Multi-source space digital-intelligent fusion base building and decision optimization method for water network management and control

Abstract

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

Description

Technical Field

[0001] This invention relates to the field of intelligent water network management and control technology, specifically to the construction of a multi-source spatial digital intelligence fusion foundation and decision optimization method for water network management and control. Background Technology

[0002] The intelligent management and control of water network systems is becoming increasingly important. The effectiveness of this management and control relies heavily on the support of multi-source spatial data. How to effectively integrate data with diverse structures and dispersed sources, and make scientific decisions based on this data, is a core technological bottleneck currently facing the field. This requires building a technological foundation capable of achieving standardized data integration, high-precision fusion, and intelligent decision optimization.

[0003] In existing technological practices, a preliminary data processing and decision-making process for water network management has been established. This process typically begins with data acquisition from multiple stakeholders, collecting multi-source data including real-time operating conditions, historical hydrological data, and geographic information. Subsequently, a data preprocessing platform cleans, converts, and unifies the coordinates of this raw data to address data heterogeneity issues. In the data fusion stage, a fusion model is constructed, often employing methods such as weighted averaging and principal component analysis to integrate information from different sources and generate a unified data view. Finally, based on the fused data, the decision-making system generates a preliminary plan by combining hydrodynamic models and spatial analysis tools, and then outputs the final management strategy through evaluation and optimization algorithms.

[0004] Existing technologies for processing multi-source heterogeneous data exhibit significant deficiencies in their standardized processes. Data format conversion and coordinate system unification typically rely on independent tools or scripts. This approach lacks systematic integration, making it difficult to achieve precise alignment of spatial references between different datasets. The resulting initial errors propagate and amplify, ultimately limiting the upper limit of data fusion and creating an unreliable data foundation.

[0005] At the data fusion level, existing methods often employ universal algorithms, such as weighted average or principal component analysis. These algorithms do not fully consider the inherent differences in characteristics between different data types, such as vectors, raster data, and 3D models. Furthermore, the weighting of data sources is often based on static or empirical judgments, failing to establish a dynamic and quantitative quality assessment mechanism. As a result, the fusion process may weaken the advantages of high-precision data or even introduce noise, failing to achieve true information complementarity and enhancement.

[0006] The existing technology suffers from a loose connection between the decision-making optimization stage and the data fusion stage. Typically, after generating an initial plan, significant manual intervention is required for evaluation and adjustment. This leads to a decline in the value of the data. High-precision fused data models fail to directly drive closed-loop optimization of the decision-making plan. The final output management strategy is often a locally optimal solution rather than a globally optimal solution, limiting the precision and intelligence of water network management. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for constructing a multi-source spatial data intelligence fusion foundation and optimizing decisions for water network management. This method solves the problems in existing technologies, such as insufficient standardization of data processing procedures, lack of specificity in data fusion algorithms, and disconnect between decision optimization and data foundation, which lead to poor reliability of the data foundation, limited accuracy of the fusion model, and low scientificity and efficiency of decision-making.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: a method for building a multi-source spatial data fusion base and optimizing decision-making for water network management. This method first acquires multi-source spatial data covering real-time monitoring data, historical hydrological data, and basic spatial data through multi-subject collaboration.

[0009] Subsequently, the acquired data was standardized using a data preprocessing platform. This platform accesses multi-source data through a universal data reading module that supports the Open Geospatial Information Consortium (OGC) standard. An automatic format conversion engine built on the GDAL and OGR libraries converts heterogeneous graphic format data from different sources, such as Shapefile, GeoJSON, and CAD, into a unified standard spatial data format. To eliminate spatial benchmark differences, the platform uses an automatic coordinate system identification and conversion tool, employing a seven-parameter conversion model to unify different coordinate systems into the National Geodetic Coordinate System 2000. Based on this, to achieve semantic uniformity of the data, the method constructs a water network ontology model. This model systematically defines core concepts such as water body types, hydraulic structures, watershed boundaries, and land cover, as well as their interrelationships. According to a pre-defined unified spatial feature attribute dictionary, standard attribute fields are added to the geometric elements extracted from the graphic data, completing systematic semantic annotation and ultimately forming a standardized spatial dataset.

[0010] In the data fusion phase, the method constructs a multi-source spatial data fusion model based on a standardized spatial dataset. First, multi-dimensional features are extracted from spatial objects, generating a feature vector for each object that includes spatial location features, geometric shape features, attribute features, and temporal features. Based on the feature type, appropriate matching algorithms are used for comparison and fusion. For example, Euclidean distance is used to calculate the positional approximation between spatial objects, and a shape similarity function is used for geometric shape matching.

[0011] To improve the reliability of the fusion results, this method assigns weights to different data sources. The weight determination process is as follows: First, a data quality evaluation index system covering data accuracy, completeness, timeliness, consistency, and current availability is constructed. Then, a fuzzy comprehensive evaluation method is used to quantitatively score each data source, forming a membership matrix. Combined with a pre-defined index weight vector The overall quality score of each data source is calculated using the following fuzzy comprehensive evaluation formula. : : In the formula: This represents the overall quality score vector; parameter This is the result vector. One of the specific elements represents the first The specific score obtained after evaluating multiple data sources. Indicates the first The overall quality score of each data source; Represents the indicator weight vector; Represents the membership matrix; This indicates the total number of data sources.

[0012] After obtaining the overall quality score, the final weight coefficients of each data source in the subsequent fusion process are calculated using the following normalization formula. : ; In the formula: Indicates the first The weight ratio of each data source in the fusion process; Indicates the first Scores from each data source; Indicates the first Scores from each data source; This indicates the total number of data sources.

[0013] For different data types, the fusion process employs specific technical approaches: For vector data, candidate matching features are efficiently retrieved by constructing spatial index structures such as R-trees or quadtrees. Subsequently, Hausdorff distance is introduced as the core metric for calculating geometric similarity to identify and match features with the same name. Hausdorff distance effectively measures the difference in spatial shape between two point sets, and its definition is as follows: ; In the formula: Represents a set and set The Hausdorff distance between them; middle Represents the set of points containing the first element; middle Represents the set of points containing the second element; Represents a set any point in it; Represents a set any point in it; Point With point The Euclidean distance between the two figures indicates that the smaller the value, the higher the similarity between the two figures in terms of spatial shape. (Supremum) represents taking the supremum (i.e., the least upper bound) of a set. (Infimum) represents the infimum (i.e. the largest lower bound) of a set. This indicates taking the maximum of the two values.

[0014] For raster data, an improved wavelet transform fusion algorithm is employed. This algorithm decomposes multispectral remote sensing images, retaining their information-rich low-frequency wavelet components, while simultaneously extracting the high-frequency wavelet components of a high-resolution digital surface model (DSM) generated from LiDAR point clouds. The fusion is then performed via inverse wavelet transform, and the fusion process can be represented as follows: ; In the formula: The image after fusion; For the wavelet low-frequency component of the multispectral image; Wavelet high-frequency components for generating images of LiDAR point clouds; This indicates the inverse wavelet transform operation.

[0015] For 3D model data, Building Information Modeling (BIM) output is used as the structural foundation, combined with Digital Elevation Model (DEM) topographic data for fusion. This fusion is achieved through spatial registration technology, specifically employing a model based on control points and iterative least squares fitting, minimizing the error function. Solving for optimal rigid body transformation moments This is to achieve automated and accurate alignment of 3D models from different sources. The error function is defined as: ; In the formula: The error function represents the sum of squared errors between the source model points and the target model points after rigid body transformation. This represents the source model point set, which contains a series of original points. ; This represents the target model point set, which contains a series of target points. , and the source model point set The points in the diagram correspond one-to-one; This represents the optimal rigid body transformation matrix (or transformation function), which rotates and translates the points of the source model to align them with the points of the target model. This is achieved by solving... Make the error function Minimum; Represents the source model point set The first in One point; Represents the target model point set The first in One point; Indicates the total number of control point pairs used for alignment; This represents the square of the Euclidean norm (L2 norm) of a vector, which is the square root of the sum of the squares of its components. In this context, it calculates the transformed vector. With the target point The square of the Euclidean distance between them.

[0016] Finally, based on the unified spatial data model generated after fusion, the decision optimization system performs data presentation and decision optimization. For data presentation, the system builds a 3D visualization platform based on WebGL technology and employs a strategy combining octree spatial partitioning and Level of Detail (LOD) technology. It dynamically loads data models of different precision according to the user's viewing distance, ensuring smooth interaction and efficient rendering of large-scale graphical data. For decision optimization, the system first combines spatial analysis algorithms and hydrodynamic models to generate preliminary control decision schemes. Then, a multi-objective decision evaluation model is established to predict and comprehensively evaluate the effects of the preliminary schemes. Finally, algorithms such as genetic optimization are used to iteratively optimize the schemes until the optimal control decision scheme is obtained. The goal of this optimization process is to find a function that makes the decision objective function... The optimal solution can be expressed as: : In the formula: This indicates the optimal control scheme.

[0017] This represents the set of feasible solutions.

[0018] The objective function represents the decision-making scheme.

[0019] Represents the set of feasible solutions One of the decision-making options; This indicates that the solution should make the objective function... Decision scheme to achieve the maximum value .

[0020] The above methods have constructed a complete technical closed loop from data acquisition, preprocessing, weighted fusion to decision optimization, effectively solving the problem of multi-source spatial data utilization in water network management and control, realizing the standardized integration and intelligent application of data assets, and providing solid data and technical support for the scientific management and precise decision-making of water network systems.

[0021] This invention provides a method for constructing a multi-source spatial digital intelligence fusion platform and optimizing decision-making for water network management. It offers the following advantages: 1. This invention establishes a standardized data preprocessing workflow, integrating automatic format conversion, coordinate system unification, and systematic semantic annotation functions to transform multi-source heterogeneous spatial data into a unified and standardized dataset. This method overcomes the integration obstacles caused by inconsistent data standards and formats, reduces the manual costs and time consumption in the data preparation stage, and provides homogeneous and standardized input data for subsequent high-level analysis.

[0022] 2. This invention employs a data quality assessment model based on fuzzy comprehensive evaluation to quantitatively evaluate different data sources and determine their fusion weights, combined with fusion algorithms tailored to specific data types. This weighting mechanism ensures that data with higher accuracy and timeliness dominates the fusion model, improving the overall accuracy and timeliness of the fusion results. Compared to methods with equal weights or single fusion methods, the generated unified spatial data model is more accurate and reliable.

[0023] 3. This invention constructs an automated process from data analysis to decision optimization by deeply coupling the fused unified spatial data model with the hydrodynamic model and the genetic optimization algorithm. This process can iteratively optimize control schemes based on data simulation and multi-objective evaluation, reducing the reliance on human experience in the decision-making process, improving the scientific nature and response speed of the decision, and providing quantitative decision-making basis for dealing with complex water network scheduling and emergencies. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the data fusion model construction process of the present invention. Figure 2 This is a flowchart illustrating the data quality evaluation index system and weight calculation method of the present invention. Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see the appendix Figure 1 - Appendix Figure 3 This invention provides a method for building a multi-source spatial digital intelligence fusion foundation and optimizing decision-making for water network management, including the following steps: First, multiple entities collaborate to acquire multi-source spatial data: multiple entities, including water network management departments, third-party data service agencies, and geographic information surveying and mapping departments, acquire multi-source spatial data through sensors, UAV remote sensing equipment, satellite positioning systems, and existing data management systems deployed at key nodes of the water network. The multi-source spatial data includes real-time monitoring data such as water level, flow rate, and water quality of the water network, historical hydrological data, and basic spatial data such as the geographic spatial coordinates, pipeline distribution, and topography of the water network. Each entity, based on its responsibilities and technical capabilities, transmits the acquired data to the data preprocessing platform in real-time or periodically, according to unified data collection frequency and format requirements, to support subsequent fusion and decision optimization. The water network management department is primarily responsible for collecting real-time monitoring data. The deployed sensors include water level sensors, flow sensors, and water quality sensors, installed at key river sections, reservoir inlets and outlets, and key nodes in the urban pipe network. These sensors collect data every minute and transmit it to the preprocessing platform in real-time via wired or wireless networks, ensuring rapid response to changes in water quality. For example, at the upstream section of a city's main river, water level sensors can provide real-time feedback on water level changes. Combined with flow sensor data from downstream nodes, this allows for dynamic assessment of water flow velocity and flux, providing a basis for flood control scheduling decisions. Third-party data service providers are responsible for integrating historical hydrological data. They regularly acquire and update historical data on floods, droughts, and other events nationwide through an industry-wide data sharing platform, with an update frequency of once a month. This data is standardized and then uploaded to the platform to enrich the system's historical response capabilities and trend analysis capabilities for extreme hydrological events. For example, the annual maximum flood hydrograph of a certain river basin from 2021 to 2024 can be used as a sample for model training, improving the accuracy of predicting similar flood events in the future. Geographic information surveying and mapping departments focus on providing high-precision spatial foundation data. They acquire high-resolution remote sensing imagery through aerial photogrammetry, combined with ground-based measurement methods to collect contour lines, land use data, and other geographic information data, updating this data annually to construct and maintain the geospatial base map of the water network. For instance, if a region experiences large areas of artificially hardened ground due to urban expansion, the latest land use data provided by the surveying and mapping department can promptly reflect this change, allowing adjustments to the model's assessment of rainwater infiltration capacity and preventing misjudgments of drainage capacity due to outdated data. Through this tripartite collaboration, comprehensive data type coverage, a clear update mechanism, and clear data transmission paths, the quality of multi-source spatial data fusion and the scientific rigor of subsequent intelligent decision-making are ensured.

[0027] Next, the data preprocessing platform optimizes the reading and preprocessing of graphic data. To address issues such as heterogeneous graphic data formats, inconsistent coordinate systems, and incompatible reading interfaces, the platform first designed a universal data reading module supporting the OGC standard. This module integrates interface protocols such as WFS, WMS, and WCS, enabling seamless access to various types of spatial data, including pipeline layers from water network management departments, remote sensing image data from third-party platforms, and contour lines or land use layers from surveying and mapping departments. Through a unified API interface specification, data from different sources and in different formats are standardized for access, improving the platform's data compatibility and scalability. After data reading, the platform incorporates an automatic format conversion engine built on the GDAL and OGR libraries. This engine supports parsing and converting various non-standard formats such as CAD, GeoTIFF, and Shapefile, converting them in real-time to unified standard spatial data formats such as GeoJSON and GML to meet the input requirements of subsequent fusion models. During the conversion process, GDAL handles the resampling and coordinate transformation of raster data, while OGR handles the geometric correction and attribute reconstruction of vector data. Their combined efforts ensure conversion accuracy and processing efficiency. The engine employs a coordinate reference system identification and matching algorithm to automatically detect the coordinate system type of the input data and process it in accordance with the system's default coordinate system, avoiding spatial mapping deviations caused by inconsistencies in coordinate systems. Its core transformation steps can be represented as follows: ; In the formula: Represents the original graphic data; Represents format conversion functions; Represents the coordinate projection transformation function; This indicates the standard data format for the output. The process is executed automatically through a pipeline, requiring no manual intervention. It possesses strong real-time processing capabilities and large data throughput, maintaining structural and semantic consistency even when faced with complex, multi-source data inputs, providing high-quality data support for subsequent spatial feature extraction and fusion modeling.

[0028] Meanwhile, the platform integrates an automatic coordinate system identification and conversion tool. As a core submodule of the automatic format conversion engine, this tool is responsible for identifying the original coordinate system type of the input data and converting it to a unified coordinate reference datum. The platform uses the National Geodetic Coordinate System 2000 as the unified spatial reference framework. This datum coordinate system has a consistent geographic positioning standard nationwide, effectively eliminating spatial errors caused by coordinate differences during cross-regional data fusion. The built-in identification module of the coordinate conversion tool automatically reads the spatial reference information embedded in each graphic data file based on the metadata parsing method of the graphic data header, and determines its type through a coordinate system dictionary and encoding rules. When an inconsistency with the target coordinate system is detected, the system automatically calls the seven-parameter transformation model to complete the coordinate projection consistency. The seven-parameter transformation model is a commonly used geodetic coordinate system transformation model that accurately corrects the original coordinates through translation, rotation, and scaling adjustments, ensuring that the positions of all source data are consistent under the same projection. Its mathematical expression is: ; In the formula: These are the original coordinate values; These are the coordinates in the target coordinate system. It is a rotation matrix; It is a three-dimensional translation vector; For a unified scale factor.

[0029] The model employs coordinate projection tools from the GDAL library for numerical calculations during the transformation process, and combines this with refined parameters released by the surveying and mapping department to improve the transformation accuracy in boundary areas or local elevation differences. The entire processing flow is embedded in the real-time execution mechanism of the automatic format conversion engine, ensuring the stability and smoothness of the transformation operation. This significantly reduces data access costs caused by heterogeneous formats and inconsistent coordinate systems, enhances the platform's compatibility and fusion accuracy with multi-source graphic data, ensures the consistency and correctness of spatial location mapping, and provides a rigorous geographic reference foundation for subsequent fusion models and 3D visualization.

[0030] After standardizing the graphic data format and unifying the coordinate system, to address the inconsistency in semantic descriptions of the same geospatial elements from different data sources, the platform further constructs an ontology model for the water network domain, systematically semantically annotating geometric elements in multi-source graphic data. The ontology model is a knowledge representation framework built around the water network structure and its related functional attributes, encompassing core concepts such as water body types, hydraulic structures, watershed boundaries, and land cover. By establishing entity classes, attribute classes, and relationship classes, it maps unstructured spatial geometric objects in the original graphic data to explicit semantics, achieving a mapping from data to knowledge. During the annotation process, the platform first identifies the types of geometric elements extracted from the graphic data, such as identifying linear objects as rivers and pipelines, and areal objects as lakes, reservoirs, and waterlogged areas. It further confirms logical attributes such as upstream and downstream locations and functional connections through spatial topological relationships. Subsequently, based on the constructed unified spatial feature attribute dictionary, the system adds standard attribute fields to each type of element, including type code, functional purpose, hydrological characteristics, region, and boundary precision level, resolving issues such as inconsistent field naming, inconsistent data types, and conflicting attribute descriptions caused by differences in source. This dictionary employs a hierarchical structure, which not only improves attribute matching efficiency but also provides a unified semantic input template for model training and decision-making systems. The establishment of semantic consistency can be represented by a mapping function: ; In the formula: Indicates the first One original graphic element; Representing the ontology model; Represents a semantic mapping function; Output Standardized semantic element data. All data that has undergone semantic unification is aggregated into a normalized spatial dataset, which serves as input for the spatial feature extraction and data fusion model in the next stage. This process not only enhances the interoperability between data from different sources but also significantly improves the semantic level's ability to control data consistency, laying a semantic foundation for data fusion and intelligent decision-making in the entire water network intelligent management and control system.

[0031] Next, the data fusion module performs graphic data fusion: After data preprocessing, the data fusion module takes over the graphic data fusion work, constructing a fusion foundation to support intelligent water network management and control. This module uses preprocessed multi-source spatial data as input, relying on standardized formats, unified coordinate systems, and semantic consistency to construct a multi-source spatial data fusion model. Through high-dimensional feature extraction and matching, it achieves effective alignment and information integration of graphic data. First, the module performs multi-dimensional feature extraction for each type of spatial object, including spatial location features, geometric shape features, attribute features, and temporal features. Spatial location features define the target's positioning accuracy through coordinate points and boundary envelopes; geometric shape features extract indicators such as side length, area, and curvature from linear, curved, or polygonal structures; attribute features involve semantic labels, functional types, and usage status; and temporal features record data acquisition or update times to support time-series analysis. The extracted features are then represented by a unified multi-dimensional vector space, assuming the data object... The feature vector is The formula is: ; In the formula: It is a position vector; Geometric vector; It is an attribute vector; This is the time vector. The fusion model uses appropriate matching algorithms to compare and combine features based on their type. Location features are calculated using Euclidean distance to determine spatial approximation; geometric features are matched using a shape similarity function; attribute features are compared using a semantic similarity model; and time features are sampled and matched using a time-difference window mechanism. The basic expression for location matching is: ; In the formula: Point With point The Euclidean distance between them; Indicates the position of the first spatial object; Indicates the position point of the second spatial object; Point The coordinates; Point The coordinates.

[0032] The geometric shape matching function is: ; In the formula: Representing two geometric shapes and Similarity score; Represents the geometry of the first object; Represents the geometry of the second object; It represents the area of ​​the intersection of two geometric shapes; Representing geometric shapes The area; Representing geometric shapes The area.

[0033] In the formula, the intersection area measures the degree of shape overlap; the closer it is to 1, the more similar the shapes are. Finally, the fusion model constructs spatial object fusion pairs based on the matching results and performs weighted fusion or attribute inheritance to generate unified data records, forming a multi-source data fusion result set. This ensures that the system can maintain fusion accuracy and consistency even when facing highly heterogeneous spatial data, thus establishing a fusion foundation.

[0034] In the process of multi-source graphic data fusion, to ensure the credibility and application value of the fusion results, the platform introduces a quality judgment mechanism based on multi-dimensional evaluation when setting data weights. It constructs a data quality evaluation index system and uses fuzzy comprehensive evaluation to calculate the weights of each data source. This index system comprehensively reflects the quality of data from different sources from five dimensions: accuracy, completeness, timeliness, consistency, and current relevance. Accuracy reflects the degree of error between the data and the actual target; completeness indicates whether the data covers the required information range; timeliness assesses the time interval between the data and the latest reality; consistency measures the logical consistency within the data or between the data and other data sources; and current relevance refers to the frequency of data updates and its ability to reflect the current state. In the specific evaluation, the platform first sets an evaluation level for each index and uses a membership function to perform fuzzy scoring on the performance of each data source under each index, forming a membership matrix. ; ; In the formula: This represents the membership matrix.

[0035] Indicates the first The data source in the first The evaluation membership degree under each indicator is then used to construct an indicator weight vector by combining expert experience or statistical analysis results. : In the formula: Represents the weight vector; Indicates the first The importance of each indicator to the overall quality evaluation; This indicates the total number of indicators and the importance of each indicator to the overall quality evaluation.

[0036] Finally, the overall quality score of the data source is calculated using the fuzzy comprehensive evaluation formula: : In the formula: This represents the overall quality score vector; Indicates the first The overall quality score of each data source; Represents the indicator weight vector; Represents the membership matrix; This indicates the total number of data sources.

[0037] To transform the scores into weight coefficients for fusion, the score vector is normalized, and the final weights are set as follows: The formula is: ; In the formula: Indicates the first The weight ratio of each data source in the fusion process; Indicates the first Scores from each data source; Indicates the first Scores from each data source; This indicates the total number of data sources. This method, while avoiding the influence of a single evaluation factor, fully considers multi-dimensional quality characteristics, improves the scientific rigor and robustness of weight allocation, provides accurate and reliable reference for data fusion, and further enhances the rationality and accuracy of the fusion results.

[0038] In the process of multi-source graphic data fusion, the system adopts a hierarchical graphic data fusion algorithm, processing different types of graphic data separately. Especially in vector data fusion, it emphasizes the introduction of spatial indexing and geometric similarity matching mechanisms to improve fusion efficiency and accuracy. The system first constructs an efficient spatial index structure, such as an R-tree or quadtree, to divide the elements in the vector data into multi-level nested minimum envelope boxes according to their spatial boundaries, enabling rapid querying and positioning of large-scale spatial data. During data fusion, for each element to be fused, the system retrieves its potentially matching candidate element set from the index structure, narrowing the comparison range by filtering significantly deviating spatial objects, thus reducing the computational burden. Among candidate elements, the platform uses Hausdorff distance as a geometric similarity metric to identify spatial objects with the same semantic identifier but geometric differences. Hausdorff distance is defined as: ; In the formula: Represents a set and set The Hausdorff distance between them; Represents the set of points containing the first element; Represents the set of points containing the second element; Represents a set any point in it; Represents a set any point in it; Point With point The Euclidean distance between the two figures indicates that the smaller the value, the higher the similarity between the two figures in terms of spatial shape. (Supremum) represents taking the supremum (i.e., the least upper bound) of a set. (Infimum) represents the infimum (i.e. the largest lower bound) of a set. This indicates that the maximum of the two values ​​is taken. The system uses this similarity as one of the matching reference values. If the Hausdorff distance between two elements is less than a set threshold and they are semantically consistent, they are judged as elements with the same name. Based on this, the platform further introduces domain rules to verify the rationality of the matching results and formulate fusion strategies. For example, in water network management, it is stipulated that the spatial coordinates of network nodes must completely coincide with the pipe endpoints. Therefore, when performing element merging, if a slight deviation between the node coordinates and the pipe endpoints is detected, the system corrects the node positions according to the principle of minimum movement to ensure logical consistency of the connection relationship. Simultaneously, to resolve conflicting elements such as duplicate labeling and boundary misalignment, the system sets fusion priorities and adjusts the attributes of the merged results based on data quality weights, using the dominant data as a benchmark and inheriting the attribute descriptions of the data with higher weights. The overall process maintains the rationality of the spatial structure while ensuring that the merged data has geometric consistency and semantic integrity, supporting the stable operation of downstream visualization and intelligent analysis tasks.

[0039] When fusing raster data such as remote sensing images, the system employs an improved wavelet transform fusion algorithm to simultaneously enhance the spatial details of the images while preserving their spectral characteristics. First, preprocessing is performed on the multispectral remote sensing images and LiDAR point cloud data, including geometric correction, radiometric normalization, and spatial resolution registration. Then, wavelet decomposition is performed on the multispectral images to extract their low-frequency components, representing the main spectral information. Simultaneously, wavelet transform is applied to the high-resolution digital surface model generated from the LiDAR point cloud to extract its high-frequency components, characterizing the spatial edges and detailed structures of ground features. The wavelet fusion is expressed as follows: ; In the formula: The image after fusion; For the wavelet low-frequency component of the multispectral image; Wavelet high-frequency components for generating images of LiDAR point clouds; This represents the inverse wavelet transform operation. The fused image generated in this way maintains the spectral consistency of the original multispectral image while significantly improving the spatial resolution, making details such as water body boundaries, dams, and building outlines clearer. Based on this, to achieve a 3D spatial fusion representation of various water network facilities, the system uses BIM modeling results as its structural foundation and combines DEM topographic data to construct a 3D fusion framework for water conservancy projects. This framework uses spatial registration technology to automatically align 3D models from different data sources, ensuring seamless connection between above-ground pipeline facilities and underground water conservancy structures in the 3D coordinate system. Spatial registration adopts a model based on control points and iterative least squares fitting, where the source model point set is... The target model point set is By solving the optimal rigid body transformation matrix Make the error function Spatial alignment of the minimum implementation model: ; In the formula: The error function represents the sum of squared errors between the source model points and the target model points after rigid body transformation. This represents the source model point set, which contains a series of original points. ; This represents the target model point set, which contains a series of target points. , and the source model point set The points in the diagram correspond one-to-one; This represents the optimal rigid body transformation matrix (or transformation function), which rotates and translates the points of the source model to align them with the points of the target model. This is achieved by solving... Make the error function Minimum; Represents the source model point set The first in One point; Represents the target model point set The first in One point; Indicates the total number of control point pairs used for alignment; This represents the square of the Euclidean norm (L2 norm) of a vector, which is the square root of the sum of the squares of its components. In this context, it calculates the transformed vector. With the target point The square of the Euclidean distance between them.

[0040] The integrated 3D spatial data model supports subsequent volume calculations, structural analysis, hydrodynamic simulations, and visualization, providing three-dimensional, refined, and structured data support for the intelligent water network management and control system.

[0041] Finally, the decision optimization system presents the data and optimizes decisions: based on the fused spatial data, the system is responsible for the final data presentation and intelligent decision support, achieving a closed-loop transformation from spatial perception to spatial cognition. The system, based on a unified spatial data model, developed a 3D visualization platform using WebGL technology, constructing an intelligent management interface that highly integrates data, vision, and interaction. The platform supports layered overlay display of multi-source graphic data, including water network structure vector layers, terrain elevation raster layers, and real-time monitoring data heatmap layers. It employs a layer management mechanism to achieve element-level visualization switching and combined rendering, and automatically adjusts the transparency and rendering order of each layer through graphic context control, ensuring clear expression and spatial logical consistency of different data types. The rendering process employs a GPU-based parallel graphics computing framework, significantly reducing CPU computation time. Real-time vertex transformation, texture mapping, and lighting simulation are performed by the shader program, improving the graphics rendering frame rate. When handling large-scale graphics data rendering needs, the system uses a strategy combining octree spatial partitioning and Level of Detail (LOD) technology. It automatically and dynamically loads data models of varying precision based on the user's viewing distance, effectively controlling memory usage and maintaining smooth interaction. The platform supports dynamic interactive tools, such as measurement tools, view controllers, profile analysis, and cross-section projection modules, enabling users to instantly query and analyze spatial data with response times controlled within milliseconds. The system incorporates a decision analysis engine that combines spatial topology analysis, watershed modeling, and real-time monitoring data inversion results for multi-dimensional calculations. This provides managers with intelligent early warnings and scheduling suggestions based on indicator thresholds. The platform's visualization analysis supports heatmap generation using a weighted kernel density estimation algorithm. : ; In the formula: For kernel functions; For smoothing parameters; To monitor the coordinates of data points; For any point where the density value needs to be estimated; To monitor the total number of data points, this method transforms the monitoring data into a continuously distributed image in space, clearly presenting risk clusters or pressure on key nodes. The fused three-dimensional spatial data, presented graphically, not only enhances the intuitiveness and timeliness of water network management decisions but also provides a panoramic view and data foundation for emergency response to sudden events, resource scheduling optimization, and long-term planning.

[0042] Based on the unified spatial data model after fusion, the system addresses diverse water network management needs, including water resource allocation, flood control and drainage, and water quality protection, by constructing an intelligent decision-making generation mechanism that integrates spatial analysis and professional models. For allocation path optimization and emergency response deployment, the system applies buffer zone analysis technology to assess the impact on water bodies surrounding sensitive areas, utilizes network analysis algorithms to construct a water network topology diagram based on hydraulic connections, and calculates the shortest flow paths and access efficiency between different nodes. For estimating the continuity of water quality status between monitoring points, Kriging interpolation is used to model pollutant concentration distribution. The Kriging interpolation method is expressed as follows: ; In the formula: Indicates the location to be estimated Water quality values; Indicates a known location Observed values; The weighting coefficients are determined based on the spatial autocorrelation structure. This represents the total number of known observation points used for estimation. The above analysis results, along with the integrated hydrodynamic and water quality diffusion models, are input into the decision logic module to simulate water flow scheduling, water level changes, and pollutant diffusion trends, generating a preliminary control plan. The generated plan is then used to construct a comprehensive evaluation system based on multi-dimensional indicators such as feasibility, effectiveness, economy, and ecological impact. The system uses a simulation platform based on historical empirical data and rule parameters to dynamically predict and visually evaluate the implementation effect of the control plan under different hydrological scenarios, seasonal variations, and emergencies. Based on this, a genetic optimization algorithm is introduced to adaptively iteratively optimize the plan. Let the objective function of the decision plan be... Using a comprehensive function of multiple evaluation indicators as the fitness criterion, the optimal decision set is solved through iterative evolution using operators such as selection, crossover, and mutation. : In the formula: This indicates the optimal control scheme.

[0043] This represents the set of feasible solutions.

[0044] The objective function represents the decision-making scheme.

[0045] Represents the set of feasible solutions One of the decision-making options; This indicates that the solution should make the objective function... Decision scheme to achieve the maximum value .

[0046] Ultimately, the system will push the optimization results to the water network management department as the basis for actual scheduling, allocation, and early warning intervention, forming a closed-loop management and control model driven by data, supported by models, and with intelligent feedback.

[0047] When the data preprocessing platform reads and optimizes graphic data, during data cleaning, for real-time monitoring data, real-time data verification rules are set. When the monitoring data exceeds the preset normal threshold range, an abnormal data marking mechanism is triggered, and at the same time, the synchronous data of adjacent nodes is automatically retrieved. A time-series-based Kalman filter algorithm is used to correct the abnormal data. For historical data, duplicate records are identified through data comparison, and the latest and most complete records are retained according to the chronological order of data collection time. When performing data format conversion, for geospatial coordinate data, coordinate data from different sources are uniformly converted to the 2000 National Geodetic Coordinate System according to the map projection and coordinate transformation parameters specified by the Ministry of Natural Resources.

[0048] When performing graphic data fusion, the data fusion module constructs a multi-source spatial data fusion model that comprehensively considers the spatial, temporal, and attribute characteristics of the data. It assigns corresponding weights to different types of data, determining these weights by considering factors such as the reliability of the data source, the time span, and spatial resolution, and calculating them using algorithms such as the analytic hierarchy process (AHP) or entropy weighting. Using the constructed fusion model, the multi-source spatial data is fused, employing feature-level or pixel-level fusion algorithms, such as weighted average and principal component analysis, to integrate spatial information from different data sources and generate a unified fused spatial data model.

[0049] When generating preliminary control and management decision plans, the decision optimization system combines the real-time operating status and historical data of the water network with machine learning algorithms such as support vector machines and random forests to predict the future operating trends of the water network, providing a reference for the generation of decision plans. When evaluating decision plans, a multi-objective decision evaluation model is established. This model uses economic, environmental, and social benefits as evaluation objectives, employs the Delphi method to determine the weights of each objective, and constructs an evaluation matrix to comprehensively evaluate the plans. When optimizing decision plans, an optimization objective function is set, such as maximizing water resource utilization efficiency and minimizing flood control costs. Nonlinear programming algorithms are then used to optimize and adjust the decision variables in the plan.

Claims

1. A method for building a multi-source spatial digital fusion base and optimizing decision-making for water network management and control, characterized in that, The method comprises the following steps: Multi-agent collaborative acquisition of multi-source spatial data, including real-time monitoring data, historical hydrological data and basic spatial data; A data preprocessing platform performs graphic data reading and preprocessing optimization, converts heterogeneous graphic format data in the multi-source spatial data into a unified standard spatial data format, unifies their coordinate systems, performs systematic semantic annotation on geometric elements, and obtains a standardized spatial data set; A data fusion module performs graphic data fusion, constructs a multi-source spatial data fusion model based on the standardized spatial data set, extracts and matches multi-dimensional features, and generates a fused unified spatial data model; A decision optimization system performs data presentation and decision optimization, generates and optimizes a management and control decision scheme based on the fused unified spatial data model.

2. The multi-source spatial digital fusion base building and decision optimization method of water network management and control according to claim 1, characterized in that, The data preprocessing platform performs graphic data reading and preprocessing optimization, comprising: Accessing the multi-source spatial data through a general data reading module supporting OGC standards; Using an automatic format conversion engine constructed based on GDAL and OGR libraries to convert the heterogeneous graphic format data into the standard spatial data format; Using a coordinate system automatic recognition and conversion tool to unify different coordinate systems into the national 2000 geodetic coordinate system through a seven-parameter conversion model.

3. The method of claim 1, wherein the method further comprises: determining a water network control strategy based on the water network control model; and outputting the water network control strategy. The systematic semantic annotation on geometric elements comprises: Constructing a water network domain ontology model, which covers core concepts of water body types, hydraulic structures, watershed boundaries and land cover; According to a unified spatial feature attribute dictionary, adding standard attribute fields to geometric elements extracted from graphic data to realize semantic unification.

4. The water network management and control multi-source spatial digital fusion base building and decision optimization method according to claim 1, characterized in that, The data fusion module performs graphic data fusion, specifically: Extracting multi-dimensional features of spatial objects to obtain feature vectors containing spatial location features, geometric shape features, attribute features and time features; According to feature types, using corresponding matching algorithms for feature comparison and combination, the matching algorithms including calculating spatial location approximation using Euclidean distance and performing geometric shape matching using a shape similarity function.

5. The water network management and control multi-source spatial digital fusion base building and decision optimization method according to claim 1, characterized in that, When constructing the multi-source spatial data fusion model, weights are set for different data sources, and the determination method of the weights comprises: Constructing a data quality evaluation index system including data accuracy, completeness, timeliness, consistency and present situation; The data sources are scored by using a fuzzy comprehensive evaluation method to form a membership matrix ; combining the indicator weight vector , calculating an overall quality score for the data source by a fuzzy comprehensive evaluation formula ​ Normalizing the overall quality score to obtain weight coefficients of each data source in the fusion process.

6. The water network management and control multi-source spatial digital fusion base building and decision optimization method according to claim 1, characterized in that, The data fusion module performs graphic data fusion, further comprising: For vector data, by constructing Candidate feature retrieval is performed using a spatial index of a tree or quadtree, and Hausdorff distance is used as a metric for calculating geometric similarity to identify and match features with the same name.

7. The method of claim 1, wherein the method further comprises: determining a water network control strategy based on the water network control model; and outputting the water network control strategy. The data fusion module performs graphic data fusion, further comprising: For raster data, an improved wavelet transform fusion algorithm is used to fuse the wavelet low-frequency components of multi-spectral remote sensing images and the wavelet high-frequency components of high-resolution digital surface models generated by LiDAR point clouds.

8. The method of claim 1, wherein the method further comprises: determining a water network control strategy based on the water network control model; and outputting the water network control strategy. The decision optimization system generates and optimizes the management and control decision scheme, comprising: Combining spatial analysis algorithms and hydrodynamic models to generate a preliminary management and control decision scheme; Establishing a multi-objective decision evaluation model to predict and comprehensively evaluate the effect of the preliminary management and control decision scheme; The genetic optimization algorithm is used to iteratively optimize the preliminary management and control decision scheme until an optimal management and control decision scheme is obtained.

9. The method of claim 1, wherein the method further comprises: determining a water network control strategy based on the water network control model; and outputting the water network control strategy. The decision optimization system performs data presentation, including: A three-dimensional visualization platform is constructed based on WebGL technology; An octree space division combined with level of detail (LOD) technology is adopted to dynamically load data models with different precision according to the distance of the user's perspective, thereby realizing smooth interaction and rendering of large-scale graphic data.

10. The multi-source spatial digital fusion base building and decision optimization method of water network management and control according to claim 1, characterized in that, The data fusion module performs graphic data fusion, further including: For three-dimensional model data, BIM modeling results are taken as a structural basis, and DEM terrain data are combined for fusion; the fusion is realized through a space registration technology, and an optimal rigid transformation matrix is solved by using a control point-based and iterative least square fitting model to realize automatic alignment processing of the three-dimensional model.

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