Drainage pipe network design optimization system based on GIS technology

Abstract

The invention discloses a drainage pipe network design optimization system based on a GIS technology, which comprises a data module, a core calculation module and an application output module, and the modules realize bidirectional data interaction through standardized interfaces; the data module is used for integrating multi-source heterogeneous data and establishing a standardized database; the core calculation layer comprises a spatial analysis module, a dynamic hydraulic calculation module and a multi-objective optimization module, and the spatial analysis module is used for analyzing a spatial association relationship, a pipe network connectivity map and a catchment partition thermodynamic diagram; the invention relates to the technical field of municipal drainage engineering. According to the drainage pipe network design optimization system based on the GIS technology, automatic integration and standardized processing of multi-source heterogeneous data are achieved, data deviation is corrected through a dynamic calibration mechanism, the data integration time is greatly shortened, the precision is improved to meet the hydraulic calculation requirement, and a reliable data basis is provided for pipe network design optimization.

Description

Technical Field

[0001] This invention relates to the field of municipal drainage engineering technology, specifically to a drainage network design optimization system based on GIS technology. Background Technology

[0002] With the acceleration of urbanization, the built-up area of ​​cities is constantly expanding, and the population density is continuously increasing, placing higher demands on the carrying capacity and operational stability of urban drainage networks. As a core component of urban infrastructure, drainage networks bear the key functions of rainwater and flood drainage, and sewage collection and transportation. The rationality of their design is directly related to the quality of urban water environment, flood control safety, and the quality of life of residents. Especially against the backdrop of frequent extreme rainfall, problems such as insufficient drainage capacity of aging networks, combined sewer overflows, and waterlogging are becoming increasingly prominent, urgently requiring scientific design optimization techniques to improve the comprehensive efficiency of the network. Currently, GIS technology, due to its powerful spatial data management and visualization capabilities, has been gradually applied to the field of drainage network design, replacing the traditional two-dimensional drawing design mode and playing a role in areas such as network spatial layout planning. At the same time, hydraulic calculation models (such as SWMM, MIKE URBAN, etc.) are widely used to simulate the hydraulic conditions of the network, and multi-objective optimization algorithms are also beginning to be applied to network parameter optimization to balance investment costs and drainage performance.

[0003] Currently, GIS technology, with its powerful spatial data management and visualization capabilities, has been gradually applied to the field of drainage network design, replacing the traditional two-dimensional drawing design mode and playing a certain role in network spatial layout planning and node location positioning. Meanwhile, hydraulic calculation models (such as SWMM and MIKE URBAN) are also widely used to simulate the hydraulic conditions of the network, providing data support for the feasibility verification of design schemes; and multi-objective optimization algorithms (such as genetic algorithms and particle swarm optimization) are beginning to be applied to network parameter optimization to balance investment costs and drainage performance. However, existing technologies still have many shortcomings, making it difficult to meet the high-precision and high-efficiency requirements of drainage network design optimization in complex urban environments: Insufficient integration capabilities of multi-source heterogeneous data; lack of standardization mechanisms leading to inconsistent data formats, redundancy, and the absence of effective verification and deviation correction mechanisms, affecting the reliability of hydraulic calculations; limited hydraulic simulation scenarios, with fixed rainfall parameters unable to cover multiple durations and current rainfall events, and unable to adapt to different regional climate types, resulting in significant deviations between simulation results and actual operating conditions; optimization schemes lack scientific rigor and comprehensiveness, often focusing on single objectives without fully considering actual engineering constraints, resulting in insufficient practicality; GIS visualization is limited to two-dimensional displays, lacking the ability to overlay three-dimensional topology with simulation results and optimization schemes, and design documents require manual writing, leading to low efficiency and inconsistent formats; most systems are closed architectures, unable to connect with urban water affairs big data platforms in real time, with high coupling of functional modules, poor compatibility and scalability, making it difficult to adapt to the needs of smart water management development. Therefore, developing a drainage network design optimization system that can achieve standardized integration of multi-source data, dynamic and accurate hydraulic simulation, multi-objective scientific optimization, integrated visualization, and standardized document output has become a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] To solve the above technical problems, the present invention is implemented through the following technical solution: a drainage network design optimization system based on GIS technology, including a data module, a core computing module and an application output module, wherein each module realizes bidirectional data interaction through a standardized interface; The data module is used to integrate multi-source heterogeneous data and establish a standardized database. The core computing layer includes a spatial analysis module, a dynamic hydraulic calculation module, and a multi-objective optimization module. The spatial analysis module is used to analyze spatial relationships and generate network connectivity maps and catchment zone heat maps. The dynamic hydraulic calculation module is used to dynamically simulate network flow, velocity, and water level under different rainfall scenarios. The multi-objective optimization module is used to output the Pareto optimal solution set. The application output module includes an integrated GIS visualization display unit and a design scheme generation unit. The GIS visualization display unit is used for overlaying and displaying the three-dimensional topology of the pipeline network, hydraulic simulation results, and optimization schemes. The design scheme generation unit is used to generate standardized design documents.

[0005] Preferably, the multi-source heterogeneous data includes GIS spatial data, pipeline attribute data, hydrological and meteorological data, and topographic elevation data. The standardized database is used for data homogenization processing. The GIS spatial data includes pipeline node coordinates, pipeline orientation, and catchment area boundary information. The hydrological and meteorological data includes spatiotemporal distribution data of rainfall with different return periods and durations.

[0006] Preferably, during the establishment of the standardized database, the data module performs consistency verification on pipeline attribute data from different sources, and corrects the elevation deviation of pipeline nodes through the overlay analysis of terrain elevation data and GIS spatial data, ensuring that the data accuracy meets the requirements of hydraulic calculation.

[0007] Preferably, the dynamic hydraulic calculation module of the core calculation module has a built-in gradient rainfall scenario library, which covers typical rainfall events from 30 minutes to 72 hours and with a return period of 2 to 100 years, and can dynamically adjust the rainfall model parameters according to the climate type of the target area.

[0008] Preferably, the multi-objective optimization module sets parameter constraints, including a pipe diameter adjustment step size of not less than 50 mm, a pipeline slope of not less than 0.002, and a manhole spacing of not more than 50 m.

[0009] Preferably, the data module supports real-time data connection with the urban water affairs big data platform, and can dynamically acquire real-time monitoring data of the pipeline network and meteorological early warning data. Through the feedback calibration mechanism of the core computing layer, the hydraulic model parameters are dynamically updated.

[0010] This invention provides a drainage pipe network design optimization system based on GIS technology. It has the following beneficial effects: (i) The drainage network design optimization system based on GIS technology realizes the automated integration and standardized processing of multi-source heterogeneous data. Through the dynamic calibration mechanism, it corrects data deviations, significantly shortens the data integration time, and improves the accuracy to meet the requirements of hydraulic calculation, providing a reliable data foundation for network design optimization.

[0011] (ii) The drainage network design optimization system based on GIS technology generates a Pareto optimal solution set that takes into account investment cost, drainage capacity and operating energy consumption by combining a gradient rainfall scenario library and a multi-objective optimization algorithm. It is suitable for different climate and rainfall conditions, and the design scheme is more in line with the actual needs of the project.

[0012] (III) This drainage network design optimization system based on GIS technology realizes the integrated display of network topology, hydraulic simulation results and optimization scheme through three-dimensional GIS visualization. Combined with the function of automatically generating standardized design documents, it greatly shortens the design cycle and reduces the cost of manual writing and analysis.

[0013] (iv) The drainage network design and optimization system based on GIS technology supports real-time connection with the urban water affairs big data platform. It adopts standardized interfaces and a general architecture, which can be adapted to different regional network renovation / new construction scenarios, and facilitates subsequent functional expansion and technology iteration. Detailed Implementation

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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.

[0015] This invention provides a technical solution: A drainage network design optimization system based on GIS technology is proposed. The system adopts a B / S architecture, with the backend developed based on Java modules, Spring modules, and Boot framework, and the frontend built using Vue.js, ArcGIS modules, API modules, for modules, and JavaScript. The database uses PostgreSQL with PostGIS spatial extension to ensure the system's stability, scalability, and spatial data processing capabilities.

[0016] I. System Deployment; 1. Hardware environment deployment; Server: Configured with Intel Xeon Gold 6230 processor, 128GB DDR4 2933MHz memory, 4TB SSD solid-state drive + 16TB SATA III mechanical hard drive, supporting RAID5 redundancy backup to ensure data security and computing efficiency; Data acquisition equipment: drones, pipeline detectors, rain gauges, and water level sensors, used to supplement the collection of topographic, pipeline, and hydrological monitoring data of the target area.

[0017] 2. Software environment deployment; Operating system: The server uses CentOS 8.5 64-bit operating system, and the client uses Windows 10 / 11 64-bit operating system; Database: PostgreSQL 14.5 + PostGIS 3.3 Spatial Extension, supporting spatial data storage and spatial query analysis; II. Specific Implementation Steps; 1. Data module implementation; The core function of this module is the integration of multi-source heterogeneous data and the construction of a standardized database. The specific implementation steps are as follows: Multi-source data acquisition and access: GIS spatial data: Obtain a 1:500 scale topographic map of the target area from the urban planning department, including basic geographic information such as roads, water systems, and buildings; obtain regional DEM data with a resolution of 0.5m through UAV lidar scanning; use a pipeline detector to conduct on-site detection of pipeline node coordinates, pipeline direction, pipe diameter, pipe material, etc., generate a pipeline detection report and convert it into SHP format; Pipeline attribute data: Obtain existing pipeline network as-built data, maintenance records, pipe diameter, pipe length, manhole location, pump station parameters, etc. from the water utility group, in formats including Excel spreadsheets and PDF documents; Hydrometeorological data: Nearly 30 years of rainfall observation data were obtained from the local meteorological bureau database, including spatiotemporal distribution data of rainfall with different return periods and durations, in CSV format; Real-time monitoring data: Through the RESTful API interface of the urban water affairs big data platform, real-time data from pipeline flow monitoring points, water level monitoring points, and rainfall monitoring stations are connected and updated every 5 minutes.

[0018] Data preprocessing and homogenization: Format conversion: Convert data in different formats such as Excel, PDF, and SHP into spatial data types and attribute data types supported by PostGIS, where pipeline nodes are point data, pipelines are line data, and catchment areas are polygon data; Define a unified data field specification and map and convert non-standard fields; Consistency verification: Verify the pipeline attribute data, such as checking whether the pipe diameter is a positive number, whether the pipeline length matches the coordinate distance, and whether the manhole number is unique. Mark abnormal data and output a verification report, which will be manually verified and corrected by staff. Elevation deviation correction: DEM data and GIS spatial data are overlaid and analyzed to extract the topographic elevation values ​​corresponding to the pipeline nodes. These values ​​are then compared with the node elevation values ​​obtained from the survey. If the deviation exceeds 5cm, the node elevation is corrected based on the topographic elevation value to ensure that the data accuracy meets the requirements of hydraulic calculation.

[0019] Standardized database construction: Create basic geographic information tables, pipeline node tables, pipeline tables, catchment area tables, hydrological and meteorological tables, monitoring data tables, etc. Among them, the pipeline node tables, pipeline tables, and catchment area tables contain spatial fields to store spatial coordinate information; Create GIST indexes for spatial fields and B-tree indexes for key fields such as pipeline number and catchment area number to improve the efficiency of spatial and attribute queries. The preprocessed multi-source data is imported into a standardized database in batches. Transaction management is used to ensure the integrity of the data entering the database. After the data is entered into the database, data consistency is checked to ensure that there is no missing or duplicate data.

[0020] Real-time data integration and dynamic updates: Through the API interface of the urban water affairs big data platform, monitoring data such as pipeline flow, water level, and rainfall, as well as meteorological early warning data, are dynamically acquired; a feedback calibration mechanism is established, which compares the real-time monitoring data with the hydraulic model simulation data every hour. If the error exceeds 10%, the least squares method is used to dynamically update the roughness, permeability coefficient and other parameters of the hydraulic model to ensure the accuracy of the model.

[0021] 2. Implementation of the core computing module; The core calculation module includes a spatial analysis module, a dynamic hydraulic calculation module, and a multi-objective optimization module. These modules work together to complete the pipeline network design optimization calculations. The specific implementation steps are as follows: 2.1 Implementation of the spatial analysis module; Analyze the spatial relationships of the pipe network to generate a connectivity map and a catchment area heat map: Using network nodes as vertices and pipelines as edges, a network topology graph model is constructed. The NetworkX library is used to store and parse the topology relationships, and to identify spatial relationships such as connected branches, upstream nodes, downstream nodes, and merging nodes in the network. Based on the topology analysis results, the ArcGIS API for JavaScript is used to draw a network connectivity map, marking different connected branches with different colors and marking the direction of water flow with arrows. The map supports interactive operations such as zooming, panning, and node querying. The target area is divided into catchment zones, and the boundaries, area, slope and other parameters of each catchment zone are determined. Based on the rainfall runoff coefficient, area and other factors of each catchment zone, the runoff capacity of each catchment zone is calculated, and a heat map of the catchment zone is generated, with the intensity of the runoff capacity represented by the color (the darker the color, the stronger the runoff capacity).

[0022] 2.2 Implementation of the dynamic hydraulic calculation module; This module is based on the SWMM hydraulic calculation engine and combines a gradient rainfall scenario library to dynamically simulate the hydraulic conditions of the pipe network under different rainfall scenarios: Construction of a gradient rainfall scenario library: According to the requirements of claim 4, a typical rainfall event library covering rainfall durations from 30 minutes to 72 hours and return periods from 2 to 100 years is constructed, specifically including the following typical scenarios: 30 minutes / 2 years, 1 hour / 5 years, 3 hours / 10 years, 6 hours / 20 years, 12 hours / 50 years, 24 hours / 50 years, and 72 hours / 100 years; the Chicago rainfall pattern is used as the rainfall intensity distribution model, and the rainfall pattern parameters (peak coefficient r=0.4) are calibrated based on the measured rainfall data of the target area (subtropical monsoon climate). Hydraulic model parameter settings: Import the pipe network parameters (pipe diameter, pipe length, slope, roughness) and catchment area parameters (area, runoff coefficient, permeability coefficient) from the standardized database into the SWMM model, and set the boundary conditions (upstream runoff input, downstream outlet water level). Dynamic hydraulic simulation: Based on the rainfall scenario or real-time weather warning data selected by the user, the SWMM model is called to perform hydraulic simulation. The simulation time step is set to 1 minute. The output data includes the flow rate, velocity, and water level of each node in the pipeline network at different times, as well as the filling degree and pressure of the pipe section. The simulation results are judged for reasonableness. If abnormal conditions such as the flow velocity exceeding 3m / s (scouring velocity) or falling below 0.6m / s (siltation velocity) or the water level exceeding the top of the pipe occur, they are marked as critical warning nodes. The hydraulic simulation results are output in the form of data tables and graphs (flow-time curves, water level-time curves), and linked to GIS spatial data to realize the correspondence between the simulation results and the spatial location of the pipeline network.

[0023] 2.3 Implementation of the multi-objective optimization module; This module uses NSGA-III (Non-dominated sorting genetic algorithm III) to achieve multi-objective optimization and outputs the Pareto optimal solution set. The specific implementation steps are as follows: Optimization Goal Setting: Three core optimization goals were identified: (1) The investment cost for pipeline construction and renovation is minimized; (2) The drainage capacity of the pipeline network is the largest (the number of overloaded nodes in the pipeline network is the smallest when the return period of the rainstorm is 100 years). (3) The pipeline network has the lowest operating energy consumption (with the lowest pumping energy consumption as a quantitative indicator). Parameter constraint settings: Set the following constraints: (1) The pipe diameter adjustment step is not less than 50mm, and the pipe diameter can be selected as 300mm, 350mm, 400mm, ..., 1500mm; (2) The pipeline slope shall not be less than 0.002; (3) The spacing between inspection wells shall not exceed 50m; (4) The flow velocity inside the pipe is controlled between 0.6-3 m / s; (5) The water level at the node should not exceed the ground elevation (to avoid water accumulation); Optimize algorithm implementation; The specific parameters are set as follows: population size 100, number of iterations 50, crossover probability 0.8, mutation probability 0.1; with pipe diameter, pipeline slope, and manhole location as optimization variables, the fitness value of each individual is calculated (a comprehensive score of the three optimization objectives). Pareto optimal solution set generation: Through non-dominated sorting and congestion calculation, the Pareto optimal solution set is selected. Each solution corresponds to a set of pipeline design optimization schemes, including optimized pipe diameter, slope, manhole location and other parameters. The Pareto optimal solution set is then dimensionality-reduced to generate an optimization scheme ranking table, sorted by investment cost from smallest to largest, for users to choose from.

[0024] 3. Implementation of the application output module; This module includes a GIS visualization unit and a design scheme generation unit, enabling the visualization of optimization results and the generation of standardized design documents: Implementation of GIS visualization display units; 3D topology display of the pipeline network; Convert two-dimensional data (nodes, pipelines) of the pipeline network into a three-dimensional model, set three-dimensional parameters such as pipeline diameter and manhole height, and display the three-dimensional spatial distribution of the pipeline network in a visualization platform, supporting three-dimensional roaming, rotation, and sectioning operations; The hydraulic simulation results are overlaid and displayed. The flow rate, velocity, and water level data output by the dynamic hydraulic calculation module are superimposed onto the three-dimensional pipe network model, and different velocity ranges (green for 0.6-1.2 m / s, yellow for 1.2-2.0 m / s, and orange for 2.0-3.0 m / s) and water level status (blue for normal water level, yellow for warning water level, and red for overload water level) are marked with different colors. Based on the result range given above, a standardized design document is generated, and the design scheme is implemented in the unit generation process.

[0025] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A GIS technology-based sewer network design optimization system, characterized by: It includes a data module, a core computing module, and an application output module. Each module achieves bidirectional data interaction through standardized interfaces. The data module is used to integrate multi-source heterogeneous data and establish a standardized database. The core computing layer includes a spatial analysis module, a dynamic hydraulic calculation module, and a multi-objective optimization module. The spatial analysis module is used to analyze spatial relationships and generate network connectivity maps and catchment zone heat maps. The dynamic hydraulic calculation module is used to dynamically simulate network flow, velocity, and water level under different rainfall scenarios. The multi-objective optimization module is used to output the Pareto optimal solution set. The application output module includes an integrated GIS visualization display unit and a design scheme generation unit. The GIS visualization display unit is used for the overlay display of the three-dimensional topology of the pipeline network, hydraulic simulation results and optimization schemes. The design scheme generation unit is used to generate standardized design documents.

2. The drainage network design optimization system based on GIS technology according to claim 1, characterized in that: The multi-source heterogeneous data includes GIS spatial data, pipeline attribute data, hydrological and meteorological data, and topographic elevation data. The standardized database is used for data homogenization processing. The GIS spatial data includes pipeline node coordinates, pipeline orientation, and catchment area boundary information. The hydrological and meteorological data includes spatiotemporal distribution data of rainfall with different return periods and durations.

3. The drainage network design optimization system based on GIS technology according to claim 1, characterized in that: During the establishment of the standardized database, the data module performs consistency verification on pipeline attribute data from different sources. Through the overlay analysis of terrain elevation data and GIS spatial data, it corrects the elevation deviation of pipeline nodes to ensure that the data accuracy meets the requirements of hydraulic calculation.

4. The drainage network design optimization system based on GIS technology according to claim 1, characterized in that: The core computing module's dynamic hydraulic calculation module has a built-in gradient rainfall scenario library, which covers typical rainfall events ranging from 30 minutes to 72 hours and with return periods of 2 to 100 years. The rainfall model parameters can be dynamically adjusted according to the climate type of the target area.

5. The drainage network design optimization system based on GIS technology according to claim 1, characterized in that: The multi-objective optimization module sets parameter constraints, including a pipe diameter adjustment step size of not less than 50 mm, a pipeline slope of not less than 0.002, and a manhole spacing of not more than 50 m.

6. The drainage network design optimization system based on GIS technology according to claim 3, characterized in that: The data module supports real-time data connection with the urban water affairs big data platform, and can dynamically acquire real-time monitoring data of the pipeline network and meteorological early warning data. Through the feedback calibration mechanism of the core computing layer, the hydraulic model parameters are dynamically updated.