Land space planning method based on big data

Abstract

The invention discloses a territorial space planning method based on big data, and belongs to the technical field of territorial space planning. The method comprises the following steps of: 1, integrating territorial space comprehensive data and realizing standardized and normalized preprocessing and semantic association of the data; 2, precise expression and cross-scale conversion of spatial element semantics are achieved; 3, dynamically evaluating and quantifying the information distortion degree and uncertainty in the cross-scale semantic conversion process, and performing intelligent error correction; 4, realizing high-fidelity restoration and semantic consistency reconstruction of the information; 5, realizing an accurate and dynamic territorial space planning decision; 6, converting the cross-scale spatial semantic data into a visual and interactive visual form, and realizing intelligent presentation and interactive analysis of the spatial data; and 7, ensuring real-time performance, safety and openness of planning data, and realizing intelligent and dynamic management of territorial space planning.

Description

Technical Field

[0001] This application relates to the technical field of territorial space planning, and more specifically, to a territorial space planning method based on big data. Background Art

[0002] With the continuous advancement of globalization and urbanization, the rational allocation and sustainable development of territorial space resources are facing unprecedented challenges. Traditional territorial space planning methods often rely on experience and static data analysis, and it is difficult to meet the increasingly complex spatial resource management requirements. In recent years, with the rapid development of technologies such as big data, cloud computing, and artificial intelligence, spatial planning methods that can achieve real-time data collection, dynamic analysis, and intelligent decision-making have gradually emerged. These emerging technologies have shown great potential in improving the efficiency of spatial resource allocation, optimizing ecological environment protection, and promoting urban sustainable development.

[0003] However, there are still many problems in the current territorial space planning methods in practical applications. First, there are great challenges in the integration and standardized processing of multi-source spatial data. The data formats, accuracies, and update frequencies of different sources vary greatly, resulting in difficult data integration. Second, there is a lack of effective processing methods for multi-scale analysis and cross-scale semantic conversion of spatial data, making it difficult to achieve high-fidelity information restoration and semantic consistency reconstruction, thus affecting the accuracy and reliability of the planning results. Third, traditional planning decision-making methods lack intelligent and dynamic support and are difficult to adapt to complex and changing development scenarios and uncertain factors.

[0004] In summary, how to achieve intelligent integration, cross-scale conversion, and efficient decision-making of multi-source spatial data through big data technology has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a territorial space planning method based on big data for the above problems, including the following steps:

[0006] Step 1, integrate the comprehensive territorial space data and perform standardized, normalized preprocessing and semantic association of the data;

[0007] Step 2, automatically extract and encode the semantic features of spatial elements at different scales to achieve accurate expression and cross-scale conversion of spatial element semantics;

[0008] Step 3, dynamically evaluate and quantify the information distortion degree and uncertainty in the cross-scale semantic conversion process;

[0009] Step 4, repair and optimize the semantic association of cross-scale spatial data to achieve high-fidelity restoration of information and reconstruction of semantic consistency;

[0010] Step 5: Simulate the spatial resource allocation, ecological environment changes, and urban expansion trends under different development scenarios to achieve precise and dynamic decision-making for territorial spatial planning;

[0011] Step 6: Convert cross-scale spatial semantic data into an intuitive and interactive visualization form to achieve intelligent presentation and interactive analysis of spatial data;

[0012] Step 7: Achieve intelligent and dynamic management of territorial spatial planning.

[0013] Furthermore, Step 1 includes the following steps:

[0014] Comprehensively collect data from different platforms;

[0015] Perform unified format conversion and standardization processing on the collected data;

[0016] By establishing a standard metadata schema and keyword mapping table, achieve semantic alignment and association of geographical entities, attributes, and relationships in different data sources, and unify the association of ground object information in remote sensing images, spatial elements in geographic information data, structural data in urban construction archives, and statistical data in population censuses to form a rich and logically consistent integrated dataset;

[0017] Conduct quality assessment on the integrated dataset, including integrity, consistency, accuracy, and timeliness checks;

[0018] Perform precise geometric correction and registration on remote sensing images and geographic information data to achieve spatial alignment and overlay of multi-source data;

[0019] Build a comprehensive metadata management system to record the source, processing process, accuracy information, and usage limitations of each data source.

[0020] Furthermore, Step 2 includes the following steps:

[0021] Extract local and global feature information of spatial elements at different scales through parallel convolution operations and fuse them to generate multi-scale features;

[0022] Dynamically adjust the weights of different-scale features to adaptively focus on the regions and channels that are most critical for spatial semantic understanding:

[0023] Achieve information interaction and gradual refinement between different-scale feature maps, construct a semantic mapping channel from coarse-grained to fine-grained, and then integrate multi-scale semantic information;

[0024] Perform abstract representation and decoupling of spatial semantic features through learnable position encoding and semantic mapping techniques;

[0025] By stabilizing the multi-scale feature distribution, alleviate the problems of gradient vanishing and overfitting, while preserving the original semantic information of the features;

[0026] Through interpolation, convolutional downsampling, and upsampling operations, ensure the continuity and consistency of features between different resolutions and scales;

[0027] Introduce a multi-source data fusion and cross-validation mechanism to reduce the risk of information omission.

[0028] Furthermore, step 3 includes the following steps:

[0029] Comprehensively and quantitatively analyze the overall information distortion degree in the cross-scale semantic conversion process;

[0030] Through random sampling and probability inference, accurately estimate the cognitive uncertainty and random uncertainty in each link of semantic conversion;

[0031] Real-time identify and correct systematic biases and random errors in semantic conversion to ensure the accuracy and consistency of semantic information;

[0032] According to the real-time evaluated information distortion degree, autonomously adjust the intensity and scope of the error correction strategy to achieve an intelligent balance between semantic integrity and conversion accuracy;

[0033] By comparing the semantic representations at different scales, construct an explicit semantic consistency loss function to minimize the semantic bias in the scale conversion process;

[0034] Through continuous feedback learning and self-correction, gradually improve the accuracy and reliability of cross-scale semantic conversion, and achieve dynamic perception and intelligent correction of information distortion.

[0035] Furthermore, step 4 includes the following steps:

[0036] Through an adaptive attention mechanism and semantic relevance evaluation, accurately capture the potential semantic connections between spatial data at different scales and reconstruct the lost semantic information;

[0037] Design a multi-task constraint loss function to optimize the semantic mapping relationship of cross-scale spatial data and maintain global semantic consistency and local detail accuracy;

[0038] Through generator-discriminator adversarial training, adaptively restore the missing or ambiguous semantic information in cross-scale spatial data, and ensure the high fidelity of semantic information at the visual and semantic levels through reconstruction loss and perceptual loss;

[0039] Dynamically quantify and adjust the semantic association strength between data at different scales to achieve fine-grained modeling and intelligent calibration of semantic relevance;

[0040] Effectively transmit key semantic information through multi-scale residual connections and information compression strategies, minimizing the loss and distortion of semantic information during scale conversion;

[0041] Through iterative feedback learning and adaptive adjustment, continuously evaluate and optimize the semantic relevance of cross-scale spatial data, realizing the dynamic reconstruction, repair, and enhancement of semantic information, and ultimately achieving the goal of high-fidelity restoration and semantic consistency reconstruction.

[0042] Furthermore, step 6 includes the following steps:

[0043] Realize the semantic mapping and intelligent conversion from abstract data to intuitive images;

[0044] Realize the free scaling, switching, and in-depth analysis of spatial semantic information;

[0045] Utilize context reasoning and dynamic association to automatically identify and highlight key semantic elements in spatial data, providing intelligent recommendations, semantic links, and context association visualization;

[0046] Realize the visual presentation of the dynamic evolution process of spatial semantic data over time and space dimensions;

[0047] By analyzing users' interaction behaviors, cognitive preferences, and professional backgrounds, intelligently recommend and generate visualization expressions suitable for different user needs;

[0048] Through responsive design and intelligent terminal adaptation, ensure the consistency and high-quality presentation of spatial semantic data visualization on different devices.

[0049] Furthermore, step 7 includes the following steps:

[0050] Establish a decentralized data governance model through multi-party secure computing, homomorphic encryption, and trusted execution environments;

[0051] Use noise addition, local training, and secure aggregation technologies to achieve cross-institutional and cross-regional spatial data collaborative learning and intelligent analysis while protecting personal and sensitive information;

[0052] Precisely control the access rights of different roles to spatial planning data through refined permission management, dynamic access policies, and context-aware technologies;

[0053] Through microservice architecture, message queues, and event sourcing technologies, realize the real-time update and multi-source collaboration of land spatial planning data;

[0054] Continuously monitor and optimize the quality of spatial planning data, and establish a closed-loop feedback mechanism for data governance;

[0055] Integrate compliance detection and data lineage tracing technologies to achieve intelligent governance and compliance management of territorial space planning data.

[0056] Compared with the prior art, the beneficial effects of this application are as follows:

[0057] This application uses big data and intelligent methods to integrate and accurately represent territorial space data across scales, and realizes high-precision, intelligent, and dynamic decision-making management of territorial space planning through dynamic assessment, error correction, scenario simulation, and multi-dimensional visualization. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of a territorial space planning method based on big data provided for an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0060] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0061] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0062] Please refer to Figure 1 , a territorial space planning method based on big data, comprising the following steps:

[0063] Step 1, integrate comprehensive territorial space data and perform standardized, normalized preprocessing and semantic association of the data;

[0064] Step 2, automatically extract and encode semantic features of spatial elements at different scales to achieve accurate expression and cross-scale conversion of spatial element semantics;

[0065] Step 3, dynamically evaluate and quantify the information distortion degree and uncertainty in the cross-scale semantic conversion process, and perform intelligent error correction;

[0066] Step 4, repair and optimize the semantic association of cross-scale spatial data to achieve high-fidelity restoration of information and reconstruction of semantic consistency;

[0067] Step 5: Simulate the spatial resource allocation, ecological environment changes, and urban expansion trends under different development scenarios to achieve precise and dynamic decision-making for territorial spatial planning;

[0068] Step 6: Convert cross-scale spatial semantic data into intuitive and interactive visualization forms to achieve intelligent presentation and interactive analysis of spatial data;

[0069] Step 7: Establish a secure and controllable spatial data sharing and privacy protection technology system to ensure the timeliness, security, and openness of planning data, and achieve intelligent and dynamic management of territorial spatial planning.

[0070] Step 1 involves the integration of a large amount of territorial spatial data from different sources and formats, including multiple fields such as Geographic Information System (GIS), remote sensing images, geological surveys, and land use. In this process, the standardization and normalization of data are crucial, aiming to unify the data format and quality so that it can be integrated across platforms and systems. Standardization not only ensures the consistency of data in different application scenarios but also effectively reduces the risks of errors and inconsistencies during further analysis and processing. Normalization processes data according to unified rules to ensure the consistency of data dimensions, units, coordinate systems, etc. At the same time, the realization of semantic association links multiple spatial data sets, enabling practical connections to be formed between different data sets. For example, through the association of land use data and environmental monitoring data, the interaction between urban expansion and ecological environment changes can be better understood. Through these steps, an accurate and reliable basis can be provided for subsequent data analysis.

[0071] In Step 2, the automatic extraction of spatial elements at different scales is the key to achieving precise territorial spatial planning. The scales of spatial elements include national scale, regional scale, urban scale, etc. As the scale changes, the expression and semantic meaning of spatial features also change. Therefore, being able to accurately extract spatial elements at different scales can effectively perform semantic conversion across different spatial scales. For example, at the global scale, the land use types may mainly focus on agriculture and nature reserves, while at the urban scale, the land use types may change to residential, commercial, and industrial land. Through automated technologies, these spatial features can be identified and extracted, and encoded to form a clear spatial semantic representation. In addition, cross-scale semantic conversion is a complex process that requires seamless connection of information across different scales to ensure effective spatial analysis and planning at different scales.

[0072] In Step 3, with the transformation of cross-scale spatial elements, information distortion and uncertainty are inevitable phenomena. Due to the different spatial characteristics at different scales, the accuracy and reliability of information may decline during the transformation process. Therefore, dynamically evaluating and quantifying these information distortions and uncertainties becomes the core link to ensure the quality of planning decisions. By applying intelligent algorithms, the degree of information distortion can be monitored and quantified in real time, and the parameters or methods of cross-scale transformation can be adjusted according to the actual situation for intelligent error correction. This error correction can not only improve the accuracy of data but also ensure the scientificity and rationality of decisions. For example, when predicting the urban expansion trend, through the quantitative analysis of information distortion and uncertainty, a more realistic prediction result of urban development trend can be obtained.

[0073] Repairing and optimizing the semantic association of cross-scale spatial data in Step 4 is the key step to ensure the consistency and high fidelity of spatial data. With the progress of data scale transformation, semantic mismatches or losses between data will occur. For example, at the regional scale, there may be inconsistencies in land use types compared with those at the national scale. To ensure high-fidelity restoration and semantic consistency, advanced repair technologies such as data fusion, deep learning, and image recognition must be adopted to optimize the semantic association of cross-scale data. This optimization can not only improve the accuracy of data but also maintain the high fidelity of the original data during cross-scale transformation, avoiding the loss and misunderstanding of important spatial information, thus ensuring the scientificity and feasibility of territorial space planning decisions.

[0074] In Step 5, by establishing a dynamic simulation model, the utilization effects of territorial space resources under different development scenarios can be evaluated in real time. These simulation scenarios include multiple factors such as population growth, economic development, urbanization process, and climate change. During the simulation process, by considering various possible scenario changes, it can help planners predict the possible future spatial demands and resource allocation situations. For example, when simulating the urban expansion trend, it can predict the impact of the urbanization process on the ecological environment, helping planners adjust the spatial planning scheme in a timely manner to avoid resource waste and ecological damage. In addition, the simulation method based on big data can achieve dynamic update and adjustment to ensure the adaptability and feasibility of planning decisions.

[0075] In Step 6, by converting cross-scale spatial semantic data into an intuitive and interactive visualization form, it can help decision-makers intuitively understand the meaning and value behind spatial data. For example, using map visualization technology to display information such as urban expansion trends and ecological environment changes can help planners visually see the resource allocation and land use changes under different scenarios. The interactive visualization platform can also, through real-time data updates and dynamic analysis functions, enable decision-makers to conduct personalized spatial data analysis according to different needs, improving the accuracy and efficiency of decision-making. In addition, the application of intelligent presentation technology can provide an easy-to-understand way of presenting spatial data for non-professional users, thereby enhancing the public's awareness and participation in spatial planning.

[0076] In Step 7, the sharing and privacy protection of spatial data are one of the key technologies to ensure the intelligent and dynamic management of territorial spatial planning. With the development of big data and artificial intelligence technologies, the acquisition, analysis, and use of spatial data have gradually penetrated into various fields. However, the sharing of data is also accompanied by the risks of privacy leakage and security issues. Therefore, it is particularly important to establish a secure and controllable spatial data sharing and privacy protection technology system. By adopting methods such as encryption technology, access control, and data desensitization, the security of spatial data during the sharing process can be ensured. In addition, real-time data update and sharing technology can ensure the timeliness and accuracy of planning data, enabling planning decisions to quickly respond to the needs of dynamic changes. Through this technology system, it is possible to ensure the coexistence of data openness and security, promoting the efficient implementation and intelligent management of territorial spatial planning.

[0077] In summary, the above seven steps form a complete system of a territorial spatial planning method based on big data. From data integration, spatial feature extraction, cross-scale transformation, to simulating development scenarios and intelligent decision-making, and then to visualization presentation and data security protection, all provide technical support for realizing accurate and dynamic territorial spatial planning decisions. This method not only improves the efficiency and accuracy of spatial planning but also provides a solid technical foundation for future intelligent and dynamic territorial management.

[0078] Furthermore, Step 1 includes the following steps:

[0079] Comprehensively collect data from different platforms, including remote sensing satellite images, geographic information data, urban construction archives, and the latest census information. Remote sensing satellite images provide extensive ground information, which can reflect surface changes and are suitable for large-scale spatial analysis; geographic information data provides precise geographic coordinate information for spatial features and is commonly used in map-making and spatial planning; urban construction archives include urban infrastructure, building information, and land use, etc., and are applicable to urban expansion and urban management; census information can provide the dynamic changes of social and economic activities and provide the population and social and economic background for planning decisions. The collection of these data not only provides rich resources for subsequent data integration and analysis but also provides basic data support for the formation of a comprehensive and accurate national territorial space plan.

[0080] Perform unified format conversion and standardization processing on the collected data to eliminate technical differences between different data sources. Unified format conversion and standardization processing of the collected data are the basis for achieving data interoperability and fusion. Different data sources usually have technical differences due to different collection methods, storage formats, semantic definitions, and unit specifications. Therefore, to address this issue, the following technical means need to be adopted: 1) Format conversion and compatibility processing: Format conversion is the primary step in dealing with technical differences between different data sources. Different data sources may be stored in various formats, such as JSON, XML, CSV, binary files, database tables, or proprietary format files. To achieve a unified format, various types of data need to be converted into the target format through data parsing tools (such as pandas, GDAL, FME) and custom scripts. For geospatial data, a common GIS standard format (such as GeoJSON, Shapefile, or PostGIS) can be adopted to make spatial data more compatible. For image data, remote sensing processing tools (such as ENVI, ERDAS) can be used to convert the original data into a common raster data format (such as GeoTIFF). At the same time, to be compatible with heterogeneous databases, data access and format conversion between different databases can be achieved through middleware technologies (such as ODBC, JDBC). 2) Standardized semantic definition: After the format is unified, the semantic definition of the data needs to be standardized to eliminate differences in field naming, unit usage, and data types between different data sources. For this purpose, domain ontology or common metadata standards (such as ISO 19115, DCAT) can be adopted to uniformly describe the concepts and relationships in the data. By establishing standardized field mapping rules, the field names, meanings in the data source are corresponded with the standardized semantic model. For example, align the "temp" field with "temperature" and unify the storage unit to the standard international unit (such as Celsius or Kelvin). Tools such as Protégé can be used to build and manage the semantic model, and combined with automatic mapping tools (such as RDF or SPARQL), to achieve semantic consistency across source fields. 3) Consistent conversion of data types: Since different data sources may store data in different data types, such as integer, floating-point, or string data, it is necessary to unify the types through type conversion tools (such as ETL tools or data conversion libraries in programming languages). For example, convert all numeric fields to floating-point for easier numerical calculation, and uniformly convert time fields to the ISO 8601 standard format (such as "YYYY-MM-DDTHH:mm:ssZ") to ensure consistent parsing of timestamps. Especially when dealing with spatial data, it is also necessary to unify the geographic coordinate reference system (CRS), usually adopting the WGS84 standard to ensure consistency in visualization and analysis of spatial data in different systems.4) Missing value handling and outlier cleaning: Missing values or outliers may occur during the collection of different data sources, and these issues must be resolved during the standardization process. For missing values, interpolation algorithms (such as linear interpolation, Kriging interpolation) can be used to fill them, or reasonable default values can be filled according to business requirements. For example, missing values in spatial data can be estimated by spatial interpolation methods; missing values in statistical data can be filled by referring to surrounding values or historical trends. For outliers, they can be removed or corrected by combining distribution detection (such as standard deviation method, box plot analysis) and rule verification (such as unit range limit) to improve data consistency and accuracy. 5) Unifying multi-source units: Since different data sources may use different measurement units (such as kilometers and miles, Celsius and Fahrenheit), the units of all data fields must be uniformly processed. Automatic conversion between units can be achieved by constructing a unit conversion function library or introducing existing conversion tools (such as the pint library in Python). For example, all distance data can be uniformly converted to meters, and temperature data can be unified to Celsius. This process of unit standardization can reduce errors in subsequent analysis and improve data usability at the same time.

[0081] By establishing a standard metadata schema and a keyword mapping table, semantic alignment and association of geographical entities, attributes, and relationships in different data sources are achieved. The feature information in remote sensing images, spatial elements in geographic information data, structural data in urban construction archives, and statistical data in population censuses are unified and associated to form an integrated dataset rich in content and consistent in logic. In the process of data integration and semantic alignment, first, a standard metadata schema needs to be established to ensure the effective docking and association of geographical entities, attributes, and relationships from different data sources. The metadata schema is the descriptive layer of data, defining the structure, semantics, constraints, and various format specifications of the data. For this purpose, adopting a unified data modeling standard, such as ISO 19115 (Geographic Information - Metadata Standard), can effectively describe the types, scales, accuracies, and format requirements of spatial data. Through this standardization process, various heterogeneous data sources, such as remote sensing images, Geographic Information System (GIS) data, urban construction archives, and population census data, can be organized and stored through the same semantic framework, thus laying a foundation for subsequent semantic alignment and data fusion. Second, the creation of the keyword mapping table is a key technical means to ensure semantic alignment of different data sources. Geographical entities, attributes, and relationships in different data sources may use different naming, classification, and annotation methods. At this time, by establishing a keyword mapping table, similar or identical content in different datasets can be unified. Specifically, using Ontology technology, standardized definitions can be assigned to each entity and attribute through a semantic network, and the vocabulary in each data source can be aligned through mapping relationships. For example, in remote sensing image data, "forest" can correspond to "woodland" in GIS data and can also be mapped to the "green belt" field in urban construction archives. Through these mapping relationships, the system can achieve interoperability and sharing of data in different fields and ensure the compatibility of cross - domain data. In the process of data integration, different types of data, such as remote sensing image data, geographic information data, urban construction archives, and population census data, have different spatial attributes and semantic characteristics. Therefore, spatial analysis and statistical methods need to be used for data association. For the feature information in remote sensing image data, image - processing techniques and machine - learning algorithms can be used for automatic extraction. For example, object classification can be carried out through a Convolutional Neural Network (CNN) to identify elements such as urban buildings, roads, and green spaces in the image. In geographic information data, the association of spatial elements can utilize spatial data models, such as vector models (points, lines, polygons) and raster models, and combined with a spatial database (such as PostGIS) for spatial indexing and query to achieve precise matching of spatial positions and geographical relationship modeling of data. The structural data in urban construction archives may involve information such as the height, use, and materials of buildings. These data can be standardized through Building Information Modeling (BIM) technology and combined with other spatial data for analysis.Census data involves a large amount of socioeconomic information. Through statistical analysis methods, population data can be correlated with other spatial data (such as geographical information and structural data) to construct a multi-dimensional dataset that reflects dynamic changes such as urban planning and regional development.

[0082] Conduct a quality assessment of the integrated dataset, including integrity, consistency, accuracy, and timeliness checks. Data quality assessment is an important link to ensure the effect of data integration. The integrated dataset often comes from different sources and time points. Therefore, before further analysis, strict quality assessment must be carried out. Integrity checks of data can ensure that all types of collected data are not lost or omitted, and ensure that key information in each data source has been fully integrated; consistency checks can discover and correct conflicts and inconsistencies between different data sources. For example, information given by different data sources within the same geographical area may vary; accuracy checks evaluate whether the data conforms to the actual situation in the real world to ensure that the data can truly reflect geographical and socioeconomic conditions; timeliness checks focus on the update time of the data to ensure that the integrated data can reflect the latest spatial changes and social dynamics. Through these checks, potential errors and problems can be discovered and corrected in a timely manner to ensure that the finally generated dataset has high quality and high reliability.

[0083] Perform precise geometric correction and registration on remote sensing images and geographical information data to achieve spatial alignment and overlay of multi-source data. Spatial alignment and overlay of remote sensing images and geographical information data are key steps to achieve cross-data-source spatial analysis. During the shooting process of remote sensing images, geometric distortions may occur due to factors such as shooting angle, satellite position, and weather. Geographical information data (such as vector data) sometimes also has certain spatial errors. To eliminate these errors, precise geometric correction and registration must be carried out to ensure that the images and geographical information data can be aligned under the same spatial framework. Geometric correction corrects the geometric distortions of the images so that the remote sensing images can accurately reflect the actual situation of the Earth's surface; registration is to compare and align the remote sensing images with existing geographical information data (such as maps, boundaries, etc.) to ensure their spatial consistency. Through these operations, multiple data sources can be superimposed and analyzed under the same coordinate system to achieve precise fusion of multi-source data, providing a solid foundation for subsequent spatial analysis and decision-making.

[0084] Build a comprehensive metadata management system to record the source, processing process, accuracy information, and usage restrictions of each data source. Metadata is data about data, containing key information such as the source, processing process, accuracy information, and usage restrictions of the data. A perfect metadata management system can help users clearly understand the background and characteristics of each data source, so as to make more accurate analysis and decisions. By recording the source and processing process of each data source, the history of the data can be traced, ensuring the transparency and verifiability of the data; the recording of high-precision information helps users understand the accuracy of the data, so as to select appropriate data when high-precision analysis is required; usage restrictions help ensure that the data is used within the legal scope, avoiding privacy violations or regulatory violations. The construction of the comprehensive metadata management system not only improves the management and utilization efficiency of the data, but also provides guarantees for the sharing and long-term use of the data.

[0085] In summary, the above steps provide a complete technical framework for data collection, processing, integration, and management. From the comprehensive collection of data to format standardization, then to semantic association, quality assessment, and spatial alignment, each step provides basic data support for subsequent territorial spatial planning. Through precise data processing and management, not only the high quality and high reliability of the data are ensured, but also a solid technical guarantee is provided for spatial analysis and decision-making. The technical effects of these steps are reflected in improving data utilization efficiency, ensuring data accuracy, and enhancing the scientificity and operability of planning decisions.

[0086] Furthermore, Step 2 includes the following steps:

[0087] Extract the local and global feature information of spatial elements at different scales through parallel convolution operations, and fuse them to generate multi-scale features, so as to achieve the effective integration and joint attention of detailed features and overall semantics. Spatial elements at different scales may show different manifestations of local and global features during the processing. Local features usually manifest as spatial detail information, such as edges, textures, etc., while global features involve spatial layout and large-scale geographical or social structures. Parallel convolution operations can extract these local and global features simultaneously by performing convolutions at multiple scales, enabling the effective integration and joint attention of detailed features and overall semantics. By processing the input data with convolution kernels of different scales, a balance can be found between local details and global context, thereby enhancing the comprehensive understanding of spatial data. For example, in urban planning, detailed features may involve the form of individual buildings, while global features may involve the overall layout of the city. Through this process, the semantic information of details and the whole is effectively fused, providing comprehensive data support for further spatial planning and decision-making.

[0088] Dynamically adjust the weights of features at different scales, adaptively focus on the regions and channels that are most critical for spatial semantic understanding, so as to improve the effectiveness of feature representation. Dynamically adjusting the weights of features at different scales is one of the core methods to improve the effectiveness of feature representation. During the process of multi-scale feature extraction, the information at different scales contributes differently to spatial semantics. Therefore, it is necessary to adaptively adjust the weights of features according to the actual situation. Some regions or channels may contain more important semantic information, while other regions or channels may contribute less. By introducing an adaptive mechanism, it is possible to automatically focus on the most critical regions when processing features at different scales. For example, in urban space analysis, some regions may change significantly in the short term, so the weights of their spatial features should be increased accordingly, while the weights of long-term stable regions can be appropriately reduced. The adaptive focusing mechanism can more intelligently understand spatial semantics, optimize the representation effect of features, avoid interference from irrelevant information, and enhance the sensitivity to key regions and channels.

[0089] Achieve information interaction and gradual refinement between feature maps at different scales, construct a semantic mapping channel from coarse-grained to fine-grained, and then integrate multi-scale semantic information. Feature information at different scales often varies in semantic expression. Coarse-grained feature maps provide a large-scale spatial background information, while fine-grained feature maps contain more local and detailed information. By achieving information interaction between these feature maps, an effective connection and information transmission channel can be established between different scales. For example, coarse-grained feature maps can provide the overall spatial layout, while fine-grained feature maps are helpful for identifying specific spatial objects. Through a step-by-step refinement method, coarse-grained feature information will gradually be transformed into fine-grained features, thus achieving an effective semantic mapping between different scales. This step-by-step refinement process can not only retain the spatial relationships at large scales but also enable accurate spatial understanding at the detailed level. This method is particularly important for the comprehensive analysis of spatial data, especially in complex urban planning and ecological environment monitoring, where it can ensure comprehensive analysis under a multi-scale spatial background.

[0090] Abstract representation and decoupling of spatial semantic features through learnable position encoding and semantic mapping techniques. In spatial data, position encoding is to combine geographical spatial information with specific semantic information, so as to better understand the relative positions and spatial relationships of spatial features. Semantic mapping techniques abstract and decouple spatial features, transforming complex spatial information into an operable feature representation. Through position encoding, the relative relationships between different spatial regions, such as distance, direction, etc., can be learned, providing an accurate spatial background for subsequent spatial analysis. Semantic mapping techniques can remove complex spatial background information and focus on more meaningful spatial features. In spatial planning, this process can help identify meaningful regions or spatial patterns, and then guide decision-making. Through this method, spatial semantic information is more accurately and efficiently abstracted and expressed, providing important support for subsequent spatial data analysis and intelligent decision-making.

[0091] By stabilizing the multi-scale feature distribution, alleviate the problems of gradient vanishing and overfitting, while maintaining the original semantic information of the features. When dealing with multi-scale features, gradient vanishing and overfitting are common problems, especially when dealing with high-dimensional data. These problems will affect the final prediction accuracy. By stabilizing the multi-scale feature distribution, these problems can be effectively alleviated. During the multi-scale feature learning process, gradually adjust the distribution of features at different scales, so that the feature information at each scale is evenly expressed, thus avoiding the loss of information or overfitting of some features during training. This stabilization can maintain the original semantic information of the features, so that when facing complex spatial data, it can still maintain sensitivity and expression ability to the original information. For example, when dealing with remote sensing image data, through a stable feature distribution, ground object information can be accurately captured, avoiding misjudgment or over-simplification of features.

[0092] Through interpolation, convolutional downsampling and upsampling operations, ensure the continuity and consistency of features between different resolutions and scales, and maintain the integrity of semantic information during scale transformation. When converting between different resolutions and scales, problems such as loss or distortion of feature information are usually faced. Through interpolation, convolutional downsampling and upsampling operations, a continuous and consistent feature representation can be effectively established between different scales. The interpolation method restores the lost detailed information by interpolating and supplementing the low-resolution image; convolutional downsampling and upsampling operations help to perform size transformation of feature maps between different scales, ensuring the consistency of features at different resolutions. Through these operations, it can be ensured that no important spatial semantic information is lost during the multi-scale feature transformation process. For example, during the resolution change process of remote sensing images, the continuity of ground object information can be maintained, ensuring that the data after scale transformation can still provide accurate spatial analysis results.

[0093] Introduce a multi-source data fusion and cross-validation mechanism to reduce the risk of information omission. The main methods include: comparing spatial elements in different data sources to cross-validate the integrity and accuracy of feature extraction; designing a feature completion strategy based on multi-modal learning to automatically identify and fill in possible missing spatial semantic features; establishing a feature integrity evaluation index to quantify and track the coverage and depth of feature extraction; adopting an active learning method to identify feature regions with low confidence and re-extract and verify deep features specifically. This step not only improves data integrity but also enhances feature expression ability. By comparing spatial elements in different data sources, the cross-validation mechanism can effectively identify and complete missing information. In addition, the multi-modal learning strategy ensures the comprehensiveness of features, while the active learning method further optimizes the quality of feature extraction by focusing on regions with low confidence. The introduction of the feature integrity evaluation index provides a quantification standard, making the entire feature extraction process more controllable and transparent.

[0094] In summary, the above steps demonstrate that in the process of multi-scale spatial data processing, precise extraction and effective integration of spatial features are achieved through technologies such as parallel convolution, multi-scale feature fusion, dynamic adjustment, and semantic mapping. Each link not only improves the ability to understand local details and global semantics but also ensures the stability and consistency of feature information at different scales. These technologies continuously optimize the representation of spatial features, enabling efficient processing of large-scale data in complex spatial environments and providing precise support for intelligent spatial planning and decision-making.

[0095] Furthermore, the multi-scale feature is expressed by the formula: F(X) = ∑ s＝1 S α s ·(Conv(X, W s ) + b s ), where F(X) is the final multi-scale feature representation, that is, the spatial feature obtained through multi-scale convolution operations and fusion; S is the number of scales, representing the number of convolution kernels at different scales; X is the input comprehensive national land space data; Conv(X, W s ) represents performing a convolution operation on the input data X using the convolution kernel W s ; b s is the bias term of the s-th convolution operation; W s is the convolution kernel of the s-th scale, which determines the receptive field size of the convolution operation; α s is the weight coefficient of the s-th scale, used to represent the importance of each scale in the final feature fusion, and is dynamically adjusted through the following formula: α s = A s (X) / ∑ s＝1 S A s (X), where As (X) is the attention score at the s-th scale, A s (X) = σ(W a ·F s (X) + b a ), where σ(·) is the activation function, b a is the bias term of the attention network, F s (X) is the feature map at the s-th scale, which is extracted from the input data X through a convolution operation; W a is the weight matrix of the attention network, which is used to calculate the attention score for each scale.

[0096] In summary, through the above technology, features can be effectively extracted and fused from multi-scale spatial data, not only retaining detailed information but also strengthening the understanding of global semantics. Dynamically adjusting the weights of different-scale features and optimizing feature selection through the attention mechanism can adaptively focus on the most critical regions and scales, thereby improving the representation ability of spatial semantic features. This method provides powerful technical support for complex national land space planning and decision-making, can conduct in-depth analysis under different scales and various spatial features, and provides accurate data support for decision-makers.

[0097] Furthermore, the feature integrity evaluation index C(X) is expressed by the formula: C(X) = (the number of semantically features extracted completely / the total expected number of semantically features) · τ, where τ is a dynamically adjusted weight factor used to measure and adjust the weight of the feature integrity evaluation index C(X) to reflect the comprehensiveness or importance of feature extraction in specific situations. τ is dynamically adjusted through multi-source data cross-validation and active learning methods to improve the comprehensiveness of feature extraction.

[0098] In summary, by introducing the feature integrity evaluation index C(X) and the dynamically adjusted weight factor τ, combined with multi-source data cross-validation and active learning methods, the comprehensiveness and accuracy of the feature extraction process have been significantly improved. Feature integrity evaluation is not limited to the analysis of static data, but through adaptive adjustment and multi-perspective verification mechanisms, it can always maintain high efficiency and accuracy in complex and changing practical applications. The combined effect of these technical means not only optimizes the quality of feature extraction but also provides a solid foundation for the intelligent processing of multi-modal data, thereby improving the robustness and generalization ability.

[0099] Furthermore, step 3 includes the following steps:

[0100] Construct a multi-dimensional information distortion evaluation index system, including multi-indexes such as semantic information entropy, mutual information, structural similarity index, and feature space distribution difference, to comprehensively and quantitatively analyze the overall information distortion degree in the cross-scale semantic conversion process. In the cross-scale semantic conversion process, the accuracy and consistency of data are crucial. Therefore, constructing a multi-dimensional information distortion evaluation index system is a key step to ensure data quality. This system includes multi-indexes such as semantic information entropy, mutual information, structural similarity index, and feature space distribution difference, which can comprehensively quantify the loss of information in the conversion process. These indexes can analyze information distortion from different perspectives, and then provide accurate evaluation and feedback. Semantic information entropy is a tool for measuring information uncertainty. By calculating the entropy value of each part in the spatial data, it can reflect the degree of information loss in the data conversion process. In cross-scale conversion, a higher information entropy means greater data variation, which may lead to the loss of important features. Therefore, the change in entropy value can effectively reveal the degree of distortion in semantic conversion. Mutual information measures the degree of information sharing between two data sets. In multi-scale data conversion, the calculation of mutual information helps to understand the similarity and correlation between data at different scales. A lower mutual information value may indicate a decrease in the correlation between data during the semantic conversion process, which helps to discover the loss of semantic information caused by scale transformation. The structural similarity index is an index for measuring the consistency of the spatial data structure, and is usually used for comparative analysis of images and spatial data. In cross-scale semantic conversion, this index can effectively reflect the possible spatial layout distortion in the conversion process by quantifying the structural changes of data at different scales. A lower structural similarity indicates that there may be a large spatial deformation or structural error in cross-scale conversion. The feature space distribution difference index helps us understand the changes in data distribution at different scales by comparing the distributions of feature spaces. When the distribution difference caused by scale conversion is large, it means the loss or variation of semantic information, which in turn affects the overall quality and usability of the data. By comprehensively using these indexes, the possible distortion phenomena in the cross-scale conversion process can be comprehensively evaluated from multiple dimensions, potential problems can be identified in a timely manner, and an effective basis can be provided for subsequent correction.

[0101] Introduce Bayesian neural network and Monte Carlo failure simulation technology. Through random sampling and probabilistic inference, accurately estimate the cognitive uncertainty and random uncertainty in each link of semantic transformation. During the process of cross-scale semantic transformation, there are a large number of uncertain factors, which come from the diversity of data, different scales, and the inconsistency of semantic expressions. The introduction of Bayesian neural network (BNN) and Monte Carlo failure simulation technology helps to accurately estimate these uncertainties and provides a reliable feedback mechanism for the model. The Bayesian neural network models the weights in the neural network as a probability distribution by introducing a probabilistic inference method. Different from traditional neural networks, the Bayesian neural network can handle the uncertainty in data and estimate the credibility of each decision through posterior inference. In cross-scale semantic transformation, the Bayesian neural network can capture the uncertainties existing in the semantic transformation process at different scales and accurately reflect the uncertainty of each link, thus providing a basis for subsequent error correction. The Monte Carlo failure simulation technology can simulate the failures in different scenarios during the semantic transformation process through random sampling and probabilistic inference. This technology helps the model to make extensive speculations and simulations when facing unknown and uncertain scenarios, so as to estimate possible errors. By conducting a large number of Monte Carlo simulations on the model output, the performance of the model at different scales can be effectively evaluated, and data support can be provided for subsequent error correction.

[0102] Through contrastive learning and dynamic weight adjustment, identify and correct the systematic biases and random errors in semantic transformation in real time to ensure the accuracy and consistency of semantic information. There are systematic biases and random errors in the semantic transformation process, which will affect the consistency and accuracy of data. Through contrastive learning and dynamic weight adjustment, these errors can be identified and corrected in real time, thereby improving the accuracy of semantic transformation. Contrastive learning is a learning method that compares the differences between different data samples. In cross-scale transformation, contrastive learning can help identify the semantic differences between different scales, thus discovering systematic biases. For example, if the features at a certain scale overly focus on local details while ignoring the global structure, the learning process can be adjusted in real time by comparing the semantic features at different scales to correct the bias. This mechanism based on contrastive learning and dynamic weight adjustment can quickly identify and correct errors, thereby improving the accuracy of semantic transformation.

[0103] Autonomously adjust the intensity and scope of the error correction strategy according to the degree of information distortion evaluated in real time, achieving an intelligent balance between semantic integrity and conversion accuracy. The degree of information distortion will continuously change during cross-scale semantic conversion. Therefore, it is necessary to autonomously adjust the intensity and scope of the error correction strategy according to the results of real-time evaluation. Through the feedback mechanism, the correction strategy can be adjusted according to the current distortion evaluation results to ensure that the semantic information of the data can be accurately repaired. For example, when the evaluated semantic distortion degree in a certain area or at a certain scale is relatively high, the correction strategy can enhance the correction intensity for that area or scale. While in the area with a relatively low semantic distortion degree, the correction strategy can be appropriately weakened to avoid the negative impact caused by overcorrection. This adaptive correction strategy can be flexibly adjusted according to different environments and requirements to ensure the accuracy of semantic conversion and the integrity of data. Through this self-adjustment mechanism, the error correction strategy can be dynamically optimized during different conversion processes, thus achieving efficient semantic conversion and information recovery.

[0104] By comparing the semantic representations at different scales, construct an explicit semantic consistency loss function to minimize the semantic deviation during the scale conversion process. In the processing of multi-scale spatial data, there will be certain differences in the semantic representations at different scales. To ensure the consistency of semantic features at different scales, it is necessary to construct an explicit semantic consistency loss function. This loss function minimizes the possible semantic deviation during the scale conversion process by comparing the semantic features at different scales, thereby improving the accuracy of semantic conversion. The introduction of the semantic consistency loss function can detect and correct the semantic deviation in the scale conversion. By calculating the semantic differences at different scales, the loss function can quantify the errors during the conversion process and adjust the model parameters through backpropagation, so as to maintain the semantic consistency between different scales to the greatest extent. This loss function can not only reduce the semantic deviation caused by scale conversion, but also make the semantic information at different scales as consistent as possible through the optimization process, improving the quality of semantic conversion.

[0105] Through continuous feedback learning and self-correction, gradually improve the accuracy and reliability of cross-scale semantic conversion, achieving dynamic perception and intelligent correction of information distortion. To improve the accuracy and reliability of cross-scale semantic conversion, it is necessary to continuously optimize the performance through continuous feedback learning and self-correction. By continuously evaluating and learning, gradually identify and correct past mistakes to improve the semantic conversion ability at different scales.

[0106] In summary, step 3 effectively improves the accuracy and reliability of cross-scale semantic conversion. By constructing a multi-dimensional information distortion evaluation system, introducing Bayesian neural networks and Monte Carlo error simulation techniques, and using methods such as contrast learning and dynamic weight adjustment, errors in the conversion can be identified and corrected in real time to ensure the accuracy and consistency of semantic information. In addition, the introduction of an explicit semantic consistency loss function and an adaptive error correction strategy can flexibly handle semantic differences at different scales, further improving the overall conversion quality. Through continuous optimization and correction, efficient and accurate semantic conversion is finally achieved.

[0107] Furthermore, the overall information distortion degree in the cross-scale semantic conversion process is comprehensively and quantitatively analyzed through the following formula: D total = λ1H(F s (X)) + λ2(1 - I(F s (X), F t (X))) + λ3(1 - SSIM(F s (X), F t (X))) + λ4D KL (P s ‖P t ), where D total is a comprehensive distortion metric representing the overall information distortion degree in the cross-scale semantic conversion process; λ1 is the weight coefficient controlling the influence of semantic information entropy in the comprehensive distortion evaluation; λ2 is the weight coefficient controlling the influence of mutual information in the comprehensive distortion evaluation; λ3 is the weight coefficient controlling the influence of the structural similarity index in the comprehensive distortion evaluation; λ4 is the weight coefficient controlling the influence of the Kullback-Leibler divergence in the comprehensive distortion evaluation; SSIM(F s (X), F t (X)) is the structural similarity between scale s and scale t; H(F s (X)) is the semantic information entropy at scale s, measuring semantic uncertainty, and the formula is: H(F s (X)) = -∑ i＝1 N P(F s (x i ))logP(F s (x i ))), P(F s (x i )) is the probability distribution of feature x i at scale s; N is the total number of features at scale s; I(F s (X), F t (X)) is the mutual information between scale s and scale t, and the formula is expressed as: I(F s (X), F t (X)) = ∑i＝1 N ∑ j＝1 M P(F s (x i ),F t (y j ))log[P(F s (x i ),F t (y j )) / P(F s (x i ))P(F t (y j ))], where M represents the total number of features at scale t, and P(F s (x i ),F t (y j )) is the joint probability of feature x i at scale s and feature y j at scale t; P(F s (x i )) and P(F t (y j )) are the marginal probability distributions of the features at scales s and t respectively. The formula is: SSIM(F s (X),F t (X)) = [(2μ s μ t + C1)(2σ st + C2)] / [(μ s 2 + μ t 2 + C1)(σ s 2 + σ t 2 + C2)], where μ s and μ t are the means at scales s and t respectively; σ s 2 and σ t 2 are the variances at scales s and t respectively; σ st is the covariance between scales s and t; C1 and C2 are small constants used to avoid a zero denominator; D KL (P s ‖P t ) is the Kullback-Leibler divergence between scales s and t, which measures the difference in feature distributions. The formula is: D KL (P s ‖P t ) = ∑

[0108] i＝1 N P s (x i )log[P s (x i ) / P t (x i )], where P s (x i ) and P t (x i ) are the probability distributions of the feature x at scales s and t, respectively. i

[0109] In summary, information distortion in cross-scale semantic transformation is inevitable, especially when dealing with complex national land spatial data. By introducing the comprehensive distortion metric formula D total , we can quantitatively analyze the distortion in the transformation process from multiple perspectives. This formula combines semantic information entropy, mutual information, structural similarity index, and Kullback-Leibler divergence. By flexibly adjusting the weight coefficients, it can comprehensively evaluate the distortion according to the characteristics of different datasets. Through this evaluation tool, it is possible to effectively identify and quantify information loss and errors in cross-scale transformation, providing an important basis for subsequent error correction and data optimization, and ultimately achieving more accurate and consistent spatial data transformation.

[0110] Furthermore, step 4 includes the following steps:

[0111] By means of an adaptive attention mechanism and semantic relevance evaluation, accurately capture the potential semantic connections between spatial data at different scales and reconstruct the lost semantic information. The adaptive attention mechanism is a technology that can automatically identify important information regions in the input data. In cross-scale spatial data processing, the adaptive attention mechanism can dynamically select and focus on the spatial regions most relevant to the target semantics. Through the attention mechanism, for data at different scales, it can automatically evaluate and capture the potential semantic connections between them. This mechanism can not only improve the accuracy of information capture but also avoid interference from irrelevant regions, thus effectively reconstructing the lost semantic information. The adaptive attention mechanism processes data at different scales by weighting features, enabling the contributions of data at different scales to be automatically adjusted according to their semantic importance during the semantic recovery process. By accurately capturing the potential semantic connections between spatial data, it is possible to effectively recover the semantic information lost during the scale transformation, enhancing the consistency and accuracy of the data after cross-scale transformation.

[0112] Design a multi-task constraint loss function to optimize the semantic mapping relationship of cross-scale spatial data, and maintain global semantic consistency and local detail accuracy. In order to optimize the semantic mapping relationship of cross-scale spatial data and maintain global semantic consistency and local detail accuracy, a multi-task constraint loss function is designed. This loss function not only focuses on the consistency of the overall data but also takes into account the retention of local details, thus achieving a balanced consideration of global and local features during the optimization process. The core advantage of the multi-task learning method lies in its ability to improve the optimization ability for multi-dimensional objectives through the joint action of different task constraints. In the semantic mapping of cross-scale data, special attention needs to be paid to maintaining details during the scale conversion process. Therefore, by setting multiple constraint tasks, the function can ensure the consistency between details and global information. This method enables the conversion of spatial data between different scales to not only maintain global semantic consistency but also restore the accuracy of local features.

[0113] Through generator-discriminator adversarial training, adaptively recover the missing or blurred semantic information in cross-scale spatial data, and ensure the high-fidelity of semantic information at the visual and semantic levels through reconstruction loss and perceptual loss. Generator-discriminator adversarial training is a very effective training strategy widely used in image generation and restoration tasks. In this step, generator-discriminator adversarial training is used to adaptively recover the missing or blurred semantic information in cross-scale spatial data. The recovery results of cross-scale data are generated through the generator network, and the discriminator judges whether these results are real or not, continuously optimizing the recovery effect. During the training process, the generator network continuously learns how to generate more realistic spatial data, while the discriminator provides feedback to the generator by evaluating the quality of the generated data, promoting its optimization. The adversarial process between the generator and the discriminator makes the recovery of semantic information more accurate at the visual and semantic levels and reduces the possible blur and distortion problems during scale conversion. By introducing reconstruction loss and perceptual loss, the generator can ensure that the data is not only visually faithful but also maintains the original semantic consistency during the recovery process of semantic information.

[0114] Dynamically quantify and adjust the semantic association strength between data of different scales to achieve fine-grained modeling and intelligent calibration of semantic relevance. During the semantic reconstruction process of cross-scale spatial data, the precise adjustment of semantic association strength is crucial. There may be different semantic relationships between data of different scales, and the strength of this relationship should be dynamically quantified and adjusted according to the actual situation. This step dynamically adjusts the semantic association strength between data of different scales based on the results of real-time evaluation, thereby achieving fine-grained modeling and intelligent calibration of semantic relevance. The advantage of this dynamic adjustment mechanism lies in its ability to flexibly control the semantic weights between scales for different data characteristics and semantic differences, ensuring that important spatial information is not lost during the conversion process. By adaptively adjusting the association strength, it is possible to accurately recover the key semantic information lost during the scale conversion process and achieve more precise semantic matching between different scales.

[0115] Through multi-scale residual connections and information compression strategies, effectively transmit key semantic information and minimize the loss and distortion of semantic information during the scale conversion process. During the processing of cross-scale data, there are usually significant feature differences between data of different scales, which may cause information loss during the scale conversion process. Multi-scale residual connections fuse the features between different scales through skip connections, enabling data to cross scale differences and transmit useful information. The information compression strategy optimizes the data representation through dimensionality reduction and feature compression, making the representation of data in the high-dimensional space more compact and information-rich. Combining these two methods can effectively minimize the loss and distortion of semantic information during the scale conversion process, thereby ensuring the efficient transmission and accurate expression of data.

[0116] Through iterative feedback learning and adaptive adjustment, continuously evaluate and optimize the semantic relevance of cross-scale spatial data, achieve dynamic reconstruction, repair, and enhancement of semantic information, and ultimately reach the goal of high-fidelity restoration and semantic consistency reconstruction. Iterative feedback learning is a learning strategy based on cyclic optimization, which is used to continuously evaluate and optimize the semantic relevance of cross-scale spatial data. The adaptive adjustment after each round of feedback can be optimized according to the evaluation results, thereby achieving dynamic reconstruction, repair, and enhancement of semantic information. This continuous feedback mechanism can continuously adjust and optimize the conversion effect of cross-scale spatial data, ensuring that during the long-term learning process, the sensitivity to semantic information distortion and loss is gradually increased, and the lost semantic features can be accurately repaired and reconstructed.

[0117] In summary, through a series of advanced technical methods, such as adaptive attention mechanism, multi-task constrained loss function, generator-discriminator adversarial training, dynamic quantization and adjustment of semantic association strength, multi-scale residual connection and information compression strategy, and iterative feedback learning, etc., a comprehensive cross-scale spatial data semantic reconstruction and optimization framework is constructed. These methods can not only finely reconstruct and recover the lost semantic information, but also improve the accuracy and consistency in the data conversion process, ensuring high fidelity and consistency of spatial data at different scales at the semantic level. Through dynamic adjustment and optimization, the recovery quality of semantic information can be continuously improved, and finally accurate semantic reconstruction and optimization of cross-scale spatial data can be achieved.

[0118] Furthermore, the calculation formula for the semantic association strength is: S final = ∑ s,t λ st · [A st · ((X s · X t ) / (‖X s ‖ ‖X t ‖))], where S final represents the final semantic association strength, which is the comprehensive evaluation result of the semantic association between data at different scales; λ st is the global semantic calibration factor, used to adjust the semantic association strength between data at different scales; X s and X t represent the data feature vectors at scale s and scale t respectively; A st is the dynamic adjustment factor, used to adjust the weight of the semantic association strength between different scales; ‖·‖ represents the norm. The calculation formula for this semantic association strength calculates the semantic association strength between different scales by integrating multiple factors (such as global semantic calibration factor, dynamic adjustment factor, feature vector similarity, etc.), realizing accurate semantic mapping and optimization of cross-scale spatial data. Through dynamic adjustment and flexible weight mechanism, the formula can ensure the high-fidelity transmission and consistency maintenance of semantic information during data conversion and reconstruction. This not only effectively improves the accuracy and stability of cross-scale data processing, but also provides strong technical support for large-scale, multi-source data fusion and national land space planning.

[0119] Furthermore, the loss and distortion of semantic information during scale conversion are minimized through the following formula: L = ∑ l＝1 S (γ1 · ‖F out (l) - F orig (l) ‖2 2 + γ2 · D (l)), where \(L\) is the comprehensive loss function; \(\gamma_1\) is a hyperparameter in the loss function, used to adjust the weight related to the feature map matching error; \(F\) orig (l) is the original feature map of the \(l\)-th layer, representing the preliminary features extracted at this scale; \(F\) out (l) is the output feature map of the \(l\)-th layer, indicating the feature map obtained after residual connection and multi-scale feature transfer; \(D\) (l) is the information distortion metric of the \(l\)-th layer, used to quantify the information loss or distortion that may occur during the scale conversion process; \(\gamma_2\) is another hyperparameter in the loss function, used to control the contribution of the information distortion metric \(D\) (l) to the final loss function. By designing a comprehensive loss function that combines the feature map matching error and the information distortion metric, the information loss and distortion during the cross-scale semantic conversion can be effectively minimized. During the multi-scale spatial data processing, the accurate matching of the feature maps and the dynamic adjustment of the distortion metric work together to ensure the high-fidelity transmission and consistency maintenance of semantic information.

[0120] Furthermore, step 5 includes the following steps:

[0121] Set different development scenario parameters and constraints, and simulate the dynamic evolution process of urban space, resource allocation, ecosystem, and socio-economic elements to achieve a fine prediction of future territorial space development. By setting different development scenario parameters and constraints, this step enables the simulation to reflect the changes under different environments and development paths. It can simulate the interactions and long-term changes of urban space, resource allocation, ecosystem, and socio-economic elements based on factors such as historical data, policy changes, and economic development trends, and then predict the future territorial space. Through multi-dimensional simulations of different scenarios, potential development trends, interdependencies between regions, and pressure points of resources and the environment can be identified. Such simulations can effectively provide a global perspective on future development for policy decision-makers, help predict potential resource bottlenecks, environmental pressures, or social contradictions, and thus provide an accurate basis for territorial space planning.

[0122] Through deep learning and probabilistic inference of historical data, diverse and differentiated development scenarios are automatically generated, and a multi-dimensional evaluation index system is established to quantify the spatial resource allocation efficiency, ecological environment carrying capacity, and urban expansion potential under different scenarios. Using deep learning and probabilistic inference techniques, multiple development scenarios can be automatically generated from historical data. These scenarios not only consider historical trends but also provide multiple possible paths for future development by simulating different policies, environmental changes, and social dynamics. In addition, a multi-dimensional evaluation index system will be established to quantitatively evaluate the spatial resource allocation efficiency, ecological environment carrying capacity, urban expansion potential, etc. under different scenarios. Deep learning can effectively capture the potential patterns in historical data and generate diverse future scenarios through probabilistic inference. This process improves the prediction accuracy and flexibility of territorial spatial planning, enabling decision-makers to consider the impacts of different policies, socioeconomic changes, and environmental changes on future territorial space from multiple perspectives. The multi-dimensional evaluation system ensures the scientific nature of scenario generation and can effectively evaluate the impacts of different scenarios through quantifiable indicators, helping decision-makers make more reasonable decisions in a complex and changing environment.

[0123] Build a multi-factor dynamic analysis model based on system dynamics and complex network theory, specifically including: identifying and quantifying the key factors affecting territorial spatial planning, such as changes in population structure, economic development level, industrial layout, infrastructure construction, ecological environment carrying capacity, climate change, etc.; constructing a multi-dimensional causal relationship network to analyze the interaction mechanisms and dynamic coupling relationships among various factors. Through the system dynamics method, establish a dynamic simulation model including stocks, flows, and feedback loops to simulate the long-term evolution trends of various factors under different scenarios; introduce complex network analysis techniques to evaluate the influence degree and transmission mechanism of key nodes (such as major infrastructure, industrial agglomeration areas) on the overall spatial system; establish a parameter adaptive calibration mechanism based on historical data to continuously optimize the prediction accuracy and robustness of the model. The multi-factor dynamic analysis model based on system dynamics and complex network theory, by identifying and quantifying the key factors of territorial spatial planning, constructing a multi-dimensional causal relationship network, applying the system dynamics simulation model and complex network analysis techniques, and finally through the adaptive calibration mechanism based on historical data, realizes an all-round and dynamic territorial spatial planning prediction tool. This model can not only accurately depict the interaction relationships between different factors but also provide spatial evolution trends on a long time scale, providing a scientific decision-making basis for the government and planning agencies. Through continuous optimization and adjustment, the model can adapt to a complex dynamic environment and demonstrate strong robustness when dealing with future uncertainties, providing important support for achieving sustainable development.

[0124] By simulating the co-evolution of resource utilization, environmental change, and urban expansion, systematic and dynamic prediction and comprehensive assessment of the development of territorial space can be achieved. Through simulating the co-evolution process of resource utilization, environmental change, and urban expansion, the interactions among resource allocation, urbanization process, and ecological environment can be analyzed more comprehensively. Such simulation can not only identify the efficiency of current resource use but also predict the long-term impacts of different resource configurations on the environment and socio-economy. The simulation of co-evolution can reveal the interdependence between resources and the environment, helping decision-makers understand the complex relationships among ecosystem carrying capacity, resource utilization efficiency, and urban expansion. Through this comprehensive prediction, profound insights into resource optimization, environmental protection, and urban expansion strategies can be provided, and it helps achieve cross-domain policy coordination and reduce potential conflicts.

[0125] By setting constraints on spatial resource allocation, ecological protection, and urban development, using evolutionary algorithms and Monte Carlo tree search techniques, optimal territorial space planning schemes can be automatically generated and evaluated. Combining evolutionary algorithms and Monte Carlo tree search techniques can automatically generate multiple territorial space planning schemes under complex constraints and evaluate their advantages and disadvantages. Evolutionary algorithms can simulate the process of natural selection and generate optimal solutions through continuous iteration and optimization, while Monte Carlo tree search can conduct random exploration and evaluation among various possible schemes to find the most likely scheme to achieve the goal. The combination of evolutionary algorithms and Monte Carlo tree search techniques can efficiently find the optimal territorial space planning scheme in a vast and complex decision space. Evolutionary algorithms provide powerful global search capabilities, while Monte Carlo tree search avoids the trap of local optimality through random exploration. Combining these two techniques can achieve the optimal allocation of territorial space under multiple constraints, improve resource utilization efficiency, and reduce potential risks.

[0126] Through the dynamic monitoring and prediction of natural disasters, ecological vulnerability, and resource constraint factors, real-time and accurate risk assessment and early warning information can be provided for territorial space planning. By dynamically monitoring and predicting natural disasters, ecological vulnerability, and resource constraint factors, real-time risk assessment and early warning information can be provided for territorial space planning. This process integrates remote sensing technology, environmental monitoring data, and geographic information system (GIS), enabling planning decisions to promptly respond to changes in the external environment. This technology can provide a real-time early warning mechanism, helping decision-makers take prompt and effective countermeasures when facing natural disasters, ecological damage, or resource depletion. This provides solid data support for emergency management, post-disaster recovery, and environmental protection, ensuring that territorial space planning is forward-looking and resilient.

[0127] Integrate the knowledge and demands of different stakeholders, and through intelligent opinion aggregation and conflict coordination, achieve the inclusiveness and scientific nature of territorial spatial planning decisions, so as to promote the rational allocation of spatial resources and sustainable development. During the territorial spatial planning process, multiple stakeholders are usually involved, including the government, enterprises, residents, and social organizations, etc. Through intelligent opinion aggregation and conflict coordination technologies, it is possible to integrate the needs and opinions of all parties, handle the conflicts between different interests, and finally reach a scientific, reasonable, and inclusive planning decision. Intelligent opinion aggregation and conflict coordination technologies can reasonably integrate the needs of different interest parties and avoid potential interest conflicts or social dissatisfaction during the planning process. This process improves the scientific nature and inclusiveness of decision-making, ensuring that territorial spatial planning not only meets the needs of economic development but also takes into account ecological protection and social fairness.

[0128] In summary, comprehensively considering the dynamic evolution of multiple factors such as urban space, resource allocation, ecological environment, and social economy can provide accurate predictions and comprehensive evaluations of the future development of territorial space. Simulate different development scenarios and generate the optimal territorial spatial planning scheme to ensure the scientific nature and rationality of decision-making. At the same time, real-time risk assessment and intelligent interest coordination provide flexibility and sustainability for territorial spatial planning. Through this series of technologies, it is not only possible to provide a reliable prediction basis for future urban development but also effectively improve the allocation efficiency of spatial resources, promote the sustainable development of the ecological environment, and drive the territorial spatial planning towards the direction of intelligence and dynamism.

[0129] Furthermore, by setting constraints on spatial resource allocation, ecological protection, and urban development, and using evolutionary algorithms and Monte Carlo tree search technologies, automatically generate and evaluate the optimal territorial spatial planning scheme, including the following steps:

[0130] According to the diversified needs of territorial spatial planning, construct composite constraint conditions including ecological protection red lines, urban development boundaries, resource carrying capacity, and infrastructure layout, and construct a multi-objective evaluation function to quantify the comprehensive benefits of the planning scheme;

[0131] Adopt the particle swarm optimization algorithm, and randomly generate an initial solution cluster according to the predefined spatial planning coding scheme. Each solution represents a potential territorial spatial planning scheme;

[0132] Introduce the Monte Carlo tree search technology to optimize the initial solution cluster. In each iteration, through random simulation and decision tree search, evaluate the potential development paths and risks of different territorial spatial planning schemes, dynamically adjust the parameters of the solutions, and gradually converge to a more optimized spatial configuration scheme to balance short-term benefits and long-term sustainable development needs;

[0133] Comprehensively evaluate the spatial planning schemes generated in each iteration to screen out the optimal planning scheme.

[0134] In summary, by combining the particle swarm optimization algorithm with the Monte Carlo tree search technique, an efficient and comprehensive method for optimizing territorial spatial planning is provided. First, the particle swarm optimization algorithm generates an initial solution cluster through multi-dimensional constraint conditions, providing a rich set of candidate solutions for further optimization. Then, the Monte Carlo tree search technique continuously optimizes the solution cluster through multiple iterations and dynamic adjustments, guiding it to gradually converge to the optimal planning solution. Finally, through comprehensive evaluation and screening, it can be ensured that the selected solution achieves a balance in aspects such as resource utilization, environmental protection, and social economy, and has long-term sustainability. This process not only improves the accuracy and scientific nature of territorial spatial planning but also ensures the flexibility and operability of the planning, providing strong technical support for the sustainable development of future urban space and resource allocation.

[0135] Furthermore, step 6 includes the following steps:

[0136] Convert cross-scale spatial semantic data into a multi-level, high-fidelity visual representation, realizing semantic mapping and intelligent conversion from abstract data to intuitive images. The visual conversion of cross-scale spatial semantic data requires accurately and efficiently mapping data of different scales and dimensions into intuitive images. The multi-level visual representation means that different levels of data (such as the global perspective and local details) can be clearly displayed, and high-fidelity ensures that semantic information is not lost or distorted during the conversion process. By performing multi-level semantic mapping of spatial data, different information from the whole to the part can be displayed, enabling users to obtain global and detailed information from different perspectives. This method can avoid over-simplification or loss of information when dealing with large-scale and complex spatial data, maintaining the integrity and accuracy of the data. High-fidelity visualization ensures the authenticity and accuracy of spatial data, allowing users to visually and clearly capture the spatial semantics of each level and each dimension. The multi-level visualization also facilitates users to freely switch views according to their needs, meeting the analysis requirements of different users.

[0137] Through multimodal interaction, provide real-time dynamic exploration of multi-dimensional and multi-scale spatial semantic data, and achieve free scaling, switching, and in-depth analysis of spatial semantic information. This dynamic exploration not only enhances the user's sense of participation but also helps the user obtain multi-dimensional information about the data in real time for further in-depth analysis of specific regions or elements. The multimodal interaction technology enables users to interact with spatial data more flexibly and intuitively. By freely scaling and switching views, users can focus on specific spatial regions or features for more detailed analysis. This real-time dynamic exploration can improve the presentation effect of the data and speed up the decision-making process. When faced with large and complex data, users can quickly locate and understand key regions through multi-dimensional interaction methods, enabling spatial data not only to be statically displayed but also to have the ability of real-time feedback, enhancing the depth of data analysis and operation efficiency.

[0138] Utilize context reasoning and dynamic association to automatically identify and highlight key semantic elements in spatial data, provide intelligent recommendations, semantic links, and context association visualization, and enhance data understanding and insight depth. Through context reasoning and dynamic association, key semantic elements in spatial data can be intelligently identified, and users can be helped to quickly focus on important content through highlighting, semantic links, etc. This technology not only improves the understandability of the data but also provides users with intelligent recommendations and associated information, thus deepening the user's insight into the data. Context reasoning and dynamic association technology avoid users getting lost in a large amount of spatial data by intelligently identifying key elements in the data. It can automatically judge which information is most critical for the current analysis task based on the context and relevance of the data and make this information more prominent and easy to interpret through visual highlighting. In addition, the visual display of semantic links and context associations can effectively guide users to conduct deeper mining in spatial data, improving the accuracy and comprehensiveness of data understanding. This function is not only a data display tool but also an intelligent analysis assistance system to help users quickly extract key information from complex data.

[0139] Through particle systems, flow field visualization, and time series visualization techniques, the visualization of the dynamic evolution process of spatial semantic data along the time and space dimensions is achieved. Particle systems, flow field visualization, and time series visualization are techniques used to display the dynamic change process of spatial data along the time and space dimensions. These techniques can transform complex dynamic data into visually understandable patterns, helping users intuitively perceive the evolution trends of spatial data in the time and space dimensions. Particle systems can effectively display dynamic changes in space and are particularly suitable for simulating complex spatial phenomena such as movement and expansion. For example, processes such as urban expansion, resource flow, and population migration can be visualized through particle systems, providing a more intuitive spatial dynamic analysis. Flow field visualization can display the flow paths and distributions of substances, energy, or information in spatial data, enabling users to clearly understand the interactions between different regions through the changes in streamlines. Time series visualization techniques can display the changes in spatial data over time, helping users observe the spatial evolution trends and supporting predictive analysis based on historical data. The combination of these techniques makes the dynamic changes of spatial data more visual, helping users better understand the underlying change patterns of the data.

[0140] By analyzing users' interaction behaviors, cognitive preferences, and professional backgrounds, intelligent recommendations and generation of visualization expressions adapted to different user needs are made to improve the pertinence of visualization and user experience. The purpose of personalized recommendation is to provide customized visualization solutions, enabling different users to obtain the most relevant information in spatial data visualization to enhance the efficiency and accuracy of data analysis. Intelligent recommendation can adjust the style and content of visualization according to users' historical interaction records and analysis requirements, enabling users to obtain the most suitable display method during use. For example, for urban planners, data related to urban expansion, resource allocation, etc. can be preferentially recommended; while for environmental protectors, data related to the ecological environment and resource consumption may be more prominent. By learning users' preferences, the user experience can be improved and the operation burden of users can be reduced. The customized visualization solution enables each user to obtain the most suitable analysis view in different task backgrounds, thus making more effective decision support.

[0141] Through responsive design and intelligent terminal adaptation, ensure the consistency and high-quality presentation of spatial semantic data visualization on different devices. Whether on desktop computers, tablet devices or mobile terminals, users can obtain the same high-quality visualization effect. Responsive design ensures that the visualization of spatial data can adapt to different screen sizes and device characteristics, so that no matter what terminal environment the user is in, they can smoothly and efficiently explore and analyze spatial data. This technology improves the cross-device experience of users, avoids visual differences between devices, and enables spatial data to maintain consistency and high-quality presentation on multiple platforms. In addition, intelligent terminal adaptation can dynamically adjust according to the performance and characteristics of the device, ensuring that on resource-constrained mobile devices, a sufficiently smooth visualization effect can still be provided, further enhancing the universality of the user experience.

[0142] In summary, through multi-level and high-fidelity spatial semantic data visualization and intelligent interaction technologies, the user's ability to understand and analyze complex spatial data has been greatly enhanced. Multimodal interaction and real-time dynamic exploration allow users to flexibly switch and deeply explore data, while context reasoning and dynamic association improve the intelligence and operability of spatial data. Technologies such as particle systems, flow field visualization, and time series visualization make the dynamic change process of spatial data more intuitive and understandable. Intelligent recommendation and customized visualization ensure the satisfaction of different user needs, while responsive design and intelligent terminal adaptation improve the consistency and visualization effect of multi-device use. Overall, this process transforms abstract spatial data into intuitive visual information, helping users make more scientific and efficient decisions in multi-dimensional and multi-scale spatial planning and analysis.

[0143] Furthermore, step 7 includes the following steps:

[0144] Through secure multi-party computation, homomorphic encryption, and trusted execution environments, a decentralized data governance model is established to ensure the integrity, security, and traceability of data sharing. In modern data governance, data security and privacy protection are key issues. Through technologies such as Secure Multi-Party Computation (SMPC), Homomorphic Encryption, and Trusted Execution Environment (TEE), the problems of data privacy leakage and sharing trust can be effectively solved. These technologies enable different parties to jointly perform calculations and analyses without exposing their respective data. Secure multi-party computation allows multiple participants to jointly compute a function without sharing the original data, while homomorphic encryption supports performing calculations on encrypted data, and the result obtained after decryption is the same as the result of performing calculations on plaintext data. The trusted execution environment provides a secure computing environment to ensure the integrity and reliability of the computing process. This combination of technologies effectively constructs a decentralized data governance model, avoiding the privacy risks of data concentration. In the absence of centralized data storage, all parties can still share data and conduct cross-institutional and cross-regional collaborative analyses. By protecting data privacy during the computing process, the security and integrity of data during sharing and use are ensured. This mechanism greatly enhances the credibility of data governance and ensures the effective protection of sensitive information.

[0145] Using techniques such as noise addition, local training, and secure aggregation, cross-institutional and cross-regional spatial data collaborative learning and intelligent analysis can be achieved while protecting personal and sensitive information. The noise addition technique disrupts the data by adding noise to it, thus avoiding the leakage of sensitive information; local training (such as federated learning) keeps the data locally, avoiding the transmission of sensitive data to a centralized server, and only model parameters and updates are shared; secure aggregation ensures that when multiple parties perform data aggregation, their respective original data will not be leaked. These technologies enable multiple organizations and institutions to jointly conduct data collaboration and intelligent analysis while ensuring data privacy. Especially in the application of federated learning, each institution can perform model training locally and only share model updates and parameters, avoiding the privacy risks of data exchange. This approach supports large-scale data collaborative learning across regions and institutions, promotes the intelligent analysis and model optimization of spatial data, and at the same time safeguards the privacy of the data.

[0146] Through refined permission management, dynamic access policies, and context-aware technologies, precisely control the access rights of different roles to spatial planning data, and achieve the precise sharing and secure governance of data. The design of refined permission management and dynamic access policies is the key to ensuring reasonable and secure permission allocation in data governance. Refined permission management can formulate specific data access control policies according to different user roles, requirements, and data sensitivities, while dynamic access policies allow the adjustment of access rights according to different contexts (such as time, location, task, etc.). Context-aware technologies dynamically adjust the permission configuration according to the real-time environment and task requirements, thus ensuring data security and compliance to the greatest extent. Through these technologies, precise control of spatial data access can be achieved, thereby ensuring data security and avoiding unauthorized access or data leakage. Refined permission management ensures that different roles (such as planners, analysts, decision-makers) can only access sensitive data related to their work. Dynamic access policies and context-aware technologies ensure the flexibility and real-time nature of data access rights under different conditions, maximizing the accuracy and security of data governance.

[0147] Through microservices architecture, message queues, and event sourcing technologies, achieve the real-time update and multi-source collaboration of territorial spatial planning data. Through the microservices architecture, modular management and independent update of territorial spatial planning data can be realized. Message queues and event sourcing technologies ensure data real-time and collaboration. Message queues support asynchronous communication between different service modules, ensuring that each module can exchange information in real time; event sourcing technology can trace the historical processes of data generation, modification, update, etc., providing transparency and traceability for data management and governance. These technologies ensure the real-time update of territorial spatial planning data and the collaborative work of multi-source data. The microservices architecture allows different data sources and functional modules to operate and update independently, avoiding single-point failures and tight coupling between modules, and having stronger scalability and fault tolerance. Message queues and event sourcing technologies ensure the efficient flow and real-time synchronization of data between different services, and can trace the source and change history of data, providing higher data transparency and governance compliance.

[0148] Continuously monitor and optimize the quality of spatial planning data, and establish a closed-loop feedback mechanism for data governance. Continuously monitoring data quality is an essential part of data governance. By setting quality indicators, conducting regular inspections, and establishing a feedback mechanism, the quality of spatial planning data can be evaluated in real time, and potential quality problems can be identified. The closed-loop feedback mechanism of data governance can ensure that data is continuously optimized during use. Through continuous monitoring and optimization, the high quality and usability of data can be maintained. Continuous data quality monitoring and optimization can not only improve the accuracy and consistency of data, but also ensure the effectiveness of data in long-term use. Through the closed-loop feedback mechanism, data governance strategies can be adjusted according to real-time feedback to ensure that spatial planning data always meets quality standards. This mechanism helps to eliminate noise, errors, and redundant information in data, improving the credibility of data and its decision-making support capabilities.

[0149] Integrate compliance detection and data lineage tracing technologies to achieve intelligent governance and compliance management of national territorial space planning data. Compliance detection can examine the entire process of data access, use, and sharing to ensure that all operations comply with relevant laws, regulations, and policy requirements. Data lineage tracing can trace the entire life cycle of data, including its source, processing process, and final use, providing support for compliance management. These technologies can effectively enhance the transparency and audibility of data governance, ensuring compliance requirements during data use. Data lineage tracing helps the system understand the flow, changes, and dependencies of data, facilitating the discovery of potential compliance risks and enabling timely measures to be taken for correction. Through these intelligent governance means, the legal and compliance risks caused by improper data governance can be greatly reduced, ensuring the security and compliance of data throughout its life cycle.

[0150] In summary, through technologies such as multi-party secure computing, homomorphic encryption, and refined permission management, an efficient, secure, and decentralized data governance system has been constructed, providing a solid technical guarantee for cross-institutional and cross-regional spatial data sharing and collaborative learning. At the same time, technologies such as microservice architecture and event sourcing ensure the real-time update, collaborative operation, and full traceability of data. Through continuous data quality monitoring, compliance detection, and data lineage tracing, the intelligent level and compliance of data governance have been further improved. Overall, this series of technical means not only strengthens data privacy protection and security, but also enhances the transparency and real-time nature of data governance, laying a foundation for the intelligent, precise, and sustainable development of national territorial space planning.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for territorial spatial planning based on big data, characterized in that It includes the following steps: Step 1: Integrate comprehensive land spatial data and perform standardized, normalized preprocessing and semantic association of the data; Step 2: Automatically extract and encode semantic features of spatial elements at different scales, and achieve accurate expression and cross-scale conversion of spatial element semantics; Step 3: Dynamically evaluate and quantify the information distortion degree and uncertainty in the cross-scale semantic conversion process; Step 4: Repair and optimize the semantic association of cross-scale spatial data, and achieve high-fidelity restoration of information and reconstruction of semantic consistency; Step 5: Simulate the spatial resource allocation, ecological environment changes and urban expansion trends under different development scenarios, and achieve accurate and dynamic decision-making for land spatial planning; Step 6: Convert cross-scale spatial semantic data into an intuitive and interactive visualization form, and achieve intelligent presentation and interactive analysis of spatial data; Step 7: Realize the intelligent and dynamic management of land spatial planning.

2. The method for territorial spatial planning based on big data according to claim 1, wherein, Step 1 includes the following steps: Comprehensively collect data from different platforms; Perform unified format conversion and standardized processing on the collected data; By establishing a standard metadata schema and keyword mapping table, achieve semantic alignment and association of geographical entities, attributes and relationships in different data sources, and uniformly associate the ground object information in remote sensing images, spatial elements in geographic information data, structural data in urban construction archives and statistical data in population censuses to form a rich and logically consistent integrated dataset; Conduct quality assessment on the integrated dataset, including integrity, consistency, accuracy and timeliness checks; Perform precise geometric correction and registration on remote sensing images and geographic information data to achieve spatial alignment and overlay of multi-source data; Construct a comprehensive metadata management system to record the source, processing process, accuracy information and usage restrictions of each data source.

3. A method for territorial space planning based on big data according to claim 1, characterized in that Step 2 includes the following steps: Extract local and global feature information of spatial elements at different scales through parallel convolution operations, and fuse to generate multi-scale features; Dynamically adjust the weights of different-scale features, and adaptively focus on the regions and channels that are most critical for spatial semantic understanding; Achieve information interaction and gradual refinement between different-scale feature maps, construct a semantic mapping channel from coarse-grained to fine-grained, and then integrate multi-scale semantic information; Perform abstract representation and decoupling of spatial semantic features through learnable position encoding and semantic mapping technologies; By stabilizing the multi-scale feature distribution, mitigate the problems of gradient disappearance and overfitting, while maintaining the original semantic information of the features; Through interpolation, convolutional downsampling and upsampling operations, ensure the continuity and consistency of features between different resolutions and scales; Introduce a multi-source data fusion and cross-validation mechanism to reduce the risk of information omission.

4. A method for territorial spatial planning based on big data according to claim 1, characterized in that, Step 3 includes the following steps: Comprehensively and quantitatively analyze the overall information distortion degree in the cross-scale semantic conversion process; Through random sampling and probability inference, accurately estimate the cognitive uncertainty and random uncertainty in each link of semantic conversion; Real-time identify and correct systematic biases and random errors in semantic conversion to ensure the accuracy and consistency of semantic information; Autonomously adjust the intensity and scope of the error correction strategy according to the degree of information distortion evaluated in real time, and achieve an intelligent balance between semantic integrity and conversion accuracy; Construct an explicit semantic consistency loss function by comparing semantic representations at different scales, and minimize the semantic deviation during the scale conversion process; Gradually improve the accuracy and reliability of cross-scale semantic conversion through continuous feedback learning and self-correction, and achieve dynamic perception and intelligent correction of information distortion.

5. A method for territorial spatial planning based on big data according to claim 1, characterized in that, Step 4 includes the following steps: Precisely capture the potential semantic connections between spatial data at different scales through an adaptive attention mechanism and semantic relevance evaluation, and reconstruct the lost semantic information; Design a multi-task constrained loss function to optimize the semantic mapping relationship of cross-scale spatial data, and maintain global semantic consistency and local detail accuracy; Through generator-discriminator adversarial training, adaptively recover the missing or ambiguous semantic information in cross-scale spatial data, and ensure the high fidelity of semantic information at the visual and semantic levels through reconstruction loss and perceptual loss; Dynamically quantify and adjust the semantic association strength between data at different scales, and achieve fine-grained modeling and intelligent calibration of semantic relevance; Effectively transmit key semantic information through multi-scale residual connections and information compression strategies, and minimize the loss and distortion of semantic information during the scale conversion process; Through iterative feedback learning and adaptive adjustment, continuously evaluate and optimize the semantic relevance of cross-scale spatial data, and achieve dynamic reconstruction, repair and enhancement of semantic information, and finally achieve the goal of high-fidelity restoration and semantic consistency reconstruction.

6. A method for territorial spatial planning based on big data according to claim 1, characterized in that, Step 6 includes the following steps: Implement semantic mapping and intelligent conversion from abstract data to intuitive images; Implement free scaling, switching and in-depth analysis of spatial semantic information; Use context reasoning and dynamic association to automatically identify and highlight key semantic elements in spatial data, and provide intelligent recommendations, semantic links and context association visualization; Implement the visual presentation of the dynamic evolution process of spatial semantic data along the time and space dimensions; Intelligently recommend and generate visualization expressions adapted to different user needs by analyzing users' interaction behaviors, cognitive preferences and professional backgrounds; Ensure the consistency and high-quality presentation of spatial semantic data visualization on different devices through responsive design and intelligent terminal adaptation.

7. A method for national territorial space planning based on big data according to claim 1, characterized in that Step 7 includes the following steps: Establish a decentralized data governance model through multi-party secure computing, homomorphic encryption and trusted execution environments; Use noise addition, local training and secure aggregation technologies to achieve cross-institutional and cross-regional spatial data collaborative learning and intelligent analysis while protecting personal and sensitive information; Precisely control the access rights of different roles to spatial planning data through refined permission management, dynamic access policies and context-aware technologies; Implement real-time updates and multi-source collaboration of national territorial space planning data through microservice architecture, message queue and event tracing technologies; Continuously monitor and optimize the quality of spatial planning data, and establish a closed-loop feedback mechanism for data governance; Integrate compliance detection and data lineage tracing technologies to achieve intelligent governance and compliance management of national territorial space planning data.

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