Geographic information software performance evaluation big data generation management system

Abstract

According to the geographic information software performance evaluation big data generation management system provided by the invention, a whole set of whole process system from production to management of a GIS software evaluation data set is established; comprising GIS map DLG error rule data set construction, GIS software evaluation big data model expansion, an error rule data and expanded GIS software evaluation big data management system and a GIS software evaluation data management prototype system. Four basic geographic data error types are covered, and model data are rapidly expanded in a semi-automatic mode based on a mirror image expansion method; data model import, model data expansion and error rule data generation and management are carried out, a data layer and a host are connected through a logic layer plug-in, unified management of model data and error rule generation data is realized based on SuperMap iServer, the quality and efficiency of geographic information product quality control and inspection are improved, and the method is suitable for large-scale popularization and application. The function completeness and fault tolerance of quality inspection software are effectively verified, and the authority of geographic information product inspection is improved.

Description

Technical Field

[0001] The present application relates to a GIS software evaluation data generation and management system, and in particular to a geographic information software performance evaluation big data generation and management system, which belongs to the field of big data software testing technology. Background Art

[0002] Geographic information systems fully integrate emerging Internet technologies such as big data and cloud computing to carry out industrial innovation. Under this trend, the structure of GIS software is more complex, and GIS software testing faces new challenges and higher requirements. GIS software evaluation has its own unique features, such as high system complexity, complex system expression, and GIS data occupying an important position in testing. In response to these difficulties, targeted evaluation strategies should be adopted to improve the test data set. The evaluation data set should include correct data and data containing error rules. Correct data can effectively verify the completeness of software functions and system stability; data containing error rules can fully verify the fault tolerance of basic geographic data quality inspection software and the completeness of quality inspection functions.

[0003] Testing of GIS software requires the application of some special testing methods and techniques, the design of reasonable and effective test cases, and more effective organization and implementation of testing. Test cases are the key to software testing. Test cases containing reasonable and unreasonable input data can more comprehensively test whether the software functions are complete, improve software quality, discover more errors, and improve software reliability.

[0004] The problems that need to be solved in the prior art multi-source vector-grating remote sensing image registration and the key technical difficulties of this application include:

[0005] (1) GIS software has a complex structure, GIS software testing is challenging and has higher requirements. The GIS software evaluation system is highly complex, the system expression is complex, and GIS data plays an important role in testing. In response to these difficulties, the existing technology lacks targeted evaluation strategies and sound test data sets. The evaluation data set lacks correct data and data containing error rules. Correct data cannot effectively verify the completeness of software functions and system stability; data with error rules cannot fully verify the fault tolerance and completeness of quality inspection functions of basic geographic data quality inspection software. The existing technology GIS software testing lacks some special testing methods and techniques, lacks reasonable and effective test cases, cannot effectively organize and implement testing, and cannot comprehensively test whether the software functions are complete, which is not conducive to improving software quality, discovering more errors, and improving software reliability.

[0006] (2) In the current GIS software testing process, due to data security and confidentiality, data production lag, sample data that does not meet functional requirements, etc., it is impossible to obtain targeted test samples and effectively verify the software functions, which seriously affects the test quality and efficiency. Although manual editing is a feasible way to provide test data and can solve the source problem of GIS test data to a certain extent, manual editing is time-consuming and has many disadvantages in terms of inspection content, inspection cost, inspection efficiency, etc., and the editing results may not fully meet the test requirements. In addition, due to different operating habits of operators, the manual editing method is greatly affected by the operators. Different operators will inadvertently introduce some errors, making the manual editing results not meet the requirements. Especially in software testing related to map quality inspection, it is necessary to test the completeness of software functions and the ability to detect various quality problems. However, due to various reasons such as security and confidentiality and data production, the data that can be provided for software testing is often small data: that is, the data volume is small, and the data types are few, which cannot cover all data types and are not representative, so the test results are often incomplete. The existing technology lacks a data set design and management system for GIS software testing combined with the actual production operations.

[0007] (3) The existing technology lacks a complete system for the whole process of GIS software evaluation data set production and management, lacks an error rule library for vector topographic maps, cannot cover the four basic geographic data error types, and lacks semi-automatic rapid expansion of model data based on the mirror extension method; it cannot import data models, expand model data, generate and manage error rule data, and cannot connect the data layer and the host through a logical layer plug-in. It lacks the unified management of model data and error rule generation data based on SuperMap iServer. Before software development, all possible error conditions are not analyzed, and these error conditions cannot be used as a data production input condition to generate evaluation data, and cannot meet the addition of data types of various landform features; there are many types of vector errors, and each error rule must correspond to an error rule operator. The existing technology cannot express these error rules in a language that can be understood by computers and add them to the evaluation data; it lacks automated evaluation data production technology, the model library does not contain data of various data categories, and it cannot automatically expand the prototype system to generate a large range of evaluation data according to actual evaluation needs. There is a lack of a dataset design and management prototype system for GIS software testing. It is impossible to effectively manage datasets in the "data model + error rule combination" mode, and it is impossible to quickly and automatically generate target sample evaluation data based on the test error types contained in the customized evaluation dataset by testers. The quality inspection of GIS software is not yet perfect, the inspection content is not comprehensive enough, and the designed algorithm still has some defects. It cannot effectively verify the functional completeness and fault tolerance of the quality inspection software, which is not conducive to improving the authority of geographic information product inspection. Summary of the invention

[0008] This application establishes a complete system for the whole process of GIS software evaluation data set from production to management, including the construction of GIS map DLG error rule data set, the expansion of GIS software evaluation big data model, the management system of error rule data and the expanded GIS software evaluation big data, and the GIS software evaluation data management prototype system; establishes an error rule library for vector topographic maps, covering four basic geographic data error types, and semi-automatically expands model data based on the mirror extension method; adopts the SuperMap plug-in development form to import data models, expand model data, generate and manage error rule data, connect the data layer and the host through the logic layer plug-in, realizes unified management of model data and error rule generation data based on SuperMap iServer, uses SuperMap iDeskop as the host program, and realizes the operation transmission between the host and the data layer through SuperMap iObjects.NET; improves the quality and efficiency of geographic information product quality control and inspection, effectively verifies the functional completeness and fault tolerance of quality inspection software, and improves the authority of geographic information product inspection.

[0009] In order to achieve the above technical effects, the technical solutions adopted in this application are as follows:

[0010] Geographic information software performance evaluation big data generation and management system, establish a complete set of full-process systems from production to management of GIS software evaluation data sets, including GIS map DLG error rule data set construction, GIS software evaluation big data model expansion, error rule data and expanded GIS software evaluation big data management system and GIS software evaluation data management prototype system; establish an error rule library for vector topographic maps, covering four basic geographic data error types, and semi-automatically expand model data based on the mirror extension method; adopt the SuperMap plug-in development form to import data models, expand model data, generate and manage error rule data, connect the data layer and the host through the logic layer plug-in, realize unified management of model data and error rule generation data based on SuperMapiServer, use SuperMap iDeskop as the host program, and realize the operation transmission between the host and the data layer through SuperMap iObjects.NET;

[0011] 1) Construction of GIS map DLG error rule dataset: Analyze all possible errors in GIS, use these errors as a data production input condition to generate evaluation data, and the vector error rule library accommodates all vector data error types; error rules cover four types of vector data quality checks in large categories, and meet the addition of data types of various landform features;

[0012] 2) GIS software evaluation big data model extension: define vector error rule operators and add them to the evaluation data; introduce mirroring into the model data extension, and combine the characteristics of DLG data to obtain that the objects represented by DLG data are composed of vector attributes and data attributes in storage. Vector attributes are points, lines, and surfaces, and data attributes are other categories of information of their abstract representative elements. Based on mirroring, DLG model extension is established from vector attributes and data attributes;

[0013] 3) GIS software evaluation big data management system: including evaluation system architecture, evaluation data set production and creation, GIS big data management, automated evaluation data production, and selecting corresponding category data contained in the model library according to actual evaluation needs to automatically expand and generate a wide range of evaluation data;

[0014] 4) GIS software evaluation data management prototype system: manages data sets of data model + error rule combination mode, generates target sample evaluation data according to the test error types contained in the custom evaluation data set, and provides browsing, display, editing and operation.

[0015] Preferably, the GIS map DLG error rule data set is constructed: based on the DLG data quality standards and the logical characteristics of eight major elements, namely, vegetation, transportation and its ancillary facilities, residential areas and their ancillary facilities, water systems and their ancillary facilities, survey control points, landforms, boundaries, and soil quality, combined with the GIS software's need to express DLG data, a DLG data error rule set is established;

[0016] (I) Mathematical accuracy error rules: including elevation accuracy, plane position accuracy, spatial reference system, and edge accuracy;

[0017] (II) Attribute precision error rules: including symbols, notes, numbers, and text, attribute precision errors include:

[0018] (1) The attribute value is not unique;

[0019] (2) Attribute values ​​are empty: a) elevation value of contour line; b) elevation value of elevation point; c) null value of control point attribute; d) null value of water tower elevation value; e) null value of sluice elevation value; f) null value of square network annotation; g) null value of house attribute value;

[0020] (3) Attribute value range error: If the elevation of the area where the elevation range is located is within 100, then check the objects with elevation values ​​within the value range of (u, 0) and (100, u) and identify them as illegal elevation points;

[0021] (4) Directed point attribute errors: a) Sluice gate: the direction of the tip is opposite to the direction of water flow, which is an error; b) The direction of river flow and ditch flow is wrong; c) The slope line should point to the direction of decreasing elevation; d) The tooth line of the steep slope should point to the direction of decreasing elevation of the contour line;

[0022] (5) Inconsistency of elevation point map attributes: the attribute content of elevation point is inconsistent with the elevation annotation content;

[0023] (6) Inconsistency of residential ground attributes: The residential ground attribute content is inconsistent with the annotation content;

[0024] (7) The classification and coding of point elements are incorrect;

[0025] (III) Completeness and accuracy error rules: data scope, spatial entity type, attribute feature classification, whether the data should be covered to the location that should be covered, reflected in data organization, data format, data layering, and file layer name errors;

[0026] (IV) Logical consistency error rules: DLG data topology rules include two aspects: 1) Topology of a layer itself: the geometric attributes of the layer are consistent, and it is divided into point layer, line layer, and surface layer; 2) The topology between two layers is divided into: point-surface topology, point-line topology, point-surface topology, line-line topology, line-surface topology, and surface-surface topology.

[0027] Preferably, vector attribute mirroring based on DLG geometry:

[0028] Point: A point is a geometric object with two-dimensional (X, Y) or (B, L) position coordinates, and an elevation value (Z) coordinate, or other values. Point elements represent survey control points, wells, township locations, shopping malls, and institutional locations. When used as points, they have no area attributes.

[0029] Line: A line is a cluster composed of a set of continuous points. A line is represented by the coordinates of a set of points (nodes). A line represents a road, river, boundary line, or landform. When used as a line object, its length cannot be zero.

[0030] Polygon: It is a closed area, representing lakes, buildings, administrative divisions, and is a ring object or multiple ring objects. However, as a surface element, its enclosed area must be a closed interval.

[0031] Layer: A collection of points, lines, and areas with similar attributes.

[0032] From the storage perspective in the database, vector attributes are points, lines, surfaces, and layers. Points in the database are stored with their names, IDs, and X and Y coordinates. Lines are stored in the database in the form of multiple points, with point numbers and position coordinates arranged in the order of connection of points on the line. Surfaces are also stored in the form of multiple points, but are connected end to end, with the first and last nodes being the same point. Considering that both lines and surfaces are stored in the form of multiple points in the database, the vector attributes of lines and surfaces are mirrored in the same way.

[0033] Point mirroring: For a certain layer, it is expanded in four directions: east, west, south, and north. The expansion principle in each direction is the same. Expand in the east direction to get the bounding box of the layer, determine the range of the layer, and set the four corner points of the bounding box, the upper, lower, left, and right points, as P 1 , P 2 , P 3 , P 4 , for the expansion of the point, P(x p ,y p ) means that in the east mirror image, the Y value of the coordinate of the point remains unchanged, while the X value becomes the original X value plus twice the distance from the right margin to point P;

[0034] Similarly, the left side of the point after mirroring in the east, west, south, and north directions corresponds to:

[0035] P E (x p +2×(x P3 -x p), y p ) Formula 1

[0036] P W (x p -2×(x p -x P1 ), y p ) Formula 2

[0037] P S (x p ,y p -2×(y p -y P2 )) Formula 3

[0038] P N (x p ,y p +2×(y P1 -y p )) Formula 4

[0039] Mirror image of a line: A line is composed of multiple continuous points. By obtaining the data records in the line layer data set, a series of point coordinate values ​​are obtained. After mirror image expansion, the coordinate relationship between corresponding points satisfies the mirror image relationship of the points.

[0040] Mirror image of a surface: The multi-points corresponding to the surface are a multi-point sequence connected end to end. The principle of mirror image expansion of a surface is the same as that of mirror image expansion of a line.

[0041] Preferably, data attribute mirroring based on DLG feature classification: vector data mirroring of points, lines and surfaces is obtained by mirroring on the vector, but the classification of features also affects the features of the features, and these attributes are called data attributes. On the basis of the mirror extension of the vector attributes, the mirror extension of the data attributes is performed;

[0042] (1) Point class: Point objects only have vector attributes and no data attribute mirroring. The geographic entity represented by the point is independent and does not have any self-elevation or self-directional contradictions in space. After vector attribute mirroring, a single point object still maintains its own correctness. In addition, when the lines and surfaces around the point are mirrored, the elevation area of ​​the point and the area surrounded by the contour lines can always remain consistent. The point does not need to be mirrored for data attributes.

[0043] (2) Line type: select three representative line elements: contour lines, power lines, and river lines;

[0044] Contour class: Select contour lines to represent a line composed of a series of points with equal or similar elevation values. After the contour line vector attribute is mirrored and expanded, when the contour line is located at the periphery of the layer frame, after mirroring, the continuity at the edge can be maintained;

[0045] For the logical consistency of DLG data, there is no need to consider how to transform at the edge. The original contour layer is mirrored in the east and north directions, and then the east mirrored data is mirrored in the north direction. The contour lines at the edge are finally formed into a ring, forming a closed contour line. In addition, the area around the contour line also maintains the continuity in elevation. For the contour line type landform line, after the vector attribute is mirrored, a one-to-one corresponding data mirroring is used, that is, the data attributes of each point on the original line are matched one by one;

[0046] River line type: represents lines with directionality due to natural conditions of geographical elevation;

[0047] Power line: A linear representation of power transportation. The biggest feature of this type of line is that it has directionality. For this type of linear data mirroring, the data attribute replication method is used to obtain a set of sequence point coordinates through vector mirroring. However, the order of these points is not mirrored according to the original arrangement, but the original directionality remains unchanged; in the eastward vector mirroring, the first point P of the line is 1 Coordinates, get a position coordinate related to it, but the serial number of this position point is not the mirrored P 1 , but the P in the new line n ,After using data attribute replication, the linear ,adjacent parts of the power line class remain consistent in direction;

[0048] Mirroring of surface data attributes: Surfaces are also composed of point sets, but compared with linear elements, which are also composed of point sets, there are two huge differences: one is that lines have directionality; the other is that lines do not need to consider closure, but surfaces do. Based on these two points, the data attributes of surfaces are also mirrored. For surface elements, the advantage is that vectors with consistent data attributes always intersect at the edges, and there will be no situations that are inconsistent with reality, such as half a house and half a lake being connected.

[0049] Preferably, DLG data mirroring extension based on SuperMap: using mirroring to extend vector attributes, maintaining good edge connection at the edges, mirroring of vector attributes and mirroring of data attributes, and processing on the storage content of points, lines and surfaces based on the plug-in library of the SuperMap platform;

[0050] Traversing layers: Based on the MapControl control of SuperMap, the MapControl control obtains the current Data, implements operations on the map layer, obtains the bounding box of the layer frame after traversal, and obtains the coordinate range of the boundary line;

[0051] Specific vector categories: For each layer, there is a certain vector category corresponding to it, and vector attributes are expanded and then data attributes are expanded in turn; for the point layer, its own elevation logic is not considered, only vector attributes are mirrored, and there is no data attribute mirroring; for lines, there are three cases. For contour line types without direction, vector attributes are mirrored first, and then data attributes are mirrored to maintain good edge connection; for power line types, directional lines that are not related to elevation, vector attributes are mirrored first, and then data attributes are copied to maintain directional consistency; for river line types, directional lines related to elevation, vector attributes are mirrored first, and data attributes are temporarily mirrored; for surfaces, vector attributes are mirrored first, and then data attributes are mirrored.

[0052] Preferably, the evaluation system architecture: the sample data and error rule management system architecture is divided into three layers: application software layer, database layer, and support layer;

[0053] Application software layer: It consists of two components, namely, simulation data production and production component and data management component. Simulation data production and production is responsible for the production and production of sample data. Its bottom layer is supported by the error rule engine, which provides the combination of error data. Data management processes the data acquisition, organization, management and update of the model database, error rule library and achievement sample library.

[0054] Database layer: stores model data, simulation data results and metadata information, which are maintained by data management software. Metadata provides the simulation test data production and production software and data management software with the required description information of various types of data, including the error types and data types in the data. The model database stores models of various data types, including topographic map models, geomorphic map models, and water system map models, simulation data results library and test sample data, and stores the results of simulation data production. The data production component writes the simulation data results into the test sample database, and the data management component is responsible for obtaining the test sample data and providing it to the testers for use;

[0055] Support layer: It is composed of network system, computer system and platform software. The platform software is SuperMap series software. iServer provides the organization and management of model data and simulation data results. SuperMap Object provides secondary development based on SuperMap to realize visual management of geographic data.

[0056] Preferably, the evaluation data set production and production: the whole system is summarized into seven modules: map data, error rule production, automatic evaluation data production, interactive evaluation data production, production data result inspection, and production data result output;

[0057] Map data: some basic functions common to all modules of the software, including the management of vector and cartographic data, various data display and browsing, symbol setting and data query and retrieval;

[0058] Error rule creation: According to the software testing requirements, data error rules are formed by designing catalog models, various terrains, error check items, data packet models and quality evaluation models;

[0059] Automatic test data generation: Based on the error rule library, it can realize the automatic, efficient and batch generation of various error data, and also realize the automatic generation of some common error data;

[0060] Test data interactive production: For some contents that are difficult to automatically produce errors, computer-assisted and convenient interactive production tools and error identification tools are provided to quickly generate, locate and mark errors;

[0061] Production data result inspection: provide viewing and confirmation of production results and error information, and calculate and evaluate production results;

[0062] Produce data result output: output error graphics, error details and simulation record tables for easy modification, filing or storage.

[0063] Preferably, GIS big data management: evaluation data management realizes the integrated management of model data, error rules and evaluation data results generated by evaluation data survival and production components, including data publishing, data query browsing, data update, error rule management and system user management modules;

[0064] (1) Data management module: comprehensive management of model data and data results. The data publishing submodule realizes batch storage and release of data and configures the storage plan. The data query and browsing submodule realizes fast conditional query of data. The query methods include metadata query and spatial information query. At the same time, it realizes multiple ways of result browsing, including tabular information, map display and calculation chart. The data update submodule realizes data maintenance and update.

[0065] (2) Error rule management module: This module implements comprehensive management of system built-in error rules and user-defined error rules. Error rules are stored in the form of files. Through the management system, users can publish user-defined error rules to the server and query, browse, update, and download them to the local computer.

[0066] (3) System management module: implements security management and daily maintenance of management software, including user management, user rights management, and system log management.

[0067] Preferably, the GIS software evaluation data management prototype system: the main interface is divided into three parts, the upper part is the menu part, the lower left part is the layer management, and the lower right part is the map display;

[0068] Data management: The menu bar on the main interface of the system consists of two parts. The upper one is the basic toolbar, including pointer, roaming, zoom in, zoom out, free zoom, and global display; the lower one is the toolbar, including opening workspace, loading model, expanding model, adding errors, and publishing model. The menu bar is classified according to the functions implemented, and is divided into three categories: basic map functions, evaluation data production, and data management;

[0069] When the user opens the workspace or loads the model directly on the server, the layer management is used to select the layer that needs to be operated, and the map display part displays the map and operation results accordingly.

[0070] Preferably, the GIS software evaluation data management prototype system includes:

[0071] (I) Basic map function module: Basic map display is a component provided by SuperMaplObjects.NET, which realizes the basic viewing of data sets of each layer. Click the zoom in, zoom out, and free zoom buttons on the panel, and use the mouse wheel to zoom in and out. Select the roaming mode to cooperate with the zoom view, and select the pointer mode to select a specific record in the data set;

[0072] (ii) Extension model: After the user selects a specific attribute set, the prototype system obtains the current NOTE node to obtain the entire current map range, and can traverse the data record content in the middle of the layer through MapControl to perform mirroring expansion;

[0073] (III) Adding error rules: To implement adding line discount errors, the user selects a certain linear data, clicks the "Element" button on the panel, and then selects "Add line discount errors" from the "Add error" drop-down box on the panel. The prototype system traverses the original linear element set, randomly searches for a point on each line element, generates a line discount error, and visually displays the place where the line discount error is added, and highlights the place where the error is added.

[0074] Compared with the prior art, the innovations and advantages of this application are:

[0075] (1) This application establishes the vector error rule dataset design and database construction technology. According to the GIS software evaluation function and performance test requirements, the basic requirements for dataset design for GIS software testing are proposed; error rules are summarized in combination with a series of vector data quality inspection standards such as national standards and industry standards; such as attribute errors and topological rule errors, vector data error rules are classified and encoded to form a vector data error rule library. A GIS software evaluation dataset generation technology based on model extension is established, which converts error rules into programming language and generates evaluation data containing various error rule types; based on various landform type model extensions, large-scale model data is automatically generated; model data is combined with data of various combined error types. The GIS software evaluation dataset management prototype system is designed and developed. According to the use requirements of GIS software evaluation for datasets, the dataset management system prototype system framework is designed; database construction and data model management methods are established, the prototype system is implemented, and different data efficiency evaluations are performed. A software has been developed that can generate test data for vector map error rules to detect the correctness of quality software functions. It can produce various types of landform features or application features to fully ensure the correctness of software functions and the authority of the test. Under normal circumstances, this data is managed uniformly by the server. When test data is needed, qualified data sets can be automatically extracted according to the test requirements of the tester.

[0076] (2) This application is based on SuperMap as a development platform, and adopts a plug-in framework for the development of evaluation data production and production software, including a host program and a plug-in object. The first is the organization and database construction of vector map error data rules, analyzing all possible error situations, and using these error situations as a data production input condition to generate evaluation data. The vector error rule library needs to accommodate all vector data error types as much as possible. Error rules should cover the four types of vector data quality checks in large categories, and be able to meet the addition of data types of various landform features. The second is the definition of vector error rule operators. There are many types of vector errors, and each error rule must correspond to an error rule operator. These error rules are expressed in a language that the computer can understand and added to the evaluation data. The third is automated evaluation data production technology. According to actual evaluation needs, users select corresponding model data, and the prototype system automatically expands and generates a wide range of evaluation data. The fourth is the data set management technology. We designed and implemented a data set design and management prototype system for GIS software testing. This system can effectively manage data sets in the "data model + error rule combination" mode. According to the test error types contained in the test data set customized by the tester, it can quickly and automatically generate target sample evaluation data, and provide browsing, display, editing, and operation functions.

[0077] (3) Aiming at the dilemma of incomplete and small GIS software evaluation data, this application proposes a complete set of GIS software evaluation data generation and management technology processes, from the establishment of an error rule library, to the automatic expansion of model data, and then to the plug-in development of the evaluation data generation and management prototype system. A vector data error rule library was established; a data automatic expansion method was constructed, and the model data expansion was realized by using the mirror replication method; a GIS evaluation data generation and management prototype system was developed, adding basic map functions, evaluation data generation functions, model data and evaluation data management functions, and the system was performance tested. The innovation also lies in the proposal of data set design and management technology for GIS software evaluation, verifying the feasibility of evaluation data set design and management for various vector terrain map error rules; developing software with simulation generation of evaluation data, which can effectively solve the problem of limited test sample data and insufficient data, and produce suitable data sets for daily GIS software testing work. Correct data can effectively verify the correctness and completeness of system functions, and data of various combined error types can help verify the completeness of the software quality inspection module function. At the same time, it can help detect the fault tolerance, stability and reliability of the detection system, thereby improving the efficiency and quality of testing.

[0078] (4) This application establishes a complete system for the whole process from production to management of GIS software evaluation data sets, including the construction of GIS map DLG error rule data set, the expansion of GIS software evaluation big data model, the management system of error rule data and the expanded GIS software evaluation big data, and the prototype system of GIS software evaluation data management; establishes an error rule library for vector topographic maps, covering four basic geographic data error types, and semi-automatically expands model data based on the mirror extension method; adopts the SuperMap plug-in development form to import data models, expand model data, generate and manage error rule data, connect the data layer and the host through the logic layer plug-in, realize the unified management of model data and error rule generation data based on SuperMap iServer, use SuperMap iDeskop as the host program, and realize the operation transmission between the host and the data layer through SuperMap iObjects.NET; improve the quality and efficiency of geographic information product quality control and inspection, effectively verify the functional completeness and fault tolerance of quality inspection software, and improve the authority of geographic information product inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a mirror image of the midpoint of the vector attribute mirror image based on the DLG geometry.

[0080] Figure 2 It is a mirror image of the midline of the vector attribute mirror based on the DLG geometry.

[0081] Figure 3 It is a mirror image of the surface in the vector attribute mirror based on the DLG geometry structure.

[0082] Figure 4 It is a schematic diagram of the mirror expansion of contour vector attributes.

[0083] Figure 5 It is a river mirror image based on the data attributes of DLG feature classification.

[0084] Figure 6 It is a model expansion method and flow chart based on SuperMap.

[0085] Figure 7 It is a system structure diagram for the production and preparation of evaluation data.

[0086] Figure 8 It is a schematic diagram of the prototype system data management loading model.

[0087] Fig. 9 It is a template data extension test list diagram.

[0088] Fig.10 It is an error rule to add a performance test list diagram. DETAILED DESCRIPTION

[0089] The following, in conjunction with the accompanying drawings, further describes the technical solution of the geographic information software performance evaluation big data generation and management system provided by the present application, so that technical personnel in this field can better understand the present application and implement it.

[0090] This application establishes a complete system for the whole process from production to management of GIS software evaluation data sets, including the design of error rule database, automated expansion of model data, generation of evaluation data by combining error rule data with expanded model data, and development of a prototype system for evaluation data and model data management.

[0091] GIS data includes digital line drawings (DLG), digital raster maps (DRG), digital orthophotos (DOM), and digital elevation models (DEM). This application focuses on establishing an error rule library for vector topographic maps, covering four basic geographic data error types.

[0092] GIS model data expansion, in order to address the dilemma of "small and incomplete" GIS evaluation data, it is necessary to expand GIS data to obtain large-scale and large-volume evaluation data. Evaluation data is divided into correct evaluation data and evaluation data containing erroneous rules, but no matter which type, it is necessary to expand the generation and increase the data volume. For GIS model data expansion, after analyzing the random generation method and the mirror expansion method, this application concluded that the random generation method has poor operability, and adopted the mirror expansion method to semi-automatically and quickly expand the model data.

[0093] Evaluation data generation and management prototype system. This application adopts the SuperMap plug-in development form, which has high flexibility and can effectively import data models, expand model data, and generate and manage error rule data. The data layer and the host are connected through the logic layer (plug-in), and SuperMapiServer is used to achieve unified management of model data and error rule generation data. SuperMapiDeskop is used as the host program, and SuperMapiObjects.NET is used to realize the operation transfer between the host and the data layer.

[0094] 1. Construction of GIS map DLG error rule dataset

[0095] According to the DLG data quality standards and the logical characteristics of eight major elements including vegetation, transportation and its ancillary facilities, settlements and their ancillary facilities, water systems and their ancillary facilities, survey control points, landforms, boundaries, and soil quality, combined with the needs of GIS software for DLG data expression, a DLG data error rule set was established.

[0096] (I) Mathematical Precision Error Rules

[0097] Including elevation accuracy, plane position accuracy, spatial reference system, and edge accuracy.

[0098] (II) Attribute precision error rules

[0099] Including symbols, notes, numbers, text, attribute precision errors include:

[0100] (1) The attribute value is not unique;

[0101] (2) Attribute values ​​are empty: a) elevation value of contour line; b) elevation value of elevation point; c) null value of control point attribute; d) null value of water tower elevation value; e) null value of sluice elevation value; f) null value of square network annotation; g) null value of house attribute value;

[0102] (3) Attribute value range error: If the elevation of the area where the elevation range is located is within 100, then check the objects with elevation values ​​within the value range of (u, 0) and (100, u) and identify them as illegal elevation points;

[0103] (4) Directed point attribute errors: a) Sluice gate: The direction of the tip is opposite to the direction of water flow, which is an error; b) The direction of river flow (point) and ditch flow (point) is wrong; c) The slope line should point to the direction of decreasing elevation; d) The tooth line of the steep slope should point to the direction of decreasing elevation of the contour line;

[0104] (5) Inconsistency of elevation point map attributes: the attribute content of elevation point is inconsistent with the elevation annotation content;

[0105] (6) Inconsistency of residential ground attributes: The residential ground attribute content is inconsistent with the annotation content;

[0106] (7) The classification and coding of point features are incorrect.

[0107] (III) Completeness and Accuracy Error Rules

[0108] The data scope, spatial entity type, attribute feature classification, whether the data should be covered to the location to be covered, is reflected in data organization, data format, data layering, and file layer name errors.

[0109] (IV) Logical consistency error rules

[0110] DLG data topology rules include two aspects: 1) The topology of a layer itself: the geometric attributes of the layer are consistent, and it is divided into point layer, line layer, and surface layer; 2) The topology between two layers is divided into: point-surface topology, point-line topology, point-surface topology, line-line topology, line-surface topology, and surface-surface topology.

[0111] 1. Point layer: The error is a point duplication topology error.

[0112] 2. Line layer:

[0113] (1) Line layer: line overlap errors, including water system line overlaps: single-line surface rivers, double-line ditches, and single-line dry ditches overlap each other;

[0114] (2) Line self-intersection error: Applicable to road lines, water system lines, boundary lines, vegetation lines, residential lines, landform lines, and soil lines;

[0115] (3) Line hanging node error: including road endpoints or vertex hanging: roads should be connected and interlinked. When a road line has a hanging node and there are other hanging nodes within a certain critical value range, it does not meet the specifications;

[0116] (4) The line has a pseudo-node error (a node that appears on a continuous arc segment), which divides the arc segment into several segments unnecessarily.

[0117] (5) Line discount errors: including water lines, transportation lines, and landform lines.

[0118] 3. Surface layer:

[0119] (1) Errors in overlapping elements (including partial overlap): including self-intersection of faces;

[0120] (2) Single feature class, surface incompleteness error: including landform surface, traffic surface, traffic subsidiary surface, boundary surface, residential ground, residential subsidiary surface, other area surface, water system surface, water system subsidiary surface, and vegetation surface, there are hanging lines and no closed surface is formed.

[0121] 4. Point-to-point topology: duplication of points, including the existence of more than one elevation point at the same longitude and latitude.

[0122] 5. Point-line topology: 1) The point falls into the line incorrectly; 2) The point does not fall into the line incorrectly; 3) The point-line elevation contradiction, including the position is between two contour lines, but the elevation value is not within the range of the two contour lines, and the difference between the elevation value of the contour line and the elevation value of the adjacent elevation point is greater than the contour interval;

[0123] 6. Point-to-surface topology: 1) The point must be on the polygon boundary but is not; 2) The point layer element is within the polygon; 3) The area element must contain at least one point of a certain type but does not contain that type of point;

[0124] 7. Line-surface topology: lines pass through surfaces or lines intersect with surfaces, including road lines passing through house surfaces, single-line rivers passing through house surfaces, contour lines passing through houses, and walls intersecting with houses.

[0125] 8. Line-line topology: 1) Lines do not fall into lines: including linear bridges that do not fall into road lines, and linear bridges that exist independently from roads; 2) Line-line intersections: including one or more intersections between two different contour lines.

[0126] 9. Face-to-face topology: 1) Feature layers cannot overlap with each other, including the intersection of road faces and house faces, the intersection of house faces and house faces, and the intersection of administrative area faces; 2) Faces do not fall into faces incorrectly, including face-shaped bridges not falling into road faces, and all faces within a province must fall into the provincial administrative area face; 3) Faces fall into faces incorrectly, including house faces falling into vegetation faces, house faces falling into water system faces, and water system faces falling into vegetation faces.

[0127] 2. Extension of Big Data Model for GIS Software Evaluation

[0128] According to the specified model, after expansion and then adding error rule data, the evaluation data is generated. For GIS data expansion, it is a key step, and finally a large amount of GIS evaluation data is obtained. At the same time, the error rules are added by the user's independent selection of error rule combinations. While expanding the amount of data, it is necessary to ensure that no new unquantifiable errors are introduced.

[0129] 1. GIS model big data expansion requirements

[0130] 1) High efficiency: GIS model data can be expanded to multiple times of its own data volume in a short period of time. The tight rhythm of GIS software development and testing puts forward requirements for GIS model data expansion. The existing GIS data manual production process cannot meet the efficiency requirements. The manual expansion method manually adds the location, geometry, and attribute value of the edited elements according to the mapping process. Everything is done under human operation, and the expansion efficiency is extremely low. Especially when it is hoped to obtain a large amount of data and quickly obtain data 10 times or even 20 times the size of the model data, manual expansion is difficult to meet the geometric growth of GIS data needs. The use of manual expansion will consume huge time costs and human resources. In addition, manual operation has no standard operating specifications and indicators, and it is difficult to guarantee the expansion results. Therefore, it is necessary to use software for automatic expansion. Of course, this expansion is absolutely impossible to be fully automated, but a semi-automated process. In today's era of rapid development of big data and cloud computing technology, the update and development of physical technology has brought revolutionary updates to software development. The geometric increase in data volume is an inevitable trend. Under this background, manual expansion of GIS data models is undoubtedly contrary to the development of the times. The use of computer software technology for automated expansion is an inevitable trend.

[0131] Ensure quality: During the process of model data expansion, ensure that the quality of the expanded model data is within the control range. The expanded model data adds error rules according to the user-defined permutations and combinations. If new error types are introduced during model expansion, they may be new errors with regular patterns. Such errors can be removed by some subsequent means. If there are no regular patterns, the addition of error rules will become uncontrolled, and error rules cannot be added under control variables. If the generated evaluation data contains errors that developers cannot quantify and describe, the types of error rules contained in the evaluation data set will be questionable, and such evaluation data cannot be used to check the completeness of GIS data quality inspection system functions, system fault tolerance and stability. Therefore, during the process of model data expansion, the quality of the extended data should be logically checked to meet the industry GIS data quality standards.

[0132] (II) Image-based big data model expansion

[0133] By introducing mirroring into model data extension and combining it with the characteristics of DLG data itself, it is found that the objects represented by DLG data are composed of vector attributes and data attributes in storage. Vector attributes are points, lines, and surfaces, and data attributes are other categories of information of their abstract representative elements. DLG model extension is established from vector attributes and data attributes based on mirroring.

[0134] 1. Vector attribute mirroring based on DLG geometry

[0135] Point: A point is a geometric object with two-dimensional (X, Y) or (B, L) position coordinates, and an elevation value (Z) coordinate, or other values. Point elements represent survey control points, wells, township locations, shopping malls, and institutional locations. When used as points, they have no area attributes.

[0136] Line: A line is a cluster composed of a set of continuous points. A line is represented by the coordinates of a set of points (nodes). A line represents a road, river, boundary line, or landform. When used as a line object, its length cannot be zero.

[0137] Polygon: It is a closed area, representing lakes, buildings, administrative divisions, and is a ring object or multiple ring objects. However, as a surface element, its enclosed area must be a closed interval.

[0138] Layer: A collection of points, lines, and areas with similar attributes.

[0139] From the storage perspective in the database, vector attributes are points, lines, surfaces, and layers. Points are stored in the database with their names, IDs, and X and Y coordinates. Lines are stored in the database in the form of multiple points, with point numbers and the position coordinates of each point arranged in the order of connection of the points on the line. Surfaces are also stored in the form of multiple points, but are connected end to end, with the first and last nodes being the same point. Considering that both lines and surfaces are stored in the form of multiple points in the database, the vector attributes of lines and surfaces are mirrored in the same way.

[0140] Point mirroring: For a certain layer, it is expanded in four directions: east, west, south, and north. The expansion principle in each direction is the same. Expand in the east direction to get the bounding box of the layer, determine the range of the layer, and set the four corner points of the bounding box, the upper, lower, left, and right points, as P 1 , P 2 , P 3 , P 4 , for the expansion of the point, P(x p ,y p ) means that in the east mirror image, the coordinate of the point remains unchanged in Y value, while the X value becomes the original X value plus twice the distance from the right margin to point P. Figure 1 .

[0141] Similarly, the left side of the point after mirroring in the east, west, south, and north directions corresponds to:

[0142] P E (x p +2×(x P3 -x p ), y p ) Formula 1

[0143] P W (x p -2×(x p -x P1 ), y p ) Formula 2

[0144] P S (x p ,y p -2×(y p -y P2 )) Formula 3

[0145] P N (x p ,y p +2×(y P1 -y p )) Formula 4

[0146] Mirror image of line: A line is composed of multiple continuous points. By obtaining the data records in the line layer dataset, we can get the coordinate values ​​of a series of points. The schematic diagram obtained after the line is mirrored in the east direction is as follows: Figure 2 As shown. Similarly, after mirror expansion, the coordinate relationship between corresponding points satisfies the mirror relationship of the points;

[0147] Mirror image of the surface: The multi-points corresponding to the surface are a multi-point sequence connected end to end. The principle of surface mirror expansion is the same as the mirror expansion of the line. The schematic diagram of the eastward expansion of the surface is as follows: Figure 3 .

[0148] 2. Data attribute mirroring based on DLG feature classification

[0149] The vector data of points, lines and surfaces are obtained by mirroring on the vector, but the classification of the features also affects the attributes of the features. This part of the attributes is called data attributes. On the basis of the mirror extension of the vector attributes, the mirror extension of the data attributes is performed.

[0150] (1) Point class: Point objects have only vector attributes and no data attribute mirroring. The geographic entity represented by the point is independent and will not have any elevation or direction contradictions in space. After vector attribute mirroring, a single point object still maintains its own correctness. In addition, when the lines and surfaces around the point are mirrored, the elevation area of ​​the point and the area surrounded by the contour lines can always remain consistent, and the point does not need to be mirrored for data attributes.

[0151] (2) Line type: select three representative line elements: contour lines, power lines, and river lines;

[0152] Contour class: Contours are selected to represent a line composed of a series of points with equal or similar elevation values. After the contour vector attribute is mirrored and expanded, when the contour is located at the periphery of the layer frame, it can maintain continuity at the edge after mirroring. The schematic diagram is as follows Figure 4 shown.

[0153] For the logical consistency of DLG data, there is no need to consider how to transform at the edge. The original contour layer is mirrored in the east and north directions, and the east mirrored data is mirrored in the north direction. The contour lines at the edge are finally formed into a ring and become a closed contour line. In addition, the area around the contour line also maintains the continuity in elevation. For contour-type landform lines, after the vector attribute is mirrored, one-to-one data mirroring can be used, that is, the data attributes of each point on the original line are matched one by one;

[0154] River line type: represents lines with directionality due to natural conditions of geographic elevation. For this type of linear vector mirror, it is fine if it is not at the edge of the frame, but once the river line is at the edge of the frame, there will be a place that violates the real logic, such as Figure 5 , that is, the directions of the two small sections of the river point to a common point at the same time, but in the real world, the directions of adjacent river sections must be the same.

[0155] Power lines: Lines representing power transportation. The biggest feature of this type of line is that it has directionality, but this directionality is not related to elevation changes. For this type of linear data mirroring, the data attribute replication method is used to obtain a set of point coordinates through vector mirroring, but the order of these points (which brings directionality) is not mirrored according to the original arrangement, but the original directionality remains unchanged. In the eastward vector mirroring, the first point P of the line passes through 1 Coordinates, get a position coordinate related to it, but the serial number of this position point is not the mirrored P l , but the P in the new line n ,It can be found that after adopting data attribute replication, the linear shape of the power line class and the adjacent parts maintain consistency in direction.

[0156] Mirroring of surface data attributes: Surfaces are also composed of point sets, but compared with linear elements, which are also composed of point sets, there are two huge differences: first, lines have directionality; second, lines do not need to consider closure, but surfaces do. Based on these two points, the data attributes of surfaces are also mirrored. For surface elements, the advantage is that vectors with consistent data attributes always intersect at the edges, and there will be no situations that are inconsistent with reality, such as half a house and half a lake connecting.

[0157] (III) DLG data mirroring extension based on SuperMap

[0158] Adopting mirror-extended vector attributes, maintaining good edge connection at the edge, mirroring of vector attributes and mirroring of data attributes, and processing on the storage content of points, lines and surfaces based on the plug-in library of the SuperMap platform.

[0159] Model expansion method and flow chart based on SuperMap, such as Figure 6 shown.

[0160] Traversing layers: Based on the MapControl control of SuperMap, the MapControl control obtains the current Data, implements operations on the map layer, obtains the bounding box of the layer frame after traversal, and obtains the coordinate range of the boundary line;

[0161] Specific vector categories: For each layer, there is a certain vector category corresponding to it, and vector attributes are expanded and then data attributes are expanded in turn; for the point layer, its own elevation logic is not considered, only vector attributes are mirrored, and there is no data attribute mirroring; for lines, there are three cases. For contour line types without direction, vector attributes are mirrored first, and then data attributes are mirrored to maintain good edge connection; for power line types, directional lines that are not related to elevation, vector attributes are mirrored first, and then data attributes are copied to maintain directional consistency; for river line types, directional lines related to elevation, vector attributes are mirrored first, and data attributes are temporarily mirrored; for surfaces, vector attributes are mirrored first, and then data attributes are mirrored.

[0162] 3. GIS Software Evaluation Big Data Management System

[0163] Develop a GIS evaluation data generation and management system, implement model data expansion and error rule addition, produce evaluation data, and implement evaluation data and model data management and mapping functions.

[0164] 1. Evaluation system architecture

[0165] The architecture of the sample data and error rule management system is divided into three layers: application software layer, database layer, and support layer;

[0166] Application software layer: It consists of two components, namely, simulation data production and production component and data management component. Simulation data production and production is responsible for the production and production of sample data. Its bottom layer is supported by the error rule engine, which provides the combination of error data. Data management processes the data acquisition, organization, management and update of the model database, error rule library and achievement sample library.

[0167] Database layer: stores model data, simulation data results and metadata information, which are maintained by data management software. Metadata provides the simulation test data production and production software and data management software with the required description information of various types of data, including the error types and data types in the data. The model database stores models of various data types, including topographic map models, geomorphic map models, and water system map models, simulation data results library and test sample data, and stores the results of simulation data production. The data production component writes the simulation data results into the test sample database, and the data management component is responsible for obtaining the test sample data and providing it to the testers for use;

[0168] Support layer: It is composed of network system, computer system and platform software. The platform software is SuperMap series software. iServer provides the organization and management of model data and simulation data results. SuperMap Object provides secondary development based on SuperMap to realize visual management of geographic data.

[0169] 2. Production and preparation of evaluation data sets

[0170] The system structure of production and preparation of evaluation data is as follows Figure 7 shown.

[0171] The whole system is summarized into seven functional modules: map data, error rule production, automatic production of evaluation data, interactive production of evaluation data, production data result inspection, and production data result output;

[0172] Map data: some basic functions common to all modules of the software, including the management of vector and cartographic data, various data display and browsing, symbol setting and data query and retrieval;

[0173] Error rule creation: According to the software testing requirements, data error rules are formed by designing catalog models, various terrains, error check items, data packet models and quality evaluation models;

[0174] Automatic test data generation: Based on the error rule library, it can realize the automatic, efficient and batch generation of various error data, and also realize the automatic generation of some common error data;

[0175] Test data interactive production: For some contents that are difficult to automatically produce errors, computer-assisted and convenient interactive production tools and error identification tools are provided to quickly generate, locate and mark errors;

[0176] Production data result inspection: provide viewing and confirmation of production results and error information, and calculate and evaluate production results;

[0177] Produce data result output: output error graphics, error details and simulation record tables for easy modification, filing or storage.

[0178] 3. GIS Big Data Management

[0179] Evaluation data management realizes the integrated management of model data, error rules and evaluation data results generated by evaluation data storage and production components, including data publishing, data query and browsing, data update, error rule management and system user management model.

[0180] (1) Data management module: comprehensive management of model data and data results. The data publishing submodule realizes batch storage and release of data and configures the storage plan. The data query and browsing submodule realizes fast conditional query of data. The query methods include metadata query and spatial information query. At the same time, it realizes multiple ways of result browsing, including tabular information, map display and calculation chart. The data update submodule realizes data maintenance and update.

[0181] (2) Error rule management module: This module implements comprehensive management of system built-in error rules and user-defined error rules. Error rules are stored in the form of files. Through the management system, users can publish user-defined error rules to the server and query, browse, update, and download them to the local computer.

[0182] (3) System management module: implements security management and daily maintenance of management software, including user management, user rights management, and system log management.

[0183] 4. GIS Software Evaluation Data Management Prototype System

[0184] The main interface is divided into three parts: the upper part is the menu part, the lower left part is the layer management, and the lower right part is the map display;

[0185] Data management: The menu bar on the main interface of the system consists of two parts. The upper one is the basic toolbar, including pointer, roaming, zoom in, zoom out, free zoom, and global display; the lower one is my toolbar, including opening workspace, loading model, expanding model, adding errors, and publishing model. The menu bar is classified according to the functions implemented, and is divided into three categories: basic map functions, evaluation data production, and data management;

[0186] When the user opens the workspace or directly loads the model on the server, the layer management is used to select the layer that needs to be operated, and the map display part will display the map and operation results accordingly.

[0187] (I) Basic map function module

[0188] Basic map display is a component provided by SuperMaplObjects.NET, which can realize basic viewing of data sets of each layer. You can click the zoom in, zoom out, and free zoom buttons on the panel, or use the mouse wheel to zoom in and out. You can choose the roaming mode to zoom in and out, or choose the pointer mode to select a specific record in the data set. It meets the basic functional requirements of map display.

[0189] (II) Extended Model

[0190] After the user selects a specific attribute set, the prototype system obtains the current NOTE node to obtain the entire current map range. Through MapControl, it can traverse the data record content in the middle of the layer and perform mirror expansion.

[0191] (III) Add error rules

[0192] To implement adding line discount errors, the user selects a certain linear data, clicks the element button on the panel, and then selects to add a line discount error through the Add Error drop-down box on the panel. The prototype system traverses the original linear element set, randomly searches for a point on each line element, generates a line discount error, and visually displays the place where the line discount error is added, and highlights the place where the error is added.

[0193] (IV) Prototype system data management

[0194] (1) Loading the model

[0195] Click the Load Model button on the main interface panel of the user stand-alone machine, select the model data published on the server, double-click to select, and the model data sets will be added one by one, such as Figure 8 .

[0196] (2) Release model

[0197] The user selects the workspace that needs to be operated, and then clicks Publish Model on the homepage panel. After the model is successfully published, the system will pop up a dialog box to indicate that the publishing is successful. The user can then load the model that he has published on the server;

[0198] The implementation process is to first obtain the data source of the token, determine whether the data source is saved in the workspace, and if not, save it. After saving, continue the publishing process, create a local workspace, create a file-type data source, import the data in the memory data source, and publish the local data to the server.

[0199] 5. Prototype system performance test

[0200] The performance test of the GIS evaluation data design and management prototype system was carried out. The system was carried out on a computer with SuperMap installed, and the performance tests of model data expansion and error rule addition were carried out respectively. The results are as follows: Fig. 9 It can be seen that the time for model data expansion is not only related to system performance, but also to the spatial complexity of the data itself. The higher the spatial complexity, the longer the model data expansion time under the same conditions. Fig.10 It can be seen that the addition of error rules is also related to the spatial complexity of the model data, because in the process of adding error rules, it is necessary to obtain records in the data set and use random sampling to select the location to add the error rule type. Therefore, it can be seen that the larger the amount of data, the longer it takes to add error rules.

Claims

1. A big data generation and management system for geographic information software performance evaluation, characterized in that: Establish a complete system from production to management of GIS software evaluation data sets, including the construction of GIS map DLG error rule data sets, the expansion of GIS software evaluation big data models, the management system of error rule data and the expanded GIS software evaluation big data, and the GIS software evaluation data management prototype system; establish an error rule library for vector topographic maps, covering four basic geographic data error types, and semi-automatically expand model data based on the mirror extension method; adopt the SuperMap plug-in development form to import data models, expand model data, generate and manage error rule data, connect the data layer and the host through the logic layer plug-in, realize the unified management of model data and error rule generation data based on SuperMapiServer, use SuperMap iDeskop as the host program, and realize the operation transmission between the host and the data layer through SuperMap iObjects.NET; 1) Construction of GIS map DLG error rule dataset: Analyze all possible errors in GIS, use these errors as a data production input condition to generate evaluation data, and the vector error rule library contains all vector data error types; Error rules cover four types of vector data quality checks in broad categories, and data types that meet various geomorphic characteristics are added; 2) GIS software evaluation big data model extension: define vector error rule operators and add them to the evaluation data; introduce mirroring into the model data extension, and combine the characteristics of DLG data to obtain that the objects represented by DLG data are composed of vector attributes and data attributes in storage. Vector attributes are points, lines, and surfaces, and data attributes are other categories of information of their abstract representative elements. Based on mirroring, DLG model extension is established from vector attributes and data attributes; 3) GIS software evaluation big data management system: including evaluation system architecture, evaluation data set production and creation, GIS big data management, automated evaluation data production, and selecting corresponding category data contained in the model library according to actual evaluation needs to automatically expand and generate a wide range of evaluation data; 4) GIS software evaluation data management prototype system: manages data sets of data model + error rule combination mode, generates target sample evaluation data according to the test error types contained in the custom evaluation data set, and provides browsing, display, editing and operation.

2. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: Construction of GIS map DLG error rule data set: Based on the DLG data quality standards and the logical characteristics of eight major elements, including vegetation, transportation and its ancillary facilities, residential areas and their ancillary facilities, water systems and their ancillary facilities, survey control points, landforms, boundaries, and soil quality, combined with the GIS software's need to express DLG data, a DLG data error rule set is established; (I) Mathematical accuracy error rules: including elevation accuracy, plane position accuracy, spatial reference system, and edge accuracy; (II) Attribute precision error rules: including symbols, notes, numbers, and text, attribute precision errors include: (1) The attribute value is not unique; (2) Attribute values ​​are empty: a) elevation value of contour line; b) elevation value of elevation point; c) null value of control point attribute; d) null value of water tower elevation value; e) null value of sluice elevation value; f) null value of square network annotation; g) null value of house attribute value; (3) Attribute value range error: If the elevation of the area where the elevation range is located is within 100, then check the objects with elevation values ​​within the value range of (u, 0) and (100, u) and identify them as illegal elevation points; (4) Directed point attribute errors: a) Sluice gate: the direction of the tip is opposite to the direction of water flow, which is an error; b) The direction of river flow and ditch flow is wrong; c) The slope line should point to the direction of decreasing elevation; d) The tooth line of the steep slope should point to the direction of decreasing elevation of the contour line; (5) Inconsistency of elevation point map attributes: the attribute content of elevation point is inconsistent with the elevation annotation content; (6) Inconsistency of residential ground attributes: The residential ground attribute content is inconsistent with the annotation content; (7) The classification and coding of point elements are incorrect; (III) Completeness and accuracy error rules: data scope, spatial entity type, attribute feature classification, whether the data should be covered to the location that should be covered, reflected in data organization, data format, data layering, and file layer name errors; (IV) Logical consistency error rules: DLG data topology rules include two aspects: 1) Topology of a layer itself: the geometric attributes of the layer are consistent, and it is divided into point layer, line layer, and surface layer; 2) The topology between two layers is divided into: point-surface topology, point-line topology, point-surface topology, line-line topology, line-surface topology, and surface-surface topology.

3. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: Vector attribute mirroring based on DLG geometry: Point: A point is a geometric object with two-dimensional (X, Y) or (B, L) position coordinates, and an elevation value (Z) coordinate, or other values. Point elements represent survey control points, wells, township locations, shopping malls, and institutional locations. When used as points, they have no area attributes. Line: A line is a cluster composed of a set of continuous points. A line is represented by the coordinates of a set of points (nodes). A line represents a road, river, boundary line, or landform. When used as a line object, its length cannot be zero. Polygon: It is a closed area, representing lakes, buildings, administrative divisions, and is a ring object or multiple ring objects. However, as a surface element, its enclosed area must be a closed interval. Layer: A collection of points, lines, and areas with similar attributes. From the storage perspective in the database, vector attributes are points, lines, surfaces, and layers. Points in the database are stored with their names, IDs, and X and Y coordinates. Lines are stored in the database in the form of multiple points, with point numbers and position coordinates arranged in the order of connection of points on the line. Surfaces are also stored in the form of multiple points, but are connected end to end, with the first and last nodes being the same point. Considering that both lines and surfaces are stored in the form of multiple points in the database, the vector attributes of lines and surfaces are mirrored in the same way. Point mirroring: For a certain layer, it is expanded in four directions: east, west, south, and north. The expansion principle in each direction is the same. The bounding box of the layer is obtained by expanding in the east direction to determine the range of the layer. The four corner points of the bounding box, top, bottom, left, and right, are set as P1, P2, P3, and P4. For the expansion of the point, P(x p ,y p ) means that in the east mirror image, the Y value of the coordinate of the point remains unchanged, while the X value becomes the original X value plus twice the distance from the right margin to point P; Similarly, the left side of the point after mirroring in the east, west, south, and north directions corresponds to: P E (x p +2×(x P3 -x p ), y p ) Formula 1 P W (x p -2×(x p -x P1 ), y p ) Formula 2 P S (x p ,y p -2×(y p -y P2 ) Formula 3 P N (x p ,y p +2×(y P1 -y p ) Formula 4 Mirror image of a line: A line is composed of multiple continuous points. By obtaining the data records in the line layer data set, a series of point coordinate values ​​are obtained. After mirror image expansion, the coordinate relationship between corresponding points satisfies the mirror image relationship of the points. Mirror image of a surface: The multi-points corresponding to the surface are a multi-point sequence connected end to end. The principle of mirror image expansion of a surface is the same as that of mirror image expansion of a line.

4. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: Data attribute mirroring based on DLG feature classification: vector data mirroring of points, lines and surfaces is obtained by mirroring on the vector, but the classification of features also affects the features of the features. This part of the attributes is called data attributes. On the basis of the mirror extension of vector attributes, the mirror extension of data attributes is performed; (1) Point class: Point objects only have vector attributes and no data attribute mirroring. The geographic entity represented by the point is independent and will not have any self-elevation or self-directional contradictions in space. After vector attribute mirroring, a single point object still maintains its own correctness. In addition, when the lines and surfaces around the point are mirrored, the elevation area of ​​the point and the area surrounded by the contour lines can always remain consistent. The point does not need to be mirrored for data attributes. (2) Line type: select three representative line elements: contour lines, power lines, and river lines; Contour class: Select contour lines to represent a line composed of a series of points with equal or similar elevation values. After the contour line vector attribute is mirrored and expanded, when the contour line is located at the periphery of the layer frame, after mirroring, the continuity at the edge can be maintained; For the logical consistency of DLG data, there is no need to consider how to transform at the edge. The original contour layer is mirrored in the east and north directions, and the east mirrored data is mirrored in the north direction. The contour lines at the edge are finally formed into a ring and become a closed contour line. In addition, the area around the contour line also maintains the continuity in elevation. For contour-type landform lines, after the vector attribute is mirrored, one-to-one data mirroring can be used, that is, the data attributes of each point on the original line are matched one by one; River line type: represents lines with directionality due to natural conditions of geographical elevation; Power line: A linear representation of power transportation. The biggest feature of this type of line is that it has directionality. For this type of linear data mirroring, the data attribute replication method is used to obtain a set of sequence point coordinates through vector mirroring, but the order of these points is not mirrored according to the original arrangement, but the original directionality remains unchanged; in the eastward vector mirroring, the first point P1 coordinate of the line is used to obtain a position coordinate related to it, but the serial number of this position point is not the P obtained by the mirroring. l , but the P in the new line n ,After using data attribute replication, the linear ,adjacent parts of the power line class remain consistent in direction; Mirroring of surface data attributes: Surfaces are also composed of point sets, but compared with linear elements, which are also composed of point sets, there are two huge differences: one is that lines have directionality; the other is that lines do not need to consider closure, but surfaces do. Based on these two points, the data attributes of surfaces are also mirrored. For surface elements, the advantage is that vectors with consistent data attributes always intersect at the edges, and there will be no situations that are inconsistent with reality, such as half a house and half a lake being connected.

5. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: DLG data mirroring extension based on SuperMap: using mirroring to extend vector attributes, maintaining good edge connection at the edges, mirroring of vector attributes and mirroring of data attributes, and processing on the storage content of points, lines and surfaces based on the plug-in library of the SuperMap platform; Traversing layers: Based on the MapControl control of SuperMap, the MapControl control obtains the current Data, implements operations on the map layer, obtains the bounding box of the layer frame after traversal, and obtains the coordinate range of the boundary line; Specific vector categories: For each layer, there is a certain vector category corresponding to it, and vector attributes are expanded and then data attributes are expanded in turn; for the point layer, its own elevation logic is not considered, only vector attributes are mirrored, and there is no data attribute mirroring; for lines, there are three cases. For contour line types without direction, vector attributes are mirrored first, and then data attributes are mirrored to maintain good edge connection; for power line types, directional lines that are not related to elevation, vector attributes are mirrored first, and then data attributes are copied to maintain directional consistency; for river line types, directional lines related to elevation, vector attributes are mirrored first, and data attributes are temporarily mirrored; for surfaces, vector attributes are mirrored first, and then data attributes are mirrored.

6. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: Evaluation system architecture: The sample data and error rule management system architecture is divided into three layers: application software layer, database layer, and support layer; Application software layer: It consists of two components: simulation data production and production component and data management component. Simulation data production and production is responsible for the production and production of sample data. Its bottom layer is supported by the error rule engine, which provides the combination of error data. Data management deals with data acquisition, organization, management and updating of model database, error rule library and achievement sample library; Database layer: stores model data, simulation data results and metadata information, which are maintained by data management software. Metadata provides the simulation test data production and production software and data management software with the required description information of various types of data, including the error types and data types in the data. The model database stores models of various data types, including topographic map models, geomorphic map models, and water system map models, simulation data results library and test sample data, and stores the results of simulation data production. The data production component writes the simulation data results into the test sample database, and the data management component is responsible for obtaining the test sample data and providing it to the testers for use; Support layer: It is composed of network system, computer system and platform software. The platform software is SuperMap series software. iServer provides the organization and management of model data and simulation data results. SuperMap Object provides secondary development based on SuperMap to realize visual management of geographic data.

7. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: Evaluation data set production and production: The entire system is divided into seven modules: map data, error rule production, automatic evaluation data production, interactive evaluation data production, production data result inspection, and production data result output; Map data: some basic functions common to all modules of the software, including the management of vector and cartographic data, various data display and browsing, symbol setting and data query and retrieval; Error rule creation: According to the software testing requirements, data error rules are formed by designing catalog models, various terrains, error check items, data packet models and quality evaluation models; Automatic test data generation: Based on the error rule library, it can realize the automatic, efficient and batch generation of various error data, and also realize the automatic generation of some common error data; Test data interactive production: For some contents that are difficult to automatically produce errors, computer-assisted and convenient interactive production tools and error identification tools are provided to quickly generate, locate and mark errors; Production data result inspection: provide viewing and confirmation of production results and error information, and calculate and evaluate production results; Produce data result output: output error graphics, error details and simulation record tables for easy modification, filing or storage.

8. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: GIS big data management: Evaluation data management realizes the integrated management of model data, error rules and evaluation data results generated by evaluation data survival and production components, including data publishing, data query and browsing, data update, error rule management and system user management modules; (1) Data management module: comprehensive management of model data and data results. The data publishing submodule realizes batch storage and release of data and configures the storage plan. The data query and browsing submodule realizes fast conditional query of data. The query methods include metadata query and spatial information query. At the same time, it realizes multiple ways of result browsing, including tabular information, map display and calculation chart. The data update submodule realizes data maintenance and update. (2) Error rule management module: This module implements comprehensive management of system built-in error rules and user-defined error rules. Error rules are stored in the form of files. Through the management system, users can publish user-defined error rules to the server and query, browse, update, and download them to the local computer. (3) System management module: implements security management and daily maintenance of management software, including user management, user rights management, and system log management.

9. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: GIS software evaluation data management prototype system: The main interface is divided into three parts, the upper part is the menu part, the lower left part is the layer management, and the lower right part is the map display; Data management: The menu bar on the main interface of the system consists of two parts. The upper one is the basic toolbar, including pointer, roaming, zoom in, zoom out, free zoom, and global display; the lower one is the toolbar, including opening workspace, loading model, expanding model, adding errors, and publishing model. The menu bar is classified according to the functions implemented, and is divided into three categories: basic map functions, evaluation data production, and data management; When the user opens the workspace or loads the model directly on the server, the layer management is used to select the layer that needs to be operated, and the map display part displays the map and operation results accordingly.

10. The geographic information software performance evaluation big data generation and management system according to claim 1, characterized in that: The GIS software evaluation data management prototype system includes: (I) Basic map function module: Basic map display is a component provided by SuperMaplObjects.NET, which realizes the basic viewing of data sets of each layer. Click the zoom in, zoom out, and free zoom buttons on the panel, and use the mouse wheel to zoom in and out. Select the roaming mode to cooperate with the zoom view, and select the pointer mode to select a specific record in the data set; (ii) Extension model: After the user selects a specific attribute set, the prototype system obtains the current NOTE node to obtain the entire current map range, and can traverse the data record content in the middle of the layer through MapControl to perform mirroring expansion; (III) Adding error rules: To implement adding line discount errors, the user selects a certain linear data, clicks the "Element" button on the panel, and then selects "Add line discount errors" from the "Add error" drop-down box on the panel. The prototype system traverses the original linear element set, randomly searches for a point on each line element, generates a line discount error, and visually displays the place where the line discount error is added, and highlights the place where the error is added.

Images