Ground feature data analysis method and system

Abstract

The invention discloses a surface feature data analysis method and system. The surface feature data analysis method comprises the following steps: acquiring surface feature data; determining a classification set, and storing the obtained surface feature data to the classification set; uniformly storing the classification set; and calling and transmitting the stored classification set to complete the analysis of the surface feature data. According to the method, a node storage and transmission calling mode is adopted, storage is carried out at any time, transmission is carried out at any time, the transmission safety of the surface feature data is greatly improved, the data copy is further created, subsequent analysis can still be completed according to the data copy after transmission abnormity occurs, and the timeliness and accuracy of analysis are guaranteed.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular, to a method and system for analyzing ground object data. Background Art

[0002] With the progress of technology, the acquisition technology of ground object data has become more and more advanced. Based on the analysis of ground object data, oil and gas geophysical prospecting, as well as environmental protection and ecological research, can be completed. In the process of analyzing ground object data, first, the acquired ground object data needs to be transmitted to the data processing center, and the data processing center then completes the subsequent analysis of the ground object data. In the process of ground object transmission, it is often transmitted through satellite communication or wireless sensor network communication. When transmitting data in this way, data encryption and identity authentication and other means are often used to ensure the security of the data. However, once this method is cracked, the security of the data will still be reduced, thereby reducing the accuracy of subsequent analysis.

[0003] Therefore, how to provide a method to strengthen data security and thus ensure the accuracy of subsequent analysis has become an urgent problem to be solved in this field. Summary of the Invention

[0004] This application proposes a method for analyzing ground object data, which specifically includes the following steps: acquiring ground object data; determining a classification set and storing the acquired ground object data in the classification set; uniformly storing the classification set; and calling and transmitting the stored classification set to complete the analysis of the ground object data.

[0005] The method for analyzing ground object data as described above, wherein the ground object data includes land cover data, ground object classification data, and remote sensing image data.

[0006] The method for analyzing ground object data as described above, wherein determining a classification set and storing the acquired ground object data in the classification set includes storing the land cover data in one or more land cover type classification sets, storing the ground object classification data in one or more ground object type classification sets, and storing the remote sensing image data in one or more remote sensing type classification sets.

[0007] The method for analyzing ground object data as described above, wherein uniformly storing the classification set includes the following sub-steps: determining the number of classification sets; determining whether unified storage is required according to the number of classification sets; if unified storage is required, creating multiple nodes; and storing multiple classification sets in the nodes according to different strategies.

[0008] The method for analyzing ground object data as described above, wherein storing multiple classification sets in the nodes according to different strategies includes: creating data copies of multiple classification sets; storing multiple classification sets in the multiple created nodes respectively; and storing the created data copies in the remaining nodes.

[0009] A ground feature data analysis system specifically includes: a ground feature data acquisition unit, a ground feature data storage unit, a classification set storage unit, and a call and analysis unit; the ground feature data acquisition unit is used to acquire ground feature data; the ground feature data storage unit is used to determine a classification set and store the acquired ground feature data into the classification set; the classification set storage unit is used to uniformly store the classification set; the call and analysis unit is used to call and transmit the stored classification set to complete the analysis of the ground feature data.

[0010] In the ground feature data analysis system as described above, the ground feature data acquired by the ground feature data acquisition unit includes land cover data, ground feature classification data, and remote sensing image data.

[0011] In the ground feature data analysis system as described above, the ground feature data storage unit storing the acquired ground feature data into the classification set includes storing the land cover data in one or more land cover type classification sets, storing the ground feature classification data in one or more ground feature type classification sets, and storing the remote sensing image data in one or more remote sensing type classification sets.

[0012] In the ground feature data analysis system as described above, the classification set storage unit uniformly storing the classification set includes the following sub-steps: determining the number of classification sets; determining whether unified storage is required according to the number of classification sets; if unified storage is required, creating multiple nodes; storing multiple classification sets in the nodes according to different strategies.

[0013] In the ground feature data analysis system as described above, storing multiple classification sets in the nodes according to different strategies includes: creating data copies of multiple classification sets; storing multiple classification sets in the multiple created nodes respectively; storing the created data copies in the remaining nodes.

[0014] This application has the following beneficial effects:

[0015] This application adopts the method of node storage and call transmission, storing and transmitting as needed, greatly increasing the transmission security of ground feature data. And this application also creates data copies, and subsequent analysis can still be completed according to the data copies after a transmission anomaly occurs, ensuring the timeliness and accuracy of the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1It is a flowchart of a method for analyzing ground object data provided by an embodiment of the present application;

[0018] Figure 2 It is a schematic internal structure diagram of an analysis system for ground object data provided by an embodiment of the present application. Detailed implementation manners

[0019] Next, in combination with the accompanying drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0020] Embodiment 1

[0021] As Figure 1 shown, this embodiment provides a method for analyzing ground object data, which specifically includes the following steps:

[0022] Step S1: Obtain ground object data.

[0023] Among them, the ground object data includes land cover data, ground object classification data, and remote sensing image data.

[0024] The land cover data provides global surface cover information, including type data such as cultivated land, forest land, and grassland.

[0025] The ground object classification data covers classifications such as buildings, roads, and water bodies, and can be used in fields such as the exploration and acquisition of seismic source operation routes in the oil and gas industry.

[0026] The remote sensing image data is obtained by remote sensing satellites and includes optical image data, radar image data, etc., and can be used to observe the surface color, vegetation conditions, etc.

[0027] Step S2: Determine the classification set and store the obtained ground object data in the classification set.

[0028] Among them, the obtained ground object data is divided into land cover type, ground object type, and remote sensing type, and the classified data is stored in the corresponding classification set (i.e., classification data set).

[0029] Since the amount of ground object data may be too large and a single classification set may not be able to fully accommodate it, it is necessary to store the ground object data in one or more classification sets.

[0030] The land cover data is stored in one or more land cover type classification sets, the ground object classification data is stored in one or more ground object type classification sets, and the remote sensing image data is stored in one or more remote sensing type classification sets.

[0031] Step S3: Uniformly store the classification sets.

[0032] The classification sets to be uniformly stored are those that have already stored data. For example, if one or more land cover classification sets and remote sensing classification sets have already stored data, then these classification sets are uniformly stored. If the land cover classification set has no stored data, it waits to complete data storage before classifying and storing the set.

[0033] The storage of the classification sets includes the following sub-steps:

[0034] Step S31: Determine the number of classification sets.

[0035] Step S32: Determine whether uniform storage is required based on the number of classification sets.

[0036] If the number of classification sets is less than the specified threshold, the classification sets can be stored synchronously or asynchronously. If the number of classification sets is greater than the specified threshold, it is considered that uniform storage is required, and step S33 is executed.

[0037] Step S33: Create multiple nodes.

[0038] Since the number of classification sets is greater than the specified threshold, it indicates that the corresponding data volume in the classification sets is large. The uniform storage system is to reduce the probability of storage congestion or storage errors caused by a large amount of stored data during storage. In this embodiment, to reduce the probability of storage errors, a scheme of creating nodes while storing is adopted.

[0039] Specifically, if one or more land cover classification sets need to be uniformly stored, multiple nodes are first created for storing the classification sets. The nodes can be virtual nodes. During the creation process, the number of nodes needs to be greater than the number of classification sets to be stored.

[0040] During the creation process, since multiple nodes are created synchronously, there may be cases where node creation is successful or fails. Therefore, this embodiment also needs to determine the success rate of node creation.

[0041] Assume that p is the probability of successfully creating one node, and q is the probability of failing to create one node. When the number of created nodes meets z, the probability of node creation failure follows a Poisson distribution, and the distribution expectation is:

[0042]

[0043] Then the probability P of successful node creation is expressed as:

[0044]

[0045] If the probability P is greater than the specified threshold, the currently created node is retained and step S34 is executed.

[0046] If the probability P is less than the specified threshold, node creation is performed again.

[0047] Step S34: Store multiple classification sets in nodes according to different strategies.

[0048] Among them, storing multiple classification sets in nodes according to different strategies includes creating corresponding data copies in multiple classification sets, and the data copies are in the form of copies of the corresponding data in the classification sets.

[0049] Store multiple classification sets separately in multiple created nodes. For example, one node stores one classification set. Store the corresponding created data copies in the remaining created nodes. For example, one node stores the corresponding data copy of one classification set.

[0050] As an example, after storing multiple land cover class classification sets and feature class classification sets, create land cover data copies in multiple land cover class classification sets and feature classification data copies in feature class classification sets, and store the above copies in one or more remaining nodes.

[0051] Among them, storing multiple classification sets in nodes according to different strategies also includes, if the classification set to be stored is a remote sensing classification set, downsample the remote sensing image data in the classification set to form image blocks, store the number of image blocks in multiple nodes, and check the number of image blocks after a specified time.

[0052] If the number of image blocks does not change after the specified storage time, it is considered that the storage is passed and step S4 is executed.

[0053] The number of image blocks L stored in p nodes is:

[0054]

[0055] Where a*b is the size of the original remote sensing image, g represents the downsampling ratio, n represents the number of downsampling layers, l represents a natural number, and p represents the number of nodes storing image blocks.

[0056] Step S4: Call and transfer the stored classification sets to complete the analysis of feature data.

[0057] Among them, if subsequent analysis is required, call the classification set in the corresponding node, transfer the classification set to the data processing center, and the data processing center obtains the corresponding feature data according to the classification set. Urban planning analysis and environmental protection analysis can be performed based on the feature data.

[0058] If data loss occurs during transmission, the node can be called again to retrieve its corresponding data copy, and subsequent analysis can be completed based on the data copy.

[0059] This embodiment adopts the method of storing and calling nodes during transmission, storing and transmitting data as needed, which greatly improves the transmission security of ground object data. In addition, this application also creates data copies, and subsequent analysis can still be completed based on the data copies after transmission anomalies occur, ensuring the timeliness and accuracy of the analysis.

[0060] Embodiment 2

[0061] As Figure 2 shown, this embodiment provides a ground object data analysis system, which specifically includes: a ground object data acquisition unit 210, a ground object data storage unit 220, a classification set storage unit 230, and a call analysis unit 240.

[0062] The ground object data acquisition unit 210 is used to acquire ground object data.

[0063] Among them, the ground object data includes land cover data, ground object classification data, and remote sensing image data.

[0064] The land cover data provides global surface cover information, including data of types such as cultivated land, forest land, and grassland.

[0065] The ground object classification data covers classifications such as buildings, roads, and water bodies, and can be used in fields such as urban planning.

[0066] The remote sensing image data is obtained by remote sensing satellites and includes optical image data, radar image data, etc., and can be used to observe surface colors, vegetation conditions, etc.

[0067] The ground object data storage unit 220 is used to determine the classification set and store the acquired ground object data into the classification set.

[0068] Among them, the acquired ground object data is divided into land cover type, ground object type, and remote sensing type, and the classified data is stored in the corresponding classification sets (i.e., classification data sets).

[0069] Since the amount of ground object data may be too large and one classification set may not be able to fully accommodate it, it is necessary to store the ground object data in one or more classification sets.

[0070] The land cover data is stored in one or more land cover type classification sets, the ground object classification data is stored in one or more ground object type classification sets, and the remote sensing image data is stored in one or more remote sensing type classification sets.

[0071] The classification set storage unit 230 is used to uniformly store the classification sets.

[0072] The classification set for unified storage is the classification set in which data has been stored. As an example, if data has been stored in one or more land cover type classification sets and remote sensing type classification sets, then the above classification sets are stored uniformly. For the land cover type classification set in which data has not been stored, classification set storage is awaited until data storage is completed.

[0073] Storing the classification set includes the following sub-steps:

[0074] Step T1: Determine the number of classification sets.

[0075] Step T2: Determine whether unified storage is required based on the number of classification sets.

[0076] If the number of classification sets is less than the specified threshold, the classification sets can be stored synchronously or asynchronously. If the number of classification sets is greater than the specified threshold, it is considered that unified storage is required, and step S33 is executed.

[0077] Step T3: Create multiple nodes.

[0078] Since the number of classification sets is greater than the specified threshold, it indicates that the corresponding data volume in the classification sets is large. The unified storage system is to reduce the probability of storage congestion or storage errors caused by a large amount of stored data during storage. In this embodiment, to reduce the probability of storage errors, a scheme of creating nodes while storing is adopted.

[0079] Specifically, if one or more land cover type classification sets need to be stored uniformly, multiple nodes are first created for storing the classification sets. The nodes can be virtual nodes. During the creation process, the number of nodes needs to be greater than the number of classification sets to be stored.

[0080] During the creation process, since multiple nodes are created synchronously, it is possible that a node creation is successful or failed. Therefore, in this embodiment, it is also necessary to determine the success rate of node creation.

[0081] Assume that p is the probability of successfully creating a node, q is the probability of failing to create a node. When the number of created nodes satisfies z, the probability of failing to create a node follows a Poisson distribution, and the distribution expectation value is:

[0082]

[0083] Then the probability P of successful node creation is expressed as:

[0084]

[0085] If the probability P is greater than the specified threshold, the currently created nodes are retained, and step S34 is executed.

[0086] If the probability P is less than the specified threshold, the creation of the node is re-performed.

[0087] Step T4: Store multiple classification sets in nodes according to different strategies.

[0088] Among them, storing multiple classification sets in nodes according to different strategies includes creating corresponding data copies in multiple classification sets, and the data copies are in the form of copies of the corresponding data in the classification sets.

[0089] Store multiple classification sets separately in the multiple created nodes, for example, one node stores one classification set. Store the created corresponding data copies in the remaining created nodes, for example, one node stores the corresponding data copy of one classification set.

[0090] As an example, after storing multiple land cover class classification sets and feature class classification sets, create land cover data copies in multiple land cover class classification sets, and feature classification data copies in feature class classification sets, and store the above copies in one or more remaining nodes.

[0091] Among them, storing multiple classification sets in nodes according to different strategies also includes, if the classification set to be stored is a remote sensing classification set, downsample the remote sensing image data in the classification set to form image blocks, store the number of image blocks in multiple nodes, and check the number of impact blocks after a specified time.

[0092] If the number of image blocks does not change after the specified storage time, it is considered that the storage is passed, and the analysis unit 240 is called.

[0093] The number L of image blocks stored in p nodes is:

[0094]

[0095] Among them, a*b is the size of the original remote sensing image, g represents the downsampling ratio, n represents the number of downsampling layers, l represents a natural number, and p represents the number of nodes storing image blocks.

[0096] Call the analysis unit 240 to perform call transmission on the stored classification set and complete the analysis of feature data.

[0097] Among them, if subsequent analysis is required, call the classification set in the corresponding node, transmit the classification set to the data processing center, and the data processing center obtains the corresponding feature data according to the classification set. Urban planning analysis and environmental protection analysis can be performed based on the feature data.

[0098] If data is lost during the transmission process, the corresponding data copy can be called again from the node, and the subsequent analysis is completed according to the data copy.

[0099] The present application further provides a computer storage medium. The computer storage medium stores computer instructions, which are used to execute the method for analyzing ground object data when called.

[0100] The embodiments disclosed by the present invention provide a computer-readable storage medium. Computer program instructions are stored in the computer-readable storage medium. When the computer program instructions run on a computer, the computer is enabled to execute the above-mentioned method for analyzing ground object data.

[0101] The embodiments of the present invention provide a processor for processing the above-mentioned method for analyzing ground object data.

[0102] In the embodiments of the present invention, the processor may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP for short), an application-specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0103] It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and combines its hardware to complete the steps of the above method.

[0104] The storage medium may be a memory, for example, it may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0105] Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0106] The present application has the following beneficial effects:

[0107] The present application adopts the method of node storage and call transmission, storing and transmitting as needed, which greatly increases the transmission security of ground object data. Moreover, the present application also creates data copies, and subsequent analysis can still be completed according to the data copies after a transmission anomaly occurs, ensuring the timeliness and accuracy of the analysis.

[0108] Although the examples referred to in the current application are described, they are for explanatory purposes only and not a limitation of the present application. Changes, additions, and / or deletions to the embodiments can be made without departing from the scope of the present application.

[0109] As described above, the above is only the specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for analyzing ground object data, characterized in that, It includes the following steps: Obtain feature data; Determine the classification set and store the obtained feature data in the classification set; Perform unified storage on the classification set; Call and transfer the stored classification set to complete the analysis of feature data.

2. The method for analyzing ground object data according to claim 1, characterized in that The feature data includes land cover data, feature classification data, and remote sensing image data.

3. The method for analyzing ground object data according to claim 1, wherein Determining the classification set and storing the obtained feature data in the classification set includes storing the land cover data in one or more land cover class classification sets, storing the feature classification data in one or more feature class classification sets, and storing the remote sensing image data in one or more remote sensing class classification sets.

4. The method for analyzing ground object data according to claim 1, characterized in that Performing unified storage on the classification set includes the following sub-steps: Determine the number of classification sets; Determine whether unified storage is required according to the number of classification sets; If unified storage is required, create multiple nodes; Store multiple classification sets in the nodes according to different strategies.

5. The method for analyzing ground object data according to claim 4, wherein Storing multiple classification sets in the nodes according to different strategies includes: Create data copies of multiple classification sets; Store multiple classification sets in the multiple created nodes respectively; Store the created data copies in the remaining nodes.

6. A ground object data analysis system, characterized in that, Specifically include: Feature data acquisition unit, feature data storage unit, classification set storage unit, and call analysis unit; The feature data acquisition unit is used to obtain feature data; The feature data storage unit is used to determine the classification set and store the obtained feature data in the classification set; The classification set storage unit is used to perform unified storage on the classification set; The call analysis unit is used to call and transfer the stored classification set to complete the analysis of feature data.

7. The feature of the ground object data analysis system according to claim 6 is that, The feature data obtained by the feature data acquisition unit includes land cover data, feature classification data, and remote sensing image data.

8. The ground feature data analysis system according to claim 6, characterized in that, The feature data storage unit storing the obtained feature data in the classification set includes storing the land cover data in one or more land cover class classification sets, storing the feature classification data in one or more feature class classification sets, and storing the remote sensing image data in one or more remote sensing class classification sets.

9. The ground object data analysis system according to claim 6, wherein, The classification set storage unit performing unified storage on the classification set includes the following sub-steps: Determine the number of classification sets; Determine whether unified storage is required according to the number of classification sets; If unified storage is required, create multiple nodes; Store multiple classification sets in the nodes according to different strategies.

10. The ground feature data analysis system according to claim 9, characterized in that, Storing multiple classification sets in the nodes according to different strategies includes: Create data copies of multiple classification sets; Store multiple classification sets in the multiple created nodes respectively; Store the created data copies in the remaining nodes.

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