A method and apparatus for processing geographic information data in and out of a database.

Abstract

This application discloses a method and apparatus for processing geographic information data in and out of a database, solving the problem that geographic information database feature data cannot be freely downloaded and stored locally. The method includes the following steps: obtaining a first data cluster of the target feature in the database; responding to GUI instructions, dividing the first data cluster along a certain range line to generate at least one original sub-data cluster; obtaining the resulting sub-data cluster; merging the resulting sub-data cluster with the remaining data cluster of the first data cluster excluding the original sub-data clusters to obtain a second data cluster, which is then stored back to replace the first data cluster in the database. This application incrementally updates the data into the database according to the download range line after surveying and mapping, achieving physical boundary alignment of feature elements.

Description

Technical Field

[0001] This application relates to the fields of computer data processing and geographic information database technology, and in particular to a method and apparatus for processing geographic information data in and out of a database. Background Technology

[0002] In the field of surveying and mapping geographic information, data entry and data edge matching are widely recognized and used. Data entry can be divided into initial entry and incremental update entry according to the order of data entry. With the development of surveying and mapping technology and the emergence of the "multi-survey integration" system, basic surveying and mapping work has become more flexible. It is no longer required to update and revise the survey regularly according to annual requirements, nor is it required to produce according to the concept of map sheet. Instead, it is required to revise and revise the survey on a project-by-project basis at any time. Therefore, how to flexibly draw the scope according to the specific project and download it from the database, revise the data within the project scope in the field, and then update it back to the original database incrementally, has become an urgent technical issue. This involves cutting and downloading the project area data according to the specified scope, updating the data after data revision and entering it into the database, and then physically merging it with the data outside the scope to reduce or avoid the problem of large amount of manual processing caused by conflicts of ground features during data entry. Summary of the Invention

[0003] This application proposes a method and apparatus for processing geographic information data in and out of a database. The computer automatically cuts and downloads the regional data of an engineering project according to a specified range, updates the data after data revision and enters the database, and then merges it with the data outside the range to perform feature element merging. This solves the problem that the geographic information database cannot freely download and store feature data locally in the prior art.

[0004] In a first aspect, embodiments of this application provide a method for processing geographic information data in and out of a database, comprising the following steps:

[0005] Retrieve the first data cluster of the target feature in the database;

[0006] In response to GUI instructions, the first data cluster is divided along a certain range line to generate at least one original sub-data cluster, the association between the original sub-data cluster and the first data cluster is determined, and the resulting sub-data cluster is obtained;

[0007] The resulting sub-data is merged with the remaining data cluster of the first data cluster after removing the original sub-data cluster to obtain the second data cluster, which is then stored back to replace the first data cluster in the database.

[0008] Preferably, after the first data cluster is divided along a certain range line and before the second data cluster is stored back, the following steps are further included:

[0009] An intermediate dataset is generated; the intermediate dataset contains data representing the association between the first data cluster and the original sub-data cluster.

[0010] In one embodiment, the intermediate dataset includes the remaining data clusters, as well as data representing the association between the remaining data clusters and the original sub-data clusters.

[0011] In some embodiments, the resulting sub-data cluster is merged with the remaining data cluster, including any of the following cases:

[0012] There are no feature points at the breakpoints where the first data cluster is divided by the range line. Feature points are generated at the breakpoints in the original sub-data clusters. The feature points at the breakpoints in the second data cluster are deleted.

[0013] The first data cluster has feature points at the breakpoints where it is divided by the range line. In the second data cluster, the feature point attributes of the resulting sub-data cluster and the first data cluster at the breakpoints are merged.

[0014] In some embodiments, at least one of the following is also included:

[0015] Set the feature codes of the resulting sub-data clusters to be the same as those of the first data cluster;

[0016] Set at least a portion of the attributes of the primitives in the resulting sub-data cluster to be the same as those in the remaining data cluster.

[0017] In one embodiment, the step of:

[0018] The feature point set of the remaining data clusters and the resulting sub-data clusters is merged;

[0019] Determine the location features, display features, and / or primitive combinations of the merged feature point set to generate the symbolic code for the second data cluster.

[0020] In one embodiment, the data content or timestamps of the original sub-data cluster and the resulting sub-data cluster are compared to determine that the data content or timestamps have changed.

[0021] Secondly, embodiments of this application also provide a geographic information data entry / exit processing apparatus for implementing the geographic information data entry / exit processing method described in any embodiment of the first aspect, comprising: an acquisition module for acquiring a first data cluster of a target feature in a database, and for acquiring a result sub-data cluster; a generation module for dividing the first data cluster along a certain range line in response to instructions from a GUI, generating at least one original sub-data cluster; a determination module for determining the association relationship between the original sub-data cluster and the first data cluster; and a merging module for merging the result sub-data cluster with the remaining data cluster of the first data cluster excluding the original sub-data cluster, to obtain a second data cluster and store it back, replacing the first data cluster in the database.

[0022] Thirdly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any one of the embodiments of the first aspect.

[0023] Fourthly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the method described in any one of the embodiments of the first aspect.

[0024] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0025] This application utilizes computer software technology to download data from a database by cutting along a range line. After the survey is completed, the data is incrementally updated and stored in the database according to the download range line, achieving complete integration of geographic features. For example, with physical borders, the features after the border are stored as a completely new geographic feature, rather than simply merging datasets. The features after physical borders form a new geographic feature data cluster, realizing the overall storage and symbolic representation of geographic features, rather than storing geographic feature information and driving geographic feature graphic display separately for multiple features before physical borders. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0027] Figure 1 A flowchart of a geographic information data import / export processing method provided in this application embodiment;

[0028] Figure 2-1 A schematic diagram of the extent line drawn for the database feature symbol style provided in this application embodiment;

[0029] Figure 2-2 A schematic diagram illustrating the segmentation and modification of database feature symbol styles provided in this application embodiment;

[0030] Figure 2-3 A schematic diagram illustrating the changes in database feature elements after segmentation, as provided in an embodiment of this application.

[0031] Figure 2-4 This is a schematic diagram illustrating the replacement of original data clusters by feature changes after database feature segmentation, as provided in an embodiment of this application.

[0032] Figure 3 This application provides a flowchart of a geographic information data inbound / outbound processing method for feature point set generation;

[0033] Figure 4 This is a structural diagram of a geographic information data entry and exit processing device according to an embodiment of this application;

[0034] Figure 5 This is a schematic diagram of a commercial server system used in the application scenario of this application. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] This application discloses a method and apparatus for processing geographic information data in and out of the database. This technology mainly realizes that basic geographic information data can be automatically cut and downloaded according to a specified range line and update information and graphic information are recorded separately. It can realize incremental data update and database entry of downloaded revised survey data based on the recorded update information. It can realize lossless physical edge joining and correct symbol display of downloaded revised survey data based on the recorded graphic information. It can realize automatic handling of conflicts in data entry and exit for multiple users or multiple projects. It can realize that a geographic entity feature can be segmented, downloaded, updated, and then entered into the database to form a new complete geographic entity feature.

[0037] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0038] Figure 1 This is a flowchart of a geographic information data entry and exit processing method according to an embodiment of this application, including steps 110-140.

[0039] Step 110: Obtain the first data cluster of the target feature in the database.

[0040] For example, read the first data cluster from the database and store it in the SourceData field of the project file.

[0041] In one embodiment of this application, the data structure and attribute data of the target features in the database are obtained as a first data cluster. The first data cluster may include the location coordinates, range, name, or feature type of the target features.

[0042] For example, the data structure of the first data cluster includes: feature codes, attribute values, primitives or primitive groups, and symbolic codes.

[0043] The aforementioned feature code refers to an identifier that represents the type or name of a feature.

[0044] The attribute values ​​include the common attributes and primitive attributes of the feature. The common attributes contain overall descriptive information about the feature, and the primitive attributes contain the characteristics of each primitive that constitutes the feature's graphic, such as feature point location information, primitive type, and display characteristics.

[0045] The symbolization code specifically includes an algorithm for generating a ground feature display graphic using at least a portion of the feature point location information, display features, and / or primitive combination information of the ground features in the first data cluster. In one embodiment, the symbolization code includes executable code for implementing primitive combination and display. In another embodiment, the symbolization code includes editable input information for triggering the graphic display program.

[0046] The display characteristics of land features refer to the visual representation of land features (such as buildings, roads, rivers, vegetation, etc.), which are expressed by attribute values.

[0047] The primitive combination information refers to the combination of basic primitives (such as points, lines, and polygons) of a ground feature according to certain rules and logic to represent the structure of complex ground features. Further, to record primitive features, the first data includes multiple feature point data, with each feature point represented by location information. These feature points include, for example, the endpoints of line primitives, the outline points of polygon primitives, and multiple inflection points of primitives, etc. At least one of these feature points can be used to locate the entire primitive, primitive group, or entire ground feature, and at least a portion of the feature point data is used as input parameters for symbolic codes.

[0048] Step 120: In response to the GUI instruction, the first data cluster is divided along a certain range line to generate at least one original sub-data cluster, and the association relationship between the original sub-data cluster and the first data cluster is determined.

[0049] In the embodiments of the application, preferably, the range line is generated in response to GUI instructions according to any engineering requirement. For example, the position coordinates of the range line are input through the GUI, or the position information of the manual operation trajectory is captured through the GUI to generate the range line.

[0050] The first data cluster is divided along a certain boundary line to form a new data cluster, which includes at least one original sub-data cluster. The original sub-data cluster refers to the small patch of terrain features represented by the first data cluster in the target area after being divided by the boundary line. The original sub-data cluster inherits the data structure of the first data cluster and can be used to display a portion of the target area's graphics. Information related to the target area, such as location coordinates or the first data cluster identifier, can also be added to the data structure. A parent-child relationship is established between the original sub-data cluster and the first data cluster, forming an association. For example, the first data cluster identifier can be used as data representing the association relationship between the original sub-data cluster and the first data cluster, and recorded in the original sub-data cluster or other intermediate files.

[0051] The boundary line can divide the target area into several parts, and the data structure of each part of the regional feature unit can also include its own outline range.

[0052] For example, a first data cluster D is divided along a certain boundary line to generate at least one sub-data cluster D1 and D2. When generating the original sub-data cluster D2, a feature code for the original sub-data cluster is generated. The attribute values ​​of the original sub-data cluster are inherited from the first data cluster. Feature points within the boundary line are extracted (if there are no feature points on the boundary where the boundary line intersects with the feature, one or more new feature points with the location attribute information of the intersection boundary can be generated in the original sub-data cluster). The symbolic code contains executable code, such as algorithms for how to implement primitive combination and display. It should be noted that when reading a sub-data cluster D2 from the first data cluster, this sub-data cluster D2 is generated for engineering updates, but it is not necessarily required to generate a remaining data cluster D1. The original geographic information database still retains the first data cluster D.

[0053] When performing operations on each part of the first data cluster separately, multiple sub-data clusters need to be generated. For example, such as Figure 2-1 As shown, this is a graphical representation of the parent feature (data is the first data cluster) before the database feature data is downloaded. Items A and B represent two ranges, that is, the first data cluster is divided into two original sub-data clusters, which are then updated by different teams. In the figure, box point 210 and arrow point 220 both represent feature information points (i.e., feature points). The arrow point is a closed point, that is, the point where the first and last points are connected.

[0054] Preferably, step 120 further includes steps 120A and / or 120B after the first data cluster is segmented along a certain range line.

[0055] Step 120A: Generate an intermediate dataset; the intermediate dataset contains data representing the association between the first data cluster and the original sub-data cluster;

[0056] In one embodiment, the first data cluster is cut along the range line, and the cut results are stored in the project file as the base map data of the job, that is, the job dataset contains the original sub-data clusters.

[0057] In one embodiment, the first data cluster, the original sub-data clusters, and the data representing the relationships are stored in a project file. For example, the identifier mapping table representing the relationships is stored in a public field of the DataMapTable in the project file.

[0058] The intermediate dataset contains original sub-data clusters with the same feature coding as the first data cluster. The attribute values ​​of the two are related. For example, the attribute value of the first data cluster of a line feature includes length. The length attribute of the original sub-data cluster can be the length attribute value of the original first data cluster, or it can be the length attribute of the first data cluster minus the length attribute of the remaining part of the first data cluster after being divided by a certain range line.

[0059] The intermediate dataset stores data representing the relationships. The advantage of this is that when the resulting sub-data cluster is stored back instead of the original sub-data cluster, it is easy to determine the first data cluster corresponding to the resulting sub-data cluster based on the relationships.

[0060] In one embodiment, the intermediate dataset may further include the remaining data clusters, and the association between the remaining data clusters and the resulting sub-data clusters is determined by the land cover codes or feature points of the remaining data clusters and the resulting sub-data clusters. Additionally, the intermediate dataset includes data representing the association between the remaining data clusters and the original sub-data clusters.

[0061] The advantage of including the remaining data clusters in the intermediate dataset is that when the resulting sub-data clusters are stored back, it is convenient to combine the resulting sub-data clusters and the remaining sub-data clusters for computation, avoiding the need to temporarily calculate the difference between the first data cluster and the original sub-data clusters.

[0062] Step 120B: Processing of original sub-data cluster split points and modification of symbolic codes.

[0063] It is understandable that line primitives in ground features are divided by dividing lines, forming breakpoints. When there are no feature points at a breakpoint, feature points should be added at the breakpoint in the original sub-data cluster. This is to avoid losing the graphic features between the breakpoint and the nearest feature point during symbolic code execution.

[0064] Preferably, in response to the GUI's range line indication, the feature point closest to the range line indication information is found, and the generated range line passes through the feature point. In this way, the breakpoints in the original sub-data clusters contain the original feature point information, so as to avoid losing the graphical features between the breakpoint and the nearest feature point during symbolic code execution.

[0065] In one embodiment, preferably, when generating the original sub-data clusters, feature point information outside the range lines is removed from the parameters of the symbolic code. It is understood that retaining feature point information outside the range lines in the parameters of the symbolic code for each original sub-data cluster would result in excessively large sub-data clusters; therefore, the symbolic code at least alters the feature point set used as input parameters.

[0066] like Figure 2-2 As shown, the symbol styles of the two project ranges formed by cutting and downloading according to the boundary lines have not yet been updated in production; they are only inherited within their respective ranges. The child features (represented as original sub-data clusters) after the parent feature in the database is segmented and downloaded establish a parent-child relationship and are stored. They still inherit the symbol styles and feature point information of the parent feature. However, to ensure correct symbol style display, data supplementation is also necessary for the child feature features. Therefore, the original sub-data clusters change during the segmentation process. For example, area A of the project adds a square point 210, while area B of the project adds an arrow point 220. Closed points must also exist. This ensures that the node trend order and display graphic style of the child feature features are the same as those of the parent feature features.

[0067] Step 130: Obtain the resulting sub-data clusters.

[0068] Within a project scope, original sub-data clusters are modified through operational testing to determine resulting sub-data clusters. For example, a new data cluster is loaded into memory and modified through production operations to form a resulting sub-data cluster.

[0069] The result sub-data clusters are stored in the result graph data file, and the association between the result sub-data clusters and the first data cluster is established.

[0070] The resulting sub-data clusters can be adapted from the original sub-data clusters. For example, after the target area is divided by the boundary line, the original sub-data clusters are stored in the data file of the operation and revision. As the operation and revision of the land features represented by the original sub-data clusters are carried out, the modified content is put into the original sub-data clusters, and finally the resulting sub-data clusters are formed.

[0071] It should be noted that this application does not concern itself with how to edit the original sub-data clusters to generate the resulting sub-data clusters. The method or apparatus of this application uses the resulting sub-data clusters as a trigger condition, for example, obtaining the resulting sub-data clusters from the project file through a dedicated data interface or in response to a save command. Figure 5 The resulting dataset shown is 550.

[0072] Preferably, step 130 further includes step 130A.

[0073] Step 130A: After obtaining the result sub-data cluster, compare at least one of the land cover codes, attribute values, and symbolic codes of the result sub-data cluster and the original sub-data cluster in the intermediate dataset to determine the association between the first data cluster and the result sub-data cluster.

[0074] The original sub-data cluster is updated to obtain the result sub-data cluster through the result sub-data cluster job dataset. The result sub-data cluster is compared with the original sub-data cluster stored in the intermediate dataset to determine the first data cluster that is associated with the result sub-data cluster.

[0075] Step 140: The resulting sub-data cluster is merged with the remaining data clusters of the first data cluster after removing the original sub-data clusters to obtain the second data cluster, which is then stored back to replace the first data cluster in the database.

[0076] Retrieve features unrelated to the boundary line from the newly added and modified feature sets, update the database directly, and remove the feature from its corresponding feature set. For the remaining features in the database feature set, add them to the pending feature set if they intersect the boundary line. The pending feature set contains multiple result sub-data clusters.

[0077] For example, the resulting sub-data cluster D2' covers a portion of the original sub-data cluster D2. It is added to the feature point difference set of the first data cluster D and the original sub-data cluster D2 to obtain the second data cluster D', which is then stored back to replace the first data cluster in the database.

[0078] Furthermore, during display, the data and graphics of the same geographic feature in the first data cluster and the result sub-data cluster outside the scope of a certain project are merged.

[0079] Before data merging, the original and resulting sub-data clusters can be located and verified using positional relationships, without relying on intermediate datasets. For example, if the positional difference between the resulting and original sub-data clusters is less than a set threshold, then the resulting sub-data cluster is considered to be the original sub-data cluster after the update job.

[0080] For example, determining that features in the first data cluster and the result sub-data cluster belong to the same geographic feature specifically includes the following steps: geographic features in the first data cluster and the result sub-data cluster are classified according to codes;

[0081] The latest data cluster is retrieved from the database. The resulting sub-data clusters and the latest data cluster are then classified according to their element codes to form element sets. It should be noted that the "latest data cluster" refers to the first data cluster before it was split, or before it was modified based on the resulting sub-data clusters. After the first data cluster is modified based on the resulting sub-data clusters, the resulting second data cluster becomes the latest data cluster, and so on. That is, the parent data cluster in the database is updated to the latest data cluster based on different job modifications. It can be understood that when multiple original sub-data clusters split from the first data cluster each generate resulting sub-data clusters, they can be successively merged and stored back into the original database.

[0082] The classification results are compared to determine if the elements belong to the same land cover; the comparison parameters include spatial and attribute information. Spatial and attribute comparisons are performed on the classification results, and then inconsistent elements are physically joined along the project scope.

[0083] Based on the set of production output elements, the system searches the element set for similar elements at the same spatial location. If similar elements are found, they are compared according to spatial point arrays, information, and attribute information. Otherwise, the system moves on to the next element. An initial threshold can be set for the location. This initial threshold, for example, determines the spacing between elements that can be automatically joined. Specifically, if it's a line, it determines the distance between the endpoints of the line to identify them as the same element. Once identified as the same element, it further checks if the attributes are the same. If all attributes are the same, automatic joining and merging are possible.

[0084] Preferably, in step 140, after determining the association between the first data cluster / remaining data cluster and the resulting sub-data cluster, it is necessary to determine whether the resulting sub-data cluster has changed compared to the original sub-data cluster.

[0085] In the optimized embodiment of this application, in response to a change in the resulting sub-data cluster compared to the original sub-data cluster, step 140 is performed to merge and store the data; otherwise, the geographic feature data is not merged and stored. The difference between the original sub-data cluster and the resulting sub-data cluster can be determined based on changes in the data content or timestamp.

[0086] In one embodiment, it is determined whether the resulting sub-data cluster and the original sub-data cluster have changed, and the data content of the original sub-data cluster and the resulting sub-data cluster are compared to see if they are the same.

[0087] The data content includes feature codes, attribute values, or symbolic codes. Typically, the feature codes of the original subdata cluster and the resulting subdata cluster remain unchanged, but there are cases where feature codes may change due to operational changes. Therefore, if the feature codes change, it is considered that both the resulting subdata cluster and the original subdata cluster have changed.

[0088] For example, comparing two elements based on feature points, spatial information, and attribute information:

[0089] If the spatial information and attribute information are consistent, the feature is considered unchanged and will not be updated.

[0090] If the spatial information is consistent but the attribute information is different, then the feature (result sub-data cluster) is added to the modified feature set, the corresponding parent feature (first data cluster) is added to the modified feature set, and the modified feature is retrieved from the database feature set or intermediate dataset. If the spatial information is inconsistent, then the feature is added to the parent feature corresponding to the newly added feature set.

[0091] For example, such as Figure 2-3 As shown, Figure 2-3 This refers to a completely new data cluster formed after the right side of the updated production area has changed significantly, replacing the original data cluster. After downloading and updating the sub-feature elements, the related elements are identified through parent-child relationships. By comparison, it is found that the sub-feature elements in project A remain unchanged, while those in project B have changed. Therefore, the two sub-feature elements need to be merged to replace the parent element. If they are determined to be related elements, they are automatically joined to form a new element (i.e., the second data cluster) that replaces the original parent element. This new cluster still inherits the feature points of the sub-feature elements and maintains the main rules of symbolic effects.

[0092] For example, a change in a feature includes changes in spatial information (a) and attribute information (b). The main ways to change a feature are: unchanged, modified, added, and deleted. When downloading the segmented data, the parent-child relationship is recorded. The type of change needs to be determined by comparison. For example, if a feature element in the updated result does not have a relationship, it is defined as added and can be directly updated to the original database. Another example is that if a feature element is deleted or modified and a relationship is found, the spatial information is compared first. If the spatial information is consistent, it is defined as modified. Since modification can also be understood as deleting the original information and adding a new one, any change in spatial information is treated as deletion or addition. The two (based on a and b) can be combined into four types of changes: ① both a and b change, ② neither a nor b change, ③ a changes but b does not change, ④ b changes but a does not change. Among them, ② does not require updating and keeps the database elements unchanged. That is, the parent feature element before the segmentation does not need to be updated and only needs to be maintained.

[0093] ④ If the spatial information remains unchanged but the attribute information has changed, it can be directly defined as the modified feature. That is, the actual location and range of the feature have not changed, only one attribute information has changed, such as the name field. Extract the parent feature feature from the database, modify this attribute directly and then replace it back.

[0094] Both ① and ③ involve changes in spatial information. Therefore, the updated elements (represented as the second data cluster) are treated as newly added elements, thus finding the relationship. This leads to the identification of the parent element (represented as the first data cluster) of the changed sub-feature element. The parent element is deleted, and the corresponding newly added feature element replaces the parent element as a new feature element to achieve the update.

[0095] In one embodiment, it is identified whether the timestamps in the resulting sub-data clusters have changed. Since modifying a data cluster generates feature attribute values ​​or primitive attribute values ​​containing creation and update times, changes in the timestamps can indicate whether the resulting sub-data cluster has changed compared to the original sub-data cluster.

[0096] It should be noted that after generating the intermediate dataset, comparison is performed by reading the intermediate dataset, which avoids reading information from the database for comparison, thus improving data security. This intermediate dataset can be cached data or stored project files.

[0097] like Figure 2-4 As shown, after being identified as a related element, it automatically "connects" to form a new element that replaces the original parent feature element. It still inherits the feature points of the child feature element and maintains the main rules of symbol effect unchanged.

[0098] Data edge matching is generally divided into geometric edge matching (solving the problem of geometric gaps, which refers to the inability to precisely connect two parts of a geographic feature separated by the data file boundary), logical edge matching (solving the problem of logical gaps, which refers to the same geographic feature having different codes or different attribute information, such as the width of a road, the elevation of contour lines, etc.), and physical edge matching (achieving physical seamlessness, realizing that various geographic elements are not only geometrically and logically seamless, but also truly merged into the same geographic element with the same attributes).

[0099] Furthermore, in step 140, the method for merging the graphics of the same geographic feature in the remaining data clusters and result sub-data clusters outside the scope of a certain project includes any of the following:

[0100] Step 140A: Perform geometric edge joining along the range line.

[0101] The feature point set of the resulting sub-data cluster is combined with the feature point set of the remaining data cluster. The position information of the feature points generated by the segmentation of a certain range in the feature point set of the resulting sub-data cluster is adjusted so that it coincides with the position information of the feature points generated by the segmentation of a certain range in the remaining data cluster, thus forming the feature point set of the second data cluster.

[0102] The original sub-data clusters are updated into result sub-data clusters after the job, which may cause misalignment in display with the remaining data clusters.

[0103] The misalignment can be caused by, for example, when the first data cluster D is decomposed into the remaining data cluster D1 and the original sub-data cluster D2, a feature point must be generated at the boundary point. The positional attributes of the feature point may change when the original sub-data cluster is edited.

[0104] Therefore, the feature point set of the resulting sub-data cluster D2' needs to be geometrically joined with the feature point set of the remaining data clusters to form the feature point set of the second data cluster D'.

[0105] The physical edge connection requires merging the breakpoints of the first data cluster divided by the range line. There are two cases for breakpoint merging: either there are feature points at the original breakpoints, or there are no feature points at the original breakpoints.

[0106] If there are no feature points at the breakpoints where the first data cluster is divided by the range line, and the original sub-data clusters generate feature points at the breakpoints, then during the merging process, the feature points at the breakpoints of the second data clusters will be deleted.

[0107] The first data cluster has a feature point at the breakpoint where it is divided by the range line. In the second data cluster, the feature point of the resulting sub-data cluster and the feature point of the first data cluster at the breakpoint are merged into a single feature point.

[0108] After feature point segmentation at the breakpoint, one feature point representing data becomes two feature points representing data. That is, the feature point in the original sub-data cluster and the same feature point in the first data cluster have the same position. However, due to the assignment, the data of the two feature points are different. Therefore, it is necessary to merge the two feature points into one feature point to make their attributes the same. This can be done by taking only one feature point (e.g., the point in the resulting sub-data cluster) as the merged feature point, or by averaging the positions of the two feature points to merge them into one point.

[0109] Step 140B: Perform attribute edge joining along the range line.

[0110] The attribute connections include logical connections and physical connections.

[0111] The logical connection refers to the process where, during the editing of the resulting sub-data clusters, different feature codes are formed, and during merging, the feature codes of the resulting sub-data clusters are set to be the same as those of the first data cluster.

[0112] The logical edge joining, for example, in response to a situation where the graphic positions on both sides of a boundary line do not correspond but belong to the same feature, physically joins the elements. The common attribute information of the resulting sub-data cluster and the remaining data clusters are set to be the same, and at least some of the element attribute expression data are set to be the same.

[0113] In this way, the mosaic image of the feature in the first data cluster and the result sub-data cluster is replaced with the complete image of the feature.

[0114] Figure 3 The symbol reshaping flowchart provided in this application embodiment further includes steps 250-260, based on steps 110-140 (i.e. steps 210-240 in the figure), to generate symbolic code for a second data cluster to replace the symbolic code for the first data cluster.

[0115] Step 250: Combine the remaining data clusters with at least a portion of the feature point set of the resulting sub-data clusters and input the symbolic code into the dataset.

[0116] Land cover types can be determined through land cover coding, and each land cover has a corresponding attribute value. The symbolization algorithm merges the feature point sets of the remaining data clusters and the resulting sub-data clusters, and determines the location features, display features, and / or primitive combinations of the merged feature point set, thereby generating the symbolization code for the second data cluster. The merged feature point set contains all or at least a portion of the feature points of the remaining data clusters and the resulting sub-data clusters before merging. These feature points are the feature points that function in generating the land cover graphics and contain the data used for the symbolization code.

[0117] These feature points generally include the beginning and end points of ground features, i.e., the closing points of the maximum range outline; as well as the closing points of the inner rings of a torus formed by empty islands; if there are multiple empty islands, there are multiple closing points of the inner rings; they also include feature point identification information that plays a special role in the symbolization effect, such as hidden points, turning points, and virtual-real boundary points. Using its own symbolization algorithm, the original feature points are read and inherited, and the symbol information is re-displayed in combination with the new coordinate data cluster. For individual re-displayed areas that differ greatly from the original image, dynamic adjustments are made within a certain threshold so that the overlapping part with the original symbol is maximized to be close to the original value without adjusting the range.

[0118] It should also be noted that different types of land features have different graphic representations, therefore different symbolization algorithms can be determined based on the land feature coding. Furthermore, the symbolization algorithm for each type of land feature is pre-defined, capable of identifying all feature points constituting the land feature elements, selecting attribute data that contributes to the construction of the land feature graphic symbol, selecting or generating the primitives or combinations of primitives constituting the land feature graphic symbol, and determining the display characteristics of the primitives constituting the land feature graphic symbol, such as color, line type, hidden surface removal, and location distribution.

[0119] Step 260: Determine the location features, display features, and / or primitive combinations of the merged feature point set, and generate the symbolic code for the second data cluster.

[0120] In step 260, after physically connecting the newly added set of ground features and the set of ground features to be determined along the boundary line, the symbol effects of the successfully merged ground features are restored:

[0121] For example, after merging geographic feature data, the symbolic information merging process includes the following steps:

[0122] Step 260A: Obtain symbolic information of ground features in the result sub-data cluster.

[0123] The symbolization information includes the symbolization code, the input dataset that triggers the code, and the truth value of the symbolization effect.

[0124] Obtain the associated features corresponding to the adjacent features, and the symbolization effect of the same spatial location, and use it as the truth value of the symbolization effect; the associated features can be two sub-features that have a mutual connection relationship, that is, sub-features on both sides of the range line, where one sub-feature is an associated feature of the other sub-feature, thus determining the resulting sub-data cluster and the first data cluster and / or the remaining data cluster associated with it.

[0125] The symbolic effect truth value represents the output value of primitive combination, display features, and location features. The symbolic effect of the child feature element ultimately replaces the parent feature element. Since the symbolic effect of the parent feature element has changed and can no longer be used, it needs to be replaced by a merged child feature element. Therefore, the symbolic effect of the child feature element can replace the effect of the parent feature element through the symbolic effect truth value.

[0126] Step 260B: Fuse the symbolic information to form the true value of the symbolic effect of the second data cluster.

[0127] Obtain symbolic information of the portion of the geographic features outside the scope of the retained project in the database (i.e., the remaining data clusters), and merge it with the symbolic information of the geographic features in the resulting sub-data clusters.

[0128] For example, restoring the closure points of features after edge joining based on spatial location. Another example is restoring island information and merging islands. This island information applies to planar features, specifically holes within planar features. A planar feature can be drawn with two closed loops using continuous lines. If a boundary line cuts a closed loop in half, then island information must be restored during merging. Another example is restoring the framework information of features based on spatial location, dynamically adjusting based on a threshold, and selecting the symbol effect closest to the true value while preserving the feature framework information of its nodes; another example is restoring information such as hidden line removal, dashed lines, and features of features; and handling special node symbol intersection effects, etc., which will not be listed here.

[0129] The software layer correctly displays information about geographic features according to map symbols and topographic map output requirements. This includes information such as closure information, point sequence, frame information, feature characteristics, hidden line removal information, and dashed line information. Furthermore, adjustments can be made based on given thresholds to create new symbol effects, and the true value of these effects is scored. The optimal symbolization scheme is obtained, and the symbolization information for that feature is reset.

[0130] It should be noted that the above configuration process is automatically completed through a symbolization algorithm. For each type of feature, based on its characteristic point description, a symbolization algorithm specifically designed for that type of feature is used to select and process the feature points and primitives in the feature feature data cluster, generating a display graphic for that type of feature. Combining the symbolization information from the first data cluster, the resulting sub-data cluster, and / or the second data cluster library, the correct display information for the feature feature symbols is determined. Preferably, this application may choose to use one set of symbolization codes to process the graphics of the second data cluster, instead of using two sets of symbolization codes to process the graphics of the resulting data cluster and the remaining data cluster separately. Further, this application may choose to use the primitive attributes related to display effects in the symbolization code of the resulting data cluster to replace the primitive attributes related to display effects in the symbolization code of the first data cluster, generating the symbolization code for the second data cluster. Then, the relevant display attribute information is adjusted according to a set threshold to determine the new symbol effect.

[0131] In surveying and mapping production, the production method still often separates graphics from databases, resulting in low work efficiency and an inability to obtain sufficiently up-to-date data within budget constraints. However, the actual annual changes in a city are limited, especially in highly urbanized areas. Therefore, the method and apparatus of this application fully consider the actual production situation and integrate data download, update into the database, automatic edge matching, and symbolic graphics into a single process. This enables a city to be updated in real time according to its specific update range, such as by completed areas. This truly reduces unnecessary production input while ensuring that the data in the region is sufficiently up-to-date, giving the data higher use value to meet other application needs.

[0132] Figure 4 This is a structural diagram of a geographic information data entry and exit processing device according to an embodiment of this application, used to implement the geographic information data entry and exit processing method described in any embodiment of this application, comprising:

[0133] The acquisition module 410 is used to acquire the first data cluster of the target feature in the database; it is also used to acquire the result sub-data cluster.

[0134] The generation module 420 is used to respond to instructions from the GUI to divide the first data cluster along a certain range line to generate at least one original sub-data cluster.

[0135] The determination module 430 is used to determine the association relationship between the original sub-data cluster and the first data cluster.

[0136] The merging module 440 is used to merge the resulting sub-data cluster with the remaining data cluster of the first data cluster after removing the original sub-data cluster, to obtain the second data cluster and store it back to replace the first data cluster in the database.

[0137] In one embodiment, the acquisition module further includes a first acquisition unit for acquiring a first data cluster of the target feature in the database.

[0138] In one embodiment, the generation module further includes a first generation unit for dividing the first data cluster along a certain range line in response to instructions from the GUI to generate at least one original sub-data cluster.

[0139] In one embodiment, the acquisition module further includes a second acquisition unit for acquiring result sub-data clusters.

[0140] In one embodiment, the determining module further includes a first determining unit for determining the association between the original sub-data cluster and the first data cluster.

[0141] In one embodiment, the generation module further includes a second generation unit for merging the resulting sub-data cluster overlay with the remaining data cluster of the first data cluster after removing the original sub-data cluster, to obtain a second data cluster and store it back to replace the first data cluster in the database.

[0142] In one embodiment, the generation module further includes a third generation unit for generating an intermediate dataset.

[0143] Specifically, it is used to implement any of the functions in steps 120-140 to determine the intermediate dataset.

[0144] In one embodiment, the generation module further includes a fourth generation unit for merging the resulting sub-data cluster with the remaining data cluster, including any of the following:

[0145] There are no feature points at the breakpoints where the first data cluster is divided by the range line. Feature points are generated at the breakpoints in the original sub-data clusters. The feature points at the breakpoints in the second data cluster are deleted.

[0146] The first data cluster has feature points at the breakpoints where it is divided by the range line. In the second data cluster, the feature point attributes of the resulting sub-data cluster and the first data cluster at the breakpoints are merged.

[0147] Specifically, it is used to implement any function in step 140A.

[0148] In one embodiment, the generation module further includes a fifth generation unit that sets the feature code of the resulting sub-data cluster to be the same as the feature code of the first data cluster.

[0149] Set the common attribute information of the resulting sub-data cluster and the remaining data cluster to be the same, and set the primitive attribute expression to be the same.

[0150] Specifically, it is used to implement any of the functions in step 140B.

[0151] In one embodiment, the generation module further includes a sixth generation unit for merging the remaining data cluster with all feature points of the resulting sub-data cluster.

[0152] The determining module further includes a second determining unit, used to determine the positional features, display features, and / or primitive combinations of the merged feature point set, and generate symbolic codes for the second data cluster.

[0153] Specifically, it is used to implement any of the functions in steps 250 to 260.

[0154] In one embodiment, the determining module further includes a third determining unit, used to determine the association between the remaining data cluster and the resulting sub-data cluster by comparing the land cover codes or feature points of the remaining data cluster and the resulting sub-data cluster when the intermediate dataset still contains the remaining data cluster.

[0155] Specifically, it is used to implement the function of determining in steps 120-140 that the intermediate dataset still contains any of the remaining data clusters.

[0156] In one embodiment, the determining module further includes a fourth determining unit, used to compare the data content or timestamps of the original sub-data cluster and the resulting sub-data cluster; and determine that the data content has changed, or the timestamp has changed, and the original sub-data cluster and the resulting sub-data cluster are different.

[0157] Specifically, it is used to implement the function of determining whether the resulting sub-data cluster and the original sub-data cluster have changed in steps 120 to 140.

[0158] The determining module further includes a fifth determining unit, used to determine the merging method, the merging method including physical edge joining along the range line; replacing the spliced ​​image of the feature in the first data cluster and the result sub-data cluster with the complete graphic of the feature.

[0159] Specifically, it is used to implement any function of the same feature graphic merging processing method in step 140B.

[0160] In one embodiment, the determining module further includes a sixth determining unit, used for classifying the geographic features of the first data cluster and the resulting sub-data cluster according to codes. It is also used to compare the spatial and attribute parameters of the two.

[0161] Specifically, it is used to achieve the function of determining in step 140 that any feature in the first data cluster and the result sub-data cluster belongs to the same land cover.

[0162] Furthermore, the fifth and sixth determining units are also used to compare the spatial and attribute parameters of the geographic elements of the first data cluster and the resulting sub-data cluster to determine the physical edge-joining method. Specifically, they are used to implement any function of the physical edge-joining method.

[0163] In one embodiment, the determining module further includes a seventh determining unit for determining the second data cluster; the generating module further includes a seventh generating unit for merging the first data cluster and the resulting sub-data cluster. Specifically, this is used to implement any function of comparing two elements according to spatial point sequences and information and attribute information.

[0164] In one embodiment, the generation module further includes an eighth generation unit for merging symbolic information. Specifically, it is used to implement any of the functions described in step 260.

[0165] In one embodiment, a geographic information database 450 is also included, which is used to store ground feature data before and after the update in the target area.

[0166] Figure 5 This is a schematic diagram of a commercial server system for the application scenario of this application. This application also proposes a geographic information data processing system for implementing the method described in any embodiment of this application, including: a geographic information database 510, a data processor 520, a GUI operation interface 530, an application terminal 540, and at least one result map dataset 550.

[0167] The geographic information database is used to store target geographic information data.

[0168] The GUI operation interface and / or application terminal are used to input setting conditions and trigger the data processor to run the acquisition module, determination module, generation module, or merging module. The data processor responds to GUI instructions to acquire target area project scope attribute data and determines the scope line through the project scope attribute data. The geographic information database is used to store relevant data of the target features, including a first data cluster and a second data cluster.

[0169] It should be understood that the specific related content listed above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0170] The specific methods for implementing the functions of the above-mentioned modules are as described in the various method embodiments of this application, and will not be repeated here.

[0171] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. For example, in Figure 5 The application system shown loads the computer program product of the method of this application. The computer program product includes a computer program or instructions that, when executed by a processor, implement the method as described in any embodiment of this application.

[0172] Therefore, this application also proposes a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any embodiment of this application.

[0173] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0176] Furthermore, this application also proposes an electronic device (or computing device) including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any embodiment of this application.

[0177] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media. Computer-readable media includes both permanent and non-persistent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by the computing device. As defined in this article, computer-readable media do not include transient media, such as modulated data signals and carrier waves.

[0178] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0179] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for processing geographic information data in and out of a database, characterized in that, Includes the following steps: Retrieve the first data cluster of the target feature in the database; In response to GUI instructions, the first data cluster is divided along a certain range line to generate at least one original sub-data cluster, and the association relationship between the original sub-data cluster and the first data cluster is determined. Obtain the resulting sub-data clusters; The resulting sub-data cluster is merged with the remaining data clusters of the first data cluster after removing the original sub-data clusters to obtain the second data cluster, which is then stored back to replace the first data cluster in the database. The resulting sub-data cluster is merged with the remaining data cluster, including any of the following cases: There are no feature points at the breakpoints where the first data cluster is divided by the range line. Feature points are generated at the breakpoints in the original sub-data clusters. The feature points at the breakpoints in the second data cluster are deleted. The first data cluster has feature points at the breakpoints where it is divided by the range line. In the second data cluster, the feature point attributes of the resulting sub-data cluster and the first data cluster at the breakpoints are merged.

2. The geographic information data entry and exit processing method according to claim 1, characterized in that, It also includes at least one of the following: Set the feature code of the resulting sub-data cluster to be the same as the feature code of the first data cluster; set at least a portion of the attributes of the features in the resulting sub-data cluster to be the same as those in the remaining data clusters.

3. The geographic information data entry and exit processing method according to claim 1, characterized in that, It also includes the following steps: The remaining data clusters are merged with the feature point sets of the resulting sub-data clusters; the positional features, display features, or primitive combinations of the merged feature point sets are determined to generate the symbolic code of the second data cluster.

4. The geographic information data entry and exit processing method according to claim 1, characterized in that, By comparing the data content or timestamps of the original sub-data cluster and the resulting sub-data cluster, it is determined that the data content or timestamps have changed.

5. The geographic information data entry and exit processing method according to any one of claims 1 to 4, characterized in that, After the first data cluster is divided along a certain range line, and before the second data cluster is stored back, the following steps are also included: An intermediate dataset is generated; the intermediate dataset contains data representing the association between the first data cluster and the original sub-data cluster.

6. The geographic information data entry and exit processing method according to any one of claims 1 to 4, characterized in that, It also includes the steps of: generating an intermediate dataset; the intermediate dataset containing the remaining data clusters, and data representing the association between the remaining data clusters and the original sub-data clusters.

7. The geographic information data entry and exit processing method according to any one of claims 1 to 4, characterized in that, After obtaining the result sub-data cluster, at least one of the land cover codes, attribute values, and symbolic codes of the result sub-data cluster and the original sub-data cluster in the intermediate dataset is compared to determine the association between the first data cluster and the result sub-data cluster.

8. A geographic information data entry and exit processing device, characterized in that, The method for processing geographic information data entry and exit as described in any one of claims 1-7 includes: The acquisition module is used to acquire the first data cluster of the target feature in the database; it is also used to acquire the result sub-data cluster. A generation module is used to respond to instructions from the GUI to divide the first data cluster along a certain range line and generate at least one original sub-data cluster; A determining module is used to determine the association between the original sub-data cluster and the first data cluster; The merging module is used to merge the resulting sub-data cluster with the remaining data cluster of the first data cluster after removing the original sub-data cluster, to obtain the second data cluster and store it back to replace the first data cluster in the database.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

Images