A sharp angle graph spot automatic identification and intelligent processing method and device

Abstract

The application discloses a kind of sharp angle figure spot automatic identification and intelligent processing method, device, it is related to geographic information investigation technical field, the method includes: S1 is calculated by setting threshold figure spot angle point angle, identifies and exports sharp angle position and attribute;S2 extracts figure spot position and angle point feature, and sharp angle figure spot is classified;S3 is respectively adopted cutting and merging, cutting angle backfill, area fusion or boundary adjustment strategy to different types of sharp angle figure spot, and through topological verification ensure that data correctness after processing;S4 is realized non-destructive processing by data backup, marking and process record;S5 constructs error processing and feedback mechanism and guarantees process stability.The application adopts the above-mentioned sharp angle figure spot automatic identification and intelligent processing method, device, can be processed by intelligent process, reduce human error, improve data accuracy, reduce repetitive labor, reduce artificial investment, shorten production cycle, make that production process is more smooth and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information investigation, in particular to a sharp corner graph spot automatic identification and intelligent processing method and device. BACKGROUND

[0002] With the rapid development of "3S" technology, China has gradually built a complete land survey and monitoring technology system and a database management and service technology system from the second national land survey to the third national land survey and the annual land change survey. However, the automation level of some links in the production process is still not high, and a large amount of manpower and material resources need to be invested. For example, in the annual land change survey project, the achievement database construction and quality inspection are necessary, and the processing of sharp corner problems in quality inspection errors is a big problem.

[0003] The annual land change survey project has a short cycle and heavy tasks, and the survey and monitoring data itself has two significant characteristics: one is large data volume, and the other is complex data relationship. In the operation process, a large number of sharp corner graph spots may be generated due to various reasons. These graph spots may be narrow sharp corners caused by improper operation in the in-house mapping process, or may be generated because the boundaries do not fit when the various related layers are superimposed and analyzed (for example, the change survey update library graph spot needs to be fitted not only with the basic library boundary issued by the state, but also with the land use layers and ownership layers of the state-issued city and village, so when the land class graph spot is cut and spatially valued, a large number of sharp corner graph spots will also be generated). According to the current technical specifications of land space survey, the appearance of sharp corner graph spots in the database is judged as a quality inspection error. For the number of such quality inspection errors, it is hundreds or even thousands. Relying on manual analysis and processing of such large and complex data is not only time-consuming and labor-intensive, but also difficult and tedious, and important information may be missed, leading to errors.

[0004] The types of sharp corner graph spots that need to be processed mainly include the following:

[0005] Convex polygon sharp corner graph spot, which refers to a graph spot with a convex sharp corner and multiple other graph spots around the perimeter;

[0006] Concave polygon sharp corner graph spot, which refers to a graph spot with a concave sharp corner and multiple other graph spots around the perimeter;

[0007] Fragment polygon with sharp corner, which refers to a graph spot with an area less than 30 square meters after cutting the update layer feature graph;

[0008] Small gap between graph spots, which refers to a graph spot with no gap between different elements in the same layer and a face crack tolerance of 0.0001 meters;

[0009] Independent sharp corner graph patches, which are independent graph patches, i.e. graph patches without other graph patches in the periphery, are relatively special and need to be manually analyzed and processed for the main reasons.

[0010] Currently, the processing schemes for sharp corner graph patches of various operating units mainly include the following two kinds:

[0011] One is manual elimination: through the inspection results of the national quality inspection software, it is determined whether the graph patches in the layer meet the requirements of the national inspection, the error position is found and modified according to the error prompt, and the sharp corner graph patches are manually processed one by one through cutting, segmentation and fusion. For example, the sharp corner part needs to be cut out manually to form an independent fragment polygon, and then the attribute relationship between layers is analyzed and merged with the adjacent large area graph patch one by one to eliminate the sharp corner. The processing scheme of manual elimination is mainly suitable for the case where the number of sharp corner graph patches is small. Although this method can ensure the accuracy of processing, it is slow, inefficient, time-consuming and labor-intensive. Moreover, due to the complexity of the change survey data, manual processing is difficult to achieve standard consistency, and other error types may be generated at the same time as the error is modified, such as operation errors leading to gaps, small fragments and blank graph patches. Therefore, manual elimination of sharp corners also needs to modify the topological errors through topological inspection, which increases the workload, and therefore requires higher experience of data processing personnel.

[0012] The second is the modification based on the toolbox of GIS software: the elimination tool in GIS software, such as the Eliminate tool in ArcGIS, selects the elements to be eliminated in the related layer, specifies the path and name of the input elements and output elements, sets the merging parameters, i.e. specifies the conditions (such as area size, shared boundary length, etc.) to merge the fragment polygons with sharp corners with adjacent large graph patches. The operation based on the toolbox of GIS can automatically merge the sharp corner graph patches, reduce the workload of manual operation, and improve the work efficiency. However, the parameter setting conditions of the toolbox are relatively limited, and the adaptability to complex data structure is insufficient, especially when dealing with graph patches with complex boundaries or irregular shapes.

[0013] No matter which scheme of the prior art, it is implemented on the basis of determining the error position through the quality inspection software. However, in actual operation, the sharp corner graph patches cannot be identified by the naked eye. Therefore, it is urgent to study the automatic processing of sharp corner graph patches and overcome the current time-consuming and labor-intensive problems. SUMMARY

[0014] The purpose of the present application is to provide a sharp corner graph patch automatic identification and intelligent processing method and device, to improve the automation level of sharp corner graph patch identification, and to reduce human errors, improve data accuracy, reduce repetitive labor, reduce labor input, shorten production cycle, make the production process smoother and more efficient through intelligent process.

[0015] To achieve the above object, the application provides a sharp spot automatic identification and intelligent processing method, the steps are as follows:

[0016] S1, sharp angle identification and extraction, comprising:

[0017] S1.1, setting the judgment threshold of sharp angle spot,

[0018] S1.2, traversing all corner points of the spot, calculating the degree of the adjacent edge angle of the corner point;

[0019] S1.3, outputting the sharp angle position coordinates and coordinate values, displaying through the graphic interface and generating the vector table containing attribute information;

[0020] S2, feature analysis and classification, comprising:

[0021] S2.1, extracting the position feature and corner point feature of the spot, and selecting the most meaningful feature from the extracted feature;

[0022] S2.2, according to the extracted feature, the sharp angle is divided into convex polygon sharp angle, concave polygon sharp angle, fragment polygon spot, small gap, and independent sharp angle which needs manual processing;

[0023] S3, processing the sharp angle spot through intelligent processing mechanism, comprising:

[0024] S3.1, for convex polygon sharp angle spot, according to the spatial relationship of adjacent spots, cutting the convex sharp angle spot and merging to the adjacent spot according to the rule;

[0025] S3.2, for concave polygon sharp angle spot, cutting angle backfilling from the adjacent graph;

[0026] S3.3, for fragment polygon spot containing sharp angle, when the spot area is less than the preset threshold, directly merging to the adjacent spot;

[0027] S3.4, for small gap between spots, eliminating the gap by filling the gap, deleting the overlap or adjusting the boundary line;

[0028] S3.5, after processing all sharp angles, re-identifying the sharp angle and verifying by using the topological checking tool, returning to S3 for processing for those with topological errors;

[0029] S4, non-destructive processing and result output, comprising:

[0030] S4.1, automatically copying the original data set to generate processing copy, avoiding losing or covering the original data in the modification process;

[0031] S4.2, add a mark field or an interpretation field in the processing data to distinguish the original data and the modified data;

[0032] S4.3, record the processing flow information, including the algorithm type, parameter setting and operation sequence;

[0033] S5, error processing and feedback mechanism

[0034] S5.1, detect the processing process exception in real time, record the error code and detailed log;

[0035] S5.2, provide a man-machine interactive feedback interface to support the operator to correct the parameters or intervene in the processing flow.

[0036] Preferably, in S1.2, the method for calculating the angle of the adjacent edges of the corner point is: the two adjacent edges of the corner point are represented by vectors A and B respectively, and the cosine of the angle θ between the vectors A and B is calculated by the following formula:

[0037]

[0038] Then the angle value of θ is obtained by the arccosine function.

[0039] Preferably, in S1.3, the attribute information of the vector table includes the coordinates of the sharp corner, the angle value and the polygon ID information.

[0040] Preferably, in S2.1, the position characteristics of the polygon include the polygon coordinates and the spatial relationship with the adjacent polygons, and the corner point characteristics of the polygon include the number of corner points, the distribution of the corner points and the sharpness of the corner points.

[0041] Preferably, in S3.1, the path algorithm for cutting the sharp corner polygon of the convex polygon adopts the Dijkstra algorithm or the cutting algorithm of the minimum circumscribed circle.

[0042] Preferably, in S3.2, the backfill operation of the sharp corner of the concave polygon is based on the Delaunay triangulation, Voronoi diagram and minimum circumscribed circle algorithm.

[0043] Preferably, in S3.4, the common point recognition algorithm is used before the fine slit processing to accurately locate the common endpoints of different line segments or polygons in the vector graphics.

[0044] Preferably, in S3.4, the fine slit elimination is based on the geometric adjustment or the Delaunay triangulation algorithm, and the topological structure of the graphics is reconstructed to fill the fine slit area.

[0045] An automatic sharp corner polygon recognition and intelligent processing device, comprising:

[0046] A sharp corner detection module for calculating the angle of the polygon and generating a position vector table;

[0047] a feature classification module for extracting position features and corner point features of the map patch and classifying sharp corners;

[0048] an intelligent processing module integrating processing algorithms and topology checking tools for different types of sharp corner map patches;

[0049] a data management module for realizing data backup, marking and process recording;

[0050] a human-computer interaction module providing a visual interface and an error feedback interface.

[0051] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0052] 1. Avoiding sharp corner map patch errors in advance: automatic processing before the quality inspection software detects errors, avoiding repeated rework, multiple processing, exporting and passing the national inspection, making the internal work of building a database time-consuming and inefficient.

[0053] 2. Improve the recognition accuracy of sharp corner map patches: automatically extract and recognize the features of sharp corner map patches to improve the recognition accuracy of sharp corner map patches and reduce human error.

[0054] 3. Realize intelligent processing of sharp corner map patches: use the mode of automatic processing to realize fast and accurate processing of sharp corner map patches and improve work efficiency.

[0055] 4. Maintain the integrity and accuracy of data: ensure the integrity and accuracy of the original data during the processing of sharp corner map patches to avoid data loss or errors caused by improper processing.

[0056] 5. Promote the development of related technologies: not only solves the problem of sharp corner map patch processing in the land change survey project, but also promotes the application and development of automatic processing in survey and monitoring projects, and provides new ideas and methods for the progress of related technologies.

[0057] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0059] Figure 1Flowchart of an embodiment of the application for automatic identification and intelligent processing of sharp spot;

[0060] Figure 2 Example of a convex polygon sharp corner spot for an embodiment of the application;

[0061] Figure 3 Example of a concave polygon sharp corner spot for an embodiment of the application;

[0062] Figure 4 Example of a fragment polygon spot with sharp corners for an embodiment of the application;

[0063] Figure 5 Example of a small gap between spots for an embodiment of the application;

[0064] Figure 6 Output display map of sharp corner positions for an embodiment of the application;

[0065] Figure 7 Sharp corner position output vector attribute table for an embodiment of the application;

[0066] Figure 8 Comparison chart of a convex polygon sharp corner spot before and after processing for an embodiment of the application;

[0067] Figure 9 Comparison chart of a concave polygon sharp corner spot before and after processing for an embodiment of the application;

[0068] Figure 10 Comparison chart of a small gap being a void before and after processing for an embodiment of the application;

[0069] Figure 11 Comparison chart of a small gap overlapping before and after processing for an embodiment of the application;

[0070] Figure 12 Comparison chart of a small gap boundary line being inaccurate before and after processing for an embodiment of the application;

[0071] Figure 13 Comparison chart of a fragment polygon sharp corner spot before and after processing for an embodiment of the application;

[0072] Figure 14 Data marking diagram for an embodiment of the application. DETAILED DESCRIPTION

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0075] Example

[0076] like Figure 1 As shown, a method for automatic identification and intelligent processing of sharp patches includes the following steps:

[0077] S1. Sharp corner recognition and extraction, including:

[0078] S1.1 Set the threshold for determining sharp-corner patches.

[0079] S1.2. Traverse all corner points of the polygon and calculate the degree measure of the angle between adjacent sides of the corner point. The method for calculating the degree measure of the angle between adjacent sides of the corner point is as follows: Represent the two adjacent sides of the corner point with vectors A and B respectively, and calculate the cosine of the angle θ between vectors A and B using the following formula:

[0080]

[0081] Then, the angle value of θ is obtained through the arctangent function arccos.

[0082] S1.3 Output the coordinates and values ​​of the sharp corner position, such as Figure 6 As shown, a vector table containing attribute information is displayed and generated through a graphical interface; the attribute information of the vector table includes the coordinates of the apex, the angle value, and the patch ID information, such as... Figure 7 As shown.

[0083] S2, Feature Analysis Classification, including:

[0084] S2.1 Extract the location features and corner features of the map features, and select the features most meaningful for classification from the extracted features; the location features of the map features include map feature coordinates, spatial relationship between adjacent map features, etc., and the corner features of the map features include the number of corners, the distribution of corners, the sharpness of corners, etc.

[0085] S2.2. Based on the extracted features, the sharp corners are classified into convex polygonal sharp corners that require intelligent processing (e.g., Figure 2 As shown), the sharp angle of the concave polygon (such as...) Figure 3 As shown), fragmented polygonal patches (such as...) Figure 4illustrated), thin slits (such as Figure 5 illustrated), and independent sharp corners that require manual processing;

[0086] S3, processing the sharp corner patches by an intelligent processing mechanism, including:

[0087] S3.1, for convex polygon sharp corner patches, cutting the convex sharp corner patches according to the spatial relationship of adjacent patches and merging them into adjacent patches according to the attribute consistency, spatial adjacency and distance, and area proportion rules. The cutting path algorithm is based on the shortest path cutting (using Dijkstra algorithm) or the minimum circumscribed circle cutting algorithm. For example, Figure 8 (a) of FIG. 1 shows the image before sharp corner processing, Figure 8 (b) of FIG. 1 shows the image after sharp corner processing.

[0088] S3.2, for concave polygon sharp corner patches, cutting and backfilling the sharp corners from the adjacent graphics. For example, Figure 9 (a) of FIG. 2 shows the image before sharp corner processing, (b) shows the image after sharp corner processing, (c) shows the image before sharp corner processing inside the layer, and (d) shows the image after sharp corner processing inside the layer.

[0089] S3.3, for sharp corner containing fragment polygon patches, when the patch area is less than a preset threshold, directly merge into adjacent patches. Fragment polygons refer to patches smaller than a related threshold, for example, the minimum mapping area of land change survey is 30 square meters. If the normal concave-convex polygon sharp corner processing method is used, it may cause the processed patch to be too small, so the direct fusion to the surrounding patch is adopted. Figure 13 (a) of FIG. 3 shows the image before fusion processing of the sharp corner containing fragment polygon patch, and (b) shows the image after fusion processing of the sharp corner containing fragment polygon patch.

[0090] S3.4, for the thin slits between patches. Thin slits usually refer to the small gaps or overlaps between patch boundary lines, which may be caused by errors in data acquisition or processing. The present application pre-processes the common point recognition algorithm for thin slit elimination, accurately locates the common endpoints of different line segments or polygons in vector graphics, quickly locks the potential common point positions that may cause thin slit problems, and then adjusts the original separate common points to coincide, thereby eliminating the thin slit; or based on the Delaunay triangulation algorithm, the topological structure of the graphics is reconstructed, and the thin slit area is filled.

[0091] If the thin slit is a gap, a filling algorithm can be used to fill the gap. For example, Figure 10 (a) of FIG. 4 shows the image before thin slit gap processing, and (b) shows the image after thin slit gap processing.

[0092] If the thin gap is caused by overlapping, the delete algorithm can be used to remove the redundant boundary lines. As shown in Figure 11 (a) represents the image before overlapping processing, and (b) represents the image after overlapping processing.

[0093] If the boundary line of the polygon element is not accurate, the adjustment algorithm can be used to adjust the position of the boundary line to eliminate the thin gap. As shown in Figure 12 (a) represents the image before thin gap gap processing, and (b) represents the image after thin gap gap processing.

[0094] S3.5, after processing all sharp corners, re-identify sharp corners and use topological inspection tools to verify whether the topological structure of the polygon is correct, and return to S3 for processing for those with topological errors, which helps to ensure that all sharp corners are repaired correctly, while avoiding the generation of other errors.

[0095] S4, non-destructive processing and result output, including:

[0096] S4.1, automatically copy the original data set to generate a processing copy, avoiding the loss or covering of the original data during the modification process;

[0097] S4.2, add a mark field or an explanation field in the processed data to distinguish between the original data and the modified data; the content of the mark field is filled with readability, and the expression is intuitive and easy to understand, all using easy-to-understand terms, as shown in Figure 14 .

[0098] S4.3, record the processing flow information, including algorithm type, parameter setting and operation sequence, which helps to reproduce the processing process when needed, or understand the impact of modification on the result.

[0099] S5, error handling and feedback mechanism

[0100] S5.1, real-time detection of processing process abnormalities, record error codes and detailed logs. Due to the complexity of data, errors and abnormal situations are inevitable in the entire automated sharp corner processing flow. In order to ensure the stability and continuity of the processing flow, a sound error handling mechanism is built. This mechanism should include error detection, recording, reporting and repair, etc. When the system encounters an exception, the corresponding processing measures can be automatically triggered, and detailed error information can be provided as much as possible to facilitate manual troubleshooting and correction.

[0101] S5.2, provide human-computer interaction feedback interface, support operation personnel to correct parameters or intervene in processing flow. In order to continuously improve the efficiency and accuracy of data processing, the feedback interface is arranged at the key processing node, so that the operation personnel can obtain the working state and result of the current stage in time, and the bottleneck problems and common error modes in the processing process are collected and analyzed regularly to guide the process improvement and innovation. Through the organic combination of the error processing and feedback mechanism, not only the existing problems can be effectively solved, but also the data processing flow can be continuously optimized and improved, so as to improve the overall work efficiency and result quality.

[0102] The sharp spot automatic identification and intelligent processing method of the application realizes the function of high-efficiency automatic processing of sharp corners, can improve work efficiency, reduce labor cost and improve data accuracy in the process of land change survey data processing. After many people and many rounds of experiments, it is found that manual elimination of sharp corners takes an average of 10 seconds per sharp corner, and a large amount of manual time is needed for topology checking after elimination. The technology can not only automatically locate the position of the sharp corner, but also automatically eliminate the sharp corner of the specified angle, which takes very little time and improves the efficiency by several tens of times. The automatic processing of sharp corner spots shows remarkable advantages in technical and application levels, not only brings convenience to natural resource monitoring projects such as land change survey, but also provides strong support for the processing and application of geographic information data.

[0103] A sharp corner spot automatic identification and intelligent processing device, comprising:

[0104] The sharp corner detection module is used for calculating the included angle of the spot and generating a position vector table;

[0105] The feature classification module is used for extracting the position feature and corner point feature of the spot, and classifying the sharp corner;

[0106] The intelligent processing module integrates processing algorithms and topology checking tools for different types of sharp corner spots;

[0107] The data management module realizes data backup, marking and process recording;

[0108] The man-machine interaction module provides a visual interface and an error feedback interface.

[0109] In addition to the corresponding system device, in combination with GIS or FME software related tools, the data processing logic of the existing project experience can be sorted out, and the sharp corner spot processing geographic processing model can be created by using the model builder.

[0110] In the application level, the sharp corner spot automatic identification and intelligent processing device has the following beneficial effects:

[0111] 1. Improve data processing efficiency

[0112] Automatic processing can quickly process a large number of sharp corner graph spots, reducing the time cost and labor cost of manual processing, and improving the overall work efficiency.

[0113] 2. Optimize resource allocation

[0114] Through automatic processing, it helps to avoid waste of resources and repeated investment, and improves the efficiency of resource utilization.

[0115] 3. Promote intelligent development

[0116] Automatic processing of sharp corner graph spots is an important part of the intelligent development of geographic information systems (GIS), remote sensing technology, etc. With the continuous progress of technology and the expansion of application scenarios, automatic processing will play an important role in more fields and promote the process of intelligent development.

[0117] The remaining technical features in the above embodiments can be flexibly selected by those skilled in the art according to actual conditions to meet different specific actual needs. However, it is obvious to those skilled in the art that these specific details are not necessary to implement the present application. In other examples, in order to avoid confusion of the present application, well-known components, structures or parts are not specifically described, and are within the technical scope defined in the claims of the present application.

[0118] Changes and variations made by those skilled in the art without departing from the spirit and scope of the present application shall be within the scope of protection of the appended claims of the present application. In the above description, a large number of specific details are set forth in order to provide a thorough understanding of the present application. However, it is obvious to those skilled in the art that these specific details are not necessary to implement the present application. In other examples, in order to avoid confusion of the present application, well-known technologies are not specifically described, such as specific construction details, operating conditions and other technical conditions.

[0119] The principles and embodiments of the present application are described in this paper, and the above examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in specific embodiments and application scope. In view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for automatic identification and intelligent processing of sharp patches, characterized in that, The steps are as follows: S1. Sharp corner recognition and extraction, including: S1.1 Set the threshold for determining sharp-corner patches. S1.

2. Traverse all corner points of the map patch and calculate the degree measure of the angle between adjacent sides of the corner point. The specific method is as follows: Represent the two adjacent sides of the corner point with vectors A and B respectively. The cosine of the angle θ between vectors A and B is calculated by the following formula: Then, the angle value of θ is obtained through the arctangent function arccos; S1.3 Output the position coordinates and coordinate values ​​of the sharp corner, display them through a graphical interface and generate a vector table containing attribute information. The attribute information of the vector table includes the sharp corner coordinates, angle values ​​and patch ID information. S2, Feature Analysis Classification, including: S2.1 Extract the location features and corner features of the map features, and select the features most meaningful for classification from the extracted features; the location features of the map features include the map feature coordinates and the spatial relationship between adjacent map features, and the corner features of the map features include the number of corners, the distribution of corners, and the sharpness of corners; S2.2 Based on the extracted features, sharp corners are divided into convex polygonal sharp corners that require intelligent processing, concave polygonal sharp corners, fragmented polygonal patches, fine slits, and independent sharp corners that require manual processing; S3. Sharp-angle patches are processed through an intelligent processing mechanism, including: S3.1 For convex polygonal sharp-corner patches, based on the spatial relationship between adjacent patches, cut the convex sharp-corner patches and merge them into adjacent patches according to rules; the path algorithm for cutting convex polygonal sharp-corner patches adopts Dijkstra's algorithm or the minimum circumcircle cutting algorithm. S3.2 For concave polygon sharp corner patches, the corner is backfilled by cutting the corner of the adjacent graphic; the backfilling operation of concave polygon sharp corners is based on Delaunay triangulation, Voronoi diagram and minimum circumcircle algorithm; S3.3 For fragmented polygonal patches with sharp corners, when the area of ​​the patch is less than a preset threshold, it is directly merged into an adjacent patch; S3.4 For small gaps between patches, eliminate gaps by filling gaps, deleting overlaps, or adjusting boundary lines; pre-processing of gaps uses a common point recognition algorithm to accurately locate the common endpoints of different line segments or polygons in vector graphics; S3.5 After processing all sharp corners, re-identify the sharp corners and verify them using the topology check tool. For those with topology errors, return to S3 for processing. S4. Non-destructive processing and result output, including: S4.1 Automatically copy the original dataset to generate a processing copy, avoiding the loss or overwriting of the original data during modification; S4.2 Add a tag or explanation field to the processed data to distinguish between the original data and the modified data; S4.3 Record processing flow information, including algorithm type, parameter settings and operation sequence; S5. Error Handling and Feedback Mechanism S5.1 Real-time detection of abnormalities during processing, recording error codes and detailed logs; S5.2 provides a human-machine interaction feedback interface to support operators in correcting parameters or intervening in the processing flow.

2. An automatic identification and intelligent processing device for sharp-corner patches using the method described in claim 1, characterized in that, include: The sharp corner detection module is used to calculate the included angle of the patch and generate a position vector table; The feature classification module is used to extract the location features and corner features of the patches, and to classify sharp corners; The intelligent processing module integrates processing algorithms and topology inspection tools for different types of sharp-corner patches; The data management module enables data backup, tagging, and process recording. The human-computer interaction module provides a visual interface and error feedback interface.

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