No-blank-space AOI data processing method and device, equipment and storage medium

Abstract

The invention provides a blank-space-free AOI data processing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the steps of obtaining no-blank-leaving AOI data, processing the no-blank-leaving AOI data to generate a processing result, determining a problem type corresponding to the no-blank-leaving AOI data according to the processing result, determining a corresponding repair strategy according to the problem types such as blank leaving, cover pressing, fragment long strips, splitting granularity and fine lines, executing the repair strategy, and repairing the no-blank-leaving AOI data. For the problem data of the part which cannot be automatically repaired, issuing the problem data to a crowdsourcing operator for manual repair in a task creating form; according to the embodiment of the invention, special repair can be carried out according to specific problem types and scenes, and the data repair efficiency and accuracy are improved.

Description

Background Art

[0002] After the production of non - blank AOI data is completed, during the application process, data quality problems such as blank spaces, overlapping, broken blocks and long strips, over - large splitting granularity, and thin lines may occur due to reasons such as production standards, business rules, and scene changes. Therefore, it is necessary to repair the data targeted.

[0003] In the prior art, the method of manually inspecting and repairing all non - blank AOI data to improve the quality of non - blank AOI data requires data operators to confirm one by one whether there are problems with the non - blank AOI data. It has the disadvantages of high labor and repair costs, inability to perform special repairs according to specific problem types and scenarios, lack of pertinence in repairing data, and inability to perform automated repairs, which affect the efficiency of data quality improvement and the accuracy of data repair.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The present disclosure provides a method, device, electronic device, and computer - readable storage medium for processing non - blank AOI data, which at least overcome the problem of low repair efficiency of non - blank AOI data in related technologies to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or will be learned in part through the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for processing non - blank AOI data is provided, including: obtaining non - blank region of interest (AOI) data; processing the non - blank AOI data to generate a processing result, and determining the problem type corresponding to the non - blank AOI data according to the processing result; determining a repair strategy according to the problem type; and executing the repair strategy to repair the non - blank AOI data.

[0008] In an embodiment of the present disclosure, the obtaining of non - blank region of interest (AOI) data includes: determining a plurality of effective surfaces corresponding to the region AOI data according to the region AOI data; aggregating the plurality of effective surfaces to obtain an aggregated surface; when the inner - ring data of the aggregated surface is greater than the inner - ring threshold and there is abnormal data in the aggregated surface, filling the inner - ring data with block data; when the inner - ring data of the aggregated surface is greater than the inner - ring threshold and there is no abnormal data in the aggregated surface, or when the inner - ring data of the aggregated surface is less than or equal to the inner - ring threshold, determining the AOI data as the non - blank AOI data.

[0009] In one embodiment of the present disclosure, the processing of the non - blank AOI data to generate a processing result and determining the problem type corresponding to the non - blank AOI data according to the processing result includes: performing a spatial intersection operation on the effective surface and the spatial data in the database to obtain one or more intersecting surfaces intersecting with the effective surface; calculating the intersection area between the effective surface and the intersecting surface, where the processing result includes the intersection area; when the intersection area is greater than the intersection threshold, determining that there is capping on the effective surface and the problem type is capping data problem.

[0010] In one embodiment of the present disclosure, the determining the repair strategy according to the problem type includes: when the problem type is the capping data problem, the repair strategy includes: performing a difference operation on the effective surface and the intersecting surface to clear the capping data, where the capping data is the data where the effective surface and the intersecting surface intersect.

[0011] In one embodiment of the present disclosure, the processing result includes the area data of each effective surface corresponding to the non - blank AOI data and / or the perimeter - to - area ratio of each effective surface; where determining the problem type corresponding to the non - blank AOI data according to the processing result includes: if there is an area data of an effective surface less than a preset fragment threshold, and / or there is a perimeter - to - area ratio of an effective surface less than a preset perimeter - to - area ratio threshold, then determining that the problem type corresponding to the non - blank AOI data is fragment and long - strip data problem.

[0012] In one embodiment of the present disclosure, the determining the repair strategy according to the problem type includes: when the processing result is that the area data of each effective surface corresponding to the non - blank AOI data is less than the fragment threshold, screening out the effective surfaces with area data less than the fragment threshold; when the perimeter - to - area ratio of each effective surface corresponding to the non - blank AOI data is less than the perimeter - to - area ratio threshold, screening out the effective surfaces with perimeter - to - area ratio less than the perimeter - to - area ratio threshold.

[0013] In one embodiment of the present disclosure, the processing of the non - blank AOI data to generate a processing result and determining the problem type corresponding to the non - blank AOI data according to the processing result includes: when the intersection data is greater than the quantity threshold, determining that the effective surface corresponding to the non - blank AOI data needs to be further split, generating a split task and determining that the problem type is split - granularity data problem.

[0014] In one embodiment of the present disclosure, the processing of the blank-free AOI data to generate a processing result and determining the problem type corresponding to the blank-free AOI data according to the processing result includes: traversing the target points corresponding to the blank-free AOI data; respectively calculating the cosine values of two vector values between the target point and the previous point and between the target point and the next point; when the cosine values of the two vector values are greater than a set value, determining that the problem type is thin line data problem.

[0015] In one embodiment of the present disclosure, determining the repair strategy according to the problem type includes: determining the target points with the cosine values of the two vector values greater than the set value as abnormal points; determining the positions where the thin lines are located according to the abnormal points; removing the abnormal points according to the positions where the thin lines are located.

[0016] In one embodiment of the present disclosure, it further includes: when the problem type is blank data problem, broken block long strip data problem or thin line data problem, creating corresponding tasks according to a preset range and sending them to the staff.

[0017] In one embodiment of the present disclosure, it further includes: when the problem type is split granularity data problem, receiving the split task created according to the split granularity data problem; determining whether the blank-free AOI data needs to be split according to the intelligence source data.

[0018] In one embodiment of the present disclosure, it further includes: when the problem type is capping data problem, determining the task identifier and task scope according to the capping data; creating a task according to the task identifier and the task scope and sending it to the staff.

[0019] In one embodiment of the present disclosure, it further includes: obtaining the repair result of executing the repair strategy, where the repair result includes: successful tasks, failed tasks, and tasks sent; where the failed tasks include: information on unsuccessful matching of repair strategies, information on unsuccessful execution of repair strategies, and the information on unsuccessful execution of repair strategies includes: un-repaired points, un-repaired lines, un-repaired surfaces or positions;

[0020] Sending the failed tasks to the staff for manual repair.

[0021] According to another aspect of the present disclosure, there is also provided a blank-free AOI data repair device, including:

[0022] A data acquisition module for acquiring blank-free area of interest (AOI) data;

[0023] A type determination module for processing the blank-free AOI data to generate a processing result and determining the problem type corresponding to the blank-free AOI data according to the processing result;

[0024] A policy determination module that determines a repair policy according to the problem type;

[0025] A data repair module that executes the repair policy to repair the non-blank AOI data.

[0026] According to another aspect of the present disclosure, an electronic device is further provided, including: a processor; and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the non-blank AOI data repair method described in any one of the above via executing the executable instructions.

[0027] According to another aspect of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored, and when the computer program is executed by a processor, the non-blank AOI data repair method described in any one of the above is implemented.

[0028] The non-blank AOI data processing method, device, electronic device and computer-readable storage medium provided by the embodiments of the present disclosure obtain non-blank area of interest (AOI) data, process the non-blank AOI data to generate a processing result, determine the problem type corresponding to the non-blank AOI data according to the processing result, determine the corresponding repair policy according to problem types such as blank space, capping, broken strips, splitting granularity, and thin lines, execute the repair policy to repair the non-blank AOI data, and for the problem data that cannot be automatically repaired, create tasks and send them to crowd-sourcing workers for manual repair, which can perform special repairs according to specific problem types and scenarios, improving the efficiency and accuracy of data repair.

[0029] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Description of the Drawings

[0030] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0031] Figure 1 Show a flowchart of a non-blank AOI data processing method in an embodiment of the present disclosure;

[0032] Figure 2 Show a flowchart of a blank AOI data processing method in an embodiment of the present disclosure;

[0033] Figure 3Schematic diagram of the problem of blank data in non - blank AOI data in an embodiment of the present disclosure;

[0034] Figure 4 Flowchart of a method for processing capping data in an embodiment of the present disclosure;

[0035] Figure 5 Schematic diagram of the problem of capping data in non - blank AOI data in an embodiment of the present disclosure;

[0036] Figure 6 Flowchart of a method for processing broken - block long - strip data in an embodiment of the present disclosure;

[0037] Figure 7 Schematic diagram of the problem of broken - block data in non - blank AOI data in an embodiment of the present disclosure;

[0038] Figure 8 Another flowchart of a method for processing broken - block long - strip data in an embodiment of the present disclosure;

[0039] Figure 9 Schematic diagram of the problem of long - strip data in non - blank AOI data in an embodiment of the present disclosure;

[0040] Figure 10 Flowchart of a method for processing split - granularity data in an embodiment of the present disclosure;

[0041] Figure 11 Schematic diagram of the problem of superposed information source data for split - granularity data in an embodiment of the present disclosure;

[0042] Figure 12 Flowchart of a method for processing thin - line data in an embodiment of the present disclosure;

[0043] Figure 13 Schematic diagram of the problem of superposed information source data for thin - line data in an embodiment of the present disclosure;

[0044] Figure 14 Another flowchart of a method for processing non - blank AOI data in an embodiment of the present disclosure;

[0045] Figure 15 Schematic diagram of the repair task for thin - line abnormal data in an AOI editing platform in an embodiment of the present disclosure;

[0046] Figure 16 Schematic diagram of a device for processing non - blank AOI data in an embodiment of the present disclosure;

[0047] Figure 17 Schematic diagram of an exemplary system architecture to which the method for processing non - blank AOI data or the device for processing non - blank AOI data according to the embodiments of the present disclosure can be applied; and

[0048] Figure 18 Shows a structural block diagram of an electronic device in an embodiment of the present disclosure. Detailed implementation manners

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0050] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0051] For ease of understanding, several terms related to the present disclosure are first explained as follows:

[0052] AOI (Area of Interest), used to mark an area that needs attention or has meaning on a surface, and can divide a blank surface into countless planes with spatial or business meanings.

[0053] The following will describe the example embodiments in detail with reference to the accompanying drawings and embodiments.

[0054] First, an AOI data processing method without blank spaces is provided in an embodiment of the present disclosure, and this method can be executed by any electronic device with computing and processing capabilities.

[0055] Figure 1 Shows a flowchart of an AOI data processing method without blank spaces in an embodiment of the present disclosure. As Figure 1 shown, the AOI data processing method without blank spaces provided in the embodiment of the present disclosure includes the following steps:

[0056] S102, Obtain AOI data of the area of interest without blank spaces.

[0057] AOI can divide a blank surface into countless planes with spatial or business meanings. It can be an area of interest in an Internet electronic map, representing geographical entity buildings or building groups in a regional shape on the map. It can also refer to the smallest and fixed unit range within a road area, which is the smallest range for collection and distribution, such as a community, an office building, a government agency, a factory building, a single building group, etc. Non - blank AOI refers to seamless block data with spatial or business meanings.

[0058] In one embodiment, non - blank AOI data is input. The non - blank AOI data is composed of seamless connections between blocks with spatial or business meanings. Each block is formed by connecting the head and tail of lines and filling the interior. Each line is given according to the topological structure. Each line L i , i ∈ {0, 1, ……, n} is defined by the coordinates on the line, and the connection relationship between them is defined by the connection points N i , i ∈ {0, 1, ……, m}. By a line, the connection points of the current broken line can be obtained.

[0059] S104. Process the non - blank AOI data to generate a processing result, and determine the problem type corresponding to the non - blank AOI data according to the processing result.

[0060] In one embodiment, the non - blank AOI data of the specified area is automatically detected according to algorithms and strategies. According to business rules and data application scenarios, the problems of non - blank AOI data are classified. Problems such as blank spaces, overlaps, fragmented long strips, splitting granularity, thin lines, etc. in the selected data can be detected.

[0061] S106. Determine the repair strategy according to the problem type.

[0062] One problem type can correspond to one repair strategy. The repair strategy can be a program for repairing the corresponding problem or the call address, identifier, etc. of the above program. The corresponding relationship between the problem type and the repair strategy can be recorded in the form of a corresponding relationship table, etc. In the corresponding relationship table, the address of the repair strategy, or the identifier of the repair strategy, etc. can be stored.

[0063] S108. Execute the repair strategy to repair the non - blank AOI data.

[0064] In one embodiment, for problems such as blank space data, overlap data, fragmented long - strip data, splitting granularity data, thin - line data, etc., determine the corresponding repair strategies and execute the repair strategies to repair the non - blank AOI data.

[0065] In one embodiment, obtain the repair result of executing the repair strategy. The repair result includes, but is not limited to: successful tasks, failed tasks, issued tasks, etc. The failed tasks can be sent to the staff for manual repair.

[0066] Failed tasks include, but are not limited to: failure to successfully match repair strategy information and failure to successfully execute repair strategy information; failure to successfully match repair strategy information means that the problem data fails to match the corresponding repair strategy. For example, when a valid surface is detected with split granularity data problems but no corresponding repair strategy for the split granularity data is matched; failure to successfully execute repair strategy information means the information corresponding to an exception occurred during the execution of the repair strategy. The information on failure to successfully execute repair strategy includes, but is not limited to: task identifier, unrepaired points, unrepaired lines, unrepaired surfaces or positions, problem types, review information, etc. For example, the location information of the unrepaired exception point.

[0067] The tasks issued are tasks for problem data that cannot be automatically repaired, which are issued to crowdworkers for manual repair in the form of creating tasks; the problem data includes, but is not limited to: blank data, capping data, fragmented long strip data, split granularity data, thin line data, etc. The crowdworker receives the task of repairing abnormal data and performs data repair operations on the AOI editing platform. First, the task name and type that the crowdworker needs to repair will be displayed in the upper left corner of the editing platform, and then there will be an associated problem data identifier, which will be automatically filled in the search box. The crowdworker only needs to press Enter to locate the problem data and perform repairs. Operations such as data merging and splitting are involved during the repair process. During the repair process, the crowdworker can load a reference AOI to view the specific location of the problem data and directly locate the problem points for repair.

[0068] Successful tasks are the repair results after successfully executing the repair strategy. For example, successfully clearing abnormal points, clearing capping surfaces, etc.

[0069] In the above embodiments, for different business rules and scenarios, problems such as blank spaces, capping, fragmentation, long thin strips, excessive split granularity, and thin lines are automatically detected and repaired through programs. The problem data that cannot be automatically repaired by the program is issued to crowdworkers in the form of creating different types of tasks. The workers perform repairs on the AOI editing platform. For specific problem scenarios, the automated detection and repair logic is used to reduce labor and repair costs, improve data quality, enhance efficiency, and improve the accuracy of data repair.

[0070] Figure 2 Show a flowchart of a method for processing blank AOI data in an embodiment of the present disclosure. As Figure 2 shown, the method for processing blank AOI data provided in the embodiment of the present disclosure includes the following steps:

[0071] S202. Determine multiple valid surfaces corresponding to the regional AOI data according to the regional AOI data;

[0072] In one embodiment, to automatically detect the problem of blank space data according to the city dimension, first, obtain the non-blank AOI data of a given area in the data master database PostGIS based on the spatial information of the urban administrative division boundary gbFence. Each face has its spatial position information, which is stored in a vector data structure. The vector data structure is a data organization method that uses points, lines, planes, and their combinations in Euclidean geometry to represent the spatial distribution of geographical entities.

[0073] The vector data structure can be divided into a simple data structure and a topological data structure. The simple data structure only records the position coordinates and attribute information of spatial objects, without topological relationships and cannot describe the relationships between them. The topological data structure not only expresses geometric positions and attributes but also represents spatial relationships. Each spatial object contains an identification code and an attribute code. For each stored spatial object, there is a unique code to connect the geometric and attribute data, and its storage method is to use a point position dictionary to record the information of points, lines, and planes.

[0074] The non-blank AOI data is first composed of a series of longitude and latitude points (e.g., POINT(longitude A latitude B)) sequentially connected to form a line (e.g., LINESTRING(longitude A latitude B, longitude C latitude D, longitude E latitude F)), and the lines are connected end to end to form a non-blank AOI face (e.g., POLYGON(longitude A latitude B, longitude C latitude D, longitude E latitude F, longitude A latitude B)). There are also often some abnormal data types in the data, such as unclosed data: referring to the missing start or end point of the face, resulting in an abnormal data where the face cannot be closed; self-intersecting data: an error type in the geometric figure validity verification. Self-intersection of face elements is the most common in the original data. Some of these errors can be discovered manually, but some require the help of a program to discover. If self-intersection is not handled, it will cause the element to be unable to be converted into the specified format or unable to complete operations such as writing into the database. The root causes of self-intersection are quite diverse. Some are due to accidental misoperations by the mapping personnel, such as repeatedly adding nodes when collecting feature nodes; and some are also caused by different data precisions set by some inspection or processing software when processing the data, which may also lead to self-intersection.

[0075] Traverse each face in the given Area of Interest (AOI) data and detect the validity of this face, that is, determine whether the face is closed and whether it is self-intersecting data. If there are problems, modify it to obtain a valid face. Modification of closed data: The program supplements the start point or end point data so that a series of longitude and latitude points are sequentially connected to form a line, and the lines are connected end to end to form face data. Modification of self-intersecting data: When processing self-intersecting data, it is required that the coordinates do not change at all. An object needs to be broken into two objects, and points are added at the intersection so that the Polygon data of only one object is broken into two objects and combined into MULTIPOLYGON data; among them, Polygon data refers to a planar surface defined by 1 outer boundary and 0 or more inner boundaries; MULTIPOLYGON data refers to a "multi-surface" whose elements are "polygons".

[0076] S204, Aggregate multiple valid faces to obtain an aggregated face;

[0077] In one embodiment, all valid faces are put into the temporary PostGIS database table. The temporary PostGIS database table stores the AOI data without blank spaces that needs to be detected. Traverse to obtain each face, and perform the aggregation Union operation on the two obtained valid faces until all valid faces have undergone such operations, and finally aggregate them into a large face, that is, the aggregated face.

[0078] S206, When the inner ring data of the aggregated face is greater than the inner ring threshold and the aggregated face has abnormal data, fill the inner ring data of the aggregated face with block data to fill in the missing blank data; Figure 3 Show a schematic diagram of the blank data problem of the AOI data without blank spaces in an embodiment of the present disclosure. Area 1 is the missing blank data.

[0079] It should be noted that the inner ring threshold is a value set automatically or manually according to user needs or historical data, etc.

[0080] S208, When the inner ring data of the aggregated face is greater than the inner ring threshold and the aggregated face has no abnormal data, or when the inner ring data of the aggregated face is less than or equal to the inner ring threshold, determine the AOI data as the AOI data without blank spaces.

[0081] In one embodiment, abnormal data includes but is not limited to: closed, self-intersecting data, etc.

[0082] In one embodiment, the inner ring data of the aggregated surface is then obtained, and the inner ring data that meets the policy requirements is filled into block data according to the set inner ring threshold; specifically, there will be some holes inside the aggregated surface formed by aggregating all the non-blank AOI data in the area, and these holes are the inner ring data. A series of longitude and latitude points of each independent inner ring data are obtained, connected in sequence to form a line, and the line is connected end to end to form a surface. It is judged whether the area of the surface is greater than the set inner ring threshold. If it is greater than the inner ring threshold, it is judged whether the surface is closed or self-intersecting data. If so, the inner ring data of the aggregated surface is filled into block data. If not, non-blank AOI data is formed. If the area of the surface is less than or equal to the set inner ring threshold, non-blank AOI data is formed.

[0083] In one embodiment, when the inner ring data of the aggregated surface is greater than the inner ring threshold and there is abnormal data in the aggregated surface, the problem type is blank data problem, and corresponding tasks are created according to the preset range and sent to the staff.

[0084] In one embodiment, when the inner ring data of the aggregated surface is greater than the inner ring threshold and there is abnormal data in the aggregated surface, the problem type is blank data problem. The inner ring data of the aggregated surface is filled into block data to fill the missing blank data, and a data identification code is generated for storage. Taking the data identification code as a clue, the task range is expanded by a preset range outside the Polygon data, such as expanding 200 meters, and sent to the crowd-sourcing operator for further inspection, merging or splitting of the filled data.

[0085] In the above embodiment, the blank data problem in the non-blank AOI data is detected and repaired, which meets the business rule that there should be no gaps and missing blank data in the non-blank AOI data, reduces the manual and repair costs, improves the data quality, efficiency and data repair accuracy.

[0086] Figure 4 The flowchart of a method for processing capping data in an embodiment of the present disclosure is shown, as Figure 4 shown, the method for processing capping data provided in the embodiment of the present disclosure includes the following steps:

[0087] S402, perform a spatial intersection operation on the valid surface and the spatial data in the database to obtain one or more intersecting surfaces that intersect with the valid surface;

[0088] In one embodiment, the capping data problem is automatically detected according to the city dimension, that is, all valid surfaces are put into the PostGIS data temporary table, each surface is traversed and obtained, and the spatial intersection Intersects operation is performed on the Polygon spatial information of the surface and the Geom spatial data of all data in the PostGIS data temporary table.

[0089] Spatial data Geom refers to spatial location information; spatial intersection Intersects refers to a method for determining whether there is an intersection inside geometric figures. If two figures have the same spatial part, that is, if their boundaries or interiors intersect, then ST_Intersects(Geom A, Geom B) returns TRUE.

[0090] When obtaining the data corresponding to a valid face, check whether there is data in the PostGIS data temporary library table that intersects with its boundary or interior. Until all the data in the PostGIS data temporary library table has been traversed, all the data that intersects with the valid face can be obtained.

[0091] S404, calculate the intersection area between the valid face and the intersecting face, where the processing result includes the intersection area;

[0092] S406, when the intersection area is greater than the intersection threshold, determine that there is an overlay on the valid face and the problem type is an overlay data problem.

[0093] It should be noted that the intersection threshold is a value set automatically or manually according to user needs or historical data, etc.

[0094] In one embodiment, obtain all the faces that intersect with the valid face, then use the spatial intersection Intersect to obtain the intersecting faces between the valid face and the intersecting faces pairwise, calculate the corresponding intersection areas of the intersecting faces, and determine whether the intersection area is greater than the intersection threshold set by the service. If it is greater, it means there is an overlay between the two faces, and the overlay face is filled.

[0095] Figure 5 Shows a schematic diagram of an overlay data problem of AOI data without blank space in an embodiment of the present disclosure. Region 2 is the overlay data.

[0096] S408, when the problem type is an overlay data problem, the repair strategy includes: performing a difference operation between the valid face and the intersecting face to clear the overlay data, where the overlay data is the data that intersects between the valid face and the intersecting face.

[0097] In one embodiment, when it is found that there is an overlay between two faces, that is, the valid face A and the intersecting face B, perform a spatial intersection Intersects operation on the Polygon spatial information of the valid face A and the Geom spatial data of all the data in the PostGIS data temporary library table to obtain the overlay face C of the two faces. Select any one of the valid face A and the intersecting face B to perform a difference operation with the overlay face C (that is, subtract the C part from the face), and the overlay face C can be attributed to any one of the valid face A and the intersecting face B, achieving the purpose of clearing the overlay data.

[0098] S410, when the problem type is an overlay data problem, determine the task identifier and task scope according to the overlay data;

[0099] S412. Create a task based on the task identifier and task scope and assign it to the staff.

[0100] In one embodiment, determine how many capping areas there are between the effective surface and the intersecting surface. If it is greater than 1, use the surface identifier corresponding to the surface with the smallest area among the two surfaces as a clue, and the task scope is expanded 200 meters outward based on the polygon data of the surface with the smallest area. The purpose of using the surface with the smallest ID as a clue is to facilitate the crowdsourcing operator to quickly and accurately locate the capping problem area and be able to operate quickly. If it is equal to 1, the capping common surface can be obtained through spatial intersection operations, and a preset distance, such as 200 meters, is expanded outward from this surface to assign tasks to the crowdsourcing operator for merging and splitting, minimizing the task scope and reducing the interference of redundant data on the operator when repairing data. If the surface identifier with the largest area is used as a clue, it increases the cost for the crowdsourcing operator to discover and find problems and reduces the efficiency of repairing data.

[0101] In the above embodiment, the capping data problem in the non - blank AOI data is detected and repaired, meeting the business rule that data capping is not allowed in the non - blank AOI data, reducing the manual and repair costs, improving the data quality, enhancing the efficiency, and improving the accuracy of data repair.

[0102] Figure 6 Show a flowchart of a method for processing fragmented long - strip data in an embodiment of the present disclosure. As Figure 6 shown, the method for processing fragmented long - strip data provided in the embodiment of the present disclosure includes the following steps:

[0103] S602. Process the non - blank AOI data to generate a processing result;

[0104] Among them, the processing result includes the area data of each effective surface corresponding to the non - blank AOI data and / or the perimeter - to - area ratio of each effective surface;

[0105] S604. Determine that the problem type corresponding to the non - blank AOI data is a fragmented long - strip data problem according to the processing result.

[0106] In one embodiment, if there is an area data of an effective surface less than a preset fragmentation threshold, and / or there is a perimeter - to - area ratio of an effective surface less than a preset perimeter - to - area ratio threshold, it is determined that the problem type corresponding to the non - blank AOI data is a fragmented long - strip data problem.

[0107] It should be noted that the fragmentation threshold and the perimeter - to - area ratio threshold are values set automatically or manually according to user needs or historical data, etc.

[0108] Figure 7 Show a schematic diagram of a fragmented data problem in non - blank AOI data in an embodiment of the present disclosure. Region 3 is fragmented data.

[0109] S606. When the area data of each valid surface corresponding to the non - blank AOI data without blank spaces is less than the fragment threshold during processing, the valid surfaces with area data less than the fragment threshold are screened out.

[0110] In one embodiment, traverse each valid surface, obtain the area data of each valid surface, and then set the fragment threshold according to the business to screen out the meaningless fragment surface data.

[0111] S608. When the problem type is the fragment long - strip data problem, create corresponding tasks according to the preset range and send them to the staff.

[0112] In the above - mentioned embodiment, in the actual data application, the fragment long - strip has no specific meaning. Therefore, in the application scenario, this part of meaningless data needs to be removed. An algorithm repair strategy is formulated for the fragment long - strip data problem for repair, and the problem data that cannot be automatically repaired by the program is sent to the crowd - sourced workers in the form of creating different types of tasks. The workers repair it on the AOI editing platform, reducing the manual and repair costs, improving the data quality, efficiency, and data repair accuracy.

[0113] Figure 8 Show a flowchart of another method for processing fragment long - strip data in an embodiment of the present disclosure. As Figure 8 shown, the method for processing fragment long - strip data provided in the embodiment of the present disclosure includes the following steps:

[0114] S802. Process the non - blank AOI data to generate a processing result.

[0115] Among them, the processing result is that the perimeter - to - area ratio of each valid surface corresponding to the non - blank AOI data is less than the perimeter - to - area ratio threshold.

[0116] S804. Determine that the problem type corresponding to the non - blank AOI data is the fragment long - strip data problem according to the processing result.

[0117] Figure 9 Show a schematic diagram of the long - strip data problem of non - blank AOI data in an embodiment of the present disclosure. Region 4 is the long - strip data.

[0118] S806. When the perimeter - to - area ratio of each valid surface corresponding to the non - blank AOI data is less than the perimeter - to - area ratio threshold, screen out the valid surfaces with a perimeter - to - area ratio less than the perimeter - to - area ratio threshold.

[0119] In one embodiment, traverse each valid surface, obtain the information of the longest side and the perimeter of each valid surface, calculate the ratio of the longest side of the valid surface to the perimeter, and then screen out the meaningless long - strip information according to the long - perimeter ratio threshold set by the business.

[0120] S808. When the problem type is the fragmented long-strip data problem, create corresponding tasks according to the preset range and distribute them to the staff. That is, using the surface identifier as a clue, the task range expands a preset distance outward from the center point of the surface. For example, it expands 200 meters outward, and then distribute the tasks to the crowd-sourcing workers for merging and splitting.

[0121] In the above embodiments, in actual data applications, fragmented long strips have no specific meaning. Therefore, in the application scenario, this part of meaningless data needs to be removed. An algorithm repair strategy is formulated for the fragmented long-strip data problem for repair, and the problem data that cannot be automatically repaired by the program is distributed to the crowd-sourcing workers in the form of creating different types of tasks. The workers perform repairs on the AOI editing platform, reducing labor and repair costs, improving data quality, enhancing efficiency, and improving data repair accuracy.

[0122] Figure 10 Show a flowchart of a split-granularity data processing method in an embodiment of the present disclosure. As Figure 10 shown, the split-granularity data processing method provided in the embodiment of the present disclosure includes the following steps:

[0123] S1002. When the intersecting data is greater than the quantity threshold, determine that the effective surface corresponding to the AOI data without blank space needs to be further split, generate a split task, and determine that the problem type is the split-granularity data problem.

[0124] It should be noted that the quantity threshold is a value automatically or manually set according to user needs or historical data, etc.

[0125] In one embodiment, traverse each effective surface, perform a spatial intersection Intersects operation on the Polygon spatial information of the effective surface and the Geom spatial data of all data in the AOI database table with blank space, and obtain the quantity of all intersecting data with the effective surface, that is, the quantity of intersecting surfaces. When the quantity of intersecting surfaces is greater than the quantity threshold set by the business rules, it is prompted that the split granularity of the effective surface is too large and needs to be further split.

[0126] The AOI database table with blank space stores another batch of AOI data that allows gaps between surfaces. Traverse each data in the PostGIS data temporary table, search for the data that intersects with its boundary or inside in the AOI database table with blank space, and obtain the number of all AOI data with blank space that intersects with this AOI data without blank space. When the quantity of intersecting surfaces is greater than the quantity threshold set by the business rules, it is prompted that the split granularity of the surface is too large and needs to be further split.

[0127] S1004. When the problem type is the split-granularity data problem, receive the split task created according to the split-granularity data problem;

[0128] In one embodiment, when the problem type is the problem of splitting granularity data, corresponding tasks are created according to a preset range and sent to the staff. That is, taking the face identifier as a clue, the task range expands a preset distance outward from the center point of the face. For example, it expands 200 meters outward, and the crowdsourcing operator is sent to perform the splitting.

[0129] S1006. Determine whether the non-white-space AOI data needs to be split according to the intelligence source data.

[0130] In one embodiment, when the problem types are problems such as white space, capping, broken blocks and long strips, splitting granularity, and thin lines, it is possible to determine whether the non-white-space AOI data needs to be split according to the intelligence source data.

[0131] In one embodiment, Figure 11 FIG. shows a schematic diagram of superimposing intelligence source data on a problem of splitting granularity data in an embodiment of the present disclosure. Region 5 is the splitting granularity data. When repairing, the operator judges whether the non-white-space AOI data needs to be further split by checking intelligence source data such as maps and remote sensing images. When further splitting is required, it is processed manually on the editing tool platform; the role of superimposing intelligence source data such as map data is to intuitively display the entity corresponding to this non-white-space AOI data on the map, and more clearly and intuitively show whether the splitting granularity of the non-white-space AOI data is too large and whether further splitting is required.

[0132] In the above embodiment, in actual data applications, the data granularity size affects application scenarios such as road area division. When the granularity is too large, further splitting is required to meet the business requirements. An algorithm repair strategy is formulated for this data problem for repair, and the problem data that cannot be automatically repaired by the program is sent to the crowdsourcing operator in the form of creating different types of tasks. The operator repairs on the AOI editing platform, reducing labor and repair costs, improving data quality, enhancing efficiency, and data repair accuracy.

[0133] Figure 12 FIG. shows a flowchart of a method for processing thin line data in an embodiment of the present disclosure. As Figure 12 shown, the method for processing thin line data provided in the embodiment of the present disclosure includes the following steps:

[0134] S1202. Traverse the target points corresponding to the non-white-space AOI data;

[0135] S1204. Calculate the cosine values of the two vector values of the target point and the previous point, and the target point and the next point respectively.

[0136] In one embodiment, traverse each valid face corresponding to the non-white-space AOI data, and then traverse each target point P corresponding to the valid face i , i ∈ {0, 1, ……, n}, from the i-th target point P of the valid face iStart and calculate P i With P i-1 And P i With P i+1 Calculate the vector values of P and P, and calculate the cosine value of the two vectors.

[0137] S1206, when the cosine value of the two vector values is greater than the set value, determine that the problem type is thin line data problem.

[0138] In one embodiment, when the cosine value of the two vector values is greater than the set value (for example, 0.9999 can be taken), it is determined that there is a thin line data problem on the effective surface.

[0139] S1208, determine the target points where the cosine value of the two vector values is greater than the set value as abnormal points;

[0140] S1210, determine the position where the thin line is located according to the abnormal points;

[0141] S1212, remove the abnormal points according to the position where the thin line is located.

[0142] In one embodiment, determine the target points where the cosine value of the two vector values is greater than the set value as abnormal points, perform a spatial intersection Intersects operation on the calculated abnormal points and the corresponding surface for removing abnormal points, and judge whether the thin line is inside or outside; Figure 13 Show a schematic diagram of thin line data problem superposed with source data in an embodiment of the present disclosure. Region 6 is the data where the detected thin line is inside, and region 7 is the data where the detected thin line is outside.

[0143] In one embodiment, to remove the data where the thin line is inside the surface, the purpose of repair can be achieved by removing abnormal points; for the thin line outside the surface, abnormal points can be removed, and then the surface formed by the points before and after the abnormal points can be filled.

[0144] In one embodiment, when the problem type is thin line data problem, create a corresponding task according to the preset range and send it to the staff. That is, taking the surface identifier as a clue, after clearing the abnormal points, the task range is expanded by a preset distance outside the polygon data, for example, expanded by 10 meters, and then sent to the crowdsourcing operator for merging and splitting.

[0145] It should be noted that the thin line data problem repair tasks are divided into two categories: manual inspection tasks and manual operation repair tasks; manual inspection tasks are relatively simple, only checking whether there are problems with the automated repair data. If there are problems, they will be repaired and reported to optimize the automated repair strategy; for manual operation repair tasks, if there are problem data, the operator needs to perform merging and splitting operations; clearing abnormal point data belongs to the operation of automated repair, but manual inspection is required. Generally, manual operation is not needed. After receiving the task, the operator only needs to check whether there are other problems. If there are no problems, the task can be directly submitted.

[0146] In the above embodiments, during the production of non - blank AOI, due to data accuracy problems, thin line problems will occur during the merging operation by the operator. Such data problems will affect the data usage of the business. For this specific problem, an algorithm repair strategy is formulated for repair, and the problem data that cannot be automatically repaired by the program is sent to the crowdsourcing operator in the form of creating different types of tasks. The operator repairs it on the AOI editing platform, reducing labor and repair costs, improving data quality, efficiency, and data repair accuracy.

[0147] Figure 14 The flowchart of another non - blank AOI data processing method in the embodiments of the present disclosure is shown. As Figure 14 shown, the non - blank AOI data processing method provided in the embodiments of the present disclosure includes the following steps:

[0148] S1402, obtain non - blank area of interest (AOI) data.

[0149] S1404, process the non - blank AOI data to generate a processing result, and determine the problem type corresponding to the non - blank AOI data according to the processing result.

[0150] In one embodiment, the non - blank AOI data input to the specified area is automatically detected according to the algorithm and strategy, and the non - blank AOI data problems are classified according to business rules and data application scenarios. Problems such as blank space, capping, broken blocks and long strips, splitting granularity, and thin lines in the selected data can be detected.

[0151] S1406, determine the repair strategy according to the problem type, and execute the repair strategy to repair the non - blank AOI data.

[0152] S1408, create a specific repair task for the data of the un - repaired problem type in the form of creating a task;

[0153] S1410, send the specific repair task to the crowdsourcing operator for manual repair;

[0154] S1412, the editing platform receives the specific repair task for repair.

[0155] In one embodiment, the crowdsourcing operator receives the abnormal data repair task and performs data repair operations on the AOI editing platform. First, the task name and type that the crowdsourcing operator needs to repair are displayed in the upper left corner of the editing platform. Then, there will be an associated problem data identifier, which is automatically filled in the search box. The crowdsourcing operator only needs to press Enter to locate the problem data and perform repairs. Operations such as data merging and splitting are involved in the repair process; Figure 15 Fig. shows a schematic diagram of a thin line abnormal data repair task on the AOI editing platform in an embodiment of the present disclosure. M is the abnormal point, area 8 and area 9 are the target areas corresponding to the thin line abnormal data repair task on the AOI editing platform, and area 9 is the problem data area located after pressing Enter in the search box.

[0156] In the above embodiment, specific problems of non-blank AOI data are automatically detected through programs for different business rules and scenarios. Algorithm repair strategies are formulated for specific problems for repair, and the problem data that cannot be automatically repaired by the program is sent to the crowdsourcing operator in the form of creating different types of tasks. The operator repairs on the AOI editing platform, reducing labor and repair costs, improving data quality, enhancing efficiency, and improving data repair accuracy.

[0157] Based on the same inventive concept, an apparatus for processing non-blank AOI data is also provided in an embodiment of the present disclosure, as described in the following embodiment. Since the principle of solving problems in this apparatus embodiment is similar to that of the above method embodiment, the implementation of this apparatus embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be described again.

[0158] Figure 16 Fig. shows a schematic diagram of an apparatus for processing non-blank AOI data in an embodiment of the present disclosure, as Figure 16 shown, the apparatus 16 for processing non-blank AOI data includes: a data acquisition module 1601, a type determination module 1602, a strategy determination module 1603, and a data repair module 1604;

[0159] The data acquisition module 1601 acquires non-blank region of interest (AOI) data;

[0160] The type determination module 1602 processes the non-blank AOI data to generate a processing result, and determines the problem type corresponding to the non-blank AOI data according to the processing result;

[0161] In one embodiment, the type determination module 1602 includes, but is not limited to: a blank data determination module, a capping data determination module, a broken block and long strip data determination module, a splitting granularity data determination module, a thin line data determination module, etc., for determining different problem data.

[0162] The strategy determination module 1603 determines a repair strategy according to the problem type;

[0163] In one embodiment, the policy determination module 1603 includes, but is not limited to, a blank space policy determination module, a capping policy determination module, a broken block and long strip policy determination module, a splitting granularity policy determination module, a thin line policy determination module, etc., for determining different repair policies.

[0164] The data repair module 1604 executes the repair policy to repair the non-blank AOI data.

[0165] In one embodiment, a task determination module is further included, which is used to obtain the repair results of executing the repair policy, where the repair results include, but are not limited to, successful tasks, failed tasks, issued tasks, etc.

[0166] In the above embodiment, for different business rules and scenarios, problems such as blank spaces, capping, broken blocks, long and thin strips, too large splitting granularity, and thin lines are automatically detected and repaired by the program. The problem data that cannot be automatically repaired by the program is sent to the crowd-sourcing operator in the form of creating different types of tasks. The operator repairs it on the AOI editing platform. For specific problem scenarios, the automatic detection and repair logic is used to reduce the manual and repair costs, improve the data quality, enhance the efficiency, and improve the data repair accuracy.

[0167] Figure 17 The figure shows a schematic diagram of an exemplary system architecture to which the non-blank AOI data processing method or non-blank AOI data processing device according to the embodiments of the present disclosure can be applied.

[0168] As Figure 17 shown, the system architecture 1700 may include terminal devices 1701, 1702, 1703, a network 1704, and a server 1705.

[0169] The network 1704 is a medium for providing a communication link between the terminal devices 1701, 1702, 1703 and the server 1705, and can be a wired network or a wireless network.

[0170] Optionally, the above-mentioned wireless network or wired network uses standard communication technologies and / or protocols. The network is usually the Internet, but can also be any network, including but not limited to any combination of a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or a virtual private network. In some embodiments, technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent the data exchanged through the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec), etc. can be used to encrypt all or some of the links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0171] The terminal devices 1701, 1702, 1703 can be various electronic devices, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, etc., for displaying the task names and types that the crowdsourcing workers need to repair.

[0172] Optionally, the clients of the application programs installed in different terminal devices 1701, 1702, 1703 are the same, or the clients of the same type of application programs based on different operating systems. Depending on the different terminal platforms, the specific form of the client of the application program can also be different. For example, the client of the application program can be a mobile phone client, a PC client, etc.

[0173] The server 1705 can be a server that provides various services, such as a background management server that supports the operations performed by the user using the terminal devices 1701, 1702, 1703. The background management server can analyze and process data such as requests received, and feedback the processing results to the terminal devices for operations such as obtaining non-white-space region of interest (AOI) data, processing the non-white-space AOI data to generate a processing result, determining the problem type corresponding to the non-white-space AOI data according to the processing result, determining a repair strategy according to the problem type, and executing the repair strategy.

[0174] Optionally, the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not make any restrictions here.

[0175] Those skilled in the art can know that Figure 17 the number of terminal devices, networks, and servers in

[0176] Those skilled in the art to which the present disclosure pertains can understand that various aspects of the present disclosure can be implemented as a system, a method, or a program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuits", "modules", or "systems" here.

[0177] Next, refer to Figure 18 to describe the electronic device 1800 according to this embodiment of the present disclosure. Figure 18 The electronic device 1800 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0178] As Figure 18 shown, the electronic device 1800 is presented in the form of a general-purpose computing device. The components of the electronic device 1800 may include, but are not limited to: at least one of the above-mentioned processing units 1810, at least one of the above-mentioned storage units 1820, and a bus 1830 that connects different system components (including the storage unit 1820 and the processing unit 1810).

[0179] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 1810, so that the processing unit 1810 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0180] For example, the processing unit 1810 may execute the following steps of the above method embodiments: obtain non - blank area of interest (AOI) data, process the non - blank AOI data to generate a processing result, determine the problem type corresponding to the non - blank AOI data according to the processing result, determine the corresponding repair strategy according to problem types such as blank space, capping, broken long strips, splitting granularity, thin lines, etc., execute the repair strategy to repair the non - blank AOI data, and for the problem data that cannot be automatically repaired, create a task and send it to the crowd - sourcing operator for manual repair.

[0181] The storage unit 1820 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 18201 and / or a cache storage unit 18202, and may further include a read - only storage unit (ROM) 18203.

[0182] The storage unit 1820 may also include a program / utilities 18204 having a set (at least one) of program modules 18205. Such program modules 18205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0183] The bus 1830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0184] The electronic device 1800 may also communicate with one or more external devices 1840 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1800, and / or may communicate with any device that enables the electronic device 1800 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 1850. And the electronic device 1800 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1860. As shown in the figure, the network adapter 1860 communicates with other modules of the electronic device 1800 through the bus 1830. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0185] Those skilled in the art can easily understand from the description of the above embodiments that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a portable hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0186] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, which can be a readable signal medium or a readable storage medium. A program product capable of implementing the above method of the present disclosure is stored thereon. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of this specification.

[0187] For example, when the program product in the embodiment of the present disclosure is executed by a processor, it implements a method with the following steps: obtaining non-blank area of interest (AOI) data, processing the non-blank AOI data to generate a processing result, determining the problem type corresponding to the non-blank AOI data according to the processing result, determining corresponding repair strategies according to problem types such as blank area, overlapping, fragment and long strip, splitting granularity, and thin line, executing the repair strategies to repair the non-blank AOI data, and for the problem data that cannot be automatically repaired, creating a task and sending it to the crowd-sourcing operator for manual repair.

[0188] More specific examples of the computer-readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0189] In the present disclosure, the computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program used by or in combination with an instruction execution system, apparatus, or device.

[0190] Optionally, the program code contained on a computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0191] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., connected through the Internet using an Internet service provider).

[0192] It should be noted that although several modules or units of a device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described modules or units may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by a plurality of modules or units.

[0193] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0194] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0195] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the appended claims.

Claims

1. A method for processing non - blank AOI data, characterized in that, Including: Obtaining non - blank area of interest (AOI) data; Processing the non - blank AOI data to generate a processing result, and determining the problem type corresponding to the non - blank AOI data according to the processing result; Determining a repair strategy according to the problem type; Executing the repair strategy to repair the non - blank AOI data.

2. The method for processing non - blank AOI data according to claim 1, characterized in that, The obtaining of non - blank area of interest (AOI) data includes: Determining a plurality of effective surfaces corresponding to the area AOI data according to the area AOI data; Aggregating the plurality of effective surfaces to obtain an aggregated surface; When the inner - ring data of the aggregated surface is greater than the inner - ring threshold and the aggregated surface has abnormal data, filling the inner - ring data with block data; When the inner - ring data of the aggregated surface is greater than the inner - ring threshold and the aggregated surface has no abnormal data, or when the inner - ring data of the aggregated surface is less than or equal to the inner - ring threshold, determining the AOI data as the non - blank AOI data.

3. The method for processing non - blank AOI data according to claim 2, characterized in that, The processing of the non - blank AOI data to generate a processing result and determining the problem type corresponding to the non - blank AOI data according to the processing result includes: Performing a spatial intersection operation between the effective surface and the spatial data in the database to obtain one or more intersecting surfaces intersecting with the effective surface; Calculating the intersection area between the effective surface and the intersecting surface, where the processing result includes the intersection area; When the intersection area is greater than the intersection threshold, determining that the effective surface has capping and the problem type is capping data problem; Among them, the determining of the repair strategy according to the problem type includes: When the problem type is the capping data problem, the repair strategy includes: performing a difference operation between the effective surface and the intersecting surface to clear the capping data, where the capping data is the data intersecting between the effective surface and the intersecting surface; When the problem type is the capping data problem, determining the task identifier and task scope according to the capping data; creating a task according to the task identifier and the task scope and sending it to the staff.

4. The method for processing non - blank AOI data according to claim 2, characterized in that, The processing result includes the area data of each effective surface corresponding to the non - blank AOI data and / or the perimeter - to - area ratio of each effective surface; Among them, the determining of the problem type corresponding to the non - blank AOI data according to the processing result includes: If there is an area data of an effective surface less than a preset fragment threshold, and / or there is a perimeter - to - area ratio of an effective surface less than a preset perimeter - to - area ratio threshold, determining that the problem type corresponding to the non - blank AOI data is fragment and long - strip data problem; Among them, the determining of the repair strategy according to the problem type includes: When the processing result is that the area data of each effective surface corresponding to the non - blank AOI data is less than the fragment threshold, screening out the effective surfaces with area data less than the fragment threshold; When the perimeter - to - area ratio of each effective surface corresponding to the non - blank AOI data is less than the perimeter - to - area ratio threshold, screening out the effective surfaces with perimeter - to - area ratio less than the perimeter - to - area ratio threshold.

5. The method for processing non - blank AOI data according to claim 2, characterized in that, The processing of the non - blank AOI data to generate a processing result and determining the problem type corresponding to the non - blank AOI data includes: When the intersecting data is greater than the quantity threshold, it is determined that the effective surface corresponding to the non-blank AOI data needs to be further split, a split task is generated, and the problem type is determined to be a split-granularity data problem; When the problem type is a split-granularity data problem, receive the split task created according to the split-granularity data problem; determine whether the non-blank AOI data needs to be split according to the intelligence source data.

6. The method for processing non - blank AOI data according to claim 1, characterized in that, Processing the non-blank AOI data to generate a processing result, and determining the problem type corresponding to the non-blank AOI data according to the processing result includes: Traverse the target points corresponding to the non-blank AOI data; Calculate the cosine values of the two vector values of the target point and the previous point, and the target point and the next point respectively; When the cosine values of the two vector values are greater than the set value, determine that the problem type is a thin-line data problem; Among them, determining the repair strategy according to the problem type includes: Determine the target points with the cosine values of the two vector values greater than the set value as abnormal points; Determine the position where the thin line is located according to the abnormal points; Remove the abnormal points according to the position where the thin line is located.

7. The method for processing AOI data without blank spaces according to claim 1, wherein, Also includes: Obtain the repair result of executing the repair strategy, where the repair result includes: successful tasks, failed tasks, and issued tasks; Among them, the failed tasks include: failure to successfully match the repair strategy information, failure to successfully execute the repair strategy information, and the failure to successfully execute the repair strategy information includes: unrepaired points, unrepaired lines, unrepaired surfaces or positions; Send the failed tasks to the staff for manual repair.

8. An apparatus for repairing AOI data without blank spaces, wherein, Includes: A data acquisition module that acquires non-blank area of interest (AOI) data; A type determination module that processes the non-blank AOI data to generate a processing result, and determines the problem type corresponding to the non-blank AOI data according to the processing result; A strategy determination module that determines a repair strategy according to the problem type; A data repair module that executes the repair strategy to repair the non-blank AOI data.

9. An electronic device, wherein, Includes: A processor; And A memory for storing executable instructions of the processor; Among them, the processor is configured to execute the non-blank AOI data processing method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the non-blank AOI data processing method according to any one of claims 1 to 7.

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