion and stitching of the orthophoto photos based on the drone flight collection path, and obtain the orthophoto image of the target area;

[0015] A grid boundary of the orthophoto image is constructed, and the orthophoto image is cropped and exported according to the grid boundary to obtain a first image data set of the target area.

[0016] Optionally, the step of correcting and processing the first image data set using a plurality of sets of satellite remote sensing image data with different resolutions to obtain a second image data set of the target area specifically includes:

[0017] Extracting a plurality of evenly distributed first ground significant control points from a plurality of groups of satellite remote sensing image data with different resolutions and a first image data set;

[0018] Determine the optimized position coordinates of each first ground significant control point based on several groups of position information for each first ground significant control point in several groups of satellite remote sensing image data with different resolutions;

[0019] According to the position coordinates of each first ground significant control point in the first image data set and the optimized position coordinates of each first ground significant control point in the satellite remote sensing image data, the geometric transformation relationship of each orthophoto cropped image in the first image data set is determined by least squares fitting method;

[0020] The geometric transformation relationship is used to perform geometric correction on the first image data set, and radiation correction is performed on the geometrically corrected first image data set according to a plurality of groups of satellite remote sensing image data with different resolutions.

[0021] Optionally, the step of correcting and processing the first image data set using a plurality of sets of satellite remote sensing image data with different resolutions to obtain a second image data set of the target area specifically includes:

[0022] Mosaic remote sensing images of a plurality of orthophoto cropped images after geometric correction and radiation correction in the first image data set to obtain a complete remote sensing image of the target area;

[0023] The complete remote sensing image is converted into a designated target data format and subjected to mask processing of the image data to obtain a second image data set of the processed target area.

[0024] Optionally, the step of performing coordinate transformation on the second image data set by using vector data to obtain a spatial data processing set of the target area specifically includes:

[0025] Extracting a number of evenly distributed second ground significant control points from the vector data and the second image data set, determining coordinate transformation parameters by using the least squares fitting method according to the position coordinates of each second ground significant control point in the vector data and the position coordinates of each second ground significant control point in the second image data set, and constructing a coordinate transformation model for the second image data set;

[0026] The coordinate transformation model is used to transform the position coordinates of each pixel point in the second image data set to obtain a spatial data processing set of the target area.

[0027] Optionally, based on the spatial data processing set, a step of constructing a spatial data slice pyramid of the target area specifically includes:

[0028] Based on the spatial data processing set, a preset spatial data slicing strategy is obtained, and slice sizes and slice formats of a target number of slice levels in the preset spatial data slicing strategy are extracted;

[0029] Based on the slice size and slice format, the spatial data processing set is sliced, and a plurality of spatial slice data obtained by slicing with different slice sizes are initialized and deployed in the order of resolution from low to high of the slice size, so as to construct a spatial data slice pyramid.

[0030] Optionally, obtaining the spatial data slice demand information of the previous structure adjustment period, and adjusting the slice deployment structure of the spatial data slice pyramid of the next structure adjustment period according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information, specifically includes:

[0031] Acquire the data slice demand information of the previous structure adjustment cycle, and extract the spatial data slice resolution requirement of each demand user in the spatial data slice demand information;

[0032] The cumulative sum of the differences between the spatial data slice resolution requirement of each user and the resolution of each slice level after the spatial data slice pyramid is adjusted is less than the preset error tolerance value, and the number of slice levels with adjusted slice sizes in the spatial data slice pyramid is minimized. The slice deployment strategy for the next structural adjustment cycle is optimized and solved.

[0033] Based on the slice deployment strategy of the next structural adjustment period, a slice deployment structural adjustment action of the spatial data slice pyramid of the next structural adjustment period is performed to update the deployment of the spatial data slice pyramid.

[0034] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a spatial data automatic processing and slicing device, comprising:

[0035] An acquisition module is used to acquire spatial data of a target area; wherein the spatial data includes UAV orthophoto data, several groups of satellite remote sensing image data of different resolutions, and vector data;

[0036] A first processing module is used to process the UAV orthophoto data to obtain a first image data set of the target area;

[0037] a second processing module, configured to correct and process the first image data set using a plurality of sets of satellite remote sensing image data of different resolutions to obtain a second image data set of the target area, and to perform coordinate transformation on the second image data set using vector data to obtain a spatial data processing set of the target area;

[0038] A construction module, used for constructing a spatial data slice pyramid of a target area based on the spatial data processing set;

[0039] The deployment adjustment module is used to obtain the spatial data slice demand information of the previous structure adjustment cycle, and adjust the slice deployment structure of the spatial data slice pyramid of the next structure adjustment cycle according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

[0040] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a spatial data automatic processing and slicing device, which includes: a memory, a processor, and a spatial data automatic processing and slicing program stored in the memory and executable on the processor, and when the spatial data automatic processing and slicing program is executed by the processor, the steps of the spatial data automatic processing and slicing method as described above are implemented.

[0041] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a spatial data automatic processing and slicing program is stored. When the spatial data automatic processing and slicing program is executed by a processor, the steps of the above-mentioned spatial data automatic processing and slicing method are implemented.

[0042] The beneficial effects of the present invention are as follows: a method, device, equipment and storage medium for automatic processing and slicing of spatial data are proposed, by acquiring spatial data of a target area, using drone orthophoto data in the spatial data to construct a first image data set, using several groups of satellite remote sensing image data of different resolutions in the spatial data to correct and process the first image data set, obtaining a second image data set of the target area, using vector data in the spatial data to perform coordinate transformation on the second image data set, obtaining a spatial data processing set of the target area, constructing a spatial data slicing pyramid of the target area according to an initial deployment strategy, and then according to the spatial data slicing resolution requirements of each demand user in the previous structural adjustment cycle, while considering the adjustment amount of each demand user using the corresponding slice level resolution and the slice level adjustment number of the server spatial data slice deployment, obtaining the optimal adjustment strategy for the next structural adjustment cycle, realizing the updating of the slice deployment structure, improving the automation and intelligence of spatial data processing efficiency, improving the rationality of spatial data slice deployment and updating, and alleviating the contradiction between the adaptability of the deployment structure and the server data deployment adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 The figure is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0045] Figure 2 It is a flowchart of an embodiment of the method for automatic processing and slicing of spatial data of the present invention.

[0046] Figure 3 It is a structural block diagram of a spatial data automatic processing and slicing device in an embodiment of the present invention.

[0047] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0050] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or it may be a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0051] Those skilled in the art will understand that Figure 1 The structure of the device shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0052] like Figure 1As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a spatial data automatic processing and slicing program.

[0053] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the spatial data automatic processing and slicing program stored in the memory 1005, and perform the following operations:

[0054] Acquire spatial data of the target area; wherein the spatial data includes drone orthophoto data, several sets of satellite remote sensing image data of different resolutions, and vector data;

[0055] Processing the UAV orthophoto data to obtain a first image data set of the target area;

[0056] Using a plurality of sets of satellite remote sensing image data with different resolutions to correct and process the first image data set, to obtain a second image data set of the target area, and using vector data to perform coordinate transformation on the second image data set, to obtain a spatial data processing set of the target area;

[0057] Based on the spatial data processing set, construct a spatial data slice pyramid of the target area;

[0058] The spatial data slice demand information of the previous structure adjustment period is obtained, and the slice deployment structure of the spatial data slice pyramid of the next structure adjustment period is adjusted according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

[0059] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the following application of the method for automatic processing and slicing of spatial data, and will not be described in detail here.

[0060] The embodiment of the present invention provides a method for automatic processing and slicing of spatial data, referring to Figure 2 , Figure 2 It is a flowchart of an embodiment of the method for automatic processing and slicing of spatial data of the present invention.

[0061] In this embodiment, the spatial data automatic processing and slicing method includes the following steps:

[0062] S100: Acquire spatial data of a target area; wherein the spatial data includes drone orthophoto data, several groups of satellite remote sensing image data of different resolutions, and vector data;

[0063] S200: Processing the UAV orthophoto data to obtain a first image data set of the target area;

[0064] S300: Correcting and processing the first image data set using a plurality of sets of satellite remote sensing image data with different resolutions to obtain a second image data set of a target area, and performing coordinate transformation on the second image data set using vector data to obtain a spatial data processing set of the target area;

[0065] S400: constructing a spatial data slice pyramid of a target area based on the spatial data processing set;

[0066] S500: Acquire spatial data slice demand information of the previous structure adjustment period, and adjust the slice deployment structure of the spatial data slice pyramid of the next structure adjustment period according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

[0067] In order to solve the above problems, this embodiment obtains spatial data of the target area, constructs a first image data set using drone orthophoto data in the spatial data, corrects and processes the first image data set using several groups of satellite remote sensing image data of different resolutions in the spatial data, obtains a second image data set of the target area, and uses vector data in the spatial data to perform coordinate transformation on the second image data set to obtain a spatial data processing set of the target area. According to the initial deployment strategy, a spatial data slice pyramid of the target area is constructed, and then according to the spatial data slice resolution requirements of each demand user in the previous structural adjustment cycle, while considering the adjustment amount of each demand user using the corresponding slice level resolution and the slice level adjustment number of the server spatial data slice deployment, the optimal adjustment strategy for the next structural adjustment cycle is obtained, the slice deployment structure is updated, the automation and intelligence of the spatial data processing efficiency are improved, the rationality of the spatial data slice deployment and update is improved, and the contradiction between the adaptability of the deployment structure and the server data deployment adjustment is alleviated.

[0068] In a preferred embodiment, the step of processing the drone orthophoto data to obtain a first image data set of the target area specifically includes:

[0069] S210: Acquire an orthophoto photo collected by a drone, and extract POS information from the orthophoto photo; wherein the POS information includes the longitude and latitude, altitude, internal orientation elements, and external orientation elements of the photo;

[0070] S220: Setting a target coordinate system, obtaining parameters of a camera configured for the drone and adding an elevation data set of the drone flight, generating a drone flight acquisition path, and performing fusion stitching of orthophoto photos based on the drone flight acquisition path to obtain an orthophoto image of the target area;

[0071] S230: constructing a grid boundary of the orthophoto image, and cropping and exporting the orthophoto image according to the grid boundary to obtain a first image data set of the target area.

[0072] In this embodiment, for the collected drone orthophotos, by extracting POS information, a drone flight collection path is generated according to the set target coordinate system and the parameters of the drone camera configuration, and the orthophotos are fused and spliced ​​according to the drone flight collection path to obtain the orthophoto image of the target area, and then the image is cropped according to the grid boundary to generate a first image data set.

[0073] In a preferred embodiment, the step of correcting and processing the first image data set using several sets of satellite remote sensing image data with different resolutions to obtain a second image data set of the target area specifically includes:

[0074] S310: extracting a plurality of evenly distributed first ground significant control points from a plurality of groups of satellite remote sensing image data with different resolutions and a first image data set;

[0075] S320: determining the optimized position coordinates of each first ground significant control point based on a plurality of sets of position information for each first ground significant control point in a plurality of sets of satellite remote sensing image data with different resolutions;

[0076] S330: Determine the geometric transformation relationship of each orthophoto cropped image in the first image data set respectively by using a least squares fitting method according to the position coordinates of each first ground significant control point in the first image data set and the optimized position coordinates of each first ground significant control point in the satellite remote sensing image data;

[0077] S340: Performing geometric correction on the first image data set using the geometric transformation relationship, and performing radiation correction on the geometrically corrected first image data set according to a plurality of groups of satellite remote sensing image data with different resolutions.

[0078] Furthermore, the step of correcting and processing the first image data set using a plurality of sets of satellite remote sensing image data of different resolutions to obtain a second image data set of the target area specifically includes:

[0079] S350: performing remote sensing image mosaicking on a plurality of orthophoto cropped images that have been subjected to geometric correction and radiation correction in the first image data set to obtain a complete remote sensing image of the target area;

[0080] S360: converting the complete remote sensing image into a designated target data format and performing mask processing on the image data to obtain a processed second image data set of the target area.

[0081] Furthermore, the steps of using vector data to transform the coordinates of the second image data set to obtain a spatial data processing set of the target area specifically include:

[0082] S370: extracting a plurality of uniformly distributed second ground significant control points from the vector data and the second image data set, determining coordinate transformation parameters by using a least squares fitting method according to the position coordinates of each second ground significant control point in the vector data and the position coordinates of each second ground significant control point in the second image data set, and constructing a coordinate transformation model for the second image data set;

[0083] S380: Using the coordinate transformation model to transform the position coordinates of each pixel point in the second image data set, to obtain a spatial data processing set of the target area.

[0084] In this embodiment, several groups of satellite remote sensing image data with different resolutions are used to perform correction processing including geometric correction and radiation correction on the first image data set, and the relatively accurate positioning information in the satellite remote sensing image data is synchronized to the first image data set collected and generated by the drone, so as to achieve the simultaneous retention of the high-resolution data characteristics of the first image data set and the high positioning accuracy characteristics of the satellite remote sensing image data, so that the generated spatial data processing set of the target area has better data quality. It should be noted that the optimization of the position coordinates of the first ground significant control point of several groups of satellite remote sensing image data with different resolutions mainly considers the positioning accuracy differences of satellite remote sensing image data with different resolutions, and replaces several groups of position information with the position coordinates with the smallest sum of distances from each first ground significant control point to reduce the impact of different positioning accuracy differences.

[0085] In a preferred embodiment, the step of constructing a spatial data slice pyramid of a target area based on the spatial data processing set specifically includes:

[0086] S410: Based on the spatial data processing set, a preset spatial data slicing strategy is obtained, and slice sizes and slice formats of a target number of slice levels in the preset spatial data slicing strategy are extracted;

[0087] S420: Based on the slice size and the slice format, the spatial data processing set is sliced, and a plurality of spatial slice data obtained by slicing with different slice sizes are initialized and deployed in order from low to high resolution of the slice size, so as to construct a spatial data slice pyramid.

[0088] Furthermore, the spatial data slice demand information of the previous structure adjustment period is obtained, and according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information, the slice deployment structure step of the spatial data slice pyramid of the next structure adjustment period is adjusted, specifically including:

[0089] S510: Acquire data slice demand information of the previous structure adjustment cycle, and extract the spatial data slice resolution requirement of each demand user in the spatial data slice demand information;

[0090] S520: Taking the cumulative sum of the differences between the spatial data slice resolution requirement of each demand user and the resolution of the slice size of each slice level after the spatial data slice pyramid is adjusted as a constraint condition, and taking the minimum number of slice levels of the adjusted slice size in the spatial data slice pyramid as the goal, optimizing and solving to obtain the slice deployment strategy for the next structural adjustment cycle;

[0091] S530: Based on the slice deployment strategy of the next structural adjustment period, execute a slice deployment structure adjustment action of the spatial data slice pyramid of the next structural adjustment period to deploy and update the spatial data slice pyramid.

[0092] In this embodiment, after obtaining the spatial data processing set of the target area, a spatial data slice pyramid of the target area is constructed according to the initial deployment strategy, and then based on the spatial data slice resolution requirements of each demand user in the previous structural adjustment cycle, while considering the adjustment amount of each demand user using the corresponding slice level resolution and the slice level adjustment number of the server spatial data slice deployment, the optimal adjustment strategy for the next structural adjustment cycle is obtained, the slice deployment structure is updated, the automation and intelligence of the spatial data processing efficiency are improved, the rationality of the spatial data slice deployment and update is improved, and the contradiction between the adaptability of the deployment structure and the server data deployment adjustment is alleviated.

[0093] Reference Figure 3 , Figure 3 It is a structural block diagram of an embodiment of the spatial data automatic processing and slicing device of the present invention.

[0094] like Figure 3 As shown, the spatial data automatic processing and slicing device proposed in the embodiment of the present invention includes:

[0095] The acquisition module 10 is used to acquire the spatial data of the target area; wherein the spatial data includes the orthophoto data of the drone, several sets of satellite remote sensing image data of different resolutions and vector data;

[0096] The first processing module 20 is used to process the drone orthophoto data to obtain a first image data set of the target area;

[0097] The second processing module 30 is used to correct and process the first image data set using a plurality of sets of satellite remote sensing image data with different resolutions to obtain a second image data set of the target area, and to perform coordinate transformation on the second image data set using vector data to obtain a spatial data processing set of the target area;

[0098] A construction module 40 is used to construct a spatial data slice pyramid of a target area based on the spatial data processing set;

[0099] The deployment adjustment module 50 is used to obtain the spatial data slice demand information of the previous structure adjustment period, and adjust the slice deployment structure of the spatial data slice pyramid of the next structure adjustment period according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

[0100] Other embodiments or specific implementations of the spatial data automatic processing and slicing device of the present invention can refer to the above-mentioned method embodiments and will not be described in detail here.

[0101] In addition, the present invention also proposes a spatial data automatic processing and slicing device, which includes: a memory, a processor, and a spatial data automatic processing and slicing program stored in the memory and runnable on the processor. When the spatial data automatic processing and slicing program is executed by the processor, the steps of the spatial data automatic processing and slicing method described above are implemented.

[0102] The specific implementation of the spatial data automatic processing and slicing device of the present application is basically the same as the above-mentioned embodiments of the spatial data automatic processing and slicing method, and will not be repeated here.

[0103] In addition, the present invention also proposes a readable storage medium, which includes a computer-readable storage medium on which a spatial data automatic processing and slicing program is stored. The readable storage medium may be Figure 1 The memory 1005 in the terminal may also be at least one of a ROM (Read-Only Memory) / RAM (Random Access Memory), a magnetic disk, and an optical disk. The readable storage medium includes a number of instructions for enabling a spatial data automatic processing and slicing device with a processor to execute the spatial data automatic processing and slicing method described in each embodiment of the present invention.

[0104] The specific implementation methods in the readable storage medium of the present application are basically the same as the above-mentioned embodiments of the automatic processing and slicing method of spatial data, and will not be repeated here.

[0105] It is understood that, in the description of this specification, the description with reference to the terms "one embodiment", "another embodiment", "other embodiments", or "first to Nth embodiments" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0106] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including 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 system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0107] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0108] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for automatic processing and slicing of spatial data, characterized in that: The following steps are involved: Acquire spatial data of the target area; wherein the spatial data includes drone orthophoto data, several sets of satellite remote sensing image data of different resolutions, and vector data; Processing the UAV orthophoto data to obtain a first image data set of the target area; Using a plurality of sets of satellite remote sensing image data with different resolutions to correct and process the first image data set, to obtain a second image data set of the target area, and using vector data to perform coordinate transformation on the second image data set, to obtain a spatial data processing set of the target area; The step of using several groups of satellite remote sensing image data with different resolutions to correct and process the first image data set to obtain the second image data set of the target area specifically includes: extracting several uniformly distributed first ground significant control points from several groups of satellite remote sensing image data with different resolutions and the first image data set; determining the optimized position coordinates of each first ground significant control point based on several groups of position information for each first ground significant control point in several groups of satellite remote sensing image data with different resolutions; and determining the optimized position coordinates of each first ground significant control point based on the position coordinates of each first ground significant control point in the first image data set and the optimized position coordinates of each first ground significant control point in the satellite remote sensing image data. Position coordinates, using the least squares fitting method to respectively determine the geometric transformation relationship of each orthophoto cropped image in the first image data set; using the geometric transformation relationship to perform geometric correction on the first image data set, and performing radiation correction on the geometrically corrected first image data set according to a plurality of sets of satellite remote sensing image data with different resolutions; performing remote sensing image mosaicking on a plurality of orthophoto cropped images that have been geometrically corrected and radiation corrected in the first image data set to obtain a complete remote sensing image of the target area; performing conversion of the complete remote sensing image to a specified target data format and performing mask processing on the image data to obtain a processed second image data set of the target area; The step of determining the optimized position coordinates of each first ground significant control point based on several groups of position information of each first ground significant control point in several groups of satellite remote sensing image data with different resolutions specifically adopts: the optimized position coordinates of each first ground significant control point are determined by replacing several groups of position information with position coordinates having the smallest sum of distances from each first ground significant control point; Among them, the step of using vector data to perform coordinate transformation on the second image data set to obtain the spatial data processing set of the target area specifically includes: extracting a number of uniformly distributed second ground significant control points from the vector data and the second image data set, determining coordinate transformation parameters by using the least squares fitting method according to the position coordinates of each second ground significant control point in the vector data and the position coordinates of each second ground significant control point in the second image data set, and constructing a coordinate transformation model for the second image data set; using the coordinate transformation model to perform coordinate transformation on the position coordinates of each pixel point in the second image data set to obtain the spatial data processing set of the target area; Based on the spatial data processing set, a spatial data slice pyramid of the target area is constructed; specifically comprising: based on the spatial data processing set, a preset spatial data slice strategy is obtained, and the slice sizes and slice formats of the target number of slice levels in the preset spatial data slice strategy are extracted; based on the slice sizes and slice formats, the spatial data processing set is sliced, and a plurality of spatial slice data obtained by slicing with different slice sizes are initialized and deployed in the order of the resolution of the slice size from low to high, so as to construct a spatial data slice pyramid; Acquiring spatial data slice demand information of a previous structural adjustment cycle, and adjusting the slice deployment structure of a spatial data slice pyramid of a next structural adjustment cycle according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information specifically includes: acquiring data slice demand information of a previous structural adjustment cycle, extracting the spatial data slice resolution requirement of each demand user in the spatial data slice demand information; taking the cumulative sum of differences between the spatial data slice resolution requirement of each demand user and the resolution of the slice size of each slice level after adjustment of the spatial data slice pyramid as a constraint condition that is less than a preset error allowable value, taking the minimum number of slice levels of the adjusted slice size in the spatial data slice pyramid as a goal, optimizing and solving to obtain a slice deployment strategy for the next structural adjustment cycle; and executing a slice deployment structure adjustment action of the spatial data slice pyramid of the next structural adjustment cycle based on the slice deployment strategy of the next structural adjustment cycle, so as to deploy and update the spatial data slice pyramid.

2. The method for automatic processing and slicing of spatial data according to claim 1, characterized in that: The steps of processing the drone orthophoto data to obtain the first image data set of the target area specifically include: Obtaining orthophotos collected by a drone, and extracting POS information from the orthophotos; wherein the POS information includes the latitude and longitude, altitude, internal orientation elements, and external orientation elements of the photos; Set the target coordinate system, obtain the parameters of the drone camera and add the elevation data set of the drone flight, generate the drone flight collection path, perform the fusion and stitching of the orthophoto photos based on the drone flight collection path, and obtain the orthophoto image of the target area; A grid boundary of the orthophoto image is constructed, and the orthophoto image is cropped and exported according to the grid boundary to obtain a first image data set of the target area.

3. A spatial data automatic processing and slicing device, characterized in that: The method for automatic processing and slicing of spatial data as claimed in any one of claims 1 to 2 comprises: An acquisition module is used to acquire spatial data of a target area; wherein the spatial data includes UAV orthophoto data, several groups of satellite remote sensing image data of different resolutions, and vector data; A first processing module is used to process the UAV orthophoto data to obtain a first image data set of the target area; a second processing module, configured to correct and process the first image data set using a plurality of sets of satellite remote sensing image data of different resolutions to obtain a second image data set of the target area, and to perform coordinate transformation on the second image data set using vector data to obtain a spatial data processing set of the target area; A construction module, used for constructing a spatial data slice pyramid of a target area based on the spatial data processing set; The deployment adjustment module is used to obtain the spatial data slice demand information of the previous structure adjustment cycle, and adjust the slice deployment structure of the spatial data slice pyramid of the next structure adjustment cycle according to the spatial data slice resolution requirement of each demand user in the spatial data slice demand information.

4. A spatial data automatic processing and slicing device, characterized in that: The spatial data automatic processing and slicing device includes: a memory, a processor, and a spatial data automatic processing and slicing program stored in the memory and executable on the processor. When the spatial data automatic processing and slicing program is executed by the processor, the steps of the spatial data automatic processing and slicing method as described in any one of claims 1 to 2 are implemented.

5. A storage medium, characterized in that: The storage medium stores a spatial data automatic processing and slicing program, which, when executed by a processor, implements the steps of the spatial data automatic processing and slicing method according to any one of claims 1 to 2.

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