Universal plane-coordinate system adaptive loading method and system for Cesium frame

Abstract

The invention discloses a universal plane coordinate system self-adaptive loading method and system for a Cesium framework, and belongs to the field of universal plane coordinate system self-adaptive loading, and the method comprises the following steps: if a coordinate offset or geometric deformation exists between a coordinate system template and actual data through judgment, adjusting parameter values of a scale factor and origin offset through iteration; obtaining a corrected coordinate mapping relation; according to the corrected coordinate mapping relation, performing spatial transformation processing on the input data to obtain standardized coordinate data compatible with the three-dimensional earth platform; carrying out superposition verification on the standardized coordinate data and a data layer loaded by the platform, carrying out secondary detection on possible parameter matching deviation, and determining final loading precision; and updating the coordinate system template library by adopting an automatic identification mechanism. According to the method, the self-adaptive loading capability of the Cesium platform on heterogeneous coordinate system data is remarkably improved, the data loading precision and the space consistency are guaranteed, and efficient and reliable technical support is provided for three-dimensional visualization application.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a general planar coordinate system adaptive loading method and system for the Cesium framework. Background Technology

[0002] In the field of geographic information visualization, 3D globe platforms are widely used in scenarios such as urban planning, emergency response, and resource management. Their core function lies in their ability to accurately overlay and display various spatial data onto a virtual globe, which directly determines the platform's practicality and reliability. Currently, most methods for loading planar coordinate system data rely on manually specifying projection parameters beforehand or are limited to specific known coordinate systems. When faced with data from diverse sources, this approach often leads to low loading efficiency and increased error risks due to cumbersome parameter configuration. This is especially true when data comes from different regions or historical projects, where inconsistent parameters further amplify the operational difficulty.

[0003] There are many types of plane coordinate systems, and different regions use significantly different projection types, central meridians, bandwidths, and scale factors. These parameters collectively determine the geometric meaning and spatial location of the coordinates. If the combined characteristics of these parameters cannot be accurately captured, the system will struggle to determine the true projection method of the data, leading to coordinate shifts or distortions during loading. Parameter differences further exacerbate the complexity of feature matching, as even a small change in a single parameter may correspond to a completely different coordinate system template, making the automatic recognition process prone to misjudgments or mismatches.

[0004] In practical applications, for example, when users need to import data from old engineering drawings of a specific location into a 3D platform, the drawings may use a custom planar coordinate system for that region, containing unique ellipsoidal parameters and origin offsets. If the platform cannot automatically extract and compare these key parameters from the data itself, operators are forced to repeatedly consult reference materials and manually try different parameter combinations. This is not only time-consuming but may also lead to misalignment of the entire data layer due to a single matching failure, affecting subsequent analysis and decision-making. Therefore, accurately identifying and adapting to the parameter characteristics of various planar coordinate systems without relying on manual intervention has become a key issue in achieving universal data loading within the Cesium framework. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a general planar coordinate system adaptive loading method and system for the Cesium framework, which addresses the shortcomings of the prior art.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A general planar coordinate system adaptive loading method for the Cesium framework includes the following steps: Step 1: Extract metadata information from the input data, perform preliminary analysis on the potential features of the plane coordinate system, obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factor, and obtain a preliminary parameter combination description. Step 2: Based on the preliminary parameter combination description, compare the results using a pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set. Step 3: By matching the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and by performing auxiliary verification based on the historical records of the data source, the most suitable coordinate system template is determined. Step 4: If the determined coordinate system template has coordinate offset or geometric deformation from the actual data, the corrected coordinate mapping relationship is obtained by iteratively adjusting the parameter values ​​of the scaling factor and origin offset. Step 5: Based on the corrected coordinate mapping relationship, perform spatial transformation processing on the input data to obtain standardized coordinate data compatible with the 3D Earth platform; Step 6: Verify the standardized coordinate data by overlaying it with the data layer already loaded on the platform, and perform a secondary detection to check for possible parameter matching deviations, thereby determining the final loading accuracy. Step 7: Based on the final loading accuracy, update the coordinate system template library using an automatic recognition mechanism, optimize the matching for subsequent inputs from similar data sources, and obtain continuously improved parameter recognition capabilities.

[0007] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 1, by extracting metadata information from the input data, preliminary analysis is performed on the potential features of the planar coordinate system to obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings, and scaling factors, thus obtaining a preliminary parameter combination description, including: Step 1.1: By parsing metadata information from the input data, performing data layering processing based on the structural characteristics of the planar coordinate system, an initial dataset containing projection parameter values, central meridian, and bandwidth settings is obtained, yielding preliminary feature extraction results; Step 1.2: Based on the preliminary feature extraction results, a data filtering method is used to define the range for the central meridian and bandwidth settings. If data points are detected to exceed the preset threshold range, abnormal data are marked to determine the first feature dataset after processing. Step 1.3: By grouping the first feature dataset, a secondary analysis is performed on the distribution characteristics of the scaling factor to obtain the grouped data subset, thus obtaining the second feature dataset associated with the projection parameter value. Step 1.4: Based on the second feature dataset, the support vector machine algorithm is used to classify the latent features. The weights of the boundary data points in the classification results are adjusted, and it is determined whether the classified third feature dataset meets the preset accuracy standard. Step 1.5: By constructing parameter combinations from the third feature dataset, data mapping is performed on the correlation between the central meridian and bandwidth settings to obtain the combined parameter description set and preliminary parameter optimization results. Step 1.6: Based on the preliminary parameter optimization results, verify the matching degree between the scaling factor and the projection parameter values. If the matching degree is lower than the preset threshold, regroup the data to determine the final parameter combination description set. Step 1.7: By formatting the output of the final parameter combination description set, data verification is performed to ensure the completeness of the description construction, and the final business description result is obtained, thus completing the parsing process of the planar coordinate system features.

[0008] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 2, based on the preliminary parameter combination description, a pre-established coordinate system template library is used for comparison, and priority is ranked according to the combination features of the central meridian and bandwidth settings to determine the closest template candidate set, including: Step 2.1: Based on the preliminary parameter combination description, extract all template records from the pre-established coordinate system template library, and compare the central meridian value with the bandwidth setting value one by one to obtain the initial similarity result; Step 2.2: Based on the initial similarity results, the cosine similarity algorithm is used to calculate the distance between the combined value feature vectors to determine the quantized similarity results; Step 2.3: By sorting the quantized similarity results in descending order, higher weights are assigned to the combination features of the central meridian value and the bandwidth setting value to obtain the priority-sorted template sequence; Step 2.4: Based on the priority-sorted template sequence, filter out records with similarity results higher than the internal threshold to determine the preliminary template candidate set; Step 2.5: By traversing each matching template in the template candidate set, extract the complete configuration of the corresponding projection parameters to obtain the expanded candidate template set; Step 2.6: If the number of records in the candidate template set is less than the preset number, the similarity threshold is lowered and the template set is re-sorted to determine the supplemented template candidate set. Step 2.7: Based on the supplemented template candidate set, select the matching template with the highest sorting position as the output to obtain the final template candidate set.

[0009] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 3, by matching the template candidate set with the ellipsoidal parameters and origin offset of the input data one by one, and performing auxiliary verification based on the historical records of the data source, the most suitable coordinate system template is determined, including: Step 3.1: Extract each record from the template candidate set and perform a preliminary comparison based on the ellipsoidal parameters of the input data to obtain the initial matching result; Step 3.2: Based on the initial matching results, obtain the relevant values ​​of the origin offset, perform a secondary verification with the corresponding records in the template candidate set, and determine the record set with high offset consistency. Step 3.3: Using historical records obtained from the data source, background information is checked on the record set with high offset consistency to determine the subset that matches the source characteristics; Step 3.4: By comparing the parameters of the coordinate templates in the subset, the records that are closest to the input data are extracted to obtain the selected template set; Step 3.5: If the number of records in the selected template set is lower than the preset threshold, extract more relevant information from the historical records and add it to the comparison process to determine the expanded template set; Step 3.6: Based on the expanded template set, perform a final sorting based on the overall consistency of ellipsoid parameters and origin offset, and obtain the coordinate templates with the highest sorting order as the output result; Step 3.7: If multiple coordinate templates in the final sorting results have similar consistency, the single template that best matches the background of the input data is determined through auxiliary verification of source analysis.

[0010] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 4, if the determined coordinate system template has coordinate offset or geometric deformation with the actual data, the corrected coordinate mapping relationship is obtained by iteratively adjusting the parameter values ​​of the scaling factor and the origin offset, including: Step 4.1: Calculate the Euclidean distance difference between corresponding point pairs using the selected coordinate system template and the actual data point set to obtain the initial residual distribution; Step 4.2: Calculate the residual mean and residual variance based on the initial residual distribution, and determine the current residual statistic; Step 4.3: Use the least squares method to perform a fitting adjustment on the scaling factor and the origin offset parameter to obtain the updated scaling factor and the new origin offset value. Step 4.4: Perform an affine transformation on the actual data point set using the updated scaling factor and the new origin offset value to obtain the corrected data point set; Step 4.5: Recalculate the distance difference between corresponding point pairs based on the corrected data point set and coordinate system template to obtain a new round of residual distribution; Step 4.6: If the residual variance of the new round of residual distribution is less than the residual variance of the previous round, then accept the current scaling factor and the new origin offset value, and continue to perform affine transformation to calculate the next round of residual distribution; otherwise, revert to the parameter values ​​of the previous round, reduce the adjustment step size, and refit. Step 4.7: Determine whether the residual variance is lower than the preset convergence threshold by the residual distribution after multiple iterations. If the convergence threshold is met, fix the current scaling factor and the new origin offset value, and output the final correction mapping relationship.

[0011] As a further preferred embodiment of the universal planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 5, spatial transformation processing is performed on the input data according to the corrected coordinate mapping relationship to obtain standardized coordinate data compatible with the three-dimensional earth platform, including: Step 5.1: Perform spatial transformation processing on the input data according to the corrected coordinate mapping relationship to obtain a standardized coordinate point set; Step 5.2: Perform projection alignment between the standardized coordinate point set and the coordinate grid of the 3D earth platform to obtain the distribution deviation of the point set on the grid; Step 5.3: Calculate the initial value of the rotation angle parameter based on the distribution deviation, and determine the initial rotation correction parameter; Step 5.4: Perform a rotation transformation on the standardized coordinate point set using the initial rotation correction parameters to obtain the rotated and corrected coordinate point set; Step 5.5: Reproject the rotated and corrected set of coordinate points onto the coordinate grid of the 3D Earth platform and calculate the projection error value. Step 5.6: If the projection error value is greater than the preset threshold, update the rotation angle parameter according to the projection error value and obtain the adjusted rotation correction parameter; Step 5.7: Repeat the rotation transformation and projection calculation on the rotated and corrected coordinate point set using the adjusted rotation correction parameters to determine the new round of projection error values; Step 5.8: If the new round of projection error value is less than or equal to the preset threshold, then fix the current rotation correction parameters and output the final coordinate data that is compatible with the 3D Earth platform.

[0012] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 6, the standardized coordinate data is superimposed and verified with the data layer already loaded on the platform, and a secondary detection is performed to check for possible parameter matching deviations, thereby determining the final loading accuracy, including: Step 6.1: Use standardized coordinates and platform data layer to perform data overlay processing to obtain preliminary overlay comparison results; Step 6.2: Based on the preliminary overlay comparison results, the overlay comparison method is used to analyze the coordinate matching degree between the standardized coordinates and the platform data layer to obtain matching degree evaluation data. Step 6.3: For the matching degree evaluation data, use the quadratic deviation value and deviation detection method to perform a standardized detection process to determine the potential deviation distribution range; Step 6.4: Obtain the relationship data between the secondary deviation value and the loading accuracy value from the deviation distribution range, calculate using the accuracy evaluation method, and determine whether the accuracy reaches the preset threshold. If the preset threshold is not reached, record the deviation point information output by the deviation detection method. Step 6.5: Based on the deviation point information, use a data consistency analysis tool to perform a local data comparison between the standardized coordinates and the platform data layer to obtain the consistency verification results; Step 6.6: If the consistency check result shows that there is inconsistency in the local data, the standardized coordinates are locally adjusted using the pre-established correction rules to obtain the adjusted coordinate data. Step 6.7: Re-execute the data overlay method with the adjusted coordinate data and the platform data layer to determine the final confirmed value.

[0013] As a further preferred embodiment of the general planar coordinate system adaptive loading method for the Cesium framework of the present invention, in step 7, based on the final loading accuracy, an automatic identification mechanism is used to update the coordinate system template library, and optimized matching is performed for subsequent inputs from similar data sources to obtain continuously improved parameter identification capabilities, including: Step 7.1: Based on the final loading accuracy, the step uses an automatic identification mechanism to extract the current data source identifier and corresponding matching parameters; Step 7.2: From the extracted matching parameters, perform parameter pre-matching processing for similar data sources in subsequent inputs to obtain the initial matching parameter set; Step 7.3: Use a clustering algorithm to group and cluster the initial matching parameter set to obtain a parameter cluster set; Step 7.4: Select the center point of each cluster from the parameter cluster set as a representative parameter; Step 7.5: Calculate the intra-cluster accuracy fluctuation value by combining representative parameters with historical accuracy records. If the accuracy fluctuation value is lower than a preset threshold, mark the corresponding cluster as a high-stability parameter cluster. Step 7.6: Use a highly stable parameter cluster to increase the weight values ​​of the corresponding parameters in the template library to obtain the adjusted template library weight distribution; Step 7.7: Update the coordinate system template library by adjusting the weight distribution of the template library to determine the optimized coordinate system template library.

[0014] A system for a general planar coordinate system adaptive loading method for the Cesium framework, comprising: The metadata extraction and preliminary analysis module is used to extract metadata information from the input data, perform preliminary analysis on the potential features of the plane coordinate system, obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factors, and obtain a preliminary parameter combination description. The template comparison and candidate set determination module is used to compare the templates based on the preliminary parameter combination description using a pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set. The parameter matching and template judgment module is used to match the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and to perform auxiliary verification based on the historical records of the data source, in order to determine the most suitable coordinate system template. The coordinate correction and mapping acquisition module is used to obtain the corrected coordinate mapping relationship by iteratively adjusting the parameter values ​​of the scaling factor and the origin offset if the determined coordinate system template has coordinate offset or geometric deformation with the actual data. The spatial transformation and standardization module is used to perform spatial transformation processing on the input data based on the corrected coordinate mapping relationship, so as to obtain standardized coordinate data that is compatible with the three-dimensional earth platform. The overlay verification and accuracy determination module is used to verify the overlay of standardized coordinate data with the data layer already loaded on the platform, perform secondary detection for possible parameter matching deviations, and determine the final loading accuracy. The template library update and optimization module is used to update the coordinate system template library based on the final loading accuracy using an automatic recognition mechanism, and to optimize and match the input from similar data sources in the future, thereby obtaining continuously improved parameter recognition capabilities.

[0015] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: This invention extracts metadata information from input data, analyzes features such as projection parameters, central meridian, and bandwidth settings to form a preliminary parameter combination, and compares it with a pre-established template library to prioritize and determine the candidate set. Subsequently, it combines ellipsoidal parameters and historical records for verification to accurately match the optimal template. If deviations exist, it iteratively adjusts the scaling factor and origin offset to correct the coordinate mapping relationship, ultimately achieving standardized data conversion and overlay verification compatible with the Cesium framework. At the same time, it optimizes subsequent matching accuracy by automatically updating the template library. This invention significantly improves the Cesium platform's adaptive loading capability for heterogeneous coordinate system data, ensuring data loading accuracy and spatial consistency, and providing efficient and reliable technical support for 3D visualization applications. Attached Figure Description

[0016] Figure 1 This is a flowchart of a general planar coordinate system adaptive loading method for the Cesium framework according to the present invention; Figure 2 This is a schematic diagram of a general planar coordinate system adaptive loading system for the Cesium framework according to the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings: The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0018] This invention provides a general planar coordinate system adaptive loading method for the Cesium framework, such as... Figure 1 As shown, it can specifically include: Step S101: By extracting metadata information from the input data, a preliminary analysis is performed on the potential features of the plane coordinate system to obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factors, thus obtaining a preliminary parameter combination description.

[0019] By parsing metadata information from the input data and performing data layering based on the structural characteristics of the planar coordinate system, an initial dataset containing projection parameter values, central meridian, and bandwidth settings is obtained, yielding preliminary feature extraction results. Based on these preliminary results, a data filtering method is used to define the range of the central meridian and bandwidth settings. If data points exceed a preset threshold, abnormal data is labeled, determining the processed first feature dataset. The first feature dataset is then grouped, and a secondary analysis of the distribution characteristics of the scaling factor is performed to obtain a subset of grouped data, resulting in a second feature dataset associated with the projection parameter values. Based on the second feature dataset, a support vector machine algorithm is used to classify the latent features. Weights are adjusted for boundary data points in the classification results, and the classification of the third feature dataset is assessed to determine if it meets a preset accuracy standard. Parameter combinations are constructed from the third feature dataset, and data mapping is performed based on the correlation between the central meridian and bandwidth settings, obtaining a combined parameter description set and preliminary parameter optimization results. Based on the preliminary parameter optimization results, the matching degree between the scaling factor and projection parameter values ​​is verified. If the matching degree is lower than a preset threshold, the data is regrouped to determine the final parameter combination description set. By formatting the final parameter combination description set and performing data verification to ensure the completeness of the description construction, the final business description result is obtained, thus completing the parsing process of the planar coordinate system features.

[0020] Extracting metadata is the first step in analyzing the characteristics of a plane coordinate system. Assuming we are dealing with geographic data for a specific region, the metadata includes information such as projection method and coordinate system type. By parsing this metadata, we can initially process the data in layers, separating projection parameter values, central meridian, and bandwidth settings to form an initial dataset. For example, projection parameter values ​​might be baseline values ​​under a specific projection method, the central meridian might be 120 degrees east longitude, and the bandwidth might be set to 6 degrees. This data lays the foundation for subsequent analysis.

[0021] During the data filtering phase, when defining the range for the central meridian and bandwidth settings, a reasonable range for the central meridian can be set as 110°E to 130°E, and the bandwidth as 5°E to 7°E. If a data point's central meridian value is 135°E, it is marked as outlier data and categorized into a pending category, ultimately forming the first feature dataset. This approach allows for rapid identification of outliers, improving data quality. In the grouping stage, when performing secondary analysis on the distribution characteristics of the scaling factor, the data can be divided into three groups based on the scaling factor. For example, a group might have a scaling factor close to 1.0, while another group might have a larger deviation. This grouping method yields the second feature dataset associated with the projection parameter values. This method helps reveal potential patterns among the data, providing support for subsequent classification.

[0022] In classification processing, when using the Support Vector Machine (SVM) algorithm to process the second feature dataset, weights can be adjusted for boundary data points. For example, some data points near the classification boundary can have their weights increased to have a greater impact on the classification result, ultimately forming the third feature dataset, which is then used to determine if it meets the accuracy criteria. This improves classification accuracy and ensures the reliability of the results.

[0023] During the parameter combination construction phase, when mapping data based on the correlation between the central meridian and bandwidth settings, the central meridian value and bandwidth value can be combined into different parameter pairs. For example, 120 degrees east longitude and 6 degrees bandwidth can be grouped together to generate a parameter description set. This method helps optimize parameter configuration and improve projection effects. In the parameter optimization result verification, if the matching degree between the scale factor and the projection parameter value is lower than a preset threshold (e.g., a matching degree of only 0.7 while the threshold is 0.8), the data is regrouped and processed to finally determine the parameter combination description set. This verification mechanism ensures the applicability of the parameters and avoids error accumulation.

[0024] During the final formatted output, the completeness of the description set is validated. This involves checking whether all necessary fields are included, such as the central meridian and bandwidth, to ensure the accuracy of the business description results. This approach improves data readability and application value, providing a reliable basis for subsequent decision-making.

[0025] Step S102: Based on the preliminary parameter combination description, compare the pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set.

[0026] Based on the initial parameter combination description, all template records are extracted from a pre-established coordinate system template library. The central meridian value and bandwidth setting value are compared one by one to obtain initial similarity results. Based on the initial similarity results, the cosine similarity algorithm is used to calculate the distance between the feature vectors of the combined values, determining the quantized similarity results. The quantized similarity results are sorted in descending order, and the combined features of the central meridian value and bandwidth setting value are assigned higher weights to obtain a priority-ranked template sequence. Based on the priority-ranked template sequence, records with similarity results higher than an internal threshold are selected to determine the initial template candidate set. By traversing each matching template in the template candidate set, the complete configuration of the corresponding projection parameters is extracted to obtain an expanded candidate template set. If the number of records in the candidate template set is less than a preset number, the similarity threshold is lowered, and the template set is re-sorted to determine a supplementary template candidate set. Based on the supplementary template candidate set, the matching template with the highest ranking position is selected as the output to obtain the final template candidate set.

[0027] In coordinate system template matching, extracting records from a pre-established template library is a fundamental step. The template library typically contains records of various standard projection configurations, such as combinations of different central meridians and bandwidths. By comparing the input central meridian values ​​with the bandwidth settings one by one, initial similarity results can be quickly obtained. This comparison helps to initially screen potential matches, improving the targeting of subsequent processing.

[0028] Assume that the central meridian in the initial parameter combination description is 123 degrees east longitude and the bandwidth is set to 6 degrees.

[0029] In one embodiment, after extracting records from the template library, the central meridian and bandwidth of each template are directly compared. If a template record has a bandwidth of 120°E and 6°E, or 126°E and 6°E, the initial similarity can be calculated based on the difference. For example, a difference of less than 5° in the central meridian is considered a high initial similarity. This method can effectively capture similar configurations and avoid interference from irrelevant templates.

[0030] In one possible implementation, before using the cosine similarity algorithm to calculate the distance to the combined value feature vector, the central meridian and bandwidth need to be converted into vector form.

[0031] The central meridian value is standardized to a vector ranging from 0 to 180 degrees, with the bandwidth value serving as another dimension. Cosine similarity quantification more accurately reflects the overall similarity between combinations, rather than a single-dimensional difference. This quantification provides a reliable basis for ranking and helps highlight truly matching templates.

[0032] When sorting the quantification similarity results in descending order, a higher weight is given to the combination of the central meridian and bandwidth.

[0033] Understandably, the central meridian often determines the core location of the projection area, so its weight can be set to 0.7, while the bandwidth weight is 0.3. In this way, records near 123 degrees East longitude will be ranked higher in the priority-sorted template sequence. This weighting mechanism optimizes sorting accuracy and ensures that key features dominate the matching process.

[0034] When filtering records with similarity higher than an internal threshold, assuming the threshold is set to 0.85, only templates with quantization results exceeding this value are retained, forming a preliminary candidate set. This filtering method significantly reduces the number of candidates and improves efficiency. If the candidate set is traversed to extract complete projection parameters, such as ellipsoid type and pseudo-eastward shift, the expanded candidate set will contain more detailed configurations, supporting subsequent verification.

[0035] In one embodiment, when the number of records in the candidate set is less than a preset value, such as less than 5, the threshold is preferably lowered to 0.8 for re-screening. This supplementary mechanism avoids matching failures due to insufficient candidates, ensuring that enough options are available. Selecting the top-ranked template from the supplementary candidate set as the final output can directly provide the optimal match, such as a complete configuration where the central meridian is closest to 123 degrees east longitude. Determining this final template candidate set helps to quickly apply it to data projection, improving the accuracy and consistency of coordinate transformation.

[0036] From another perspective, if the input central meridian is 117 degrees east longitude and the bandwidth is 6 degrees, through the aforementioned weighted cosine similarity and threshold adjustment, the final candidate set may include the standard template of 117 degrees east longitude and its neighboring variants. This multi-faceted matching method can stably output reliable templates in data from different regions, reduce the need for manual intervention, and provide a solid foundation for fine-tuning the projection parameters.

[0037] Step S103: By matching the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and performing auxiliary verification based on the historical records of the data source, the most suitable coordinate system template is determined.

[0038] By extracting each record from the template candidate set and performing a preliminary comparison with the ellipsoidal parameters of the input data, an initial matching result is obtained. Based on the initial matching result, the relevant value of the origin offset is obtained and a secondary verification is performed with the corresponding record in the template candidate set to determine the set of records with high offset consistency. Using historical records obtained from the data source location, background information is checked on the set of records with high offset consistency to identify a subset that matches the characteristics of the source location. By comparing the parameters of the coordinate templates in the subset, the record that is closest to the input data is extracted to obtain the selected template set. If the number of records in the selected template set is less than a preset threshold, more relevant information is extracted from the historical records and added to the comparison process to determine the expanded template set. Based on the expanded template set, a final ranking is performed based on the comprehensive consistency of the ellipsoidal parameters and the origin offset, and the top-ranked coordinate templates are obtained as the output result. If multiple coordinate templates in the final ranking result have similar consistency, the single template that best matches the background of the input data is determined through auxiliary verification by source analysis.

[0039] In the process of selecting coordinate templates, a crucial step is to perform a preliminary comparison of the ellipsoidal parameters of the input data after extracting each record from the candidate template set. This comparison helps to quickly identify records with high ellipsoidal consistency and avoids irrelevant configurations interfering with subsequent verification.

[0040] Assuming the input data uses the Beijing 54 ellipsoid, while the candidate set includes various types such as the Krasovsky ellipsoid and the GRS80 ellipsoid, initial matching results can be obtained by directly comparing ellipsoid names or key parameters, such as the semi-major axis value. This preliminary screening can significantly improve processing efficiency and ensure that subsequent steps are more targeted.

[0041] In one embodiment, after obtaining the origin offset related value based on the initial matching result, a secondary verification is performed with the corresponding record in the template candidate set, which can further determine the record set with high offset consistency.

[0042] If the origin of the input data is offset by 500 kilometers eastward and 0 kilometers northward, records in the template with offset values ​​close to this combination are preferentially retained. This secondary verification evaluates consistency through difference and effectively eliminates templates with similar ellipsoids but large offset differences, thus forming a more accurate record set. This mechanism helps improve the reliability of matching and reduce projection errors.

[0043] By using historical records obtained from the data source to verify the background information of record sets with high offset consistency, a subset that matches the characteristics of the source can be identified.

[0044] In one possible implementation, if the data originates from the Chinese region and historical records show that the Xi'an 80 ellipsoid is commonly used in combination with specific offsets, then after verification, templates matching the characteristics of that region are retained. This background verification takes into account regional habits, optimizes the quality of subsets, and ensures that the templates match the actual application background. This approach can result in more stable coordinate transformation effects.

[0045] By comparing the parameters of the coordinate templates in the subset, the records that are closest to the input data can be extracted to obtain a selected template set.

[0046] By comparing auxiliary parameters such as false northward shift and projection type, if all key values ​​of a template are closest to the input, it is included in the selection set. This fine-grained comparison highlights the best match and supports high-precision output.

[0047] In one embodiment, if the number of records in the selected template set is less than a preset threshold, such as less than three, more relevant information is extracted from historical records to supplement the comparison process, thus determining an expanded template set. This supplementation mechanism avoids failures due to insufficient candidates, ensuring that enough options are available, thereby improving the robustness of the system.

[0048] Based on the comprehensive consistency of the expanded template set for ellipsoidal parameters and origin offset, the final sorting is performed, and the coordinate templates with the highest sorting values ​​are obtained as the output results.

[0049] When calculating overall consistency, the ellipsoid can be given a higher weight, such as 0.6, while the offset weight is 0.4, so that the core parameters dominate the sorting. This weighted sorting can accurately highlight the optimal template and improve transformation consistency.

[0050] In one possible implementation, if multiple coordinate templates in the final sorting result have similar consistency, the single template that best matches the background of the input data is determined through auxiliary verification by source analysis.

[0051] When multiple template consistency scores are close to 0.95, a commonly used configuration is selected based on historical preferences from the source region. This auxiliary verification resolves ambiguities, ensuring a single, reliable output template and supporting efficient applications. This combination of multi-layered verification and sorting significantly reduces the need for manual adjustments, improving the overall accuracy and efficiency of coordinate processing.

[0052] Step S104: If the determined coordinate system template has coordinate offset or geometric deformation with the actual data, the corrected coordinate mapping relationship is obtained by iteratively adjusting the parameter values ​​of the scaling factor and the origin offset.

[0053] The initial residual distribution is obtained by calculating the Euclidean distance difference between corresponding point pairs using the selected coordinate system template and the actual data point set. The residual mean and residual variance are calculated based on the initial residual distribution to determine the current residual statistics. The scaling factor and origin offset parameters are adjusted using the least squares method to obtain updated scaling factors and new origin offset values. An affine transformation is performed on the actual data point set using the updated scaling factors and new origin offset values ​​to obtain the corrected data point set. The distance difference between corresponding point pairs is recalculated based on the corrected data point set and the coordinate system template to obtain a new round of residual distribution. If the residual variance of the new round of residual distribution is less than that of the previous round, the current scaling factor and new origin offset values ​​are accepted, and the affine transformation is performed to calculate the next round of residual distribution; otherwise, the calculation reverts to the previous parameter values, reduces the adjustment step size, and refits. The residual distribution after multiple iterations is used to determine if the residual variance is lower than a preset convergence threshold. If the convergence threshold is met, the current scaling factor and new origin offset values ​​are fixed, and the final corrected mapping relationship is output.

[0054] Calculating the initial residual distribution is a crucial step in matching and adjusting the coordinate system template with the actual data point set. The residual distribution reflects the distance differences between the data points and the template points, and analyzing these differences can provide a preliminary assessment of the degree of mismatch.

[0055] Assuming the actual data set originates from measurement data of a certain region, and the template is a known standard coordinate system, calculating the Euclidean distance difference between two sets of point pairs may reveal larger differences for some pairs, indicating a systematic offset or inconsistency in scale. In this case, an initial residual mean and variance can be obtained by statistically analyzing the differences of all point pairs, providing a basis for subsequent adjustments.

[0056] When using the least squares method for parameter fitting, adjusting the scaling factor and the origin offset is crucial. Assuming an initial scaling factor of 1.0, but calculations reveal that the actual data set is generally too small, a new scaling factor, such as 1.05, might be obtained after fitting. Simultaneously, the origin offset is adjusted to 200 meters eastward and 150 meters northward. This adjustment is iterated multiple times to ensure the data set gradually approaches the template set, forming a more closely fitting mapping relationship.

[0057] During the fitting process, attention should be paid to the step size of parameter adjustment. Too large a step size may cause the results to diverge, while too small a step size will affect efficiency. Therefore, it is necessary to dynamically adjust the step size according to the changes in residuals.

[0058] After performing the affine transformation, the distance difference between the corrected data point set and the template generates a new round of residual distribution. Assuming the variance of the residuals in the previous round was 5.2, and it decreases to 4.8 in the new round, it indicates the adjustment direction is correct, and parameter optimization can continue. Conversely, if the variance rises to 5.5, it is necessary to revert to the previous round of parameters and reduce the step size to try again. This iterative mechanism ensures that parameter adjustments gradually converge to a better state by continuously comparing changes in the residual distribution.

[0059] When determining whether the residual variance has reached the convergence threshold, a specific criterion can be set, such as stopping iteration when the variance is less than 0.5. Assuming that after five iterations the variance drops to 0.3, satisfying the condition, the current scaling factor and offset value are fixed as the final result. This approach effectively avoids over-iteration while ensuring the stability of the correction mapping relationship.

[0060] The convergence threshold should be set in conjunction with the actual data characteristics. If it is too lenient, it may lead to insufficient accuracy, while if it is too strict, it may fail to converge.

[0061] Looking at the iterative adjustment process from multiple perspectives reveals that the optimization of the residual distribution is not only a result of parameter adjustment but also closely related to the quality of the data set. Assuming the data set contains outliers that may interfere with the calculation of the residual mean, preprocessing the data before iteration to remove obvious outliers before fitting can further improve the reliability of the mapping relationship.

[0062] Outlier identification can be based on statistical methods, such as setting an upper limit for distance difference, and temporarily excluding point pairs that exceed this range from the calculation.

[0063] In the extended approach, if the data point set covers a large area, a single scaling factor may not be sufficient. In such cases, parameter adjustment by partitioning the data point set can be considered. Assuming the data point set is divided into northern and southern regions, the scaling factor and offset values ​​are calculated for each region, followed by a local affine transformation, and finally the results are merged. This partitioning strategy can adapt to complex data distributions and improve the overall adaptability of the correction.

[0064] Step S105: Based on the corrected coordinate mapping relationship, perform spatial transformation processing on the input data to obtain standardized coordinate data compatible with the three-dimensional earth platform.

[0065] Based on the corrected coordinate mapping, a spatial transformation is performed on the input data to obtain a standardized coordinate point set. The standardized coordinate point set is then projected and aligned with the 3D earth platform's coordinate grid to obtain the distribution deviation of the point set on the grid. Initial values ​​for the rotation angle parameters are calculated based on the distribution deviation to determine the initial rotation correction parameters. The standardized coordinate point set is then rotated using these initial rotation correction parameters to obtain a rotated-corrected coordinate point set. This rotated-corrected coordinate point set is then reprojected onto the 3D earth platform's coordinate grid, and the projection error value is calculated. If the projection error value is greater than a preset threshold, the rotation angle parameters are updated based on the projection error value to obtain the adjusted rotation correction parameters. The rotation transformation and projection calculation are repeated on the rotated-corrected coordinate point set using the adjusted rotation correction parameters to determine a new round of projection error values. If the new round of projection error values ​​is less than or equal to the preset threshold, the current rotation correction parameters are fixed, and the final coordinate data compatible with the 3D earth platform is output.

[0066] When processing spatial transformations of coordinate data, it's helpful to first understand the underlying principles. Spatial transformation involves adjusting the position of input data based on a corrected coordinate mapping relationship, aiming to make the data points conform to a standardized coordinate system. For example, in a geographic survey project, the original data point set might originate from field collection, but due to equipment or environmental factors, the data may be offset. By applying a corrected mapping relationship, these point sets can be adjusted to a unified reference plane, forming a standardized coordinate point set. This adjustment lays the foundation for subsequent integration with a 3D globe platform.

[0067] In the projection alignment of the standardized coordinate point set with the 3D earth platform's coordinate grid, the core issue lies in identifying the distribution deviation of the point set on the grid. Suppose that after the standardized point set is projected onto the grid, it is found that the overall deviation is 50 meters eastward and 30 meters northward. This deviation can be quantified by analyzing the relative positional relationship between the point set and the grid. The calculation of the distribution deviation provides a basis for determining the subsequent rotation angle parameters, ensuring the accuracy of the correction direction.

[0068] To determine the initial value of the rotation angle parameter and implement the rotation transformation, we can consider a specific scenario. Assume that, through distribution deviation analysis, the initial rotation angle parameter is set to a 3-degree clockwise rotation, and then a rotation transformation is performed on the standardized coordinate point set. After the transformation, the point set's position on the grid is closer to the target, but some error still exists. This preliminary rotation correction lays the foundation for subsequent optimization.

[0069] In the process of calculating the projection error value and determining whether it has reached the preset threshold, assuming the preset threshold is 2 meters, and the projection error value after the initial rotation correction is 3.5 meters, exceeding the threshold range, it is necessary to update the rotation angle parameter based on the error value, for example, adjusting it to a clockwise rotation of 3.2 degrees, and then re-perform the rotation transformation and projection calculation. In this way, the error can be gradually reduced, ensuring the alignment accuracy between the point set and the mesh.

[0070] In the next round of projection error calculation and parameter adjustment, assuming the error value drops to 1.8 meters after adjustment, which is below the preset threshold of 2 meters, the current rotation correction parameters can be fixed and used as the final output. This iterative adjustment method effectively improves the compatibility of coordinate data, providing reliable support for the display and analysis of the 3D globe platform.

[0071] In the final coordinate data output stage, it is assumed that after multiple rounds of adjustments, the output coordinate data perfectly matches the grid of the 3D earth platform. This result not only improves the visualization of the data but also provides a precise basis for subsequent spatial analysis and decision-making. Especially in the application of geographic information systems, this high-precision coordinate data can significantly optimize the efficiency of route planning and resource allocation.

[0072] As an extension, if the point set has a large distribution range, rotation correction can be performed regionally. Assuming the data is divided into eastern and western parts, the rotation angle parameters are calculated for each part, followed by local transformation and projection alignment, and finally the results are merged. This partitioning strategy can adapt to complex data distributions, improve the adaptability of the overall correction, and provide a flexible solution for large-scale data processing.

[0073] Step S106: By overlaying the standardized coordinate data with the data layer already loaded on the platform for verification, a secondary detection is performed to check for possible parameter matching deviations, and the final loading accuracy is determined.

[0074] Preliminary overlay comparison results are obtained by overlaying standardized coordinates with the platform data layer. Based on these results, the coordinate matching degree between the standardized coordinates and the platform data layer is analyzed using the overlay comparison method to obtain matching degree evaluation data. For this matching degree evaluation data, a standardized detection process using quadratic deviation values ​​and deviation detection methods is implemented to determine the potential deviation distribution range. From this range, the relationship between the quadratic deviation value and the loading accuracy value is obtained, and an accuracy evaluation method is used to calculate whether the accuracy reaches a preset threshold. If not, the deviation point information output by the deviation detection method is recorded. Based on this deviation point information, a data consistency analysis tool is used to perform a local data comparison between the standardized coordinates and the platform data layer to obtain consistency verification results. If the consistency verification results show inconsistencies in local data, pre-established correction rules are used to locally adjust the standardized coordinates, resulting in adjusted coordinate data. The data overlay process is then re-executed with the adjusted coordinate data and the platform data layer to determine the final confirmed value.

[0075] When processing the overlay of standardized coordinates and platform data layers, it's helpful to first understand the underlying principles. Data overlay involves comparing the spatial alignment of standardized coordinates and platform data layers to initially identify their spatial relationship. In a geographic information processing project, the standardized coordinates originate from previously calibrated data, while the platform data layer serves as the foundational grid layer of a 3D earth platform. Through overlay comparison, the degree of overlap in certain areas can be visually observed, laying the groundwork for subsequent analysis.

[0076] When performing coordinate matching analysis on the overlay comparison results, a specific scenario can be considered. Suppose that after initial overlay, it is found that the standardized coordinates have a high overlap rate with the platform's data layer in the southern region, while there is a significant misalignment in the northern region. This difference can be quantified through matching degree evaluation; for example, the matching degree in the south is 85%, while in the north it is only 60%. This evaluation data provides directional guidance for subsequent deviation detection.

[0077] When using quadratic deviation values ​​and deviation detection methods to determine the range of deviation distribution, a methodological approach can be explored. Quadratic deviation values ​​typically reflect the secondary offset between data points, while deviation detection methods identify abnormal areas through a systematic process. Suppose that in the project mentioned above, the detection revealed that the deviation distribution in the northern region was concentrated within a specific grid, with an average deviation value of 5.2 meters. Determining this distribution range helps to focus on the problem area.

[0078] When evaluating the accuracy of data relating secondary deviation values ​​to loading accuracy values, the implementation method can be analyzed in detail. Assuming a preset accuracy threshold of 3.0 meters, while the current loading accuracy is 4.5 meters, it clearly fails to meet the standard. Accuracy evaluation methods can further identify the main source of the deviation, such as system errors during data acquisition. This judgment provides a basis for subsequent adjustments.

[0079] When recording deviation point information and performing local data comparison, the process can be approached from the perspective of consistency verification. Suppose that data consistency analysis tools reveal inconsistencies in altitude between certain points in the northern region and the platform data layer; for example, the standardized coordinates show an altitude of 120 meters, while the platform data layer shows 115 meters. This verification result indicates the need for local adjustments.

[0080] For local adjustments, pre-established correction rules can be used. For example, if the rules stipulate that points with a height deviation exceeding 3 meters must be adjusted proportionally to a value closer to the platform data layer, the adjusted height value might be corrected to 117 meters, more closely matching the platform data layer. This adjustment method ensures local data consistency.

[0081] When re-executing the data overlay method to determine the final confirmed value, the closed-loop nature of the process should be considered. Assuming that after the adjusted coordinate data is overlaid again with the platform data layer, the overall matching degree improves to over 90%, and the deviation value drops to 2.8 meters, below the preset threshold, this result indicates that the adjustment is effective, and the final confirmed value can be used as a reliable output. This closed-loop processing method significantly improves the reliability of data processing, providing solid support for subsequent geographic information applications.

[0082] Step S107: Based on the final loading accuracy, the coordinate system template library is updated using an automatic recognition mechanism, and the matching is optimized for inputs from similar data sources in the future to obtain continuously improved parameter recognition capabilities.

[0083] Based on the final loading accuracy, an automatic identification mechanism is used to extract the current data source identifier and corresponding matching parameters. From the extracted matching parameters, parameter pre-matching processing is performed for similar data sources input later to obtain an initial matching parameter set. A clustering algorithm is used to group and cluster the initial matching parameter set to obtain a parameter cluster set. The center point of each cluster is selected from the parameter cluster set as a representative parameter. The accuracy fluctuation value within the cluster is calculated by combining the representative parameter with historical accuracy records. If the accuracy fluctuation value is lower than a preset threshold, the corresponding cluster is marked as a high-stability parameter cluster. The weight values ​​of the corresponding parameters in the template library are increased using the high-stability parameter clusters to obtain the adjusted template library weight distribution. The coordinate system template library is updated using the adjusted template library weight distribution to determine the optimized coordinate system template library.

[0084] In geographic information processing projects, an automatic identification mechanism is used to extract the current data source identifier and corresponding matching parameters based on the final loading accuracy. This process can be understood as intelligently identifying source information from verified coordinate data, thereby providing rapid parameter association for similar data in the future.

[0085] Assuming a standardized coordinate dataset originates from a satellite calibration source with a loading accuracy of 2.8 meters, the automatic identification mechanism will extract the source's identifier and matching parameters such as projection type and offset value to form an initial reference.

[0086] In one embodiment, parameter pre-matching is performed from the extracted matching parameters for similar data sources that will be input later, to obtain an initial set of matching parameters. This pre-matching helps reduce manual intervention; for example, when a new batch of data is input from the same satellite source, the system directly applies the previously extracted parameters for preliminary alignment, resulting in an initial set containing multiple similar parameters, such as projection parameters and scale factors.

[0087] A clustering algorithm is used to group the initial matching parameter set into clusters, resulting in a set of parameter clusters. This clustering process is based on the similarity between parameters, grouping similar parameters together.

[0088] In one possible implementation, the initial set is assumed to contain 10 matching parameters, which are then clustered into 3 clusters. One cluster contains parameters with offset values ​​in the range of 1-2 meters, while the other cluster covers parameters with a scale factor close to 1.0001. This grouping facilitates pattern recognition.

[0089] The center point of each cluster is selected from the set of parameter clusters as a representative parameter. This center point represents the most typical parameter value within the cluster. For example, in the offset value cluster, the center point may be offset by 1.5 meters, thus serving as the reference for that cluster.

[0090] The accuracy fluctuation value within a cluster is calculated by combining representative parameters with historical accuracy records. If the accuracy fluctuation value is lower than a preset threshold, the corresponding cluster is marked as a high-stability parameter cluster.

[0091] If historical records show that the accuracy of this cluster fluctuates between 2.5 and 3.0 meters, and the calculated fluctuation value is 0.3 meters, which is lower than the preset threshold of 0.5 meters, then it is marked as high stability. This marking helps to prioritize the use of reliable parameters and improve the overall matching efficiency.

[0092] In one embodiment, a cluster of highly stable parameters is used to increase the weight values ​​of corresponding parameters in the template library, resulting in an adjusted template library weight distribution. This weight increase means that highly stable parameters have a greater influence in future matching; for example, the original weight is increased from 0.6 to 0.85, thereby optimizing template selection.

[0093] The coordinate system template library is updated by adjusting its weight distribution to determine the optimized version. This update ensures the template library is better suited to real-world high-precision scenarios. In the geographic information platform, when processing data from the northern region, the optimized template can directly call high-weight parameters, reducing the recurrence of biases and improving the stability and reliability of data loading. This mechanism, through continuous learning of historical accuracy, forms an adaptive optimization loop, effectively supporting the accuracy consistency of large-scale coordinate processing.

[0094] This invention provides a general-purpose planar coordinate system adaptive loading system for the Cesium framework, such as... Figure 2 As shown, it includes: The metadata extraction and preliminary analysis module is used to extract metadata information from the input data, perform preliminary analysis on the potential features of the plane coordinate system, obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factors, and obtain a preliminary parameter combination description. The template comparison and candidate set determination module is used to compare the templates based on the preliminary parameter combination description using a pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set. The parameter matching and template judgment module is used to match the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and to perform auxiliary verification based on the historical records of the data source, in order to determine the most suitable coordinate system template. The coordinate correction and mapping acquisition module is used to obtain the corrected coordinate mapping relationship by iteratively adjusting the parameter values ​​of the scaling factor and the origin offset if the determined coordinate system template has coordinate offset or geometric deformation with the actual data. The spatial transformation and standardization module is used to perform spatial transformation processing on the input data based on the corrected coordinate mapping relationship, so as to obtain standardized coordinate data that is compatible with the three-dimensional earth platform. The overlay verification and accuracy determination module is used to verify the overlay of standardized coordinate data with the data layer already loaded on the platform, perform secondary detection for possible parameter matching deviations, and determine the final loading accuracy. The template library update and optimization module is used to update the coordinate system template library based on the final loading accuracy using an automatic recognition mechanism, and to optimize and match the input from similar data sources in the future, thereby obtaining continuously improved parameter recognition capabilities.

[0095] This invention extracts metadata information from input data, analyzes features such as projection parameters, central meridian, and bandwidth settings to form a preliminary parameter combination, and compares it with a pre-established template library to prioritize and determine the candidate set. Subsequently, it combines ellipsoidal parameters and historical records for verification to accurately match the optimal template. If deviations exist, it iteratively adjusts the scaling factor and origin offset to correct the coordinate mapping relationship, ultimately achieving standardized data conversion and overlay verification compatible with the Cesium framework. At the same time, it optimizes subsequent matching accuracy by automatically updating the template library. This invention significantly improves the Cesium platform's adaptive loading capability for heterogeneous coordinate system data, ensuring data loading accuracy and spatial consistency, and providing efficient and reliable technical support for 3D visualization applications.

[0096] It will be understood by those skilled in the art that the above descriptions are merely preferred examples of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent substitutions for some of the technical features. All modifications and equivalent substitutions made within the spirit and principles of the invention should be included within the scope of protection of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0097] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A general planar coordinate system adaptive loading method for the Cesium framework, characterized in that, Specifically, it includes the following steps: Step 1: Extract metadata information from the input data, perform preliminary analysis on the potential features of the plane coordinate system, obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factor, and obtain a preliminary parameter combination description. Step 2: Based on the preliminary parameter combination description, compare the results using a pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set. Step 3: By matching the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and by performing auxiliary verification based on the historical records of the data source, the most suitable coordinate system template is determined. Step 4: If the determined coordinate system template has coordinate offset or geometric deformation from the actual data, the corrected coordinate mapping relationship is obtained by iteratively adjusting the parameter values ​​of the scaling factor and origin offset. Step 5: Based on the corrected coordinate mapping relationship, perform spatial transformation processing on the input data to obtain standardized coordinate data compatible with the 3D Earth platform; Step 6: Verify the standardized coordinate data by overlaying it with the data layer already loaded on the platform, and perform a secondary detection to check for possible parameter matching deviations, thereby determining the final loading accuracy. Step 7: Based on the final loading accuracy, update the coordinate system template library using an automatic recognition mechanism, optimize the matching for subsequent inputs from similar data sources, and obtain continuously improved parameter recognition capabilities.

2. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 1, metadata information is extracted from the input data to perform preliminary analysis on the potential features of the planar coordinate system. This yields a preliminary feature set including projection parameter values, central meridian, bandwidth settings, and scale factors, resulting in a preliminary description of the parameter combination, including: Step 1.1: By parsing metadata information from the input data, performing data layering processing based on the structural characteristics of the planar coordinate system, an initial dataset containing projection parameter values, central meridian, and bandwidth settings is obtained, yielding preliminary feature extraction results; Step 1.2: Based on the preliminary feature extraction results, a data filtering method is used to define the range for the central meridian and bandwidth settings. If data points are detected to exceed the preset threshold range, abnormal data are marked to determine the first feature dataset after processing. Step 1.3: By grouping the first feature dataset, a secondary analysis is performed on the distribution characteristics of the scaling factor to obtain the grouped data subset, thus obtaining the second feature dataset associated with the projection parameter value. Step 1.4: Based on the second feature dataset, the support vector machine algorithm is used to classify the latent features. The weights of the boundary data points in the classification results are adjusted, and it is determined whether the classified third feature dataset meets the preset accuracy standard. Step 1.5: By constructing parameter combinations from the third feature dataset, data mapping is performed on the correlation between the central meridian and bandwidth settings to obtain the combined parameter description set and preliminary parameter optimization results. Step 1.6: Based on the preliminary parameter optimization results, verify the matching degree between the scaling factor and the projection parameter values. If the matching degree is lower than the preset threshold, regroup the data to determine the final parameter combination description set. Step 1.7: By formatting the output of the final parameter combination description set, data verification is performed to ensure the completeness of the description construction, and the final business description result is obtained, thus completing the parsing process of the planar coordinate system features.

3. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 2, based on the preliminary parameter combination description, a pre-established coordinate system template library is used for comparison. Priority is assigned to the combined features of the central meridian and bandwidth settings to determine the closest candidate template set, including: Step 2.1: Based on the preliminary parameter combination description, extract all template records from the pre-established coordinate system template library, and compare the central meridian value with the bandwidth setting value one by one to obtain the initial similarity result; Step 2.2: Based on the initial similarity results, the cosine similarity algorithm is used to calculate the distance between the combined value feature vectors to determine the quantized similarity results; Step 2.3: By sorting the quantized similarity results in descending order, higher weights are assigned to the combination features of the central meridian value and the bandwidth setting value to obtain the priority-sorted template sequence; Step 2.4: Based on the priority-sorted template sequence, filter out records with similarity results higher than the internal threshold to determine the preliminary template candidate set; Step 2.5: By traversing each matching template in the template candidate set, extract the complete configuration of the corresponding projection parameters to obtain the expanded candidate template set; Step 2.6: If the number of records in the candidate template set is less than the preset number, the similarity threshold is lowered and the template set is re-sorted to determine the supplemented template candidate set. Step 2.7: Based on the supplemented template candidate set, select the matching template with the highest sorting position as the output to obtain the final template candidate set.

4. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 3, the ellipsoidal parameters and origin offset of the template candidate set are matched one by one with the input data, and historical records from the data source are used for auxiliary verification to determine the most suitable coordinate system template, including: Step 3.1: Extract each record from the template candidate set and perform a preliminary comparison based on the ellipsoidal parameters of the input data to obtain the initial matching result; Step 3.2: Based on the initial matching results, obtain the relevant values ​​of the origin offset, perform a secondary verification with the corresponding records in the template candidate set, and determine the record set with high offset consistency. Step 3.3: Using historical records obtained from the data source, background information is checked on the record set with high offset consistency to determine the subset that matches the source characteristics; Step 3.4: By comparing the parameters of the coordinate templates in the subset, the records that are closest to the input data are extracted to obtain the selected template set; Step 3.5: If the number of records in the selected template set is lower than the preset threshold, extract more relevant information from the historical records and add it to the comparison process to determine the expanded template set; Step 3.6: Based on the expanded template set, perform a final sorting based on the overall consistency of ellipsoid parameters and origin offset, and obtain the coordinate templates with the highest sorting order as the output result; Step 3.7: If multiple coordinate templates in the final sorting results have similar consistency, the single template that best matches the background of the input data is determined through auxiliary verification of source analysis.

5. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 4, if the determined coordinate system template has coordinate offset or geometric deformation compared to the actual data, the corrected coordinate mapping relationship is obtained by iteratively adjusting the scaling factor and origin offset parameters, including... Step 4.1: Calculate the Euclidean distance difference between corresponding point pairs using the selected coordinate system template and the actual data point set to obtain the initial residual distribution; Step 4.2: Calculate the residual mean and residual variance based on the initial residual distribution, and determine the current residual statistic; Step 4.3: Use the least squares method to perform a fitting adjustment on the scaling factor and the origin offset parameter to obtain the updated scaling factor and the new origin offset value. Step 4.4: Perform an affine transformation on the actual data point set using the updated scaling factor and the new origin offset value to obtain the corrected data point set; Step 4.5: Recalculate the distance difference between corresponding point pairs based on the corrected data point set and coordinate system template to obtain a new round of residual distribution; Step 4.6: If the residual variance of the new round of residual distribution is less than the residual variance of the previous round, then accept the current scaling factor and the new origin offset value, and continue to perform affine transformation to calculate the next round of residual distribution; otherwise, revert to the parameter values ​​of the previous round, reduce the adjustment step size, and refit. Step 4.7: Determine whether the residual variance is lower than the preset convergence threshold by the residual distribution after multiple iterations. If the convergence threshold is met, fix the current scaling factor and the new origin offset value, and output the final correction mapping relationship.

6. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 5, based on the corrected coordinate mapping relationship, spatial transformation processing is performed on the input data to obtain standardized coordinate data compatible with the 3D globe platform, including: Step 5.1: Perform spatial transformation processing on the input data according to the corrected coordinate mapping relationship to obtain a standardized coordinate point set; Step 5.2: Perform projection alignment between the standardized coordinate point set and the coordinate grid of the 3D earth platform to obtain the distribution deviation of the point set on the grid; Step 5.3: Calculate the initial value of the rotation angle parameter based on the distribution deviation, and determine the initial rotation correction parameter; Step 5.4: Perform a rotation transformation on the standardized coordinate point set using the initial rotation correction parameters to obtain the rotated and corrected coordinate point set; Step 5.5: Reproject the rotated and corrected set of coordinate points onto the coordinate grid of the 3D Earth platform and calculate the projection error value. Step 5.6: If the projection error value is greater than the preset threshold, update the rotation angle parameter according to the projection error value and obtain the adjusted rotation correction parameter; Step 5.7: Repeat the rotation transformation and projection calculation on the rotated and corrected coordinate point set using the adjusted rotation correction parameters to determine the new round of projection error values; Step 5.8: If the new round of projection error value is less than or equal to the preset threshold, then fix the current rotation correction parameters and output the final coordinate data that is compatible with the 3D Earth platform.

7. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 6, the standardized coordinate data is overlaid and verified with the data layer already loaded on the platform. A secondary detection is performed to check for possible parameter matching deviations, thus determining the final loading accuracy, including: Step 6.1: Use standardized coordinates and platform data layer to perform data overlay processing to obtain preliminary overlay comparison results; Step 6.2: Based on the preliminary overlay comparison results, the overlay comparison method is used to analyze the coordinate matching degree between the standardized coordinates and the platform data layer to obtain matching degree evaluation data. Step 6.3: For the matching degree evaluation data, use the quadratic deviation value and deviation detection method to perform a standardized detection process to determine the potential deviation distribution range; Step 6.4: Obtain the relationship data between the secondary deviation value and the loading accuracy value from the deviation distribution range, calculate using the accuracy evaluation method, and determine whether the accuracy reaches the preset threshold. If the preset threshold is not reached, record the deviation point information output by the deviation detection method. Step 6.5: Based on the deviation point information, use a data consistency analysis tool to perform a local data comparison between the standardized coordinates and the platform data layer to obtain the consistency verification results; Step 6.6: If the consistency check result shows that there is inconsistency in the local data, the standardized coordinates are locally adjusted using the pre-established correction rules to obtain the adjusted coordinate data. Step 6.7: Re-execute the data overlay method with the adjusted coordinate data and the platform data layer to determine the final confirmed value.

8. The general planar coordinate system adaptive loading method for the Cesium framework according to claim 1, characterized in that, In step 7, based on the final loading accuracy, an automatic recognition mechanism is used to update the coordinate system template library. This optimizes matching for subsequent inputs from similar data sources, resulting in continuously improved parameter recognition capabilities, including: Step 7.1: Based on the final loading accuracy, the step uses an automatic identification mechanism to extract the current data source identifier and corresponding matching parameters; Step 7.2: From the extracted matching parameters, perform parameter pre-matching processing for similar data sources in subsequent inputs to obtain the initial matching parameter set; Step 7.3: Use a clustering algorithm to group and cluster the initial matching parameter set to obtain a parameter cluster set; Step 7.4: Select the center point of each cluster from the parameter cluster set as a representative parameter; Step 7.5: Calculate the intra-cluster accuracy fluctuation value by combining representative parameters with historical accuracy records. If the accuracy fluctuation value is lower than a preset threshold, mark the corresponding cluster as a high-stability parameter cluster. Step 7.6: Use a highly stable parameter cluster to increase the weight values ​​of the corresponding parameters in the template library to obtain the adjusted template library weight distribution; Step 7.7: Update the coordinate system template library by adjusting the weight distribution of the template library to determine the optimized coordinate system template library.

9. A system based on the universal planar coordinate system adaptive loading method for the Cesium framework according to any one of claims 1 to 8, characterized in that, include: The metadata extraction and preliminary analysis module is used to extract metadata information from the input data, perform preliminary analysis on the potential features of the plane coordinate system, obtain a preliminary feature set including projection parameter values, central meridian, bandwidth settings and scale factors, and obtain a preliminary parameter combination description. The template comparison and candidate set determination module is used to compare the templates based on the preliminary parameter combination description using a pre-established coordinate system template library, prioritize the combination features of the central meridian and bandwidth settings, and determine the closest template candidate set. The parameter matching and template judgment module is used to match the ellipsoidal parameters and origin offset of the template candidate set with the input data one by one, and to perform auxiliary verification based on the historical records of the data source, in order to determine the most suitable coordinate system template. The coordinate correction and mapping acquisition module is used to obtain the corrected coordinate mapping relationship by iteratively adjusting the parameter values ​​of the scaling factor and the origin offset if the determined coordinate system template has coordinate offset or geometric deformation with the actual data. The spatial transformation and standardization module is used to perform spatial transformation processing on the input data based on the corrected coordinate mapping relationship, so as to obtain standardized coordinate data that is compatible with the three-dimensional earth platform. The overlay verification and accuracy determination module is used to verify the overlay of standardized coordinate data with the data layer already loaded on the platform, perform secondary detection for possible parameter matching deviations, and determine the final loading accuracy. The template library update and optimization module is used to update the coordinate system template library based on the final loading accuracy using an automatic recognition mechanism, and to optimize and match the input from similar data sources in the future, thereby obtaining continuously improved parameter recognition capabilities.

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