Cim-based urban flood control and drainage capacity evaluation method and system and medium

Abstract

The method, system, and medium for assessing urban flood control and drainage capacity based on CIM include the following steps: acquiring and constructing inland river CIM sub-models, flood storage and detention area CIM sub-models, and pipeline network CIM sub-models based on static basic data and dynamic monitoring data; calculating the inland river water storage capacity based on the inland river CIM sub-model; calculating the flood storage capacity based on the flood storage and detention area CIM sub-model; generating the pipeline network topology based on the pipeline network CIM sub-model and applying the SWMM model to analyze the theoretical flow capacity of the pipeline network; acquiring and analyzing real-time monitoring data of the pipeline network and information on urban flooding points, and correcting the SWMM model parameters based on the analysis results; establishing a multi-objective optimization function based on static basic data, dynamic monitoring data, inland river water storage capacity, flood storage capacity, and the corrected SWMM model; solving the Pareto optimal solution set using the particle swarm optimization algorithm, and outputting the scheduling scheme after weighted sorting.

Description

Technical Field

[0001] This invention relates to the field of urban flood control technology, and in particular to a method, system and medium for assessing urban flood control and drainage capacity based on CIM. Background Technology

[0002] Traditional urban flood control solutions are primarily developed using expert experience, with plans proposed based on historical dispatching experience. These plans are then discussed and refined in meetings before implementation. This approach is not only highly subjective but also time-consuming, and there is a possibility that the optimal solution may not have been identified.

[0003] Furthermore, existing technologies do not integrate City Information Modeling (CIM), resulting in fragmented multi-source data and independent analysis of each subsystem, making it impossible to form a unified basis for dynamic assessment of city-level rainwater carrying capacity.

[0004] It is evident that existing technologies have limitations in achieving real-time collaborative assessment and optimized scheduling of urban lifeline rainwater harvesting capacity. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for assessing urban flood control and drainage capacity based on CIM, comprising the following steps: Based on static basic data and dynamic monitoring data, construct CIM sub-models for inland waterways, flood storage and detention areas, and pipeline networks respectively; static basic data includes at least pipeline network data, inland waterway data, flood storage and detention area data, and flood control and drainage project data; dynamic monitoring data includes at least real-time monitoring data for pipeline networks, inland waterways, flood storage and detention areas, and flood control and drainage projects. Based on the inland river CIM sub-model, the inland water storage capacity of the river section is calculated; Calculate flood storage capacity based on the CIM sub-model of flood storage and detention areas; Based on the CIM sub-model of the pipeline network, the topological relationship of the pipeline network is generated, and the theoretical flow capacity of the pipeline network is analyzed by applying the SWMM model. Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and correct the SWMM model parameters based on the analysis results; Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, a multi-objective optimization function is established. The Pareto optimal solution set is obtained by using the particle swarm optimization algorithm, and the scheduling scheme is output after weighted sorting.

[0006] Optionally, the pipeline network data shall include at least the basic attribute data of the pipeline network, the pipeline network connection data, the pipeline network facility data, and the pipeline network detection and evaluation data; Inland waterway data should include at least basic information data on inland waterways, river network and water system data, cross-sectional data, inland waterway facility data, and data related to inland waterway storage and drainage capacity. Data on flood storage and detention areas should include at least the basic information of the flood storage and detention areas, the engineering facilities of the flood storage and detention areas, the flood storage capacity of the flood storage and detention areas, the flood risk assessment of the flood storage and detention areas, and the emergency plans for the use of the flood storage and detention areas. Flood control and drainage project data should include at least the design parameters, scheduling procedures, geographic information data, and geometric attribute information of each facility; Real-time monitoring data for inland rivers should include at least inland river water level and inland river flow data; real-time monitoring data for flood storage and detention areas should include at least flood storage and detention area water level data; real-time monitoring data for pipe networks should include at least pipe network liquid level, pipe network flow, and pipe network flow direction data; and real-time monitoring data for flood control and drainage projects should include at least various operating parameters of each facility.

[0007] Optionally, based on the inland river CIM sub-model, the calculation of the inland river storage capacity of the river segment includes at least the following steps: The river complexity index is calculated based on inland waterway data, and the corresponding preset clipping interval is obtained based on the river complexity index. Along the centerline of the river channel, the cross-section of the CIM sub-model of the inland river system is cut according to the preset cutting interval to obtain multiple coarse cross-sections. The rough cross section is matched and corrected with the cross section data, and the missing cross section after correction is filled by interpolation to obtain the corrected cross section group, which includes multiple corrected cross sections. Based on the corrected cross-sectional data and inland river water level data, the inland river storage capacity of the river section is calculated.

[0008] Optionally, the flood storage capacity is calculated based on the CIM sub-model of the flood storage and detention area, including at least the following steps: Acquire DEM data of the flood storage and detention area and lake boundary data; Based on the CIM sub-model of the flood storage and detention area, the lake area of ​​the flood storage and detention area is divided into grid cells, and effective grid cells are obtained by trimming according to the lake boundary. Extract the elevation value of the center point of each grid cell, combine it with the water level data of the flood storage area to calculate the water storage capacity of all submerged grid cells, and sum them up to obtain the flood storage capacity.

[0009] Optionally, based on the CIM sub-model of the pipeline network, the topological relationship of the pipeline network is generated, and the theoretical flow capacity of the pipeline network is analyzed using the SWMM model, including at least the following steps: Acquire topographic elevation data and land use type data; Extracting basic attribute data of the pipeline network based on the pipeline network CIM sub-model; Based on the basic attribute data of the pipeline network and SWMM software, pipe segments and nodes are constructed, pipe segment parameters, node types and critical storage ratio of the pipeline network are set, and terrain elevation data is associated. Sub-catchment areas are divided based on land use type data, and boundary conditions are set; Import rainfall data and calibrate the SWMM model parameters based on historical pipeline level and flow data; Run the SWMM model to simulate and extract key data for each pipe segment to quantitatively analyze the theoretical flow capacity of the pipe network. The theoretical flow capacity includes at least the initial filling degree, initial water storage capacity, and initial flow capacity.

[0010] Optionally, real-time monitoring data of the pipe network and information on urban flooding points are acquired and analyzed, and the SWMM model parameters are corrected based on the analysis results, including at least the following steps: Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points; The initial water storage capacity, initial filling degree, initial flow capacity and real-time monitoring data were compared and analyzed. The parameters of the SWMM model were corrected based on the results of the comparative analysis.

[0011] Optionally, based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, a multi-objective optimization function is established, including at least the following steps: Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, the various facilities of the flood control and drainage project are initialized. Construct a multi-objective optimization function, with objectives including at least minimizing pipeline overflow risk, minimizing scheduling time, and simplifying scheduling measures; Each objective is quantified, and corresponding indicators and calculation methods are determined.

[0012] Optionally, the Pareto optimal solution set is obtained by using a particle swarm optimization algorithm, and the scheduling scheme is output after weighted sorting, including at least the following steps: Using real-number encoding, the combined operational states of various facilities in flood control and drainage projects are mapped into particle position vectors; The fitness value of each particle is calculated using a multi-objective optimization function. Record and update the individual extreme value of each particle and the global extreme value of the entire population; The particles are iteratively updated using the velocity and position update formulas of the particle swarm optimization algorithm, and the Pareto optimal solution set is solved by combining adaptively adjusted inertia weights and learning factors. Based on the target priority, the Pareto optimal solution set is weighted and sorted to output the scheduling scheme.

[0013] Corresponding to the aforementioned CIM-based urban flood control and drainage capacity assessment method, this invention provides a CIM-based urban flood control and drainage capacity assessment system, which includes: The CIM sub-model construction module is used to acquire and construct inland river CIM sub-models, flood storage and detention area CIM sub-models, and pipeline network CIM sub-models based on static basic data and dynamic monitoring data. The static basic data includes at least pipeline network data, inland river data, flood storage and detention area data, and flood control and drainage project data. The dynamic monitoring data includes at least real-time monitoring data of pipeline network, inland river, flood storage and detention area, and flood control and drainage project data. The calculation module is used to calculate the inland water storage capacity of a river section based on the inland river CIM sub-model; and to calculate the flood storage capacity based on the flood storage and detention area CIM sub-model. The SWMM model building module is used to generate the topological relationship of the pipeline network based on the pipeline network CIM sub-model, and to apply the SWMM model to analyze the theoretical flow capacity of the pipeline network. The calibration module is used to acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and to calibrate the SWMM model parameters based on the analysis results. The modeling module is used to model static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model. The evaluation module is used to solve the Pareto optimal solution set using the particle swarm optimization algorithm, and output the scheduling scheme after weighted sorting.

[0014] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a CIM-based urban flood control and drainage capacity assessment program, wherein when the CIM-based urban flood control and drainage capacity assessment program is executed by a processor, it implements the steps of the CIM-based urban flood control and drainage capacity assessment method described above.

[0015] Compared with existing technologies, this invention solves the problem of real-time collaborative assessment and optimized scheduling of urban lifeline rainwater carrying capacity by constructing a CIM sub-model, calculating inland river water storage and flood storage capacity, and establishing and solving a multi-objective optimization function based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and a corrected SWMM model.

[0016] Compared with existing technologies, this invention improves the automation and accuracy of water system analysis by automatically cutting cross-sections of inland waterways and calculating the inland water storage capacity of river sections; it can quickly respond to changes in inland water levels and provide real-time hydrological information for urban flood control and drainage, thereby effectively preventing and managing flood risks.

[0017] Compared with existing technologies, this invention achieves refined management of flood storage and detention areas by dividing them into grids and calculating their flood storage capacity; it can more accurately assess the flood storage capacity of flood storage and detention areas, providing important decision support for urban flood control and drainage.

[0018] Compared with existing technologies, this invention provides a scientific basis for the performance evaluation and optimization of urban drainage systems by generating the topological relationship of the pipe network and quantitatively analyzing the initial fullness, initial water storage capacity and initial flow capacity of the pipe network; it helps to improve the operating efficiency and flood control and drainage capacity of urban drainage systems.

[0019] Compared with existing technologies, this invention improves the prediction accuracy of the model by acquiring real-time monitoring data of the pipeline network and information on urban flooding points, and correcting the parameters of the SWMM model; it helps to more accurately assess the actual performance of the pipeline network and provides more reliable decision support for flood control and drainage.

[0020] Compared with existing technologies, this invention optimizes the scheduling scheme by establishing a multi-objective optimization function, which comprehensively considers multiple key objectives of flood control and drainage projects; it helps to improve the rationality and effectiveness of the scheduling scheme, thereby better addressing urban flooding problems.

[0021] Compared with existing technologies, this invention solves the Pareto optimal solution set by particle swarm optimization algorithm and performs weighted sorting, which can quickly determine the optimal scheduling scheme according to actual needs and priorities, thereby improving the response speed and processing capacity of urban flood control and drainage. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a simplified flowchart of an embodiment of the urban flood control and drainage capacity assessment method based on CIM of the present invention; Figure 2 This is a schematic diagram of cross-sectional cutting of an embodiment of the urban flood control and drainage capacity assessment method based on CIM of the present invention; Figure 3 This is a schematic diagram illustrating the matching and correction of rough cross-sections and cross-sectional data in an embodiment of the urban flood control and drainage capacity assessment method based on CIM of the present invention. Figure 4 This is a schematic diagram of effective grid cells obtained by trimming an embodiment of the urban flood control and drainage capacity assessment method based on CIM of the present invention; Figure 5 A framework diagram of an embodiment of a CIM-based urban flood control and drainage capacity assessment system. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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, not all embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. 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.

[0024] like Figure 1 As shown, the present invention provides a method for assessing urban flood control and drainage capacity based on CIM, which includes the following steps: Based on static basic data and dynamic monitoring data, construct CIM sub-models for inland waterways, flood storage and detention areas, and pipeline networks respectively; static basic data includes at least pipeline network data, inland waterway data, flood storage and detention area data, and flood control and drainage project data; dynamic monitoring data includes at least real-time monitoring data for pipeline networks, inland waterways, flood storage and detention areas, and flood control and drainage projects. Based on the inland river CIM sub-model, the inland water storage capacity of the river section is calculated; Calculate flood storage capacity based on the CIM sub-model of flood storage and detention areas; Based on the CIM sub-model of the pipeline network, the topological relationship of the pipeline network is generated, and the theoretical flow capacity of the pipeline network is analyzed by applying the SWMM model. Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and correct the SWMM model parameters based on the analysis results; Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, a multi-objective optimization function is established. The Pareto optimal solution set is obtained by using the particle swarm optimization algorithm, and the scheduling scheme is output after weighted sorting.

[0025] This invention solves the problem of real-time collaborative assessment and optimized scheduling of urban lifeline rainwater carrying capacity by constructing a CIM sub-model, calculating inland river water storage and flood storage capacity, and establishing and solving a multi-objective optimization function based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and a corrected SWMM model.

[0026] In this embodiment, the pipeline network data includes at least basic pipeline network attribute data, pipeline network connection data, pipeline network facility data, and pipeline network detection and evaluation data; Inland waterway data should include at least basic information data on inland waterways, river network and water system data, cross-sectional data, inland waterway facility data, and data related to inland waterway storage and drainage capacity. Data on flood storage and detention areas should include at least the basic information of the flood storage and detention areas, the engineering facilities of the flood storage and detention areas, the flood storage capacity of the flood storage and detention areas, the flood risk assessment of the flood storage and detention areas, and the emergency plans for the use of the flood storage and detention areas. Flood control and drainage projects (sluice gates, pumps, dams, etc.) should include at least the design parameters, scheduling procedures, geographic information data, and geometric attribute information of each facility. Real-time monitoring data for inland rivers should include at least inland river water level and inland river flow data; real-time monitoring data for flood storage and detention areas should include at least flood storage and detention area water level data; real-time monitoring data for pipe networks should include at least pipe network liquid level, pipe network flow, and pipe network flow direction data; and real-time monitoring data for flood control and drainage projects should include at least various operating parameters of each facility.

[0027] For example, data collection requirements and formats can be found in Table 1 below. It should be noted that Table 1 is for illustrative purposes only and should be adjusted according to actual needs.

[0028]

[0029] Table 1 Data Collection Requirements and Format Reference Table To acquire and construct CIM sub-models for inland waterways, flood storage and detention areas, and pipeline networks based on static basic data and dynamic monitoring data, the following steps are required: Acquire basic data and dynamic monitoring data, and perform preprocessing; The preprocessed data is classified and integrated to construct multidimensional datasets of pipeline networks, inland rivers, and flood storage and detention areas, which are then stored in object-relational databases and spatial databases. Based on the preprocessed data, inland river CIM sub-models, flood storage and detention area CIM sub-models, and pipeline network CIM sub-models were constructed and integrated into the CIM platform.

[0030] Constructing a CIM sub-model of the pipeline network involves using preprocessed pipeline network data to build a 3D pipeline network model, including geometric and attribute models of facilities such as drainage pipes, manholes, and pumping stations. Through data association and fusion, precise matching and integration of pipeline network facilities with the surrounding environment are achieved, forming a complete CIM sub-model of the pipeline network.

[0031] The construction of inland river CIM sub-models is based on static basic data and dynamic monitoring data of inland rivers. A three-dimensional model of the inland river is constructed, which includes elements such as river morphology and hydraulic facilities. At the same time, dynamic information such as water level and flow rate is incorporated to generate dynamic inland river CIM sub-models, which intuitively show the real-time status of inland rivers.

[0032] Constructing a CIM sub-model for flood storage and detention areas involves integrating geographic information, engineering facility data, and flood risk assessment data of the flood storage and detention areas to build a three-dimensional model of the flood storage and detention areas. This includes modeling elements such as boundaries and flood storage and detention engineering facilities, and combining dynamic monitoring data to form a CIM sub-model for flood storage and detention areas with real-time data update capabilities.

[0033] Furthermore, urban information models can be constructed, integrating the pipeline network CIM sub-model, the inland river CIM sub-model, and the flood storage and detention area CIM sub-model into the CIM platform for visualization. This supports multi-angle browsing, querying, and analysis, providing city managers and decision-makers with an intuitive view of the operational status of urban infrastructure.

[0034] Preferably, a data sharing and exchange mechanism is also used to regularly update static basic data and dynamic monitoring data, and dynamically maintain the pipeline network CIM sub-model, inland river CIM sub-model and flood storage and detention area CIM sub-model.

[0035] Preferably, the preprocessing of static basic data includes at least normalization and standardization to ensure data validity and usability. For example, this involves establishing data standards, defining a data standard framework, and specifying format standards for different data types, such as column names, data types, and date formats for tabular data. Unified coding is implemented based on geographic information, facility types, etc., to ensure data consistency and interoperability. Data quality requirements are set, including standards for accuracy, completeness, timeliness, consistency, and reliability. Static basic data undergoes normalization and standardization. Data cleaning removes duplicate, erroneous, and incomplete data, and outliers are corrected. Data from different sources is converted to a unified format, especially geographic information is registered to the same coordinate system, and attribute data is standardized.

[0036] Preprocessing of dynamic monitoring data includes at least data cleaning, time alignment, data smoothing, and data fusion. Common issues with dynamic monitoring data include outliers / noisy data and missing values. Outlier and missing data preprocessing models can be built using Spark. Data is partitioned by time, location, or monitoring point. UDFs (User-Defined Functions), RDDAPI (Resilient Distributed Dataset Interface), and DataFrame distributed data containers are used for batch processing of partitioned data to improve the automation of data processing. Data is smoothed using resampling to reduce the impact of outliers. The Laida criterion or other rules are used to identify and handle outliers, automatically removing or marking abnormal data. Missing system data is completed using methods such as linear interpolation, spline interpolation, or Kriging to ensure data accuracy and completeness.

[0037] In this embodiment, calculating the inland water storage capacity of a river segment based on the inland river CIM sub-model includes at least the following steps: The river complexity index is calculated based on the river channel curvature, the total number of confluence points between tributaries and the main channel at full channel water level, and the length of the wetting centerline. Based on the river complexity index, obtain the corresponding preset pruning interval (see Table 2 below for an example of the correspondence between the river complexity index and the preset pruning interval). It should be noted that the correspondence between the river complexity index and the preset pruning interval can be adjusted as needed. For example, the interval can be increased when computing power is insufficient or accuracy requirements are not high.

[0038] Table 2. Examples of the correspondence between river complexity index and preset clipping interval. The formula for calculating the river complexity index is as follows: RCI = (S × (1 + #J) / (RL)) × 100; where S represents curvature, reflecting the degree of tortuosity of the river channel, #J represents the total number of confluence points between tributaries and the main channel at full water level, reflecting the branching complexity of the river channel, and RL represents the length of the wet centerline, which can be replaced by the river length. Along the centerline of the river channel, the CIM sub-model of the inland river system is cross-sectionally cut according to a preset cutting interval to obtain multiple coarse-cut cross-sections (see reference). Figure 2 ); Match any rough cut cross section with the cross section data, and perform geometric transformation correction on the rough cut cross section to make the rough cut cross section match the cross section in shape and size; preferably, the geometric transformation includes at least one of translation, scaling, and rotation; The transformation parameters are obtained using the least squares method, and the remaining coarse-cut cross-sections are corrected to minimize the difference between the coarse-cut cross-sections and the measured cross-sections. Cross-section interpolation is then performed to fill in the missing cross-sections after correction, resulting in a corrected cross-section group (see reference). Figure 3 The correction section group includes multiple correction sections; Based on the data from the corrected cross-section group and the inland river water level, the water storage area of ​​each corrected cross-section is calculated, and the water storage capacity of the river section is obtained by integral summation.

[0039] Specifically, each correction cross-section can be discretized into multiple tiny units, such as triangles or trapezoids. Based on the inundation range of the river channel at different water levels, it is determined whether each tiny unit is submerged. For submerged tiny units, their areas are calculated and summed to obtain the water storage area of ​​the i-th correction cross-section at a specific water level. The specific calculation formula is as follows: ; in, Let n represent the area of ​​the j-th submerged micro-unit on the i-th correction cross section, and n be the total number of submerged micro-units on the i-th correction cross section.

[0040] The water storage area of ​​the corrected cross section at a specific water level By performing integration and summation, the river segment between two adjacent cross-sections is approximated as a prism, whose volume can be expressed as: =( + ) / 2× ; in, This represents the water storage capacity of the i-th river segment. and These represent the water storage areas of two adjacent cross sections. for and The distance between two adjacent cross sections.

[0041] Furthermore, by integrating and summing the water storage of all river sections, the water storage of the entire inland river system can be obtained. The specific calculation formula is as follows: ; in, The total water storage capacity of the inland river system is represented by the value of i, which ranges from [1, m], where m is the total number of river sections included in the inland river system.

[0042] This invention improves the automation and accuracy of water system analysis by automatically cutting cross-sections of inland waterways and calculating the inland water storage capacity of river sections; it can quickly respond to changes in inland water levels and provide real-time hydrological information for urban flood control and drainage, thereby effectively preventing and managing flood risks.

[0043] Preferably, cross-sectional interpolation completion can be performed manually or using interpolation algorithms. For missing cross-sectional locations, various interpolation algorithms can be used to generate missing cross-sectional data based on the geometric characteristics of adjacent known cross-sections. Common interpolation algorithms include linear interpolation, spline interpolation, and Kriging interpolation. In linear interpolation, it is assumed that the cross-sectional geometry changes linearly between adjacent known cross-sections, and the shape parameters of the missing cross-section are calculated using a simple linear interpolation formula. Spline interpolation uses smooth spline curves to fit the feature points of known cross-sections, thereby obtaining the shape of the missing cross-section. Kriging interpolation is a geostatistical interpolation method that considers the spatial correlation of data and can more accurately reflect the changing trend of cross-sectional geometry.

[0044] In this embodiment, the calculation of flood storage capacity based on the CIM sub-model of the flood storage and detention area includes at least the following steps: Acquire DEM data of the flood storage and detention area and lake boundary data; Based on the CIM sub-model of the flood storage and detention area, the lake area of ​​the flood storage and detention area is divided into grid cells; the grid size can be determined according to actual needs and calculation accuracy requirements, preferably, the grid side length is between 1 and 10 meters; By overlaying the grid cells onto the lake boundary and performing analysis, the effective grid cells (see reference) within the lake area are obtained. Figure 4 ), forming a gridded model of the lake; Extract the elevation value of the center point of each grid cell, combine it with the water level data of the flood storage area, and calculate the water storage capacity of each submerged grid cell according to the water level-area relationship; The flood storage capacity is obtained by summing the water storage volumes of all submerged grid cells; the specific calculation formula is as follows: ; in: V Indicates the flood storage capacity of the lake; This represents the current water level; This represents the elevation value of the center point of the o-th grid cell.

[0045] This invention enables refined management of flood storage and detention areas by dividing them into grids and calculating their flood storage capacity; it can more accurately assess the flood storage capacity of flood storage and detention areas, providing important decision support for urban flood control and drainage.

[0046] In this embodiment, based on the pipeline network CIM sub-model, the topological relationship of the pipeline network is generated, and the theoretical flow capacity of the pipeline network is analyzed using the SWMM model, including at least the following steps: Acquire topographic elevation data and land use type data; Based on the CIM sub-model of the pipeline network, basic attribute data of the pipeline network is extracted and the data is cleaned. The basic attribute data of the pipeline network includes at least the spatial location, length, diameter, slope, node type and location information of the pipe segment. Check whether the basic attribute data of the pipeline network after data cleaning meets the input standards of the SWMM model; Based on the basic attribute data of the pipeline network and SWMM software, pipe segments and nodes are constructed, pipe segment parameters, node types and critical storage ratio of the pipeline network are set, and terrain elevation data is associated to simulate the flow characteristics of rainwater at different elevations; the pipe segment parameters include at least the length, diameter and slope of the pipe segment. Sub-catchments are divided based on land use type data, and boundary conditions (such as outlet water level and external flow process) are set. Import rainfall data to simulate the drainage response of the pipe network under different rainfall intensities and durations, and calibrate the SWMM model parameters based on historical pipe network liquid level data and pipe network flow data to ensure that the simulation results are highly consistent with the actual drainage situation, thus guaranteeing the reliability and accuracy of the model; preferably, the rainfall data includes historical rainfall data and / or preset rainfall data; The SWMM model is run to simulate and calculate in real time the changes in liquid level and flow rate within the pipe section, as well as the liquid level fluctuations at nodes. Key data for each pipe section are extracted to quantitatively analyze the theoretical flow capacity of the pipe network. The theoretical flow capacity includes at least the initial filling degree (a technical parameter measuring the proportion of space occupied by water flow in the pipe channel in a gravity drainage system), the initial water storage capacity (calculated based on static water, i.e., the water storage under full pipe without pressure), and the initial flow capacity (the basic gravity flow capacity calculated according to the Manning formula within the design return period; when the design return period is exceeded, the pressure flow model is used for calculation, referring to the "Outdoor Drainage Design Standard" GB50014-2021). Preferably, the key data includes at least the water storage capacity, overflow volume, flow rate change data for each pipe section, and liquid level change data for each node.

[0047] This invention provides a scientific basis for the performance evaluation and optimization of urban drainage systems by generating the topological relationship of the pipe network and quantitatively analyzing the initial fullness, initial water storage capacity and initial flow capacity of the pipe network; it helps to improve the operating efficiency and flood control and drainage capacity of urban drainage systems.

[0048] In this embodiment, real-time monitoring data of the pipeline network is acquired, and information on urban flood-prone areas (including the distribution of flood-prone areas and real-time monitoring data of flooding) is collected simultaneously. These data sensors are transmitted in real-time to the CIM platform data center, forming the foundational dataset for big data analysis. Big data analytics are used to deeply mine the collected monitoring data. Time series analysis captures the dynamic changes in flow rate and liquid level, cluster analysis identifies common characteristics of flood-prone areas, and regression analysis establishes a quantitative relationship between flow rate, liquid level, and flooding occurrence. This allows for accurate assessment of the pipeline network's actual water storage capacity, actual overflow level, and actual flow capacity.

[0049] In this embodiment, acquiring and analyzing real-time monitoring data of the pipeline network and information on urban flooding points, and correcting the SWMM model parameters based on the analysis results, includes at least the following steps: Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points; The initial water storage capacity, initial filling degree, initial flow capacity and real-time monitoring data were compared and analyzed. The parameters of the SWMM model were corrected based on the results of the comparative analysis.

[0050] This invention improves the prediction accuracy of the model by acquiring real-time monitoring data of the pipeline network and information on waterlogging points, and by correcting the parameters of the SWMM model; it helps to more accurately assess the actual performance of the pipeline network and provides more reliable decision support for flood control and drainage.

[0051] Furthermore, by incorporating field surveys and monitoring data, the network attribute parameters (such as junctions, outlets, water storage facilities, orifices, Manning roughness, loss coefficient, maximum network depth, inlet / outlet offset, etc.) and rainfall characteristic parameters in the SWMM model can be updated to make the model more closely reflect actual drainage conditions. Through this iterative process of comparison and correction, the accuracy of model evaluation is gradually improved. Based on data monitoring, big data analysis, model evaluation, and comprehensive comparison and correction, a final comprehensive evaluation conclusion is formed.

[0052] In this embodiment, a multi-objective optimization function is established based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, including at least the following steps: Based on flood control and drainage project data, coordinate transformation, projection transformation and data fusion algorithms are used to integrate the established inland river CIM sub-model, flood storage and detention area CIM sub-model and pipeline network CIM sub-model to ensure the consistency of all data in space and time. Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, the various facilities of the flood control and drainage project are initialized, such as the opening degree of the sluice gates and the flow rate of the pumping stations, and a parameter table of the engineering facilities is established. Construct a multi-objective optimization function, with objectives including at least minimizing pipeline overflow risk, minimizing scheduling time, and simplifying scheduling measures; Each objective is quantified, and corresponding indicators and calculation methods are determined.

[0053] This invention optimizes the scheduling scheme by establishing a multi-objective optimization function, which comprehensively considers multiple key objectives of flood control and drainage projects. This helps to improve the rationality and effectiveness of the scheduling scheme, thereby better addressing urban flooding problems.

[0054] Preferably, the risk of pipeline overflow can be quantified by the frequency and volume of overflow indices. A pipeline overflow risk assessment model is constructed by simulating the hydraulic behavior of the pipeline using a Swing Model (SWMM) to calculate the overflow frequency and volume indices. For example, pipeline overflow risk... It can be represented as: ; in, Let be the overflow frequency of pipeline node a. b is the overflow amount of this node, and b is the total number of nodes in the pipeline network; The scheduling time is quantified based on the time from the occurrence of the flood to the normal operation of each engineering facility; the scheduling time is defined as the time required from the occurrence of the scheduling event to all engineering facilities reaching the expected operating state, and the scheduling time is determined through time series analysis and critical path method (CPM); The complexity of scheduling measures is quantified based on the number of operational steps and the types of facilities involved. For example, a scheduling measure complexity assessment index can be constructed, covering all steps such as issuing, receiving, executing, and responding to scheduling orders. The complexity of scheduling measures can be calculated by comprehensively considering factors such as the number of operational steps, the types of facilities involved, and the difficulty of operation.

[0055] In this embodiment, the Pareto optimal solution set is obtained by using the particle swarm optimization algorithm, and the scheduling scheme is output after weighted sorting. This process includes at least the following steps: The operating parameters of each engineering facility are represented by real number encoding, and the operating status combination of each facility in the flood control and drainage project is mapped into a particle position vector. For example, the A gate of the flood control and drainage project has states such as 1 gate open, 3 gates open, fully open, and different gate positions. The pumping unit of the B pumping station of the flood control and drainage project has scheduling states with different drainage volumes such as 2 stopped and 1 started, 2 started and 1 stopped. After mapping, they can form permutations and combinations. The fitness value of each particle is calculated using a multi-objective optimization function, and is represented as a vector containing the particle's performance on each optimization objective. Record the historical best position (individual extreme value) for each particle and the best position of the entire population (global extreme value). In each iteration, the current fitness value of the particle is compared with the fitness values ​​corresponding to the individual extreme value and the global extreme value, and the individual extreme value and the global extreme value are updated accordingly. The particles are iteratively updated using the velocity and position update formulas of the particle swarm optimization algorithm, and the Pareto optimal solution set is solved by combining adaptively adjusted inertia weights and learning factors. Based on the target priority, the Pareto optimal solution set is weighted and sorted to output the scheduling scheme.

[0056] This invention uses a particle swarm optimization algorithm to solve for the Pareto optimal solution set and performs weighted sorting, which can quickly determine the optimal scheduling scheme according to actual needs and priorities, thereby improving the response speed and processing capacity of urban flood control and drainage.

[0057] In this embodiment, the speed update formula is as follows: ; The specific formula for position update is as follows: ; in, These are inertia weights, c1 and c2 are learning factors, and r1 and r2 are random numbers. It refers to the number of particles, or the number of iterations. This represents the optimal solution for this particle up to the current iteration number. This represents the optimal solution among all particles up to the current iteration number. Indicates the first The position of each particle. Indicates the first +1 particle velocity.

[0058] Set a termination condition, which can be the maximum number of iterations or a convergence condition (such as the population diversity index being below a threshold). In each iteration, perform operations such as fitness calculation, individual extreme value and global extreme value update, particle velocity and position update, etc., until the termination condition is met.

[0059] In the iterative process of the particle swarm optimization (PSO) multi-objective optimization algorithm, an adaptive self-learning-based iterative optimization technique was adopted. First, an adaptive parameter adjustment strategy was designed based on indicators such as population diversity and convergence speed. The parameters of the PSO algorithm were then dynamically adjusted according to these indicators. For example, population diversity indicators (such as the average distance between particles) and convergence speed indicators (such as the rate of change of fitness values) were used to evaluate the population state, and the inertia weights were adjusted according to predefined rules. And learning factors c1 and c2. When the population diversity decreases, the inertia weight is appropriately increased to enhance the global search capability of the particles; when the convergence speed is slow, the inertia weight is appropriately decreased to improve the local search accuracy, thereby improving the performance and efficiency of the algorithm.

[0060] Simultaneously, a self-learning mechanism is introduced, employing reinforcement learning algorithms to enable particles to learn from their own historical search experience and the evolutionary trends of the population, continuously optimizing their search paths and directions. Particles can adjust their speed and position update strategies based on their advantageous directions during historical iterations and the global optimal trend of the population, improving the accuracy and efficiency of the search.

[0061] Based on the objective priority, after weighting and sorting the Pareto optimal solution set, the output scheduling scheme includes at least the following steps: For the Pareto optimal solution set, draw the Pareto front plot, analyze the performance of each scheduling scheme combination on different optimization objectives, and show the trade-off relationship between different schemes; Based on actual needs and decision-making objectives, determine the priority of each optimization objective; Based on the priority of each optimization objective, the Analytic Hierarchy Process (AHP) or the fuzzy comprehensive evaluation method is used to assign weights to each objective. Based on the weight allocation results for each objective, a weighted sum is calculated for each combination of scheduling schemes in the Pareto optimal solution set to obtain a comprehensive score. The specific calculation formula is as follows: ; in, The weights of target k are... is the fitness value of the scheme on the target k, and P is the total number of scheduling schemes; After ranking the scheduling schemes based on their comprehensive scores, the scheduling schemes are output.

[0062] Furthermore, the method of the present invention also includes: executing a scheduling scheme and acquiring real-time operating parameters and hydrological data of flood control and drainage engineering facilities; The real-time operating parameters and hydrological data of flood control and drainage engineering facilities are compared with the expected effects of the scheduling plan; Model predictive control (MPC) algorithm is used to adjust and optimize the scheduling scheme in real time.

[0063] like Figure 2 As shown, the present invention also provides a CIM-based urban flood control and drainage capacity assessment system, which includes: CIM sub-model construction module 10 is used to acquire and construct inland river CIM sub-models, flood storage and detention area CIM sub-models, and pipeline network CIM sub-models based on static basic data and dynamic monitoring data, respectively; the static basic data includes at least pipeline network data, inland river data, flood storage and detention area data, and flood control and drainage project data; the dynamic monitoring data includes at least real-time monitoring data of pipeline network, inland river, flood storage and detention area, and flood control and drainage project. The calculation module 20 is used to calculate the inland water storage capacity of the river section based on the inland river CIM sub-model; and to calculate the flood storage capacity based on the flood storage and detention area CIM sub-model. The SWMM model building module 30 is used to generate the topological relationship of the pipeline network based on the pipeline network CIM sub-model, and to apply the SWMM model to analyze the theoretical flow capacity of the pipeline network. The calibration module 40 is used to acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and to calibrate the SWMM model parameters based on the analysis results. Modeling module 50 is used to model static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model. Evaluation module 60 is used to solve the Pareto optimal solution set through particle swarm optimization algorithm, and output the scheduling scheme after weighted sorting.

[0064] Furthermore, the system of the present invention may also include an optimization module for real-time adjustment and optimization of the scheduling scheme; specifically including: executing the scheduling scheme and acquiring the operating parameters and hydrological data of flood control and drainage engineering facilities in real time; comparing the real-time operating parameters and hydrological data of flood control and drainage engineering facilities with the expected effect of the scheduling scheme; and using a model predictive control algorithm (MPC) to adjust and optimize the scheduling scheme in real time.

[0065] This invention also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement... Figure 1 The method for assessing urban flood control and drainage capacity based on CIM is shown. The computer-readable storage medium can be a read-only memory, a hard disk, or an optical disk, etc.

[0066] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments and storage medium embodiments, since they are basically similar to method embodiments, the descriptions are relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0067] Furthermore, in this document, 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 limitation, 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.

[0068] The foregoing description illustrates and describes preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for evaluating urban flood control and drainage capacity based on CIM, characterized in that, Includes the following steps: Based on static foundational data and dynamic monitoring data, construct CIM sub-models for inland waterways, flood storage and detention areas, and pipeline networks, respectively. Static foundational data includes at least pipeline network data, inland waterway data, flood storage and detention area data, and flood control and drainage project data. Dynamic monitoring data includes at least real-time monitoring data for pipeline networks, inland waterways, flood storage and detention areas, and flood control and drainage projects. Specifically, pipeline network data includes at least basic pipeline attribute data, pipeline connection data, pipeline facility data, and pipeline inspection and evaluation data. Inland waterway data includes at least basic inland waterway information data, river network data, cross-sectional data, inland waterway facility data, and inland waterway storage and drainage data. Capacity-related data; Flood storage and detention area data should at least include basic information on flood storage and detention areas, engineering facilities, flood storage capacity, flood risk assessment, and operational plans; Flood control and drainage engineering data should at least include design parameters, scheduling procedures, geographic information data, and geometric attribute information for each facility; Real-time monitoring data for inland rivers should at least include inland river water level and flow data; real-time monitoring data for flood storage and detention areas should at least include flood storage and detention area water level data; real-time monitoring data for pipe networks should at least include pipe network liquid level, pipe network flow, and pipe network flow direction data; real-time monitoring data for flood control and drainage engineering should at least include various operating parameters for each facility. The calculation of inland water storage capacity of a river segment based on the inland river CIM sub-model includes at least the following steps: calculating the river complexity index based on inland river data and obtaining the corresponding preset clipping interval based on the river complexity index; clipping the inland river system CIM sub-model along the river centerline according to the preset clipping interval to obtain multiple coarse clipped cross sections; matching and correcting the coarse clipped cross sections with the cross section data, and interpolating and completing the positions of missing cross sections after correction to obtain a corrected cross section group, which includes multiple corrected cross sections; and calculating the inland water storage capacity of the river segment based on the corrected cross section group and inland river water level data. Calculate flood storage capacity based on the CIM sub-model of flood storage and detention areas; Based on the CIM sub-model of the pipeline network, the topological relationship of the pipeline network is generated, and the theoretical flow capacity of the pipeline network is analyzed using the SWMM model. This process includes at least the following steps: acquiring topographic elevation data and land use type data; extracting basic attribute data of the pipeline network based on the CIM sub-model; constructing pipe segments and nodes based on the basic attribute data and SWMM software, setting pipe segment parameters, node types, and the critical storage ratio of the pipeline network, and associating them with topographic elevation data; dividing sub-catchments according to land use type data and setting boundary conditions; importing rainfall data and calibrating the SWMM model parameters based on historical pipeline level data and pipeline flow data; running the SWMM model simulation and extracting key data for each pipe segment to quantitatively analyze the theoretical flow capacity of the pipeline network. The theoretical flow capacity includes at least initial fullness, initial water storage capacity, and initial flow capacity. Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and correct the SWMM model parameters based on the analysis results; Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, a multi-objective optimization function is established. The objectives include at least minimizing the risk of pipeline overflow, minimizing scheduling time, and simplifying scheduling measures. The Pareto optimal solution set is obtained by using the particle swarm optimization algorithm, and the scheduling scheme is output after weighted sorting. Calculating flood storage capacity based on the CIM sub-model of flood storage and detention areas includes at least the following steps: Acquire DEM data of the flood storage and detention area and lake boundary data; Based on the CIM sub-model of the flood storage and detention area, the lake area of ​​the flood storage and detention area is divided into grid cells, and effective grid cells are obtained by trimming according to the lake boundary. Extract the elevation value of the center point of each grid cell, combine it with the water level data of the flood storage area to calculate the water storage capacity of all submerged grid cells, and sum them up to obtain the flood storage capacity.

2. The CIM-based urban flood control and drainage capacity assessment method according to claim 1, characterized in that, Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and correct the SWMM model parameters based on the analysis results, including at least the following steps: Acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points; The initial water storage capacity, initial filling degree, initial flow capacity and real-time monitoring data were compared and analyzed. The parameters of the SWMM model were corrected based on the results of the comparative analysis. 3.The CIM-based urban flood control and drainage capacity assessment method according to claim 1, characterized in that, Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, a multi-objective optimization function is established, including at least the following steps: Based on static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model, the various facilities of the flood control and drainage project are initialized. Construct a multi-objective optimization function; Each objective is quantified, and corresponding indicators and calculation methods are determined.

4. The CIM-based urban flood control and drainage capacity assessment method according to claim 3, characterized in that, The process of finding the Pareto optimal solution set using the particle swarm optimization algorithm, and then outputting a scheduling scheme after weighted sorting, includes at least the following steps: Using real-number encoding, the operational status combination of various facilities in flood control and drainage projects is mapped into particle position vectors; The fitness value of each particle is calculated using a multi-objective optimization function. Record and update the individual extreme value of each particle and the global extreme value of the entire population; The particles are iteratively updated using the velocity and position update formulas of the particle swarm optimization algorithm, and the Pareto optimal solution set is solved by combining adaptively adjusted inertia weights and learning factors. Based on the target priority, the Pareto optimal solution set is weighted and sorted to output the scheduling scheme.

5. The city flood control and drainage capacity assessment system based on CIM, characterized in that, include: The CIM sub-model construction module is used to acquire and construct inland river CIM sub-models, flood storage and detention area CIM sub-models, and pipeline network CIM sub-models based on static basic data and dynamic monitoring data. Static basic data includes at least pipeline network data, inland river data, flood storage and detention area data, and flood control and drainage project data. Dynamic monitoring data includes at least real-time monitoring data of pipeline networks, inland rivers, flood storage and detention areas, and flood control and drainage projects. Specifically, pipeline network data includes at least basic pipeline attribute data, pipeline connection data, pipeline facility data, and pipeline inspection and evaluation data. Inland river data includes at least basic inland river information data, river network data, cross-sectional data, and inland river facility data. Data related to the water storage and drainage capacity of inland rivers; data on flood storage and detention areas should include at least basic information on flood storage and detention areas, engineering facilities of flood storage and detention areas, flood storage capacity of flood storage and detention areas, flood risk assessment of flood storage and detention areas, and emergency plans for the use of flood storage and detention areas; data on flood control and drainage projects should include at least the design parameters, scheduling procedures, geographic information data, and geometric attribute information of each facility; real-time monitoring data of inland rivers should include at least inland river water level and inland river flow data; real-time monitoring data of flood storage and detention areas should include at least flood storage and detention area water level data; real-time monitoring data of pipe networks should include at least pipe network liquid level, pipe network flow and pipe network flow direction data; and real-time monitoring data of flood control and drainage projects should include at least the various operating parameters of each facility. The calculation module is used to calculate the inland water storage capacity of a river segment based on the inland river CIM sub-model, and includes at least the following steps: calculating the river complexity index based on inland river data, and obtaining the corresponding preset clipping interval based on the river complexity index; clipping the inland river system CIM sub-model along the river centerline according to the preset clipping interval to obtain multiple coarse clipped cross sections; matching and correcting the coarse clipped cross sections with the cross section data, and interpolating and completing the positions of missing cross sections after correction to obtain a corrected cross section group, which includes multiple corrected cross sections. The process involves: 1) Calculating the inland river storage capacity based on the corrected cross-section group and inland river water level data; 2) Calculating the flood storage capacity based on the CIM sub-model of the flood storage and detention area, including at least the following steps: Obtaining the DEM data and lake boundary data of the flood storage and detention area; 3) Dividing the lake area of ​​the flood storage and detention area into grid cells based on the CIM sub-model of the flood storage and detention area, and trimming the effective grid cells according to the lake boundary; 4) Extracting the elevation value of the center point of each grid cell, combining it with the water level data of the flood storage and detention area to calculate the water storage capacity of all submerged grid cells, and summing the results. The SWMM model building module is used to generate the topological relationship of a pipeline network based on the pipeline network CIM sub-model and to apply the SWMM model to analyze the theoretical flow capacity of the pipeline network. It includes at least the following steps: acquiring topographic elevation data and land use type data; extracting basic attribute data of the pipeline network based on the pipeline network CIM sub-model; constructing pipe segments and nodes based on the basic attribute data and SWMM software, setting pipe segment parameters, node types, and the critical storage ratio of the pipeline network, and associating them with topographic elevation data; dividing sub-catchments according to land use type data and setting boundary conditions; importing rainfall data and calibrating the SWMM model parameters based on historical pipeline level data and pipeline flow data; running the SWMM model simulation and extracting key data for each pipe segment to quantitatively analyze the theoretical flow capacity of the pipeline network. The theoretical flow capacity includes at least initial fullness, initial water storage capacity, and initial flow capacity. The calibration module is used to acquire and analyze real-time monitoring data of the pipeline network and information on urban flooding points, and to calibrate the SWMM model parameters based on the analysis results. The modeling module is used to model static basic data, dynamic monitoring data, inland river water storage, flood storage capacity, and the corrected SWMM model. The evaluation module is used to solve the Pareto optimal solution set through the particle swarm optimization algorithm, and output the scheduling scheme after weighted sorting. The objectives include at least minimizing the pipeline overflow risk, minimizing the scheduling time, and the simplest scheduling measures.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a CIM-based urban flood control and drainage capacity assessment program, which, when executed by a processor, implements the steps of the CIM-based urban flood control and drainage capacity assessment method as described in any one of claims 1 to 4.

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