Drainage basin urban water system multivariate spatio-temporal data integration and topology analysis method based on graph database

Abstract

The invention provides a watershed urban water system multivariate spatio-temporal data integration and topology analysis method based on a graph database. The watershed urban water system multivariate spatio-temporal data integration and topology analysis method comprises the steps of multivariate data integration, graph database construction, multi-source data hooking and topology analysis and data verification. Integrating meteorological monitoring, river pipe network monitoring, water supply and other multivariate data, and constructing a distributed database through space-time alignment and deep fusion; pipe network GIS data is converted into a graph database model, nodes represent entities such as inspection wells, and edges represent connection relations such as pipelines; the drainage partition and monitoring equipment data are hooked through the joint index to form a complete data network; and running a quality balance algorithm to verify the data, and outputting a pipe network health report. The method solves the problems of data islands, poor relevance and low topological analysis efficiency of a traditional relational database, supports efficient query of complex topological relations, is high in data fusion and verification capability, is suitable for urban drainage network, river management and comprehensive water environment treatment, and effectively improves the implementation effect of a large water environment treatment project.

Description

Technical Field

[0001] This invention relates to the field of smart water management technology, and in particular to a method for multi-dimensional spatiotemporal data integration and topology analysis of urban water systems in watersheds based on graph databases. Background Technology

[0002] Traditional urban water system data management in river basins often employs relational databases, which suffers from data silos, poor correlation, and low efficiency in topology analysis. While GIS systems can partially address spatial data visualization, they struggle to efficiently integrate dynamic monitoring data with static pipeline network data and lack the ability to rapidly analyze complex topological relationships. Furthermore, existing methods are deficient in data cleaning, monitoring point optimization, and verification of dynamic and static data, hindering the effectiveness of large-scale water environment management projects such as the Yangtze River protection initiative. Summary of the Invention

[0003] This invention provides a method for integrating and analyzing multi-dimensional spatiotemporal data of urban water systems in watersheds based on graph databases, which solves the problems of data silos, low efficiency of topology analysis, and insufficient data fusion and verification in traditional technologies.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for multi-dimensional spatiotemporal data integration and topological analysis of urban water system in a watershed based on graph databases includes the following steps: a) Multi-source data integration: Integrate meteorological monitoring data, river network monitoring data, water supply data, remote sensing image data, and population statistics data, and construct a distributed database of urban water systems in the basin through spatiotemporal alignment and deep fusion calculation; b) Graph database construction: Convert the pipeline network GIS data into a graph database model, where nodes represent inspection wells and drainage zone entities, and edges represent pipeline and river connection relationships; c) Multi-source data connection: Through a joint indexing system, monitoring equipment and drainage zone data are dynamically linked to the topology nodes and edges of the graph database to form a complete data network; d) Topology analysis and data verification: Run the quality balance algorithm to verify the data and output a health report of the urban water system network in the watershed.

[0005] Furthermore, the multi-source data integration mentioned in step a includes constructing a meteorological monitoring database, a river network monitoring database, a water supply monitoring database, and a catchment area database.

[0006] Furthermore, the meteorological monitoring database includes rainfall monitoring equipment data and rainfall monitoring data. The rainfall monitoring equipment data includes the unique equipment number, monitoring equipment node number, equipment installation time, dismantling time, longitude coordinates, latitude coordinates, and monitoring indicators. The rainfall monitoring data includes monitoring time, rainfall, and monitoring equipment area information.

[0007] Furthermore, the river network monitoring database includes river network monitoring equipment data and river network monitoring data. The river network monitoring equipment data includes equipment monitoring indicators and quality inspection thresholds. The monitoring indicators include water level, flow rate, flow velocity, and COD. The river network monitoring data includes water level and parameters related to anomaly checks.

[0008] Furthermore, the water supply monitoring database is constructed using a web crawler algorithm to achieve dynamic updates of water supply data, including the name of the water supply community, the name of the sub-community, the water supply baseline, and the water supply curve coefficient.

[0009] Furthermore, the watershed zoning database is based on the river network database and the population distribution data of the community, and is organized into watershed sub-catchment, stormwater drainage zoning and sewage drainage zoning databases. The attributes of the watershed sub-catchment and stormwater drainage zoning include name, area, land type, impermeability, and slope, while the attributes of the sewage drainage zoning include community name, area, street, and population.

[0010] Furthermore, the graph database model described in step b is implemented by importing CSV files containing Node and Relationship attributes into the graph database. The import process is executed using an automated script, which includes configurations for forced import and skipping duplicate nodes.

[0011] Furthermore, the multi-source data connection mentioned in step c includes drainage zone connection and monitoring equipment connection. The drainage zone is connected to the corresponding node according to the topology relationship. Among the monitoring equipment, the level gauge is connected to the graph database node, the flow meter is connected to the graph database relationship, and new monitoring indicator attributes and monitoring equipment name attributes are added.

[0012] Furthermore, the quality balance algorithm described in step d is based on the data network in the graph database to perform fusion verification of dynamic monitoring data and static pipeline network data, including outlier checks and data consistency verification.

[0013] Furthermore, the graph database uses Neo4j, and the distributed database uses MongoDB and GDB. The method is applied to urban drainage network management, river management, and comprehensive water environment management projects.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. A graph database-based topology analysis system supports efficient querying of complex topological relationships, solving the problem of low efficiency in traditional topology analysis methods.

[0015] 2. Achieve deep integration of diverse data such as meteorological, pipeline, and water supply data to break down data silos and improve data correlation.

[0016] 3. By linking multiple data sources and using quality assessment algorithms, we enhance data verification capabilities and ensure data accuracy.

[0017] 4. It is applicable to urban drainage network management, river management and comprehensive water environment management, and can improve the implementation effect of large-scale water environment management projects such as the Yangtze River protection, and has broad application prospects. Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the multi-source data preprocessing process for urban water systems in a watershed, as described in this invention. Figure 2 This is a schematic diagram of the rain gauge monitoring device of the present invention; Figure 3 This is a schematic diagram of the rain gauge monitoring data of the present invention; Figure 4 This is a schematic diagram of the data from the river network monitoring equipment of the present invention; Figure 5 This is a schematic diagram of the river network monitoring data of the present invention; Figure 6 This is a schematic diagram of the structure of a single data entry for water supply monitoring data in this invention; Figure 7 This is a map of the sub-catchment areas of the watershed in this invention; Figure 8 This is a rainwater drainage zoning diagram of the present invention; Figure 9 This is a wastewater drainage zoning diagram of the present invention; Figure 10 This is a schematic diagram of the attributes of nodes in the drainage zoning connection diagram database of the present invention; Figure 11 This is a schematic diagram of the level gauge connection in the diagram database of this invention; Figure 12 This is a schematic diagram of the flow meter connection in the diagram database of this invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be further described in detail with reference to the accompanying drawings.

[0020] Taking Jiujiang City as a pilot, tipping bucket rain gauges were selected as rainfall monitoring equipment, submersible level gauges were installed in inspection wells, and electromagnetic flow meters were installed in pipelines to collect indicators such as water level and flow rate. Neo4j-community-4.4.7 was selected as the graph database, and MongoDB and GDB were used as the distributed databases. Web crawler algorithms were used to crawl the population distribution and water supply dynamic data of the community.

[0021] The collected data includes Jiujiang City's pipeline network GIS data, meteorological monitoring data, river pipeline network monitoring data, water supply data, remote sensing image data, and community population statistics, ensuring that the data covers the watershed sub-catchment areas, rainwater drainage zones, and sewage drainage zones.

[0022] A method for multi-dimensional spatiotemporal data integration and topological analysis of urban water system in a watershed based on graph databases, specifically including the following steps: 1. Achievement of multi-source data integration Integrating diverse data sources such as meteorological monitoring, river network monitoring, water supply data, remote sensing imagery, and population statistics, this data undergoes spatiotemporal alignment and deep fusion calculations to form a distributed database of the urban water system in the watershed. The multi-source data preprocessing process for the urban water system in the watershed is as follows: Figure 1 As shown.

[0023] 1.1 Meteorological Monitoring Database Construction: The name and attribute information of each rain gauge are stored in the rain gauge monitoring equipment table, including the unique equipment number, monitoring equipment node number, installation time, dismantling time, longitude coordinates, latitude coordinates, and rainfall monitoring indicators; rainfall monitoring data are stored according to monitoring time, rainfall, and monitoring equipment area, with the data structure as follows: Figure 2 As shown in Table 2, the meanings of each key-value pair are explained in the table. Figure 3 As shown in Table 3, the meanings of each key-value pair are explained in the table.

[0024] Table 2 - Description of Rainfall Monitoring Equipment Data Structure

[0025] Table 3 – Description of Rain Gauge Monitoring Data Structure

[0026] 1.2 Construction of River Pipeline Network Monitoring Database: This database stores the monitoring indicators and quality inspection thresholds of river pipeline network monitoring equipment. Monitoring indicators include water level, flow rate, flow velocity, and COD. River pipeline network monitoring data records water level and outlier detection parameters, including single-point average value checks, multi-point average value checks, and abrupt change checks. The data structure is as follows: Figure 4 As shown in Table 4, the meanings of each key-value pair are explained in Table 4; and as shown in Table 5. Figure 5 As shown in Table 5, the meanings of each key-value pair are explained in the table.

[0027] Table 4 - Description of Data Structure for River Pipeline Network Monitoring Equipment

[0028] Table 5 - Description of River Pipeline Network Monitoring Data Structure

[0029] 1.3 Construction of Water Supply Monitoring Database: Water supply data is dynamically updated using a web crawler algorithm. The database stores the name of the water supply community, sub-community name, water supply baseline, and water supply curve coefficients. The data structure is as follows: Figure 6 As shown in Table 6, the meanings of each key-value pair are explained in the table.

[0030] Table 6 – Explanation of Water Supply Monitoring Data Structure

[0031] 1.4 Construction of Catchment Zoning Database: Based on the river network database and community population distribution data, a database of sub-catchment areas, stormwater drainage zones, and sewage drainage zones for the Jiujiang City watershed was constructed. The sub-catchment areas and stormwater drainage zones include attributes such as name, area, land type, impermeability, and slope. Figure 7 and Figure 8 As shown; the sewage drainage zoning includes the community name, area, street, and population attributes, such as... Figure 9 As shown.

[0032] 2. Graph Database Construction and Data Integration 2.1 Topology Model Conversion: Convert the pipeline network GIS data into CSV files with Node and Relationship attributes, store them in the graph database input folder, and execute the automated script neo4j-adminimport--force=true--skip-duplicate-nodes=true--database=neo4j--nodes=import / NodeInfo.csv--relationships=import / LinkInfo.csv to convert the pipeline network relationship database into a graph database. Nodes represent entities such as manholes and drainage zones, and edges represent connections such as pipelines and waterways.

[0033] 2.2 Multi-source data integration: Drainage zones are integrated with corresponding nodes according to topological relationships, forming node attributes that include sewage drainage zones and rainwater drainage zones, such as... Figure 10 As shown; the level gauge is connected to the graph database node according to its installation location, and the flow meter is connected to the graph database relation. New monitoring indicator attributes `mon_type` and monitoring device name attributes `mongo_id` are added. The connection diagram is shown below. Figure 11 and Figure 12 As shown.

[0034] 3. Topology analysis and data verification The quality balance algorithm is run, and based on the complete data network in the graph database, dynamic monitoring data and static pipeline network data are fused and verified. Invalid data is removed through outlier checks and data consistency checks. Finally, the Jiujiang City pipeline network health report is output, which includes pipeline network topology analysis results, data quality assessment and abnormal point warning.

[0035] Working principle This method solves the data silo problem by integrating diverse spatiotemporal data to construct a distributed database; it converts pipeline GIS data into a graph database model, leveraging the advantages of graph databases in efficiently handling complex topological relationships to achieve rapid topological relationship queries; it connects multi-source data through a joint index system to form a complete data network; and finally, it verifies the data using a quality assessment algorithm to ensure data accuracy, providing decision support for watershed urban water system management.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for multi-dimensional spatiotemporal data integration and topological analysis of urban water system in a watershed based on graph databases, characterized in that, Includes the following steps: a) Multi-source data integration: Integrate meteorological monitoring data, river network monitoring data, water supply data, remote sensing image data, and population statistics data, and construct a distributed database of urban water systems in the basin through spatiotemporal alignment and deep fusion calculation; b) Graph database construction: Convert the pipeline network GIS data into a graph database model, where nodes represent inspection wells and drainage zone entities, and edges represent pipeline and river connection relationships; c) Multi-source data connection: Through a joint indexing system, monitoring equipment and drainage zone data are dynamically linked to the topology nodes and edges of the graph database to form a complete data network; d) Topology analysis and data verification: Run the quality balance algorithm to verify the data and output a health report of the urban water system network in the watershed.

2. The method for multi-dimensional spatiotemporal data integration and topological analysis of urban water system based on graph database as described in claim 1, characterized in that, The multi-source data integration mentioned in step a includes constructing a meteorological monitoring database, a river network monitoring database, a water supply monitoring database, and a catchment area database.

3. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 2, characterized in that, The meteorological monitoring database includes rainfall monitoring equipment data and rainfall monitoring data. The rainfall monitoring equipment data includes the unique equipment number, monitoring equipment node number, equipment installation time, dismantling time, longitude coordinates, latitude coordinates, and monitoring indicators. The rainfall monitoring data includes monitoring time, rainfall, and monitoring equipment area information.

4. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 2, characterized in that, The river network monitoring database includes river network monitoring equipment data and river network monitoring data. The river network monitoring equipment data includes equipment monitoring indicators and quality inspection thresholds. The monitoring indicators include water level, flow rate, flow velocity, and COD. The river network monitoring data includes water level and parameters related to anomaly checks.

5. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 2, characterized in that, The water supply monitoring database is constructed using a web crawler algorithm to achieve dynamic updates of water supply data, including the name of the water supply community, the name of the sub-community, the water supply baseline, and the water supply curve coefficient.

6. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 2, characterized in that, The catchment area database is based on the river network database and the population distribution data of the community, and is organized into a database of watershed sub-catchment areas, stormwater drainage areas and sewage drainage areas. The attributes of the watershed sub-catchment areas and stormwater drainage areas include name, area, land type, impermeability and slope, while the attributes of the sewage drainage areas include community name, area, street and population.

7. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 1, characterized in that, The graph database model described in step b is implemented by importing CSV files containing Node and Relationship attributes into the graph database. The import process is executed by an automated script, which includes configurations for forced import and skipping duplicate nodes.

8. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 1, characterized in that, The multi-source data connection mentioned in step c includes drainage zone connection and monitoring equipment connection. Drainage zones are connected to corresponding nodes according to topology. Among the monitoring devices, level gauges are connected to graph database nodes, flow meters are connected to graph database relationships, and new monitoring indicator attributes and monitoring device name attributes are added.

9. The method for multi-dimensional spatiotemporal data integration and topology analysis of urban water system based on graph database as described in claim 1, characterized in that, The quality balance algorithm described in step d is based on the data network in the graph database and performs fusion verification on dynamic monitoring data and static pipeline network data, including outlier checks and data consistency verification.

10. The method for multi-dimensional spatiotemporal data integration and topological analysis of urban water system based on graph database as described in claim 1, characterized in that, The graph database used is Neo4j, and the distributed databases are MongoDB and GDB. The method is applied to urban drainage network management, river management, and comprehensive water environment management projects.

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