Geographic information collection method and device based on GIS technology, equipment and medium

Abstract

The application relates to a GIS technology-based geographic information collection method, device, equipment and medium, wherein the method acquires satellite images, unmanned aerial vehicle LiDAR point clouds and ground sensor data through multi-source geographic data collection to generate a multi-source geographic data set and an environmental parameter matrix; a credibility vector is generated by performing dynamic credibility evaluation on the environmental parameter matrix; adaptive weight distribution is performed on the credibility vector in combination with an equipment inherent error coefficient to generate a dynamic weight vector; the multi-source geographic data set and the dynamic weight vector are subjected to spatio-temporal fusion to generate a fused geographic information matrix through coordinate registration and weighted fusion; finally, closed-loop distortion calibration is performed based on a verification reference point, system errors are corrected by using the credibility vector, and an optimized geographic information database is generated. The method can effectively overcome measurement deviations caused by environmental factors, realize intelligent fusion and quality control of multi-source heterogeneous geographic data, and meet application requirements of high-precision geographic spatial analysis.

Description

Technical Field

[0001] This invention relates to the field of geographic information acquisition technology, specifically to a geographic information acquisition method, device, equipment, and medium based on GIS technology. Background Technology

[0002] The in-depth application of Geographic Information System (GIS) technology in fields such as resource surveys, environmental monitoring, and smart city management has always relied on high-quality geospatial data. Geographic information acquisition methods, as the core technological means of acquiring this fundamental data, have the core task of comprehensively and accurately acquiring and integrating spatial and attribute information reflecting the Earth's surface conditions through various technical approaches such as satellite remote sensing, aerial mapping, and ground sensing, thereby constructing a reliable digital spatial foundation. With the ever-increasing demands for data quality and timeliness, how to effectively and collaboratively utilize heterogeneous geographic data from different platforms and time periods has become a key focus of current development in this technological field.

[0003] However, existing geographic information acquisition methods still face significant challenges in dealing with complex environments and multi-source data fusion. Specifically, changes in environmental factors directly affect the measurement accuracy of various sensing devices, leading to persistent systematic biases between data collected at different times and under different conditions. Furthermore, when fusing multi-source data with varying accuracy, resolution, and reliability, the lack of an effective mechanism for dynamically and quantitatively evaluating data quality and adaptively weighting the fusion process often results in the fusion results retaining individual errors and failing to achieve complementary advantages, ultimately limiting the overall accuracy and reliability of geospatial databases. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a geographic information acquisition method, device, equipment and medium based on GIS technology that can adaptively correct environmental deviations and realize intelligent fusion of multi-source data.

[0005] The objective of this invention is achieved through the following solution:

[0006] In a first aspect, the present invention provides a geographic information acquisition method based on GIS technology, comprising the following steps:

[0007] S1: Collect multi-source geographic data for the target area. Simultaneously acquire satellite imagery data, UAV LiDAR point cloud data, and ground sensor data through the GNSS positioning module to generate a multi-source geographic dataset and environmental parameter matrix.

[0008] S2: Perform dynamic credibility assessment on the environmental parameter matrix, calculate real-time credibility score based on the deviation between environmental parameter values ​​and equipment calibration parameter values, and generate environmental parameter credibility vector;

[0009] S3: Adaptively assign weights to the environmental parameter confidence vector, calculate the weight quotient by combining the preset equipment inherent error coefficients and normalize it to generate a dynamic weight vector;

[0010] S4: Perform spatiotemporal fusion of multi-source geographic datasets and dynamic weight vectors. After eliminating spatial differences through coordinate registration, perform weighted fusion calculation to generate a fused geographic information matrix.

[0011] S5: Perform closed-loop distortion calibration on the fused geographic information matrix, calculate the spatial distortion degree based on the verification benchmark point with known precise coordinates, and use the environmental parameter confidence vector to correct the system error, generating an optimized geographic information database for high-precision geospatial analysis.

[0012] In one embodiment, S1 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0013] S11: The satellite image data of the target area is acquired synchronously through the GNSS positioning module. Radiometric calibration and atmospheric correction are performed on the satellite image data to eliminate radiometric distortion caused by the sensor itself and atmospheric scattering, and generate pre-processed corrected image data.

[0014] S12: Synchronously acquire UAV LiDAR point cloud data of the target area through the GNSS positioning module, perform noise reduction and ground point classification processing on the UAV LiDAR point cloud data, filter out flight noise and separate accurate ground elevation information to generate classified LiDAR point cloud data.

[0015] S13: The ground sensor data of the target area is synchronously acquired through the GNSS positioning module. The ground sensor data is processed by time synchronization and spatial interpolation. The asynchronous readings are unified to the standard timestamp and a continuous spatial parameter field is generated to generate an environmental parameter matrix.

[0016] S14: Spatial registration and format unification of the corrected image data and classified LiDAR point cloud data are performed to integrate them into a multi-source geographic dataset.

[0017] In one embodiment, step S2 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0018] S21: Perform sliding time window division on the environmental parameter matrix, extract a subset of parameters of a specific time length according to the collected time series for independent analysis, and generate parameter time series segments;

[0019] S22: Perform statistical feature extraction on the time series segments of the parameters, calculate the mean, variance and number of outliers of the parameters in each segment, and generate parameter stability index;

[0020] S23: Perform fuzzy logic reasoning on the parameter stability index, compare the stability index with the equipment calibration parameter range to assess the degree of credibility, and generate an environmental parameter credibility vector.

[0021] In one embodiment, step S3 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0022] S31: Sort and partition the environmental parameter confidence vector, divide the confidence values ​​into high and low intervals and assign different basic weight coefficients to generate a basic weight vector.

[0023] S32: Normalize and reciprocal transform the preset inherent error coefficients of the equipment to convert the error coefficients into accuracy indicators that are positively correlated with the weights, and generate the equipment accuracy coefficients.

[0024] S33: Perform weighted synthesis processing on the basic weight vector and the device accuracy coefficient, merge the two types of coefficients according to a preset ratio and normalize them into a probability distribution to generate a dynamic weight vector.

[0025] In one embodiment, step S4 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0026] S41: Perform spatial coordinate registration on multi-source geographic datasets. By matching feature points, the coordinate systems of different data sources are aligned to the same spatial reference system, generating spatially aligned geospatial data.

[0027] S42: Perform gridded resampling on spatially aligned geospatial data, interpolating data of different resolutions into a geographic grid of uniform size to generate grid data with consistent scale.

[0028] S43: Perform weighted fusion processing on grid data with consistent scale and dynamic weight vector, and perform pixel-level fusion calculation based on the weight values ​​of each data source in the grid cell to generate a fused geographic information matrix.

[0029] In one embodiment, step S5 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0030] S51: Perform verification benchmark point extraction processing on the fused geographic information matrix, extract local matrix data at the control point locations with known precise coordinates, and generate a benchmark point sample set;

[0031] S52: Calculate the spatial distortion of the reference point sample set, compare the Euclidean distance between the sample coordinate values ​​and the true coordinate values, and generate a spatial distortion vector.

[0032] S53: Perform multiple regression analysis on the spatial distortion vector and the environmental parameter credibility vector, establish a compensation model for distortion and credibility, and reverse-correct the system parameters to generate an optimized geographic information database.

[0033] Secondly, the present invention provides a geographic information acquisition device based on GIS technology, which is configured with the following modules:

[0034] The multi-source geographic data acquisition module is used to acquire multi-source geographic data of the target area. It synchronously acquires satellite imagery data, UAV LiDAR point cloud data and ground sensor data through the GNSS positioning module to generate multi-source geographic datasets and environmental parameter matrices.

[0035] The environmental parameter credibility assessment module is used to dynamically assess the credibility of the environmental parameter matrix, calculate the real-time credibility score based on the deviation between the environmental parameter values ​​and the equipment calibration parameter values, and generate an environmental parameter credibility vector.

[0036] The adaptive weight allocation module is used to adaptively allocate weights to the environmental parameter confidence vector. It calculates the weight quotient by combining the preset device inherent error coefficients and performs normalization processing to generate a dynamic weight vector.

[0037] The geographic data spatiotemporal fusion module is used to perform spatiotemporal fusion of multi-source geographic datasets and dynamic weight vectors. After eliminating spatial differences through coordinate registration, it performs weighted fusion calculation to generate a fused geographic information matrix.

[0038] The closed-loop distortion calibration module is used to perform closed-loop distortion calibration on the fused geographic information matrix. It calculates the spatial distortion degree based on the verification benchmark points with known precise coordinates and corrects the system error using the environmental parameter confidence vector, generating an optimized geographic information database for high-precision geospatial analysis.

[0039] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned geographic information collection methods based on GIS technology.

[0040] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-mentioned geographic information collection methods based on GIS technology.

[0041] In summary, the geographic information acquisition method based on GIS technology provided in this application achieves spatiotemporal synchronization of multi-source data through a GNSS positioning module during the data acquisition stage, effectively establishing the inherent correlation between data and laying the foundation for subsequent fusion processing. By dynamically assessing the reliability of the environmental parameter matrix, the reliability of each data source under different environmental conditions can be accurately quantified, enabling precise perception of data quality. Adaptive weight allocation based on the environmental parameter reliability vector combined with the inherent error coefficient of the equipment dynamically adjusts the contribution of each data source in the fusion process according to the actual data quality, achieving an intelligent filtering effect that highlights advantageous data and suppresses inferior data. Processing multi-source geographic datasets and dynamic weight vectors through a combination of coordinate registration and weighted fusion enables deep integration of heterogeneous data in terms of spatial benchmarks and numerical characteristics, effectively eliminating data inconsistencies caused by differences in acquisition methods and resolutions. Finally, a closed-loop distortion calibration mechanism based on verification benchmarks continuously corrects system errors and uses the previously generated environmental parameter reliability vector for error tracing, ensuring that the final optimized geographic information database has high spatial accuracy and logical consistency. This approach fundamentally changes the limitations of isolated processing of each stage in the traditional geographic information collection process. It establishes a complete quality control system from data collection, quality assessment, intelligent fusion to accuracy verification, significantly improving the reliability and applicability of geospatial data products and providing a solid data foundation for high-precision geospatial analysis.

[0042] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0043] Figure 1 A flowchart illustrating a geographic information collection method based on GIS technology, provided for an embodiment of this application;

[0044] Figure 2 This is a schematic diagram of the structure of a geographic information acquisition device based on GIS technology, provided for another embodiment of this application. Detailed Implementation

[0045] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] In one embodiment, such as Figure 1 As shown, a geographic information collection method based on GIS technology is provided. This embodiment illustrates the method applied to a terminal, but it is understood that the method can also be applied to a server, or to a device including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0048] S1: Collect multi-source geographic data for the target area. Simultaneously acquire satellite imagery data, UAV LiDAR point cloud data, and ground sensor data through the GNSS positioning module to generate a multi-source geographic dataset and environmental parameter matrix.

[0049] Specifically, the system divides the target area into grids, defines the regional boundary coordinates based on the national geodetic coordinate system, generates a vector file of the acquisition range, and clarifies the coverage overlap ratio of satellite imagery, UAV LiDAR, and ground sensors to ensure data redundancy meets fusion requirements. The system selects high-precision GNSS receivers supporting multiple frequency bands and achieves time synchronization between multiple devices using second pulse signals, controlling the synchronization error range. Satellite imagery data, after radiometric correction, is obtained from the National Satellite Data Sharing Platform, covering multiple spectral bands and possessing specific swath width attributes. The UAV LiDAR system, equipped with lidar, synchronizes its position information with the GNSS equipment in real time through the UAV flight control system, completing data acquisition according to a set flight path, ensuring the flight path overlap rate meets data acquisition requirements. Ground sensing units are uniformly deployed within the target area, including sensors related to atmospheric temperature and humidity, air pressure, visibility, and terrain elevation. All sensors use a unified sampling frequency and transmit data in real time via wireless transmission technology.

[0050] Preferably, the system performs multi-device collaborative data acquisition. GNSS receiving devices generate timestamps at a set frequency and synchronize these timestamps to the satellite image receiving terminal, the UAV flight control system, and the ground sensor network, ensuring that all data are correlated by timestamp. During acquisition, satellite imagery is stored frame by frame, LiDAR point clouds are stored in a specific format, including 3D coordinates and reflection intensity information, and ground sensor data is stored in a specific format, including measured values ​​of various parameters and timestamps. The system performs preliminary structuring processing on the acquired data: satellite imagery data is converted to a format supported by specific software, and a pixel grayscale matrix is ​​extracted; LiDAR point cloud data is denoised using a specific library, and a statistical filtering algorithm is used to remove discrete points, generating a 3D coordinate matrix; ground sensor data is sorted by timestamp to generate an attribute data matrix. Finally, the system integrates the three types of data by timestamp and spatial coordinate index to form a multi-source geographic dataset. The system extracts all environmental measurement parameters and constructs an environmental parameter matrix. The matrix dimensions are determined by the total number of acquired timestamps and the number of environmental parameter types, and the matrix elements represent the measured value of a certain type of environmental parameter at a specific timestamp.

[0051] S2: Perform dynamic credibility assessment on the environmental parameter matrix, calculate real-time credibility score based on the deviation between environmental parameter values ​​and equipment calibration parameter values, and generate environmental parameter credibility vector.

[0052] Specifically, the system calibrates each environmental sensor in a standard laboratory environment, acquiring calibration parameter values. These calibration parameters are stored in the equipment calibration database as a reliability assessment benchmark. The system preprocesses the data in the environmental parameter matrix using specific criteria, calculating the mean and standard deviation of each environmental parameter. Data exceeding a specific multiple of the mean and standard deviation are identified as outliers and replaced using linear interpolation based on valid data points before and after them, ensuring data continuity. For each time stamp, the system calculates the relative deviation between the measured and calibrated values ​​for each type of environmental parameter. The relative deviation is obtained by dividing the absolute value of the difference between the measured and calibrated values ​​by the calibrated value.

[0053] Preferably, the system can use a non-linear scoring function to calculate real-time reliability. This function is designed based on the weighted impact of environmental parameters on the data acquisition equipment. The function includes an environmental impact coefficient, which is preset according to the equipment type. The reliability value range is a specific interval, and the value is positively correlated with the data reliability under the environmental parameters. The system arranges all environmental parameter reliability scores at each timestamp in a preset order to form an environmental parameter reliability vector. The vector dimension is consistent with the number of environmental parameter types and is updated in real time with the timestamp, ensuring that the reliability assessment can dynamically reflect the impact of environmental parameter changes on data quality.

[0054] S3: Adaptively assign weights to the environmental parameter confidence vector, calculate the weight quotient by combining the preset device inherent error coefficients and normalize it to generate a dynamic weight vector.

[0055] Specifically, the system conducts repeated data acquisition experiments under standard conditions to statistically analyze the inherent errors of each data source and determine the inherent error coefficients of the equipment. These coefficients correspond to three types of data sources: satellite imagery, UAV LiDAR, and ground sensors. The inherent error coefficients are obtained by statistically analyzing the measurement errors of different data sources under standard conditions. For each timestamp, the system calculates the average reliability of each data source: the average reliability of temperature and visibility for satellite imagery, the average reliability of temperature and humidity for LiDAR, and the average reliability of all environmental parameters for ground sensors. Based on the average reliability and the inherent error coefficients of the equipment, a weighted quotient is calculated. This quotient comprehensively reflects the synergistic effect of environmental reliability and the inherent accuracy of the equipment, and the magnitude of the quotient is positively correlated with the overall reliability of the data source.

[0056] Furthermore, the system normalizes the weight quotients by dividing each quotient by the sum of all weight quotients, ensuring that the sum of weights from each data source is a specific value. This generates dynamic weights, which are dynamically adjusted in real-time based on the reliability of environmental parameters, giving higher weights to more reliable data sources. The system then arranges the normalized weights of each data source in a preset order, forming a dynamic weight vector. The vector dimension matches the number of data source types, serving as the core weight basis for subsequent data fusion and ensuring that the fusion process can dynamically adjust the fusion ratio based on the real-time reliability of the data sources.

[0057] S4: Perform spatiotemporal fusion of multi-source geographic datasets and dynamic weight vectors. After eliminating spatial differences through coordinate registration, perform weighted fusion calculation to generate a fused geographic information matrix.

[0058] Specifically, the system adopts a unified national geodetic coordinate system and eliminates spatial differences between multi-source data through a specific zoning method, providing a unified spatial benchmark for multi-source data fusion. During satellite image registration, the system uses a specific feature point detection algorithm to extract image feature points, controls the number of feature points extracted, performs feature point matching using a specific algorithm to control the mismatch rate range, and establishes an affine transformation model based on the matched feature points to perform geometric correction on the satellite image, controlling the registration error range. During LiDAR point cloud registration, the system uses a specific feature point extraction algorithm to extract key feature points from the point cloud, matches them with the feature points registered with the satellite image, and completes spatial alignment between the point cloud and the image through an iterative nearest-point algorithm, controlling the registration error range.

[0059] During the registration process of ground sensor data, the system converts the GNSS coordinates of the sensor deployment location into coordinates in the target coordinate system and associates them with the imagery and point cloud data of the corresponding grid to ensure spatial consistency. For the registered multi-source data, the system performs weighted fusion calculations according to dynamic weight vectors. The fusion model adopts a weighted summation form, where each component corresponds to the relevant parameter values ​​of satellite imagery, LiDAR point cloud, and ground sensors at specific coordinates. Satellite imagery provides ground feature attribute values, LiDAR point cloud provides 3D coordinates and reflectance values, and ground sensors provide attribute measurement values. During the fusion process, the system uses bilinear interpolation to rasterize the discrete data to ensure the spatial continuity of the fusion result. The system divides the target area into grids and stores the fused geographic information parameters in grid cells, generating a fused geographic information matrix. The matrix dimensions are determined by the number of grid rows, columns, and parameter types. Matrix elements represent a specific type of geographic information parameter for a specific grid, using a specific storage format to support efficient spatial indexing and data querying.

[0060] S5: Perform closed-loop distortion calibration on the fused geographic information matrix, calculate the spatial distortion degree based on the verification benchmark point with known precise coordinates, and use the environmental parameter confidence vector to correct the system error, generating an optimized geographic information database for high-precision geospatial analysis.

[0061] Specifically, the system uniformly deploys verification benchmarks within the target area, covering edge areas, central areas, and typical feature areas. GNSS static measurement technology is used to acquire the precise coordinates of these benchmarks, controlling the range of horizontal and vertical accuracy. The benchmark coordinates are stored in a benchmark database as a reference for distortion calibration. For each grid cell in the fused geographic information matrix, the system extracts its fused coordinates and calculates the spatial distortion relative to the nearest benchmark coordinates using a spatial distance formula. If the distortion exceeds a preset threshold, the grid cell is deemed to have significant distortion and requires calibration. The system establishes an error correction model based on the environmental parameter confidence vector to calibrate the geographic information of distorted grid cells. The calibration model includes factors such as parameter type correction coefficients and the mean of the environmental parameter confidence vector. The parameter type correction coefficients are set according to the geographic information parameter types, and the mean of the environmental parameter confidence vector is the average value of the confidence of the relevant environmental parameters corresponding to that grid cell. During the correction process, iterative calculations are used to control the distortion within the preset threshold, ensuring calibration accuracy.

[0062] Preferably, the system employs a specific spatial database management system to construct an optimized geographic information database, which includes three core tables. The geographic information basic table stores the 3D coordinates, land cover type codes, spectral characteristics, attribute measurements, and calibrated accuracy indicators of grid cells. The metadata table stores metadata information such as data acquisition time, equipment model, dynamic weight vector, environmental parameter matrix, and distortion calibration records. The spatial index table uses a specific index structure to establish the mapping relationship between grid cell coordinates and geographic information, controlling the spatial query response time range. The database supports specific standards, is seamlessly compatible with mainstream GIS software, provides spatial data services, and meets the application needs of high-precision geospatial analysis.

[0063] In summary, the geographic information acquisition method based on GIS technology provided in this application achieves spatiotemporal synchronization of multi-source data through a GNSS positioning module during the data acquisition stage, effectively establishing the inherent correlation between data and laying the foundation for subsequent fusion processing. By dynamically assessing the reliability of the environmental parameter matrix, the reliability of each data source under different environmental conditions can be accurately quantified, enabling precise perception of data quality. Adaptive weight allocation based on the environmental parameter reliability vector combined with the inherent error coefficient of the equipment dynamically adjusts the contribution of each data source in the fusion process according to the actual data quality, achieving an intelligent filtering effect that highlights advantageous data and suppresses inferior data. Processing multi-source geographic datasets and dynamic weight vectors through a combination of coordinate registration and weighted fusion enables deep integration of heterogeneous data in terms of spatial benchmarks and numerical characteristics, effectively eliminating data inconsistencies caused by differences in acquisition methods and resolutions. Finally, a closed-loop distortion calibration mechanism based on verification benchmarks continuously corrects system errors and uses the previously generated environmental parameter reliability vector for error tracing, ensuring that the final optimized geographic information database has high spatial accuracy and logical consistency. This approach fundamentally changes the limitations of isolated processing of each stage in the traditional geographic information collection process. It establishes a complete quality control system from data collection, quality assessment, intelligent fusion to accuracy verification, significantly improving the reliability and applicability of geospatial data products and providing a solid data foundation for high-precision geospatial analysis.

[0064] In one embodiment, S1 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0065] S11: The satellite image data of the target area is acquired synchronously through the GNSS positioning module. The satellite image data is radiometrically calibrated and atmospherically corrected to eliminate radiometric distortion caused by the sensor itself and atmospheric scattering, and to generate pre-processed corrected image data.

[0066] Specifically, the system establishes a data synchronization link with the satellite image receiving terminal through GNSS positioning equipment. During the reception of satellite image data of the target area, GNSS positioning information is linked in real time, so that each frame of satellite imagery is accompanied by corresponding spatial coordinates and timestamp information. The system performs radiometric calibration processing, establishing a correspondence between the sensor output signal and the true radiance of ground objects based on the response characteristics of the satellite sensor, and correcting the radiometric distortion caused by the inconsistency of the sensor's own response through a radiometric calibration model.

[0067] During radiometric calibration, the system uses the sensor's factory-set radiometric calibration coefficients and combines them with attitude and orbital parameters provided by the satellite platform to convert the grayscale value of each pixel in the image into a physically meaningful radiance value. After radiometric calibration, the system performs atmospheric correction processing. Based on an atmospheric transmission model, it analyzes the absorption and scattering of solar radiation by the atmosphere and constructs an atmospheric correction equation to eliminate interference from atmospheric scattering, absorption, and aerosols on the image's radiation signal. During atmospheric correction, the system combines the target area's geographical location and atmospheric condition data corresponding to the imaging time to determine the key parameters required for atmospheric correction. It then iteratively calculates and corrects the radiance value of each band of the image, ultimately generating pre-processed corrected image data to ensure that the image data accurately reflects the actual radiation characteristics of the ground features.

[0068] S12: Synchronously acquire UAV LiDAR point cloud data of the target area through the GNSS positioning module, perform noise reduction and ground point classification processing on the UAV LiDAR point cloud data, filter out flight noise and separate accurate ground elevation information to generate classified LiDAR point cloud data.

[0069] Specifically, the system scans the target area using a LiDAR device mounted on a UAV, while simultaneously synchronizing the LiDAR scan data with spatial location information in real time using GNSS positioning equipment. This ensures that each LiDAR point cloud data set contains corresponding 3D coordinates and timestamp information. The system then performs point cloud denoising processing. Based on the spatial distribution characteristics and statistical features of the point cloud data, a point cloud denoising algorithm is used to filter the acquired LiDAR point cloud data. During the denoising process, the system analyzes the spatial density and distance distribution patterns of the point cloud data, identifying discrete points that deviate from the normal distribution range. These discrete points mainly originate from flight noise generated by equipment vibration and air turbulence interference during flight.

[0070] Furthermore, the system uses algorithms to calculate the spatial relationships between each point in the point cloud data and its neighboring points, eliminating discrete points that do not conform to spatial distribution patterns and retaining valid point cloud data. After denoising, the system performs ground point classification processing. Based on the spatial undulation characteristics of the terrain and the elevation distribution patterns of the point cloud data, a ground point classification algorithm is used to classify the valid point cloud data. During the classification process, the system sets the judgment conditions for ground point identification. By analyzing parameters such as the elevation change rate of the point cloud data and the slope between adjacent points, it distinguishes between ground points and non-ground points, separating information such as vegetation and buildings corresponding to non-ground points, retaining ground point cloud data that can reflect the true surface morphology, and generating classified LiDAR point cloud data.

[0071] S13: The ground sensor data of the target area is synchronously acquired through the GNSS positioning module. The ground sensor data is processed by time synchronization and spatial interpolation. The asynchronous readings are unified to the standard timestamp and a continuous spatial parameter field is generated to generate an environmental parameter matrix.

[0072] Specifically, the system collects environmental parameter data through a ground-based sensor network deployed in the target area. This network includes various types of sensors, each collecting environmental parameters across different dimensions. The system uses GNSS positioning equipment to synchronize data from all ground sensors. This equipment generates a unified standard timestamp, and the system associates the raw data collected by each sensor with this timestamp, performing time calibration on out-of-sync sensor readings. During time synchronization, the system analyzes the acquisition time deviations of each sensor, corrects the acquisition time of the raw data based on the standard timestamp, and unifies all sensor data to the same time reference system, ensuring consistency of data from different sensors across the time dimension.

[0073] After time synchronization is completed, the system performs spatial interpolation processing. Since the ground sensors are discretely deployed, the collected data are discrete spatial point data. Based on the spatial coordinates of the discrete points and the corresponding parameter measurements, the system uses a spatial interpolation algorithm to construct a continuous spatial parameter field. During the spatial interpolation process, the system analyzes the spatial distance relationships and parameter distribution patterns between discrete points. By calculating the parameter estimates for unknown spatial locations, the discrete sensor data is transformed into continuous parameter distribution data covering the entire target area, which is then integrated to form an environmental parameter matrix. This matrix contains environmental parameter information for different spatial locations and time points within the target area.

[0074] S14: Spatial registration and format unification of the corrected image data and classified LiDAR point cloud data are performed to integrate them into a multi-source geographic dataset.

[0075] Specifically, the system calls upon preprocessed corrected image data and classified LiDAR point cloud data to perform spatial registration. A unified spatial coordinate system is used as the registration benchmark. By extracting feature information from the corrected image data and classified LiDAR point cloud data, the spatial correspondence between the two types of data is established. During spatial registration, the system extracts ground feature points from the corrected image data and corresponding terrain feature points from the classified LiDAR point cloud data. A feature point matching algorithm determines the correspondence between feature points in the two types of data. Based on the coordinate differences of the corresponding feature points, a coordinate transformation model is constructed to adjust the coordinates of the corrected image data or classified LiDAR point cloud data, eliminating spatial positional deviations between the two types of data and ensuring consistency in spatial dimensions.

[0076] After spatial registration, the system performs data format unification processing. Following a preset GIS data standard format, the system converts the storage formats of the corrected image data and the classified LiDAR point cloud data, ensuring both types of data use the same file structure and encoding method, thus guaranteeing data format compatibility. After format unification, the system integrates the two types of data based on spatial coordinates and timestamp information, establishing an association index between the data. It associates and stores the ground feature radiation information contained in the corrected image data with the surface elevation information contained in the classified LiDAR point cloud data, forming a multi-source geographic dataset. This provides a structurally unified and spatially consistent foundation of data for subsequent multi-source data fusion processing.

[0077] In one embodiment, step S2 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0078] S21: Perform sliding time window partitioning on the environmental parameter matrix, extract a subset of parameters of a specific time length according to the collected time series for independent analysis, and generate parameter time series segments.

[0079] Specifically, the system first sorts the acquisition time series corresponding to the environmental parameter matrix to ensure that all parameter data are arranged in the order of acquisition, providing an ordered data foundation for sliding time window partitioning. The system then performs sliding time window partitioning, setting window movement rules around the time series. The window slides sequentially along the time series at fixed steps, and after each slide, parameter data within the corresponding time length is extracted as an independent parameter subset. During partitioning, the system maintains a consistent window time length, ensuring that each parameter subset contains the same time span, while avoiding duplicate or omitted parameter data. The system identifies each extracted parameter subset, with the identification information including the window start and end times, and establishes a correlation with the original data in the environmental parameter matrix.

[0080] Through continuous sliding window operations, the system divides the complete environmental parameter time series into multiple continuous and non-overlapping parameter time series segments. Each segment corresponds to a specific time interval and contains measurement data for all environmental parameters within that interval, providing structured data support for subsequent independent analysis of the stability of parameters within each time interval. During the segmentation process, the system verifies the data integrity of each segment in real time, ensuring that there are no missing or abnormally truncated parameter data within each segment. If data loss occurs, the window position is adjusted and the segment is re-trunculated to ensure that all generated parameter time series segments meet the basic requirements for independent analysis.

[0081] S22: Perform statistical feature extraction on the time series segments of the parameters, calculate the mean, variance and number of outliers of the parameters in each segment, and generate parameter stability index.

[0082] Specifically, the system calls each time series segment after the parameter division and performs statistical feature extraction processing on various environmental parameters within each segment. The system iterates through all data for a certain type of environmental parameter within a single segment, sums all data to obtain a total, and calculates the mean of that type of parameter within the segment by the ratio of the total to the number of data points. The mean reflects the central tendency of the parameter within that time interval. Further, the system calculates the variance of that type of parameter by dividing the sum of the squared differences between each data point and the mean by the number of data points. The variance reflects the degree of dispersion of the parameter data within the segment. Simultaneously, the system performs outlier identification, setting outlier judgment rules based on the distribution patterns of the parameter data. By comparing each data point with the distribution range of parameters within the segment, it identifies data exceeding the normal distribution range and counts the number of outliers. The number of outliers reflects abnormalities in the parameter data.

[0083] Preferably, after calculating the mean, variance, and number of outliers for all environmental parameters within each time series segment, the system integrates these three statistical characteristics of various parameters in a preset order to form a parameter stability index for each segment. The system then standardizes the format of the integrated parameter stability index to ensure a unified data structure for stability indices of different segments and parameter types. This facilitates subsequent fuzzy logic inference and comparison, and the stability index comprehensively reflects the distribution characteristics and anomalies of environmental parameters within each time interval.

[0084] S23: Perform fuzzy logic reasoning on the parameter stability index, compare the stability index with the equipment calibration parameter range to assess the degree of credibility, and generate an environmental parameter credibility vector.

[0085] Specifically, the system acquires the equipment calibration parameter range, which is determined based on calibration results under standard equipment conditions and serves as a reference benchmark for reliability assessment. The system compares the parameter stability index corresponding to each parameter's time series segment with the equipment calibration parameter range. This comparison covers three statistical characteristics of the stability index: mean, variance, and number of outliers. The system performs fuzzy logic inference processing to establish a fuzzy inference rule base. This rule base contains the correspondence between different combinations of stability indicators and reliability levels. The rule base is established based on the influence of environmental parameters on the accuracy of collected data, and is formed by analyzing the correlation between parameter stability and data reliability.

[0086] During the inference process, the system inputs the comparison results into the fuzzy inference model. The model performs logical operations based on the deviation of the indicators from the calibrated parameter range, combined with preset rules in the fuzzy rule base, to determine the credibility of various environmental parameters within the corresponding time interval. The system classifies the credibility of various environmental parameters within each parameter's time series segment into levels, and the classification results are quantified numerically to form a credibility value for each parameter type. The system arranges the credibility values ​​of all environmental parameters within each time segment according to parameter type and time segment order, integrating them to form an environmental parameter credibility vector. During vector construction, the system maintains a one-to-one correspondence between credibility values, parameter time series segments, and environmental parameter types, ensuring that each element in the vector clearly points to the quantified credibility result of a certain type of environmental parameter within a specific time interval.

[0087] In one embodiment, step S3 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0088] S31: Sort and partition the environmental parameter confidence vector, divide the confidence values ​​into high and low intervals and assign different basic weight coefficients to generate a basic weight vector.

[0089] Specifically, the system calls the generated environment parameter confidence vector and, based on the numerical relationships of the confidence values ​​in the vector, arranges them in a fixed order to form a complete ordered sequence, ensuring that each confidence value occupies a unique position in the sequence and that the sequence covers all confidence values. The system determines the interval division rules based on the distribution characteristics of the confidence values ​​and determines the interval boundaries by calculating the interval division threshold. The calculation of the division threshold follows the following formula:

[0090]

[0091] in, The threshold value representing the division of the k-th interval. Represents the confidence vector of environmental parameters. This represents the minimum value in the environmental parameter confidence vector. K represents the maximum value in the environmental parameter confidence vector, and K represents the number of preset intervals.

[0092] Furthermore, the system divides the ordered sequence into multiple continuous intervals based on a partitioning threshold, ensuring that each interval fully covers the corresponding numerical range and that the confidence values ​​within each interval have similar distribution characteristics. The system assigns a corresponding basic weight coefficient to each interval, following a positive correlation between the confidence value and the basic weight coefficient, through a mapping function. Implement weight assignment, where Represents the basic weighting coefficient. Represents the confidence value within the interval. This represents a pre-defined positive correlation mapping relationship. The system arranges the basic weight coefficients corresponding to the interval to which each confidence value belongs in the original environmental parameter confidence vector according to the element order, forming a basic weight vector. The dimension of this vector is consistent with that of the environmental parameter confidence vector, and each element corresponds to the basic weight of the corresponding confidence value in the original vector.

[0093] S32: Normalize and reciprocal transform the preset inherent error coefficients of the equipment, convert the error coefficients into accuracy indicators that are positively correlated with the weights, and generate the equipment accuracy coefficients.

[0094] Specifically, the system extracts the inherent error coefficients of each preset data source corresponding to the device. These coefficients are determined based on the device's own characteristics and are used to characterize the error level of different data sources under standard conditions. Each data source corresponds to a unique inherent error coefficient. The system normalizes all inherent error coefficients of the devices to eliminate the dimensional differences between different inherent error coefficients, ensuring that the processed coefficients are within a uniform numerical range. The normalization process follows the formula:

[0095]

[0096] in, This represents the normalized inherent error coefficient of the equipment. This represents the inherent error coefficient of the original equipment. This represents the minimum value among all the inherent error coefficients of the original equipment. This represents the maximum value among all original inherent error coefficients of the equipment. After normalization, the system performs a reciprocal transformation on the normalized inherent error coefficients to establish an inverse correlation between the error coefficients and the accuracy index. The transformation process follows the formula below:

[0097]

[0098] in, This represents the device's accuracy coefficient. Through reciprocal transformation, the smaller the normalized error coefficient, the larger the corresponding device accuracy coefficient, allowing the device accuracy coefficient to directly characterize the device's inherent accuracy level. The system, through the aforementioned continuous processing, transforms the inherent error coefficient, originally representing the error level, into a device accuracy coefficient representing the accuracy level; each data source corresponds to a unique device accuracy coefficient.

[0099] S33: Perform weighted synthesis processing on the basic weight vector and the device accuracy coefficient, merge the two types of coefficients according to a preset ratio and normalize them into a probability distribution to generate a dynamic weight vector.

[0100] Specifically, the system calls a preset fusion ratio, which defines the proportion of the basic weight vector and the device accuracy coefficient in the final weight composition. This ratio is preset based on the actual application needs and data characteristics of geographic information collection. The system performs a weighted synthesis calculation on the basic weight vector and the device accuracy coefficient according to the fusion ratio. The synthesis process follows the formula:

[0101]

[0102] in, Represents the initial fusion coefficient. This represents the preset blending ratio. Represents the elements in the basic weight vector. This represents the device accuracy coefficient of the corresponding data source. Using this formula, the system proportionally calculates and superimposes each element of the basic weight vector with the corresponding device accuracy coefficient of the data source to obtain a preliminary fusion coefficient. This coefficient integrates the basic weight corresponding to the reliability of environmental parameters and the accuracy coefficient corresponding to the inherent accuracy of the device, comprehensively reflecting the overall reliability of the data source under the dual influence of environmental factors and its own characteristics. After synthesis, the system normalizes all preliminary fusion coefficients to ensure that the processed coefficients meet the probability distribution requirements. The normalization process follows the formula below:

[0103]

[0104] in, Represents the normalized dynamic weights. This represents the sum of all initial fusion coefficients. The normalized coefficients form a dynamic weight vector, the dimension of which is consistent with the number of data source types. Each element corresponds to the dynamic weight of a data source, providing the core basis for subsequent weighted fusion of multi-source data and ensuring that the fusion process takes into account both environmental impact and equipment accuracy.

[0105] In one embodiment, step S4 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0106] S41: Perform spatial coordinate registration on multi-source geographic datasets. By matching feature points, the coordinate systems of different data sources are aligned to the same spatial reference system, generating spatially aligned geospatial data.

[0107] Specifically, the system calls upon multi-source geographic datasets and extracts feature points from different data sources. Feature point extraction is based on the spatial structure features and land cover contour information of the data, ensuring that the extracted feature points possess spatial discriminability and stability. The system performs matching processing on the feature points extracted from each data source, determining the correspondence between feature points from different data sources by calculating the spatial topological relationships and attribute similarity of the feature points, and eliminating invalid correspondences generated during the matching process. The system constructs a coordinate transformation model, following the formula:

[0108]

[0109] in, Represents the coordinates in the target space reference frame. The coordinates representing the original data source. Represents the coordinate transformation matrix. This represents the coordinate offset. The system uses the matched feature points to solve for the coordinate transformation matrix and offset, and substitutes the original coordinates of all data sources into the transformation model to perform coordinate transformation, so that the coordinate systems of different data sources are aligned to the same spatial reference frame. After the transformation is completed, the system verifies the spatial overlap of the transformed feature points to confirm the registration effect, ensuring that there is no misalignment or deviation in the spatial position of each data source, and generating spatially aligned geospatial data.

[0110] S42: Perform gridded resampling on spatially aligned geospatial data, interpolating data of different resolutions into a geographic grid of uniform size to generate grid data with consistent scale.

[0111] Specifically, the system determines the geographic grid division rules based on the spatial extent of the target area and the data fusion requirements. These rules must ensure that the geographic grid completely covers the target area, and that the spatial division method is compatible with the accuracy requirements of subsequent fusion calculations. The system divides the target area into continuous and non-overlapping geographic grids according to the division rules, forming a unified grid framework. The system resamples the spatially aligned geospatial data and uses an interpolation algorithm to interpolate the original data at different resolutions into a geographic grid of uniform size. The interpolation process follows the formula below:

[0112]

[0113] in, The interpolation result representing the grid cell. The interpolation weights represent the interpolation weights corresponding to the original data points. The attribute value representing the original data point. This represents the number of original data points involved in the interpolation calculation of a single grid cell. The system uses this formula to calculate the attribute value of each grid cell, converting data at different resolutions into grid data with a consistent scale. During the resampling process, the system maintains the spatial distribution and attribute characteristics of the original data, avoiding data distortion and ensuring that the generated grid data is completely uniform in spatial scale, meeting the scale requirements for subsequent weighted fusion.

[0114] S43: Perform weighted fusion processing on grid data with consistent scale and dynamic weight vector, and perform pixel-level fusion calculation based on the weight values ​​of each data source in the grid cell to generate a fused geographic information matrix.

[0115] Specifically, the system calls upon grid data of consistent scale and dynamic weight vectors to establish a correspondence between grid cells and dynamic weights, ensuring that each grid cell can be associated with the corresponding weight value from each data source. The system performs pixel-level weighted fusion calculations, performing fusion operations on each grid cell based on the weight values ​​from each data source, using the following formula:

[0116]

[0117] in, The fusion result representing a single grid cell. This represents the dynamic weight value of the k-th data source in that grid cell. This represents the attribute value of the k-th data source in that grid cell. This represents the total number of data sources participating in the fusion. The system performs the fusion operation on all grid cells sequentially in spatial order, overlaying and integrating the attribute information of each data source according to its corresponding weights. During the fusion process, the system maintains the spatial index of the grid cells to ensure that the fusion result accurately corresponds to the spatial location of the target area. After the fusion calculation of all grid cells is completed, the system organizes the fusion results of each grid cell in spatial order to form a fused geographic information matrix, where each element corresponds to the comprehensive geographic information of a grid cell.

[0118] In one embodiment, step S5 of the geographic information collection method based on GIS technology provided by the present invention specifically includes the following steps:

[0119] S51: Perform verification benchmark point extraction processing on the fused geographic information matrix, extract local matrix data at the control point locations with known precise coordinates, and generate a benchmark point sample set.

[0120] Specifically, the system calls the generated fused geographic information matrix and simultaneously retrieves control point data with known precise coordinates to establish a spatial relationship between the two types of data. The system uses spatial indexing technology to determine the corresponding position of each control point in the fused geographic information matrix, and sets local matrix truncation rules based on this position. These truncation rules are defined by the following formula:

[0121]

[0122] in, Represents the spatial extent of the local matrix. and These represent the planar coordinate components of the control point. This parameter represents the extended range of the local matrix. The system extracts local matrix data for the corresponding region from the fused geographic information matrix according to this spatial range, ensuring that the local matrix data completely includes the geographic information surrounding the control point and avoiding missing sample information due to incomplete data extraction. The system performs structured processing on the extracted local matrix data, extracting core data from the local matrix corresponding to each control point as samples. The core data consists of the matrix elements corresponding to the control point coordinates and surrounding related elements; the selection of related elements is based on spatial adjacency. The system organizes the sample data corresponding to all control points in order of control point number, establishing a one-to-one correspondence between each sample data and its corresponding known precise coordinate information, ultimately generating a benchmark point sample set. This sample set provides direct data support for subsequent spatial distortion calculations, ensuring that distortion calculations can be verified based on real control points.

[0123] S52: Calculate the spatial distortion of the reference point sample set, compare the Euclidean distance between the sample coordinate values ​​and the true coordinate values, and generate a spatial distortion vector.

[0124] Specifically, the system calls the benchmark sample set, extracting the coordinate values ​​corresponding to each sample one by one, while simultaneously retrieving the known precise coordinate values ​​for each sample, ensuring that the order of calling the two types of coordinate data is consistent. The system performs dimension matching processing on the sample coordinate values ​​and the known precise coordinate values ​​to ensure that the number of coordinate components in both types of coordinate values ​​is completely consistent, and that the coordinate reference system remains unified, avoiding calculation errors caused by differences in dimension or reference system. The system uses the Euclidean distance formula to calculate the spatial distortion of each sample, the formula being:

[0125]

[0126] in, Represents the spatial distortion of a single sample. Represents the number of coordinate components. The i-th component represents the sample coordinate value. This represents the i-th component with known precise coordinates. The system calculates spatial distortion for each sample sequentially according to the order of the reference point sample set, and arranges all calculated spatial distortion values ​​in the order of their corresponding samples to form a spatial distortion vector. The dimension of the spatial distortion vector is consistent with the number of samples in the reference point sample set, and each element corresponds to the spatial distortion value of one sample. This vector fully reflects the spatial distortion of the fused geographic information matrix at each control point location.

[0127] S53: Perform multiple regression analysis on the spatial distortion vector and the environmental parameter credibility vector, establish a compensation model for distortion and credibility, and reverse-correct the system parameters to generate an optimized geographic information database.

[0128] Specifically, the system calls the spatial distortion vector and the environmental parameter confidence vector, performs data preprocessing on both types of vectors, removes missing terms, and ensures that the data lengths of the two types of vectors are completely consistent, meeting the data requirements of multiple regression analysis. The system uses multiple regression analysis to establish a compensation model between spatial distortion and environmental parameter confidence. The regression model formula is as follows:

[0129]

[0130] in, Represents spatial distortion. Represents the regression constant term. Represents the number of environmental parameters. The regression coefficient representing the reliability of the j-th environmental parameter. This represents the confidence value of the j-th environmental parameter. This represents the random error term. The system determines the coefficients of each term in the regression model through mathematical operations, enabling the model to accurately describe the influence of environmental parameter reliability on spatial distortion and ensuring that the model's fit conforms to the data patterns. Based on the established compensation model, the system derives the correction amounts for the system parameters using the following formula:

[0131]

[0132] in, This represents the correction amount for system parameters. This represents the estimated value of the regression coefficient. This represents the confidence vector of environmental parameters. The system applies the correction to the original system parameters, performing a global correction on the fused geographic information matrix. The correction process is executed element by element to ensure that each element receives corresponding error compensation.

[0133] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0134] Based on the same inventive concept, this application also provides a GIS-based geographic information acquisition device for implementing the above-mentioned GIS-based geographic information acquisition method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more GIS-based geographic information acquisition device embodiments provided below can be found in the limitations of the GIS-based geographic information acquisition method described above, and will not be repeated here.

[0135] Preferably, such as Figure 2 As shown, the present invention provides a geographic information acquisition device 600 based on GIS technology, which is configured with the following modules:

[0136] The multi-source geographic data acquisition module 610 is used to acquire multi-source geographic data of the target area. It synchronously acquires satellite imagery data, UAV LiDAR point cloud data and ground sensor data through the GNSS positioning module to generate a multi-source geographic dataset and environmental parameter matrix.

[0137] The environmental parameter credibility assessment module 620 is used to perform dynamic credibility assessment on the environmental parameter matrix, calculate the real-time credibility score based on the deviation between the environmental parameter values ​​and the equipment calibration parameter values, and generate an environmental parameter credibility vector.

[0138] The adaptive weight allocation module 630 is used to adaptively allocate weights to the environmental parameter confidence vector, calculate the weight quotient by combining the preset device inherent error coefficients and perform normalization processing to generate a dynamic weight vector.

[0139] The geographic data spatiotemporal fusion module 640 is used to perform spatiotemporal fusion of multi-source geographic datasets and dynamic weight vectors. After eliminating spatial differences through coordinate registration, it performs weighted fusion calculation to generate a fused geographic information matrix.

[0140] The closed-loop distortion calibration module 650 is used to perform closed-loop distortion calibration on the fused geographic information matrix. It calculates the spatial distortion degree based on the verification benchmark point with known precise coordinates and corrects the system error using the environmental parameter confidence vector, generating an optimized geographic information database for high-precision geospatial analysis.

[0141] Preferably, the multi-source geographic data acquisition module 610 provided in this application is configured with the following units:

[0142] The satellite image preprocessing unit is used to synchronously acquire satellite image data of the target area through the GNSS positioning module, perform radiometric calibration and atmospheric correction on the satellite image data, eliminate radiometric distortion caused by the sensor itself and atmospheric scattering, and generate preprocessed corrected image data.

[0143] The LiDAR point cloud classification unit is used to synchronously acquire UAV LiDAR point cloud data of the target area through the GNSS positioning module, perform noise reduction and ground point classification processing on the UAV LiDAR point cloud data, filter out flight noise and separate accurate ground elevation information to generate classified LiDAR point cloud data.

[0144] The ground sensor data processing unit is used to synchronously acquire ground sensor data of the target area through the GNSS positioning module, perform time synchronization and spatial interpolation processing on the ground sensor data, unify asynchronous readings to a standard timestamp and generate a continuous spatial parameter field, and generate an environmental parameter matrix.

[0145] The geographic data integration unit is used to spatially register and unify the format of corrected image data and classified LiDAR point cloud data, and integrate them into a multi-source geographic dataset.

[0146] Preferably, the environmental parameter reliability assessment module 620 provided in this application is configured with the following units:

[0147] The parameter window partitioning unit is used to perform sliding time window partitioning on the environmental parameter matrix, extracting a subset of parameters of a specific time length according to the collected time series for independent analysis, and generating parameter time series segments.

[0148] The statistical feature extraction unit is used to perform statistical feature extraction processing on parameter time series segments, calculate the mean, variance and number of outliers of the parameters in each segment, and generate parameter stability index.

[0149] The credibility fuzzy inference unit is used to perform fuzzy logic inference processing on the parameter stability index, compare the stability index with the equipment calibration parameter range to evaluate the credibility level, and generate an environmental parameter credibility vector.

[0150] Preferably, the adaptive weight allocation module 630 provided in this application is configured with the following units:

[0151] The weight partitioning assignment unit is used to sort and partition the environmental parameter confidence vector, divide the confidence values ​​into high and low intervals and assign different basic weight coefficients to generate a basic weight vector.

[0152] The error coefficient conversion unit is used to normalize and reciprocal transform the preset inherent error coefficients of the equipment, convert the error coefficients into accuracy indicators that are positively correlated with the weights, and generate the equipment accuracy coefficients.

[0153] The dynamic weight synthesis unit is used to perform weighted synthesis processing on the basic weight vector and the device accuracy coefficient. It merges the two types of coefficients according to a preset ratio and normalizes them into a probability distribution to generate a dynamic weight vector.

[0154] Preferably, the geographic data spatiotemporal fusion module 640 provided in this application is configured with the following units:

[0155] The spatial coordinate registration unit is used to perform spatial coordinate registration processing on multi-source geographic datasets. By matching feature points, the coordinate systems of different data sources are aligned to the same spatial reference system, generating spatially aligned geospatial data.

[0156] The gridded resampling unit is used to perform gridded resampling processing on spatially aligned geospatial data, interpolating data of different resolutions into a geographic grid of uniform size to generate grid data with consistent scale.

[0157] The grid data weighted fusion unit is used to perform weighted fusion processing on grid data of consistent scale and dynamic weight vector. It performs pixel-level fusion calculation based on the weight values ​​of each data source in the grid unit to generate a fused geographic information matrix.

[0158] Preferably, the closed-loop distortion calibration module 650 provided in this application is configured with the following units:

[0159] The benchmark extraction unit is used to perform benchmark extraction processing on the fused geographic information matrix. It extracts local matrix data at the control point locations with known precise coordinates and generates a benchmark sample set.

[0160] The spatial distortion calculation unit is used to calculate the spatial distortion of the reference point sample set, compare the Euclidean distance between the sample coordinate values ​​and the true coordinate values, and generate a spatial distortion vector.

[0161] The regression analysis correction unit is used to perform multivariate regression analysis on the spatial distortion vector and the environmental parameter confidence vector, establish a compensation model for distortion and confidence, and reverse correct the system parameters to generate an optimized geographic information database.

[0162] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described geographic information collection method based on GIS technology.

[0163] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described geographic information collection method based on GIS technology.

[0164] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0165] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A geographic information acquisition method based on GIS technology, characterized in that, Includes the following steps: S1: Collect multi-source geographic data for the target area. Simultaneously acquire satellite imagery data, UAV LiDAR point cloud data, and ground sensor data through the GNSS positioning module to generate a multi-source geographic dataset and environmental parameter matrix. S2: Perform dynamic reliability assessment on the environmental parameter matrix, calculate real-time reliability score based on the deviation between environmental parameter values ​​and equipment calibration parameter values, and generate an environmental parameter reliability vector; wherein, the environmental parameter values ​​are used to indicate the environmental measurement parameters measured by the ground sensors in the environmental parameter matrix during the actual data acquisition process; the equipment calibration parameter values ​​are used to indicate the calibration parameter values ​​obtained by calibrating each environmental sensor in a standard laboratory environment; S3: Adaptively assign weights to the environmental parameter confidence vector, calculate the weight quotient by combining the preset device inherent error coefficients and normalize it to generate a dynamic weight vector; S4: Perform spatiotemporal fusion on the multi-source geographic dataset and the dynamic weight vector, eliminate spatial differences through coordinate registration, and then perform weighted fusion calculation to generate a fused geographic information matrix; S5: Perform closed-loop distortion calibration on the fused geographic information matrix, calculate the spatial distortion degree based on the verification benchmarks with known precise coordinates, and correct the system error using the environmental parameter confidence vector to generate an optimized geographic information database for high-precision geospatial analysis; wherein, the known precise coordinates are obtained by uniformly distributing verification benchmarks within the target area, covering the edge area, the central area, and typical feature areas, and using GNSS static measurement technology to obtain the precise coordinates of the verification benchmarks; The multi-source geographic dataset and the environmental parameter matrix are obtained through the following steps: S11: Simultaneously acquire satellite image data of the target area through the GNSS positioning module, perform radiometric calibration and atmospheric correction on the satellite image data, eliminate radiometric distortion caused by sensor itself and atmospheric scattering, and generate pre-processed corrected image data. S12: Synchronously acquire UAV LiDAR point cloud data of the target area through the GNSS positioning module, perform noise reduction and ground point classification processing on the UAV LiDAR point cloud data, filter out flight noise and separate accurate ground elevation information to generate classified LiDAR point cloud data. S13: The ground sensor data of the target area is synchronously acquired through the GNSS positioning module. The ground sensor data is processed by time synchronization and spatial interpolation. The asynchronous readings are unified to the standard timestamp and a continuous spatial parameter field is generated to generate an environmental parameter matrix. S14: Spatial registration and format unification are performed on the corrected image data and the classified LiDAR point cloud data to form a multi-source geographic dataset; The fused geographic information matrix is ​​calculated through the following steps: S41: Perform spatial coordinate registration processing on the multi-source geographic dataset, and match the coordinate systems of different data sources to the same spatial reference system through feature point matching to generate spatially aligned geospatial data. S42: Perform gridded resampling on the spatially aligned geospatial data, interpolating data of different resolutions into a geographic grid of uniform size to generate grid data with consistent scale. S43: Perform weighted fusion processing on the grid data and the dynamic weight vector with consistent scale, and perform pixel-level fusion calculation based on the weight values ​​of each data source in the grid cell to generate a fused geographic information matrix.

2. The method according to claim 1, characterized in that, S2 includes: S21: Perform sliding time window division on the environmental parameter matrix, extract a subset of parameters of a specific time length according to the collected time series for independent analysis, and generate parameter time series segments; S22: Perform statistical feature extraction processing on the time series segments of the parameters, calculate the mean, variance and number of outliers of the parameters in each segment, and generate parameter stability index; S23: Perform fuzzy logic reasoning on the stability index of the parameters, compare the stability index with the range of equipment calibration parameters to evaluate the degree of credibility, and generate an environmental parameter credibility vector.

3. The method according to claim 1, characterized in that, S3 includes: S31: Sort and partition the environmental parameter confidence vector, divide the confidence values ​​into high and low intervals and assign different basic weight coefficients to generate a basic weight vector. S32: Normalize and reciprocal transform the preset inherent error coefficients of the equipment to convert the error coefficients into accuracy indicators that are positively correlated with the weights, and generate the equipment accuracy coefficients. S33: Perform weighted synthesis processing on the basic weight vector and the device accuracy coefficient, merge the two types of coefficients according to a preset ratio and normalize them into a probability distribution to generate a dynamic weight vector.

4. The method according to any one of claims 1-3, characterized in that, S5 includes: S51: Perform verification benchmark point extraction processing on the fused geographic information matrix, extract local matrix data at the control point positions with known precise coordinates, and generate a benchmark point sample set; S52: Perform spatial distortion calculation on the reference point sample set, compare the Euclidean distance between the sample coordinate values ​​and the true coordinate values, and generate a spatial distortion vector. S53: Perform multivariate regression analysis on the spatial distortion vector and environmental parameter credibility vector, establish a compensation model for distortion and credibility, and reverse-correct the system parameters to generate an optimized geographic information database.

5. A geographic information acquisition device based on GIS technology, characterized in that, The device includes: The multi-source geographic data acquisition module is used to acquire multi-source geographic data of the target area. It synchronously acquires satellite imagery data, UAV LiDAR point cloud data and ground sensor data through the GNSS positioning module to generate multi-source geographic datasets and environmental parameter matrices. The environmental parameter reliability assessment module is used to perform dynamic reliability assessment on the environmental parameter matrix, calculate a real-time reliability score based on the deviation between the environmental parameter values ​​and the equipment calibration parameter values, and generate an environmental parameter reliability vector; wherein, the environmental parameter values ​​are used to indicate the environmental measurement parameters measured by the ground sensors in the environmental parameter matrix during the actual data acquisition process; the equipment calibration parameter values ​​are used to indicate the calibration parameter values ​​obtained by calibrating each environmental sensor in a standard laboratory environment; An adaptive weight allocation module is used to adaptively allocate weights to the environmental parameter confidence vector, calculate the weight quotient by combining the preset device inherent error coefficients and perform normalization processing to generate a dynamic weight vector. The geographic data spatiotemporal fusion module is used to perform spatiotemporal fusion of the multi-source geographic dataset and the dynamic weight vector. After eliminating spatial differences through coordinate registration, it performs weighted fusion calculation to generate a fused geographic information matrix. The closed-loop distortion calibration module is used to perform closed-loop distortion calibration on the fused geographic information matrix. It calculates the spatial distortion degree based on the verification benchmark point with known precise coordinates and corrects the system error using the environmental parameter confidence vector, thereby generating an optimized geographic information database for high-precision geospatial analysis. The known precise coordinates are obtained by uniformly distributing verification benchmark points within the target area, covering the edge area, the central area, and typical feature areas, and using GNSS static measurement technology. The multi-source geographic dataset and the environmental parameter matrix are obtained through the following steps: Satellite imagery data of the target area is acquired synchronously through the GNSS positioning module. Radiometric calibration and atmospheric correction are performed on the satellite imagery data to eliminate radiometric distortion caused by the sensor itself and atmospheric scattering, and to generate pre-processed corrected imagery data. The UAV LiDAR point cloud data of the target area is acquired synchronously through the GNSS positioning module. The UAV LiDAR point cloud data is then processed for noise reduction and ground point classification. Flight noise is filtered out and accurate ground elevation information is separated to generate classified LiDAR point cloud data. Ground sensor data of the target area is acquired synchronously through the GNSS positioning module. The ground sensor data is then processed by time synchronization and spatial interpolation to unify the asynchronous readings to a standard timestamp and generate a continuous spatial parameter field, thereby generating an environmental parameter matrix. Spatial registration and format unification are performed on the corrected image data and the classified LiDAR point cloud data to form a multi-source geographic dataset. The fused geographic information matrix is ​​calculated through the following steps: Spatial coordinate registration is performed on the multi-source geographic dataset. By matching feature points, the coordinate systems of different data sources are aligned to the same spatial reference system, generating spatially aligned geospatial data. The spatially aligned geospatial data is subjected to gridded resampling processing, interpolating data of different resolutions into a geographic grid of uniform size to generate grid data with consistent scale. The grid data and the dynamic weight vector with consistent scale are weighted and fused. Pixel-level fusion calculations are performed based on the weight values ​​of each data source in the grid cell to generate a fused geographic information matrix.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

Images