Common map-oriented quality inspection method and system, and storage medium

Abstract

The invention relates to the technical field of geographic information processing, and discloses a common map-oriented quality inspection method and system and a storage medium, and the method comprises the steps: obtaining a map to be subjected to quality inspection and N standard maps, and extracting geometric data, attribute data and topological data; performing data preprocessing on the map to be subjected to quality inspection and N standard maps; based on the N standard maps, screening a reference map; constructing geometric data indexes, attribute data indexes and topological data indexes of the map to be subjected to quality inspection and the standard map relative to the reference map; generating a geometric data index standard value, an attribute data index standard value and a topological data index standard value; respectively comparing the geometric data index, the attribute data index and the topological data index of the map to be subjected to quality inspection relative to the reference map with corresponding standard values, and generating a quality inspection result; according to the invention, the quality inspection efficiency is improved, and the objectivity and reliability of the quality inspection result are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of geographic information processing technology, specifically to a quality inspection method, system, and storage medium for general maps. Background Technology

[0002] With the rapid development of the geographic information industry, general maps, as an important carrier of geographic information, are widely used in navigation, planning, education, and other fields. The quality of general maps directly affects their application effectiveness; therefore, map quality inspection is of paramount importance.

[0003] In existing technologies, ordinary map quality inspection mainly relies on manual inspection and simple computer-aided verification methods. Manual inspection suffers from low efficiency, strong subjectivity, and easy omissions, especially when the map data volume is large, making it difficult to meet the needs of large-scale, high-efficiency quality inspection. Simple computer-aided verification methods usually only check a single attribute of the map, often focusing only on indicators such as coordinate accuracy, lacking comprehensive testing of multiple dimensions such as geometric accuracy and attribute completeness, resulting in inaccurate and unreliable quality inspection results. At the same time, existing technologies do not make full use of multi-source standard map data to construct accurate quality inspection benchmarks, resulting in insufficient objectivity and authority of the quality inspection standards. In addition, for the analysis and feedback of quality inspection results, existing technologies cannot quickly locate the root cause of quality problems, which is not conducive to subsequent map data correction work. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a quality inspection method, system, and storage medium for general maps, which solves the problems of low efficiency, poor accuracy, and incomplete coverage dimensions in existing map quality inspection methods.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A quality inspection method for general maps includes the following steps: S1. Obtain the quality inspection map of the same area and scale. N Zhang Standard Map, extract the map to be inspected and N Geometric data, attribute data, and topological data of a standard map; S2, Regarding the quality inspection map and N Zhang's standard map underwent data preprocessing to generate a preprocessed map to be inspected. N Zhang Standard Map; S3, Obtain preprocessed data N The grayscale values ​​of each pixel in the standard map are calculated, and the average difference between any standard map and other standard maps is selected as the benchmark map by selecting the standard map with the smallest average difference. S4. Based on the baseline map and the preprocessed map to be inspected, construct the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the baseline map. S5, Preprocessing N Step S4 is executed for each standard map, generating geometric data indicators, attribute data indicators, and topological data indicators for each standard map relative to the baseline map, calculating the mean values ​​for each, and generating standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators. S6. Compare the geometric data indicators, attribute data indicators, and topological data indicators from step S4 with their corresponding standard values ​​to determine whether the ordinary map to be inspected is qualified and generate the quality inspection results.

[0006] A system applying a quality inspection method for general maps includes: The data acquisition module is used to acquire the map to be inspected from the same area and scale. N Zhang Standard Map, extract the map to be inspected and N Geometric data, attribute data, and topological data of a standard map; The data preprocessing module is used to receive the map to be inspected and... N Zhang's standard map is used to perform coordinate normalization, data cleaning, and topology repair to generate a preprocessed map for quality inspection. N Zhang Standard Map; The baseline map filtering module is used to receive preprocessed data. N The standard map is calculated by extracting the grayscale values ​​of pixels and calculating the average difference between any standard map and other standard maps. The standard map with the smallest average difference is selected as the benchmark map. The quality inspection index construction module is used to receive the preprocessed map to be inspected and the benchmark map, and to construct the geometric data index, attribute data index, and topological data index of the map to be inspected relative to the benchmark map using the benchmark map as a reference. The quality inspection standard value generation module is used to receive preprocessed values. N Using the benchmark map as a reference, the standard map generates geometric data indicators, attribute data indicators, and topological data indicators for each standard map relative to the benchmark map. The same type of indicators are summed and averaged to generate standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators. The quality inspection judgment and result generation module is used to receive the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the base map, as well as their corresponding standard values. Based on the preset judgment rules, it determines whether the ordinary map to be inspected is qualified and generates the quality inspection result.

[0007] A computationally readable storage medium, comprising: The storage medium contains a computer program, which, when executed by a processor, implements any of the aforementioned quality inspection methods for ordinary maps.

[0008] The present invention has the following beneficial effects: This invention proposes a quality inspection method, system, and storage medium for general maps. By introducing standard map data, it constructs a multi-dimensional quality inspection index system with geometric accuracy, attribute integrity, and topological consistency as its core. It also utilizes the statistical mean method to generate objective standard values, achieving standardized quality inspection throughout the entire process from benchmark map selection and index construction to automatic judgment. This not only avoids subjective biases caused by single standards or human experience but also significantly improves the objectivity and reliability of the quality inspection results. Simultaneously, through automated comparison and judgment, it greatly improves quality inspection efficiency and reduces human error, providing a scientific, efficient, and quantifiable technical means for the quality assessment and optimization of general maps, effectively ensuring the accuracy and usability of map data in various application scenarios. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a quality inspection method for general maps proposed in this invention. Figure 2 This is a schematic diagram of the structure of a quality inspection system for general maps proposed in this invention. Detailed Implementation

[0010] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0011] like Figure 1 As shown, a quality inspection method for general maps includes the following steps: S1. Obtain the quality inspection map of the same area and scale. N Zhang Standard Map, extract the map to be inspected and N The geometric data, attribute data, and topological data of a standard map.

[0012] In this embodiment, firstly, in the cartographic database, the map to be inspected and the map of the same region and scale are obtained. N Zhang Standard Map, the map to be inspected is... M ,Will N Zhang Standard Map is recorded as , ,..., ,and N The sample size is ≥3, the purpose of which is to ensure that the sample size meets the statistical significance requirement; then, the map to be inspected is extracted. M With standard map The three core data types include geometric data, attribute data, and topological data. Geometric data includes the coordinate set of ground features and shape parameters of ground features. The coordinate set of ground features includes the latitude and longitude of point ground features, the coordinate string of line ground features, and the boundary coordinates of area ground features. The shape parameters of ground features include the perimeter and area of ​​area ground features and the curvature of line ground features. Attribute data includes the attribute labels of ground features, such as name, category, and level. Topological data includes the topological relationships between ground features, such as the connection relationship of road nodes and the inclusion relationship of regional ground features.

[0013] S2, Regarding the quality inspection map and N Zhang's standard map underwent data preprocessing to generate a preprocessed map to be inspected. N Zhang Standard Map.

[0014] In this embodiment, by comparing the quality inspection map with... N The three types of data in the standard map undergo unified data preprocessing to eliminate data format and noise interference. This includes coordinate normalization, data cleaning, and topological consistency checks and repairs, as detailed below: Specifically, step S2 includes S21-S23: S21, Connect the map to be inspected with N The standard map is normalized to the coordinates and converted to the geocentric coordinate system to generate a unified coordinate map for quality inspection. N Zhang Standard Map.

[0015] In this embodiment, the coordinate normalization process involves converting all map data to the CGCS2000 National Geodetic Coordinate System (Geocentric Geodetic Coordinate System) to ensure consistent coordinate references. In practical applications, however, professional Geographic Information System (GIS) software can be used for coordinate transformation as needed, such as QGIS software.

[0016] S22. Connect the quality inspection map with unified coordinates. N Duplicate and empty features were removed from the standard map, generating a cleaned map ready for quality inspection. N Zhang Standard Map.

[0017] In this embodiment, the data cleaning process involves removing duplicate features (judged by a coordinate overlap rate ≥ 95% within the same feature category) and deleting invalid data (features with empty attribute fields) to improve data accuracy. The specific operation process is as follows: 1) Duplicate Feature Removal: First, feature categories are filtered using the attribute query function of GIS software. Features in the map data are categorized and filtered by feature type, such as roads, buildings, and rivers. For example, in QGIS, open the attribute table, use the "Select by Attribute" tool, enter the query conditions, and select all features of that category, such as "Feature Category = 'Road'". Then, coordinate overlap calculation and duplicate determination are performed. For features of the same category, spatial analysis tools are used to calculate their coordinate overlap. In QGIS, the "Intersection" tool can be used to perform an intersection operation on the selected features of the same category to obtain the geometric information of the intersection area. By calculating the area (for polygon features), length (for line features), etc., of the intersection area and the ratio of them to the original features, it is determined whether the coordinate overlap is ≥95%. If the overlap is ≥95%, it is determined to be a duplicate feature. One of them is kept, and the rest are deleted. Finally, duplicate features are removed. 2) Deleting invalid data: Delete features with empty attribute fields. In the attribute table of the GIS software, filter out features with empty attribute fields, such as name, category, level, etc. For example, in the QGIS attribute table, find the corresponding attribute field, use the "Select by attribute" tool, enter the query conditions to select these invalid features, and then perform the deletion operation, such as "Name IS NULL".

[0018] S23. Data cleaning based on topology rules. N Perform topology checks and repairs on the standard map, and generate the topology-repaired map. N Zhang's standard map, ultimately resulting in a preprocessed map awaiting quality inspection and... N Zhang Standard Map.

[0019] In this embodiment, the process involves topological consistency checking and repair, i.e., checking the standard map. The topological data was used to correct obvious logical errors, such as suspended connections of road nodes, to ensure the topological integrity of the standard map itself. The map under quality inspection... M No repair is performed, thus preserving the original topology for quality inspection; the specific operation process is as follows: 1) Standard Map Topology error detection: In GIS software, establish topology rules; for example, in QGIS software, use the topology inspector plugin to set and check topology rules, i.e., by loading a map containing a standard map. To import the vector layers of the feature dataset into the QGIS project, enable the Topology Inspector plugin. In the Topology Inspector panel, add the layers to be inspected, and then set topology rules for each layer. For example, add topology rules based on road feature classes. That is, for road nodes, select the "Endpoints must be connected" rule to detect logical errors such as dangling connections of road nodes. Then run the topology check to generate a topology error report, which displays the existing topology errors, such as dangling road nodes and incorrect regional feature inclusion relationships. 2) Topology Error Repair: Based on the topology error report, locate the specific error location; for example, for a suspended road node connection error, find the suspended road node on the map; use the editing tools in QGIS software to repair it; if the road nodes are not connected correctly, use the "Edit Vertex" tool to move the suspended node to the correct connection position and connect it with other road nodes or line segments; if the regional feature inclusion relationship is incorrect, such as a regional feature incorrectly including features that should not be included, adjust the boundaries of the regional features to conform to the correct inclusion relationship; for maps under quality inspection... M No topology repair operation is performed; the original topology state is preserved for subsequent detection.

[0020] S3, Obtain preprocessed data N The grayscale values ​​of each pixel in a standard map are used to calculate the average difference between any one standard map and other standard maps. The standard map with the smallest average difference is selected as the benchmark map.

[0021] In this embodiment, for N After preprocessing the standard maps, the grayscale values ​​of each pixel in each standard map are obtained (using libraries such as OpenCV and Pillow in Python, or software such as MATLAB to read the image and obtain its pixel grayscale values). The pixel-level differences between any two standard maps are calculated, and then the average difference (average error) between each standard map and other standard maps is calculated. The standard map with the smallest average difference is selected as the benchmark map. Since a small average difference means that the standard map has a small overall difference from most other standard maps, it is more representative. Using it as the benchmark can reduce the extreme errors in subsequent comparisons, and also avoid the extreme errors of randomly selecting standard maps as benchmark maps in existing methods. It also makes the selected benchmark map reliable.

[0022] Specifically, step S3 includes S31-S33: S31. Calculate the pixel-level differences between any standard map and other standard maps, i.e.:

[0023] in, Represents standard map With standard map Pixel-level differences and , These represent the x and y coordinates of the pixel, respectively. Represents standard map At pixel grayscale value at that location Represents standard map At pixel The grayscale value at that location.

[0024] In this embodiment, by summing the absolute values ​​of the grayscale differences of each pixel, the overall difference between the two standard maps can be effectively measured, and the smaller the sum of the differences, the more similar the two maps are.

[0025] S32. Based on the pixel-level differences between any standard map and other standard maps, calculate the average difference between any standard map and other standard maps, i.e.:

[0026] in, Represents standard map Average difference from other standard maps This indicates the total number of standard maps.

[0027] In this embodiment, the average difference between a single standard map and all other standard maps is calculated, and the sum of the differences is divided by the number of other standard maps. It can effectively reflect the average level of difference between the standard map and the overall set of standard maps.

[0028] S33. Select the standard map with the smallest average difference and use it as the baseline map.

[0029] S4. Based on the baseline map and the preprocessed map to be inspected, construct the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the baseline map. Specifically, step S4 includes: S41. Based on the baseline map and the preprocessed map to be inspected, construct geometric data indicators of the map to be inspected relative to the baseline map, including coordinate deviation rate and shape similarity indicators, specifically: S411. Obtain the deviation values ​​of all coordinates of each feature in the geometric data of the map to be inspected relative to the reference map. Simultaneously, obtain the upper and lower limits of all coordinates of that feature on the reference map. Calculate the percentage of the average deviation value of all deviation values ​​of that feature to the upper and lower limits of all coordinates of that feature, and use this as the coordinate deviation rate indicator, i.e.:

[0030] in, This indicates the coordinate deviation rate index. This indicates the number of coordinate points for a specific geographical feature. , These represent the first and second features of a certain ground feature in the geometric data of the map to be inspected. The x and y coordinates of each coordinate. , These represent the first and second corresponding features in the geometric data of the base map. The x and y coordinates of each coordinate. , These represent the maximum values ​​of all x and y coordinates of the feature in the geometric data of the base map. , These represent the minimum values ​​of all horizontal and vertical coordinates of the feature in the geometric data of the base map.

[0031] In this embodiment, the deviation between the coordinates of the feature in the map to be inspected and the coordinates of the feature in the reference map is calculated. Combined with the upper and lower limits of the coordinates of the feature in the reference map, the coordinate deviation rate is obtained. This rate measures the degree of deviation of the coordinates of a feature in the map to be inspected. Furthermore, by relating the deviation to the coordinate range of the feature itself, the proportion of the deviation relative to the spatial span of the feature can be reflected.

[0032] S412. Using the Hausdorff distance method, calculate the Hausdorff distance of each feature in the geometric data of the map to be inspected relative to the reference map, and use it as a shape similarity index, i.e.:

[0033] in, This represents the set of coordinate points of a certain feature in the geometric data of the map to be inspected. This represents the set of coordinate points in the geometric data of the base map that correspond to the same features on the map to be inspected. Represents the set of coordinate points With coordinate point set The Hausdorff distance, a shape similarity index, This indicates taking the maximum value. This indicates taking the minimum value. This represents the first position of a certain feature in the geometric data of the map to be inspected. The coordinates of the first coordinate point correspond to the same ground feature in the geometric data of the base map. Euclidean distance between coordinate points.

[0034] In this embodiment, the Hausdorff distance is introduced to capture the spatial distribution differences between two point sets (sets of feature coordinate points) as a whole, thereby measuring the similarity of feature shapes. Simultaneously, it considers the "least mismatched" parts between the two point sets (bidirectional maximum and minimum distances). Compared to simple geometric feature comparisons, such as area and perimeter, it can more comprehensively and accurately capture the overall differences in feature shapes, including details such as edge convexity and overall contour offset. Furthermore, this method can also be used in scenarios such as high-precision map matching and feature change detection, more sensitively detecting subtle shape differences and providing a more reliable quantitative means for assessing the consistency and accuracy of map shapes. For example, in the shape detection of linear or planar features such as roads and rivers, it can effectively identify shape deviations caused by surveying errors or actual changes in features, assisting in map quality checks and update decisions.

[0035] S42. Based on the baseline map and the preprocessed map to be inspected, construct attribute data indicators for the map to be inspected relative to the baseline map, including attribute missing rate and attribute error rate, specifically: S421. Obtain the attribute fields of each feature in the attribute data of the map to be inspected, and simultaneously obtain the attribute fields of the corresponding features in the attribute data of the benchmark map. By comparison, obtain the number of missing attribute fields for each feature in the attribute data of the map to be inspected. Calculate the percentage of the number of missing attribute fields for each feature in the attribute data of the map to be inspected to the total number of attribute fields for the corresponding feature in the attribute data of the benchmark map, and use this percentage as the attribute missing rate indicator.

[0036] in, Indicates the attribute missing rate. This indicates the number of missing attribute fields for a certain feature in the attribute data of the map to be inspected. This represents the total number of attribute fields corresponding to the land features in the attribute data of the base map.

[0037] In this embodiment, this step compares the attribute fields of corresponding features in the map to be inspected with those in the benchmark map to calculate the percentage of missing attribute fields for each feature in the attribute data of the map to be inspected relative to the total number of attribute fields for the corresponding feature in the attribute data of the benchmark map. This yields the attribute missing rate for a particular feature, providing a more intuitive reflection of the degree of attribute loss in the map to be inspected compared to the benchmark map. Therefore, this method can accurately quantify the attribute loss situation of each feature in the map to be inspected, providing a strong basis for the integrity assessment of map attributes. For example, during map updates or quality checks, it can quickly identify which features have attribute missing issues, facilitating targeted supplementation and correction. Furthermore, calculating based on the total number of attribute fields in the benchmark map gives the attribute missing rate relative significance, intuitively reflecting the proportion of missing attributes relative to the complete attribute system. This helps assessors to more clearly understand the quality status of map attribute data, assisting in map data management and optimization decisions.

[0038] S422. Compare the attribute values ​​of each feature's attribute field in the attribute data of the map to be inspected with the attribute values ​​of the corresponding feature's attribute field in the attribute data of the benchmark map. Obtain the number of inconsistent attribute values ​​for each feature's attribute field in the attribute data of the map to be inspected. Calculate the percentage of inconsistent attribute values ​​for each feature's attribute field in the attribute data of the map to be inspected relative to the total number of inconsistent attribute values ​​for the corresponding feature's attribute field in the attribute data of the benchmark map. Use this percentage as the attribute error rate indicator.

[0039] in, This represents the attribute error rate metric. This indicates the number of attribute fields in the attribute data of a certain feature in the map to be inspected that are inconsistent with the attribute fields in the attribute data of the corresponding feature in the base map.

[0040] In this embodiment, this step compares the attribute values ​​of corresponding feature attribute fields in the map to be inspected and the benchmark map, calculates the percentage of inconsistent attribute values ​​relative to the total number of corresponding feature attribute fields in the benchmark map, and obtains the attribute error. This provides a more intuitive reflection of the degree of error in the feature attribute values ​​of the map to be inspected relative to the benchmark map. Therefore, this method can accurately quantify the attribute value error of each feature in the map to be inspected, providing a strong basis for the accuracy assessment of map attribute data. For example, during map updates or quality checks, it can quickly identify which feature attribute values ​​have errors, facilitating targeted corrections. At the same time, the calculation based on the total number of attribute fields in the benchmark map gives the attribute error rate relative significance, intuitively reflecting the proportion of attribute value errors relative to the complete attribute system. This helps assessors to more clearly understand the quality status of map attribute data, assists in map data management and optimization decisions, and ensures the reliability of maps in various applications such as navigation and planning.

[0041] S43. Based on the baseline map and the preprocessed map to be inspected, construct topological data indicators for the map to be inspected relative to the baseline map, including the topological error rate indicator, specifically: S431. Compare the baseline map and the map to be inspected, obtain the topological error quantity of the road network and regional features in the map to be inspected, and simultaneously obtain the total number of road nodes and regional features in the map to be inspected. Calculate the degree of topological relationship error of the map to be inspected relative to the baseline map, and use this as the topological error rate index, i.e.:

[0042] in, This represents the topology error rate metric. This indicates the amount of topological errors in the road network of the map to be inspected. This indicates the amount of topological errors in the features of the area in the map to be inspected. This represents the total number of road nodes in the map to be inspected. This indicates the total number of features in the area of ​​the map to be inspected.

[0043] In this embodiment, this step compares the baseline map and the map to be inspected, counts the number of topological errors in the road network and regional features in the map to be inspected, and calculates the topological error rate by combining the total number of road nodes and regional features in the map to be inspected. This reflects the degree of error in the topological relationships of the features in the map to be inspected relative to the baseline map. Therefore, this method can quantify the degree of error in the topological relationships of the features in the map to be inspected relative to the baseline map, providing a strong basis for the accuracy assessment of map topological relationships. For example, during map updates or quality checks, topological errors in road networks and regional features can be quickly identified, facilitating targeted corrections. At the same time, the calculation based on the total number of road nodes and regional features in the map to be inspected itself gives the topological error rate relative significance, which can intuitively reflect the proportion of topological errors relative to the total number of relevant features in the map to be inspected. This helps assessors to more clearly understand the quality status of map topological data, assists in map data management and optimization decisions, and ensures the correctness of map topological relationships in various applications such as navigation and geographic analysis.

[0044] S5, Preprocessing N Step S4 is executed for each standard map, generating geometric data indicators, attribute data indicators, and topological data indicators for each standard map relative to the base map, calculating the mean values ​​for each, and generating standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators.

[0045] In this embodiment, by... N Each standard map calculates various indicators relative to the base map, and then averages these indicators to obtain a standard value. This not only reduces errors from single data sources by utilizing multi-source standard data, but also makes the obtained standard value more representative and authoritative, providing a reliable benchmark for subsequent quality inspection of maps awaiting quality control. The specific operation process is as follows: Specifically, step S5 includes S51-S54: S51, Preprocessing N Step S4 is executed for each standard map to generate geometric data indicators, attribute data indicators, and topological data indicators relative to the base map.

[0046] In this embodiment, the same index calculation process (step S4) is performed on each standard map as on the aforementioned map to be inspected, ensuring the consistency of index calculation between the standard map and the map to be inspected, making subsequent comparisons based on these indicators comparable, and laying the foundation for generating accurate standard values.

[0047] S52. Sum and average the geometric data indicators of each standard map relative to the base map to generate standard values ​​for geometric data indicators, including standard values ​​for coordinate deviation rate and shape similarity.

[0048] In this embodiment, the summation and averaging of geometric data indicators to generate standard values ​​can integrate the commonalities of geometric features (such as coordinates, shapes, etc.) in multiple standard maps, eliminate the local deviations that may exist in a single standard map, and obtain standard values ​​of geometric indicators such as coordinate deviation rate and shape similarity. These standard values ​​can accurately measure the degree of fit between the geometric elements of the map to be inspected and the standard geometric elements, ensuring the accuracy of geometric dimension quality inspection.

[0049] S53. Sum and average the attribute data indicators of each standard map relative to the baseline map to generate standard values ​​for the attribute data indicators, including the standard values ​​for the attribute missing rate indicator and the standard values ​​for the attribute error rate indicator.

[0050] In this embodiment, the attribute data indicators are summed and averaged to generate standard values. The attribute features of multiple standard maps (such as the completeness of attribute items and the accuracy of attribute values) are integrated to obtain standard values ​​of attribute indicators such as attribute missing rate and attribute error rate. These standard values ​​can effectively detect the completeness and correctness of the attribute data of the map to be inspected, and ensure the comprehensiveness of the attribute dimension quality inspection.

[0051] S54. Sum and average the topology data indicators of each standard map relative to the base map to generate standard values ​​for the topology data indicators, including the standard value for the topology error rate indicator.

[0052] In this embodiment, the topological data indicators are summed and averaged to generate standard values. By integrating the topological relationship features of multiple standard maps, the obtained standard values ​​of topological indicators such as the topological error rate can accurately assess the rationality and correctness of the topological structure of the map to be inspected, and ensure the reliability of the topological dimension quality inspection.

[0053] S6. Compare the geometric data indicators, attribute data indicators, and topological data indicators from step S4 with their corresponding standard values ​​to determine whether the ordinary map to be inspected is qualified and generate the quality inspection results.

[0054] In this embodiment, by accurately comparing the three dimensions of the map to be inspected—geometric, attribute, and topological—with the corresponding standard values, a quality inspection logic of "dimension-based verification and comprehensive qualification judgment" is constructed. This logic can systematically identify quality problems in the map to be inspected in terms of spatial location, feature form, data integrity, and topological relationships, and ultimately generate objective and quantifiable quality inspection results. This provides a direct basis for map quality assessment and optimization, while ensuring the standardization of the quality inspection process and the reliability of the results.

[0055] Specifically, step S6 includes S61-S65: S61. Determine whether the coordinate deviation rate indicators of the map to be inspected relative to the reference map are all less than the standard value of the corresponding coordinate deviation rate indicator. If so, the accuracy of the coordinates of all features on the map to be inspected is qualified, and proceed to step S62. Otherwise, the accuracy of the coordinates of features on the map to be inspected is unqualified.

[0056] In this embodiment, this step focuses on the core quality dimension of map coordinate accuracy. Through the judgment rule that "all coordinate deviation rate indicators are less than the standard value," it achieves a comprehensive verification of the coordinate accuracy of individual features, and even the entire map. On the one hand, this step can accurately locate features with out-of-standard coordinates (such as road centerline offset, excessive building vertex coordinate deviation, etc.), avoiding the impact of local coordinate errors on the overall map usability (such as navigation positioning deviation). On the other hand, the rule of "all indicators must meet the standard to be qualified" strictly ensures the consistency of map coordinate data, laying a spatial benchmark for subsequent detection of dimensions such as shape accuracy and topological relationships. Furthermore, if this step fails, it can directly prompt the user to prioritize correcting coordinate problems, improving map optimization efficiency.

[0057] S62. Determine whether all shape similarity indices of the map to be inspected relative to the base map are less than the standard value of the corresponding shape similarity index. If so, the shape accuracy of each feature in the map to be inspected is qualified, and proceed to step S63. Otherwise, the shape accuracy of the features in the map to be inspected is unqualified.

[0058] In this embodiment, the accuracy of feature shapes is assessed. By comparing shape similarity indices with standard values, the potential quality issue of "coordinate deviations being acceptable but shape distortions" is resolved. Examples include roads appearing as broken lines when they should be straight, or lake outlines differing significantly from standard shapes. The technical value of this step lies in two aspects: First, it extends the detection from "location" to "shape," ensuring that features not only have accurate coordinates but also accurately reflect their geometric characteristics in reality, meeting the map's requirement for "shape authenticity," such as the need for building shapes in urban planning maps to match actual shapes. Second, the rule of "all less than the standard value" prevents local shape distortions from affecting map readability and professionalism. If the assessment is unsatisfactory, it can provide targeted guidance to users to correct feature outline data, improving the integrity of the map's geometric quality.

[0059] S63. Determine whether the missing rate of each attribute of the map to be inspected relative to the base map is less than the standard value of the corresponding missing rate. If so, there are no missing attribute fields for any feature in the map to be inspected, and proceed to step S64. Otherwise, there are missing feature attribute fields in the map to be inspected.

[0060] In this embodiment, this step focuses on the "completeness" of attribute data. By verifying the attribute missing rate index, it solves the problem of maps having "geometric features but no attributes" or "incomplete attributes," such as road features missing the "road grade" attribute or buildings missing the "use" attribute. Its technical effects are reflected in two aspects: First, it ensures the "information completeness" of map data. Attribute data is a key description of the features of geographical features. Missing core attributes will cause the map to fail to meet the needs of actual applications. For example, a navigation map missing the "speed limit" attribute cannot display speed limit information. Second, by clarifying the rule that "no missing attributes are acceptable," it provides a quality standard for attribute data collection and entry. If it is judged to be unacceptable, the missing attribute fields can be directly located to guide users to supplement and improve the data, thereby enhancing the practicality and information value of the map data.

[0061] S64. Determine whether the error rate indicators of each attribute of the map to be inspected relative to the base map are all less than the standard value of the corresponding attribute error rate indicator. If so, there are no attribute value errors in the features of the map to be inspected, and proceed to step S65. Otherwise, there are feature attribute value errors in the map to be inspected.

[0062] In this embodiment, this step focuses on judging the "accuracy" of attribute data. By comparing the attribute error rate index, it solves the quality problem of "attributes exist but their values ​​are incorrect," such as a road "speed limit" attribute that should be 60km / h but is entered as 120km / h, or a building "number of floors" attribute that should be 10 floors but is entered as 20 floors. The technical significance of this step is twofold: First, it avoids erroneous attributes from misleading users' decisions, such as incorrect speed limit information that may lead to traffic violations, thus ensuring the "reliability" of map attribute data. Second, by using the rule of "all less than the standard value," it strictly controls the error ratio of attribute values, ensuring that the attribute data is consistent with the characteristics of real-world features. If the judgment is unqualified, the erroneous attribute value can be accurately located, guiding users to correct data entry errors and improving the credibility of map attribute data.

[0063] S65. Determine whether each topological error rate index of the map to be inspected relative to the base map is less than the standard value of the corresponding topological error rate index. If so, the topological structure of the map to be inspected is qualified, and the quality inspection of the map to be inspected is finally qualified. Otherwise, the topological structure of the map to be inspected is unqualified, that is, the quality inspection of the map to be inspected is unqualified.

[0064] In this embodiment, this step serves as the final determination of topological relationships. By verifying the topological error rate index, it solves the problem of "topological logical confusion" between features, such as roads and rivers appearing separate when they should intersect, or two adjacent buildings overlapping. Its core technical effects are: first, ensuring the "spatial logical consistency" between map features. Topological relationships are key to a map reflecting the spatial connections between features. Incorrect topological relationships will cause the map to fail to correctly express the actual connections between features. For example, in a pipeline map, pipes that should be connected appear broken, which will affect pipeline planning. Second, as the "final checkpoint" in the entire quality inspection process, only when the topological structure is qualified can the map be judged as qualified overall, avoiding "logical contradictions" in subsequent applications caused by topological errors. For example, navigation route planning may fail to generate the correct route due to road topological errors. If the map is deemed unqualified, it can guide users to correct the topological relationships between features, such as adding connected nodes and deleting overlapping areas, to ensure the correctness and integrity of the map's spatial logic.

[0065] In summary, the quality inspection method for ordinary maps proposed in this invention achieves the following technical effects through the construction of benchmarks using multiple standard maps, multi-dimensional indicator detection, and objective comparison and judgment: 1) Objectivity in benchmark selection: Benchmark maps are selected based on pixel grayscale value differences, avoiding the subjectivity of manual selection and ensuring the representativeness and authority of the benchmark maps, providing a reliable reference for subsequent quality inspection; 2) Comprehensive indicator system: Three major dimensions of indicators—geometric, attribute, and topological—are constructed, covering the core quality characteristics of map data, overcoming the limitations of single-dimensional detection, and achieving a comprehensive evaluation of map quality; 3) Scientific standard value generation... 4) Automated quality inspection: Standard values ​​are calculated by averaging multiple standard map indicators and integrating features from multiple authoritative data sources to eliminate the influence of single map biases, making the quality inspection standards more objective and universal; 5) Automated quality inspection judgment: Automatic judgment is achieved through quantitative comparison of indicators and standard values, reducing human intervention errors, improving quality inspection efficiency and consistency, and generating clear quality inspection results to provide accurate basis for map quality optimization; 6) Closed-loop standardization: From data acquisition and preprocessing to indicator construction, standard generation and result judgment, a complete standardized process is formed to ensure the standardization and comparability of map quality inspections in different batches and regions.

[0066] like Figure 2 As shown, a system applying a quality inspection method for general maps includes: The data acquisition module is used to acquire the map to be inspected from the same area and scale. N Zhang Standard Map, extract the map to be inspected and N The geometric data, attribute data, and topological data of a standard map.

[0067] In this embodiment, the module supports reading various map data formats (such as SHP, GeoJSON, KML, etc.) and achieves compatible acquisition of map data from different sources through a geographic data interface.

[0068] The data preprocessing module is used to receive the map to be inspected and... N Zhang's standard map is used to perform coordinate normalization, data cleaning, and topology repair to generate a preprocessed map for quality inspection. N Zhang Standard Map.

[0069] In this embodiment, the module compares the received map to be inspected with... N Data processing is performed on the standard map to ensure the consistency and accuracy of subsequent data processing; and during topology repair, only [the following needs to be done]. N The standard map undergoes topology repair, while the map awaiting quality inspection is not repaired, so as to preserve its original topology for quality inspection.

[0070] The baseline map filtering module is used to receive preprocessed data. N By extracting the grayscale values ​​of pixels from a standard map, the average difference between any standard map and other standard maps is calculated, and the standard map with the smallest average difference is selected as the benchmark map.

[0071] In this embodiment, the module calculates the difference by comparing pixel-level grayscale values ​​and uses the mean algorithm to process the difference data between multiple maps, ensuring that the selected benchmark map has the highest representativeness and authority.

[0072] The quality inspection index construction module is used to receive the preprocessed map to be inspected and the baseline map, and to construct the geometric data index, attribute data index, and topological data index of the map to be inspected relative to the baseline map, using the baseline map as a reference.

[0073] In this embodiment, the module constructs geometric data indicators (coordinate deviation rate, shape similarity), attribute data indicators (attribute missing rate, attribute error rate), and topological data indicators (topological error rate) of the map to be inspected relative to the benchmark map, thereby ensuring the accuracy and consistency of indicator calculation.

[0074] The quality inspection standard value generation module is used to receive preprocessed values. N Using a standard map and a baseline map as a reference, geometric data indicators, attribute data indicators, and topological data indicators are generated for each standard map relative to the baseline map. The same type of indicators are summed and averaged to generate standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators.

[0075] In this embodiment, the module generates geometric, attribute, and topological data indicators for each standard map relative to the base map, and performs summation and averaging operations on similar indicators to generate standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators, thus forming a complete quality inspection standard system. Furthermore, it uses a statistical averaging method to process multiple sets of indicator data, eliminating possible biases in a single standard map and ensuring the objectivity and reliability of the standard values.

[0076] The quality inspection judgment and result generation module is used to receive the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the base map, as well as their corresponding standard values. Based on the preset judgment rules, it determines whether the ordinary map to be inspected is qualified and generates the quality inspection result.

[0077] In this embodiment, the geometric, attribute, and topological data indicators of the map to be inspected are compared with their corresponding standard values. According to the preset judgment rules, if all indicators are less than the corresponding standard values, the map is qualified. The system determines whether the map to be inspected is qualified and generates a quality inspection result report that shows the map as qualified or contains details of unqualified items. This realizes the automated comparison and judgment logic of multi-dimensional indicators, supports the generation of quality inspection reports with pictures and text, and clearly shows the quality status and problems of the map to be inspected.

[0078] A computationally readable storage medium, comprising: The storage medium contains a computer program, which, when executed by a processor, implements any of the aforementioned quality inspection methods for ordinary maps.

[0079] In this embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, it can implement the method of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0080] Furthermore, the aforementioned computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium; the readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof; more specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] Furthermore, the aforementioned computer program can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer device, partially on the user's device, as a standalone software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0082] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0083] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A quality inspection method for general maps, characterized in that, Includes the following steps: S1. Obtain the quality inspection map of the same area and scale. N Zhang Standard Map, extract the map to be inspected and N Geometric data, attribute data, and topological data of a standard map; S2, Regarding the quality inspection map and N Zhang's standard map underwent data preprocessing to generate a preprocessed map to be inspected. N Zhang Standard Map; S3, Obtain preprocessed data N The grayscale values ​​of each pixel in the standard map are calculated, and the average difference between any standard map and other standard maps is selected as the benchmark map by selecting the standard map with the smallest average difference. S4. Based on the baseline map and the preprocessed map to be inspected, construct the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the baseline map. S5, Preprocessing N Step S4 is executed for each standard map, generating geometric data indicators, attribute data indicators, and topological data indicators for each standard map relative to the baseline map, calculating the mean values ​​for each, and generating standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators. S6. Compare the geometric data indicators, attribute data indicators, and topological data indicators from step S4 with their corresponding standard values ​​to determine whether the ordinary map to be inspected is qualified and generate the quality inspection results.

2. The quality inspection method for general maps according to claim 1, characterized in that, Step S2 specifically includes: S21, Connect the map to be inspected with N The standard map is normalized to the coordinates and converted to the geocentric coordinate system to generate a unified coordinate map for quality inspection. N Zhang Standard Map; S22. Connect the quality inspection map with unified coordinates. N Duplicate and empty features were removed from the standard map, generating a cleaned map ready for quality inspection. N Zhang Standard Map; S23. Data cleaning based on topology rules. N Perform topology checks and repairs on the standard map, and generate the topology-repaired map. N Zhang's standard map, ultimately resulting in a preprocessed map awaiting quality inspection and... N Zhang Standard Map.

3. The quality inspection method for general maps according to claim 1, characterized in that, Step S3 specifically includes: S31. Calculate the pixel-level differences between any standard map and other standard maps, i.e.: in, Represents standard map With standard map Pixel-level differences and , These represent the x and y coordinates of the pixel, respectively. Represents standard map At pixel grayscale value at that location Represents standard map At pixel The grayscale value at that location; S32. Based on the pixel-level differences between any standard map and other standard maps, calculate the average difference between any standard map and other standard maps, i.e.: in, Represents standard map Average difference from other standard maps Indicates the total number of standard maps; S33. Select the standard map with the smallest average difference and use it as the baseline map.

4. The quality inspection method for general maps according to claim 1, characterized in that, Step S4 specifically includes: S41. Based on the baseline map and the preprocessed map to be inspected, construct geometric data indicators of the map to be inspected relative to the baseline map, including coordinate deviation rate and shape similarity indicators, specifically: S411. Obtain the deviation values ​​of all coordinates of each feature in the geometric data of the map to be inspected relative to the reference map. Simultaneously, obtain the upper and lower limits of all coordinates of that feature on the reference map. Calculate the percentage of the average deviation value of all deviation values ​​of that feature to the upper and lower limits of all coordinates of that feature, and use this as the coordinate deviation rate indicator, i.e.: in, This indicates the coordinate deviation rate index. This indicates the number of coordinate points for a specific geographical feature. , These represent the first and second features of a certain ground feature in the geometric data of the map to be inspected. The x and y coordinates of each coordinate. , These represent the first and second corresponding features in the geometric data of the base map. The x and y coordinates of each coordinate. , These represent the maximum values ​​of all x and y coordinates of the feature in the geometric data of the base map. , These represent the minimum values ​​of all horizontal and vertical coordinates of the feature in the geometric data of the base map; S412. Using the Hausdorff distance method, calculate the Hausdorff distance of each feature in the geometric data of the map to be inspected relative to the reference map, and use it as a shape similarity index, i.e.: in, This represents the set of coordinate points of a certain feature in the geometric data of the map to be inspected. This represents the set of coordinate points in the geometric data of the base map that correspond to the same features on the map to be inspected. Represents the set of coordinate points With coordinate point set The Hausdorff distance, a shape similarity index, This indicates taking the maximum value. This indicates taking the minimum value. This represents the first position of a certain feature in the geometric data of the map to be inspected. The coordinates of the first coordinate point correspond to the same ground feature in the geometric data of the base map. Euclidean distance between coordinate points; S42. Based on the baseline map and the preprocessed map to be inspected, construct attribute data indicators for the map to be inspected relative to the baseline map, including attribute missing rate indicators and attribute error rate indicators, specifically: S421. Obtain the attribute fields of each feature in the attribute data of the map to be inspected, and simultaneously obtain the attribute fields of the corresponding features in the attribute data of the benchmark map. By comparison, obtain the number of missing attribute fields for each feature in the attribute data of the map to be inspected. Calculate the percentage of the number of missing attribute fields for each feature in the attribute data of the map to be inspected to the total number of attribute fields for the corresponding feature in the attribute data of the benchmark map, and use this percentage as the attribute missing rate indicator. in, Indicates the attribute missing rate. This indicates the number of missing attribute fields for a certain feature in the attribute data of the map to be inspected. This represents the total number of attribute fields corresponding to the land features in the attribute data of the base map; S422. Compare the attribute values ​​of each feature's attribute field in the attribute data of the map to be inspected with the attribute values ​​of the corresponding feature's attribute field in the attribute data of the benchmark map. Obtain the number of inconsistent attribute values ​​for each feature's attribute field in the attribute data of the map to be inspected. Calculate the percentage of inconsistent attribute values ​​for each feature's attribute field in the attribute data of the map to be inspected relative to the total number of inconsistent attribute values ​​for the corresponding feature's attribute field in the attribute data of the benchmark map. Use this percentage as the attribute error rate indicator. in, This represents the attribute error rate metric. This indicates the number of attribute fields in the attribute data of a certain feature in the map to be inspected that are inconsistent with the attribute fields in the attribute data of the corresponding feature in the base map. S43. Based on the baseline map and the preprocessed map to be inspected, construct topological data indicators for the map to be inspected relative to the baseline map, including the topological error rate indicator, specifically: S431. Compare the baseline map with the map to be inspected, obtain the topological error quantity of road networks and regional features in the map to be inspected, and simultaneously obtain the total number of road nodes and regional features in the map to be inspected. Calculate the topological relationship error rate of the map to be inspected relative to the baseline map, and use this as the topological error rate index, i.e.: in, This represents the topology error rate metric. This indicates the amount of topological errors in the road network of the map to be inspected. This indicates the amount of topological errors in the features of the area in the map to be inspected. This represents the total number of road nodes in the map to be inspected. This indicates the total number of features in the area of ​​the map to be inspected.

5. The quality inspection method for general maps according to claim 4, characterized in that, Step S5 specifically includes: S51, Preprocessing N Step S4 is executed for each standard map to generate geometric data indicators, attribute data indicators, and topological data indicators relative to the base map. S52. Sum and average the geometric data indicators of each standard map relative to the base map to generate standard values ​​of geometric data indicators, including the standard value of the coordinate deviation rate indicator and the standard value of the shape similarity indicator. S53. Sum and average the attribute data indicators of each standard map relative to the baseline map to generate standard values ​​of the attribute data indicators, including the standard value of the attribute missing rate indicator and the standard value of the attribute error rate indicator. S54. Sum and average the topology data indicators of each standard map relative to the base map to generate standard values ​​for the topology data indicators, including the standard value for the topology error rate indicator.

6. The quality inspection method for general maps according to claim 5, characterized in that, Step S6 specifically includes: S61. Determine whether the coordinate deviation rate indicators of the map to be inspected relative to the reference map are all less than the standard value of the corresponding coordinate deviation rate indicator. If so, the accuracy of the coordinates of the features in the map to be inspected is qualified, and proceed to step S62. Otherwise, the accuracy of the coordinates of the features in the map to be inspected is unqualified. S62. Determine whether all shape similarity indices of the map to be inspected relative to the base map are less than the standard value of the corresponding shape similarity index. If so, the shape accuracy of each feature in the map to be inspected is qualified, and proceed to step S63. Otherwise, the shape accuracy of the features in the map to be inspected is unqualified. S63. Determine whether the missing rate index of each attribute of the map to be inspected relative to the base map is less than the standard value of the corresponding missing rate index. If so, there are no missing attribute fields for each feature in the map to be inspected, and proceed to step S64. Otherwise, there are missing feature attribute fields in the map to be inspected. S64. Determine whether the error rate index of each attribute of the map to be inspected relative to the base map is less than the standard value of the corresponding attribute error rate index. If so, there are no attribute value errors in the features of the map to be inspected, and proceed to step S65. Otherwise, there are feature attribute value errors in the map to be inspected. S65. Determine whether each topological error rate index of the map to be inspected relative to the base map is less than the standard value of the corresponding topological error rate index. If so, the topological structure of the map to be inspected is qualified, and the quality inspection of the map to be inspected is finally qualified. Otherwise, the topological structure of the map to be inspected is unqualified, that is, the quality inspection of the map to be inspected is unqualified.

7. A quality inspection system for general maps, characterized in that, The quality inspection method for general maps as described in claims 1-6 includes: The data acquisition module is used to acquire the map to be inspected from the same area and scale. N Zhang Standard Map, extract the map to be inspected and N Geometric data, attribute data, and topological data of a standard map; The data preprocessing module is used to receive the map to be inspected and... N Zhang's standard map is used to perform coordinate normalization, data cleaning, and topology repair to generate a preprocessed map for quality inspection. N Zhang Standard Map; The baseline map filtering module is used to receive preprocessed data. N The standard map is calculated by extracting the grayscale values ​​of pixels and calculating the average difference between any standard map and other standard maps. The standard map with the smallest average difference is selected as the benchmark map. The quality inspection index construction module is used to receive the preprocessed map to be inspected and the benchmark map, and to construct the geometric data index, attribute data index, and topological data index of the map to be inspected relative to the benchmark map using the benchmark map as a reference. The quality inspection standard value generation module is used to receive preprocessed values. N Using the benchmark map as a reference, the standard map generates geometric data indicators, attribute data indicators, and topological data indicators for each standard map relative to the benchmark map. The same type of indicators are summed and averaged to generate standard values ​​for geometric data indicators, attribute data indicators, and topological data indicators. The quality inspection judgment and result generation module is used to receive the geometric data indicators, attribute data indicators, and topological data indicators of the map to be inspected relative to the base map, as well as their corresponding standard values. Based on the preset judgment rules, it determines whether the ordinary map to be inspected is qualified and generates the quality inspection result.

8. A computationally readable storage medium, characterized in that, include: The storage medium stores a computer program, which, when executed by a processor, implements the quality inspection method for ordinary maps as described in any one of claims 1-6.

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