A geographic information survey calibration method and system based on data analysis

Abstract

The application discloses a kind of geographic information survey calibration methods and systems based on data analysis, S1 is collected geographic information dataset by a variety of data acquisition equipment;S2 is preprocessed to geographic information dataset;S3 And generate spatial topology matrix;S4 is calculated using spatial topology matrix to the topological entropy of geographic information dataset;S5 is according to topological entropy calculation result analysis the spatial consistency between multiple data sources;S6 is to the area of topological entropy difference remarkable and constructs error distribution model;S7 is according to error distribution model adjustment geographic information data's calibration parameter;S8 is to the consistency optimization of calibration after geographic information data.This application has significantly improved the calibration efficiency, also effectively solved the calibration unstable problem caused by data real-time insufficient in dynamic scene.

Description

Technical Field

[0001] This invention relates to the field of geographic information technology, and in particular to a geographic information surveying and calibration method and system based on data analysis. Background Technology

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

[0003] Currently, mainstream geographic information data calibration technologies mainly rely on traditional model matching and manual calibration methods. They typically fuse and correct multi-source data by setting fixed geometric models or spatial features. Specifically, existing methods are mostly focused on adjusting the accuracy of a single data source, such as achieving consistency in some areas by fitting spatial point clouds or overlaying and aligning images with surveying and mapping data. However, when dealing with different data sources, these technologies are often difficult to unify effectively due to significant differences in data types, resolutions, and spatial scales.

[0004] Meanwhile, existing automated calibration methods typically rely on simple rule-based models and lack in-depth analysis of the complex relationships between multi-source data. Most algorithms only optimize the geometric matching relationships of geographic information while ignoring the impact of data distribution characteristics and topological structure on consistent calibration, resulting in insufficient accuracy of calibration results in complex terrain or dynamic scenarios. In addition, existing technologies generally lack dynamic adjustment mechanisms and cannot update parameters according to changes in real-time data during the calibration process, further limiting their application in high-precision and high-dynamic scenarios.

[0005] In summary, existing geographic information data calibration technologies have significant shortcomings in multi-source data consistency analysis, accuracy assurance in complex scenarios, and efficiency of dynamic adjustment. These shortcomings not only reduce the calibration accuracy and reliability of multi-source geographic information data but also restrict 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 objective of this invention is to propose a geographic information survey calibration method and system based on data analysis. This invention significantly improves calibration efficiency and effectively solves the problem of calibration instability caused by insufficient real-time data in dynamic scenarios.

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

[0008] S1. Collect geographic information datasets using various data acquisition devices;

[0009] S2. Preprocess the geographic information dataset;

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

[0011] S4. Calculate the topological entropy of the geographic information dataset using the spatial topology matrix. Calculate the topological entropy value for each of the multiple data sources as a basic indicator for consistency analysis between data sources.

[0012] S5. Analyze the spatial consistency between multiple data sources based on the topological entropy calculation results, and identify regions and data characteristics 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, construct an error distribution model for regions with significant differences in topological entropy;

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

[0015] S8. Perform consistency optimization on 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, S1 includes the following steps:

[0017] S11. Generate a geographic information dataset using various data acquisition devices. The geographic information dataset includes the following subsets:

[0018] Remote sensing image dataset D r Multi-band data, including surface imagery, was collected by drones.

[0019] Surveying Instrument Dataset D t Spatial coordinates are collected and recorded by surveying instruments;

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

[0021] S12. Establish a unified parametric representation for the geographic information dataset D:

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

[0023] Optionally, S2 includes the following steps:

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

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

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

[0027] S24. Perform spatial resolution matching on geographic information datasets under a unified coordinate system, so that each data subset can be stored and analyzed at the same spatial resolution:

[0028]

[0029] in, For resolution-matched remote sensing image data, This refers to the data from the surveying instrument after resolution matching. This is the GNSS data after resolution matching.

[0030] Optionally, S3 includes the following steps:

[0031] S31. For the preprocessed geographic information dataset D f Geometric feature extraction is performed on ground features, including boundary points, center points, and geometric shapes, generating a geometric feature set G = {g1, g2, ..., g...}. n}, where g n Represents the geometric feature parameters of the nth feature, including spatial coordinates (x, y, z);

[0032] S32. Analyze the adjacency relationships between ground features based on the geometric feature set G, and generate the adjacency relationship matrix A:

[0033] A = [a ij ];

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

[0035] Adjacency is determined by the spatial distance d between ground features. ij Determine if d ij ≤d threshold d threshold If a represents the spatial distance threshold for adjacency relationships, then a ij =1;

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

[0037]

[0038] Among them, V i For ground features g i spatial volume, w ij For ground features g i With ground features g j Spatial distance weighting;

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

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

[0041] Where P represents the set of spatial characteristics, including the distribution density ρ of each land feature. i and concentration c i ;

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

[0043] Optionally, S4 includes the following steps:

[0044] S41. Introduce multi-layered correlation weights W between ground features based on the spatial topology matrix M. ij :

[0045]

[0046] Among them, W ij For ground features g i With ground features g j The overall correlation weight is denoted by α, β, and γ, which are weight adjustment factors representing the influence of topological relationship, distribution density, and concentration on the overall weight, respectively.

[0047] S42. Based on the correlation weight W ij Generate the weighted space topology matrix M W =[Wij The correlation strength of land features is processed hierarchically, and the correlation matrix of each layer l is defined.

[0048]

[0049] Among them, T l-1 and T l The weight threshold range for the l-th layer. This indicates an association matrix that retains only a specific weight range;

[0050] S43. The correlation matrix for each level Calculate the hierarchical topological entropy H l :

[0051]

[0052] Among them, H l Let be the topological entropy of the l-th layer, which characterizes the orderliness and consistency of the distribution of ground features. The correlation weights of the l-th layer;

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

[0054]

[0055] Where L is the total number of layers, λ l is the weighting factor for the l-th layer, 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... Consistency deviation ΔH s :

[0058]

[0059] Where, ΔH s For data source Consistency deviation, H avg The average topological entropy value for all data sources;

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

[0061]

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

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

[0064]

[0065] Among them, T diff R is a significance threshold used to filter regions with large differences. diff This is the set of indexes for all regions that meet the criteria.

[0066] S54. For regions with significant differences R diff Extract relevant data features, including the geometric features of the land cover, adjacency features, and spatial distribution characteristics of the region.

[0067] Optionally, S6 includes the following steps:

[0068] S61. Based on the set R of regions with significant differences in topological entropy diff For each region (i,j), a comprehensive spatial error model E is established. ij :

[0069]

[0070] in, For the position deviation error model, For the proportional error model, For rotational error model;

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

[0072]

[0073] in, For data source The position vector of the middle region (i,j). The average position vector of region (i,j) across all data sources, where n is the number of data sources;

[0074] S63. Establish a proportionality error model for the spatial proportions of regions with significant differences.

[0075]

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

[0077] S64. Establish a rotation error model for spatial rotation in regions of significant difference.

[0078]

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

[0080] S65, Based on the comprehensive spatial error model E ij Generate an error distribution model E for regions with significant differences in topological entropy. model To quantify the spatial error characteristics of each significantly different region:

[0081] E model ={E ij |(i,j)∈R diff};

[0082] Among them, R diff This is the set of indexes for all regions that meet the criteria.

[0083] Optionally, S7 includes the following steps:

[0084] S71. Based on the generated error distribution model E model For each region with significant differences in topological entropy, a set of calibration parameters is set, including a scaling factor, a rotation angle, and a position offset vector. The scaling factor is used to adjust the spatial scaling error of geographic information data within 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. By analyzing the proportional error, rotational error and positional deviation in the error distribution model, the corresponding updated values ​​of the calibration parameters are calculated. The updated values ​​are related to the magnitude and trend of the error in the error distribution model and are used to optimize the set of calibration parameters.

[0086] S73. Apply the updated calibration parameter values ​​to the initialized calibration parameter set to obtain the updated scale factor, rotation angle, and position offset vector. Use the updated calibration parameters to adjust the geographic information data in areas with significant differences. The adjusted geographic information data is represented by changes in the spatial scale, rotation angle, and position coordinates of the data.

[0087] S74. After adjusting the calibration parameters, recalculate the error distribution model for the adjusted geographic information data. Dynamically update the calibration parameter set by analyzing the error changes before and after the adjustment. Each iteration is stopped by judging whether to stop based on the error convergence criterion until the error distribution model meets the preset consistency threshold.

[0088] A data analysis-based geographic information surveying and calibration system executes a data analysis-based geographic information surveying and calibration method, including the following modules:

[0089] The data acquisition module is used to generate geographic information datasets through various data acquisition devices. The geographic information datasets include remote sensing image data, surveying instrument data, and GNSS data. The data acquisition module supports multi-source data input and provides temporal and spatial reference information for data acquisition.

[0090] The data preprocessing module is used to perform format conversion, noise filtering, coordinate system unification, and spatial resolution matching on the collected geographic information dataset, so that the geographic information dataset has a consistent time and spatial reference.

[0091] The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information dataset, extract the geometric features, adjacency relationships and spatial distribution characteristics of ground features, and generate a spatial topology matrix to represent the relationship between ground features;

[0092] The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, which characterizes the orderliness and consistency of the distribution of ground features among data sources, and calculates the topological entropy value for multiple data sources separately as a basic indicator for consistency analysis.

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

[0094] The error modeling module is used to establish an error distribution model for regions with significant differences in topological entropy. The error distribution model includes a positional deviation model, a proportional error model, and a rotational error model, which are used to quantify the spatial error characteristics of regions with significant differences.

[0095] The dynamic calibration module is used to adjust the calibration parameter set based on the error distribution model, including the scaling factor, rotation angle and position offset vector. It uses a dynamic feedback mechanism to update the calibration parameters in real time and correct regions with differences in topological entropy.

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

[0097] The beneficial effects of this invention are:

[0098] (1) This invention constructs a spatial topology network and calculates topological entropy to comprehensively characterize the orderliness and consistency of the distribution of ground features. Compared with the traditional calibration method based solely on geometric features, this invention uses topological entropy as a quantitative indicator to accurately identify significantly different regions in multi-source data and extract relevant data features for further analysis. The topological entropy-driven calibration method can dynamically adapt to the characteristic differences between different data sources, significantly improving the accuracy of calibration results in complex scenarios and demonstrating superior robustness and accuracy in multi-source data fusion.

[0099] (2) This invention quantifies the error characteristics of significantly different regions by constructing a multidimensional error model of position deviation, proportional error and rotation error, and updates the calibration parameters in real time by combining a dynamic feedback mechanism. It can adjust the proportional factor, rotation angle and position offset vector according to the dynamic changes of error distribution during the calibration process, and realize the automatic iterative optimization of calibration parameters. The dynamic calibration mechanism not only significantly improves the calibration efficiency, but also effectively solves the problem of calibration instability caused by insufficient real-time data 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 acquisition to consistency calibration. The systematic design significantly reduces the degree of human intervention and improves calibration efficiency. At the same time, because each module is independent and closely cooperates, the system has high scalability and can flexibly adjust or integrate more functional modules according to actual needs, thereby meeting the needs of complex and ever-changing application scenarios. Attached Figure Description

[0101] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0102] Figure 1 This is a flowchart of a geographic information surveying and calibration method and system based on data analysis proposed in this invention. Detailed Implementation

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

[0104] refer to Figure 1 A geographic information surveying and calibration method based on data analysis includes the following steps:

[0105] S1. Collect geographic information datasets using various data acquisition devices;

[0106] S2. Preprocess the geographic information dataset;

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

[0108] S4. Calculate the topological entropy of the geographic information dataset using the spatial topology matrix. Calculate the topological entropy value for each of the multiple data sources as a basic indicator for consistency analysis between data sources.

[0109] S5. Analyze the spatial consistency between multiple data sources based on the topological entropy calculation results, and identify regions and data characteristics 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, construct an error distribution model for regions with significant differences in topological entropy;

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

[0112] S8. Perform consistency optimization on 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 embodiment, S1 includes the following steps:

[0114] S11. Generate a geographic information dataset using various data acquisition devices. The geographic information dataset includes the following subsets:

[0115] Remote sensing image dataset D r Multi-band data, including surface imagery, was collected by drones.

[0116] Surveying Instrument Dataset D t Spatial coordinates are collected and recorded by surveying instruments;

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

[0118] S12. Establish a unified parametric representation for the geographic information dataset D:

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

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

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

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

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

[0124] S24. Perform spatial resolution matching on geographic information datasets under a unified coordinate system, so that each data subset can be stored and analyzed at the same spatial resolution:

[0125]

[0126] in, For resolution-matched remote sensing image data, This refers to the data from the surveying instrument after resolution matching. This is the GNSS data after resolution matching.

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

[0128] S31. For the preprocessed geographic information dataset D f Geometric feature extraction is performed on ground features, including boundary points, center points, and geometric shapes, generating a geometric feature set G = {g1, g2, ..., g...}. n}, where g n Represents the geometric feature parameters of the nth feature, including spatial coordinates (x, y, z);

[0129] S32. Analyze the adjacency relationships between ground features based on the geometric feature set G, and generate the adjacency relationship matrix A:

[0130] A = [a ij ];

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

[0132] Adjacency is determined by the spatial distance d between ground features. ij Determine if d ij ≤d threshold d threshold If a represents the spatial distance threshold for adjacency relationships, then a ij =1;

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

[0134]

[0135] Among them, V i For ground features g i spatial volume, w ij For ground features g i With ground features g j Spatial distance weighting;

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

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

[0138] Where P represents the set of spatial characteristics, including the distribution density ρ of each land feature. i and concentration c i ;

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

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

[0141] S41. Introduce multi-layered correlation weights W between ground features based on the spatial topology matrix M. ij :

[0142]

[0143] Among them, W ij For ground features g i With ground features g jThe overall correlation weight is denoted by α, β, and γ, which are weight adjustment factors representing the influence of topological relationship, distribution density, and concentration on the overall weight, respectively.

[0144] S42. Based on the correlation weight W ij Generate the weighted space topology matrix M W =[W ij The correlation strength of land features is processed hierarchically, and the correlation matrix of each layer l is defined.

[0145]

[0146] Among them, T l-1 and T l The weight threshold range for the l-th layer. This indicates an association matrix that retains only a specific weight range;

[0147] S43. The correlation matrix for each level Calculate the hierarchical topological entropy H l :

[0148]

[0149] Among them, H l Let be the topological entropy of the l-th layer, which characterizes the orderliness and consistency of the distribution of ground features. The correlation weights of the l-th layer;

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

[0151]

[0152] Where L is the total number of layers, λ l is the weighting factor for the l-th layer, used to reflect the contribution of different layers to the overall topological entropy.

[0153] In this embodiment, 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... Consistency deviation ΔH s :

[0155]

[0156] Where, ΔH s For data source Consistency deviation, H avg The average topological entropy value for all data sources;

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

[0158]

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

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

[0161]

[0162] Among them, T diff R is a significance threshold used to filter regions with large differences. diff This is the set of indexes for all regions that meet the criteria.

[0163] S54. For regions with significant differences R diff Extract relevant data features, including the geometric features of the land cover, adjacency features, and spatial distribution characteristics of the region.

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

[0165] S61. Based on the set R of regions with significant differences in topological entropy diff For each region (i,j), a comprehensive spatial error model E is established. ij :

[0166]

[0167] in, For the position deviation error model, For the proportional error model, For rotational error model;

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

[0169]

[0170] in, For data source The position vector of the middle region (i,j). The average position vector of region (i,j) across all data sources, where n is the number of data sources;

[0171] S63. Establish a proportionality error model for the spatial proportions of regions with significant differences.

[0172]

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

[0174] S64. Establish a rotation error model for spatial rotation in regions of significant difference.

[0175]

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

[0177] S65, Based on the comprehensive spatial error model E ij Generate an error distribution model E for regions with significant differences in topological entropy. model To quantify the spatial error characteristics of each significantly different region:

[0178] E model ={E ij |(i,j)∈R diff};

[0179] Among them, R diff This is the set of indexes for all regions that meet the criteria.

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

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

[0182] S72. By analyzing the proportional error, rotational error and positional deviation in the error distribution model, the corresponding updated values ​​of the calibration parameters are calculated. The updated values ​​are related to the magnitude and trend of the error in the error distribution model and are used to optimize the set of calibration parameters.

[0183] S73. Apply the updated calibration parameter values ​​to the initialized calibration parameter set to obtain the updated scale factor, rotation angle, and position offset vector. Use the updated calibration parameters to adjust the geographic information data in areas with significant differences. The adjusted geographic information data is represented by changes in the spatial scale, rotation angle, and position coordinates of the data.

[0184] S74. After adjusting the calibration parameters, recalculate the error distribution model for the adjusted geographic information data. Dynamically update the calibration parameter set by analyzing the error changes before and after the adjustment. Each iteration is stopped by judging whether to stop based on the error convergence criterion until the error distribution model meets the preset consistency threshold.

[0185] A data analysis-based geographic information surveying and calibration system executes a data analysis-based geographic information surveying and calibration method, including the following modules:

[0186] The data acquisition module is used to generate geographic information datasets through various data acquisition devices. The geographic information datasets include remote sensing image data, surveying instrument data, and GNSS data. The data acquisition module supports multi-source data input and provides temporal and spatial reference information for data acquisition.

[0187] The data preprocessing module is used to perform format conversion, noise filtering, coordinate system unification, and spatial resolution matching on the collected geographic information dataset, so that the geographic information dataset has a consistent time and spatial reference.

[0188] The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information dataset, extract the geometric features, adjacency relationships and spatial distribution characteristics of ground features, and generate a spatial topology matrix to represent the relationship between ground features;

[0189] The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, which characterizes the orderliness and consistency of the distribution of ground features among data sources, and calculates the topological entropy value for multiple data sources separately as a basic indicator for consistency analysis.

[0190] The data consistency analysis module is used to analyze the spatial consistency of different data sources based on the topological entropy calculation results, identify regions 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 error distribution models for regions with significant differences in topological entropy. The error distribution models include positional deviation models, proportional error models, and rotational error models, which are used to quantify the spatial error characteristics of regions with significant differences.

[0192] The dynamic calibration module is used to adjust the calibration parameter set based on the error distribution model, including the scaling factor, rotation angle and position offset vector. It uses a dynamic feedback mechanism to update the calibration parameters in real time and correct regions with differences in topological entropy.

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

[0194] Example 1:

[0195] The example is applied in a city. In order to improve its disaster monitoring and management capabilities, the city plans to use multi-source geographic information data to identify and dynamically update urban flood risk areas with high precision. 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 instrument data, and GNSS ground station data. The data have significant differences in collection range, time scale, and spatial accuracy. The resolution of the remote sensing image reaches 0.2 meters, the point density of the surveying data collection is 1 point / square meter, and the GNSS ground station updates the location and elevation data every 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 the data, traditional methods often resulted in large calibration errors under complex terrain conditions. In low-lying areas, the calibration results could even deviate by as much as 3 meters, seriously affecting the accuracy of identifying flood-risk areas.

[0197] This embodiment utilizes the method of the present invention to conduct surveying and calibration of the above-mentioned multi-source geographic information data to achieve higher accuracy and consistency. The data collection period is from June 1, 2024 to June 10, 2024, covering the urban center area and the surrounding low-lying area within 10 kilometers, with a total data volume of approximately 20TB.

[0198] Geographic information data was collected using various data acquisition devices, including UAV remote sensing imagery, point cloud data collected by surveying instruments, and GNSS ground station location data. The remote sensing imagery data covered an area of ​​100 square kilometers and was collected daily from 9:00 AM to 11:00 AM; the surveying point cloud data was concentrated on building clusters in the city center and was collected daily from 2:00 PM to 4:00 PM; the GNSS ground station data was recorded in real time on a minute-by-minute basis, covering the entire experimental area.

[0199] Subsequently, the data preprocessing module of this invention is used to perform format conversion, noise filtering, and coordinate system unification on the collected data. The remote sensing images and surveying data adopt the UTM coordinate system, while the original GNSS data is acquired in the WGS84 coordinate system. The data is unified to the UTM coordinate system through preprocessing.

[0200] Next, a spatial topological network of the urban area was constructed based on the preprocessed data. Boundary points, geometric features, and adjacency relationships of ground features were extracted to generate a spatial topological matrix to characterize the spatial correlation between data sources. The topological entropy values ​​of remote sensing imagery, surveying and mapping data, and GNSS data were calculated using the topological entropy calculation module. The results showed that the topological entropy value of remote sensing imagery 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 differed significantly from that of other data sources.

[0201] Based on the topological entropy calculation results, the consistency analysis module identified areas with significant differences, concentrated in the southeastern part of the urban low-lying area. The geometric features and spatial distribution characteristics of the ground features in these areas were extracted to construct an error distribution model. The analysis revealed that the average positional deviation of GNSS data in this area was 2.5 meters, the rotation error was 0.3 degrees, and the scaling error was 1.8%.

[0202] By using a dynamic calibration module to iteratively optimize the calibration parameter set, including the scale factor, rotation angle, and position offset vector, the calibration was performed on areas with significant differences. After four 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] To verify the effectiveness of the method of this invention, it was compared with the traditional geometric matching calibration method. The same training samples and experimental conditions were used. The training samples included 500 randomly selected land features: urban building areas, roads, and rivers. The positional deviation, rotation error, and scaling error after calibration were calculated respectively.

[0204] Table 1 Comparison of Experimental Data

[0205]

[0206] Experimental results show that the method of the present invention is significantly better than the traditional method in terms of position deviation, rotation error and proportional error. It performs well 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, which improves efficiency 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] This invention comprehensively characterizes the orderliness and consistency of ground feature distribution by constructing a spatial topological network and calculating topological entropy. Compared with traditional calibration methods that rely solely on geometric features, this invention uses topological entropy as a quantitative indicator to accurately identify significantly different regions in multi-source data and extract relevant data features for further analysis. The topological entropy-driven calibration method can dynamically adapt to the characteristic differences between different data sources, significantly improving the accuracy of calibration results in complex scenarios and demonstrating superior robustness and accuracy in multi-source data fusion.

[0209] This invention quantifies the error characteristics of significantly different regions by constructing a multidimensional error model of position deviation, scaling error, and rotation error, and updates the calibration parameters in real time by combining a dynamic feedback mechanism. During the calibration process, the scaling factor, rotation angle, and position offset vector can be adjusted according to the dynamic changes in the error distribution, realizing automatic iterative optimization of calibration parameters. The dynamic calibration mechanism not only significantly improves calibration efficiency, but also effectively solves the problem of calibration instability caused by insufficient real-time data in dynamic scenarios.

[0210] This 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 acquisition to consistency calibration. The systematic design significantly reduces the degree of human intervention and improves calibration efficiency. At the same time, because each module is independent and closely cooperates, the system has high scalability and can be flexibly adjusted or integrated with more functional modules according to actual needs, thereby meeting the needs of complex and ever-changing application scenarios.

[0211] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A geographic information surveying and calibration method based on data analysis, characterized in that, Includes the following steps: S1. Collect geographic information datasets using various data acquisition devices; S2. Preprocess the geographic information dataset; S3. Construct a spatial topology network based on the preprocessed geographic information dataset and generate a spatial topology matrix; S4. Calculate the topological entropy of the geographic information dataset using the spatial topology matrix. Calculate the topological entropy value for each of the multiple data sources as a basic indicator for consistency analysis between data sources. S5. Analyze the spatial consistency between multiple data sources based on the topological entropy calculation results, and identify regions and data characteristics 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, construct an error distribution model for regions with significant differences in topological entropy; S7. Adjust the calibration parameters of geographic information data according to the error distribution model, and use a dynamic feedback mechanism to update the calibration parameters in real time to correct areas with significant differences in topological entropy; S8. Perform consistency optimization on 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. The geographic information surveying and calibration method based on data analysis according to claim 1, characterized in that, S1 includes the following steps: S11. Generate a geographic information dataset using various data acquisition devices. The geographic information dataset includes the following subsets: Remote sensing image dataset D r Multi-band data, including surface imagery, was collected by drones. Surveying Instrument Dataset D t Spatial coordinates are collected and recorded by surveying instruments; Global Navigation Satellite System Dataset D g Data is collected by GNSS equipment, providing geographic coordinates and timestamp data; S12. Establish a unified parametric representation for the geographic information dataset D: D={D r ,D t ,D g }。 3. The geographic information surveying and calibration method based on data analysis according to claim 1, characterized in that, S2 includes the following steps: S21. Perform format conversion on a subset of the geographic information dataset D so that all data are stored in a standardized format; S22. Apply a noise filtering algorithm to the unified format geographic information dataset to remove random errors or environmental noise generated during the data collection process; S23. Transform the noise-filtered geographic information dataset to a unified spatial reference coordinate system; S24. Perform spatial resolution matching on geographic information datasets under a unified coordinate system, so that each data subset can be stored and analyzed at the same spatial resolution: in, For resolution-matched remote sensing image data, This refers to the data from the surveying instrument after resolution matching. This is the GNSS data after resolution matching.

4. The geographic information surveying and calibration method based on data analysis according to claim 1, characterized in that, S3 includes the following steps: S31. For the preprocessed geographic information dataset D f Geometric feature extraction is performed on ground features, including boundary points, center points, and geometric shapes, generating a geometric feature set G = {g1, g2, ..., g...}. n }, where g n Represents the geometric feature parameters of the nth feature, including spatial coordinates (x, y, z); S32. Analyze the adjacency relationships between ground features based on the geometric feature set G, and generate the adjacency relationship matrix A: A=[a ij ]; Among them, a ij =1 represents the ground feature g i With ground features g j There is an adjacency relationship, a ij =0 indicates that the ground feature g i With ground features g j There is no adjacency relationship; Adjacency is determined by the spatial distance d between ground features. ij Determine if d ij ≤d threshold d threshold If a represents the spatial distance threshold for adjacency relationships, then a ij =1; S33. Calculate the spatial distribution density ρ of ground features using the geometric feature set G and the adjacency matrix A. i and concentration c i : Among them, V i For ground features g i spatial volume, w ij For ground features g i With ground features g j Spatial distance weighting; S34. Based on the geometric feature set G of ground features, the adjacency matrix A, and the spatial distribution density ρ i and concentration c i Constructing a spatial topology network T: T = (G, A, P); Where P represents the set of spatial characteristics, including the distribution density ρ of each land feature. i and concentration c i ; S35. Generate a spatial topology matrix M using a spatial topology network T.

5. The geographic information surveying and calibration method based on data analysis according to claim 1, characterized in that, S4 includes the following steps: S41. Introduce multi-layered correlation weights W between ground features based on the spatial topology matrix M. ij : Among them, W ij For ground features g i With ground features g j The overall correlation weight is denoted by α, β, and γ, which are weight adjustment factors representing the influence of topological relationship, distribution density, and concentration on the overall weight, respectively. S42. Based on the correlation weight W ij Generate the weighted space topology matrix M W =[W ij The correlation strength of land features is processed hierarchically, and the correlation matrix of the l-th layer is defined. Among them, T l-1 and T l The weight threshold range for the l-th layer. Represents the correlation matrix of the l-th layer; S43. The correlation matrix of the l-th layer Calculate the hierarchical topological entropy H l : Among them, H l Let be the topological entropy of the l-th layer, which characterizes the orderliness and consistency of the distribution of ground features. The correlation weights of the l-th layer; S44. Calculate the topological entropy values ​​H of all layers. l The overall topological entropy H is obtained by synthesis. total : Where L is the total number of layers, λ l is the weighting factor for the l-th layer, used to reflect the contribution of different layers to the overall topological entropy.

6. The geographic information surveying and calibration method based on data analysis according to claim 5, characterized in that, S5 includes the following steps: S51. Analyze the overall consistency of each data source based on the overall topology entropy value, and calculate the data source... Consistency deviation ΔH s : Where, ΔH s For data source Consistency deviation, H avg The average topological entropy value for all data sources; S52. Utilizing the hierarchical topological entropy value H l For each data source Calculate the topological entropy difference index at the regional level. in, Indicates data source Topological entropy difference index at the mid-region level For data source The hierarchical topological entropy value of the middle region (i,j), The average topological entropy value of the same level region; S53. Regional-level topological entropy difference index Determine the set of regions R with significant differences diff : Among them, T diff This is a significance threshold used to filter out regions with large differences; S54. For regions with significant differences R diff Extract relevant data features, including the geometric features of the land cover, adjacency features, and spatial distribution characteristics of the region.

7. A geographic information surveying and calibration method based on data analysis according to claim 6, characterized in that, S6 includes the following steps: S61. Based on the set of regions R with significant differences in topological entropy diff For each region (i,j), a comprehensive spatial error model E is established. ij : in, For the position deviation error model, For the proportional error model, For rotational error model; S62. For the significantly different regions (i,j), calculate the location deviation error model using location data from different data sources. in, For data source The position vector of the middle region (i,j). The average position vector of region (i,j) across all data sources, where n is the number of data sources; S63. Establish a proportionality error model for the spatial proportions of regions with significant differences. in, For data source The spatial proportion of the middle region (i,j) This represents the average spatial proportion of region (i,j) across all data sources. S64. Establish a rotation error model for spatial rotation in regions of significant difference. in, For data source The rotation angle of the middle region (i,j) This represents the average rotation angle of region (i,j) across all data sources. S65, Based on the comprehensive spatial error model E ij Generate an error distribution model E for regions with significant differences in topological entropy. model To quantify the spatial error characteristics of each significantly different region: HAVE BEEN model ={E ij ∣(i,j)∈R diff }。 8. The geographic information surveying and calibration method based on data analysis according to claim 1, characterized in that, S7 includes the following steps: S71. Based on the generated error distribution model E model For each region with significant differences in topological entropy, a set of calibration parameters is set, including a scaling factor, a rotation angle, and a position offset vector. The scaling factor is used to adjust the spatial scaling error of geographic information data within the region, the rotation angle is used to correct spatial rotation error, and the position offset vector is used to correct position deviation. S72. By analyzing the proportional error, rotational error and positional deviation in the error distribution model, the corresponding updated values ​​of the calibration parameters are calculated. The updated values ​​are related to the magnitude and trend of the error in the error distribution model and are used to optimize the set of calibration parameters. S73. Apply the updated calibration parameter values ​​to the initialized calibration parameter set to obtain the updated scale factor, rotation angle, and position offset vector. Use the updated calibration parameters to adjust the geographic information data in areas with significant differences. The adjusted geographic information data is represented by changes in the spatial scale, rotation angle, and position coordinates of the data. S74. After adjusting the calibration parameters, recalculate the error distribution model for the adjusted geographic information data. Dynamically update the calibration parameter set by analyzing the error changes before and after the adjustment. Each iteration is stopped by judging whether to stop based on the error convergence criterion until the error distribution model meets the preset consistency threshold.

9. A geographic information surveying and calibration system based on data analysis, comprising the geographic information surveying and calibration method based on data analysis as described in any one of claims 1-8, characterized in that, Includes the following modules: The data acquisition module is used to generate geographic information datasets through various data acquisition devices. The geographic information datasets include remote sensing image data, surveying instrument data, and GNSS data. The data acquisition module supports multi-source data input and provides temporal and spatial reference information for data acquisition. The data preprocessing module is used to perform format conversion, noise filtering, coordinate system unification, and spatial resolution matching on the collected geographic information dataset, so that the geographic information dataset has a consistent time and spatial reference. The spatial topology analysis module is used to construct a spatial topology network based on the preprocessed geographic information dataset, extract the geometric features, adjacency relationships and spatial distribution characteristics of ground features, and generate a spatial topology matrix to represent the relationship between ground features; The topological entropy calculation module is used to calculate the topological entropy of geographic information data using the spatial topological matrix, which characterizes the orderliness and consistency of the distribution of ground features among data sources, and calculates the topological entropy value for multiple data sources separately as a basic indicator for consistency analysis. The data consistency analysis module is used to analyze the spatial consistency of different data sources based on the topological entropy calculation results, identify regions with significant differences in topological entropy, and extract relevant data features, including geometric features, adjacency relationships, and distribution characteristics. The error modeling module is used to establish an error distribution model for regions with significant differences in topological entropy. The error distribution model includes a positional deviation model, a proportional error model, and a rotational error model, which are used to quantify the spatial error characteristics of regions with significant differences. The dynamic calibration module is used to adjust the calibration parameter set based on the error distribution model, including the scaling factor, rotation angle and position offset vector. It uses a dynamic feedback mechanism to update the calibration parameters in real time and correct regions with differences in topological entropy. The calibration result output module is used to output the geographic information data after consistency calibration as a calibrated dataset, and supports export in multiple formats.

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