Geographic information surveying and calibration method and system based on data analysis

Abstract

The invention discloses a geographic information surveying and calibration method and system based on data analysis. The method comprises the steps that S1, a geographic information data set is collected through multiple data collection devices; s2, preprocessing the geographic information data set; s3, generating a spatial topology matrix; s4, calculating the topological entropy of the geographic information data set by using the spatial topological matrix; s5, analyzing spatial consistency among the plurality of data sources according to a topological entropy calculation result; s6, constructing an error distribution model for the region with the significant topological entropy difference; s7, adjusting calibration parameters of the geographic information data according to the error distribution model; and S8, performing consistency optimization on the calibrated geographic information data. According to the method, the calibration efficiency is remarkably improved, and the problem of unstable calibration caused by insufficient data real-time performance in a dynamic scene is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a geographic information survey and calibration method and system based on data analysis. Background Art

[0002] With the rapid development of geographic information technology, geographic information data is increasingly used in many fields such as urban planning, environmental monitoring, and disaster warning. In the collection and processing of large-scale spatial information, how to accurately calibrate multi-source geographic information data has become a hot and difficult issue in current research. As an important means to ensure the consistency and accuracy of multi-source data, geographic information survey and calibration methods directly affect the reliability of subsequent data analysis and decision-making.

[0003] At present, mainstream geographic information data calibration technology mainly relies on traditional model matching and manual calibration methods, usually by setting fixed geometric models or spatial features to fuse and correct multi-source data. Specifically, existing methods mostly focus on the accuracy adjustment of a single data source, such as achieving consistency in some areas through fitting of spatial point clouds or overlay alignment of images and surveying and mapping data. However, when processing different data sources, this type of technology is often difficult to effectively unify due to significant differences in data type, resolution and spatial scale.

[0004] At the same time, existing automated calibration methods usually rely on simple regularized models and lack in-depth analysis of the complex correlations between multi-source data. Most algorithms only optimize the geometric matching relationship of geographic information and ignore the impact of data distribution characteristics and topological structure on consistency calibration, resulting in insufficient accuracy of calibration results in complex terrain or dynamic scenes. In addition, the existing technology generally lacks a dynamic adjustment mechanism and cannot update parameters according to changes in real-time data during the calibration process, further limiting its application in high-precision and high-dynamic scenes.

[0005] In summary, the existing geographic information data calibration technology has significant defects in multi-source data consistency analysis, complex scene accuracy assurance and dynamic adjustment efficiency. It not only reduces the calibration accuracy and reliability of multi-source geographic information data, but also restricts the application potential of geographic information technology in a wider range of fields. There is an urgent need for a method and system that can effectively solve the above problems. Summary of the invention

[0006] One purpose of the present invention is to propose a geographic information survey calibration method and system based on data analysis, which significantly improves the calibration efficiency and effectively solves the calibration instability problem caused by insufficient data real-time performance in dynamic scenarios.

[0007] A geographic information survey and calibration method based on data analysis according to an embodiment of the present invention comprises the following steps:

[0008] S1. Collect geographic information data sets through various data collection devices;

[0009] S2. Preprocessing geographic information datasets;

[0010] S3. construct a spatial topology network based on the preprocessed geographic information dataset and generate a spatial topology matrix;

[0011] S4. Use the spatial topology matrix to calculate the topological entropy of the geographic information dataset, and calculate the topological entropy values ​​of multiple data sources respectively as the basic indicator for consistency analysis between data sources;

[0012] S5. Analyze the spatial consistency among multiple data sources based on the calculation results of topological entropy, and identify the regions and data features with significant differences in topological entropy by comparing the topological entropy values ​​of different data sources;

[0013] S6. Based on the consistency analysis results between data sources, an error distribution model is constructed for areas with significant differences in topological entropy;

[0014] S7. Adjust the calibration parameters of geographic information data according to the error distribution model, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the areas with significant differences in topological entropy;

[0015] S8. Optimize the consistency of the calibrated geographic information data, and verify the calibration effect by further calculating the optimized topological entropy value. If the optimized topological entropy value meets the set consistency threshold, the calibration process is completed, otherwise return to S6 for iterative correction.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. Generate geographic information datasets using a variety of data collection devices. The geographic information datasets include the following subsets:

[0018] Remote sensing image dataset D r , collected by drones, including multi-band data of surface images;

[0019] Surveying and Mapping Instrument Dataset D t , collected by surveying and mapping instruments, and the spatial coordinates are recorded;

[0020] Global Navigation Satellite System Dataset D g , collected by GNSS equipment, providing geographic coordinates and timestamp data;

[0021] S12. Establish a unified parameterized expression of geographic information dataset D:

[0022] D={D r ,D t ,D g}.

[0023] Optionally, S2 includes the following steps:

[0024] S21. Convert the format of the subset in the geographic information dataset D so that all data are stored in a standardized format;

[0025] S22. Apply noise filtering algorithm to the unified format geographic information data set to remove random errors or environmental noise generated during the collection process;

[0026] S23. Converting the noise-filtered geographic information dataset to a unified spatial reference coordinate system;

[0027] S24. Match the spatial resolution of geographic information datasets in a unified coordinate system so that each data subset can be stored and analyzed at the same spatial resolution:

[0028]

[0029] in, is the remote sensing image data after resolution matching, The data of surveying and mapping instruments after resolution matching. It is the GNSS data after resolution matching.

[0030] Optionally, S3 includes the following steps:

[0031] S31. Preprocessed geographic information dataset D f Extract the geometric features of the objects, including the boundary points, center points and geometric shapes of the objects, and generate a geometric feature set G = {g1, g2, ..., g n}, where g n Represents the geometric characteristic parameters of the nth feature, including spatial coordinates (x, y, z);

[0032] S32. Analyze the adjacency relationship between objects based on the geometric feature set G and generate an adjacency relationship matrix A:

[0033] A=[a ij ];

[0034] Among them, a ij =1 indicates ground feature g i With ground features j There is an adjacency relationship, a ij =0 indicates ground feature g i With ground features j There is no adjacency relationship;

[0035] The adjacency relationship is determined by the spatial distance d between the features. ij Judge, if d ij ≤d threshold , d threshold represents the spatial distance threshold of the adjacency relationship, then a ij =1;

[0036] S33. Calculate the spatial distribution density ρ of the object using the geometric feature set G and the adjacency matrix A i and concentration i :

[0037]

[0038] Among them, V i For the ground feature i The spatial volume, w ij For the ground feature i With ground features j The spatial distance weight of

[0039] S34. Based on the geometric feature set G of the ground object, the adjacency matrix A, and the spatial distribution density ρ i and concentration i Construct a spatial topological network T:

[0040] T = (G, A, P);

[0041] Among them, P represents the set of spatial characteristics, including the distribution density of each feature ρ i and concentration i ;

[0042] S35. Generate a spatial topology matrix M using the spatial topology network T.

[0043] Optionally, S4 includes the following steps:

[0044] S41. Introduce the multi-layer correlation weights W between objects based on the spatial topological matrix M ij :

[0045]

[0046] Among them, W ij For the ground feature i With ground features j The comprehensive correlation weight of , α, β, γ are weight adjustment factors, which respectively represent the influence of topological relationship, distribution density and centrality on the overall weight;

[0047] S42. According to the relevance weight W ij Generate weighted space topology matrix M W =[Wij ], and process the correlation strength of the objects in layers, defining the correlation matrix of each layer l

[0048]

[0049] Among them, T l-1 and T l is the weight threshold range of the lth layer, means that only the correlation matrix with a specific weight range is retained;

[0050] S43. Correlation matrix for each layer Calculate the hierarchical topological entropy H l :

[0051]

[0052] Among them, H l is the topological entropy of the lth layer, which represents the orderliness and consistency of the distribution of objects. is the relevance weight of the lth layer;

[0053] S44. Set the topological entropy value H of all layers l The overall topological entropy H is obtained by synthesis total :

[0054]

[0055] Among them, L is the total number of layers, λ l is the weight factor of the lth layer, which is used to reflect the contribution of different layers to the overall topological entropy.

[0056] Optionally, S5 includes the following steps:

[0057] S51. Analyze the overall consistency of each data source based on the comprehensive topological entropy value and calculate the data source The consistency deviation ΔH s :

[0058]

[0059] Where ΔH s For data source The consistency deviation, H avg is the average topological entropy value of all data sources;

[0060] S52. Using the hierarchical topological entropy value H l , for each data source Calculate the topological entropy difference index at the regional level

[0061]

[0062] in, Represents the data source The topological entropy difference of region (i,j) in For data source The hierarchical topological entropy value of region (i,j) in is the average topological entropy value of the region at the same level;

[0063] S53. Based on regional difference indicators Determine the set of regions with significant differences R diff :

[0064]

[0065] Among them, T diff is the significance threshold, which is used to screen the areas with large differences. diff Index set for all regions that meet the conditions;

[0066] S54. For the region with significant difference R diff , extract relevant data features, including the geometric features of regional objects, adjacency relationship features and spatial distribution characteristics.

[0067] Optionally, the S6 includes the following steps:

[0068] S61. R, a set of regions with significant differences based on topological entropy diff , establish a comprehensive spatial error model E for each region (i, j) ij :

[0069]

[0070] in, is the position deviation error model, is the proportional error model, is the rotation error model;

[0071] S62. For the significantly different regions (i, j), the position deviation error model is calculated using the position data from different data sources

[0072]

[0073] in, For data source The position vector of region (i,j) in is the average value of the location vector of region (i, j) in all data sources, and n is the number of data sources;

[0074] S63. Establish a proportional error model based on the spatial proportion of the significantly different areas

[0075]

[0076] in, For data source The spatial proportion of region (i,j) in is the average spatial proportion of region (i, j) in all data sources.

[0077] S64. Establish a rotation error model for spatial rotation of significantly different regions

[0078]

[0079] in, For data source The rotation angle of region (i,j) in is the average rotation angle of region (i, j) in all data sources.

[0080] S65, based on the comprehensive spatial error model E ij Generate error distribution model E for regions with significant topological entropy differences model , to quantify the spatial error characteristics of each significantly different area:

[0081] E model ={E ij |(i,j)∈R diff};

[0082] Among them, R diff A collection of all region indexes that meet the conditions.

[0083] Optionally, the S7 includes the following steps:

[0084] S71. Based on the generated error distribution model E model , for each region with significant topological entropy difference, a calibration parameter set is set, including a scale factor, a rotation angle and a position offset vector, wherein the scale factor is used to adjust the spatial scale error of the geographic information data in the region, the rotation angle is used to correct the spatial rotation error, and the position offset vector is used to correct the position deviation;

[0085] S72. Calculate the corresponding calibration parameter update values ​​by analyzing the proportional error, rotation error and position deviation in the error distribution model, respectively. The update value is related to the error size and its change trend in the error distribution model, and is used to optimize the calibration parameter set;

[0086] S73. Apply the calibration parameter update value to the initialized calibration parameter set to obtain the updated scale factor, rotation angle and position offset vector, and use the updated calibration parameters to adjust the geographic information data in the area with significant differences. The adjusted geographic information data is expressed as changes in the spatial scale, rotation angle and position coordinates of the data;

[0087] S74. After the calibration parameters are adjusted, the error distribution model of the adjusted geographic information data is recalculated, and the calibration parameter set is dynamically updated by analyzing the error changes before and after the adjustment. Each iteration is determined by the error convergence standard to determine whether to stop until the error distribution model meets the preset consistency threshold.

[0088] A geographic information survey and calibration system based on data analysis, which implements a geographic information survey and calibration method based on data analysis, includes the following modules:

[0089] Data collection module, used to generate geographic information data sets through a variety of data collection equipment. Geographic information data sets include remote sensing image data, surveying and mapping instrument data, and GNSS data. The data collection module supports multi-source data input and provides time and space reference information for data collection;

[0090] The data preprocessing module is used to convert the format of the collected geographic information data set, filter noise, match the coordinate system and spatial resolution, so that the geographic information data set has a consistent time and space reference;

[0091] The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information data set, extract the geometric features, adjacency relations and spatial distribution characteristics of the objects, and generate a spatial topology matrix to represent the correlation between the objects;

[0092] The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, characterize the orderliness and consistency of the distribution of objects between data sources, and calculate the topological entropy values ​​for multiple data sources respectively as the basic indicator for consistency analysis;

[0093] Data consistency analysis module, which is used to analyze the spatial consistency of different data sources based on the topological entropy calculation results, identify areas with significant differences in topological entropy, and extract relevant data features, including geometric features, adjacency relationships, and distribution characteristics;

[0094] An error modeling module is used to establish an error distribution model for regions with significant differences in topological entropy, wherein the error distribution model includes a position deviation model, a proportional error model, and a rotation error model, which are used to quantify the spatial error characteristics of regions with significant differences;

[0095] Dynamic calibration module, which is used to adjust the calibration parameter set based on the error distribution model, including the scale factor, rotation angle and position offset vector, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the area of ​​topological entropy difference;

[0096] The calibration result output module is used to output the geographic information data after consistency calibration as a calibrated data set, and supports export in multiple formats.

[0097] The beneficial effects of the present invention are:

[0098] (1) The present invention comprehensively characterizes the orderliness and consistency of the distribution of objects by constructing a spatial topological network and calculating the topological entropy. Compared with the traditional calibration method based only on geometric features, the present invention uses topological entropy as a quantitative indicator to accurately identify the areas with significant differences in multi-source data and extract relevant data features for further analysis. The calibration method driven by topological entropy can dynamically adapt to the characteristic differences between different data sources, significantly improve the accuracy of the calibration results in complex scenes, and show superior robustness and accuracy in multi-source data fusion.

[0099] (2) The present invention quantifies the error characteristics of the area with significant differences by constructing a multidimensional error model of position deviation, proportional error and rotation error, and updates the calibration parameters in real time in combination with a dynamic feedback mechanism. During the calibration process, the proportional factor, rotation angle and position offset vector can be adjusted according to the dynamic changes of the error distribution, thereby realizing automatic iterative optimization of the calibration parameters. The dynamic calibration mechanism not only significantly improves the calibration efficiency, but also effectively solves the problem of unstable calibration caused by insufficient data real-time performance in dynamic scenarios.

[0100] (3) The present invention designs a modular system architecture. The system supports unified input and processing of multiple data sources and can automatically complete the entire process from data collection to consistency calibration. The systematic design significantly reduces the degree of human participation and improves the efficiency of calibration. At the same time, since each module is independent and closely coordinated, the system has a high degree of scalability and can be flexibly adjusted or integrated with more functional modules according to actual needs, thereby meeting the needs of complex and changing application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0102] Figure 1 The present invention provides a flowchart of a geographic information survey and calibration method and system based on data analysis. DETAILED DESCRIPTION

[0103] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0104] refer to Figure 1 , a geographic information survey and calibration method based on data analysis, comprising the following steps:

[0105] S1. Collect geographic information data sets through various data collection devices;

[0106] S2. Preprocessing geographic information datasets;

[0107] S3. construct a spatial topology network based on the preprocessed geographic information dataset and generate a spatial topology matrix;

[0108] S4. Use the spatial topology matrix to calculate the topological entropy of the geographic information dataset, and calculate the topological entropy values ​​of multiple data sources respectively as the basic indicator for consistency analysis between data sources;

[0109] S5. Analyze the spatial consistency among multiple data sources based on the calculation results of topological entropy, and identify the regions and data features with significant differences in topological entropy by comparing the topological entropy values ​​of different data sources;

[0110] S6. Based on the consistency analysis results between data sources, an error distribution model is constructed for areas with significant differences in topological entropy;

[0111] S7. Adjust the calibration parameters of geographic information data according to the error distribution model, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the areas with significant differences in topological entropy;

[0112] S8. Optimize the consistency of the calibrated geographic information data, and verify the calibration effect by further calculating the optimized topological entropy value. If the optimized topological entropy value meets the set consistency threshold, the calibration process is completed, otherwise return to S6 for iterative correction.

[0113] In this implementation, S1 includes the following steps:

[0114] S11. Generate geographic information datasets using a variety of data collection devices. The geographic information datasets include the following subsets:

[0115] Remote sensing image dataset D r , collected by drones, including multi-band data of surface images;

[0116] Surveying and Mapping Instrument Dataset D t , collected by surveying and mapping instruments, and the spatial coordinates are recorded;

[0117] Global Navigation Satellite System Dataset Dg , collected by GNSS equipment, providing geographic coordinates and timestamp data;

[0118] S12. Establish a unified parameterized expression of geographic information dataset D:

[0119] D={D r ,D t ,D g}.

[0120] In this implementation, S2 includes the following steps:

[0121] S21. Convert the format of the subset in the geographic information dataset D so that all data are stored in a standardized format;

[0122] S22. Apply noise filtering algorithm to the unified format geographic information data set to remove random errors or environmental noise generated during the collection process;

[0123] S23. Converting the noise-filtered geographic information dataset to a unified spatial reference coordinate system;

[0124] S24. Match the spatial resolution of geographic information datasets in a unified coordinate system so that each data subset can be stored and analyzed at the same spatial resolution:

[0125]

[0126] in, is the remote sensing image data after resolution matching, The data of surveying and mapping instruments after resolution matching. It is the GNSS data after resolution matching.

[0127] In this implementation, S3 includes the following steps:

[0128] S31. Preprocessed geographic information dataset D f Extract the geometric features of the objects, including the boundary points, center points and geometric shapes of the objects, and generate a geometric feature set G = {g1, g2, ..., g n}, where g n Represents the geometric characteristic parameters of the nth feature, including spatial coordinates (x, y, z);

[0129] S32. Analyze the adjacency relationship between objects based on the geometric feature set G and generate an adjacency relationship matrix A:

[0130] A=[a ij ];

[0131] Among them, a ij =1 indicates ground feature gi With ground features j There is an adjacency relationship, a ij =0 indicates ground feature g i With ground features j There is no adjacency relationship;

[0132] The adjacency relationship is determined by the spatial distance d between the features. ij Judge, if d ij ≤d threshold , d threshold represents the spatial distance threshold of the adjacency relationship, then a ij =1;

[0133] S33. Calculate the spatial distribution density ρ of the object using the geometric feature set G and the adjacency matrix A i and concentration i :

[0134]

[0135] Among them, V i For the ground feature i The spatial volume, w ij For the ground feature i With ground features j The spatial distance weight of

[0136] S34. Based on the geometric feature set G of the ground object, the adjacency matrix A, and the spatial distribution density ρ i and concentration i Construct a spatial topological network T:

[0137] T = (G, A, P);

[0138] Among them, P represents the set of spatial characteristics, including the distribution density of each feature ρ i and concentration i ;

[0139] S35. Generate a spatial topology matrix M using the spatial topology network T.

[0140] In this implementation, S4 includes the following steps:

[0141] S41. Introduce the multi-layer correlation weights W between objects based on the spatial topological matrix M ij :

[0142]

[0143] Among them, W ij For the ground feature i With ground features jThe comprehensive correlation weight of , α, β, γ are weight adjustment factors, which respectively represent the influence of topological relationship, distribution density and centrality on the overall weight;

[0144] S42. According to the relevance weight W ij Generate weighted space topology matrix M W =[W ij ], and process the correlation strength of the objects in layers, defining the correlation matrix of each layer l

[0145]

[0146] Among them, T l-1 and T l is the weight threshold range of the lth layer, means that only the correlation matrix with a specific weight range is retained;

[0147] S43. Correlation matrix for each layer Calculate the hierarchical topological entropy H l :

[0148]

[0149] Among them, H l is the topological entropy of the lth layer, which represents the orderliness and consistency of the distribution of objects. is the relevance weight of the lth layer;

[0150] S44. Set the topological entropy value H of all layers l The overall topological entropy H is obtained by synthesis total :

[0151]

[0152] Among them, L is the total number of layers, λ l is the weight factor of the lth layer, which is used to reflect the contribution of different layers to the overall topological entropy.

[0153] In this implementation, S5 includes the following steps:

[0154] S51. Analyze the overall consistency of each data source based on the comprehensive topological entropy value and calculate the data source The consistency deviation ΔH s :

[0155]

[0156] Where ΔH s For data source The consistency deviation, H avg is the average topological entropy value of all data sources;

[0157] S52. Using the hierarchical topological entropy value H l , for each data source Calculate the topological entropy difference index at the regional level

[0158]

[0159] in, Represents the data source The topological entropy difference of region (i,j) in For data source The hierarchical topological entropy value of region (i,j) in is the average topological entropy value of the region at the same level;

[0160] S53. Based on regional difference indicators Determine the set of regions with significant differences R diff :

[0161]

[0162] Among them, T diff is the significance threshold, which is used to screen the areas with large differences. diff Index set for all regions that meet the conditions;

[0163] S54. For the region with significant difference R diff , extract relevant data features, including the geometric features of regional objects, adjacency relationship features and spatial distribution characteristics.

[0164] In this implementation, S6 includes the following steps:

[0165] S61. R, a set of regions with significant differences based on topological entropy diff , establish a comprehensive spatial error model E for each region (i, j) ij :

[0166]

[0167] in, is the position deviation error model, is the proportional error model, is the rotation error model;

[0168] S62. For the significantly different regions (i, j), the position deviation error model is calculated using the position data from different data sources

[0169]

[0170] in, For data source The position vector of region (i,j) in is the average value of the location vector of region (i, j) in all data sources, and n is the number of data sources;

[0171] S63. Establish a proportional error model based on the spatial proportion of the significantly different areas

[0172]

[0173] in, For data source The spatial proportion of region (i,j) in is the average spatial proportion of region (i, j) in all data sources.

[0174] S64. Establish a rotation error model for spatial rotation of significantly different regions

[0175]

[0176] in, For data source The rotation angle of region (i,j) in is the average rotation angle of region (i, j) in all data sources.

[0177] S65, based on the comprehensive spatial error model E ij Generate error distribution model E for regions with significant topological entropy differences model , to quantify the spatial error characteristics of each significantly different area:

[0178] E model ={E ij |(i,j)∈R diff};

[0179] Among them, R diff A collection of all region indexes that meet the conditions.

[0180] In this implementation, S7 includes the following steps:

[0181] S71. Based on the generated error distribution model E model , for each region with significant topological entropy difference, a calibration parameter set is set, including a scale factor, a rotation angle, and a position offset vector. The scale factor is used to adjust the spatial scale error of the geographic information data in the region, the rotation angle is used to correct the spatial rotation error, and the position offset vector is used to correct the position deviation;

[0182] S72. Calculate the corresponding calibration parameter update values ​​by analyzing the proportional error, rotation error and position deviation in the error distribution model, respectively. The update value is related to the error size and its change trend in the error distribution model, and is used to optimize the calibration parameter set;

[0183] S73. Apply the calibration parameter update value to the initialized calibration parameter set to obtain the updated scale factor, rotation angle and position offset vector, and use the updated calibration parameters to adjust the geographic information data in the area with significant differences. The adjusted geographic information data is expressed as changes in the spatial scale, rotation angle and position coordinates of the data;

[0184] S74. After the calibration parameters are adjusted, the error distribution model of the adjusted geographic information data is recalculated, and the calibration parameter set is dynamically updated by analyzing the error changes before and after the adjustment. Each iteration is determined by the error convergence standard to determine whether to stop until the error distribution model meets the preset consistency threshold.

[0185] A geographic information survey and calibration system based on data analysis, which implements a geographic information survey and calibration method based on data analysis, includes the following modules:

[0186] Data collection module, used to generate geographic information data sets through a variety of data collection equipment. Geographic information data sets include remote sensing image data, surveying and mapping instrument data, and GNSS data. The data collection module supports multi-source data input and provides time and space reference information for data collection;

[0187] The data preprocessing module is used to convert the format of the collected geographic information data set, filter noise, match the coordinate system and spatial resolution, so that the geographic information data set has a consistent time and space reference;

[0188] The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information data set, extract the geometric features, adjacency relations and spatial distribution characteristics of the objects, and generate a spatial topology matrix to represent the correlation between the objects;

[0189] The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, characterize the orderliness and consistency of the distribution of objects between data sources, and calculate the topological entropy values ​​for multiple data sources respectively as the basic indicator for consistency analysis;

[0190] Data consistency analysis module, which is used to analyze the spatial consistency of different data sources based on the topological entropy calculation results, identify areas with significant differences in topological entropy, and extract relevant data features, including geometric features, adjacency relationships, and distribution characteristics;

[0191] The error modeling module is used to establish an error distribution model for areas with significant differences in topological entropy. The error distribution model includes a position deviation model, a proportional error model, and a rotation error model, which are used to quantify the spatial error characteristics of areas with significant differences.

[0192] Dynamic calibration module, which is used to adjust the calibration parameter set based on the error distribution model, including the scale factor, rotation angle and position offset vector, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the area of ​​topological entropy difference;

[0193] The calibration result output module is used to output the geographic information data after consistency calibration as a calibrated data set, and supports export in multiple formats.

[0194] Embodiment 1:

[0195] The embodiment is applied in a certain city. In order to improve the disaster monitoring and management capabilities, the city plans to use multi-source geographic information data to perform high-precision identification and dynamic updating of urban flood risk areas. The city is located in the south, with complex terrain and frequent rainfall. The multi-source geographic information data mainly includes UAV remote sensing image data, surveying and mapping instrument collection data and GNSS ground station data. The data have significant differences in collection range, time scale and spatial accuracy. The resolution of remote sensing images reaches 0.2 meters, the point density of surveying and mapping data collection is 1 point / square meter, and the GNSS ground station updates the position and elevation data once a minute, covering a wide area.

[0196] In the past, cities used traditional geometric matching methods to calibrate geographic information data. However, due to significant spatial and temporal differences between data, traditional methods often have large calibration errors under complex terrain conditions. In low-lying areas, the calibration result deviation is as high as 3 meters, which seriously affects the identification accuracy of flood risk areas.

[0197] This embodiment uses the method of the present invention to survey and calibrate the above-mentioned multi-source geographic information data to achieve higher accuracy and consistency. The data collection time is from June 1, 2024 to June 10, 2024, covering the city center area and the surrounding 10 kilometers of low-lying areas, and the total data volume is about 20TB.

[0198] Geographic information data is collected through a variety of data collection equipment, including drone remote sensing images, surveying and mapping instrument point cloud data and GNSS ground station location data. Remote sensing image data covers an area of ​​100 square kilometers and is collected from 9:00 to 11:00 a.m. every day; surveying and mapping point cloud data is concentrated on the building complex in the city center and is collected from 14:00 to 16:00 every day; GNSS ground station data is recorded in real time in minutes and covers the entire experimental area.

[0199] Subsequently, the data preprocessing module of the present invention is used to perform format conversion, noise filtering and coordinate system conversion on the collected data. The remote sensing image and surveying and mapping data use the UTM coordinate system respectively, while the GNSS data is originally collected in the WGS84 coordinate system and unified into the UTM coordinate system through preprocessing.

[0200] Next, the spatial topological network of the urban area was constructed based on the preprocessed data. The boundary points, geometric features and adjacency relationships of the objects were extracted to generate a spatial topological matrix to characterize the spatial correlation between data sources. The topological entropy values ​​of remote sensing images, surveying and mapping data, and GNSS data were calculated through the topological entropy calculation module. The results showed that the topological entropy value of remote sensing images was 1.82, that of surveying and mapping data was 1.76, and that of GNSS data was 2.15, indicating that the spatial distribution of GNSS data was significantly different from that of other data sources.

[0201] Based on the results of topological entropy calculation, the consistency analysis module was used to identify areas with significant differences, which were concentrated in the southeast of the city's low-lying areas. The geometric features and spatial distribution characteristics of the objects in these areas were extracted to construct an error distribution model. The analysis found that the average position deviation of GNSS data in this area was 2.5 meters, the rotation error was 0.3 degrees, and the scale error was 1.8%.

[0202] The dynamic calibration module is used to iteratively optimize the calibration parameter set, including the scale factor, rotation angle and position offset vector, to calibrate the areas with significant differences. After 4 iterations, the calibration parameters converged, and the average position deviation of the adjusted GNSS data from other data sources was reduced to 0.4 meters, the rotation error was reduced to 0.05 degrees, and the scale error was reduced to 0.3%.

[0203] In order to verify the effectiveness of the proposed method, it is compared with the traditional geometric matching calibration method. The same training samples and experimental conditions are used. The training samples include three types of land features, namely urban building areas, roads and rivers, which are randomly selected, with a total of 500 sample points. The position deviation, rotation error and scale error after calibration are calculated respectively:

[0204] Table 1 Experimental data comparison table

[0205]

[0206] Experimental results show that the method of the present invention is significantly superior to traditional methods in terms of position deviation, rotation error and scale error, and performs outstandingly in river areas with complex terrain. At the same time, the calibration time of the method of the present invention in the entire experimental area is 12 hours, while the traditional method requires 24 hours, and the efficiency is improved by 50%.

[0207] In summary, this embodiment verifies the effectiveness and superiority of the present invention in complex terrain scenarios. Through precise error modeling and dynamic calibration, it achieves consistent calibration of multi-source geographic information data, providing higher-precision data support for urban flood risk monitoring.

[0208] The present invention comprehensively characterizes the orderliness and consistency of the distribution of land objects by constructing a spatial topological network and calculating the topological entropy. Compared with the traditional calibration method based only on geometric features, the present invention uses topological entropy as a quantitative indicator to accurately identify areas with significant differences in multi-source data and extract relevant data features for further analysis. The calibration method driven by topological entropy can dynamically adapt to the characteristic differences between different data sources, significantly improve the accuracy of the calibration results in complex scenes, and show superior robustness and accuracy in multi-source data fusion.

[0209] The present invention quantifies the error characteristics of areas with significant differences by constructing a multidimensional error model of position deviation, proportional error and rotation error, and updates the calibration parameters in real time in combination with a dynamic feedback mechanism. During the calibration process, the proportional factor, rotation angle and position offset vector can be adjusted according to the dynamic changes of the error distribution, thereby realizing automatic iterative optimization of the calibration parameters. The dynamic calibration mechanism not only significantly improves the calibration efficiency, but also effectively solves the problem of unstable calibration caused by insufficient data real-time performance in dynamic scenarios.

[0210] The present invention designs a modular system architecture. The system supports unified input and processing of multiple data sources and can automatically complete the whole process from data collection to consistency calibration. The systematic design significantly reduces the degree of human participation and improves the efficiency of calibration. At the same time, since each module is independent and closely cooperates, the system has a high degree of scalability and can be flexibly adjusted or integrated with more functional modules according to actual needs, so as to meet the needs of complex and changing application scenarios.

[0211] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A geographic information survey and calibration method based on data analysis, characterized in that: The steps include: S1. Collect geographic information data sets through various data collection devices; S2. Preprocessing geographic information datasets; S3. construct a spatial topology network based on the preprocessed geographic information dataset and generate a spatial topology matrix; S4. Use the spatial topology matrix to calculate the topological entropy of the geographic information dataset, and calculate the topological entropy values ​​of multiple data sources respectively as the basic indicator for consistency analysis between data sources; S5. Analyze the spatial consistency among multiple data sources based on the calculation results of topological entropy, and identify the regions and data features with significant differences in topological entropy by comparing the topological entropy values ​​of different data sources; S6. Based on the consistency analysis results between data sources, an error distribution model is constructed for areas with significant differences in topological entropy; S7. Adjust the calibration parameters of geographic information data according to the error distribution model, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the areas with significant differences in topological entropy; S8. Optimize the consistency of the calibrated geographic information data, and verify the calibration effect by further calculating the optimized topological entropy value. If the optimized topological entropy value meets the set consistency threshold, the calibration process is completed, otherwise return to S6 for iterative correction.

2. A geographic information survey and calibration method based on data analysis according to claim 1, characterized in that: The S1 comprises the following steps: S11. Generate geographic information datasets using a variety of data collection devices. The geographic information datasets include the following subsets: Remote sensing image dataset D r , collected by drones, including multi-band data of surface images; Surveying and Mapping Instrument Dataset D t , collected by surveying and mapping instruments, recording spatial coordinates; Global Navigation Satellite System Dataset D g , collected by GNSS equipment, providing geographic coordinates and timestamp data; S12. Establish a unified parameterized expression of geographic information dataset D: D={D r ,D t ,D g }。 3. A geographic information survey and calibration method based on data analysis according to claim 1, characterized in that: The S2 comprises the following steps: S21. Convert the format of the subset in the geographic information dataset D so that all data are stored in a standardized format; S22. Apply noise filtering algorithm to the unified format geographic information data set to remove random errors or environmental noise generated during the collection process; S23. Converting the noise-filtered geographic information dataset to a unified spatial reference coordinate system; S24. Match the spatial resolution of geographic information datasets in a unified coordinate system so that each data subset can be stored and analyzed at the same spatial resolution: in, is the remote sensing image data after resolution matching, The data of surveying and mapping instruments after resolution matching. It is the GNSS data after resolution matching.

4. A geographic information survey and calibration method based on data analysis according to claim 1, characterized in that: The S3 comprises the following steps: S31. Preprocessed geographic information dataset D f Extract the geometric features of the objects, including the boundary points, center points and geometric shapes of the objects, and generate a geometric feature set G = {g1, g2, ..., g n }, where g n Represents the geometric characteristic parameters of the nth feature, including spatial coordinates (x, y, z); S32. Analyze the adjacency relationship between objects based on the geometric feature set G and generate an adjacency relationship matrix A: A=[a ij ]; Among them, a ij =1 indicates ground feature g i With ground features j There is an adjacency relationship, a ij =0 indicates ground feature g i With ground features j There is no adjacency relationship; The adjacency relationship is determined by the spatial distance d between the features. ij Judge, if d ij ≤d threshold , d threshold represents the spatial distance threshold of the adjacency relationship, then a ij =1; S33. Calculate the spatial distribution density ρ of the object using the geometric feature set G and the adjacency matrix A i and concentration i : Among them, V i For the ground feature i The spatial volume, w ij For the ground feature i With ground features j The spatial distance weight of S34. Based on the geometric feature set G of the ground object, the adjacency matrix A, and the spatial distribution density ρ i and concentration i Construct a spatial topological network T: T = (G, A, P); Among them, P represents the set of spatial characteristics, including the distribution density of each feature ρ i and concentration i ; S35. Generate a spatial topology matrix M using the spatial topology network T.

5. The method for geographic information survey and calibration based on data analysis according to claim 1, characterized in that: The S4 comprises the following steps: S41. Introduce the multi-layer correlation weights W between objects based on the spatial topological matrix M ij : Among them, W ij For the ground feature i With ground features j The comprehensive correlation weight of , α, β, γ are weight adjustment factors, which respectively represent the influence of topological relationship, distribution density and centrality on the overall weight; S42. According to the relevance weight W ij Generate weighted space topology matrix M W =[W ij ], and process the correlation strength of the objects in layers, defining the correlation matrix of each layer l Among them, T l-1 and T l is the weight threshold range of the lth layer, means that only the correlation matrix with a specific weight range is retained; S43. Correlation matrix for each layer Calculate the hierarchical topological entropy H l : Among them, H l is the topological entropy of the lth layer, which represents the orderliness and consistency of the distribution of objects. is the relevance weight of the lth layer; S44. Set the topological entropy value H of all layers l The overall topological entropy H is obtained by synthesis total : Among them, L is the total number of layers, λ l is the weight factor of the lth layer, which is used to reflect the contribution of different layers to the overall topological entropy.

6. A geographic information survey and calibration method based on data analysis according to claim 1, characterized in that: The S5 comprises the following steps: S51. Analyze the overall consistency of each data source based on the comprehensive topological entropy value and calculate the data source The consistency deviation ΔH s : Where ΔH s For data source The consistency deviation, H avg is the average topological entropy value of all data sources; S52. Using the hierarchical topological entropy value H l , for each data source Calculate the topological entropy difference index at the regional level in, Represents the data source The topological entropy difference of region (i,j) in For data source The hierarchical topological entropy value of region (i,j) in is the average topological entropy value of the region at the same level; S53. Based on regional difference indicators Determine the set of regions with significant differences R diff : Among them, T diff is the significance threshold, which is used to screen the areas with large differences. diff Index set for all regions that meet the conditions; S54. For the region with significant difference R diff , extract relevant data features, including the geometric features of regional objects, adjacency relationship features and spatial distribution characteristics.

7. A geographic information survey and calibration method based on data analysis according to claim 1, characterized in that: The S6 comprises the following steps: S61. R, a set of regions with significant differences based on topological entropy diff , establish a comprehensive spatial error model E for each region (i, j) ij : in, is the position deviation error model, is the proportional error model, is the rotation error model; S62. For the significantly different regions (i, j), the position deviation error model is calculated using the position data from different data sources in, For data source The position vector of region (i,j) in is the average value of the location vector of region (i, j) in all data sources, and n is the number of data sources; S63. Establish a proportional error model based on the spatial proportion of the significantly different areas in, For data source The spatial proportion of region (i,j) in is the average spatial proportion of region (i, j) in all data sources. S64. Establish a rotation error model for spatial rotation of significantly different regions in, For data source The rotation angle of region (i,j) in is the average rotation angle of region (i, j) in all data sources. S65, based on the comprehensive spatial error model E ij Generate error distribution model E for regions with significant topological entropy differences model , to quantify the spatial error characteristics of each significantly different area: HAVE BEEN model ={E ij ∣(i,j)∈R diff }; Among them, R diff A collection of all region indexes that meet the conditions.

8. The method for geographic information survey and calibration based on data analysis according to claim 1, characterized in that: The S7 comprises the following steps: S71. Based on the generated error distribution model E model , for each region with significant topological entropy difference, a calibration parameter set is set, including a scale factor, a rotation angle and a position offset vector, wherein the scale factor is used to adjust the spatial scale error of the geographic information data in the region, the rotation angle is used to correct the spatial rotation error, and the position offset vector is used to correct the position deviation; S72. Calculate the corresponding calibration parameter update values ​​by analyzing the proportional error, rotation error and position deviation in the error distribution model, respectively. The update value is related to the error size and its change trend in the error distribution model, and is used to optimize the calibration parameter set; S73. Apply the calibration parameter update value to the initialized calibration parameter set to obtain the updated scale factor, rotation angle and position offset vector, and use the updated calibration parameters to adjust the geographic information data in the area with significant differences. The adjusted geographic information data is expressed as changes in the spatial scale, rotation angle and position coordinates of the data; S74. After the calibration parameters are adjusted, the error distribution model of the adjusted geographic information data is recalculated, and the calibration parameter set is dynamically updated by analyzing the error changes before and after the adjustment. Each iteration is determined by the error convergence standard to determine whether to stop until the error distribution model meets the preset consistency threshold.

9. A geographic information survey and calibration system based on data analysis, which executes the geographic information survey and calibration method based on data analysis according to any one of claims 1 to 8, characterized in that: Includes the following modules: Data collection module, used to generate geographic information data sets through a variety of data collection equipment. Geographic information data sets include remote sensing image data, surveying and mapping instrument data, and GNSS data. The data collection module supports multi-source data input and provides time and space reference information for data collection; The data preprocessing module is used to convert the format of the collected geographic information data set, filter noise, match the coordinate system and spatial resolution, so that the geographic information data set has a consistent time and space reference; The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information data set, extract the geometric features, adjacency relations and spatial distribution characteristics of the objects, and generate a spatial topology matrix to represent the correlation between the objects; The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, characterize the orderliness and consistency of the distribution of objects between data sources, and calculate the topological entropy values ​​for multiple data sources respectively as the basic indicator for consistency analysis; Data consistency analysis module, which is used to analyze the spatial consistency of different data sources based on the topological entropy calculation results, identify areas with significant differences in topological entropy, and extract relevant data features, including geometric features, adjacency relationships, and distribution characteristics; An error modeling module is used to establish an error distribution model for regions with significant differences in topological entropy, wherein the error distribution model includes a position deviation model, a proportional error model, and a rotation error model, which are used to quantify the spatial error characteristics of regions with significant differences; Dynamic calibration module, which is used to adjust the calibration parameter set based on the error distribution model, including the scale factor, rotation angle and position offset vector, and use the dynamic feedback mechanism to update the calibration parameters in real time to correct the area of ​​topological entropy difference; The calibration result output module is used to output the geographic information data after consistency calibration as a calibrated data set, and supports export in multiple formats.

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