CN-122019680-A - Basin urban water system multi-element space-time data integration and topology analysis method based on graph database

CN122019680ACN 122019680 ACN122019680 ACN 122019680ACN-122019680-A

Abstract

The invention provides a basin urban water system multi-element space-time data integration and topology analysis method based on a graph database. Integrating multiple data such as meteorological monitoring, river pipe network monitoring and water supply, constructing a distributed database through space-time alignment and depth fusion, converting pipe network GIS data into a graph database model, enabling nodes to represent entities such as inspection wells and edges to represent connection relations of pipelines and the like, hanging drainage subareas and monitoring equipment data through joint indexes to form a complete data network, checking data through a quality balance algorithm, and outputting pipe network health reports. The method solves the problems of island data, poor relevance and low topology analysis efficiency of the traditional relational database, supports efficient query of complex topological relations, has strong data fusion and verification capability, is suitable for urban drainage pipe network and river management and comprehensive water environment treatment, and effectively improves the implementation effect of large-scale water environment treatment projects.

Inventors

WANG DIANCHANG
ZHANG JUN
HUANG WEI
WU KUNMING
TANG LIHUA
HU ZUKANG
Ni Yufang

Assignees

中国长江三峡集团有限公司
长江生态环保集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. The basin urban water system multi-element space-time data integration and topology analysis method based on the graph database is characterized by comprising the following steps of: a) Integrating meteorological monitoring data, river channel pipe network monitoring data, water supply data, remote sensing image data and demographic data, and constructing a river basin urban water system distributed database through space-time alignment and depth fusion calculation; b) The construction of a graph database, namely converting the GIS data of a pipe network into a graph database model, wherein nodes represent inspection wells and drainage partition entities, and edges represent the connection relation of pipelines and river channels; c) The multi-source data hooking is that monitoring equipment and drainage partition data are dynamically related to topological nodes and edges of a graph database through a joint index system to form a complete data network; d) And (3) topology analysis and data verification, namely, verifying data by using a quality balance algorithm, and outputting a river basin urban water system management network health report.
2. The method for integrating and analyzing the multi-element space-time data of the river basin urban water system based on the graph database as set forth in claim 1, wherein the multi-element data integration in the step a comprises the steps of constructing a meteorological monitoring database, a river channel network monitoring database, a water supply monitoring database and a catchment area database.
3. The method for integrating and analyzing the multiple space-time data of the river basin urban water system based on the graph database according to claim 2, wherein the weather monitoring database comprises rainfall monitoring equipment data and rainfall monitoring data, the rainfall monitoring equipment data comprises equipment unique numbers, monitoring equipment node numbers, equipment installation time, dismantling time, longitude coordinates, latitude coordinates and monitoring indexes, and the rainfall monitoring data comprises monitoring time, rainfall and monitoring equipment area information.
4. The method for integrating and analyzing the multi-element space-time data of the river basin urban water system based on the graph database according to claim 2, wherein the river basin pipe network monitoring database comprises river basin pipe network monitoring equipment data and river basin pipe network monitoring data, the river basin pipe network monitoring equipment data comprises equipment monitoring indexes and quality inspection thresholds, the monitoring indexes comprise water level, flow rate, flow velocity and COD, and the river basin pipe network monitoring data comprises water level and abnormal value inspection related parameters.
5. The method for integrating and analyzing the multi-element space-time data of the river basin urban water system based on the graph database according to claim 2, wherein the water supply monitoring database is constructed by a web crawler algorithm to realize the dynamic update of water supply data, and comprises a water supply cell name, a sub-cell name, a water supply base number and a water supply curve coefficient.
6. The method for integrating and topologically analyzing the multiple space-time data of the river basin urban water system based on the graph database as claimed in claim 2, wherein the catchment partition database is based on river channel network database and community population distribution data, so as to form a river basin sub-catchment area, a rainwater drainage partition and a sewage drainage partition database by combing, the river basin sub-catchment area and the rainwater drainage partition attribute comprise names, areas, places, impermeability and gradients, and the sewage drainage partition attribute comprises community names, areas, streets and population.
7. The method for integrating and analyzing the multi-element space-time data of the river basin urban water system based on the graph database according to claim 1, wherein the graph database model in the step b is realized by importing Node attribute and Relationship attribute CSV files into the graph database, the importing process is executed by adopting an automatic script, and the script comprises the configuration of forced importing and skipping repeated nodes.
8. The method for integrating and analyzing the multi-dimensional space-time data of the basin urban water system based on the graph database according to claim 1, wherein in the step c, the multi-source data hanging comprises hanging of drainage subareas and hanging of monitoring equipment, the drainage subareas are connected to corresponding nodes according to topological relations, a liquid level meter in the monitoring equipment is connected to the graph database nodes, a flow meter is connected to the graph database relations, and monitoring index attributes and monitoring equipment name attributes are added.
9. The method for integrating and analyzing the multiple space-time data of the river basin urban water system based on the graph database according to claim 1, wherein the quality balance algorithm in the step d is based on a data network in the graph database to perform fusion check on dynamic monitoring data and static pipe network data, and the fusion check comprises outlier check and data consistency check.
10. The method for integrating and topologically analyzing the multiple space-time data of the river basin urban water system based on the graph database as claimed in claim 1, wherein the graph database adopts neo4j, the distributed database adopts mongoldb and gdb, and the method is applied to urban drainage pipe network management, river management and comprehensive water environment treatment projects.

Description

Basin urban water system multi-element space-time data integration and topology analysis method based on graph database Technical Field The invention relates to the technical field of intelligent water affairs, in particular to a basin urban water system multi-element space-time data integration and topology analysis method based on a graph database. Background The traditional watershed urban water system data management mostly adopts a relational database, and has the problems of data island, poor relevance, low topology analysis efficiency and the like. In the prior art, although the GIS system can partially solve the problem of space data display, the fusion of dynamic monitoring data and static pipe network data is difficult to process efficiently, and the capability of fast analyzing complex topological relations is lacking. In addition, the existing method has defects in the aspects of data cleaning, monitoring distribution optimization and dynamic and static data verification, and restricts the implementation effect of large water environment treatment projects such as Yangtze river large protection and the like. Disclosure of Invention The invention provides a basin urban water system multi-element space-time data integration and topology analysis method based on a graph database, which solves the problems of data island, low topology analysis efficiency and insufficient data fusion and verification in the traditional technology. In order to achieve the above purpose, the present invention adopts the following technical scheme: A basin urban water system multi-element space-time data integration and topology analysis method based on a graph database comprises the following steps: a) Integrating meteorological monitoring data, river channel pipe network monitoring data, water supply data, remote sensing image data and demographic data, and constructing a river basin urban water system distributed database through space-time alignment and depth fusion calculation; b) The construction of a graph database, namely converting the GIS data of a pipe network into a graph database model, wherein nodes represent inspection wells and drainage partition entities, and edges represent the connection relation of pipelines and river channels; c) The multi-source data hooking is that monitoring equipment and drainage partition data are dynamically related to topological nodes and edges of a graph database through a joint index system to form a complete data network; d) And (3) topology analysis and data verification, namely, verifying data by using a quality balance algorithm, and outputting a river basin urban water system management network health report. Furthermore, the multi-element data integration in the step a comprises the steps of constructing a meteorological monitoring database, a river pipe network monitoring database, a water supply monitoring database and a catchment subarea database. Furthermore, the weather monitoring database comprises rainfall monitoring equipment data and rainfall monitoring data, wherein the rainfall monitoring equipment data comprises equipment unique numbers, monitoring equipment node numbers, equipment installation time, dismantling time, longitude coordinates, latitude coordinates and monitoring indexes, and the rainfall monitoring data comprises monitoring time, rainfall and monitoring equipment area information. Furthermore, the river pipe network monitoring database comprises river pipe network monitoring equipment data and river pipe network monitoring data, the river pipe network monitoring equipment data comprises equipment monitoring indexes and quality inspection thresholds, the monitoring indexes comprise water levels, flow rates, flow speeds and COD, and the river pipe network monitoring data comprises water levels and abnormal value inspection related parameters. Furthermore, the water supply monitoring database is constructed through a web crawler algorithm to realize dynamic updating of water supply data, and comprises a water supply district name, a sub-district name, a water supply base number and a water supply curve coefficient. Furthermore, the catchment subarea database is based on river channel pipe network database and community population distribution data, so that a river basin subarea, a rainwater drainage subarea and a sewage drainage subarea database are formed by combing, the attributes of the river basin subarea and the rainwater drainage subarea comprise names, areas, ground types, water impermeability and gradients, and the attributes of the sewage drainage subarea comprise community names, areas, streets and population. Furthermore, in the step b, the graph database model is realized by importing Node attribute and Relationship attribute CSV files into the graph database, the importing process is executed by adopting an automatic script, and the script comprises configuration of forced importing and skipping repeated nodes. Furthermore, the mul