Geographic information data processing method and system based on multi-source data fusion

Abstract

The invention discloses a geographic information data processing method and system based on multi-source data fusion, and relates to the technical field of electrical digital data processing, and the method comprises the following steps: carrying out the analysis processing of preliminarily processed multi-source geographic data, obtaining a first weather feature, carrying out the dynamic correction of the first weather feature, obtaining a second weather feature, and carrying out the processing of the second weather feature; carrying out fusion calculation on the second weather features to obtain target geographic data, unifying remote sensing image data and GIS vector data coordinates by using a geodetic coordinate system, solving traditional spatial dislocation and tamping data consistency in combination with common control point iterative optimization, and extracting weather data by distinguishing effective ranges of color values, so as to obtain the target geographic data. According to the method, weather characteristics are corrected according to seasonal time periods in combination with historical data of the same period, interference is avoided, the recognition precision is improved, the target geographic data are ensured to be complete and controllable through space alignment, data screening and integrity check, and the high-precision requirements of smart cities, disaster early warning and land planning are met.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a geographic information data processing method and system based on multi-source data fusion. Background Technology

[0002] In recent years, with the rapid development of smart city construction, ecological environment monitoring, natural disaster early warning, and land spatial planning, the application scenarios of geographic information data have been continuously expanding, and the requirements for its accuracy, timeliness, and multi-dimensional correlation have become increasingly stringent. As the core support of geographic information systems, digital data processing technology directly determines the reliability and application value of geographic information services. Currently, geographic information data mainly comes from multiple sources, including remote sensing imagery and GIS vector data. Among them, remote sensing imagery data has the advantage of macro-dynamic monitoring, while GIS vector data has precise spatial topological attributes. The fusion of the two can effectively make up for the limitations of single-source data. Therefore, multi-source geographic data fusion has become the core development direction in the field of geographic information, especially in scenarios such as cross-time period geographic change analysis and terrain assessment under complex weather conditions, which places higher demands on the collaborative processing capabilities of multi-source data.

[0003] However, existing multi-source geographic data processing technologies still have many problems that urgently need to be solved: First, multi-source data often suffers from spatial misalignment due to inconsistent original coordinate systems, and the number of common control points is mostly fixed, making it impossible to adjust the number through iterative optimization to reduce coordinate transformation errors, which directly affects the spatial consistency of subsequent data fusion. Second, in the process of obtaining weather features, the judgment relies solely on a single color value, failing to effectively distinguish invalid data such as abnormal color values ​​and non-sky area color values, and lacking a dynamic correction mechanism that combines historical data from the same period and seasonal and time-period differences, resulting in the accuracy of weather feature extraction being greatly affected by environmental interference. Third, the multi-source data fusion stage lacks an alignment mechanism centered on a unified spatial benchmark, and there is a lack of strict screening of the associated data of geographic coordinates, weather features, and terrain information, making it easy for the generated geographic data to have excessive errors and missing key elements, which is difficult to meet the needs of high-precision geographic information applications. Summary of the Invention

[0004] The technical problem addressed by this invention is that existing multi-source geographic data processing technologies still have many unresolved issues: First, multi-source data often suffers from spatial misalignment due to inconsistent original coordinate systems, and the number of common control points is often fixed, making it impossible to reduce coordinate transformation errors through iterative optimization, directly affecting the spatial consistency of subsequent data fusion. Second, in obtaining weather features, relying solely on a single color value for judgment fails to effectively distinguish invalid data such as abnormal color values ​​and non-sky area color values, and lacks a dynamic correction mechanism that incorporates historical data from the same period and seasonal and time-period differences, resulting in significant environmental interference affecting the accuracy of weather feature extraction. Third, the multi-source data fusion stage lacks an alignment mechanism centered on a unified spatial benchmark, and lacks rigorous screening of associated data on geographic coordinates, weather features, and terrain information, making the final generated geographic data prone to excessive errors and missing key elements, failing to meet the needs of high-precision geographic information applications.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: A geographic information data processing method based on multi-source data fusion includes the following steps: Step S100: Analyze and process the pre-processed multi-source geographic data to obtain the first weather feature, and dynamically correct the first weather feature to obtain the second weather feature; Step S200: Perform fusion calculation on the second weather feature to obtain target geographic data.

[0006] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the method includes: preliminary processing of the multi-source geographic data, wherein the preliminary processing specifically includes: The multi-source geographic data includes remote sensing image data of the object to be analyzed and GIS vector data of the object to be analyzed. Perform coordinate transformation on the remote sensing image data and GIS vector data; The coordinate transformation logic includes: Select common control points of the remote sensing image data and common control points of the GIS vector data. The common control points of the remote sensing image data are n non-collinear points within the geographic range of the remote sensing image data, and the common control points of the GIS vector data are p corner points on the boundary of the geographic range of the GIS vector data. Furthermore, both n and p are positive integers greater than or equal to 3; Using a geodetic coordinate system, remote sensing image data and GIS vector data from multi-source geographic data are uniformly converted to the geodetic coordinate system. Based on the common control points, a coordinate mapping relationship is established through an affine transformation algorithm to achieve coordinate transformation. The remote sensing image data after coordinate transformation includes the first topographic feature information of the area to be analyzed; The GIS vector data after coordinate transformation includes the second terrain feature information of the area to be analyzed; The mapping relationship includes a first mapping relationship and a second mapping relationship; The first mapping relationship is the mapping relationship between the original coordinate system of the remote sensing image data and the geodetic coordinate system of the remote sensing image data; The second mapping relationship is the mapping relationship between the original coordinate system of GIS vector data and the geodetic coordinate system of GIS vector data.

[0007] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, wherein: for the number n and p of the common control points, iterative optimization is performed by increasing the number of common control points; The conditions for iterative optimization include: When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is greater than or equal to the first value, n and p are increased by 1 respectively based on the current values, and the newly added common control point is introduced into the coordinate mapping relationship. The coefficients in the coordinate mapping relationship are adaptively modified so that the input and output values ​​conform to the mathematical relationship of affine transformation. When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is less than the first value, the iteration of the values ​​of n and p is stopped. The first value represents the maximum straight-line distance between the converted common control point and the actual coordinate point.

[0008] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the process of adding timestamps to the preliminarily processed multi-source geographic data and performing analysis includes: The pre-processed multi-source geographic data consists of remote sensing image data and GIS vector data after coordinate transformation. After adding timestamps to the pre-processed multi-source geographic data, remote sensing image data is selected from the multi-source geographic data as the source for weather type feature extraction. The weather type characteristics include sunny weather characteristics, cloudy weather characteristics, partly cloudy weather characteristics, and precipitation characteristics; The effective weather data is extracted based on the color values ​​of pixels in the remote sensing image data. The extraction logic includes: When the color value is within the first valid value range, the corresponding color information is determined to be valid data and extracted. The color information is the parameter information corresponding to the color value, and the parameter information includes hue, saturation and brightness. When the color value exceeds the second valid value range, the corresponding color information is determined to be invalid data and is not extracted. The color information refers to the parameter information corresponding to abnormal color values, non-sky area color values, and distorted color values ​​caused by blurring or occlusion of remote sensing image data. The second valid value range refers to the range of color values ​​from which valid parameter information cannot be extracted.

[0009] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, wherein: the correlation between extracted color information and weather type features is established; The color parameter range of the association relationship is obtained by dividing the hue value range, saturation value range, and brightness value range within the first valid value range, specifically including: The first color parameter range corresponds to sunny weather characteristics, the second color parameter range corresponds to cloudy weather characteristics, the third color parameter range corresponds to partly cloudy weather characteristics, and the fourth color parameter range corresponds to precipitation characteristics. Based on the aforementioned correlation, the extracted valid color information is matched to the corresponding color parameter range and transformed into the corresponding first weather feature data.

[0010] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the automatic compensation of the first weather feature data based on historical data includes: Obtain color information from historical remote sensing image data of the same period and region, and the corresponding data between the corresponding data and the actual weather type characteristics of the same period and region. By analyzing the deviation pattern between the color parameter range in the corresponding data and the actual weather type characteristics, a compensation coefficient is generated. The color information in the remote sensing image data of the same historical period and the same region is classified according to the weather type characteristics, corresponding to the actual weather type characteristics of the same period and the same region. Calculate the color parameter range extracted under each weather type feature, and based on the deviation between the median of the color parameter range and the median of the standard color parameter corresponding to the actual weather type feature, statistically analyze the distribution frequency and fluctuation amplitude of the deviation value under different seasons and time periods. The seasons include spring, summer, autumn and winter, and the time periods include early morning, morning, noon, afternoon and night. The deviation pattern is that the color parameter range corresponding to sunny weather characteristics shifts towards higher values ​​for brightness parameters during the midday period in summer; the color parameter range corresponding to cloudy weather characteristics shifts towards lower values ​​for saturation parameters during the morning period in winter; and the color parameter range corresponding to precipitation characteristics shifts towards lower values ​​for brightness parameters and higher values ​​for saturation parameters during summer.

[0011] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the first weather feature data is dynamically corrected using the compensation coefficient to obtain the second weather feature data. The dynamic correction includes a first operation and a second operation; The first operation is to match a suitable target compensation coefficient from the generated compensation coefficients based on the weather type characteristics, season, and time period corresponding to the first weather feature data. The second operation is to apply the target compensation coefficient to the color parameter range corresponding to the weather type feature in the first weather feature data, adjust the median of the color parameter range, and make the deviation between the adjusted median and the median of the standard color parameter corresponding to the actual weather type feature fall within a preset allowable range. The compatibility between the adjusted color parameter range and the first weather feature data is judged in real time. If the compatibility does not reach the preset compatibility threshold, the compensation coefficient is updated based on the color information in the latest historical remote sensing image data of the same period and the same region and the corresponding data between the actual weather type features of the same period and the same region. The first and second operations are repeated until the compatibility reaches the preset compatibility threshold to obtain the second weather feature data. The second weather feature parameter includes color parameter intervals corresponding to sunny, cloudy, partly cloudy, and precipitation features after dynamic correction. The deviation between the median of the color parameter interval and the median of the standard color parameter corresponding to the actual weather type feature is within a preset allowable range. It also includes the weather type feature, season, and time period identifier corresponding to the color parameter interval.

[0012] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the method involves fusing and calculating the second weather feature parameters to obtain the target geographic data, specifically including: Using the public control point after geodetic coordinate system transformation as the benchmark, the geographical range corresponding to the second weather feature parameter is aligned with the geographical range of the GIS vector data and remote sensing image data after coordinate transformation, so that the second weather feature parameter under the same geographical coordinate point matches the spatial location of the multi-source geographical data. Based on the season and time period in the second weather feature parameters, the second weather feature parameters are combined and grouped according to the season and time period. Within each group, the weather type features under the same geographic coordinates are matched with the second terrain feature information in the GIS vector data, forming a data pair of geographic coordinates, season, time period, weather type and terrain feature information. Based on the first value, filter the related data pairs that meet the conditions; The conditions include: The associated data pairs correspond to geographic coordinates that are within the overlapping geographic range of the coordinate-transformed remote sensing image data and GIS vector data. The weather type feature in the associated data pair is consistent with the color information and the weather type feature, and the consistent association means that the color parameter range corresponding to the weather type feature is within the first valid value range. The second terrain feature information of the GIS vector data in the associated data pair does not conflict with the original second terrain feature information of the GIS vector data. The lack of conflict means that the second terrain feature information is under the same geographic coordinates, and the error between the second terrain feature information and the original second terrain feature information is less than the first value.

[0013] As a preferred embodiment of the geographic information data processing method based on multi-source data fusion described in this invention, the filtered associated data pairs are arranged in geographic coordinate order to form a structured dataset including geographic coordinate values ​​of the geodetic coordinate system, weather type characteristics, season, time period, and terrain feature parameters. The dataset is then subjected to an integrity check and entries with missing data elements are removed to obtain the target geographic data. The data elements include geographic coordinate values ​​in the geodetic coordinate system, weather type characteristics, season, time period, and terrain feature parameters; The criteria for the integrity check are that each entry in the structured dataset includes complete geodetic coordinates, weather type characteristics, season, time period, and terrain feature parameters.

[0014] The geographic coordinate accuracy of the target geographic data is consistent with the accuracy of the coordinate transformation, the weather type feature accuracy is consistent with the accuracy of the second weather feature parameter, and the overall data error is less than the first value.

[0015] A geographic information data processing system based on multi-source data fusion includes a processing module and a fusion module; The processing module analyzes and processes the pre-processed multi-source geographic data to obtain a first weather feature, and then dynamically corrects the first weather feature to obtain a second weather feature. The fusion module performs fusion calculations on the second weather feature to obtain the target geographic data.

[0016] The beneficial effects of this invention are as follows: By combining geodetic coordinate system-remote sensing image data and GIS vector data coordinates with iterative optimization of public control points, the spatial misalignment problem caused by traditional fixed control points is solved, thus consolidating the foundation for data spatial consistency. Weather data is extracted by distinguishing the effective range of color values, and weather characteristics are dynamically corrected according to seasonal periods by combining historical data from the same period, avoiding feature deviations caused by environmental interference and improving the accuracy of weather type identification. Finally, through spatial alignment, filtering of associated data pairs, and integrity checks, the integrity of target geographic data elements is ensured and the overall error is controllable. This invention can efficiently meet the needs of smart cities, disaster early warning, and land planning scenarios for high-precision geographic information. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a geographic information data processing method based on multi-source data fusion, provided in one embodiment of the present invention.

[0018] Figure 2 This is a basic flowchart of a geographic information data processing system based on multi-source data fusion, provided as an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Example 1, referring to Figure 1 As an embodiment of the present invention, a geographic information data processing method based on multi-source data fusion is provided, comprising the following steps: Step S100: Analyze and process the pre-processed multi-source geographic data to obtain the first weather feature, and dynamically correct the first weather feature to obtain the second weather feature. Step S200: Perform fusion calculation on the second day's weather features to obtain the target geographic data.

[0021] In one embodiment, the pre-processed multi-source geographic data is analyzed to obtain a first weather feature. This first weather feature is then dynamically corrected to obtain a more accurate second weather feature. The second weather feature is then fused and integrated with geographic correlation information from the multi-source geographic data to generate the target geographic data. By extracting and optimizing weather features, the accuracy of weather feature data can be effectively improved, avoiding the impact of deviations in the initial features on subsequent applications. The fusion calculation achieves an effective correlation between weather features and geographic information. The generated target geographic data can meet the needs of regional environmental monitoring and agricultural production planning scenarios for geographic and weather-related data, ensuring the practical value and reliability of the system in real-world applications.

[0022] Preliminary processing of multi-source geographic data is performed, specifically including: Multi-source geographic data includes remote sensing image data of the object to be analyzed and GIS vector data of the object to be analyzed; Perform coordinate transformation between remote sensing image data and GIS vector data; The coordinate transformation logic includes: Select common control points for remote sensing image data and common control points for GIS vector data. The common control points for remote sensing image data are n non-collinear points within the geographic range of the remote sensing image data, and the common control points for GIS vector data are p corner points on the boundary of the geographic range of the GIS vector data. And both n and p are positive integers greater than or equal to 3; Using a geodetic coordinate system, remote sensing image data and GIS vector data from multi-source geographic data are uniformly converted to the geodetic coordinate system. Based on common control points, coordinate mapping relationships are established through affine transformation algorithms to achieve coordinate transformation. The remote sensing image data after coordinate transformation includes the first topographic feature information of the area to be analyzed; The GIS vector data after coordinate transformation includes secondary topographic feature information of the area to be analyzed; The mapping relationship includes the first mapping relationship and the second mapping relationship; The first mapping relationship is the mapping relationship between the original coordinate system of the remote sensing image data and the geodetic coordinate system of the remote sensing image data. The second mapping relationship is the mapping relationship between the original coordinate system of GIS vector data and the geodetic coordinate system of GIS vector data.

[0023] For the number of common control points n and p, iterative optimization is performed by increasing the number of common control points; The conditions for iterative optimization include: When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is greater than or equal to the first value, n and p are increased by 1 respectively based on the current values, and the newly added common control point is introduced into the coordinate mapping relationship. The coefficients in the coordinate mapping relationship are adaptively modified so that the input and output values ​​conform to the mathematical relationship of affine transformation. When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is less than the first value, the iteration of the values ​​of n and p is stopped. The first value represents the maximum straight-line distance between the transformed common control point and the actual coordinate point.

[0024] In one embodiment, preliminary processing of multi-source geographic data is performed, using remote sensing image data (such as satellite remote sensing imagery of a county) and GIS vector data (such as GIS topographic vector map of the county) as the processing objects. The core process revolves around coordinate transformation and iterative optimization. The specific process is as follows: First, a coordinate transformation operation is performed, selecting common control points for the two types of data. The common control points for the remote sensing image data are n non-collinear points within its geographic range (n is a positive integer greater than or equal to 3, for example, selecting 3 evenly distributed road intersections within the county). The common control points for the GIS vector data are p corner points on the boundary of its geographic range (p The coordinates are positive integers greater than or equal to 3 (e.g., selecting three corner vertices of the county boundary). Then, a geodetic coordinate system (e.g., the 2000 geodetic coordinate system) is used to uniformly transform both types of data to this geodetic coordinate system. Based on the aforementioned common control points, an affine transformation algorithm is used to establish mapping relationships (the first mapping relationship is the correspondence between the original coordinate system of the remote sensing image data and the geodetic coordinate system; the second mapping relationship is the correspondence between the original coordinate system of the GIS vector data and the geodetic coordinate system). This completes the coordinate transformation. The transformed remote sensing image data includes the first topographic feature information of the area to be analyzed (e.g., the elevation gradient within the area), and the transformed GIS vector data includes the area to be analyzed. The second terrain feature information of the domain (such as contour line parameters within the region) is used. Simultaneously, iterative optimization is performed on the number of common control points, n and p. The conditions for iterative optimization are set as follows: when the error value between any common control point after coordinate transformation and its corresponding actual coordinate point (such as the coordinates of a road intersection measured in the field) is greater than or equal to a first value, n and p are increased by 1 respectively based on the current values ​​(e.g., n increases from 3 to 4, p increases from 3 to 4), and the newly added common control point is incorporated into the coordinate mapping relationship. The mapping relationship coefficients are adaptively changed to conform to the affine transformation mathematical relationship. Iteration stops when the error value is less than the first value, where the first value is set to 5 meters (i.e., after transformation). The maximum straight-line distance between public control points and actual coordinate points is set based on historical experience in processing multi-source geographic data in the same area, as well as the high-precision requirements of geographic data in smart city and disaster early warning scenarios. It balances accuracy and processing efficiency. In this preliminary processing, the targeted selection of public control points can ensure the geometric stability of coordinate mapping. The iterative optimization mechanism can control the coordinate transformation error within 5 meters, effectively solving the problem of coordinate system differences between remote sensing imagery and GIS vector data, avoiding spatial misalignment in subsequent data processing. At the same time, the retained terrain feature information also provides reliable geographic basic data support for subsequent weather feature extraction and data fusion.

[0025] Adding timestamps to the pre-processed multi-source geographic data and performing analysis includes: The preliminarily processed multi-source geographic data consists of remote sensing image data and GIS vector data after coordinate transformation; After adding timestamps to the pre-processed multi-source geographic data, remote sensing image data is selected from the multi-source geographic data as the source for weather type feature extraction. Weather type characteristics include sunny weather characteristics, cloudy weather characteristics, partly cloudy weather characteristics, and precipitation characteristics; The effective weather data is extracted based on the color values ​​of pixels in remote sensing image data. The extraction logic includes: When the color value is within the first valid value range, the corresponding color information is determined to be valid data and extracted. The color information is the parameter information corresponding to the color value, which includes hue, saturation and brightness. When a color value exceeds the second valid value range, the corresponding color information is determined to be invalid data and is not extracted. Color information refers to the parameter information corresponding to abnormal color values, non-sky area color values, and distorted color values ​​caused by blurring or occlusion of remote sensing image data. The second valid value range refers to the range of color values ​​from which valid parameter information cannot be extracted.

[0026] In one embodiment, timestamps are added and analyzed to the pre-processed multi-source geographic data (i.e., remote sensing image data after geodetic coordinate system transformation, such as a high-resolution satellite remote sensing image of a township in September 2024; and GIS vector data after transformation to the same coordinate system, such as the topographic vector map of the township). The specific process is as follows: First, timestamps are added to the two types of pre-processed multi-source geographic data. The timestamps are set to the hour based on the actual data acquisition time (e.g., remote sensing image data was acquired on 2024-09-10). At 10:00, a corresponding timestamp is added, and the GIS vector data is synchronously linked to the timestamp for that time period. Next, remote sensing image data is selected from multi-source geographic data as the source for weather type feature extraction. The weather type features to be extracted include sunny, cloudy, partly cloudy, and precipitation features. Subsequently, based on the RGB color values ​​of the pixels in the remote sensing image data, valid weather data is extracted. The extraction logic is set as follows: the first valid value range is set to R (180-255), G (180-255), and B (200-255) based on the statistical analysis of pixel colors in historical sunny weather remote sensing images of the same area. When the pixel color value is within this range, the corresponding color information (including hue 180-220°, saturation 20%-40%, and brightness 70%-90%) is determined. The parameter information is considered valid data and extracted. The second valid value range is set as R (0-50), G (0-50), B (0-50) (corresponding to abnormal dark colors) and R (30-80), G (100-180), B (30-80) (corresponding to non-sky areas of ground vegetation). When the pixel color value exceeds this range, the corresponding color information (such as distorted color values ​​caused by cloud cover, parameter information corresponding to color values ​​in non-sky areas) is determined to be invalid data and is not extracted. In this process, the addition of timestamps can achieve accurate correlation between weather features and collection time periods. Based on the logic of valid data extraction with clearly defined color value ranges, invalid interference data can be eliminated, significantly improving the accuracy of subsequent weather type feature identification. At the same time, focusing on remote sensing image data as the extraction source can make full use of its intuitive visual information advantages, laying a reliable data foundation for the subsequent derivation of the first weather feature.

[0027] Establish the correlation between the extracted color information and weather type characteristics; The color parameter ranges for the correlation relationship are derived based on the ranges of hue, saturation, and brightness values ​​within the first valid value range, specifically including: The first color parameter range corresponds to sunny weather characteristics, the second color parameter range corresponds to cloudy weather characteristics, the third color parameter range corresponds to partly cloudy weather characteristics, and the fourth color parameter range corresponds to precipitation characteristics. Based on the correlation, the extracted valid color information is matched to the corresponding color parameter range and transformed into the corresponding first weather feature data.

[0028] In one embodiment, after obtaining valid color information, a correlation between it and weather type characteristics is further established: the color parameter range of this correlation is divided based on the range of hue, saturation, and brightness values ​​within the first valid value range. The values ​​of each range are set according to the statistical results of color parameters of remote sensing images of the same area under different weather conditions in history. The first color parameter range is hue 190-210°, saturation 25%-35%, and brightness 80%-90%, corresponding to clear sky characteristics (matching the color pattern of bright sky and moderate saturation in historical clear sky images). The second color parameter range is hue 180-19... The first color parameter range, with 0°, saturation 20%-25%, and brightness 70%-75%, corresponds to cloudy weather characteristics (consistent with the darker skies and lower saturation in historical cloudy weather images). The second color parameter range, with hue 190-205°, saturation 22%-32%, and brightness 75%-80%, corresponds to partly cloudy weather characteristics (between sunny and cloudy parameters, adapting to the uneven brightness of the sky in partly cloudy weather). The third color parameter range, with hue 180-195°, saturation 30%-38%, and brightness 70%-78%, corresponds to precipitation characteristics (referring to the darker skies and slightly higher saturation in historical precipitation weather images). Finally, based on the above relationships, the extracted effective color information (such as hue 200°, saturation 30%, and brightness 85% of a pixel in a remote sensing image) is matched to the corresponding color parameter range. If it falls into the first color parameter range, it is converted into the first weather feature data corresponding to sunny weather; if it falls into other ranges, it is converted into the first weather feature data corresponding to cloudy, partly cloudy, or precipitation weather.

[0029] Automatic compensation of primary weather characteristic data based on historical data includes: Obtain color information from historical remote sensing image data of the same period and region, and the corresponding data between the data and the actual weather type characteristics of the same period and region. By analyzing the deviation pattern between the color parameter range in the corresponding data and the actual weather type characteristics, a compensation coefficient is generated. The color information in historical remote sensing image data of the same period and the same region is classified according to the weather type characteristics, corresponding to the actual weather type characteristics of the same period and the same region. Calculate the color parameter range extracted under each weather type feature, and based on the deviation between the median of the color parameter range and the median of the standard color parameter corresponding to the actual weather type feature, statistically analyze the distribution frequency and fluctuation range of the deviation value under different seasons and time periods. The seasons include spring, summer, autumn, and winter, and the time periods include the early morning, morning, noon, afternoon, and night. The deviation pattern is as follows: the color parameter range corresponding to sunny weather characteristics shifts towards higher values ​​for brightness parameters during midday in summer; the color parameter range corresponding to cloudy weather characteristics shifts towards lower values ​​for saturation parameters during early morning in winter; and the color parameter range corresponding to precipitation characteristics shifts towards lower values ​​for brightness parameters and higher values ​​for saturation parameters in summer.

[0030] The first weather feature data is dynamically corrected using a compensation coefficient to obtain the second weather feature data; Dynamic correction includes a first operation and a second operation; The first operation is to match the appropriate target compensation coefficient from the generated compensation coefficients based on the weather type characteristics, season and time period corresponding to the first weather feature data. The second operation is to apply the target compensation coefficient to the color parameter range of the corresponding weather type feature in the first weather feature data, adjust the median of the color parameter range, and make the deviation between the adjusted median and the median of the standard color parameter corresponding to the actual weather type feature fall within the preset allowable range. The system continuously assesses the compatibility between the adjusted color parameter range and the first weather feature data. If the compatibility does not reach the preset compatibility threshold, the system updates the compensation coefficient based on the color information in the latest historical remote sensing image data of the same period and region and the corresponding data between the actual weather type features of the same period and region. The system then repeats the first and second operations until the compatibility reaches the preset compatibility threshold, thus obtaining the second weather feature data. The second weather feature parameters include color parameter ranges corresponding to sunny, cloudy, partly cloudy, and precipitation features after dynamic correction. The deviation between the median of the color parameter range and the median of the standard color parameter corresponding to the actual weather type feature is within a preset allowable range. It also includes the weather type feature, season, and time period identifier corresponding to the color parameter range.

[0031] In one embodiment, the first weather feature data is automatically compensated and dynamically corrected based on historical data to obtain the second weather feature data. The specific process is as follows: First, an automatic compensation operation is performed to obtain relevant data from the same historical period and region of the area to be analyzed (e.g., a county). Specifically, the color information of remote sensing images from the same period of the past three years as the data being processed (e.g., if the data being processed is from June 2024, then June of each year from 2021 to 2023 is used) and the corresponding data of the actual weather type characteristics (sunny, cloudy, partly cloudy, and precipitation characteristics) recorded by meteorological stations in the same period and region are selected. After classifying the corresponding data according to the weather type characteristics, the median of the color parameter interval extracted under each weather type is calculated (e.g., median hue of 195°, median saturation of 30%, and median brightness of 85% under the sunny category). This median is then compared with the median of the standard color parameters for that weather type (industry-recognized standard: sunny hue 200°, saturation 30%, brightness 85%; cloudy hue...). The deviation values ​​were obtained by comparing 185°, saturation 22%, and brightness 72%. The distribution frequency and fluctuation range of these deviation values ​​were then statistically analyzed in different seasons (spring March-May, summer June-August, autumn September-November, winter December-February) and different time periods (early morning 0-6 am, morning 6-12 am, noon 12-2 pm, afternoon 2-6 pm, nighttime 6-24 pm). The deviation patterns were analyzed to find that the characteristic color parameter range of sunny days shifted towards higher brightness values ​​at noon in summer (e.g., the median brightness value was 3%-5% higher than the standard), the characteristic of cloudy days shifted towards lower saturation values ​​in the morning in winter (e.g., the median saturation value was 2%-4% lower than the standard), and the characteristic of precipitation shifted towards lower brightness and higher saturation values ​​in summer (e.g., the median brightness value was 4%-6% lower and the median saturation value was 3%-5% higher). Based on this, compensation coefficients were generated (e.g., the brightness compensation coefficient for sunny days at noon in summer was -4%, and the saturation compensation coefficient for cloudy days in the morning in winter was +3%).

[0032] Subsequently, the compensation coefficient is used to dynamically correct the first weather feature data, which includes two steps: The first step is to match a suitable target compensation coefficient (e.g., brightness -4%) from the generated coefficients based on the weather type (e.g., sunny), season (summer), and time period (noon) corresponding to the first weather feature data; The second step is to apply the target compensation coefficient to the color parameter range of the corresponding weather type, adjust the median of the range (e.g., the original median brightness of sunny day is 88%, and after adjustment it is 84%), so that the deviation between the adjusted median and the standard median falls within the preset allowable range (±2%, set according to the error control requirements of historical data processing). At the same time, the adaptability of the adjusted color parameter range with the first weather feature data is judged in real time (adaptability is calculated by data matching degree, with a preset adaptability threshold of 90%, set according to the data accuracy standard). If the adaptability does not reach 90%, the compensation coefficient is updated based on the latest supplemented historical data of the same period and region (e.g., newly added summer noon sunny day data in June 2023), and the above two steps are repeated until the adaptability reaches the standard, and finally the second weather feature data is obtained. The second weather feature data includes corrected color parameter ranges corresponding to various weather types (deviation less than or equal to ±2%), as well as corresponding weather type, season, and time period identifiers. This process mines precise deviation patterns from historical data and combines them with a dynamic iterative correction mechanism to effectively eliminate weather feature deviations caused by seasonal and time period differences, significantly improving the accuracy and reliability of the second weather feature data and providing high-precision weather feature support for subsequent geographic data fusion.

[0033] The target geographic data is obtained by fusing and calculating the second day's weather characteristic parameters, specifically including: Using the public control points after geodetic coordinate system transformation as the benchmark, the geographical range corresponding to the second weather feature parameter is aligned with the geographical range of the GIS vector data and remote sensing image data after coordinate transformation, so that the second weather feature parameter under the same geographical coordinate point is spatially matched with the multi-source geographic data. Based on the season and time period in the second weather feature parameters, the second weather feature parameters are combined and grouped according to the season and time period. Within each group, the weather type features under the same geographic coordinates are matched with the second terrain feature information in the GIS vector data, forming a data pair of geographic coordinates, season, time period, weather type and terrain feature information. Based on the first value, filter the related data pairs that meet the conditions; The conditions include: The geographic coordinates of the associated data pairs are within the overlapping geographic range of the coordinate-transformed remote sensing image data and GIS vector data; The weather type features and color information in the associated data pair are consistent with the weather type features. The consistency of the association means that the color parameter range corresponding to the weather type feature is within the first valid value range. The second terrain feature information of the GIS vector data in the associated data pair does not conflict with the original second terrain feature information of the GIS vector data. The non-conflicting information is under the same geographic coordinates, and the error between the second terrain feature information and the original second terrain feature information is less than the first value.

[0034] The filtered associated data pairs are organized in geographic coordinate order to form a structured dataset that includes geographic coordinate values ​​in the geodetic coordinate system, weather type characteristics, season, time period, and terrain feature parameters. The dataset is then checked for completeness and entries with any missing data element are removed to obtain the target geographic data. Data elements include geographic coordinates in the geodetic coordinate system, weather type characteristics, season, time period, and topographic feature parameters; The criteria for integrity checks are that each entry in the structured dataset includes complete geodetic coordinates, weather type characteristics, season, time period, and terrain feature parameters.

[0035] The geographic coordinate accuracy of the target geographic data is consistent with the accuracy of the coordinate transformation, the weather type feature accuracy is consistent with the accuracy of the second weather feature parameter, and the overall data error is less than the first value.

[0036] In one embodiment, the second weather feature parameters (including dynamically corrected color parameter ranges for sunny, cloudy, partly cloudy, and precipitation, as well as weather type, season, and time period identifiers) are fused and calculated to generate target geographic data. The specific process is as follows: First, using the public control points transformed by a geodetic coordinate system (such as the 2000 National Geodetic Coordinate System) (such as the four road intersections and boundary corners previously selected in a county) as a benchmark, the geographic range corresponding to the second weather feature parameters (such as the weather feature coverage area of ​​the county at noon in June 2024) is spatially aligned with the geographic range of the GIS vector data (including the second terrain feature information, such as contour line parameters) and remote sensing image data transformed by the same coordinate system. This ensures that the spatial location of the second weather feature parameters (such as the sunny weather feature at noon in summer at this point) and the multi-source geographic data is accurately matched at the same geographic coordinate point (such as the location of 118.5°E, 32.3°N within the county), avoiding spatial misalignment.

[0037] Next, based on the season (e.g., spring, March-May; summer, June-August) and time period (e.g., 0-6 AM; 12-2 PM) in the second weather feature parameters, group them by season + time period combination (e.g., summer + noon group; winter + morning group). Within each group, match the weather type features under the same geographic coordinates (e.g., the sunny weather feature of a certain coordinate point) with the second terrain feature information in the GIS vector data (e.g., the altitude of 250 meters and the contour line density of the coordinate point) one by one, forming a correlation data pair of geographic coordinates-season-time period-weather type-terrain feature (e.g., 118.5°E, 32.3°N - summer - noon - sunny weather - altitude 250 meters).

[0038] Subsequently, based on the first value (the maximum straight-line distance between the converted public control point and the actual coordinate point, set to 5 meters according to historical experience in processing the same area), the associated data pairs that meet the following three conditions are selected: First, the geographic coordinates corresponding to the associated data pairs are within the overlapping geographic range of the converted remote sensing image and GIS vector data (i.e., the coordinates are covered by both types of data at the same time); second, the color parameter range corresponding to the weather type feature in the associated data pairs is within the first valid value range (previously set to RGB R180-255, G180-255, B200-255), ensuring that the correlation between weather type and color information is consistent; third, under the same geographic coordinates, the error between the second terrain feature information (e.g., altitude 250 meters) of the GIS vector data in the associated data pairs and the original second terrain feature information (e.g., actual measured altitude 252 meters) is less than 5 meters, ensuring that the terrain data is conflict-free.

[0039] Finally, the filtered associated data were organized in latitude and longitude order to form a structured dataset including geographic coordinates, weather type characteristics, season, time period, and terrain feature parameters. This dataset underwent a completeness check, with the criterion being that each entry fully includes all five data elements. Entries lacking any element (such as missing time period identifiers) were removed, resulting in the target geographic data. The geographic coordinate accuracy of this target geographic data is consistent with the previous coordinate transformation accuracy (error less than or equal to 5 meters), the weather type feature accuracy is consistent with the second day's weather feature parameter accuracy (median color parameter deviation from the standard median is less than or equal to ±2%), and the overall data error is less than 5 meters.

[0040] This fusion computing process ensures data location accuracy through spatial alignment, eliminates invalid and conflicting data through multi-condition filtering, and ensures data standardization through integrity checks. The final generated target geographic data can accurately associate geographic coordinates, weather, topography, and spatiotemporal information, which can directly meet the needs of agricultural production planning (such as crop irrigation planning in areas with sunny weather at noon in summer) and regional environmental monitoring (such as humidity monitoring in low-altitude areas with cloudy weather in winter), effectively improving the practical value and application accuracy of geographic information data.

[0041] Example 2, refer to Figure 2 In another embodiment of the present invention, which differs from the first embodiment, a geographic information data processing system based on multi-source data fusion is provided, including a processing module and a fusion module; The processing module analyzes and processes the pre-processed multi-source geographic data to obtain the first weather feature, and then dynamically corrects the first weather feature to obtain the second weather feature. The fusion module performs fusion calculations on the second day's weather features to obtain the target geographic data.

[0042] In one embodiment, the processing module and the fusion module work together to process and fuse multi-source geographic data to output target geographic data that meets the requirements. The core function of the processing module is to analyze and process the initially processed multi-source geographic data, extracting a first weather feature and then dynamically correcting it to obtain a more accurate second weather feature. The fusion module takes the second weather feature output by the processing module, performs fusion calculations, and integrates the second weather feature with the geographic correlation information in the multi-source geographic data to finally generate the target geographic data. Through the extraction and optimization of weather features by the processing module, the accuracy of weather feature data can be effectively improved, avoiding the impact of deviations in the initial features on subsequent applications. The fusion calculations of the fusion module achieve effective correlation between weather features and geographic information. The generated target geographic data can meet the needs of regional environmental monitoring and agricultural production planning scenarios for geographic and weather-related data, ensuring the practical value and reliability of the system in actual applications.

[0043] This invention solves the spatial misalignment problem caused by traditional fixed control points by combining geodetic coordinate system-remote sensing image data and GIS vector data coordinates with iterative optimization of public control points, thus consolidating the foundation for data spatial consistency. By distinguishing the effective range of color values ​​to extract weather data, and combining historical data from the same period to dynamically correct weather characteristics according to seasonal time periods, it avoids feature deviations caused by environmental interference and improves the accuracy of weather type identification. Finally, through spatial alignment, filtering of associated data pairs, and integrity checks, it ensures the integrity of target geographic data elements and that the overall error is controllable. It can efficiently meet the needs of smart cities, disaster early warning, and land planning scenarios for high-precision geographic information.

[0044] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A geographic information data processing method based on multi-source data fusion, characterized in that, Includes the following steps: Step S100: Analyze and process the pre-processed multi-source geographic data to obtain the first weather feature, and dynamically correct the first weather feature to obtain the second weather feature; Step S200: Perform fusion calculation on the second weather feature to obtain target geographic data.

2. The geographic information data processing method based on multi-source data fusion as described in claim 1, characterized in that: Preliminary processing of multi-source geographic data is performed, specifically including: The multi-source geographic data includes remote sensing image data of the object to be analyzed and GIS vector data of the object to be analyzed. Perform coordinate transformation on the remote sensing image data and GIS vector data; The coordinate transformation logic includes: Select common control points of the remote sensing image data and common control points of the GIS vector data. The common control points of the remote sensing image data are n non-collinear points within the geographic range of the remote sensing image data, and the common control points of the GIS vector data are p corner points on the boundary of the geographic range of the GIS vector data. Furthermore, both n and p are positive integers greater than or equal to 3; Using a geodetic coordinate system, remote sensing image data and GIS vector data from multi-source geographic data are uniformly converted to the geodetic coordinate system. Based on the common control points, a coordinate mapping relationship is established through an affine transformation algorithm to achieve coordinate transformation. The remote sensing image data after coordinate transformation includes the first topographic feature information of the area to be analyzed; The GIS vector data after coordinate transformation includes the second terrain feature information of the area to be analyzed; The mapping relationship includes a first mapping relationship and a second mapping relationship; The first mapping relationship is the mapping relationship between the original coordinate system of the remote sensing image data and the geodetic coordinate system of the remote sensing image data; The second mapping relationship is the mapping relationship between the original coordinate system of GIS vector data and the geodetic coordinate system of GIS vector data.

3. The geographic information data processing method based on multi-source data fusion as described in claim 2, characterized in that: For the number of common control points n and p, iterative optimization is performed by increasing the number of common control points; The conditions for iterative optimization include: When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is greater than or equal to the first value, n and p are increased by 1 respectively based on the current values, and the newly added common control point is introduced into the coordinate mapping relationship. The coefficients in the coordinate mapping relationship are adaptively modified so that the input and output values ​​conform to the mathematical relationship of affine transformation. When the error between any common control point after coordinate transformation and its corresponding actual coordinate point is less than the first value, the iteration of the values ​​of n and p is stopped. The first value represents the maximum straight-line distance between the converted common control point and the actual coordinate point.

4. The geographic information data processing method based on multi-source data fusion as described in claim 3, characterized in that: Adding timestamps to the pre-processed multi-source geographic data and performing analysis includes: The pre-processed multi-source geographic data consists of remote sensing image data and GIS vector data after coordinate transformation. After adding timestamps to the pre-processed multi-source geographic data, remote sensing image data is selected from the multi-source geographic data as the source for weather type feature extraction. The weather type characteristics include sunny weather characteristics, cloudy weather characteristics, partly cloudy weather characteristics, and precipitation characteristics; The effective weather data is extracted based on the color values ​​of pixels in the remote sensing image data. The extraction logic includes: When the color value is within the first valid value range, the corresponding color information is determined to be valid data and extracted. The color information is the parameter information corresponding to the color value, and the parameter information includes hue, saturation and brightness. When the color value exceeds the second valid value range, the corresponding color information is determined to be invalid data and is not extracted. The color information refers to the parameter information corresponding to abnormal color values, non-sky area color values, and distorted color values ​​caused by blurring or occlusion of remote sensing image data. The second valid value range refers to the range of color values ​​from which valid parameter information cannot be extracted.

5. The geographic information data processing method based on multi-source data fusion as described in claim 4, characterized in that: Establish the correlation between the extracted color information and weather type characteristics; The color parameter range of the association relationship is obtained by dividing the hue value range, saturation value range, and brightness value range within the first valid value range, specifically including: The first color parameter range corresponds to sunny weather characteristics, the second color parameter range corresponds to cloudy weather characteristics, the third color parameter range corresponds to partly cloudy weather characteristics, and the fourth color parameter range corresponds to precipitation characteristics. Based on the aforementioned correlation, the extracted valid color information is matched to the corresponding color parameter range and transformed into the corresponding first weather feature data.

6. The geographic information data processing method based on multi-source data fusion as described in claim 5, characterized in that: Automatic compensation of the first weather feature data based on historical data includes: Obtain color information from historical remote sensing image data of the same period and region, and the corresponding data between the corresponding data and the actual weather type characteristics of the same period and region. By analyzing the deviation pattern between the color parameter range in the corresponding data and the actual weather type characteristics, a compensation coefficient is generated. The color information in the remote sensing image data of the same historical period and the same region is classified according to the weather type characteristics, corresponding to the actual weather type characteristics of the same period and the same region. Calculate the color parameter range extracted under each weather type feature, and based on the deviation between the median of the color parameter range and the median of the standard color parameter corresponding to the actual weather type feature, statistically analyze the distribution frequency and fluctuation amplitude of the deviation value under different seasons and time periods. The seasons include spring, summer, autumn and winter, and the time periods include early morning, morning, noon, afternoon and night. The deviation pattern is that the color parameter range corresponding to sunny weather characteristics shifts towards higher values ​​for brightness parameters during the midday period in summer; the color parameter range corresponding to cloudy weather characteristics shifts towards lower values ​​for saturation parameters during the morning period in winter; and the color parameter range corresponding to precipitation characteristics shifts towards lower values ​​for brightness parameters and higher values ​​for saturation parameters during summer.

7. The geographic information data processing method based on multi-source data fusion as described in claim 6, characterized in that: The first weather feature data is dynamically corrected using the compensation coefficient to obtain the second weather feature data; The dynamic correction includes a first operation and a second operation; The first operation is to match a suitable target compensation coefficient from the generated compensation coefficients based on the weather type characteristics, season, and time period corresponding to the first weather feature data. The second operation is to apply the target compensation coefficient to the color parameter range corresponding to the weather type feature in the first weather feature data, adjust the median of the color parameter range, and make the deviation between the adjusted median and the median of the standard color parameter corresponding to the actual weather type feature fall within a preset allowable range. The compatibility between the adjusted color parameter range and the first weather feature data is judged in real time. If the compatibility does not reach the preset compatibility threshold, the compensation coefficient is updated based on the color information in the latest historical remote sensing image data of the same period and the same region and the corresponding data between the actual weather type features of the same period and the same region. The first and second operations are repeated until the compatibility reaches the preset compatibility threshold to obtain the second weather feature data. The second weather feature parameter includes color parameter intervals corresponding to sunny, cloudy, partly cloudy, and precipitation features after dynamic correction. The deviation between the median of the color parameter interval and the median of the standard color parameter corresponding to the actual weather type feature is within a preset allowable range. It also includes the weather type feature, season, and time period identifier corresponding to the color parameter interval.

8. The geographic information data processing method based on multi-source data fusion as described in claim 7, characterized in that: The second weather characteristic parameter is fused and calculated to obtain the target geographic data, specifically including: Using the public control point after geodetic coordinate system transformation as the benchmark, the geographical range corresponding to the second weather feature parameter is aligned with the geographical range of the GIS vector data and remote sensing image data after coordinate transformation, so that the second weather feature parameter under the same geographical coordinate point matches the spatial location of the multi-source geographical data. Based on the season and time period in the second weather feature parameters, the second weather feature parameters are combined and grouped according to the season and time period. Within each group, the weather type features under the same geographic coordinates are matched with the second terrain feature information in the GIS vector data, forming a data pair of geographic coordinates, season, time period, weather type and terrain feature information. Based on the first value, filter the related data pairs that meet the conditions; The conditions include: The associated data pairs correspond to geographic coordinates that are within the overlapping geographic range of the coordinate-transformed remote sensing image data and GIS vector data. The weather type feature in the associated data pair is consistent with the color information and the weather type feature, and the consistent association means that the color parameter range corresponding to the weather type feature is within the first valid value range. The second terrain feature information of the GIS vector data in the associated data pair does not conflict with the original second terrain feature information of the GIS vector data. The lack of conflict means that they are under the same geographic coordinates, and the error between the second terrain feature information and the original second terrain feature information is less than the first value.

9. The geographic information data processing method based on multi-source data fusion as described in claim 8, characterized in that: The filtered associated data pairs are organized in geographic coordinate order to form a structured dataset that includes geographic coordinate values ​​in the geodetic coordinate system, weather type characteristics, season, time period, and terrain feature parameters. The dataset is then checked for completeness and entries with any missing data element are removed to obtain the target geographic data. The data elements include geographic coordinate values ​​in the geodetic coordinate system, weather type characteristics, season, time period, and terrain feature parameters; The criteria for the integrity check are that each entry in the structured dataset includes complete geodetic coordinate system geographic coordinates, weather type characteristics, season, time period, and terrain feature parameters. The geographic coordinate accuracy of the target geographic data is consistent with the accuracy of the coordinate transformation, the weather type feature accuracy is consistent with the accuracy of the second weather feature parameter, and the overall data error is less than the first value.

10. A geographic information data processing system based on multi-source data fusion, applied to the geographic information data processing method based on multi-source data fusion as described in any one of claims 1-9, characterized in that, Includes a processing module and a fusion module; The processing module analyzes and processes the pre-processed multi-source geographic data to obtain a first weather feature, and then dynamically corrects the first weather feature to obtain a second weather feature. The fusion module performs fusion calculations on the second weather feature to obtain the target geographic data.

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