Detection point data library building, warehousing and quality inspection method

Abstract

The invention discloses a detection point data library building, warehousing and quality inspection method, and relates to the technical field of large-scale topographic map detection point data library building. A detection point database is built, and data of multiple detection points are sorted and imported into the detection point database; a standard detection point database is formed after data format conversion and attribute assignment; and performing quality inspection on the stored detection point database items, and outputting a quality inspection result based on the detection point database quality inspection table. Through standard research and application, the technical standard of large-scale topographic map detection point acquisition and database establishment is established, and the inspection work efficiency and the detection point result reuse level are improved. Detection point database construction project quality inspection is carried out according to design requirements, after inspection is completed, modification opinions are fed back to database construction personnel, modification is completed, and reinspection is submitted until achievement acceptance requirements are met, the consistency of detection point database construction is improved, and basic data are accumulated for achieving automatic quality inspection and achievement evaluation.

Description

Technical Field

[0001] The invention relates to the technical field of database construction of detection point data of large-scale topographic maps, and in particular to a method for database construction, storage and quality inspection of detection point data. Background Art

[0002] Check points are the ground features and geomorphic features collected for topographic map position accuracy detection, including plane check points, elevation check points, and flat-high check points;

[0003] Plane detection points are feature points of land objects collected to detect the plane position accuracy of topographic maps; elevation detection points are feature points of land objects and landforms collected to detect the elevation accuracy of topographic maps; plane-elevation detection points are collected to detect the plane position and elevation accuracy of topographic maps, and can also be used as feature points of plane detection points and elevation detection points.

[0004] In recent years, with the continuous advancement of the construction of a new basic surveying and mapping system and the construction of a real 3D China, there is an increasing demand for the rapid acquisition and efficient management of topographic map detection points at 1:500, 1:1000, and 1:2000. The current quality inspection standards for topographic maps have made clear provisions for the number, distribution, and mathematical accuracy statistical methods of detection points, which has important guiding significance in ensuring the position accuracy of topographic maps.

[0005] At the same time, with the advent of the era of intelligent surveying and mapping, surveying and mapping quality inspection institutions, enterprises and institutions have also conducted research on the standardization of inspection points, automated measurement, and the construction of knowledge bases. Huang Changdi and others conducted research on the construction and application of surveying and mapping quality inspection resource libraries and knowledge bases, and proposed a surveying and mapping quality inspection knowledge base framework, using inspection points, topography, house surfaces, etc. over the years as data sources for the mathematical precision knowledge base. Hu Ruisheng and others standardized the DOM inspection point data, constructed a management system, and explored its application, realizing the rapid detection of DOM for geographic national conditions monitoring. Building a knowledge base for surveying and mapping inspection points and conducting research on automated quality inspection technologies can improve the application rate of reference data, the convenience of query, the standardization of inspection processes, and the consistency of problem statements, further improving quality inspection efficiency.

[0006] However, due to the variety of ways of acquiring testing points, the formats of the collected testing points are diverse. There are problems such as insufficient number of testing points, lack of image screenshot references, and incomplete information records. There are certain limitations on the scope of application of the inspection, and further standardization is needed.

[0007] Practice has proved that by building a database of test point data, establishing the organizational relationship between the original coordinate file of the test point, collecting basic information, precision information, and collecting instrument information, and through unified numbering, the uniqueness and traceability of the test point data are ensured, providing guarantee for the reusability of the test point results. The idea of ​​integrating test point collection, sorting and database construction has reached a consensus in the industry, but there is a lack of standards for building databases for test point data. It is urgent to formulate relevant standards to achieve the effect of technology and data sharing;

[0008] After the database of the inspection point data is built, errors, omissions or duplications may occur during the data entry process, and there may also be problems with the entry attributes, so the quality of the inspection point data needs to be checked. Summary of the invention

[0009] In order to solve the technical problems of database construction and quality inspection of detection point data, the present invention provides a method for database construction, storage and quality inspection of detection point data. The following technical solutions are adopted:

[0010] The method for building and storing the detection point data and quality inspection includes the following steps:

[0011] Step 1, respectively obtaining multiple detection point data, the detection point data including detection point coordinate files, collection registration forms, field photos and image screenshots with detection points collected and marked, and respectively arranging the multiple detection point coordinate files according to the set standards;

[0012] Step 2: Establish a test point database, and sort and import data of multiple test points into the test point database;

[0013] Step 3: After data format conversion and attribute assignment, a standard detection point database is formed;

[0014] Step 4: Perform a quality check on the completed inspection point database items to check whether the database's spatial reference system, data file standardization, database structure correctness, and database data information integrity meet the requirements, and prepare a inspection point database quality checklist, and output the quality check results based on the inspection point database quality checklist.

[0015] By adopting the above technical solutions, in order to adapt to the development of surveying and mapping results quality inspection technology and actual application needs, fill the gap in the standardization of 1:500, 1:1000, and 1:2000 topographic map position accuracy detection point collection and database construction, and combine the practical experience of detection point reuse, analyze and organize the existing detection point data collection indicators and technical methods, as well as similar database construction requirements, establish detection point database technical standards that meet the actual needs of inspection work, and focus on the research of detection point data standards, collection method standards, and database construction standards.

[0016] Through standard research and application, technical standards for the collection and database construction of large-scale topographic map inspection points were established, which improved the efficiency of inspection work and the level of reuse of inspection point results.

[0017] By conducting quality inspections on the completed inspection point database projects, the inspection point database construction project quality inspection checks whether the database's spatial reference system, data file standardization, database structure correctness, database data information integrity, etc. meet the requirements according to the design requirements, and fills in the inspection point database quality inspection form. After the inspection is completed, the modification opinions will be fed back to the database construction personnel, and the modifications will be submitted for review until the results acceptance requirements are met, thereby improving the consistency of the inspection point database construction and accumulating basic data for the realization of automated quality inspection and results evaluation.

[0018] Optionally, in step 1, organizing the detection point coordinate file includes the following methods:

[0019] When using a measuring instrument to collect the coordinates of the detection points, the coordinate file is directly exported from the measuring instrument and organized into the corresponding detection point coordinate file based on the detection instrument;

[0020] If the coordinate data in the detection point coordinate file only includes plane coordinates and elevation, the plane coordinates and elevation are converted to obtain longitude and latitude coordinates. The detection point coordinate file record format is: detection point number, detection point type, latitude, longitude, geodetic height, north coordinate, east coordinate, elevation, collection date; if the local coordinate system is used, it is converted to the 2000 National Geodetic Coordinate System coordinates;

[0021] The naming format of the detection point coordinate file is: instrument number, acquisition method, central meridian, acquisition date;

[0022] When the measuring instrument is RTK, calculate the Gaussian plane coordinates, convert the longitude and latitude coordinates into Gaussian plane coordinates according to the given projection band, calculate the normal height, and convert the normal height in the HBCORS system based on the original longitude and latitude, geodetic height and instrument height;

[0023] The method of converting and obtaining normal height in the HBCORS system is to extract the detection point number, longitude and latitude, geodetic height, and instrument height of the detection point from the original detection point file, organize them into a text format that meets the online conversion requirements of the HBCORS system, upload them to the HBCORS online location service system for self-service conversion, obtain the Gaussian plane coordinates, elevation, and belt number of the detection point, and obtain the central meridian of the detection point, the Gaussian plane coordinates without belt number, and the 1985 elevation through string batch processing, and add them to the detection point coordinate file;

[0024] When the measuring instrument is a total station, the DLG detection points collected by the polar coordinate method only have plane coordinates but no longitude and latitude coordinates. The detection point number, x north coordinate, y east coordinate and elevation are extracted from the original file to form a text file, which is submitted to the HBCORS online location service system for self-service conversion to obtain the longitude and latitude coordinates and geoid height under the specified central meridian.

[0025] By adopting the above technical solution, when using a measuring instrument to collect detection points, the coordinate file should be directly exported from the measuring instrument, and organized into a detection point coordinate file with the detection instrument as the unit; when using existing results to collect detection points, the detection points collected within the scope of the existing results should be organized into a detection point coordinate file; the original data only collected plane coordinates and elevations, and coordinate conversion should be performed to obtain longitude and latitude coordinates. The detection point record format is: detection point number, detection point type, latitude, longitude, geodetic height, north coordinate, east coordinate, elevation, collection date; detection point coordinates and longitude and latitude, the unit is "degree", and the value is taken to 9 decimal places; plane coordinates, geodetic height, elevation, the unit is "meter", the value is taken to 3 decimal places, and when using the local coordinate system, it needs to be converted to the 2000 national geodetic coordinate system coordinates; the detection point coordinate file can be named in the form of "instrument number + collection method + central meridian + collection date"; the detection point coordinate file related information should be registered in the collection registration form.

[0026] Optionally, use low-altitude drone aerial photogrammetry to collect field photos of the detection points. When collecting the field photos of the detection points, the main ground object points photographed are placed in the middle of the drawing. There are position reference objects in the field photos. The shooting distance is 5m to 20m, and the resolution is not less than 1 million pixels.

[0027] The image screenshots with the detection points marked are cropped from the reference data, which includes topographic maps and satellite images. The detection point positions are marked with obvious marks with the detection point positions as the center points, and the images with set length and width pixel sizes are cropped.

[0028] By adopting the above technical solution, field photos and image screenshots are organized. Field photos and image screenshots with marked detection points are stored as picture files, and folders are created by category to store pictures of different categories; the picture file name should be unique, the file name should be consistent with the detection point number, and an identification code and picture sequence number are attached. The naming rule is: "Identification code + detection point number + picture sequence number", where the field photo identification code is replaced by the letters "ZP" and the image screenshot is replaced by "YX"; the picture sequence number is serially numbered according to the number of field photos (or image screenshots) contained in each detection point, and the picture sequence number retains 2 digits, and a "0" is added in front of less than two digits.

[0029] The readability of the inspection points can be enhanced by adding field photos and image screenshots, providing accurate basis for reusing the inspection points.

[0030] Optionally, step 2 includes the following sub-steps:

[0031] Step 21, create a library, allocate physical space for each type of data, and create a detection point data table;

[0032] Step 22, data preparation, performing consistency conversion on the detection point coordinate file sorted in step 2 according to the requirements of data organization, and the consistency conversion includes code conversion, format conversion, coordinate transformation and projection conversion;

[0033] Step 23, storing the detection point spatial data and attributes in the database according to the data organization method;

[0034] Step 24, detection point file storage, the detection point file is stored in the form of a file, the folder is named according to the project name, and the detection point coordinate file, collection registration form, field photo directory and image screenshot directory are stored in sequence.

[0035] Database creation. According to the requirements of database organization, physical space is allocated to each type of data, relevant parameters are set, data tables are created, etc. The scalability of the database should be considered when allocating physical space. The organized detection point coordinate files are converted for consistency according to the requirements of data organization, mainly including code conversion, format conversion, coordinate transformation, projection conversion, etc.

[0036] The spatial data and attributes of the detection points are stored in the database according to the data organization method. Data can be stored manually or in batches by program.

[0037] The detection point files are stored in the form of files. The folders are named according to the project name, and the detection point coordinate files, collection registration forms, field photo directories, and image screenshot directories are stored in sequence.

[0038] The data at the inspection points should be updated as needed to keep the data realistic.

[0039] Database security protection, confidentiality, and database backup and recovery should comply with the requirements of GB / T 33453.

[0040] The quality requirements of the database should meet the requirements of GB / T 33453, and at the same time meet the requirements of integrity, logical consistency, conceptual consistency, value range consistency, format consistency, attribute accuracy, etc.

[0041] Optionally, step 23 includes the following sub-steps:

[0042] Step 231, input the path information of the attachment of the detection point data, the path information of the attachment of the detection point data includes the path corresponding to the detection point screenshot and the photo, and indicates the detailed location of the detection point, and uses specific direction terms to describe the position relationship, and the specific direction terms are east, south, west, north, inside, outside, up, down, up and down;

[0043] The position relationship description is consistent with the position relationship between the detection point and the ground object in the photo;

[0044] Step 232, input the detection point source information;

[0045] Step 233, input the accuracy information, fill in the actual detection result scale according to the accuracy of the detection point corresponding to the surveying and mapping results, and fill in the corresponding level for the level control point;

[0046] Step 234, enter the detection point type. The detection point type is divided into detection type and detection result type. The detection type is determined according to the source and purpose of the detection point, and is marked by letter code in the detection point record string in the detection point file, such as P for plane detection, G for elevation detection, and PG for flat-elevation detection; the detection result type is determined according to the detection type used for detection at the detection point, and the detection result type is reflected in the naming of the detection point file.

[0047] By adopting the above technical solution, the attachment path of the detection point is entered, and the detection point data is accompanied by actual photos and image screenshots. When entering the detection point, it is also necessary to enter the detection point image screenshot, the corresponding path of the photo, and indicate the detailed location of the detection point in the location description. Use words that can specifically and accurately express the position relationship, such as east, south, west, north, inside, outside, up, down, and on and off the ridge, to describe the location, which should be consistent with the location relationship between the detection point and the ground object in the photo, such as "the southwest corner of the flower bed is the landing point".

[0048] Enter the source information of the detection points. The source information of the detection points includes information such as the collection date, which can be extracted from the collection registration form; the project number is based on the station's annual inspection and acceptance project number, and is supplemented and entered during the database construction process. Projects with report numbers are numbered according to the 4-digit year + 3-digit report sequence number, and projects without project report numbers are numbered in sequence with the 4-digit year + 900 / 901 / 902, etc.

[0049] According to the accuracy of the surveying and mapping results corresponding to the detection point, fill in the actual detection result scale; fill in the level of the grade control point, such as GPS control point A, B, C, etc.; fill in the image resolution of the image control point. The accuracy information represents the position accuracy of the detection point that can achieve this type of result at this scale.

[0050] The detection point types are divided into "detection type" and "detection result" types. The "detection type" is determined according to the source and purpose of the detection point, and is marked with letter codes in the detection point record string in the detection point file, such as P for plane detection, G for elevation detection, PG for flat-elevation detection, etc. The detection result type is determined according to the detection type used for detection at the detection point, which has been reflected in the naming of the detection point file.

[0051] Optionally, step 3 includes the following sub-steps:

[0052] Step 31, input the unified number of the inspection point;

[0053] Step 32, data format conversion and attribute assignment, after entering all the inspection points in the Excel entry template, add auxiliary information decimal precision and decimal latitude, batch convert degrees, minutes and seconds into decimal degrees through Excel formulas, edit the conversion formula in Excel, and perform batch conversion;

[0054] Step 33, spatial processing, adds xy data in ArcGIS and converts the detection points into a geographic database in decimal degree format.

[0055] By adopting the above technical solution, the numbering rules of the test points stipulate the numbering components and rules, including the acquisition method, the administrative division code, the collection date and the serial number, to ensure that the test point numbers in the whole database are not repeated. At the same time, the newly collected test points can be numbered independently without affecting the numbers of the test points already in the database.

[0056] After entering all the inspection points in the Excel entry template, add the auxiliary information decimal precision and decimal latitude, and convert the degrees, minutes and seconds into decimal degrees in batches through Excel formulas. This work involves editing the conversion formula in Excel and performing the conversion in batches.

[0057] Optionally, in step 4, if there is a detection point that exceeds the administrative area boundary, the location information of the detection point is mapped to the nearest administrative area.

[0058] The detection points are spatially superimposed with the county-level administrative area surface elements, and the names of the county-level administrative areas to which they belong are attached; the detection points are spatially superimposed with the city-level administrative area surface elements, and the names of the city-level administrative areas to which they belong are attached. Some detection points are beyond the provincial boundaries. These points are mainly satellite image ground control points and encryption points. The processing method is to correspond these points to adjacent administrative areas to avoid the detection stations where these satellite image ground control points and encryption points are located being unable to be normally stored and processed in the database.

[0059] Optionally, quality checks include the following steps:

[0060] Step 41, determining whether the geodetic datum, elevation datum, and map projection parameter settings of the detection point database are correct;

[0061] Step 42, determine whether the data organization and data table naming of the detection point database meet the requirements of the design document;

[0062] Step 43, determine whether the definition of each attribute item in the data table is correct;

[0063] Step 44, determining whether the spatial topological relationship of the detection points in the detection point library is correct, including point overlap and point duplication;

[0064] Step 45, determine whether each attribute item in the detection point database is complete;

[0065] Step 46, judging whether the formats of various electronic data results submitted are correct, and whether the data file storage, organization, file format, and naming meet the requirements of the design document;

[0066] Step 47: Check whether the paper results submitted are complete;

[0067] A quality inspection table for the inspection point database is prepared based on the quality inspection results of steps 41 to 47.

[0068] Optionally, a quality checklist for the detection point database is prepared, and each quality check item in steps 41 to 47 is checked based on the quality checklist for the detection point database, and the result item indicating whether it meets the requirements is filled in.

[0069] Optionally, in step 4, during each inspection process, the specific items that do not meet the requirements are filled in the inspection opinion record form. After completion, the inspection opinion record form is fed back to the database construction personnel of the inspection point. The database construction personnel of the inspection point modify and improve the inspection opinion record form. After the modification and improvement, re-inspection is carried out according to steps 41 to 47 until all quality inspection items are met.

[0070] 10. The method for building and storing the detection point data and inspecting the quality of the detection point data according to claim 7, wherein the topological relationship inspection in step 44 comprises the following steps:

[0071] Step 441, reading the spatial coordinate data of all detection points from the database;

[0072] Step 442, checking whether there are two or more detection points that completely overlap in space;

[0073] Step 443, check whether there are identical point records in the database, where identical point records refer to those with identical coordinates and attributes.

[0074] By adopting the above technical solution, the quality inspection of the detection point database construction project checks whether the spatial reference system of the database, the standardization of data files, the correctness of the database structure, the integrity of database data information, etc. meet the requirements according to the design requirements, fills in the detection point database quality inspection form, and records quality problems in the inspection opinion record form.

[0075] A combination of automated quality inspection tools and manual inspection can be used to improve the efficiency and accuracy of quality inspection.

[0076] The check of point overlap and point duplication in step 44 may be performed using an automated method, for example, by implementing automated checking based on a python tool.

[0077] After the inspection is completed, the modification suggestions will be fed back to the database construction personnel, and the modifications will be submitted for review until the results acceptance requirements are met, thereby improving the consistency of the construction of the testing point database and accumulating basic data for the realization of automated quality inspection and results evaluation.

[0078] In summary, the present invention includes at least one of the following beneficial technical effects:

[0079] The present invention can provide a method for database construction and storage and quality inspection of detection point data, filling the gap in the standardization of the acquisition and database construction of position accuracy detection points for large-scale topographic maps such as 1:500, 1:1000, and 1:2000. Through standard research and application, the technical standard for the acquisition and database construction of detection points for large-scale topographic maps is established, which improves the inspection work efficiency and the level of reuse of detection point results.

[0080] Improved the efficiency of data collection and organization at test points. Guided and standardized the inspection personnel to collect data at test points, organize original documents, and organize photos, which improved the efficiency of data collection and organization at test points.

[0081] The construction of the detection point database has been unified. In order to build a highly reliable on-site detection point database, the specifications for coding, format conversion, attachment organization and attribute attachment have been provided, and the construction of the detection point database has been unified.

[0082] Basic data has been accumulated to realize automated quality inspection and results evaluation. The inspection point database is an important part of the construction of the inspection resource library, providing a data basis for the rapid inspection and evaluation of the mathematical accuracy of surveying and mapping results;

[0083] The quality inspection of the detection point database construction project is to check whether the spatial reference system of the database, the standardization of data files, the correctness of the database structure, the integrity of the database data information, etc. meet the requirements according to the design requirements, fill in the detection point database quality inspection form, and record the quality problems in the inspection opinion record form. After the inspection is completed, the modification opinions will be fed back to the database construction personnel, and the modifications will be submitted for review until the results acceptance requirements are met, so as to improve the consistency of the detection point database construction and accumulate basic data for the realization of automated quality inspection and results evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic diagram of the process of the detection point data database building and quality inspection method of the present invention; DETAILED DESCRIPTION

[0085] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0086] The embodiment of the present invention discloses a method for building a database and storing and quality inspection of detection point data.

[0087] Reference Figure 1, Embodiment 1, a method for building a database and quality inspection of detection point data, comprising the following steps:

[0088] Step 1, respectively obtaining multiple detection point data, the detection point data including detection point coordinate files, collection registration forms, field photos and image screenshots with detection points collected and marked, and respectively arranging the multiple detection point coordinate files according to the set standards;

[0089] Step 2: Establish a test point database, and sort and import data of multiple test points into the test point database;

[0090] Step 3: After data format conversion and attribute assignment, a standard detection point database is formed;

[0091] Step 4: Perform a quality check on the completed inspection point database items to check whether the database's spatial reference system, data file standardization, database structure correctness, and database data information integrity meet the requirements, and prepare a inspection point database quality checklist, and output the quality check results based on the inspection point database quality checklist.

[0092] In order to adapt to the development of surveying and mapping results quality inspection technology and actual application needs, fill the gap in the standardization of 1:500, 1:1000, and 1:2000 topographic map position accuracy detection point collection and database construction, and combine the practical experience of detection point reuse, analyze and organize the existing detection point data collection indicators and technical methods, as well as similar database construction requirements, establish detection point database technical standards that meet the actual needs of inspection work, and focus on the research of detection point data standards, collection method standards, and database construction standards.

[0093] Through standard research and application, technical standards for the collection and database construction of large-scale topographic map inspection points were established, which improved the efficiency of inspection work and the level of reuse of inspection point results.

[0094] By conducting quality inspections on the completed inspection point database projects, the inspection point database construction project quality inspection checks whether the database's spatial reference system, data file standardization, database structure correctness, database data information integrity, etc. meet the requirements according to the design requirements, and fills in the inspection point database quality inspection form. After the inspection is completed, the modification opinions will be fed back to the database construction personnel, and the modifications will be submitted for review until the results acceptance requirements are met, thereby improving the consistency of the inspection point database construction and accumulating basic data for the realization of automated quality inspection and results evaluation.

[0095] In Example 2, in step 1, organizing the detection point coordinate file includes the following method:

[0096] When using a measuring instrument to collect the coordinates of the detection points, the coordinate file is directly exported from the measuring instrument and organized into the corresponding detection point coordinate file based on the detection instrument;

[0097] If the coordinate data in the detection point coordinate file only includes plane coordinates and elevation, the plane coordinates and elevation are converted to obtain longitude and latitude coordinates. The detection point coordinate file record format is: detection point number, detection point type, latitude, longitude, geodetic height, north coordinate, east coordinate, elevation, collection date; if the local coordinate system is used, it is converted to the 2000 National Geodetic Coordinate System coordinates;

[0098] The naming format of the detection point coordinate file is: instrument number, acquisition method, central meridian, acquisition date;

[0099] When the measuring instrument is RTK, calculate the Gaussian plane coordinates, convert the longitude and latitude coordinates into Gaussian plane coordinates according to the given projection band, calculate the normal height, and convert the normal height in the HBCORS system based on the original longitude and latitude, geodetic height and instrument height;

[0100] The method of converting and obtaining normal height in the HBCORS system is to extract the detection point number, longitude and latitude, geodetic height, and instrument height of the detection point from the original detection point file, organize them into a text format that meets the online conversion requirements of the HBCORS system, upload them to the HBCORS online location service system for self-service conversion, obtain the Gaussian plane coordinates, elevation, and belt number of the detection point, and obtain the central meridian of the detection point, the Gaussian plane coordinates without belt number, and the 1985 elevation through string batch processing, and add them to the detection point coordinate file;

[0101] When the measuring instrument is a total station, the DLG detection points collected by the polar coordinate method only have plane coordinates but no longitude and latitude coordinates. The detection point number, x north coordinate, y east coordinate and elevation are extracted from the original file to form a text file, which is submitted to the HBCORS online location service system for self-service conversion to obtain the longitude and latitude coordinates and geoid height under the specified central meridian.

[0102] When using a surveying instrument to collect detection points, the coordinate file should be directly exported from the surveying instrument, and organized into a detection point coordinate file based on the detection instrument; when using existing results to collect detection points, organize the detection points collected within the scope of the existing results into a detection point coordinate file; the original data only collected plane coordinates and elevations, and coordinate conversion should be performed to obtain longitude and latitude coordinates. The detection point record format is: detection point number, detection point type, latitude, longitude, geodetic height, north coordinate, east coordinate, elevation, collection date; the latitude and longitude of the detection point coordinates are in degrees, and the value is taken to 9 decimal places; the plane coordinates, geodetic height, and elevation are in meters, and the value is taken to 3 decimal places. When using the local coordinate system, it needs to be converted to the 2000 National Geodetic Coordinate System coordinates; the detection point coordinate file can be named in the form of "instrument number + collection method + central meridian + collection date"; the detection point coordinate file related information should be registered in the collection registration form.

[0103] Use low-altitude drone aerial photogrammetry to collect field photos of the inspection points. When collecting field photos of the inspection points, the main ground object point photographed is placed in the middle of the drawing. There are position reference objects in the field photos. The shooting distance is 5m to 20m, and the resolution is not less than 1 million pixels.

[0104] The image screenshots with the detection points marked are cropped from the reference data, which includes topographic maps and satellite images. The detection point positions are marked with obvious marks with the detection point positions as the center points, and the images with set length and width pixel sizes are cropped.

[0105] Field photos and image screenshots are organized. Field photos and image screenshots with marked detection points are stored as image files, and folders are created by category to store images of different categories; the image file name should be unique, the file name should be consistent with the detection point number, and an identification code and image serial number should be attached. The naming rule is: "Identification code + detection point number + image serial number", where the field photo identification code is replaced by the letters "ZP" and the image screenshot is replaced by "YX"; the image serial number is serially numbered according to the number of field photos (or image screenshots) contained in each detection point, and the image serial number retains 2 digits, and a "0" is added in front of less than two digits.

[0106] The readability of the inspection points can be enhanced by adding field photos and image screenshots, providing accurate basis for reusing the inspection points.

[0107] In embodiment 3, step 2 comprises the following sub-steps:

[0108] Step 21, create a library, allocate physical space for each type of data, and create a detection point data table;

[0109] Step 22, data preparation, performing consistency conversion on the detection point coordinate file sorted in step 2 according to the requirements of data organization, and the consistency conversion includes code conversion, format conversion, coordinate transformation and projection conversion;

[0110] Step 23, storing the detection point spatial data and attributes in the database according to the data organization method;

[0111] Step 24, detection point file storage, the detection point file is stored in the form of a file, the folder is named according to the project name, and the detection point coordinate file, collection registration form, field photo directory and image screenshot directory are stored in sequence.

[0112] Database creation. According to the requirements of database organization, physical space is allocated to each type of data, relevant parameters are set, data tables are created, etc. The scalability of the database should be considered when allocating physical space. The organized detection point coordinate files are converted for consistency according to the requirements of data organization, mainly including code conversion, format conversion, coordinate transformation, projection conversion, etc.

[0113] The spatial data and attributes of the detection points are stored in the database according to the data organization method. Data can be stored manually or in batches by program.

[0114] The detection point files are stored in the form of files. The folders are named according to the project name, and the detection point coordinate files, collection registration forms, field photo directories, and image screenshot directories are stored in sequence.

[0115] The data at the inspection points should be updated as needed to keep the data realistic.

[0116] Database security protection, confidentiality, and database backup and recovery should comply with the requirements of GB / T 33453.

[0117] The quality requirements of the database should meet the requirements of GB / T 33453, and at the same time meet the requirements of integrity, logical consistency, conceptual consistency, value range consistency, format consistency, attribute accuracy, etc.

[0118] In Embodiment 4, step 23 includes the following sub-steps:

[0119] Step 231, input the path information of the attachment of the detection point data, the path information of the attachment of the detection point data includes the path corresponding to the detection point screenshot and the photo, and indicates the detailed location of the detection point, and uses specific direction terms to describe the position relationship, and the specific direction terms are east, south, west, north, inside, outside, up, down, up and down;

[0120] The position relationship description is consistent with the position relationship between the detection point and the ground object in the photo;

[0121] Step 232, input the detection point source information;

[0122] Step 233, input the accuracy information, fill in the actual detection result scale according to the accuracy of the detection point corresponding to the surveying and mapping results, and fill in the corresponding level for the level control point;

[0123] Step 234, enter the detection point type. The detection point type is divided into detection type and detection result type. The detection type is determined according to the source and purpose of the detection point, and is marked by letter code in the detection point record string in the detection point file, such as P for plane detection, G for elevation detection, and PG for flat-elevation detection; the detection result type is determined according to the detection type used for detection at the detection point, and the detection result type is reflected in the naming of the detection point file.

[0124] Enter the attached path of the detection point. The detection point data will be accompanied by on-site photos and image screenshots. When entering the detection point, it is necessary to enter the detection point image screenshots and the corresponding path of the photos at the same time, and indicate the detailed location of the detection point in the location description. Use words that can specifically and accurately express the position relationship, such as east, south, west, north, inside, outside, up, down, and on and off the ridge, etc., which should be consistent with the position relationship between the detection point and the ground object in the photo, such as "the southwest corner of the flower bed is the landing point".

[0125] Enter the source information of the detection points. The source information of the detection points includes information such as the collection date, which can be extracted from the collection registration form; the project number is based on the station's annual inspection and acceptance project number, and is supplemented and entered during the database construction process. Projects with report numbers are numbered according to the 4-digit year + 3-digit report sequence number, and projects without project report numbers are numbered in sequence with the 4-digit year + 900 / 901 / 902, etc.

[0126] According to the accuracy of the surveying and mapping results corresponding to the detection point, fill in the actual detection result scale; fill in the level of the grade control point, such as GPS control point A, B, C, etc.; fill in the image resolution of the image control point. The accuracy information represents the position accuracy of the detection point that can achieve this type of result at this scale.

[0127] The detection point types are divided into "detection type" and "detection result" types. The "detection type" is determined according to the source and purpose of the detection point, and is marked with letter codes in the detection point record string in the detection point file, such as P for plane detection, G for elevation detection, PG for flat-elevation detection, etc. The detection result type is determined according to the detection type used for detection at the detection point, which has been reflected in the naming of the detection point file.

[0128] In embodiment 5, step 3 comprises the following sub-steps:

[0129] Step 31, input the unified number of the inspection point;

[0130] Step 32, data format conversion and attribute assignment, after entering all the inspection points in the Excel entry template, add auxiliary information decimal precision and decimal latitude, batch convert degrees, minutes and seconds into decimal degrees through Excel formulas, edit the conversion formula in Excel, and perform batch conversion;

[0131] Step 33, spatial processing, adds xy data in ArcGIS and converts the detection points into a geographic database in decimal degree format.

[0132] The numbering rules for test points specify the numbering components and rules, including the acquisition method, administrative division code, collection date and serial number, to ensure that the test point numbers in the entire database are not repeated. At the same time, newly collected test points can be numbered independently without affecting the numbers of the test points already in the database.

[0133] After entering all the inspection points in the Excel entry template, add the auxiliary information decimal precision and decimal latitude, and convert the degrees, minutes and seconds into decimal degrees in batches through Excel formulas. This work involves editing the conversion formula in Excel and performing the conversion in batches.

[0134] In Example 6, in step 4, if there is a detection point that exceeds the administrative area boundary, the location information of the detection point is mapped to the nearest administrative area.

[0135] The detection points are spatially superimposed with the county-level administrative area surface elements, and the names of the county-level administrative areas to which they belong are attached; the detection points are spatially superimposed with the city-level administrative area surface elements, and the names of the city-level administrative areas to which they belong are attached. Some detection points are beyond the provincial boundaries. These points are mainly satellite image ground control points and encryption points. The processing method is to correspond these points to adjacent administrative areas to avoid the detection stations where these satellite image ground control points and encryption points are located being unable to be normally stored and processed in the database.

[0136] Embodiment 7, quality inspection comprises the following steps:

[0137] Step 41, determining whether the geodetic datum, elevation datum, and map projection parameter settings of the detection point database are correct;

[0138] Step 42, determine whether the data organization and data table naming of the detection point database meet the requirements of the design document;

[0139] Step 43, determine whether the definition of each attribute item in the data table is correct;

[0140] Step 44, determining whether the spatial topological relationship of the detection points in the detection point library is correct, including point overlap and point duplication;

[0141] Step 45, determine whether each attribute item in the detection point database is complete;

[0142] Step 46, judging whether the formats of various electronic data results submitted are correct, and whether the data file storage, organization, file format, and naming meet the requirements of the design document;

[0143] Step 47: Check whether the paper results submitted are complete;

[0144] A quality inspection table for the inspection point database is prepared based on the quality inspection results of steps 41 to 47.

[0145] Prepare a quality checklist for the detection point database, see Table 1, check each quality check item in steps 41 to 47 based on the quality checklist for the detection point database, and fill in the result item of whether it meets the requirements.

[0146] Table 1

[0147]

[0148]

[0149] Example 8, in step 4, during each inspection process, the specific items that do not meet the requirements are filled in the inspection opinion record form, see Table 2. After completion, the inspection opinion record form is fed back to the database construction personnel of the detection point. The database construction personnel of the detection point modify and improve the inspection opinion record form. After modification and improvement, re-inspection is carried out according to steps 41 to 47 until all quality inspection items are met.

[0150] Table 2

[0151]

[0152]

[0153] In Example 9, the topological relationship check in step 44 includes the following steps:

[0154] Step 441, reading the spatial coordinate data of all detection points from the database;

[0155] Step 442, checking whether there are two or more detection points that completely overlap in space;

[0156] Step 443, check whether there are identical point records in the database, where identical point records refer to those with identical coordinates and attributes.

[0157] The quality inspection of the detection point database construction project shall check whether the spatial reference system of the database, the standardization of data files, the correctness of the database structure, the integrity of the database data information, etc. meet the requirements according to the design requirements, fill in the detection point database quality inspection form, and record the quality problems in the inspection opinion record form.

[0158] A combination of automated quality inspection tools and manual inspection can be used to improve the efficiency and accuracy of quality inspection.

[0159] The check of point overlap and point duplication in step 44 may be performed using an automated method, for example, by implementing automated checking based on a python tool.

[0160] The following is a code example using Python to check whether there is point overlap:

[0161]

[0162] The following is a code example of using Python to check whether there are duplicate points:

[0163]

[0164] After the inspection is completed, the modification suggestions will be fed back to the database construction personnel, and the modifications will be submitted for review until the results acceptance requirements are met, thereby improving the consistency of the construction of the testing point database and accumulating basic data for the realization of automated quality inspection and results evaluation.

[0165] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for building a database and quality inspection of detection point data, characterized in that: The following steps are involved: Step 1, respectively obtaining multiple detection point data, the detection point data including detection point coordinate files, collection registration forms, field photos and image screenshots with detection points collected and marked, and respectively arranging the multiple detection point coordinate files according to the set standards; Step 2: Establish a test point database, and sort and import data of multiple test points into the test point database; Step 3: After data format conversion and attribute assignment, a standard detection point database is formed; Step 4: Perform a quality check on the completed inspection point database items to check whether the database's spatial reference system, data file standardization, database structure correctness, and database data information integrity meet the requirements, and prepare a inspection point database quality checklist, and output the quality check results based on the inspection point database quality checklist.

2. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: In step 1, the following methods are used to organize the detection point coordinate files: When using a measuring instrument to collect the coordinates of the detection points, the coordinate file is directly exported from the measuring instrument and organized into the corresponding detection point coordinate file based on the detection instrument; If the coordinate data in the detection point coordinate file only includes plane coordinates and elevation, the plane coordinates and elevation are converted to obtain longitude and latitude coordinates. The detection point coordinate file record format is: detection point number, detection point type, latitude, longitude, geodetic height, north coordinate, east coordinate, elevation, collection date; if the local coordinate system is used, it is converted to the 2000 National Geodetic Coordinate System coordinates; The naming format of the detection point coordinate file is: instrument number, acquisition method, central meridian, acquisition date; When the measuring instrument is RTK, calculate the Gaussian plane coordinates, convert the longitude and latitude coordinates into Gaussian plane coordinates according to the given projection band, calculate the normal height, and convert the normal height in the HBCORS system based on the original longitude and latitude, geodetic height and instrument height; The method of converting and obtaining normal height in the HBCORS system is to extract the detection point number, longitude and latitude, geodetic height, and instrument height of the detection point from the original detection point file, organize them into a text format that meets the online conversion requirements of the HBCORS system, upload them to the HBCORS online location service system for self-service conversion, obtain the Gaussian plane coordinates, elevation, and belt number of the detection point, and obtain the central meridian of the detection point, the Gaussian plane coordinates without belt number, and the 1985 elevation through string batch processing, and add them to the detection point coordinate file; When the measuring instrument is a total station, the DLG detection points collected by the polar coordinate method only have plane coordinates but no longitude and latitude coordinates. The detection point number, x north coordinate, y east coordinate and elevation are extracted from the original file to form a text file, which is submitted to the HBCORS online location service system for self-service conversion to obtain the longitude and latitude coordinates and geoid height under the specified central meridian.

3. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: Use low-altitude drone aerial photogrammetry to collect field photos of the inspection points. When collecting the inspection points, the main ground point photographed is placed in the middle of the drawing, and there are position reference objects in the field photos; The image screenshots with the detection points marked are cropped from the reference data, which includes topographic maps and satellite images. The detection point positions are marked with obvious marks with the detection point positions as the center points, and the images with set length and width pixel sizes are cropped.

4. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: Step 2 includes the following sub-steps: Step 21, create a library, allocate physical space for each type of data, and create a detection point data table; Step 22, data preparation, performing consistency conversion on the detection point coordinate file sorted in step 2 according to the requirements of data organization, and the consistency conversion includes code conversion, format conversion, coordinate transformation and projection conversion; Step 23, storing the detection point spatial data and attributes in the database according to the data organization method; Step 24, detection point file storage, the detection point file is stored in the form of a file, the folder is named according to the project name, and the detection point coordinate file, collection registration form, field photo directory and image screenshot directory are stored in sequence.

5. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: Step 23 includes the following sub-steps: Step 231, input the path information of the attachment of the detection point data, the path information of the attachment of the detection point data includes the path corresponding to the detection point screenshot and the photo, and indicates the detailed location of the detection point, and uses specific direction terms to describe the position relationship, and the specific direction terms are east, south, west, north, inside, outside, up, down, up and down; The position relationship description is consistent with the position relationship between the detection point and the ground object in the photo; Step 232, input the detection point source information; Step 233, input the accuracy information, fill in the actual detection result scale according to the accuracy of the detection point corresponding to the surveying and mapping results, and fill in the corresponding level for the level control point; Step 234, enter the detection point type. The detection point type is divided into detection type and detection result type. The detection type is determined according to the source and purpose of the detection point, and is marked by letter code in the detection point record string in the detection point file, such as P for plane detection, G for elevation detection, and PG for flat-elevation detection; the detection result type is determined according to the detection type used for detection at the detection point, and the detection result type is reflected in the naming of the detection point file.

6. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: Step 3 includes the following sub-steps: Step 31, input the unified number of the inspection point; Step 32, data format conversion and attribute assignment, after entering all the inspection points in the Excel entry template, add auxiliary information decimal precision and decimal latitude, batch convert degrees, minutes and seconds into decimal degrees through Excel formulas, edit the conversion formula in Excel, and perform batch conversion; Step 33, spatial processing, adds xy data in ArcGIS and converts the detection points into a geographic database in decimal degree format.

7. The method for building and storing test point data and quality inspection according to claim 1, characterized in that: The quality check includes the following steps: Step 41, determining whether the geodetic datum, elevation datum, and map projection parameter settings of the detection point database are correct; Step 42, determine whether the data organization and data table naming of the detection point database meet the requirements of the design document; Step 43, determine whether the definition of each attribute item in the data table is correct; Step 44, determining whether the spatial topological relationship of the detection points in the detection point library is correct, including point overlap and point duplication; Step 45, determine whether each attribute item in the detection point database is complete; Step 46, judging whether the formats of various electronic data results submitted are correct, and whether the data file storage, organization, file format, and naming meet the requirements of the design document; Step 47: Check whether the paper results submitted are complete; A quality inspection table for the inspection point database is prepared based on the quality inspection results of steps 41 to 47.

8. The method for building and storing test point data and quality inspection according to claim 7, characterized in that: Prepare a quality checklist for the detection point database, check each quality check item in steps 41 to 47 based on the quality checklist for the detection point database, and fill in the result item indicating whether it meets the requirements.

9. The method for building and storing test point data and quality inspection according to claim 8, characterized in that: In step 4, during each inspection process, the specific items that do not meet the requirements are filled in the inspection opinion record form. After completion, the inspection opinion record form is fed back to the database construction personnel of the inspection point. The database construction personnel of the inspection point modify and improve the inspection opinion record form. After modification and improvement, re-inspection is carried out according to steps 41 to 47 until all quality inspection items are met.

10. The method for building and storing test point data and quality inspection according to claim 7, characterized in that: The topological relationship check in step 44 includes the following steps: Step 441, reading the spatial coordinate data of all detection points from the database; Step 442, checking whether there are two or more detection points that completely overlap in space; Step 443, check whether there are identical point records in the database, where identical point records refer to those with identical coordinates and attributes.

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