A surveying and mapping geographic data management method and system based on cloud computing

Abstract

The present invention relates to the technical field of data management, and in particular to a surveying and mapping geographic data management method and system based on cloud computing. The method comprises the following steps: acquiring surveying and mapping geographic data; matching surveying and mapping semantic information on surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; performing distributed spatiotemporal consistency calibration on surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data; performing local coordinate data partitioning on standard multi-source surveying and mapping geographic data to generate local coordinate partitioning data sets; performing surveying and mapping geographic high-dimensional mapping on local coordinate partitioning data sets to generate surveying and mapping geographic high-dimensional mapping data; performing global coordinate system conversion on surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data. The present invention improves the efficiency and quality of data management through technologies such as semantic matching, spatiotemporal calibration, high-dimensional mapping, coordinate conversion, and distributed storage.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a surveying and mapping geographic data management method and system based on cloud computing. Background Art

[0002] In the early days, the management of surveying and mapping geographic data mainly relied on local storage and single-machine processing, with low data access and processing efficiency, which could not meet the needs of large-scale, high-precision surveying and mapping projects. With the improvement of computer hardware performance, local area networks and server clusters have gradually become mainstream, and data management capabilities have improved, but there are still problems such as difficulty in data sharing and limited processing capabilities. The rise of cloud computing technology has brought revolutionary changes to the management of surveying and mapping geographic data. Through virtualization and distributed computing technologies, cloud computing can provide elastic and scalable computing and storage resources, significantly improving the efficiency and flexibility of surveying and mapping data processing. Nowadays, the management of surveying and mapping geographic data has gradually migrated to the cloud. The cloud platform makes it possible to process, analyze and share massive surveying and mapping data in real time by providing large-scale storage and powerful parallel computing capabilities. However, the current traditional methods are prone to loss of accuracy or high conversion complexity when converting between different coordinate systems. At the same time, when processing large-scale surveying and mapping data, they face the problems of low storage efficiency and inconvenient retrieval, which leads to low management efficiency and quality. Summary of the invention

[0003] Based on this, it is necessary to provide a surveying and mapping geographic data management method and system based on cloud computing to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a surveying and mapping geographic data management method based on cloud computing includes the following steps:

[0005] Step S1: Acquire surveying and mapping geographic data; perform surveying and mapping semantic information matching on the surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data;

[0006] Step S2: partitioning the standard multi-source surveying and mapping geographic data into local coordinate data to generate a local coordinate partitioning data set; performing surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data set to generate surveying and mapping geographic high-dimensional mapping data; performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data;

[0007] Step S3: optimizing the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set; constructing a multi-level topological relationship network for the surveying and mapping geographic optimization data set to generate surveying and mapping geographic topological relationship network data; performing spatial temporal dynamic analysis on the surveying and mapping geographic optimization data set based on the surveying and mapping geographic topological relationship network data to generate a spatial temporal dynamic data set;

[0008] Step S4: Intelligently distribute and store the spatiotemporal dynamic data set and the surveying and mapping geographic optimization data set to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on the distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for the distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to execute surveying and mapping geographic data management operations.

[0009] The present invention ensures the semantic consistency of surveying and mapping data from different sources through mapping semantic information matching, making data integration smoother. Distributed spatiotemporal consistency calibration solves the problem of data inconsistency from different time and space sources, ensures the uniformity and accuracy of data, and the generated standard multi-source mapping and geographic data lays a unified foundation for subsequent processing, improving the availability and processing efficiency of data. Local coordinate data partitioning enables large-scale mapping and surveying data to be processed by partitions, reduces the pressure of single-point processing, and improves the overall processing efficiency. Through mapping and surveying geographic high-dimensional mapping, data can be analyzed at a higher dimension, capturing more complex geographic features, and improving the depth analysis capability of data. Global coordinate system conversion ensures the accurate conversion of data between different coordinate systems, avoiding the common problem of precision loss in traditional methods. Data precision optimization eliminates errors in the mapping process, improves the overall accuracy of data, and ensures the reliability of data. The construction of a multi-level topological relationship network enhances the spatial structure description capability of data, making the spatial relationship of geographic data clearer and more coherent. Spatial temporal dynamic analysis enables mapping and surveying geographic data to be dynamically updated and analyzed over time, enhancing the timeliness and dynamic monitoring capability of data. Intelligent distributed storage optimizes the storage method of large-scale surveying and mapping geographic data, supports parallel processing and storage of massive data, and improves the storage efficiency of the system. The multidimensional conditional retrieval function enables users to quickly find the required data, improves the speed and accuracy of data access, and supports complex query requirements. The construction of the data warehouse provides a systematic management platform for surveying and mapping geographic data, integrates various types of data, facilitates unified management, analysis and sharing, and greatly improves the operability and management efficiency of the data. Therefore, the present invention improves the efficiency and quality of data management through technologies such as semantic matching, spatiotemporal calibration, high-dimensional mapping, coordinate conversion, and distributed storage.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire surveying and mapping geographic data;

[0012] Step S12: performing data preprocessing on the surveying and mapping geographic data to generate processed surveying and mapping geographic data, wherein the data preprocessing includes data cleaning, data denoising, data format unification, and data outlier processing;

[0013] Step S13: performing surveying and mapping semantic information matching on the processed surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; performing data alignment on the surveying and mapping geographic semantic matching data to generate surveying and mapping geographic information alignment data;

[0014] Step S14: Perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic information alignment data to generate standard multi-source surveying and mapping geographic data.

[0015] The present invention can significantly improve the quality and accuracy of original surveying and mapping geographic data through data preprocessing (such as cleaning, denoising, format unification and outlier processing), providing a more reliable basis for subsequent analysis. The surveying and mapping semantic information matching and data alignment steps can extract and unify the semantic information in the surveying and mapping data, making the spatial and semantic information of the data more consistent and accurate. Distributed spatiotemporal consistency calibration ensures the spatial and temporal consistency of surveying and mapping data from different sources and time periods, thereby generating standardized multi-source data. This is particularly important for application scenarios that require multi-source data fusion. Systematic steps reduce errors and inconsistencies in the data integration process and improve the efficiency and accuracy of data processing. Standardized multi-source surveying and mapping geographic data provides a high-quality data foundation for subsequent geographic information system (GIS) analysis, three-dimensional modeling and other complex data processing tasks.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: assigning local coordinate identifiers to the standard multi-source surveying and mapping geographic data to obtain local coordinate identifiers of the surveying and mapping geographic data; partitioning the standard multi-source surveying and mapping geographic data by the local coordinate identifiers of the surveying and mapping geographic data to generate a local coordinate partition data set;

[0018] Step S22: constructing a multivariate space transformation model for the local coordinate partition data set by a generalized reverse projection method to generate a local transformation model data set;

[0019] Step S23: performing surveying and mapping geography high-dimensional mapping on the local coordinate partition data set based on the local transformation model data set to generate surveying and mapping geography high-dimensional mapping data;

[0020] Step S24: performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data.

[0021] The present invention makes detailed analysis and processing of local areas of data more efficient by assigning local coordinate identifiers and partitioning data. This helps to accurately locate specific areas of data and improve the reliability of local analysis. By constructing a multivariate spatial transformation model through a generalized reverse projection method, it is possible to handle complex spatial relationships and transformations, and enhance the compatibility and processing capabilities of different data sources and formats. High-dimensional mapping based on local transformation model data sets can effectively convert surveying and mapping geographic data into higher-dimensional spatial information, providing a rich data perspective for further analysis and application. Global coordinate system conversion ensures the coordination and consistency of surveying and mapping geographic data on a global scale, so that the integration and application of data are not restricted by different coordinate systems, and enhances the availability and interoperability of data. The local to global coordinate conversion process supports data processing and application requirements at different levels, from details to global data consistency, and provides a solid foundation for large-scale data analysis and the construction of geographic information systems.

[0022] Preferably, step S22 includes the following steps:

[0023] Step S221: performing feature decomposition on the local coordinate partition data set to generate geographic surveying and mapping spatial feature data;

[0024] Step S222: using a generalized reverse projection method to perform spatial relationship modeling on the geographic surveying and mapping spatial feature data to generate a preliminary spatial relationship model;

[0025] Step S223: performing local feature mapping on the preliminary spatial relationship model to generate a local feature mapping matrix; iteratively optimizing the local feature mapping matrix to generate a geographic surveying and mapping regression model set;

[0026] Step S224: performing spatial interpolation on the geographic surveying and mapping regression model set to generate geographic surveying and mapping spatial interpolation data; performing local coordinate transformation on the local coordinate partition data set through the geographic surveying and mapping spatial interpolation data to generate a local transformation model data set.

[0027] The present invention can extract key spatial features in geographic surveying and mapping data by performing feature decomposition on local coordinate partition data sets, laying a solid foundation for subsequent modeling and optimization. The spatial feature data is modeled using the generalized reverse projection method, which can accurately capture the spatial relationship in the data and generate a preliminary spatial relationship model, providing a preliminary framework for more complex spatial analysis. The generation and iterative optimization process of the local feature mapping matrix can refine the spatial features of the local area, making the local feature mapping more accurate, and generate a geographic surveying and mapping regression model set, improving the adaptability and predictive ability of the model to local data. Spatial interpolation is performed through the geographic surveying and mapping regression model set, which can generate more accurate spatial data in the local area, which helps to fill the gaps between data and improve the accuracy of local coordinate transformation. The data set after local coordinate transformation can more accurately represent the spatial relationship of geographic surveying and mapping data, provide reliable data support for the subsequent construction of multivariate spatial transformation models, and improve the quality and consistency of the overall transformation process.

[0028] Preferably, step S3 comprises the following steps:

[0029] Step S31: optimizing the data accuracy of standard multi-source surveying and mapping geographic data by using surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set;

[0030] Step S32: extracting topological basic units from the surveying and mapping geography optimization data set to obtain a basic topological unit set; constructing a topological relationship based on the basic topological unit set to generate topological relationship structure data;

[0031] Step S33: constructing a multi-level topological relationship network for the surveying and mapping geography optimization data set through the topological relationship structure data to generate surveying and mapping geography topological relationship network data;

[0032] Step S34: Performing spatial temporal dynamic analysis on the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to generate a spatial temporal dynamic data set.

[0033] The present invention optimizes the accuracy of standard multi-source surveying and mapping geographic data through local conversion model data set, which can significantly improve the accuracy and reliability of data and provide a more accurate data basis for subsequent analysis. The extraction of topological basic units and the construction of topological relationships can divide data into basic geographic units and establish relationships between them. This helps to systematically organize and analyze geographic data and improve the clarity and logic of data structure. The multi-level topological relationship network constructed based on topological relationship structure data makes the relationship of geographic data more comprehensive and in-depth. In this way, the spatial correlation of data can be better understood and complex geographic information systems and applications can be supported. Spatial time series dynamic analysis can reveal the dynamic characteristics of geographic data changing over time and generate spatial time series dynamic data sets. This provides important support for monitoring changes in the geographical environment, predicting future trends, and making dynamic decisions. Through precision optimization, topological analysis and dynamic analysis, step S3 makes the surveying and mapping geographic data more reasonable, accurate and dynamic in structure, thereby improving the overall effect of data integration and application, and providing higher quality data support for various geographic information applications.

[0034] Preferably, step S33 includes the following steps:

[0035] Step S331: performing topological hierarchical decomposition on the topological relationship structure data to generate topological hierarchical decomposition data, wherein the topological hierarchical decomposition data includes high-level topological decomposition data and low-level topological decomposition data;

[0036] Step S332: constructing a surface-line-point relationship network for the surveying and mapping geography optimization data set through high-level topological decomposition data to generate high-level topological network data; constructing a local connection relationship network for the surveying and mapping geography optimization data set through low-level topological decomposition data to generate low-level topological network data;

[0037] Step S333: performing topological network hierarchical fusion of high-level topological network data and low-level topological network data to generate multi-level fused topological network data; performing topological relationship weight calculation on the multi-level fused topological network data to obtain topological weight data;

[0038] Step S334: construct a topological relationship network for the multi-level fused topological network data according to the topological weight data to generate surveying and mapping geographic topological relationship network data.

[0039] The present invention makes the relationship and structure of data at different levels clearly defined by hierarchical decomposition of topological relationship structure data. This hierarchical processing simplifies and systematizes complex geographical relationships, making it easy to manage and analyze. The surface-line-point relationship network generated by high-level topological decomposition data and the local connection relationship network generated by low-level topological decomposition data enable geographical data to be effectively expressed at different levels. This multidimensional network construction can describe the different relationships and structures of geographical data in detail. The high-level and low-level topological network data are hierarchically fused to generate multi-level fused topological network data, providing a comprehensive perspective on geographical data. This fusion can integrate information at different levels and improve the integrity and consistency of data. Through topological relationship weight calculation, the influence and importance of different topological relationships can be quantified, providing a scientific basis for the construction of topological networks. This weight calculation helps identify and optimize key relationships and enhances the practicality and accuracy of topological networks. The topological relationship network is constructed according to the topological weight data to ensure the accuracy of the data of the surveying and mapping geographical topological relationship network in structure and relationship. This provides a solid foundation for subsequent geographic information analysis and applications, such as spatial planning and decision support.

[0040] Preferably, step S34 includes the following steps:

[0041] Step S341: extracting the time series and spatial position change of the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to obtain the surveying and mapping geography time series data and the surveying and mapping geography spatial position change data;

[0042] Step S342: dividing the surveying and mapping geographic time series data and geographic spatial position change data into data sets to generate a model training set and a model test set; using the time series regression model algorithm and the spatial association model algorithm to perform model training on the model training set to generate a time series regression pre-model and a spatial association pre-model; using the model test set to perform model optimization iteration on the time series regression pre-model and the spatial association pre-model, thereby generating a time series regression model and a spatial association model;

[0043] Step S343: fusing the time series regression model and the spatial correlation model in time and space to generate a time and space correlation model;

[0044] Step S344: Use the spatiotemporal correlation model to predict the spatiotemporal dynamic change trend of the surveying and mapping geographic optimization data set to generate a spatial time series dynamic data set.

[0045] The present invention can accurately capture the temporal change trend and spatial distribution change of data by extracting the time series and spatial position change of surveying and mapping geographic data. This information extraction provides a basis for understanding the dynamic characteristics of geographic data. The data is divided into a training set and a test set, and the time series regression model and the spatial association model algorithm are used for training and optimization, so that an efficient prediction model can be generated. In this way, the generalization ability and prediction accuracy of the model can be improved, and the accurate analysis of actual data can be ensured. The time series regression model and the spatial association model are fused in time and space to generate a time and space association model, which can comprehensively consider the dynamic changes of time and space. This fusion model provides a comprehensive analysis and prediction capability for the dynamic changes of geographic data. The time and space association model is used to predict the trend of dynamic changes in time and space, and a spatial time series dynamic data set is generated, so that the prediction of future trends is more accurate and reliable. This is of great significance for application scenarios such as long-term planning, dynamic monitoring and decision support. Through time and space dynamic analysis and prediction, dynamic data support can be provided for complex geographic information systems, and the understanding and prediction capabilities of geographical phenomena are enhanced. This helps to formulate more accurate planning and management strategies and improve the actual application effect of geographic data.

[0046] Preferably, step S4 comprises the following steps:

[0047] Step S41: performing distributed data compression on the spatiotemporal dynamic data set and the surveying and mapping geography optimization data set to generate distributed surveying and mapping geography compression data; performing data encryption on the distributed surveying and mapping geography compression data to generate distributed surveying and mapping geography encryption data;

[0048] Step S42: Based on cloud computing, the distributed surveying and mapping geography encrypted data is stored in a distributed storage architecture to generate distributed surveying and mapping geography storage data; data redundancy and recovery mechanisms are introduced into the distributed surveying and mapping geography storage data to generate a surveying and mapping geography redundant data set;

[0049] Step S43: constructing a search index based on the redundant surveying and mapping geographic data set to obtain a surveying and mapping geographic index; performing a multi-dimensional conditional search on the distributed surveying and mapping geographic storage data based on the surveying and mapping geographic index to generate a multi-dimensional search result set;

[0050] Step S44: construct a data warehouse for distributed surveying and mapping geographic storage data through a multi-dimensional search result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

[0051] The present invention can effectively protect the security of surveying and mapping geographic data through distributed data compression and encryption. Compression reduces storage requirements, and encryption ensures the security of data during transmission and storage, preventing data leakage and illegal access. The distributed storage architecture and data redundancy and recovery mechanism based on cloud computing can improve the reliability and availability of storage. Distributed storage provides an efficient data storage solution, while the redundancy mechanism enhances the reliability of data and ensures that data can be recovered in the event of a failure. By building a retrieval index and performing multi-dimensional conditional retrieval, the efficiency and accuracy of data retrieval can be significantly improved. The index accelerates the data retrieval process, and the multi-dimensional retrieval provides a flexible query capability, allowing users to quickly find the required data according to different conditions. By building an intelligent distributed geographic data repository, efficient data management is achieved. The construction of a data warehouse makes the storage, management and analysis of surveying and mapping geographic data more intelligent and systematic, and supports complex data management operations. Distributed storage and multi-dimensional retrieval functions enhance the system's ability to process large-scale data, support more complex geographic data analysis and applications, and meet the needs of high-performance data management in a big data environment.

[0052] Preferably, step S43 includes the following steps:

[0053] Step S431: classify the key fields of the surveying and mapping geography redundant data set to generate surveying and mapping geography key field classification data; construct a search index for the surveying and mapping geography redundant data set through the surveying and mapping geography key field classification data to generate a surveying and mapping geography index;

[0054] Step S432: performing retrieval data collection on the distributed surveying and mapping geographic storage data based on the surveying and mapping geographic index to obtain retrieval field data; performing retrieval field pattern analysis on the retrieval field data to generate retrieval field pattern data, wherein the retrieval field pattern data includes a single field query pattern and a multi-field query pattern;

[0055] Step S433: Calculate the key field matching rate according to the search field data and the surveying and mapping geography index through the single field query mode to generate the index matching degree; perform initial information search and sorting on the distributed surveying and mapping geography storage data through the index matching degree to generate the single field surveying and mapping geography search and sorting data;

[0056] Step S434: performing a multi-field correlation analysis on the initial surveying and mapping geographic retrieval sorting data through a multi-field query mode to generate multi-field correlation analysis data; using the multi-field correlation analysis data to perform sorting optimization on the single-field surveying and mapping geographic retrieval sorting data to generate multi-field surveying and mapping geographic retrieval sorting data;

[0057] Step S435: Integrate the search results of the single-field surveying and mapping geographic search and ranking data and the multi-field surveying and mapping geographic search and ranking data to generate a multi-dimensional search result set.

[0058] The present invention improves the accuracy and efficiency of data retrieval by classifying key fields and constructing indexes for redundant surveying and mapping geographic data sets. The generation of classification and indexing enables data structured storage, simplifies the retrieval process, and accelerates data access. By analyzing the retrieval field mode, a single-field query mode and a multi-field query mode are supported. The single-field query mode can quickly find data of a specific field, while the multi-field query mode allows more complex query conditions, thereby improving the flexibility and accuracy of retrieval. Single-field surveying and mapping geographic retrieval sorting data is generated through key field matching rate calculation and initial information retrieval sorting. This process ensures the relevance of the retrieval results and the rationality of the sorting, and provides users with preliminary high-quality retrieval results. Through multi-field relevance analysis, the single-field retrieval sorting data is sorted and optimized to generate more accurate multi-field surveying and mapping geographic retrieval sorting data. This multi-field analysis improves the relevance of the retrieval results and ensures that users obtain the data that best meets their needs. The single-field and multi-field retrieval sorting data are integrated to generate a multidimensional retrieval result set, providing a comprehensive retrieval result view. This integration enables users to obtain more comprehensive and accurate retrieval results, thereby improving data access and utilization efficiency.

[0059] In this specification, a surveying and mapping geographic data management system based on cloud computing is provided, which is used to execute the above-mentioned surveying and mapping geographic data management method based on cloud computing. The surveying and mapping geographic data management system based on cloud computing includes:

[0060] The semantic matching module is used to obtain surveying and mapping geographic data; match surveying and mapping semantic information on surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data;

[0061] The coordinate mapping module is used to perform local coordinate data partitioning on standard multi-source surveying and mapping geographic data to generate local coordinate partitioning data sets; perform surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data sets to generate surveying and mapping geographic high-dimensional mapping data; perform global coordinate system conversion on surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data;

[0062] The topological relationship modeling module is used to optimize the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate surveying and mapping geographic optimization data sets; to construct a multi-level topological relationship network for the surveying and mapping geographic optimization data sets to generate surveying and mapping geographic topological relationship network data; to perform spatial temporal dynamic analysis on the surveying and mapping geographic optimization data sets based on the surveying and mapping geographic topological relationship network data to generate spatial temporal dynamic data sets;

[0063] The data storage module is used to perform intelligent distributed storage of spatiotemporal dynamic data sets and surveying and mapping geographic optimization data sets to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

[0064] The beneficial effect of the present invention is that, through mapping semantic information matching and distributed spatiotemporal consistency calibration, the inconsistency problem of multi-source mapping geographic data in semantics and time and space is solved, the uniformity and standardization of data are ensured, and the accuracy and reliability of subsequent processing are improved. Through local coordinate partitioning and high-dimensional mapping, the local processing capability of data is optimized, and then the accurate conversion of data between different coordinate systems is ensured through global coordinate system conversion, which improves the efficiency and accuracy of data processing. Through precision optimization and topological relationship network construction, the fineness and structural integrity of data are enhanced, and further through spatial time series dynamic analysis, the dynamic update and analysis capabilities of data are realized, and the comprehensive analysis value of data is improved. Through intelligent distributed storage and multi-dimensional conditional retrieval, efficient management and rapid access of large-scale mapping geographic data are realized, and through the construction of data warehouse, the systematicness and operability of data are improved, and complex mapping geographic data management operations are supported. Therefore, the present invention improves the efficiency and quality of data management through semantic matching, spatiotemporal calibration, high-dimensional mapping, coordinate conversion, distributed storage and other technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the steps of a surveying and mapping geographic data management method based on cloud computing;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0067] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0068] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0069] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and / or processor methods and / or microcontroller methods.

[0072] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve this, please refer to Figures 1 to 4 , a surveying and mapping geographic data management method based on cloud computing, comprising the following steps:

[0074] Step S1: Acquire surveying and mapping geographic data; perform surveying and mapping semantic information matching on the surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data;

[0075] Step S2: partitioning the standard multi-source surveying and mapping geographic data into local coordinate data to generate a local coordinate partitioning data set; performing surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data set to generate surveying and mapping geographic high-dimensional mapping data; performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data;

[0076] Step S3: optimizing the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set; constructing a multi-level topological relationship network for the surveying and mapping geographic optimization data set to generate surveying and mapping geographic topological relationship network data; performing spatial temporal dynamic analysis on the surveying and mapping geographic optimization data set based on the surveying and mapping geographic topological relationship network data to generate a spatial temporal dynamic data set;

[0077] Step S4: Intelligently distribute and store the spatiotemporal dynamic data set and the surveying and mapping geographic optimization data set to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on the distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for the distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to execute surveying and mapping geographic data management operations.

[0078] The present invention ensures the semantic consistency of surveying and mapping data from different sources through mapping semantic information matching, making data integration smoother. Distributed spatiotemporal consistency calibration solves the problem of data inconsistency from different time and space sources, ensures the uniformity and accuracy of data, and the generated standard multi-source mapping and geographic data lays a unified foundation for subsequent processing, improving the availability and processing efficiency of data. Local coordinate data partitioning enables large-scale mapping and surveying data to be processed by partitions, reduces the pressure of single-point processing, and improves the overall processing efficiency. Through mapping and surveying geographic high-dimensional mapping, data can be analyzed at a higher dimension, capturing more complex geographic features, and improving the depth analysis capability of data. Global coordinate system conversion ensures the accurate conversion of data between different coordinate systems, avoiding the common problem of precision loss in traditional methods. Data precision optimization eliminates errors in the mapping process, improves the overall accuracy of data, and ensures the reliability of data. The construction of a multi-level topological relationship network enhances the spatial structure description capability of data, making the spatial relationship of geographic data clearer and more coherent. Spatial temporal dynamic analysis enables mapping and surveying geographic data to be dynamically updated and analyzed over time, enhancing the timeliness and dynamic monitoring capability of data. Intelligent distributed storage optimizes the storage method of large-scale surveying and mapping geographic data, supports parallel processing and storage of massive data, and improves the storage efficiency of the system. The multidimensional conditional retrieval function enables users to quickly find the required data, improves the speed and accuracy of data access, and supports complex query requirements. The construction of the data warehouse provides a systematic management platform for surveying and mapping geographic data, integrates various types of data, facilitates unified management, analysis and sharing, and greatly improves the operability and management efficiency of the data. Therefore, the present invention improves the efficiency and quality of data management through technologies such as semantic matching, spatiotemporal calibration, high-dimensional mapping, coordinate conversion, and distributed storage.

[0079] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a surveying and mapping geographic data management method based on cloud computing of the present invention. In this example, the surveying and mapping geographic data management method based on cloud computing includes the following steps:

[0080] Step S1: Acquire surveying and mapping geographic data; perform surveying and mapping semantic information matching on the surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data;

[0081] In an embodiment of the present invention, geographic data is obtained from multiple surveying and mapping data sources, including satellite images, aerial data, ground measurement data, LiDAR data, etc. Ensure that the acquired data covers different formats and resolutions to ensure the wide applicability of the data. Use GIS (Geographic Information System) tools or other professional data processing software to import the collected surveying and mapping geographic data. Perform basic data preprocessing, such as coordinate system conversion, resolution adjustment and format unification, to ensure that the data can be used consistently in subsequent steps. Define relevant semantic information categories (such as roads, water bodies, buildings, etc.) according to the characteristics and application requirements of surveying and mapping geographic data. Formulate corresponding semantic label assignment rules to correctly match different categories of geographic information to corresponding semantic categories. Apply an automatic matching algorithm based on machine learning or a rule engine to perform semantic information matching on surveying and mapping geographic data, generate surveying and mapping geographic semantic matching data, identify the semantic category of each geographic information data, and ensure the consistency and accuracy of the data. Perform spatiotemporal consistency calibration in a distributed computing environment, which involves the use of a cloud computing platform or a cluster computing architecture. Distribute surveying and mapping geographic semantic matching data to multiple computing nodes for parallel processing to improve computing efficiency. Perform time-space alignment on the data generated by the distributed computing nodes to ensure that the data processed by each node is consistent in time and space. Apply the consistency calibration algorithm to correct the existing time or space errors and generate calibrated surveying and mapping geographic data. Integrate the calibrated distributed surveying and mapping geographic data to generate standard multi-source surveying and mapping geographic data to ensure data integrity and consistency.

[0082] Step S2: partitioning the standard multi-source surveying and mapping geographic data into local coordinate data to generate a local coordinate partitioning data set; performing surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data set to generate surveying and mapping geographic high-dimensional mapping data; performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data;

[0083] In an embodiment of the present invention, local coordinate identifiers are assigned to standard multi-source surveying and mapping geographic data. Unique local coordinate identifiers are assigned according to the characteristics of the geographic area or data. According to the local coordinate identifiers, the standard multi-source surveying and mapping geographic data are divided into multiple local coordinate partitions. These partitions should cover the entire data area to ensure that each area has an independent coordinate identifier. The local coordinate partition data are aggregated to generate local coordinate partition data sets. These data sets contain the geographic information after partitioning for subsequent high-dimensional mapping. Spatial features such as terrain, landforms, buildings, etc. are extracted from the local coordinate partition data sets. A high-dimensional mapping algorithm is applied to perform high-dimensional space mapping on the local coordinate partition data to generate a high-dimensional feature space. Based on the high-dimensional mapping model, the local coordinate partition data is mapped to a high-dimensional space to generate surveying and mapping geographic high-dimensional mapping data. This data is used for subsequent global coordinate system conversion. A suitable global coordinate system (such as WGS84, UTM, etc.) is selected for global coordinate system conversion. This coordinate system should be consistent with the global surveying and mapping standard. A global coordinate system conversion algorithm (such as coordinate transformation formula, projection transformation, etc.) is applied to convert the surveying and mapping geographic high-dimensional mapping data. Ensure that the data can be correctly mapped to the global coordinate system. Integrate the converted data into a standard data format under the global coordinate system to generate surveying and mapping geographic global coordinate conversion data.

[0084] Step S3: optimizing the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set; constructing a multi-level topological relationship network for the surveying and mapping geographic optimization data set to generate surveying and mapping geographic topological relationship network data; performing spatial temporal dynamic analysis on the surveying and mapping geographic optimization data set based on the surveying and mapping geographic topological relationship network data to generate a spatial temporal dynamic data set;

[0085] In the embodiment of the present invention, by selecting a suitable data accuracy optimization algorithm, such as the least square method, Kalman filtering, etc., the accuracy and reliability of the data are improved. According to the characteristics and requirements of the surveying and mapping data, the algorithm parameters are set to ensure the optimization effect. The global coordinate conversion data of surveying and mapping geography is used as a benchmark to optimize the accuracy of standard multi-source surveying and mapping geography data. By comparing and adjusting, the errors and inconsistencies in the data are eliminated to generate surveying and mapping geography optimization data sets, which should have higher spatial accuracy and consistency, and provide a reliable basis for subsequent analysis. Basic topological units (such as nodes, edges, faces, etc.) are extracted from the surveying and mapping geography optimization data sets as basic elements for constructing a topological relationship network. These topological units are sorted to generate a basic topological unit set. The relationship between the basic topological units (such as connection, adjacency, inclusion, etc.) is defined to construct topological relationship structure data. Based on the basic topological unit set and the topological relationship structure data, a multi-level topological relationship network is constructed to generate surveying and mapping geography topological relationship network data. The network structure should reflect the complexity and hierarchy of geographic data. Time series data, including spatial data and dynamic characteristics of time changes, are extracted from the surveying and mapping geography optimization data sets. Analyze the spatial position changes in surveying and mapping geography data, such as terrain changes, building construction, etc. Select appropriate spatial time series analysis models, such as time series regression models and dynamic system models, to analyze the time series dynamic changes of spatial data. Use surveying and mapping geography optimization datasets to train and optimize models to ensure that the models can accurately capture the dynamic change trends in the data. Apply dynamic analysis models to analyze surveying and mapping geography optimization datasets to generate spatial time series dynamic datasets. This dataset should reflect the temporal evolution and spatial dynamic changes of geographic data.

[0086] Step S4: Intelligently distribute and store the spatiotemporal dynamic data set and the surveying and mapping geographic optimization data set to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on the distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for the distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to execute surveying and mapping geographic data management operations.

[0087] In an embodiment of the present invention, by selecting a suitable distributed storage architecture, such as a distributed file system (such as HDFS), a distributed database (such as Cassandra, MongoDB) or a cloud storage solution (such as AWS S3, Azure BlobStorage). According to the data volume, access mode and storage requirements, the spatiotemporal dynamic data set and the surveying and mapping geography optimization data set are divided into multiple data blocks or fragments. The data is compressed to save storage space, for example, using a compression algorithm (such as GZIP, LZ4). The data is encrypted to ensure the security of the data, for example, using an AES encryption algorithm. The compressed and encrypted data is distributed and stored in the selected distributed storage architecture to generate distributed surveying and mapping geography storage data. A suitable data index structure is designed to support multi-dimensional conditional retrieval. Inverted indexes, B-tree indexes, spatial indexes, etc. can be used. Index construction is implemented in a distributed storage system to generate a retrieval index for surveying and mapping geography data. Based on the index, multi-dimensional conditional retrieval is performed on the distributed surveying and mapping geography storage data. The retrieval conditions include geographic location, time range, data type, etc. A multi-dimensional retrieval result set is generated according to the retrieval conditions, including data fragments or records that meet the conditions. Design the architecture of the data warehouse, including data model, storage structure and data access interface. Integrate multi-dimensional search result sets into the data warehouse and perform data cleaning, transformation and loading (ETL) processing. Build a data warehouse in a distributed storage system or database system to generate an intelligent distributed geographic data repository. Configure data management functions such as data backup, recovery, and permission control to ensure the efficient operation of the data warehouse.

[0088] Preferably, step S1 comprises the following steps:

[0089] Step S11: Acquire surveying and mapping geographic data;

[0090] Step S12: performing data preprocessing on the surveying and mapping geographic data to generate processed surveying and mapping geographic data, wherein the data preprocessing includes data cleaning, data denoising, data format unification, and data outlier processing;

[0091] Step S13: performing surveying and mapping semantic information matching on the processed surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; performing data alignment on the surveying and mapping geographic semantic matching data to generate surveying and mapping geographic information alignment data;

[0092] Step S14: Perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic information alignment data to generate standard multi-source surveying and mapping geographic data.

[0093] In an embodiment of the present invention, by identifying and selecting data sources for surveying and mapping, including satellite images, aerial photography, ground measurement, etc., surveying and mapping geographic data are collected from various data sources. It may include high-resolution images, laser radar point cloud data, terrain data, etc. Remove errors, missing values ​​or inconsistent data in the data. Use data cleaning algorithms such as filling missing values, correcting erroneous records, etc. Apply denoising algorithms (such as mean filtering, wavelet transform, etc.) to remove noise in the data to ensure data quality. Convert surveying and mapping data from different sources into a unified format, such as adjusting images of different resolutions to the same resolution, or converting point cloud data into a unified coordinate system. Identify and process outliers (such as outliers) in the data. Use statistical methods or machine learning models to detect and correct these outliers. Extract semantic information from the processed surveying and mapping data, such as land object categories, terrain features, etc. Use image segmentation, feature extraction and other technologies. Match the extracted semantic information with the existing geographic semantic database to generate surveying and mapping geographic semantic matching data. Use a registration algorithm (such as image registration, point cloud registration, etc.) to align the surveying and mapping geographic semantic matching data to generate surveying and mapping geographic information alignment data. Analyze the consistency of surveying and mapping geographic information alignment data in time and space. Use spatiotemporal analysis algorithms to detect and correct spatiotemporal deviations. Apply distributed calibration models (such as spatiotemporal modeling, spatial interpolation in geographic information systems, etc.) to calibrate the spatiotemporal consistency of data. Generate standard multi-source surveying and mapping geographic data from the calibrated data to ensure its consistency in time and space, suitable for subsequent data analysis and application.

[0094] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0095] Step S21: assigning local coordinate identifiers to the standard multi-source surveying and mapping geographic data to obtain local coordinate identifiers of the surveying and mapping geographic data; partitioning the standard multi-source surveying and mapping geographic data by the local coordinate identifiers of the surveying and mapping geographic data to generate a local coordinate partition data set;

[0096] Step S22: constructing a multivariate space transformation model for the local coordinate partition data set by a generalized reverse projection method to generate a local transformation model data set;

[0097] Step S23: performing surveying and mapping geography high-dimensional mapping on the local coordinate partition data set based on the local transformation model data set to generate surveying and mapping geography high-dimensional mapping data;

[0098] Step S24: performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data.

[0099] In an embodiment of the present invention, the standard multi-source surveying and mapping geographic data is divided into a plurality of local areas according to the spatial distribution of the geographic data. For example, the blocks are divided according to the geographic coordinate grid or regional features. A unique coordinate identifier is assigned to each local area. These identifiers can be codes based on geographic locations, such as geographic coordinates, regional numbers, etc. The standard multi-source surveying and mapping geographic data is partitioned using the local coordinate identifiers. The data of each local area is extracted and marked as a local coordinate partition data set. The data of each partition is stored in a database to form a systematic local coordinate partition data set for subsequent processing and analysis. A generalized inverse projection method suitable for the data type is selected, such as using a generalized inverse matrix or a multivariate linear regression model for spatial transformation. Spatial feature analysis is performed on each local coordinate partition data set to estimate the parameters of the transformation model. The model parameters can be solved using the least squares method or other optimization algorithms. The estimated model parameters are applied to each local coordinate partition to generate a local transformation model data set. These model data sets include spatial transformation relationships and model parameters for each partition. A suitable high-dimensional mapping algorithm, such as principal component analysis (PCA), linear discriminant analysis (LDA), etc., is selected to perform high-dimensional mapping of the data. Use the model parameters in the local transformation model dataset to map the local coordinate partition dataset to a high-dimensional space to generate surveying and mapping geography high-dimensional mapping data. Integrate the high-dimensional mapped data into a unified high-dimensional space to ensure that the data has a consistent representation in the high-dimensional space for further analysis and processing. Select a suitable global coordinate system conversion method, such as using affine transformation, projection transformation, etc., to convert the high-dimensional mapping data into the global coordinate system. According to the requirements of the global coordinate system, calculate the parameters required for the conversion, such as projection parameters, affine matrix, etc. Apply the global coordinate system conversion method to the surveying and mapping geography high-dimensional mapping data to generate surveying and mapping geography global coordinate conversion data. Verify the converted data to ensure its accuracy and consistency in the global coordinate system.

[0100] Preferably, step S22 includes the following steps:

[0101] Step S221: performing feature decomposition on the local coordinate partition data set to generate geographic surveying and mapping spatial feature data;

[0102] Step S222: using a generalized reverse projection method to perform spatial relationship modeling on the geographic surveying and mapping spatial feature data to generate a preliminary spatial relationship model;

[0103] Step S223: performing local feature mapping on the preliminary spatial relationship model to generate a local feature mapping matrix; iteratively optimizing the local feature mapping matrix to generate a geographic surveying and mapping regression model set;

[0104] Step S224: performing spatial interpolation on the geographic surveying and mapping regression model set to generate geographic surveying and mapping spatial interpolation data; performing local coordinate transformation on the local coordinate partition data set through the geographic surveying and mapping spatial interpolation data to generate a local transformation model data set.

[0105] In an embodiment of the present invention, necessary preprocessing, such as denoising and normalization, is performed on the local coordinate partition data set. Appropriate eigendecomposition techniques, such as principal component analysis (PCA) and singular value decomposition (SVD), are selected. The processed local coordinate partition data set is subjected to eigendecomposition to extract the main spatial feature information, generate geographic surveying and mapping spatial feature data, describe the main spatial features of each local area, and generate a feature matrix containing the main spatial features for subsequent modeling and analysis. A generalized inverse projection method (such as generalized inverse matrix or spatial linear regression) is selected for spatial relationship modeling. The preliminary parameters of the spatial relationship model are calculated using the geographic surveying and mapping spatial feature data. A preliminary spatial relationship model is established, which describes the spatial relationship in the local coordinate partition data set. The preliminary spatial relationship model is verified to check its fitting effect and accuracy on the spatial features. The preliminary spatial relationship model is applied to the local feature mapping to generate a local feature mapping matrix. This matrix represents the mapping relationship of the spatial features in the local coordinate system. A suitable optimization algorithm (such as gradient descent, least squares method, etc.) is selected to iteratively optimize the local feature mapping matrix. The local feature mapping matrix is ​​updated according to the optimization results to generate a geographic surveying and mapping regression model set. These regression model sets are used to more accurately describe the spatial relationship of local coordinate data. Select appropriate spatial interpolation methods (such as Kriging interpolation, inverse distance weighting, etc.) and apply them to the geographic surveying and mapping regression model set. Use the regression model set for spatial interpolation to generate geographic surveying and mapping spatial interpolation data. These data describe the interpolation results of geographic surveying and mapping data in the global space. Use the geographic surveying and mapping spatial interpolation data to perform local coordinate transformation on the local coordinate partition dataset and generate a local transformation model dataset, which contains the local coordinate transformation results obtained by the spatial interpolation method.

[0106] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0107] Step S31: optimizing the data accuracy of standard multi-source surveying and mapping geographic data by using surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set;

[0108] Step S32: extracting topological basic units from the surveying and mapping geography optimization data set to obtain a basic topological unit set; constructing a topological relationship based on the basic topological unit set to generate topological relationship structure data;

[0109] Step S33: constructing a multi-level topological relationship network for the surveying and mapping geography optimization data set through the topological relationship structure data to generate surveying and mapping geography topological relationship network data;

[0110] Step S34: Performing spatial temporal dynamic analysis on the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to generate a spatial temporal dynamic data set.

[0111] In an embodiment of the present invention, the accuracy of standard multi-source surveying and mapping geographic data is optimized by using the model parameters in the local conversion model data set. Data correction, adjustment and correction are performed according to the model. The optimized data is precision-checked to ensure that it meets the expected accuracy standard. The optimized data is organized into a surveying and mapping geographic optimization data set for subsequent topological analysis and relationship network construction. Basic topological units are extracted from the surveying and mapping geographic optimization data set, and these units include basic geographic elements, such as points, lines, and surfaces. The extracted basic topological units are organized into sets to ensure that the spatial and attribute information of each unit is correctly recorded. Topological relationships, such as adjacency, inclusion, and intersection, are defined based on the basic topological unit set. Topological relationship structure data is constructed to describe the topological relationship between units, such as network structure, regional division, etc. The hierarchical structure of a multi-level topological relationship network is designed, including different topological levels and relationships. The surveying and mapping geographic topological relationship network data is generated using the topological relationship structure data. The network can include topological structures at different levels, such as regional networks, road networks, spatial partition networks, etc. The generated topological relationship network is optimized to ensure the connectivity, integrity and accuracy of the network. Integrate the data of each level in the topological network to form a complete surveying and mapping geographic topological relationship network data set. Combine the surveying and mapping geographic topological relationship network data with the time series data to prepare for time series dynamic analysis. Select appropriate spatial time series analysis methods, such as dynamic network analysis, time series pattern recognition, etc. Perform spatial time series dynamic analysis on the integrated data, detect and describe the changes and dynamic characteristics of the topological relationship network at different time points, generate a spatial time series dynamic data set, and record the dynamic changes of the topological relationship network, such as changes in network structure and time series changes of geographic elements.

[0112] Preferably, step S33 includes the following steps:

[0113] Step S331: performing topological hierarchical decomposition on the topological relationship structure data to generate topological hierarchical decomposition data, wherein the topological hierarchical decomposition data includes high-level topological decomposition data and low-level topological decomposition data;

[0114] Step S332: constructing a surface-line-point relationship network for the surveying and mapping geography optimization data set through high-level topological decomposition data to generate high-level topological network data; constructing a local connection relationship network for the surveying and mapping geography optimization data set through low-level topological decomposition data to generate low-level topological network data;

[0115] Step S333: performing topological network hierarchical fusion of high-level topological network data and low-level topological network data to generate multi-level fused topological network data; performing topological relationship weight calculation on the multi-level fused topological network data to obtain topological weight data;

[0116] Step S334: construct a topological relationship network for the multi-level fused topological network data according to the topological weight data to generate surveying and mapping geographic topological relationship network data.

[0117] In an embodiment of the present invention, the topological relationship structure data is hierarchically decomposed by selecting an appropriate decomposition method (such as hierarchical clustering or network decomposition technology). The topological structure is decomposed into high-level topological data, which describe the main topological relationships and structural features of the global scope. The topological structure is further decomposed into low-level topological data, which describe the detailed topological relationships and connection features of the local scope. The high-level and low-level topological decomposition data are sorted separately to ensure that the data of each level is complete and easy to process later. The surface-line-point relationship network is defined according to the high-level topological decomposition data. The surface represents the geographical area, the line represents the connection path, and the point represents the key geographical element. Construct high-level topological network data to record the relationship and connection between the surface-line-point. Construct high-level topological network data to record the relationship and connection between the surface-line-point. Construct low-level topological network data to record the local connection relationship and network structure. Select a suitable fusion method (such as weighted fusion or graph theory method) to fuse the high-level topological network data and the low-level topological network data. Integrate the high-level and low-level topological network data to generate multi-level fused topological network data. Ensure that the topological relationships at different levels are reasonably integrated during the fusion process. Define the calculation method of topological relationship weights, such as based on connectivity, distance, or importance. Calculate the weights of each relationship in the fused topological network data and generate topological weight data. Construct a topological relationship network for multi-level fused topological network data based on the topological weight data. Use weight information to optimize the network structure, improve the network's expressiveness and accuracy, generate the final surveying and mapping geographic topological relationship network data, and record the various levels and weight information of the network. Verify the constructed topological relationship network to ensure its accuracy and effectiveness. Check whether the nodes, edges, and relationships in the network meet expectations.

[0118] Preferably, step S34 includes the following steps:

[0119] Step S341: extracting the time series and spatial position change of the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to obtain the surveying and mapping geography time series data and the surveying and mapping geography spatial position change data;

[0120] Step S342: dividing the surveying and mapping geographic time series data and geographic spatial position change data into data sets to generate a model training set and a model test set; using the time series regression model algorithm and the spatial association model algorithm to perform model training on the model training set to generate a time series regression pre-model and a spatial association pre-model; using the model test set to perform model optimization iteration on the time series regression pre-model and the spatial association pre-model, thereby generating a time series regression model and a spatial association model;

[0121] Step S343: fusing the time series regression model and the spatial correlation model in time and space to generate a time and space correlation model;

[0122] Step S344: Use the spatiotemporal correlation model to predict the spatiotemporal dynamic change trend of the surveying and mapping geographic optimization data set to generate a spatial time series dynamic data set.

[0123] In an embodiment of the present invention, time series data is extracted from a surveying and mapping geography optimization data set by using surveying and mapping geography topological relationship network data. These data record the changes in geographic elements over time, generate surveying and mapping geography time series data, and represent the evolution and trend of geographic elements over time. Spatial position change data is extracted from surveying and mapping geography topological relationship network data. These data record the changes in spatial position of geographic elements, generate surveying and mapping geography spatial position change data, and describe the changes in spatial position of geographic elements. The surveying and mapping geography time series data and spatial position change data are divided into a model training set and a model test set. Ensure that the divided data set is representative and sufficient. Sort and mark the data set to prepare for model training and testing. Select a suitable time series regression model algorithm (such as ARIMA, LSTM, etc.) for training. Use the model training set to train the time series regression model to generate a time series regression pre-model. Select a suitable spatial association model algorithm (such as spatial regression, spatial autoregression, etc.) for training. Use the model training set to train the spatial association model to generate a spatial association pre-model. Use the model test set to optimize and iterate the time series regression pre-model and the spatial association pre-model, adjust the model parameters, and improve the prediction performance. Generate the final time series regression model and spatial association model through optimization iteration. Select a suitable spatiotemporal association fusion method (such as fusion algorithm, ensemble learning method) to fuse the time series regression model and the spatial association model, generate a spatiotemporal association model that comprehensively considers the time series and spatial relationship, and integrates the model's time series prediction ability and spatial analysis ability. Use the spatiotemporal association model to predict the surveying and mapping geographic optimization data set, analyze the spatiotemporal dynamic change trend in the data, generate a spatial time series dynamic data set, and record the predicted spatiotemporal dynamic change trend, including the change prediction of geographic elements in time and space. Verify the generated spatial time series dynamic data set to ensure the accuracy and practicality of the prediction results.

[0124] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0125] Step S41: performing distributed data compression on the spatiotemporal dynamic data set and the surveying and mapping geography optimization data set to generate distributed surveying and mapping geography compression data; performing data encryption on the distributed surveying and mapping geography compression data to generate distributed surveying and mapping geography encryption data;

[0126] Step S42: Based on cloud computing, the distributed surveying and mapping geography encrypted data is stored in a distributed storage architecture to generate distributed surveying and mapping geography storage data; data redundancy and recovery mechanisms are introduced into the distributed surveying and mapping geography storage data to generate a surveying and mapping geography redundant data set;

[0127] Step S43: constructing a search index based on the redundant surveying and mapping geographic data set to obtain a surveying and mapping geographic index; performing a multi-dimensional conditional search on the distributed surveying and mapping geographic storage data based on the surveying and mapping geographic index to generate a multi-dimensional search result set;

[0128] Step S44: construct a data warehouse for distributed surveying and mapping geographic storage data through a multi-dimensional search result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

[0129] In the embodiment of the present invention, a spatiotemporal dynamic data set and a surveying and mapping geography optimization data set are selected as compression objects. A suitable large-scale data compression algorithm (such as Huffman coding, LZ77, etc.) is applied to perform data compression to ensure that the compression process adapts to the distributed environment, generate distributed surveying and mapping geography compressed data, and reduce the overhead of data storage and transmission. A suitable encryption algorithm (such as AES, RSA, etc.) is selected to encrypt the distributed surveying and mapping geography compressed data. The data is encrypted to generate distributed surveying and mapping geography encrypted data to ensure data security and privacy protection. A distributed storage architecture is configured based on a cloud computing platform (such as AWS, Azure, etc.). The distributed surveying and mapping geography encrypted data is uploaded and stored on the cloud computing platform to generate distributed surveying and mapping geography storage data. A data redundancy mechanism (such as RAID, backup strategy) is configured to improve the availability and reliability of data, and a surveying and mapping geography redundant data set is generated for data recovery and fault tolerance. A suitable retrieval index structure (such as B-tree, inverted index, etc.) is selected based on the characteristics of the stored data. An index is constructed for the surveying and mapping geography redundant data set to obtain a surveying and mapping geography index. Multidimensional retrieval conditions (such as time, spatial range, data attributes, etc.) are set according to user needs. Perform multi-dimensional conditional retrieval of distributed geospatial data based on geospatial indexes to generate multi-dimensional retrieval result sets. Design appropriate database architecture and data models to support intelligent distributed geospatial data repositories. Build data warehouses based on multi-dimensional retrieval result sets to generate intelligent distributed geospatial data repositories. Perform geospatial data management operations such as data query, update, and maintenance through intelligent distributed geospatial data repositories. Continuously monitor and optimize data warehouse performance to ensure the efficiency and accuracy of data management operations.

[0130] In this specification, a surveying and mapping geographic data management system based on cloud computing is provided, which is used to execute the above-mentioned surveying and mapping geographic data management method based on cloud computing. The surveying and mapping geographic data management system based on cloud computing includes:

[0131] The semantic matching module is used to obtain surveying and mapping geographic data; match surveying and mapping semantic information on surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data;

[0132] The coordinate mapping module is used to perform local coordinate data partitioning on standard multi-source surveying and mapping geographic data to generate local coordinate partitioning data sets; perform surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data sets to generate surveying and mapping geographic high-dimensional mapping data; perform global coordinate system conversion on surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data;

[0133] The topological relationship modeling module is used to optimize the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate surveying and mapping geographic optimization data sets; to construct a multi-level topological relationship network for the surveying and mapping geographic optimization data sets to generate surveying and mapping geographic topological relationship network data; to perform spatial temporal dynamic analysis on the surveying and mapping geographic optimization data sets based on the surveying and mapping geographic topological relationship network data to generate spatial temporal dynamic data sets;

[0134] The data storage module is used to perform intelligent distributed storage of spatiotemporal dynamic data sets and surveying and mapping geographic optimization data sets to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

[0135] The beneficial effect of the present invention is that, through mapping semantic information matching and distributed spatiotemporal consistency calibration, the inconsistency problem of multi-source mapping geographic data in semantics and time and space is solved, the uniformity and standardization of data are ensured, and the accuracy and reliability of subsequent processing are improved. Through local coordinate partitioning and high-dimensional mapping, the local processing capability of data is optimized, and then the accurate conversion of data between different coordinate systems is ensured through global coordinate system conversion, which improves the efficiency and accuracy of data processing. Through precision optimization and topological relationship network construction, the fineness and structural integrity of data are enhanced, and further through spatial time series dynamic analysis, the dynamic update and analysis capabilities of data are realized, and the comprehensive analysis value of data is improved. Through intelligent distributed storage and multi-dimensional conditional retrieval, efficient management and rapid access of large-scale mapping geographic data are realized, and through the construction of data warehouse, the systematicness and operability of data are improved, and complex mapping geographic data management operations are supported. Therefore, the present invention improves the efficiency and quality of data management through semantic matching, spatiotemporal calibration, high-dimensional mapping, coordinate conversion, distributed storage and other technologies.

[0136] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0137] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A surveying and mapping geographic data management method based on cloud computing, characterized in that: The following steps are involved: Step S1: Acquire surveying and mapping geographic data; perform surveying and mapping semantic information matching on the surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; Conduct distributed spatiotemporal consistency calibration on surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data; Step S2: partitioning the standard multi-source surveying and mapping geographic data into local coordinate data to generate a local coordinate partition data set; Performing geography high-dimensional mapping on the local coordinate partition data set to generate geography high-dimensional mapping data; performing global coordinate system conversion on the geography high-dimensional mapping data to generate geography global coordinate conversion data; Step S3: optimizing the data accuracy of standard multi-source surveying and mapping geographic data by using surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set; constructing a multi-level topological relationship network for the surveying and mapping geographic optimization data set to generate surveying and mapping geographic topological relationship network data; Performing spatial temporal dynamic analysis on the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to generate a spatial temporal dynamic data set; step S3 includes the following steps: Step S31: optimizing the data accuracy of standard multi-source surveying and mapping geographic data by using surveying and mapping geographic global coordinate conversion data to generate a surveying and mapping geographic optimization data set; Step S32: extracting topological basic units from the surveying and mapping geography optimization data set to obtain a basic topological unit set; constructing a topological relationship based on the basic topological unit set to generate topological relationship structure data; Step S33: constructing a multi-level topological relationship network for the surveying and mapping geography optimization data set through the topological relationship structure data to generate surveying and mapping geography topological relationship network data; Step S34: Performing spatial temporal dynamic analysis on the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to generate a spatial temporal dynamic data set; Step S34 includes the following steps: Step S341: extracting the time series and spatial position change of the surveying and mapping geography optimization data set according to the surveying and mapping geography topological relationship network data to obtain the surveying and mapping geography time series data and the surveying and mapping geography spatial position change data; Step S342: dividing the surveying and mapping geographic time series data and geographic spatial position change data into data sets to generate a model training set and a model test set; using the time series regression model algorithm and the spatial association model algorithm to perform model training on the model training set to generate a time series regression pre-model and a spatial association pre-model; using the model test set to perform model optimization iteration on the time series regression pre-model and the spatial association pre-model, thereby generating a time series regression model and a spatial association model; Step S343: fusing the time series regression model and the spatial correlation model in time and space to generate a time and space correlation model; Step S344: using the spatiotemporal correlation model to predict the spatiotemporal dynamic change trend of the surveying and mapping geographic optimization data set to generate a spatial time series dynamic data set; Step S4: Intelligently distribute and store the spatial time series dynamic data set and the surveying and mapping geographic optimization data set to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on the distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for the distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to execute surveying and mapping geographic data management operations.

2. The method for managing surveying and mapping geographic data based on cloud computing according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire surveying and mapping geographic data; Step S12: performing data preprocessing on the surveying and mapping geographic data to generate processed surveying and mapping geographic data, wherein the data preprocessing includes data cleaning, data denoising, data format unification, and data outlier processing; Step S13: performing surveying and mapping semantic information matching on the processed surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; performing data alignment on the surveying and mapping geographic semantic matching data to generate surveying and mapping geographic information alignment data; Step S14: Perform distributed spatiotemporal consistency calibration on the surveying and mapping geographic information alignment data to generate standard multi-source surveying and mapping geographic data.

3. The method for managing surveying and mapping geographic data based on cloud computing according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: assigning local coordinate identifiers to the standard multi-source surveying and mapping geographic data to obtain local coordinate identifiers of the surveying and mapping geographic data; partitioning the standard multi-source surveying and mapping geographic data by the local coordinate identifiers of the surveying and mapping geographic data to generate a local coordinate partition data set; Step S22: constructing a multivariate space transformation model for the local coordinate partition data set by a generalized reverse projection method to generate a local transformation model data set; Step S23: performing surveying and mapping geography high-dimensional mapping on the local coordinate partition data set based on the local transformation model data set to generate surveying and mapping geography high-dimensional mapping data; Step S24: performing global coordinate system conversion on the surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data.

4. The method for managing surveying and mapping geographic data based on cloud computing according to claim 3, characterized in that: Step S22 includes the following steps: Step S221: performing feature decomposition on the local coordinate partition data set to generate geographic surveying and mapping spatial feature data; Step S222: using a generalized reverse projection method to perform spatial relationship modeling on the geographic surveying and mapping spatial feature data to generate a preliminary spatial relationship model; Step S223: performing local feature mapping on the preliminary spatial relationship model to generate a local feature mapping matrix; iteratively optimizing the local feature mapping matrix to generate a geographic surveying and mapping regression model set; Step S224: performing spatial interpolation on the geographic surveying and mapping regression model set to generate geographic surveying and mapping spatial interpolation data; performing local coordinate transformation on the local coordinate partition data set through the geographic surveying and mapping spatial interpolation data to generate a local transformation model data set.

5. The method for managing surveying and mapping geographic data based on cloud computing according to claim 1, characterized in that: Step S33 includes the following steps: Step S331: performing topological hierarchical decomposition on the topological relationship structure data to generate topological hierarchical decomposition data, wherein the topological hierarchical decomposition data includes high-level topological decomposition data and low-level topological decomposition data; Step S332: constructing a surface-line-point relationship network for the surveying and mapping geography optimization data set through high-level topological decomposition data to generate high-level topological network data; constructing a local connection relationship network for the surveying and mapping geography optimization data set through low-level topological decomposition data to generate low-level topological network data; Step S333: performing topological network hierarchical fusion of high-level topological network data and low-level topological network data to generate multi-level fused topological network data; performing topological relationship weight calculation on the multi-level fused topological network data to obtain topological weight data; Step S334: construct a topological relationship network for the multi-level fused topological network data according to the topological weight data to generate surveying and mapping geographic topological relationship network data.

6. The method for managing surveying and mapping geographic data based on cloud computing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing distributed data compression on the spatiotemporal dynamic data set and the surveying and mapping geography optimization data set to generate distributed surveying and mapping geography compression data; performing data encryption on the distributed surveying and mapping geography compression data to generate distributed surveying and mapping geography encryption data; Step S42: Based on cloud computing, the distributed surveying and mapping geography encrypted data is stored in a distributed storage architecture to generate distributed surveying and mapping geography storage data; data redundancy and recovery mechanisms are introduced into the distributed surveying and mapping geography storage data to generate a surveying and mapping geography redundant data set; Step S43: constructing a search index based on the redundant surveying and mapping geographic data set to obtain a surveying and mapping geographic index; performing a multi-dimensional conditional search on the distributed surveying and mapping geographic storage data based on the surveying and mapping geographic index to generate a multi-dimensional search result set; Step S44: construct a data warehouse for distributed surveying and mapping geographic storage data through a multi-dimensional search result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

7. The method for managing surveying and mapping geographic data based on cloud computing according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: classify the key fields of the surveying and mapping geography redundant data set to generate surveying and mapping geography key field classification data; construct a search index for the surveying and mapping geography redundant data set through the surveying and mapping geography key field classification data to generate a surveying and mapping geography index; Step S432: performing retrieval data collection on the distributed surveying and mapping geographic storage data based on the surveying and mapping geographic index to obtain retrieval field data; performing retrieval field pattern analysis on the retrieval field data to generate retrieval field pattern data, wherein the retrieval field pattern data includes a single field query pattern and a multi-field query pattern; Step S433: Calculate the key field matching rate according to the search field data and the surveying and mapping geography index through the single field query mode to generate the index matching degree; perform initial information search and sorting on the distributed surveying and mapping geography storage data through the index matching degree to generate the single field surveying and mapping geography search and sorting data; Step S434: performing a multi-field correlation analysis on the initial surveying and mapping geographic retrieval sorting data through a multi-field query mode to generate multi-field correlation analysis data; using the multi-field correlation analysis data to perform sorting optimization on the single-field surveying and mapping geographic retrieval sorting data to generate multi-field surveying and mapping geographic retrieval sorting data; Step S435: Integrate the search results of the single-field surveying and mapping geographic search and ranking data and the multi-field surveying and mapping geographic search and ranking data to generate a multi-dimensional search result set.

8. A surveying and mapping geographic data management system based on cloud computing, characterized in that: Used to execute the surveying and mapping geographic data management method based on cloud computing as claimed in claim 1, the surveying and mapping geographic data management system based on cloud computing comprises: The semantic matching module is used to obtain surveying and mapping geographic data; match surveying and mapping semantic information on surveying and mapping geographic data to generate surveying and mapping geographic semantic matching data; perform distributed spatiotemporal consistency calibration on surveying and mapping geographic semantic matching data to generate standard multi-source surveying and mapping geographic data; The coordinate mapping module is used to perform local coordinate data partitioning on standard multi-source surveying and mapping geographic data to generate local coordinate partitioning data sets; perform surveying and mapping geographic high-dimensional mapping on the local coordinate partitioning data sets to generate surveying and mapping geographic high-dimensional mapping data; perform global coordinate system conversion on surveying and mapping geographic high-dimensional mapping data to generate surveying and mapping geographic global coordinate conversion data; The topological relationship modeling module is used to optimize the data accuracy of standard multi-source surveying and mapping geographic data through surveying and mapping geographic global coordinate conversion data to generate surveying and mapping geographic optimization data sets; to construct a multi-level topological relationship network for the surveying and mapping geographic optimization data sets to generate surveying and mapping geographic topological relationship network data; to perform spatial temporal dynamic analysis on the surveying and mapping geographic optimization data sets based on the surveying and mapping geographic topological relationship network data to generate spatial temporal dynamic data sets; The data storage module is used to perform intelligent distributed storage of spatial time series dynamic data sets and surveying and mapping geographic optimization data sets to generate distributed surveying and mapping geographic storage data; perform multi-dimensional conditional retrieval on distributed surveying and mapping geographic storage data to generate a multi-dimensional retrieval result set; construct a data warehouse for distributed surveying and mapping geographic storage data through the multi-dimensional retrieval result set to generate an intelligent distributed geographic data repository to perform surveying and mapping geographic data management operations.

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