A Method for Intelligent Edge Merging of Fragmented Areas in Two Maps Based on the Land Survey Cloud Platform

Abstract

This application discloses a method for intelligent edge-fitting and merging of fragmented areas on a land survey cloud platform, relating to the field of geographic information science and technology. The method includes: acquiring multiple operational patches and multiple reference patches from the land survey cloud platform; performing overlay analysis on each operational patch and each reference patch to obtain overlay analysis results and fragmented areas to be processed; determining a target merging object from the surrounding patches of the fragmented area to be processed based on the correlation information between the fragmented area to be processed and the surrounding patches; and merging the fragmented area to be processed and the target merging object according to the overlay analysis results to obtain the target patch. This method solves the technical problem of existing technologies being unable to achieve accurate merging of fragmented areas on the land survey cloud platform while ensuring topological and ownership consistency due to the lack of benchmark reference data and compliance constraints.

Description

Technical Field

[0001] This application relates to the field of geographic information science and technology, and in particular to a method for intelligent edge merging of dual map patch fragments based on a land survey cloud platform. Background Technology

[0002] As geospatial data applications become more refined and routine, multi-source data fusion and continuous updates have become commonplace. This places demands on the automation, high precision, and strong compliance requirements for processing fragmented patches within the massive map data of the land survey cloud platform. Especially in fields such as land surveys and urban planning, it is necessary not only to efficiently eliminate redundant fragmented areas caused by manual editing or multi-source overlay, but also to ensure that the merged results strictly meet topological consistency and ownership compliance, while fully preserving processing traces for traceability.

[0003] Currently, existing technologies mainly rely on manual merging, automatic merging based on a single area threshold, or single-patch input matching. Manual merging is extremely inefficient, highly subjective, and difficult to guarantee the consistency of batch data; automatic merging based on threshold screening uses only area as the criterion, resulting in coarse merging boundaries, high topological anomaly rates, and a lack of compliance checks on attributes such as ownership, which can easily lead to logical chaos in the merged data; single-patch input matching lacks benchmark reference data, making it difficult to accurately determine the optimal merging object, and it has poor accuracy in edge-fitting processing of irregular patches and insufficient traceability.

[0004] Therefore, existing technologies cannot fully utilize benchmark reference data to achieve accurate, compliant, and traceable intelligent merging of fragmented patches while ensuring that the merging results strictly comply with topological rules and ownership constraints. In other words, there is a technical problem of how to make optimal merging decisions for fragmented patches under compliance constraints under the collaboration of multi-source data.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a method for intelligent edge-fitting and merging of fragmented areas based on a land survey cloud platform, which aims to solve the technical problem of how to make optimal merging decisions for fragmented areas under compliance constraints under the collaboration of multi-source data.

[0007] To achieve the above objectives, this application proposes a method for intelligent edge-fitting and merging of fragmented surfaces on dual maps based on a land survey cloud platform. The method includes: Multiple operational and reference map features were obtained from the land survey cloud platform; Based on the superposition analysis of each of the described work patches and each of the described reference patches, the superposition analysis results and the fragmented surfaces to be processed are obtained; Based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed, the target merging object is determined from the surrounding patches of the fragment to be processed; Based on the overlay analysis results, the fragmented surface to be processed and the target merged object are merged to obtain the target patch.

[0008] In one embodiment, the overlay analysis results include intersection regions, difference regions, and boundary association information; The method of performing overlay analysis based on each of the described operational patches and each of the described reference patches to obtain overlay analysis results and fragmented surfaces to be processed includes: Based on the overlay analysis of each of the described operational map pieces and each of the described reference map pieces, the intersection region, the difference region, and the boundary association information are obtained. Based on the intersection region, the difference region, and the boundary association information, multiple regions to be filtered are obtained; Based on preset area conditions and preset shape complexity conditions, each of the regions to be screened is screened to obtain candidate regions; Based on the overlay analysis results, the candidate regions are subjected to correlation screening to obtain the fragmented surfaces to be processed.

[0009] In one embodiment, determining the target merging object from the surrounding patches of the fragment to be processed based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed includes: Multiple reference patches located within a preset radius range of the fragmented surface to be processed are used as corresponding peripheral patches; The correlation information between the fragmented surface to be processed and each of the surrounding patches is quantitatively analyzed, wherein the correlation information includes spatial adjacency, common edge features, ownership consistency, attribute similarity and topological compatibility; Each of the surrounding map features is scored based on the correlation information to obtain a comprehensive score for each of the surrounding map features; Based on the comprehensive score, the target merging object is determined from each of the surrounding patches.

[0010] In one embodiment, the step of merging the fragmented surface to be processed with the target merging object based on the overlay analysis result to obtain the target patch includes: Based on the overlay analysis results, the boundary association information of the fragmented surface to be processed and the target merging object is obtained; The boundary nodes of the fragmented surface to be processed are matched with the boundary nodes of the target merging object based on the boundary association information to obtain the boundary nodes to be merged. The target patch is obtained by fitting the boundary between the fragmented surface to be processed and the target merging object based on the boundary nodes to be merged.

[0011] In one embodiment, after merging the fragmented surface to be processed with the target merged object based on the overlay analysis result to obtain the target patch, the method further includes: Obtain the original fragment source layer name, original fragment identifier, preset area condition, preset shape complexity condition, and processing mark status corresponding to the fragment to be processed; Based on the original fragmented surface source layer name, the original fragmented surface identifier, the preset area condition, the preset morphological complexity condition, and the processing mark status, a traceability field group for the target patch is established.

[0012] In one embodiment, after merging the fragmented surface to be processed with the target merged object based on the overlay analysis result to obtain the target patch, the method further includes: Obtain the merged patch data within the preset spatial range; The image data is divided into multiple regions to be detected; A full topology verification was performed on the region to be detected to obtain the verification results; When the verification result indicates that there is a topological error in the area to be detected, the error information is determined, and a merging strategy is determined based on the error information; The unprocessed fragments of the region to be detected are merged according to the merging strategy to obtain the target detection region; The target detection area is taken as the area to be detected, and the process of performing full topology verification on the area to be detected and obtaining the verification result is repeated until the verification result shows that the area to be detected has no topology errors. Then, the area to be detected is determined to meet the spatial data topology integrity requirements.

[0013] In one embodiment, determining a merging strategy based on the error information includes: Based on the error information, a topology error distribution map is generated, wherein the error information includes the error type, error location, and error impact range; Based on the topological error distribution map, the relationship list between the unprocessed fragments in the area to be detected and the unprocessed fragments is traced to obtain the cause of the error; Based on the causes of the errors, determine the merging strategy.

[0014] Furthermore, to achieve the above objectives, this application also proposes a dual-map patch / fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform. The dual-map patch / fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform includes: The input module is used to obtain multiple operational map features and multiple reference map features from the land survey cloud platform; The processing module is used to perform overlay analysis based on each of the operation patches and each of the reference patches to obtain the overlay analysis results and the fragmented surfaces to be processed; The filtering module is used to determine the target merging object from the surrounding patches of the fragment to be processed based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed. The output module is used to merge the fragmented surface to be processed with the target merged object according to the overlay analysis results to obtain the target patch.

[0015] Furthermore, to achieve the above objectives, this application also proposes a dual-map patch fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the dual-map patch fragmentation intelligent edge-fitting and merging processing method based on a land survey cloud platform as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the intelligent edge-fitting and merging processing method for dual map patch fragments based on the land survey cloud platform described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent edge-fitting and merging processing method for dual-map patch fragmentation based on the land survey cloud platform described above.

[0018] This application obtains multiple operational and reference map features from the land survey cloud platform; performs overlay analysis on each operational and reference map feature to obtain overlay analysis results and fragmented surfaces to be processed; determines a target merging object from the surrounding map features of the fragmented surface to be processed based on the correlation information between the fragmented surface to be processed and its surrounding map features; and merges the fragmented surface to be processed and the target merging object according to the overlay analysis results to obtain the target map feature. This solves the technical problem of existing technologies being unable to achieve accurate fragmented surface merging while ensuring topological and ownership consistency due to the lack of benchmark reference data and compliance constraints. It achieves seamless connection of the merged map feature boundaries and ensures the topological correctness and ownership compliance of the merged result. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform in this application. Figure 2 This is a comparison diagram of the processing effect provided in Embodiment 1 of the intelligent edge-fitting and merging processing method for dual map patch fragments based on the land survey cloud platform of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform in this application; Figure 4 This is a schematic diagram of the overall process of the second embodiment of the intelligent edge-fitting and merging method for dual map patch fragments based on the land survey cloud platform in this application; Figure 5 This is a schematic diagram of the module structure of the intelligent edge-fitting and merging processing device for dual-map fragmented surfaces based on the land survey cloud platform according to an embodiment of this application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the intelligent edge-fitting and merging processing method for dual-map fragmented surfaces based on the land survey cloud platform in this application embodiment.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is as follows: obtain multiple operational map patches and multiple reference map patches from the land survey cloud platform; perform overlay analysis based on each operational map patch and each reference map patch to obtain overlay analysis results and fragmented surfaces to be processed; determine the target merging object from the surrounding map patches of the fragmented surface to be processed according to the correlation information between the fragmented surface to be processed and the surrounding map patches of the fragmented surface to be processed; merge the fragmented surface to be processed and the target merging object according to the overlay analysis results to obtain the target map patch.

[0026] In this embodiment, for ease of description, the following description will focus on the intelligent edge-fitting and merging system for dual-map patch fragmentation based on the land survey cloud platform.

[0027] Existing technologies primarily rely on manual merging, automatic merging based on a single area threshold, or single-patch input matching. Manual merging is extremely inefficient, highly subjective, and struggles to guarantee consistency in batch data. Automatic merging based on threshold selection uses only area as the criterion, resulting in coarse merging boundaries, high topological anomalies, and a lack of compliance checks on attributes such as ownership, easily leading to logical inconsistencies in the merged data. Single-patch input matching lacks benchmark reference data, making it difficult to accurately determine the optimal merging object, and it suffers from poor edge-fitting accuracy and insufficient traceability for irregular patches.

[0028] This application provides a solution that accurately identifies the fragmented surfaces to be processed and their relationships with surrounding surfaces by simultaneously importing and overlaying the work samples and reference samples. It automatically determines the optimal target for merging from the surrounding samples and then performs boundary fitting and topology reconstruction based on the overlay analysis results. This solves the technical problem of existing technologies failing to achieve accurate fragmented surface merging while ensuring topological and ownership consistency due to the lack of benchmark reference data and compliance constraints. Compared with existing technologies, this solution achieves seamless boundary connection of merged samples and ensures the topological correctness and ownership compliance of the merged results.

[0029] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as the intelligent edge-fitting and merging processing system for dual-map patch fragmentation based on the land survey cloud platform. The following description uses the intelligent edge-fitting and merging processing system for dual-map patch fragmentation based on the land survey cloud platform as an example to illustrate this embodiment and the subsequent embodiments.

[0030] Based on this, the embodiments of this application provide a method for intelligent edge-fitting and merging of fragmented surfaces in dual-map data based on a land survey cloud platform, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform of this application.

[0031] In this embodiment, the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform includes steps S10~S40: Step S10: Obtain multiple operational map patches and multiple reference map patches from the land survey cloud platform; It should be noted that the National Land Survey Cloud Platform refers to a geospatial data service platform for national land survey and monitoring applications. This platform integrates multi-source and multi-temporal national land survey results data, providing online services such as data storage, query, acquisition, and analysis. It is the data infrastructure supporting natural resource survey and monitoring work. The operational patch refers to the patch in the target data layer to be processed, which usually includes fragmented patches generated by manual editing or multi-source overlay. The reference patch refers to the patch in the benchmark data layer with complete topological structure and standardized attribute information, providing a benchmark reference for fragmented patch merging.

[0032] Understandably, acquiring multiple operational and reference map features simultaneously from the land survey cloud platform can avoid problems such as lack of standards for boundary correction, lack of basis for selecting merged objects, and non-compliance of merged data with specifications due to the absence of benchmark references, thereby improving the accuracy of fragmented area merging processing and the standardization and compliance of the resulting data.

[0033] Step S20: Perform overlay analysis based on each of the operation patches and each of the reference patches to obtain the overlay analysis results and the fragmented surfaces to be processed; It should be noted that the overlay analysis results include intersection regions, difference regions, and boundary association information. The intersection region refers to the part of the working map patch that overlaps with the reference map patch in space. The difference region refers to the part of the two that do not overlap in space. The boundary association information refers to the spatial relationship between the two boundaries. The fragmented surfaces to be processed refer to the fragmented map patches that need to be merged identified from the overlay analysis results.

[0034] Understandably, traditional methods rely solely on a single area threshold to screen fragmented surfaces without considering the baseline information of reference patches for spatial relationship determination, resulting in insufficient accuracy in fragmented surface identification and a tendency to misscreen or miss. Therefore, step S20 avoids the inaccuracy of fragmented surface identification caused by relying solely on the area threshold, thereby improving the accuracy of fragmented surface screening.

[0035] In one feasible implementation, step S20 may include: performing overlay analysis based on each of the operational patches and each of the reference patches to obtain intersection regions, difference regions, and boundary association information; obtaining multiple regions to be screened based on the intersection regions, the difference regions, and the boundary association information; screening each region to be screened based on preset area conditions and preset morphological complexity conditions to obtain candidate regions; and performing correlation screening on the candidate regions based on the overlay analysis results to obtain the fragmented surfaces to be processed.

[0036] It should be noted that the areas to be screened are regions that may be fragmented based on the results of overlay analysis. The preset area condition refers to the area threshold parameter set in advance to determine the fragmented surface. The preset morphological complexity condition refers to the threshold parameter set in advance to determine whether the fragmented surface morphology is too narrow or irregular. The candidate areas are the areas retained after double screening of area and morphology. The fragmented surfaces to be processed are the fragmented surface patches that are finally confirmed to need to be merged after correlation screening.

[0037] Specifically, spatial overlay operations are performed on the working patch and the reference patch to obtain the intersection region, difference region, and boundary association information in their spatial relationship. Then, regions with an area smaller than a preset area threshold and a morphological complexity exceeding a preset morphological threshold are extracted from the difference region as candidate regions. The boundary association information in the overlay analysis results is used to verify the boundary association between the candidate regions and the reference patch. Isolated noise regions without effective boundary association with the reference patch, patches with missing ownership information, or obviously erroneous patches are removed, thus obtaining the fragmented surface to be processed.

[0038] For example, common fragmented surface types include narrow-slit overlapping fragmented surfaces, isolated fragmented surfaces, elongated fragmented surfaces after data overlay, and irregular fragmented surfaces after data overlay. Narrow-slit overlapping fragmented surfaces and isolated fragmented surfaces are formed due to differences in the operating habits of data collectors, non-standard data collection processes, or deviations in line connection and boundary positioning during manual editing, resulting in inaccurate connection of map patch boundaries. Elongated fragmented surfaces and irregular fragmented surfaces after data overlay are formed during the integration of data from multiple departments and time periods due to inconsistencies in data standards, coordinate systems, and classification rules, or by the overlay calculation of multiple feature layers (such as topography, land use, and ownership layers). These fragmented surfaces have no clear natural geographical correspondence and are redundant objects derived from data processing.

[0039] In this embodiment, by overlaying and analyzing the working patch and the reference patch, multi-dimensional screening is performed on the difference region based on area threshold, morphological complexity threshold and boundary correlation. This solves the problem of low recognition accuracy, easy omission or misjudgment caused by the existing technology that only uses a single area threshold to screen fragmented surfaces.

[0040] The above are merely feasible implementations of step S20 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S20.

[0041] Step S30: Based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed, determine the target merging object from the surrounding patches of the fragment to be processed; It should be noted that the surrounding patches of the fragment to be processed refer to the reference patches that are adjacent or close to the fragment to be processed in space; the association information refers to the quantitative information describing the degree of matching between the fragment to be processed and the surrounding patches in terms of spatial adjacency, common edge features, ownership consistency, attribute similarity and topological compatibility; the target merging object refers to the optimal patch selected from the surrounding patches for merging with the fragment to be processed.

[0042] Understandably, existing methods rely solely on simple distance or area rules when determining merge targets, lacking quantitative assessments of multi-dimensional factors such as ownership consistency and topological compatibility. This leads to inappropriate selection of merge targets and a lack of compliance guarantees. Therefore, step S30 can avoid the one-sidedness and compliance risks in the selection of merge targets, thereby improving the scientific accuracy of the determination of merge targets.

[0043] In one feasible implementation, step S30 may include: taking multiple reference patches located within a preset radius of the fragment to be processed as corresponding multiple surrounding patches; quantitatively analyzing the association information between the fragment to be processed and each of the surrounding patches, wherein the association information includes spatial adjacency, common edge features, ownership consistency, attribute similarity, and topological compatibility; scoring each of the surrounding patches according to the association information to obtain a comprehensive score corresponding to each of the surrounding patches; and determining a target merging object from each of the surrounding patches based on the comprehensive score.

[0044] It should be noted that the preset radius range refers to the spatial retrieval radius set with the fragment to be processed as the center, used to delineate the search range of surrounding patches; spatial adjacency relationship refers to whether the fragment and surrounding patches are directly adjacent, indirectly adjacent, or not adjacent; common edge characteristics refer to the length of the shared boundary between the fragment and surrounding patches and its proportion of the total boundary of the fragment; ownership consistency refers to whether the fragment and surrounding patches are consistent in terms of ownership attributes such as owner and type of use; attribute similarity refers to the degree of matching between the two in terms of attributes such as land type and land use nature; topological compatibility refers to whether the topological structure of the surrounding patches is complete and whether the boundaries are stable; the comprehensive score is a quantitative score obtained by weighting and summing the above indicators according to preset weights.

[0045] Specifically, using the geometric center of each fragment to be processed as a reference, all reference patches are retrieved within a preset radius as surrounding patches; the length and proportion of the common edge between the fragment to be processed and each surrounding patch are calculated one by one, the consistency of the ownership field is compared, the similarity of the attribute field is calculated, and the topological stability of the surrounding patches is evaluated; each indicator is weighted and summed according to different weights of spatial adjacency, common edge characteristics, ownership consistency, attribute similarity, and topological compatibility to obtain the comprehensive score of each surrounding patch; the surrounding patch with the highest comprehensive score is selected as the target merging object.

[0046] For example, the system uses multi-dimensional indicators to quantitatively analyze the matching degree between the fragmented surface and each surrounding patch, and constructs an association relationship evaluation system. The core indicators and weight allocation are shown in Table 1.

[0047] Table 1

[0048] Based on the above indicators, the system uses a weighted summation algorithm to calculate the matching score of each surrounding patch and automatically constructs a multi-dimensional association list for each fragment. The list is arranged in descending order of matching score, clearly presenting the quantitative matching results of the fragment with all surrounding candidate merge objects, including scores of various indicators, total ranking, and association basis. Based on the association information in the association list, the system uses "maximum common edge ratio priority" as the core weight factor, combined with auxiliary factors such as attribute similarity and topological compatibility, to calculate the comprehensive score of the candidate merge objects. The object with the highest comprehensive score is selected as the initial merge candidate to ensure that the boundary is naturally connected and the topological structure is stable after merging. The maximum common edge ratio is calculated as: the length of the common edge between the fragment and the candidate object ÷ the total boundary length of the fragment × 100%. The higher the ratio, the better the boundary fit.

[0049] Furthermore, since both parties to the merger must have completely identical location and ownership information, the location and ownership information of both parties can be verified by using fields such as the owner, type of use right, and scope of ownership in the relationship list. At the same time, in order to avoid destroying the logical structure and hierarchical relationship of the original operation data, it is necessary to strictly prohibit the reverse merging of fragmented areas into the superior or same-level core areas of the original operation area.

[0050] In this embodiment, by constructing a multi-dimensional weighted scoring system that includes spatial adjacency, common edge features, ownership consistency, attribute similarity, and topological compatibility, the problem of insufficient decision-making basis and easy mismatch caused by existing technologies that only match merging objects based on distance or a single rule is solved.

[0051] The above are merely feasible implementations of step S30 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S30.

[0052] Step S40: Merge the fragmented surface to be processed with the target merged object according to the overlay analysis results to obtain the target patch; It should be noted that the target patch refers to the complete patch that meets the topological specifications and attribute requirements after the fragmented surface to be processed is merged with the target merged object.

[0053] Understandably, existing methods simply overlay boundaries during the merging process without utilizing the boundary association information obtained from overlay analysis to accurately match and fit boundary nodes, resulting in abrupt boundary connections, jagged edges, or topological gaps after merging. Therefore, step S40 can avoid unnatural boundary connections and topological anomalies after merging, thereby improving the boundary continuity and topological correctness of the merged patches.

[0054] In one feasible implementation, step S40 may include: obtaining boundary association information between the fragmented surface to be processed and the target merging object based on the overlay analysis results; matching the boundary nodes of the fragmented surface to be processed with the boundary nodes of the target merging object according to the boundary association information to obtain the boundary nodes to be merged; and fitting the boundary between the fragmented surface to be processed and the target merging object based on the boundary nodes to be merged to obtain the target patch.

[0055] It should be noted that the boundary association information between the fragmented surface to be processed and the target merged object refers to the spatial correspondence between the common boundary position and the boundary nodes on both sides obtained from the overlay analysis; the boundary nodes of the fragmented surface to be processed refer to the sequence of coordinate points that constitute the boundary line of the fragmented surface to be processed; the boundary nodes of the target merged object refer to the sequence of coordinate points that constitute the boundary line of the target merged object; and the boundary nodes to be merged refer to the pair of boundary nodes on both sides of the common boundary between the fragmented surface to be processed and the target merged object that need to be fitted.

[0056] Specifically, based on the boundary association information in the overlay analysis results, the boundary nodes of the fragmented surfaces and the merged objects are accurately matched, and relevant smoothing algorithms are used to eliminate the jagged edges and abruptness of the boundaries, ensuring the continuity of the boundaries after merging. At the same time, the common edges of the fragmented surfaces and the merged objects are automatically deleted, and the boundary topology of the merged patches is reconstructed to ensure that the patches are non-overlapping and seamless.

[0057] Furthermore, it is necessary to integrate the attribute information of both parties according to preset rules, prioritize the retention of the core attributes of the merged object, supplement the special attributes of the fragments, and ensure the integrity and consistency of the attribute data. The merged patch data can also be stored in a temporary processing layer, and the merged status and association identifier can be marked to provide data support for subsequent topology verification.

[0058] For example, such as Figure 2 As shown, Figure 2 This is a comparison image showing the processing effects of this application, illustrating the changes in the boundary of the same area before and after processing. Figure 2In the first pair of effect comparison cases, before processing, there were two abnormal regions: one abnormal region was smaller than the area threshold, and the other was larger than the area threshold. Their boundaries with the surrounding reference patches were abrupt, exhibiting a jagged, discontinuous state. After processing, the abnormal region smaller than the area threshold was eliminated, and the abnormal region larger than the area threshold was identified as fragmented surfaces and merged with adjacent surrounding patches. (The last sentence appears to be incomplete and possibly refers to a different context.) Figure 2 In the second comparison case, before processing, there was a gap and overlap between the processed patch and the reference patch due to misalignment of the boundaries; this overlap area was the fragmented surface to be processed. After processing, based on the overlay analysis results, the boundary association information between the fragmented surface to be processed and the target merging object was extracted, clarifying the position of their common boundary and the spatial correspondence of the boundary nodes on both sides. The boundary nodes of the fragmented surface to be processed were registered and aligned with the corresponding boundary nodes of the target merging object, using the node pairs on both sides of the common boundary as the boundary nodes to be merged. Smooth interpolation fitting was performed on the boundary nodes to be merged, deleting redundant common edges between the fragmented surface and the merging object, and regenerating a continuous and natural fusion boundary. The resulting target patch eliminated the gaps and overlaps present before processing, and the boundary changed from the original jagged fracture to a smooth and continuous connection. The outer contour of the patch maintained geometric consistency with the boundary of the surrounding reference patch, achieving seamless boundary fitting without topological errors.

[0059] In this embodiment, the boundary nodes of the fragmented surface and the merged object are accurately matched and smoothly fitted by utilizing the boundary association information in the overlay analysis results, which solves the problem of rough boundary processing and low fitting accuracy in the existing method when merging fragmented surfaces.

[0060] The above are merely feasible implementations of step S40 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S40.

[0061] In one feasible implementation, step S40 may include: obtaining the original fragment source layer name, original fragment identifier, preset area condition, preset morphological complexity condition, and processing mark information corresponding to the fragment to be processed; and establishing a traceability field group for the target patch based on the original fragment source layer name, the original fragment identifier, the preset area condition, the preset morphological complexity condition, and the processing mark information.

[0062] It should be noted that the original fragment source layer name refers to the layer name to which the fragment to be processed belongs in the original data; the original fragment identifier refers to the unique code of the fragment to be processed in the original data; the preset area condition corresponding to the fragment to be processed refers to the area threshold parameter used when filtering the fragment; the preset morphological complexity condition corresponding to the fragment to be processed refers to the morphological complexity threshold parameter used when filtering the fragment; the processing mark status refers to the mark information that records the merging processing status of the fragment; the traceability field group refers to a set of attribute fields attached to the target patch attribute table to record key parameters and source information of the merging process. The specific fields of the traceability field group are shown in Table 2.

[0063] Table 2

[0064] Specifically, after the merging is completed, the original layer source, unique identifier, area and shape filtering parameters used, and merging processing mark of each fragment to be processed are extracted from the processing log. This information is integrated into a traceability field group and attached to the generated target patch attribute table to achieve full traceability of the merging operation.

[0065] It is important to note that this application will automatically create complete mirror copies of the original and reference polygons. The copies are completely identical to the original data, including spatial coordinates, attribute information, topological relationships, metadata, etc. The original data is stored in a separate read-only database with access control set, which can only be viewed by the system administrator. No modification or deletion operations are allowed throughout the process. All merging and correction operations are performed in the copy dataset. The copy data is physically isolated from the original data to avoid the risk of data pollution, loss, or damage during processing.

[0066] In this embodiment, by including a traceability field group containing the original fragment identifier, processing parameters, and processing status in the merged target patch record, the problem of the inability to trace the merging result due to the lack of processing trace records in the prior art is solved.

[0067] The above are merely feasible implementations of step S40 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S40.

[0068] This embodiment provides a method for intelligent edge-fitting and merging of fragmented areas based on a land survey cloud platform. It obtains multiple operational and reference map features from the platform; performs overlay analysis on each operational and reference map feature to obtain the overlay analysis results and the fragmented area to be processed; determines the target merging object from the surrounding map features of the fragmented area to be processed based on the correlation information between the fragmented area to be processed and its surrounding map features; and merges the fragmented area to be processed and the target merging object according to the overlay analysis results to obtain the target map feature. This method solves the technical problem of existing technologies that cannot achieve accurate fragmented area merging while ensuring topological and ownership consistency due to the lack of benchmark reference data and compliance constraints. It achieves seamless connection of the merged map feature boundaries and ensures the topological correctness and ownership compliance of the merged result.

[0069] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 The step S40 of the intelligent edge-fitting and merging method for dual map patch fragmentation based on the land survey cloud platform further includes steps S41 to S46: Step S41: Obtain the merged patch data within the preset spatial range; It should be noted that the preset spatial range refers to the pre-defined geographic spatial area that needs to be topologically verified; the merged patch data refers to the set of patches generated after the merging process in step S40.

[0070] Specifically, after completing one round of fragment merging, all merged patch data located within the current work area in the processing layer are extracted and used as input data for subsequent topology verification.

[0071] It is understandable that since the merging operation involves boundary modification and node reconstruction, which may introduce new topological errors, step S41 is performed to prevent merged patch data from being directly entered into the database without verification, thereby improving the completeness of data quality control.

[0072] Step S42: Divide the patch data to obtain multiple regions to be detected; It should be noted that the area to be detected refers to the spatial unit into which the merged patch data is divided according to preset rules for performing topology verification block by block.

[0073] For example, a regular grid division method can be adopted to uniformly divide the spatial range of the merged patch data into multiple rectangular areas to be detected according to a preset grid size, so as to perform block-based parallel verification and improve the processing efficiency of large-scale data.

[0074] It is understandable that since the merged patch data may be large and the overall traversal verification is inefficient, step S42 can avoid the time-consuming problem of single-threaded verification of the full data, thereby improving the processing efficiency of topology verification.

[0075] Step S43: Perform full topology verification on the region to be detected to obtain the verification result.

[0076] It should be noted that full topology verification refers to performing a complete topology rule check on all patches within the detection area, including checking for topology errors such as gaps, overlaps, discontinuous boundaries, or hanging nodes between patches; the verification result refers to the conclusion of whether the region output after full topology verification has topology errors and its detailed information.

[0077] Specifically, after all fragmented surfaces are initially merged, the system automatically launches a comprehensive topology check engine. Based on relevant topology rules and industry-defined rules, it performs full topology verification on all merged patch data. The core check contents are shown in Table 3 below: Table 3

[0078] It is understandable that since the merging operation may produce overlaps or gaps in local areas, step S43 can prevent topological defects in the merged patch data from going undetected, thereby improving the quality assurance level before the data is entered into the database.

[0079] Step S44: When the verification result indicates that there is a topological error in the area to be detected, determine the error information and determine the merging strategy based on the error information.

[0080] It should be noted that topological errors refer to specific problems in the spatial relationships of polygon data that violate topological rules; error information refers to information describing the type, location, and scope of impact of topological errors.

[0081] Specifically, when the verification results show that there is a topology error in a certain area to be detected, the error type, error location, and error impact range are automatically recorded, and the cause of the error is analyzed accordingly to formulate a corresponding re-merging strategy.

[0082] Understandably, since different types of topology errors require different repair methods, performing step S44 can avoid using a single, blind repair method for topology errors, thereby improving the targeting and effectiveness of error repair.

[0083] In one feasible implementation, step S44, determining the merging strategy based on the error information, may include: generating a topology error distribution map based on the error information, wherein the error information includes error type, error location, and error impact range; tracing the relationship list between the unprocessed fragments and the unprocessed fragments in the detection area based on the topology error distribution map to obtain the error cause; and determining the merging strategy based on the error cause.

[0084] It should be noted that a topology error distribution map is a visualization of topology errors marked on a map according to their spatial location; error type can refer to error categories such as gaps, overlaps, and hanging nodes; error location refers to the spatial coordinates of the topology error; error impact range refers to the range of patches affected by the topology error; the correlation list refers to a quantitative record of the correlation information between fragments and surrounding patches; error cause refers to the root cause of the topology error; and merging strategy refers to the repair plan formulated based on the error cause.

[0085] Specifically, all verified topological errors are categorized by type and location to generate a topological error distribution map, visually representing the spatial distribution of errors. For each topological error, the original unprocessed fragments and their associated lists in that region are traced back, and the matching scores are compared with the boundary fitting parameters to determine whether the error is caused by common edge matching deviation or improper weight constraint filtering. Based on the cause, the merging strategy is adjusted, such as expanding the search radius or correcting the matching weights.

[0086] In this embodiment, the cause of the error is located by generating a topology error distribution map and tracing back the relationship list of the fragments to be processed, which solves the problem that the existing technology cannot trace the cause of the merged topology error and accurately repair it.

[0087] The above are merely feasible implementations of step S44 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S44.

[0088] Step S45: Merge the unprocessed fragments of the region to be detected according to the merging strategy to obtain the target detection region.

[0089] It should be noted that the target detection area refers to the area obtained after re-merging the fragmented surfaces involved in the topology error according to the corrected merging strategy.

[0090] Specifically, based on the merging strategy determined in step S44, the matching and fusion process of fragmented surfaces and merged objects is re-executed to repair the areas involved in the original topology errors and generate target detection areas with correct topological relationships.

[0091] Understandably, since the revised merging strategy more accurately adapts to the actual spatial relationships, step S45 can avoid the residual topological errors caused by the original merging strategy, thereby improving the topological correctness of the merging result.

[0092] Step S46: Take the target detection area as the area to be detected, and return to the step of performing full topology verification on the area to be detected to obtain the verification result, until the verification result shows that the area to be detected has no topology errors, and determine that the area to be detected meets the spatial data topology integrity requirements.

[0093] It should be noted that the requirement for topological integrity of spatial data can refer to the fact that the patch data is seamless, non-overlapping, has continuous boundaries and no hanging nodes in space, and fully conforms to the topological rules of the data model.

[0094] Specifically, the repaired target detection region is used as new input to re-perform full topology verification; if errors still exist in the verification, the correction continues and the process is iterated; if no errors are found in the verification, the region is determined to have met the topology integrity standard, and the correction process for that region ends.

[0095] Understandably, since a single merge may not completely eliminate all topology problems, step S46 is performed to prevent incomplete topology repair from causing erroneous data to enter downstream applications, thereby improving the reliability of data delivery quality.

[0096] This embodiment provides a method for intelligent edge-fitting and merging of fragmented areas in dual-map patches based on a land survey cloud platform. The method involves acquiring merged patch data within a preset spatial range; dividing the patch data into multiple areas to be detected; performing full topological verification on these areas to obtain verification results; identifying topological errors in the areas to be detected based on the verification results; determining a merging strategy based on the error information; merging the fragmented areas to be processed within the areas to be detected according to the merging strategy to obtain a target detection area; using the target detection area as the area to be detected and returning to the step of performing full topological verification on the areas to be detected to obtain verification results, until the verification results indicate that the areas to be detected have no topological errors, thus determining that the areas to be detected meet the spatial data topological integrity requirements. By employing a closed-loop mechanism of block-based cyclic topological verification and error cause tracing and correction, the method avoids the problems of undiscovered residual topological errors after merging and the inability of a single repair method to specifically correct them. It solves the technical problem of uncontrollable data quality due to the lack of effective closed-loop verification after merging operations in existing technologies, thereby achieving closed-loop assurance of the topological integrity of the merged patch data.

[0097] For example, to help understand the implementation process of the intelligent edge-fitting and merging method for dual-map fragmented surfaces based on the land survey cloud platform obtained by combining this embodiment with the above embodiment one, please refer to... Figure 4 , Figure 4 A schematic diagram of the overall process for a dual-map patch / fragmentation intelligent edge-fitting and merging method based on a land survey cloud platform is provided, specifically: The overall process relies on the collaborative work of six core modules: the input module, the intelligent overlay analysis module, the intelligent fragmentation processing module, the topology optimization verification module, the standardized results output module, and the collaborative control and feedback module. At the start of processing, the input module receives data from both the working and reference patches, performs data import, format conversion, and integrity verification. If the verification passes, the data is transferred to the intelligent overlay analysis module. This module first calibrates the coordinates of the two source patches, then performs spatial overlay operations, outputting overlay analysis results that include information on intersection areas, difference areas, and boundary relationships. If the verification fails, the collaborative control and feedback module triggers an anomaly feedback, prompting manual intervention.

[0098] Based on the overlay analysis results, the fragmented surface intelligent processing module first performs multi-dimensional precise screening and extraction of fragmented surfaces. This involves selecting candidate regions from the differential areas based on preset area and morphological complexity conditions, and then using the overlay analysis results to perform correlation screening on these candidate regions, obtaining the fragmented surfaces to be processed and their basic information. Next, the module enters the intelligent association construction stage between the fragmented surfaces and surrounding features. It searches for surrounding reference features within a preset radius, quantitatively analyzing multi-dimensional association information such as spatial adjacency, common edge characteristics, ownership consistency, attribute similarity, and topological compatibility. After weighted scoring, it generates a list of association relationships sorted in descending order of comprehensive score. Intelligent attribution determination and compliant merging of fragmented surfaces are then performed, using the largest common edge ratio as the core for weighted decision-making. After verification by dual compliance constraints of ownership consistency and data hierarchy, the target merging object is determined. In special cases where no suitable merging object exists, the search radius can be expanded through the collaborative control and feedback module, or the area can be marked for manual processing. After determining the target merging object, based on the boundary association information between the fragmented surfaces to be processed and the target merging object in the overlay analysis results, the boundary nodes of the two are matched and the boundary is fitted, completing edge fusion and topological reconstruction to obtain the target feature. After merging, the original fragment source layer name, original fragment identifier, preset area conditions and preset morphological complexity conditions, and processing mark information of the fragment to be processed are obtained from the processing log. A traceability field group for the target patch is established to achieve full-process traceability of processing traces.

[0099] After obtaining the target patch, the process enters the topology optimization and verification module. First, merged patch data within a preset spatial range is acquired and spatially gridded to obtain multiple regions to be detected. A full topology verification is performed on each region to check for topology errors such as gaps, overlaps, discontinuous boundaries, or hanging nodes, yielding verification results. If the verification result indicates a topology error in a region, error information including error type, location, and impact range is identified, generating a topology error distribution map. Based on the topology error distribution map, the unprocessed fragments and their associated lists are traced back to analyze the error causes, and a corrected merging strategy is determined based on these causes. The fragments within the region are then merged again according to the corrected merging strategy to obtain the target detection region. This target detection region is then used as a new region to be detected and a full topology verification is performed again. This process is iterated until the verification result shows no topology errors, confirming that the region meets the spatial data topology integrity requirements.

[0100] It should also be noted that the standardized output module performs non-destructive processing to complete the sealing of raw data, annotation of traceability fields, and integration of output data, outputting standardized output data with topological compliance. The collaborative control and feedback module continuously monitors anomalies, records logs, and provides human-machine collaborative feedback throughout the entire process to ensure the stability and controllability of the processing.

[0101] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent edge-fitting and merging method for dual map patch fragments based on the land survey cloud platform. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0102] This application also provides a dual-map patch merging and processing device based on a land survey cloud platform. Please refer to... Figure 5 The intelligent edge-fitting and merging processing device for dual map patch fragments based on the land survey cloud platform includes: Input module 10 is used to obtain multiple operational map features and multiple reference map features from the land survey cloud platform; Processing module 20 is used to perform overlay analysis based on each of the operation patches and each of the reference patches to obtain overlay analysis results and fragmented surfaces to be processed; The filtering module 30 is used to determine the target merging object from the surrounding patches of the fragment to be processed based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed. Output module 40 is used to merge the fragmented surface to be processed with the target merged object according to the superposition analysis result to obtain the target patch.

[0103] The intelligent edge-fitting and merging processing device for dual-map patch fragmentation areas based on the land survey cloud platform provided in this application adopts the intelligent edge-fitting and merging processing method for dual-map patch fragmentation areas based on the land survey cloud platform in the above embodiments, which can solve the technical problem of how to make optimal merging decisions for fragmentation areas under compliance constraints under multi-source data collaboration. Compared with the prior art, the beneficial effects of the intelligent edge-fitting and merging processing device for dual-map patch fragmentation areas based on the land survey cloud platform provided in this application are the same as the beneficial effects of the intelligent edge-fitting and merging processing method for dual-map patch fragmentation areas based on the land survey cloud platform provided in the above embodiments, and other technical features in the intelligent edge-fitting and merging processing device for dual-map patch fragmentation areas based on the land survey cloud platform are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0104] The processing module 20 is further configured to perform overlay analysis based on each of the operation patches and each of the reference patches to obtain intersection regions, difference regions and boundary association information; obtain multiple regions to be screened based on the intersection regions, the difference regions and the boundary association information; screen each region to be screened based on preset area conditions and preset morphological complexity conditions to obtain candidate regions; and perform correlation screening on the candidate regions based on the overlay analysis results to obtain the fragmented surfaces to be processed.

[0105] The filtering module 30 is further configured to: identify multiple reference patches located within a preset radius of the fragmented surface to be processed as corresponding surrounding patches; quantitatively analyze the association information between the fragmented surface to be processed and each of the surrounding patches, wherein the association information includes spatial adjacency, common edge features, ownership consistency, attribute similarity, and topological compatibility; score each of the surrounding patches according to the association information to obtain a comprehensive score for each of the surrounding patches; and determine the target merging object from each of the surrounding patches based on the comprehensive score.

[0106] The output module 40 is further configured to obtain the boundary association information between the fragmented surface to be processed and the target merging object based on the overlay analysis results; match the boundary nodes of the fragmented surface to be processed with the boundary nodes of the target merging object according to the boundary association information to obtain the boundary nodes to be merged; and fit the boundary between the fragmented surface to be processed and the target merging object based on the boundary nodes to be merged to obtain the target patch.

[0107] The output module 40 is further configured to obtain the original fragment source layer name, original fragment identifier, preset area condition, preset morphological complexity condition and processing mark status corresponding to the fragment to be processed; and to establish a traceability field group for the target patch based on the original fragment source layer name, the original fragment identifier, the preset area condition, the preset morphological complexity condition and the processing mark status.

[0108] The output module 40 is also used to acquire merged patch data located within a preset spatial range; The patch data is divided into multiple regions to be detected; a full topological verification is performed on the regions to be detected to obtain verification results; when the verification results indicate that there are topological errors in the regions to be detected, error information is determined, and a merging strategy is determined based on the error information; the unprocessed fragments of the regions to be detected are merged according to the merging strategy to obtain a target detection region; the target detection region is used as the regions to be detected, and the process of performing a full topological verification on the regions to be detected to obtain verification results is repeated until the verification results indicate that there are no topological errors in the regions to be detected, at which point it is determined that the regions to be detected meet the spatial data topological integrity requirements.

[0109] The output module 40 is further configured to generate a topology error distribution map based on the error information, wherein the error information includes error type, error location and error impact range; trace the relationship list between the unprocessed fragments in the detection area and the unprocessed fragments based on the topology error distribution map to obtain the error cause; and determine a merging strategy based on the error cause.

[0110] This application provides a dual-map fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform. The dual-map fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform includes: at least one processor; and a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to execute the dual-map fragmentation intelligent edge-fitting and merging processing method based on a land survey cloud platform described in Embodiment 1 above.

[0111] The following is for reference. Figure 6 This document illustrates a structural schematic diagram of a dual-pattern fragmented surface intelligent edge-fitting and merging processing device based on a land survey cloud platform, suitable for implementing embodiments of this application. The dual-pattern fragmented surface intelligent edge-fitting and merging processing device based on a land survey cloud platform in this application embodiment can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6The intelligent edge-fitting and merging processing device for dual map patch fragments based on the land survey cloud platform shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0112] like Figure 6 As shown, the intelligent edge-merging processing device for dual-pattern fragmented surfaces based on the land survey cloud platform may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the intelligent edge-merging processing device for dual-pattern fragmented surfaces based on the land survey cloud platform. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the dual-pattern fragmented surface intelligent edge-merging processing device based on the land survey cloud platform to exchange data wirelessly or via wired communication with other devices. Although the figure shows a dual-pattern fragmented surface intelligent edge-merging processing device based on the land survey cloud platform with various systems, it should be understood that implementing or possessing all the systems shown is not required. More or fewer systems can be implemented alternatively.

[0113] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0114] The intelligent edge-fitting and merging processing device for dual-map fragmented areas based on the land survey cloud platform provided in this application adopts the intelligent edge-fitting and merging processing method for dual-map fragmented areas based on the land survey cloud platform in the above embodiments, which can solve the technical problem of how to make optimal merging decisions for fragmented areas under compliance constraints under multi-source data collaboration. Compared with the prior art, the beneficial effects of the intelligent edge-fitting and merging processing device for dual-map fragmented areas based on the land survey cloud platform provided in this application are the same as the beneficial effects of the intelligent edge-fitting and merging processing method for dual-map fragmented areas based on the land survey cloud platform provided in the above embodiments, and other technical features in the intelligent edge-fitting and merging processing device for dual-map fragmented areas based on the land survey cloud platform are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0117] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the intelligent edge-fitting and merging method for dual-pattern fragmented surfaces based on the land survey cloud platform in the above embodiments.

[0118] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0119] The aforementioned computer-readable storage medium may be included in the intelligent edge-fitting and merging processing equipment for dual-map fragmented surfaces based on the land survey cloud platform; or it may exist independently and not be assembled into the intelligent edge-fitting and merging processing equipment for dual-map fragmented surfaces based on the land survey cloud platform.

[0120] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the intelligent edge-fitting and merging processing device for dual-plot fragmented surfaces based on the land survey cloud platform, the device performs the following: acquires multiple operational plots and multiple reference plots from the land survey cloud platform; performs overlay analysis on each operational plot and each reference plot to obtain overlay analysis results and fragmented surfaces to be processed; determines a target merging object from the surrounding plots of the fragmented surface to be processed based on the correlation information between the fragmented surface to be processed and the surrounding plots of the fragmented surface to be processed; and merges the fragmented surface to be processed and the target merging object according to the overlay analysis results to obtain a target plot.

[0121] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0123] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0124] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform. This method can solve the technical problem of how to make optimal fragmentation merging decisions under compliance constraints under multi-source data collaboration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform provided in the above embodiments, and will not be repeated here.

[0125] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described intelligent edge-fitting and merging processing method for dual-map patch fragmentation based on a land survey cloud platform.

[0126] The computer program product provided in this application can solve the technical problem of how to make optimal merging decisions for fragmented areas under compliance constraints under multi-source data collaboration. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent edge-fitting and merging processing method for dual-pattern fragmented areas based on the land survey cloud platform provided in the above embodiments, and will not be repeated here.

[0127] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for intelligent edge-fitting and merging of fragmented areas on dual-map surfaces based on a land survey cloud platform, characterized in that, The method includes: Multiple operational and reference map features were obtained from the land survey cloud platform; Based on the superposition analysis of each of the described work patches and each of the described reference patches, the superposition analysis results and the fragmented surfaces to be processed are obtained; Based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed, the target merging object is determined from the surrounding patches of the fragment to be processed; Based on the overlay analysis results, the fragmented surface to be processed and the target merged object are merged to obtain the target patch.

2. The method as described in claim 1, characterized in that, The overlay analysis results include information on intersection regions, difference regions, and boundary associations. The method of performing overlay analysis based on each of the described operational patches and each of the described reference patches to obtain overlay analysis results and fragmented surfaces to be processed includes: Based on the overlay analysis of each of the described operational map pieces and each of the described reference map pieces, the intersection region, the difference region, and the boundary association information are obtained. Based on the intersection region, the difference region, and the boundary association information, multiple regions to be filtered are obtained; Based on preset area conditions and preset shape complexity conditions, each of the regions to be screened is screened to obtain candidate regions; Based on the overlay analysis results, the candidate regions are subjected to correlation screening to obtain the fragmented surfaces to be processed.

3. The method as described in claim 1, characterized in that, The step of determining the target merging object from the surrounding patches of the fragment to be processed based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed includes: Multiple reference patches located within a preset radius range of the fragmented surface to be processed are used as corresponding peripheral patches; The correlation information between the fragmented surface to be processed and each of the surrounding patches is quantitatively analyzed, wherein the correlation information includes spatial adjacency, common edge features, ownership consistency, attribute similarity and topological compatibility; Each of the surrounding map features is scored based on the correlation information to obtain a comprehensive score for each of the surrounding map features; Based on the comprehensive score, the target merging object is determined from each of the surrounding patches.

4. The method as described in claim 1, characterized in that, The step of merging the fragmented surface to be processed with the target merged object based on the overlay analysis results to obtain the target patch includes: Based on the overlay analysis results, the boundary association information of the fragmented surface to be processed and the target merging object is obtained; The boundary nodes of the fragmented surface to be processed are matched with the boundary nodes of the target merging object based on the boundary association information to obtain the boundary nodes to be merged. The target patch is obtained by fitting the boundary between the fragmented surface to be processed and the target merging object based on the boundary nodes to be merged.

5. The method as described in claim 1, characterized in that, After merging the fragmented surface to be processed with the target merged object based on the overlay analysis results to obtain the target patch, the method further includes: Obtain the original fragment source layer name, original fragment identifier, preset area condition, preset shape complexity condition, and processing mark status corresponding to the fragment to be processed; Based on the original fragmented surface source layer name, the original fragmented surface identifier, the preset area condition, the preset morphological complexity condition, and the processing mark status, a traceability field group for the target patch is established.

6. The method as described in claim 1, characterized in that, After merging the fragmented surface to be processed with the target merged object based on the overlay analysis results to obtain the target patch, the method further includes: Obtain the merged patch data within the preset spatial range; The image data is divided into multiple regions to be detected; A full topology verification was performed on the region to be detected to obtain the verification results; When the verification result indicates that there is a topological error in the area to be detected, the error information is determined, and a merging strategy is determined based on the error information; The unprocessed fragments of the region to be detected are merged according to the merging strategy to obtain the target detection region; The target detection area is taken as the area to be detected, and the process of performing full topology verification on the area to be detected and obtaining the verification result is repeated until the verification result shows that the area to be detected has no topology errors. Then, the area to be detected is determined to meet the spatial data topology integrity requirements.

7. The method as described in claim 6, characterized in that, Determine the merging strategy based on the error information, including: Based on the error information, a topology error distribution map is generated, wherein the error information includes the error type, error location, and error impact range; Based on the topological error distribution map, the relationship list between the unprocessed fragments in the area to be detected and the unprocessed fragments is traced to obtain the cause of the error; Based on the causes of the errors, determine the merging strategy.

8. A dual-map patch / fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform, characterized in that, The device includes: The input module is used to obtain multiple operational map features and multiple reference map features from the land survey cloud platform; The processing module is used to perform overlay analysis based on each of the operation patches and each of the reference patches to obtain the overlay analysis results and the fragmented surfaces to be processed; The filtering module is used to determine the target merging object from the surrounding patches of the fragment to be processed based on the correlation information between the fragment to be processed and the surrounding patches of the fragment to be processed. The output module is used to merge the fragmented surface to be processed with the target merged object according to the overlay analysis results to obtain the target patch.

9. A dual-map patch / fragmentation intelligent edge-fitting and merging processing device based on a land survey cloud platform, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent edge-fitting and merging processing method for dual-pattern fragmented surfaces based on the land survey cloud platform as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the intelligent edge-fitting and merging method for dual-map patch fragmentation based on the land survey cloud platform as described in any one of claims 1 to 7.

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