Spatial variation data identification method, system, and device, and storage medium

Abstract

Disclosed in the present invention are a spatial variation data identification method, system, and device, and a storage medium. The method comprises: on the basis of a GeoSOT global subdivision framework, performing grid subdivision on an original geographic dataset, to obtain original geographic dataset spatial grid codes; calling a grid coding engine to obtain incremental package spatial grid codes; on the basis of the original geographic dataset spatial grid codes and the incremental package spatial grid codes, determining dataset information to be updated, and determining candidate dataset spatial grid codes by means of association rules on the basis of the dataset information to be updated; and determining associated dataset information on the basis of the candidate dataset spatial grid codes and the incremental package spatial grid codes. In the present invention, candidate dataset spatial grid codes are quickly identified by means of dataset category association rules, subcategory association rules and spatial grid code association rules, and associated dataset information is positioned by means of the spatial grid code association rules on the basis of the candidate dataset spatial grid codes and incremental package spatial grid codes, thereby improving the identification efficiency of spatial variation data.

Description

Spatial variation data identification method, system, device and storage medium Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, device and storage medium for identifying spatially varying data. Background Art

[0002] In the process of identifying and processing spatially varying data, existing research has often employed different spatial entity matching strategies to establish relationships between multi-source data. However, regardless of the matching strategy used, spatial similarity measures are essential. These methods consider the directional, topological, and distance relationships of the data. However, these methods require extensive spatial searches, resulting in low efficiency and difficulty ensuring accuracy. Therefore, how to quickly identify spatially varying data has become a pressing issue.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art.

[0004] Summary of the Invention

[0005] The main purpose of the present invention is to provide a method, system, device and storage medium for identifying spatially varying data, aiming to solve the technical problem of how to quickly identify spatially varying data.

[0006] To achieve the above object, the present invention provides a method for identifying spatially varying data, the method comprising:

[0007] The original geographic dataset is gridded based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset;

[0008] Calling the grid coding engine to obtain the incremental packet space grid code;

[0009] Determining the dataset information to be updated based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package, and determining the candidate dataset spatial grid code based on the dataset information to be updated by using association rules, wherein the association rules include dataset category association rules, subcategory association rules, and spatial grid code association rules;

[0010] The associated dataset information is determined according to the candidate dataset spatial grid code and the incremental packet spatial grid code to realize spatial variation data identification.

[0011] Optionally, the step of calling the trellis coding engine to obtain the incremental packet space trellis code includes:

[0012] The incremental packet spatial grid code is obtained through a grid coding engine according to the original geographic data set spatial grid code.

[0013] Optionally, the step of determining the candidate dataset spatial grid code by association rules based on the information of the dataset to be updated includes:

[0014] Determining derived associated dataset category information based on the dataset information to be updated using dataset category association rules;

[0015] Determining the subcategory information of the linked dataset by using subcategory association rules according to the derived linked dataset category information;

[0016] Determining a linked dataset subclass spatial grid code according to the linked dataset subclass information;

[0017] The candidate data set spatial grid codes are determined according to the associated data set subclass spatial grid codes by using spatial grid code association rules.

[0018] Optionally, the step of determining the associated dataset information according to the candidate dataset spatial grid code and the incremental packet spatial grid code includes:

[0019] The associated dataset information is determined according to the candidate dataset spatial grid code and the incremental packet spatial grid code by using the spatial grid code association rule.

[0020] Optionally, the step of determining the associated dataset spatial grid code according to the candidate dataset spatial grid code and the incremental packet spatial grid code by using the spatial grid code association rule includes:

[0021] Determining a spatial grid code association relationship between the candidate data set spatial grid code and the incremental packet spatial grid code by using the spatial grid code association rule;

[0022] The associated data set information is determined according to the spatial grid code association relationship.

[0023] In addition, to achieve the above-mentioned purpose, the present invention further proposes a spatial variation data identification system, the spatial variation data identification system comprising:

[0024] The gridding module is used to grid the original geographic dataset based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset;

[0025] A calculation module, used for calling a grid coding engine to obtain a spatial grid code of an incremental packet;

[0026] a processing module, configured to determine information of a dataset to be updated based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package, and to determine spatial grid codes of candidate datasets based on the information of the dataset to be updated using association rules, wherein the association rules include dataset category association rules, subcategory association rules, and spatial grid code association rules;

[0027] A positioning module is used to determine the associated dataset information according to the candidate dataset spatial grid code and the incremental packet spatial grid code to realize spatial change data identification.

[0028] In addition, to achieve the above-mentioned purpose, the present invention also proposes a spatial change data identification device, which includes: a memory, a processor, and a spatial change data identification program stored in the memory and executable on the processor, wherein the spatial change data identification program is configured to implement the steps of the spatial change data identification method described above.

[0029] In addition, to achieve the above-mentioned purpose, the present invention further proposes a storage medium, on which a spatial variation data recognition program is stored. When the spatial variation data recognition program is executed by a processor, the steps of the spatial variation data recognition method described above are implemented.

[0030] The present invention first grids the original geographic dataset based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset, then calls the grid coding engine to obtain the spatial grid code of the incremental package, and then determines the information of the dataset to be updated based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package. The spatial grid code of the candidate dataset is determined based on the information of the dataset to be updated through association rules. The association rules include dataset category association rules, subcategory association rules and spatial grid code association rules. Finally, the associated dataset information is determined based on the spatial grid code of the candidate dataset and the spatial grid code of the incremental package to realize spatial change data identification. Compared with the prior art that uses different spatial homonymous entity matching strategies to establish the association relationship of multi-source data, but is inseparable from the spatial similarity measurement method, requires a large amount of spatial search, resulting in low efficiency and low accuracy, the present invention can quickly identify the spatial grid code of the candidate dataset through the dataset category association rules, subcategory association rules and spatial grid code association rules. Then, the associated dataset information is quickly located based on the spatial grid code association rules of the candidate dataset and the spatial grid code of the incremental package, thereby improving the efficiency of spatial change data identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG1 is a schematic diagram of the structure of a spatial variation data recognition device in a hardware operating environment according to an embodiment of the present invention;

[0032] FIG2 is a schematic flow chart of a first embodiment of a method for identifying spatially varying data according to the present invention;

[0033] FIG3 is a schematic diagram of a data processing flow of a first embodiment of a method for identifying spatially varying data according to the present invention;

[0034] FIG4 is a diagram showing a data set association relationship mapping relationship according to a first embodiment of a method for identifying spatially varying data according to the present invention;

[0035] FIG5 is a structural block diagram of the first embodiment of the spatial variation data identification system of the present invention.

[0036] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0037] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] 1 , which is a schematic diagram of the structure of a spatial variation data recognition device in a hardware operating environment according to an embodiment of the present invention.

[0039] As shown in Figure 1, the spatial variation data identification device may include: a processor 1001, such as a central processing unit (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 user interface 1003 may optionally 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 wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0040] Those skilled in the art will appreciate that the structure shown in FIG1 does not limit the spatial variation data identification device, and may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0041] As shown in FIG. 1 , the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a spatial change data recognition program.

[0042] In the spatial change data identification device shown in Figure 1, the network interface 1004 is mainly used to communicate data sets with a network server; the user interface 1003 is mainly used to interact with the user on the data set; the processor 1001 and the memory 1005 in the spatial change data identification device of the present invention can be set in the spatial change data identification device. The spatial change data identification device calls the spatial change data identification program stored in the memory 1005 through the processor 1001 and executes the spatial change data identification method provided by the embodiment of the present invention.

[0043] An embodiment of the present invention provides a method for identifying spatially varying data. Referring to FIG. 2 , FIG. 2 is a flow chart illustrating a first embodiment of the method for identifying spatially varying data according to the present invention.

[0044] In this embodiment, the spatial variation data identification method includes the following steps:

[0045] Step S10: Gridding the original geographic dataset based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset.

[0046] It is easy to understand that the execution subject of this embodiment can be a spatial change data identification system with functions such as data set processing, network communication and program running, or other computer equipment with similar functions, etc., and this embodiment is not limited.

[0047] In a specific implementation, the original geographic dataset can be understood as the geographic dataset currently stored in the system.

[0048] It should be understood that the original geographic dataset needs to be gridded using the global gridding framework of GeoSOT, and the divided grids need to be encoded to obtain the spatial grid code of the original geographic dataset.

[0049] It should also be noted that the original geographic dataset spatial grid code contains first-level spatial grid codes, second-level spatial grid codes, and third-level spatial grid codes.

[0050] The first-level space grid code includes the second-level space grid code, the second-level space grid code includes the third-level space grid code, and so on.

[0051] Step S20: calling the trellis coding engine to obtain the incremental packet space trellis code.

[0052] It should be noted that the incremental package spatial grid code is obtained through the grid coding engine based on the spatial grid code of the original geographic data set. The incremental package is the specific data that needs to be updated. The incremental package spatial grid code can be a second-level spatial grid code, or a third-level spatial grid code, etc.

[0053] Step S30: Determine the dataset information to be updated based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package, and determine the candidate dataset spatial grid code through association rules based on the dataset information to be updated. The association rules include dataset category association rules, subcategory association rules and spatial grid code association rules.

[0054] In this embodiment, referring to FIG3 , FIG3 is a schematic diagram of a data processing flow of the first embodiment of the spatial change data identification method of the present invention. The information of the dataset to be updated includes the dataset to be updated and the spatial grid code of the dataset to be updated. The spatial grid code of the dataset to be updated can be a first-level spatial grid code. The information of the dataset to be updated can be dataset A or dataset B, etc., which is not limited in this embodiment.

[0055] It should also be noted that the dataset information to be updated is the dataset information that needs to be modified due to changes in the original geographic dataset, and the dataset information that needs to be modified includes specific data that needs to be modified and specific data that does not need to be modified.

[0056] Furthermore, the processing method for determining the candidate dataset spatial grid code through association rules based on the dataset information to be updated is to determine the derived associated dataset category information through dataset category association rules based on the dataset information to be updated; determine the associated dataset subcategory information through subcategory association rules based on the derived associated dataset category information; determine the associated dataset subcategory spatial grid code based on the associated dataset subcategory information; and determine the candidate dataset spatial grid code through spatial grid code association rules based on the associated dataset subcategory spatial grid code.

[0057] In this embodiment, in order to ensure the linkage changes of the basic dataset and its associated datasets, a three-level mapping rule including dataset category association rules, subcategory association rules and spatial grid code association rules is adopted. After the basic dataset (i.e., the dataset to be updated) changes, its associated datasets are quickly discovered and located, and then the changes of the associated datasets are completed. This can effectively ensure the version consistency and content timeliness of the basic dataset and its associated datasets.

[0058] The first-level association rules are dataset category association rules. The base dataset determines the category information of the derived association datasets (product datasets, thematic datasets, and service datasets); product datasets derive associated service datasets; and thematic datasets derive associated service datasets. Using dataset category association rules, when establishing associations, the datasets to be associated are first categorized, reducing the number of dataset association comparisons. Refer to Figure 4, which shows a dataset association mapping diagram for the first embodiment of the spatially varying data identification method of the present invention.

[0059] It should also be noted that the subcategory information of the linked dataset is determined through subcategory association rules based on the derived category information of the linked dataset.

[0060] The second-level association rules are sub-category association rules. By establishing a sub-category association rule mapping table, dataset types that require associations are searched in the sub-category association mapping table. Only datasets that meet the rules (i.e., the sub-category information of the associated datasets) can establish associations. For example, for a topographic map dataset, datasets that can be associated include surveying and mapping basic geographic entities, surveying and mapping thematic geographic entity datasets, surveying and mapping map library integrated products, and surveying and mapping raster tile services.

[0061] It should be understood that when the association rules need to be adjusted, only the following association mapping table needs to be modified:

[0062] It should also be noted that the spatial grid code of the associated dataset subclass is determined based on the associated dataset subclass information; the spatial grid code of the candidate dataset is determined based on the spatial grid code of the associated dataset subclass through the spatial grid code association rule. The third-level association rule is the spatial grid code association rule. Through the spatial grid code of the dataset, it is calculated whether the spatial range of the dataset has an intersection. Only when the datasets have an intersection can an association relationship be established. It mainly includes four intersection relationships: intersection, adjacent, separated, and inclusion. The spatial grid code association rule table is shown below:

[0063] Intersection relationship judgment mainly involves calculations to determine whether there is an intersection relationship between specified grids or grid sets; adjacent relationship judgment mainly involves calculations to determine whether there is an adjacent relationship between specified grids or grid sets, and what kind of adjacent relationship it is (edge ​​adjacent, corner adjacent, and three-dimensional face adjacent, etc.); separation relationship judgment mainly involves calculations to determine whether two grids or grid sets are completely within the spatial range of the other.

[0064] It should also be noted that it is determined whether there is an intersection between the associated dataset subclass spatial grid code and the dataset to be updated spatial grid code, and the candidate dataset spatial grid code is determined based on the associated dataset subclass spatial grid codes that have an intersection.

[0065] It should also be noted that the associated dataset subclass spatial grid code can be a secondary spatial grid code. It is necessary to determine the primary spatial grid code based on the associated dataset subclass spatial grid code, and use the primary spatial grid code corresponding to the associated dataset subclass spatial grid code as the candidate dataset spatial grid code.

[0066] Step S40: Determine associated dataset information based on the candidate dataset spatial grid code and the incremental packet spatial grid code to achieve spatially varying data identification.

[0067] In a specific implementation, the associated dataset information is determined according to the spatial grid code of the candidate dataset and the spatial grid code of the incremental packet through the spatial grid code association rule.

[0068] The associated dataset information includes the associated dataset space grid code and the associated dataset. It should be understood that the candidate dataset includes the associated dataset.

[0069] It should also be noted that the spatial grid code association relationship between the candidate dataset spatial grid code and the incremental packet spatial grid code is determined by the spatial grid code association rule, and the associated dataset information is determined based on the spatial grid code association relationship.

[0070] The spatial grid code association relationship may be an intersection relationship between the spatial grid codes.

[0071] In this embodiment, the original geographic dataset is first gridded based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset. Then, the grid coding engine is called to obtain the spatial grid code of the incremental package. Then, the information of the dataset to be updated is determined based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package. The spatial grid code of the candidate dataset is determined based on the information of the dataset to be updated through association rules. The association rules include dataset category association rules, subcategory association rules, and spatial grid code association rules. Finally, the information of the associated dataset is determined based on the spatial grid code of the candidate dataset and the spatial grid code of the incremental package to achieve spatial change data identification. Compared with the prior art that uses different spatial homonymous entity matching strategies to establish the association relationship of multi-source data, but is inseparable from the spatial similarity measurement method and requires a large amount of spatial search, resulting in low efficiency and low accuracy, this embodiment uses dataset category association rules, subcategory association rules, and spatial grid code association rules to quickly identify the spatial grid code of the candidate dataset. Then, the spatial grid code association rules are used to quickly locate the associated dataset information based on the spatial grid code of the candidate dataset and the spatial grid code of the incremental package, thereby improving the efficiency of spatial change data identification.

[0072] 5 , which is a structural block diagram of a first embodiment of a spatial variation data identification system according to the present invention.

[0073] As shown in FIG5 , the spatial variation data recognition system proposed in an embodiment of the present invention includes:

[0074] The gridding module 5001 is used to grid the original geographic dataset based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset.

[0075] In a specific implementation, the original geographic dataset can be understood as the geographic dataset currently stored in the system.

[0076] It should be understood that the original geographic dataset needs to be gridded using the global gridding framework of GeoSOT, and the divided grids need to be encoded to obtain the spatial grid code of the original geographic dataset.

[0077] It should also be noted that the original geographic dataset spatial grid code contains first-level spatial grid codes, second-level spatial grid codes, and third-level spatial grid codes.

[0078] The first-level space grid code includes the second-level space grid code, the second-level space grid code includes the third-level space grid code, and so on.

[0079] The operation module 5002 is used to call the grid coding engine to obtain the incremental packet space grid code.

[0080] It should be noted that the incremental package spatial grid code is obtained through the grid coding engine based on the spatial grid code of the original geographic data set. The incremental package is the specific data that needs to be updated. The incremental package spatial grid code can be a second-level spatial grid code, or a third-level spatial grid code, etc.

[0081] Processing module 5003 is used to determine the dataset information to be updated based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package, and to determine the candidate dataset spatial grid code through association rules based on the dataset information to be updated, wherein the association rules include dataset category association rules, subcategory association rules and spatial grid code association rules.

[0082] In this embodiment, referring to FIG3 , FIG3 is a schematic diagram of a data processing flow of the first embodiment of the spatial change data identification method of the present invention. The information of the dataset to be updated includes the dataset to be updated and the spatial grid code of the dataset to be updated. The spatial grid code of the dataset to be updated can be a first-level spatial grid code. The information of the dataset to be updated can be dataset A or dataset B, etc., which is not limited in this embodiment.

[0083] It should also be noted that the dataset information to be updated is the dataset information that needs to be modified due to changes in the original geographic dataset, and the dataset information that needs to be modified includes specific data that needs to be modified and specific data that does not need to be modified.

[0084] Furthermore, the processing method for determining the candidate dataset spatial grid code through association rules based on the dataset information to be updated is to determine the derived associated dataset category information through dataset category association rules based on the dataset information to be updated; determine the associated dataset subcategory information through subcategory association rules based on the derived associated dataset category information; determine the associated dataset subcategory spatial grid code based on the associated dataset subcategory information; and determine the candidate dataset spatial grid code through spatial grid code association rules based on the associated dataset subcategory spatial grid code.

[0085] In this embodiment, in order to ensure the linkage changes of the basic dataset and its associated datasets, a three-level mapping rule including dataset category association rules, subcategory association rules and spatial grid code association rules is adopted. After the basic dataset (i.e., the dataset to be updated) changes, its associated datasets are quickly discovered and located, and then the changes of the associated datasets are completed. This can effectively ensure the version consistency and content timeliness of the basic dataset and its associated datasets.

[0086] The first-level association rules are dataset category association rules. The base dataset determines the category information of the derived association datasets (product datasets, thematic datasets, and service datasets); product datasets derive associated service datasets; and thematic datasets derive associated service datasets. Using dataset category association rules, when establishing associations, the datasets to be associated are first categorized, reducing the number of dataset association comparisons. Refer to Figure 4, which shows a dataset association mapping diagram for the first embodiment of the spatially varying data identification method of the present invention.

[0087] It should also be noted that the subcategory information of the linked dataset is determined through subcategory association rules based on the derived category information of the linked dataset.

[0088] The second-level association rules are sub-category association rules. By establishing a sub-category association rule mapping table, dataset types that require associations are searched in the sub-category association mapping table. Only datasets that meet the rules (i.e., the sub-category information of the associated datasets) can establish associations. For example, for a topographic map dataset, datasets that can be associated include surveying and mapping basic geographic entities, surveying and mapping thematic geographic entity datasets, surveying and mapping map library integrated products, and surveying and mapping raster tile services.

[0089] It should be understood that when the association rules need to be adjusted, only the following association mapping table needs to be modified:

[0090] It should also be noted that the spatial grid code of the associated dataset subclass is determined based on the associated dataset subclass information; the spatial grid code of the candidate dataset is determined based on the spatial grid code of the associated dataset subclass through the spatial grid code association rule. The third-level association rule is the spatial grid code association rule. Through the spatial grid code of the dataset, it is calculated whether the spatial range of the dataset has an intersection. Only when the datasets have an intersection can an association relationship be established. It mainly includes four intersection relationships: intersection, adjacent, separated, and inclusion. The spatial grid code association rule table is shown below:

[0091] Intersection relationship judgment mainly involves calculations to determine whether there is an intersection relationship between specified grids or grid sets; adjacent relationship judgment mainly involves calculations to determine whether there is an adjacent relationship between specified grids or grid sets, and what kind of adjacent relationship it is (edge ​​adjacent, corner adjacent, and three-dimensional face adjacent, etc.); separation relationship judgment mainly involves calculations to determine whether two grids or grid sets are completely within the spatial range of the other.

[0092] It should also be noted that it is determined whether there is an intersection between the associated dataset subclass spatial grid code and the dataset to be updated spatial grid code, and the candidate dataset spatial grid code is determined based on the associated dataset subclass spatial grid codes that have an intersection.

[0093] It should also be noted that the associated dataset subclass spatial grid code can be a secondary spatial grid code. It is necessary to determine the primary spatial grid code based on the associated dataset subclass spatial grid code, and use the primary spatial grid code corresponding to the associated dataset subclass spatial grid code as the candidate dataset spatial grid code.

[0094] The positioning module 5004 is configured to determine the associated dataset information based on the candidate dataset spatial grid code and the incremental packet spatial grid code, so as to realize spatial variation data identification.

[0095] In a specific implementation, the associated dataset information is determined according to the spatial grid code of the candidate dataset and the spatial grid code of the incremental packet through the spatial grid code association rule.

[0096] The associated dataset information includes the associated dataset space grid code and the associated dataset. It should be understood that the candidate dataset includes the associated dataset.

[0097] It should also be noted that the spatial grid code association relationship between the candidate dataset spatial grid code and the incremental packet spatial grid code is determined by the spatial grid code association rule, and the associated dataset information is determined based on the spatial grid code association relationship.

[0098] The spatial grid code association relationship may be an intersection relationship between the spatial grid codes.

[0099] In this embodiment, the original geographic dataset is first gridded based on the global gridding framework of GeoSOT to obtain the spatial grid code of the original geographic dataset. Then, the grid coding engine is called to obtain the spatial grid code of the incremental package. Then, the information of the dataset to be updated is determined based on the spatial grid code of the original geographic dataset and the spatial grid code of the incremental package. The spatial grid code of the candidate dataset is determined based on the information of the dataset to be updated through association rules. The association rules include dataset category association rules, subcategory association rules, and spatial grid code association rules. Finally, the information of the associated dataset is determined based on the spatial grid code of the candidate dataset and the spatial grid code of the incremental package to achieve spatial change data identification. Compared with the prior art that uses different spatial homonymous entity matching strategies to establish the association relationship of multi-source data, but is inseparable from the spatial similarity measurement method and requires a large amount of spatial search, resulting in low efficiency and low accuracy, this embodiment uses dataset category association rules, subcategory association rules, and spatial grid code association rules to quickly identify the spatial grid code of the candidate dataset. Then, the spatial grid code association rules are used to quickly locate the associated dataset information based on the spatial grid code of the candidate dataset and the spatial grid code of the incremental package, thereby improving the efficiency of spatial change data identification.

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

[0101] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0102] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this 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 read-only memory / random access memory, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0104] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying spatially varying data, characterized in that, The spatial change data recognition method includes the following steps: Based on the global dissection framework of GeoSOT, perform grid dissection on the original geographic data set to obtain the spatial grid codes of the original geographic data set; Call the grid coding engine to obtain the spatial grid codes of the incremental package; Determine the information of the data set to be updated according to the spatial grid codes of the original geographic data set and the spatial grid codes of the incremental package, and determine the spatial grid codes of the candidate data sets through association rules according to the information of the data set to be updated. The association rules include data set category association rules, subclass association rules, and spatial grid code association rules; Determine the information of the associated data set according to the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package to achieve the recognition of spatial change data.

2. The method according to claim 1, characterized in that, The step of calling the grid coding engine to obtain the spatial grid codes of the incremental package includes: Obtain the spatial grid codes of the incremental package through the grid coding engine according to the spatial grid codes of the original geographic data set.

3. The method according to claim 1, wherein The step of determining the spatial grid codes of the candidate data sets through association rules according to the information of the data set to be updated includes: Determine the derived associated data set category information through the data set category association rule according to the information of the data set to be updated; Determine the associated data set subclass information through the subclass association rule according to the derived associated data set category information; Determine the spatial grid codes of the associated data set subclasses according to the information of the associated data set subclasses; Determine the spatial grid codes of the candidate data sets through the spatial grid code association rule according to the spatial grid codes of the associated data set subclasses.

4. The method according to claim 3, wherein The step of determining the information of the associated data set according to the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package includes: Determine the information of the associated data set through the spatial grid code association rule according to the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package.

5. The method according to claim 4, wherein The step of determining the associated data set information of the associated data set according to the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package through the spatial grid code association rule includes: Determine the spatial grid code association relationship between the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package through the spatial grid code association rule; Determine the information of the associated data set according to the spatial grid code association relationship.

6. A spatial variation data recognition system, characterized in that, The spatial change data recognition system includes: A dissection module for performing grid dissection on the original geographic data set based on the global dissection framework of GeoSOT to obtain the spatial grid codes of the original geographic data set; An operation module for calling the grid coding engine to obtain the spatial grid codes of the incremental package; A processing module for determining the information of the data set to be updated according to the spatial grid codes of the original geographic data set and the spatial grid codes of the incremental package, and determining the spatial grid codes of the candidate data sets through association rules according to the information of the data set to be updated. The association rules include data set category association rules, subclass association rules, and spatial grid code association rules; A positioning module for determining the information of the associated data set according to the spatial grid codes of the candidate data set and the spatial grid codes of the incremental package to achieve the recognition of spatial change data.

7. A spatial variation data recognition device, characterized in that, The device includes: a memory, a processor, and a spatially varying data recognition program stored on the memory and executable on the processor, the spatially varying data recognition program being configured to implement the steps of the spatially varying data recognition method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, A spatially varying data recognition program is stored on the storage medium, and when the spatially varying data recognition program is executed by a processor, the steps of the spatially varying data recognition method according to any one of claims 1 to 5 are implemented.

Images