A method and system for evaluating the resilience of a drainage system based on pipe network disease parameterization

Abstract

The application discloses a kind of based on pipe network disease parameterization drainage system flexibility evaluation method and system.The method includes: by obtaining multi-source basic data to construct classified disease database, using parameterization mapping rule to convert respectively into the physical parameters of water power model that characterizes overflow capacity and hydraulic resistance of operation and structure;Based on the parameter, construct one-dimensional and two-dimensional coupled water power model, compare the time series simulation results of ideal and present situation working condition;Through multidimensional normalization loss function, calculate comprehensive health performance function, and obtain comprehensive flexibility index by full cycle integration, realize flexibility quantitative evaluation.At the same time, combined with simulation results and disease data, identify waterlogging vulnerable node and its disaster-causing internal cause.The application realizes the leap of evaluation benchmark from design state to real state, can accurately quantify the reduction effect of disease on system performance, and provides scientific targeting basis for priority ranking of pipe network dredging, repair and updating and reconstruction scheme.

Description

Technical Field

[0001] This invention relates to the field of urban drainage and flood control and hydrodynamic simulation and evaluation technology, and in particular to a method and system for evaluating the resilience of drainage systems based on pipeline defect parameterization. Background Technology

[0002] In densely built-up coastal areas and old urban districts, drainage pipe networks are affected by complex underlying environments such as long-term high-load service, seawater backwater, and groundwater level fluctuations. These conditions commonly result in a complex mix of operational and structural defects, including pipe siltation, scale buildup, mixed connections of rainwater and sewage, localized deformation, misaligned connections, and reverse slopes. These existing defects continuously compress the effective water passage cross-section at the physical level and significantly increase boundary frictional resistance at the hydraulic level. Consequently, the actual drainage capacity of the system under extreme rainfall conditions is far lower than the nominal design specifications, becoming a core hidden danger restricting the effectiveness of urban flood control and, in severe cases, leading to the paralysis of localized drainage functions.

[0003] In existing flood control evaluation systems for drainage systems, most still rely on static or quasi-dynamic assessment methods based on idealized design parameters. While these methods can output full flow or water accumulation results for specific operating conditions, they have significant technical limitations: First, existing technologies fail to explicitly and quantitatively transform qualitative defect information obtained from pipe network endoscopic inspections (such as CCTV and QV) into the underlying physical constraint parameters of the model. This leads to the simulation calculation's baseline implicitly treating pipe network components as initially intact. Due to the lack of precise expression of the impact of existing defects, the assessment conclusions systematically deviate from the actual disturbance resistance level of aging pipe networks. Second, existing assessment schemes often lack a precise characterization of the dynamic water exchange process between pressurized flow in underground pipe networks and surface runoff, making it difficult to identify the cascading failure mechanisms of urban flooding disasters underground and on the surface through deeply integrated simulation methods.

[0004] Even more challenging is that while resilience theory provides a new framework for risk diagnosis, existing evaluation indicators mostly focus on the overflow results at a specific moment under a single return period, failing to simultaneously capture the dynamic evolution characteristics of the system throughout the entire process of rainstorm impact, such as "disturbance absorption—functional maintenance—post-disaster recovery." Furthermore, existing macro-resilience indicators often struggle to be applied down to specific engineering repair schemes, resulting in assessment conclusions that remain largely theoretical and cannot directly support the prioritization of upgrades and renovations of existing drainage systems, targeted repair decisions, and intelligent operation scheduling optimization under complex boundary conditions.

[0005] Therefore, there is an urgent need to propose a technical solution that can deeply couple the parameterization mechanism of pipeline network defects, utilize one-dimensional and two-dimensional coupled hydrodynamic simulation, and quantitatively evaluate flood control resilience based on a dimensionless comprehensive health performance function. By constructing a resilience assessment framework applicable to complex background conditions, this solution can quantitatively reveal the dynamic attenuation mechanism of system robustness impairment and recovery hysteresis caused by existing defects, thereby meeting the practical needs of modern urban refined governance and resilience enhancement for risk diagnosis and engineering decision-making. Summary of the Invention

[0006] To address the aforementioned technical problems in the existing technology, this invention proposes a method and system for assessing the resilience of drainage systems based on pipeline defect parameterization.

[0007] According to a first aspect of the present invention, a method for assessing the resilience of a drainage system based on pipeline defect parameterization is proposed, comprising: S1. Acquire multi-source basic data of the study area, including pipeline endoscopic inspection data. After preprocessing and topological logic verification of the multi-source basic data, establish the spatial mapping relationship between pipeline facilities and surface grid. Construct a classification disease database based on pipeline endoscopic inspection data to form the basic data foundation for model inference. The classification disease database includes operational defects and structural defects. S2, based on a classification disease database, uses preset parameterized mapping rules to transform operational and structural defects into physical parameters of a hydrodynamic model that characterize effective flow capacity and hydraulic resistance. S3, based on the foundational data of model deduction and the physical parameters of the hydrodynamic model, constructs a one-dimensional and two-dimensional coupled hydrodynamic model, sets up two comparative conditions: ideal pipeline network condition and current defect condition, and conducts time-series simulation under a typical design rainstorm scenario. S4, based on time-series simulation results, constructs a multidimensional normalized loss function covering underground pipe network overload, surface inundation area, and surface water volume. Based on the multidimensional normalized loss function, calculates a dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance, and performs full-cycle integral calculation to obtain a comprehensive resilience index. By comparing the index difference between ideal pipe network operating conditions and current defective operating conditions, obtain the quantitative assessment results of drainage system resilience.

[0008] In the above technical solution, by directly parameterizing qualitative pipeline disease data into hydrodynamic physical characteristics and introducing an ideal-to-current simulation mechanism, an objective, dynamic and quantitative relative quantitative evaluation of the resilience level of the drainage system under the whole process of rainstorm disturbance is realized, which effectively solves the problem that traditional assessment is divorced from the physical health status.

[0009] In some specific embodiments, the multi-source basic data also includes drainage network attribute data, topographic data, and meteorological and hydrological data. By introducing multi-dimensional spatiotemporal data such as topographic data and meteorological and hydrological data, the high fidelity of the resilience assessment model in runoff generation and boundary condition setting is ensured.

[0010] In some specific embodiments, step S1 includes: S11. Obtain geographic information system (GIS) data on underground pipelines in the study area, extracting pipeline topology connections, spatial coordinates of manholes and pipe segments, pipe bottom elevation, pipe diameter, and material properties; obtain topographic data, including digital elevation models, measured road control points, and land use maps; obtain meteorological and hydrological data, including minute-level rainfall sequence data; and obtain pipeline endoscopic inspection data, including closed-circuit television endoscopic inspection or periscope inspection results. S12 unifies the coordinate system and elevation benchmark of the multi-source basic data, and fills in the missing values ​​of key fields in the order of priority of measured values, interpolation of adjacent pipe sections, and design ledger values ​​as a backup; a directed topology graph is constructed using a topology logic verification algorithm to verify hydraulic connectivity. The topology logic verification includes: checking the correlation between pipe section endpoints and inspection wells, checking the consistency between pipe section flow direction and elevation, eliminating isolated / suspended / repeated / self-loop pipe sections, identifying slope anomalies, and merging nodes with a distance of less than 0.5m to 2.0m within a preset tolerance to generate a one-dimensional pipe network skeleton with complete hydraulic connectivity.

[0011] S13. In the geographic information system environment, establish the spatial mapping relationship between one-dimensional pipeline facility nodes and two-dimensional surface runoff calculation grid to establish the hydraulic correlation describing the exchange of groundwater and surface water. S14 interprets the endoscopic inspection data of the pipeline, identifies and extracts operational defects including pipeline siltation and scale buildup, as well as structural defects including pipeline deformation, misalignment, collapse, or local reverse slope, and spatially associates the operational and structural defects with the corresponding pipeline network facility nodes or pipe sections to generate a classified disease database.

[0012] In the above technical solution, a low-level data architecture with high hydraulic connectivity and spatial index accuracy is constructed through standardized preprocessing, topology verification and multi-dimensional spatial association, providing a high-precision physical environment foundation for subsequent model inference.

[0013] In some specific embodiments, the physical parameters of the hydrodynamic model include: the effective cross-sectional area corresponding to operational and structural defects and used to characterize flow capacity; and the corrected Manning roughness corresponding to operational defects and used to characterize hydraulic resistance.

[0014] In some specific embodiments, the effective cross-sectional area is reduced and corrected using the siltation rate in operational defects to obtain the effective cross-sectional area. The correction formula is as follows: In the formula, This is the corrected effective cross-sectional area of ​​the water passage; This represents the original cross-sectional area; The siltation rate is calculated. By reducing and correcting the physical cross-section, the direct encroachment of siltation on the effective water-receiving space of the pipeline is accurately depicted, improving the accuracy of overflow prediction.

[0015] In some specific embodiments, the correction formula for Manning roughness is as follows: In the formula, This is the corrected Manning roughness. The roughness is the same as the original design Manning roughness. This is the resistance amplification factor caused by siltation, which can be determined based on the model calibration. The siltation rate; This is a correction term for additional resistance caused by adhesion, scaling, or pipe wall roughness. By correcting the roughness coefficient, the surge in hydraulic head loss caused by increased pipe wall roughness is quantitatively reflected, demonstrating the impact of operational defects on confluence efficiency.

[0016] In some specific embodiments, in the coupled one-dimensional and two-dimensional hydrodynamic model, the one-dimensional pipe network model uses the dynamic wave module to solve the Saint-Venant equations to obtain the changes in underground pipe water level and flow rate, while the two-dimensional surface runoff model uses the shallow water equation for gridded calculation to obtain the evolution process of surface runoff. The one-dimensional pipe network model and the two-dimensional surface runoff model exchange water bidirectionally through manholes or storm drain nodes. By employing coupled solution technology, the bidirectional interaction process of "underground pipe pressure overflow" and "surface runoff evolution" is simulated, realistically reproducing the physical generation mechanism of urban flooding.

[0017] In some specific embodiments, the comprehensive health performance function is processed with equal weights, and the formula is as follows:

[0018] In the formula, For comprehensive health performance functions; for The proportion of the number of inspection well nodes that are constantly experiencing full flow, backflow, or overflow to the maximum number of nodes under the baseline rainstorm scenario; for The proportion of the two-dimensional grid area where the water depth exceeds the disaster control threshold at any given moment to the maximum flooded area under the baseline rainstorm scenario; for The proportion of the instantaneous total volume of water remaining on the urban surface to the maximum volume of water accumulation under the baseline heavy rainfall scenario. , , These are the weights for node overload loss, surface inundation loss, and surface water volume loss, respectively, and they satisfy the following conditions: .

[0019] In the above technical solution, a general quantitative benchmark that can comprehensively characterize the entire cycle of "performance drop-minimum maintenance-recovery" of the system is constructed by normalizing and integrating multi-dimensional indicators, thereby enhancing the comprehensiveness of the evaluation results.

[0020] In some specific embodiments, the overall resilience index is obtained by integrating the following formula:

[0021] In the formula, As a comprehensive resilience index; This is the moment when rainfall begins; This marks the point at which the system has essentially recovered to a stable operating state. This is a comprehensive health performance function.

[0022] In the above technical solution, the discrete time-series performance fluctuations are transformed into a continuous resilience index by using the time integration algorithm, thereby realizing a holistic characterization of the system's disaster resistance robustness and post-disaster recovery capability.

[0023] In some specific embodiments, after obtaining the quantitative assessment results of the drainage system's resilience, the method further includes: Based on preset quantitative screening rules, areas where the evaluation indicators exceed the set threshold under current defect conditions are identified as waterlogging-vulnerable nodes. Combining time-series simulation results with a classification disease database, the dominant internal causes leading to the decline in the resilience of waterlogging-vulnerable nodes are identified through disaster-causing internal cause tracing logic. By screening vulnerable nodes and performing cause-tracing diagnosis, the macro-level system assessment is extended to the micro-level lesion location, providing a technical closed loop for drainage management from problem discovery to cause analysis.

[0024] In some specific embodiments, the quantitative screening rule is as follows: under the current defect conditions, high-risk nodes with overflow water volume exceeding a preset water volume threshold, and surrounding surface water depth exceeding a preset depth threshold and duration exceeding a preset time threshold are identified as vulnerable nodes to waterlogging. Establishing a composite screening criterion that includes water volume, depth, and time effectively filters out minor interference signals, ensuring the accuracy of high-risk vulnerability point identification and its practical engineering value.

[0025] In some specific embodiments, the logic for tracing the internal causes of disasters includes: If the proportion of the basic liquid level in the dry season to the pipe diameter of the vulnerable waterlogging node before rainfall is greater than the preset space occupancy threshold, then the main internal cause of the disaster is diagnosed as the initial storage space encroachment caused by the misconnection of rainwater and sewage. If the rate of rise of the operating water level at a vulnerable point to waterlogging exceeds the preset backlog threshold during rainfall, and the corresponding downstream pipe section has a siltation record in the classification disease database, then the primary cause of the disaster is diagnosed as hydraulic obstruction caused by pipe siltation.

[0026] In the above technical solution, by constructing a causal logic chain based on the initial state and dynamic response characteristics, the dominant causes of waterlogging are determined, providing a scientific basis for accurately taking dredging or remediation measures.

[0027] In some specific embodiments, the dynamic wave module solves the Saint-Venant equations, assuming the coordinates along the pipe axis are... The time is The pipeline flow rate is The cross-sectional area of ​​the water passage is The water depth is The bottom elevation of the pipe is Total head is The friction gradient along the route is The slope of the pipe bottom is Lateral inflow is Then the continuity equation and the momentum equation are respectively: The continuity equation is:

[0028] The momentum equation is:

[0029] Among them, friction slope This can be expressed by Manning's formula as:

[0030] In the formula: For pipeline flow rate; This refers to the cross-sectional area of ​​the water passage. The lateral inflow per unit length of the pipe is expressed in m² / s. It is the acceleration due to gravity; This refers to the total head within the pipe. This refers to the elevation of the bottom of the pipe. The depth of the water inside the pipe; The slope of frictional resistance along the route; The hydraulic radius; The roughness is the Manning roughness after parameterization correction for disease.

[0031] In the aforementioned technical solution, the Saint-Venant equations based on dynamic wave theory are used for solving, achieving high-precision mechanical analysis of the evolution process of unsteady flow in underground pipe networks. This model comprehensively captures pressure fluctuations, hydraulic gradient changes, and inertial force effects caused by rapid changes in flow velocity within the pipeline, ensuring the physical realism of simulations of pipe flow rate, manhole water level, and pressurized overflow processes under complex pipe network topologies and variable rainfall loads.

[0032] In some specific embodiments, the construction of the two-dimensional surface runoff model includes: assuming the water depth in the two-dimensional surface grid is... ,along direction and The average flow velocities in the directions are respectively and The surface elevation is Rainfall intensity is Infiltration loss is The exchange flow per unit area between a one-dimensional node and a two-dimensional mesh is Then the two-dimensional shallow water equation can be written as: Continuity equation:

[0033] Directional momentum equation:

[0034] Directional momentum equation:

[0035] In the formula: The water depth is represented by a two-dimensional surface grid, in meters. , Two-dimensional surface grids in , Average flow velocity in the direction of flow, in m / s; The time is calculated in seconds (s). , Calculate the plane coordinates of the two-dimensional Earth surface; The acceleration due to gravity is taken as 9.81 m / s². This is the ground elevation, in meters (m). Rainfall intensity, expressed in m / s; The infiltration or initial loss reduction strength is expressed in m / s. This represents the flow rate per unit area exchanged between a one-dimensional pipeline node and a two-dimensional surface grid, expressed in m³ / s. The roughness of the two-dimensional surface is the Manning roughness.

[0036] According to a second aspect of the invention, a computer-readable storage medium is provided on which one or more computer programs are stored, which, when executed by a computer processor, implement the method described above.

[0037] According to a third aspect of the present invention, a drainage system resilience assessment system based on pipeline defect parameterization is proposed, comprising: The digital infrastructure module is configured to acquire multi-source basic data, including pipeline endoscopic inspection data, for the study area. After preprocessing and topological logic verification of the multi-source basic data, a spatial mapping relationship between pipeline facilities and surface grid is established. A categorized defect database is constructed based on the pipeline endoscopic inspection data to form the basic data foundation for model deduction. The categorized defect database includes operational defects and structural defects. The physical parameter conversion module is configured to convert operational and structural defects into physical parameters of the hydrodynamic model that characterize effective flow capacity and hydraulic resistance, based on a classified disease database and using preset parameterized mapping rules. The time-series simulation module is configured to construct a one-dimensional and two-dimensional coupled hydrodynamic model based on the model extrapolation base data and the physical parameters of the hydrodynamic model. It sets two comparison conditions: ideal pipeline network conditions and current defect conditions, and performs time-series simulation under typical design rainstorm scenarios. The resilience quantification assessment module is configured to construct a multidimensional normalized loss function based on time-series simulation results, which covers underground pipe network overload, surface inundation area, and surface water volume. Based on the multidimensional normalized loss function, a dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance is calculated, and a full-cycle integral calculation is performed to obtain a comprehensive resilience index. By comparing the index difference between ideal pipe network operating conditions and current defective operating conditions, the resilience quantification assessment results of the drainage system are obtained.

[0038] This invention proposes a method and system for assessing the resilience of drainage systems based on pipeline defect parameterization, which has the following technical advantages: 1. This invention establishes a parameterized mapping mechanism for pipeline defects, directly transforming qualitative endoscopic defects (such as operational, structural, and misconnection defects) into physical control parameters of the hydrodynamic model. This fundamentally shifts the assessment benchmark from an ideal design state to the actual current state. Compared to the limitations of traditional assessment methods that ignore the degradation of the pipeline's baseline health, this invention can accurately characterize the cross-sectional shrinkage caused by pipe siltation, the surge in resistance caused by pipe wall damage, and the continuous encroachment of misconnection baseflow on the initial storage space. This greatly improves the fidelity and objectivity of the assessment results in the complex operating environment of old urban areas, truly reflecting the actual physical performance of old drainage systems.

[0039] 2. This invention relies on a deep coupling simulation technology of one-dimensional underground pipe networks and two-dimensional surface runoff to simultaneously depict the entire physical process of underground confined flow evolution, overflow at manhole nodes, and spatiotemporal evolution of surface water accumulation. This dynamic extrapolation method based on time-series simulation not only facilitates the complete identification of the functional degradation path of the drainage system under the impact of rainstorms, but also quantitatively reconstructs the cascading failure response of "local overload—upstream backflow—node overflow—surface inundation" induced by local defects. This allows the assessment to move beyond a static description of water accumulation risk, and instead ascends to a mechanistic analysis of the system's functional maintenance capacity and failure mechanisms, providing rigorous dynamic support for accurately identifying vulnerable nodes in urban flooding.

[0040] 3. This invention constructs a standardized quantitative evaluation system composed of a multidimensional normalized loss function, a comprehensive health performance function, and a comprehensive resilience index, achieving a unified characterization of the system's resilience, functional maintenance capabilities, and post-disaster recovery capabilities. Through dimensionless processing, this system eliminates interference from rainfall recurrence intervals, regional spatial scales, and topographical differences, facilitating intuitive horizontal comparisons and performance benchmarking across different operating conditions, areas, and renovation schemes. This invention is applicable not only to the "physical examination" of the existing drainage capacity in high-density old urban areas but also to the quantitative evaluation of the expected effects before and after the implementation of projects such as pipe network dredging, repair, and expansion. This provides a targeted engineering basis for prioritizing the renovation and upgrading of drainage pipe networks, achieving a leap from intuitive experience-based judgment to rational quantitative diagnosis. Attached Figure Description

[0041] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Many anticipated advantages of the embodiments and other embodiments of the invention will be readily recognized as they become better understood through reference to the following detailed description. Other features, objects, and advantages of this application will become more apparent from reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a drainage system resilience assessment method based on pipeline defect parameterization, according to an embodiment of this application. Figure 2 This is a schematic diagram of the overall technical flow of a specific embodiment of this application; Figure 3 This is a comparison chart of the dynamic evolution curves of the comprehensive health performance function of an ideal pipeline network under a 5a-year design rainstorm and a current defective network under a specific embodiment of this application. Figure 4 This is a framework diagram of a drainage system resilience assessment system based on pipeline defect parameterization, which is a specific embodiment of this application. Detailed Implementation The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] Figure 1 A flowchart illustrating a drainage system resilience assessment method based on pipeline defect parameterization according to an embodiment of this application is shown. Figure 1 As shown, the method includes the following steps: S1: Acquire multi-source basic data of the study area, including pipeline endoscopic inspection data. After preprocessing and topological logic verification of the multi-source basic data, establish the spatial mapping relationship between pipeline facilities and surface grid. Construct a classification disease database based on pipeline endoscopic inspection data to form the basic data foundation for model inference. The classification disease database includes operational defects and structural defects.

[0044] In some specific embodiments, step S1 includes: S11, Acquisition of Multi-Source Basic Data: Acquire GIS ledger data of underground pipelines in the study area, extracting pipeline topology connections, spatial coordinates of manholes and pipe segments, pipe bottom elevation, pipe diameter and material properties; acquire topographic data, including digital elevation model (DEM), measured road control points and land use status map; acquire meteorological and hydrological data, including minute-level rainfall sequence data; acquire pipeline endoscopic inspection data, including CCTV or QV endoscopic inspection results and historical waterlogging inspection records.

[0045] Specifically, the study acquired and processed attribute data of underground pipe network facilities, extracting GIS ledger data including topological connections, spatial coordinates of manholes and pipe segments, pipe bottom elevation, pipe diameter, material, and slope, to construct a one-dimensional pipe network hydrodynamic model framework. Simultaneously, it acquired geomorphic parameters such as the digital elevation model (DEM), measured road control points, and land use status maps of the study area to generate a two-dimensional surface runoff calculation base map, thereby finely characterizing spatial heterogeneity parameters such as impermeability and Manning roughness of the underlying surface. Furthermore, it introduced dynamic boundary-driven data to acquire minute-level rainfall sequence data from regional automatic rain gauges. Additionally, it collected pipeline CCTV (closed-circuit television inspection) and QV (periscope inspection) endoscopic inspection results, as well as historical flood season waterlogging point inspection records, and other defect and measured verification data, providing experimental basis for subsequent model parameter calibration and vulnerability analysis.

[0046] S12, Data Standardization Preprocessing and Topology Verification: The coordinate system and elevation benchmark of the multi-source basic data are unified, and missing values ​​of key fields are filled in the order of measured value first, interpolation of adjacent pipe sections, and design ledger value as a backup; a directed topology graph is constructed using a topology logic verification algorithm and hydraulic connectivity is verified. The topology logic verification includes: checking the correlation between pipe section endpoints and inspection wells, checking the consistency between pipe section flow direction and elevation, eliminating isolated / suspended / repeated / self-loop pipe sections, identifying slope anomalies, and merging nodes with a distance of less than 0.5m to 2.0m within a preset tolerance to generate a one-dimensional pipe network skeleton with complete hydraulic connectivity.

[0047] Specifically, unified preprocessing is performed on multi-source basic data. First, drainage network GIS ledgers, topographic elevation data, road control point data, land use data, and pipeline endoscopic inspection data are unified to the same plane coordinate system and elevation datum. For missing values ​​in key fields such as manhole elevation, pipe bottom elevation, pipe diameter, pipe slope, and material, they are filled in according to the order of "prioritizing measured values ​​for the same pipe segment, interpolating values ​​from adjacent connected pipe segments, and using design ledger values ​​as a fallback." Then, a directed topology graph is constructed with manholes as nodes and pipe segments as edges to verify the network topology. Topology verification includes: (1) Check whether the start and end points of the pipe section are both connected to valid inspection well nodes; (2) Check whether the flow direction of the pipe section is consistent with the elevation of the bottom of the upstream and downstream pipes; (3) Check for isolated nodes, suspended pipe sections, duplicate pipe sections, and self-loop connections; (4) Check whether there are sudden changes in the bottom elevation, abnormal reverse slope, or missing slope between adjacent pipe sections; (5) Nodes with endpoint distances less than the preset tolerance are merged by adsorption. The preset tolerance can be 0.5m to 2.0m. (6) Manually review and mark topological conflict points that cannot be corrected by automatic rules.

[0048] After the above processing, a one-dimensional drainage network skeleton with complete hydraulic connectivity is generated, providing a basic topology for the subsequent one-dimensional-two-dimensional coupled model.

[0049] S13. In the geographic information system environment, establish the spatial mapping relationship between one-dimensional pipeline facility nodes and two-dimensional surface runoff calculation grid to establish the hydraulic correlation describing the exchange of groundwater and surface water. S14, Construction of Classified Disease Database: Interpret the pipeline endoscopic inspection data, identify and extract operational defects including pipeline siltation and scale buildup, as well as structural defects including pipeline deformation, misalignment, collapse, or local reverse slope, and spatially associate the operational and structural defects with the corresponding pipeline network facility nodes or pipe sections to generate a classified disease database.

[0050] S2: Based on the classification disease database, and using the preset parameterized mapping rules, operational defects and structural defects are transformed into physical parameters of the hydrodynamic model that characterize the effective flow capacity and hydraulic resistance.

[0051] In some specific embodiments, the physical parameters of the hydrodynamic model include: the effective cross-sectional area corresponding to operational and structural defects and used to characterize flow capacity; the corrected Manning roughness corresponding to operational defects and used to characterize hydraulic resistance; and the dry-day basic liquid level term corresponding to stormwater and sewage mixing defects and used to characterize the initial storage space.

[0052] Furthermore, the effective cross-sectional area is reduced and corrected using the siltation rate from operational defects to obtain the effective cross-sectional area. The correction formula is as follows: In the formula, This is the corrected effective cross-sectional area of ​​the water passage; This represents the original cross-sectional area; This refers to the siltation rate.

[0053] Furthermore, the corrected formula for Manning roughness is as follows: In the formula, This is the corrected Manning roughness. The roughness is the same as the original design Manning roughness. This is the resistance amplification factor caused by siltation, which can be determined based on the model calibration. The siltation rate; This is a correction term for additional resistance caused by adhesion, scaling, or rough pipe walls.

[0054] S3: Based on the foundational data of the model derivation and the physical parameters of the hydrodynamic model, a one-dimensional and two-dimensional coupled hydrodynamic model is constructed. Two comparative conditions are set: ideal pipeline network condition and current defect condition. Time series simulation is carried out under typical design rainstorm scenarios.

[0055] In some specific embodiments, the one-dimensional and two-dimensional coupled hydrodynamic model is constructed using InfoWorks ICM. The one-dimensional pipe network is based on the dynamic wave module to solve the Saint-Venant equations to simulate the confined flow and gravity flow states of underground pipelines. The two-dimensional surface runoff is calculated using the shallow water equation in a gridded manner to simulate the flooding evolution of surface runoff. The bidirectional water exchange between the underground pipe network and the surface runoff is realized through manholes or rainwater inlets as coupling points.

[0056] Specifically, the two-dimensional surface runoff model uses shallow water equations to describe the evolution of surface water depth and planar flow velocity. Let the water depth in the two-dimensional surface grid be... ,along direction and The average flow velocities in the directions are respectively and The surface elevation is Rainfall intensity is Infiltration loss is The exchange flow per unit area between a one-dimensional node and a two-dimensional mesh is Then the two-dimensional shallow water equation can be written as: Continuity equation:

[0057] Directional momentum equation:

[0058] Directional momentum equation:

[0059] In the formula: The water depth is represented by a two-dimensional surface grid, in meters. , Two-dimensional surface grids in , Average flow velocity in the direction of flow, in m / s; The time is calculated in seconds (s). , Calculate the plane coordinates of the two-dimensional Earth surface; The acceleration due to gravity is taken as 9.81 m / s². This is the ground elevation, in meters (m). Rainfall intensity, expressed in m / s; The infiltration or initial loss reduction strength is expressed in m / s. This represents the flow rate per unit area exchanged between a one-dimensional pipeline node and a two-dimensional surface grid, expressed in m³ / s. The roughness of the two-dimensional surface is the Manning roughness.

[0060] One-dimensional underground pipe networks and two-dimensional surface grids exchange water bidirectionally through inspection wells or storm drain nodes. Let the first... Each inspection well node corresponds to a two-dimensional grid. The water level in the inspection well is Two-dimensional grid water level is The elevation of the manhole cover or drain inlet is The water level difference is defined as:

[0061] When the water level in the inspection well is higher than the surface grid water level and higher than the manhole cover elevation, the underground pipe network overflows to the surface, and the exchange flow rate is:

[0062] When the water level in the two-dimensional surface grid is higher than the water level in the inspection well and there is standing water on the surface, the surface water flows back to the underground pipe network through the rainwater inlet or inspection well. The exchange flow direction is opposite, which can be uniformly represented as:

[0063] Among them, when and At that time, take:

[0064] In the formula: For the first The exchange flow rate between each inspection well node and the two-dimensional surface grid is expressed in m³ / s. The exchange flow coefficient can be determined based on the type of manhole cover, rainwater inlet, or inspection well opening. The equivalent opening area of ​​the inspection well or rainwater inlet, in m². The water level at the inspection well node is expressed in meters (m). This corresponds to the water level in a two-dimensional surface grid, in meters (m). Elevation of manhole cover or drain inlet, in meters; This is a symbolic function used to represent the direction of water exchange.

[0065] The source term per unit area entering the 2D grid is:

[0066] in, This represents the area of ​​a two-dimensional surface grid. If a manhole node is mapped to multiple two-dimensional grids simultaneously, the mapping is performed according to the weights. distribute:

[0067] Specifically, the one-dimensional underground pipe network is solved using the InfoWorks dynamic wave module to solve the Saint-Venant equations, including: Let the coordinates along the pipeline axis be... The time is The pipeline flow rate is The cross-sectional area of ​​the water passage is The water depth is The bottom elevation of the pipe is Total head is The friction gradient along the route is The slope of the pipe bottom is Lateral inflow is Then the continuity equation and the momentum equation are respectively: The continuity equation is:

[0068] The momentum equation is:

[0069] Among them, friction slope This can be expressed by Manning's formula as:

[0070] In the formula: This represents the pipeline flow rate, measured in m³ / s. This refers to the cross-sectional area of ​​the water passage, expressed in m². The lateral inflow per unit length of the pipe is expressed in m² / s. The acceleration due to gravity is taken as 9.81 m / s². The total head of water in the pipe is expressed in meters (m). This is the elevation of the pipe bottom, in meters (m). The depth of water inside the pipe is expressed in meters (m). The slope of frictional resistance along the route; The hydraulic radius is expressed in meters (m). The roughness is the Manning roughness after parameterization correction for disease.

[0071] For pipe sections with siltation defects, if the CCTV / QV test results can provide the siltation rate, then the siltation rate is defined. This is the ratio of the area occupied by siltation to the original cross-sectional area of ​​the water passage. The corrected effective cross-sectional area of ​​the water passage is:

[0072] In the formula: The corrected effective cross-sectional area of ​​the water passage is expressed in m². This represents the original cross-sectional area of ​​the water passage, in m². This represents the siltation rate or cross-sectional occupancy rate, with a value ranging from 0 to 1.

[0073] For pipe sections where only the qualitative disease level is given without the measured siltation rate, the CCTV / QV siltation level can be used. Mapped to siltation rate ,For example:

[0074] The corrected Manning roughness can be expressed as:

[0075] In the formula: This is the corrected Manning roughness. The roughness is the same as the original design Manning roughness. This is the resistance amplification factor caused by siltation, which can be determined based on model calibration, and is generally taken as 1.2; The siltation rate; This is a correction term for additional resistance caused by adhesion, scaling, or rough pipe walls.

[0076]

[0077] For structural defects, a structural reduction factor can be set:

[0078] in, It is classified as a structural defect. This is the structural reduction factor, typically taken as 0.04.

[0079] The effective cross-sectional area of ​​the water passage after structural modification is:

[0080] S4: Based on the time-series simulation results, a multidimensional normalized loss function is constructed that covers underground pipe network overload, surface inundation area and surface water volume. Based on the multidimensional normalized loss function, a dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance is calculated, and a full-cycle integral calculation is performed on it to obtain the comprehensive resilience index. By comparing the index difference between the ideal pipe network working condition and the current defective working condition, the quantitative assessment result of the drainage system resilience is obtained. In some specific embodiments, to address the limitations of traditional drainage design methods that rely on static discrimination to reflect the system's functional evolution throughout the entire impact process, a dimensionless comprehensive health performance function is introduced to describe the real-time operational status of the drainage system during a disaster. This function collaboratively characterizes the system's functional losses through three dimensions: underground, surface, and overall regulation and storage. First, an underground pipe network overload loss term is constructed using the output of a one-dimensional model. Second, referring to the safety limits for water depth and passage in standards such as the "Technical Specification for Urban Flood Control," a disaster control threshold is set, and a normalized surface water vulnerability function composed of two-dimensional surface inundation area and surface water volume is constructed to characterize the degree of damage to urban functions caused by surface water at different times. Finally, by selecting three types of instantaneous indicators—one-dimensional node overload, two-dimensional surface inundation, and system volume exceeding limits—and performing multi-dimensional normalization fusion, a comprehensive health performance function is constructed and integrated over the entire cycle to ultimately obtain the comprehensive resilience index.

[0081] Specifically, the overload loss function of underground pipeline network nodes Defined as The number of nodes in inspection wells that are constantly experiencing full flow, backflow, or overflow. This represents the number of inspection well nodes in the study area that experienced the largest instances of full flow, backflow, or overflow under a 50-year return period rainstorm scenario. The ratio is used to reflect the degree of instantaneous failure of the physical flow capacity of the underground pipe network relative to the design standard, and its expression is: Surface inundation over-limit loss function Defined as The water depth at any given time exceeds the disaster control threshold. area of ​​two-dimensional grid This accounts for the largest flooded area in the study area during a once-in-50-year rainstorm. The proportion; considering that a water depth of 0.15m can lead to urban traffic paralysis and flooding into building interiors, therefore, the disaster control threshold is... The value is set to 0.15m to characterize the physical blocking effect of drainage failure on urban surface social activities, and its expression is: System volume over-limit loss function Defined as The instantaneous total volume of water remaining on the urban surface This accounts for the largest water accumulation volume during a 50-year rainstorm in the study area. The ratio is used to reflect the instantaneous instability state of the system's comprehensive regulation and discharge capacity under extreme operating conditions, and its expression is: In the quantitative evaluation phase, since the above three types of physical loss indicators have all been normalized to... Given the dimensional ranges and the interconnectedness of these ranges within the flood-causing chain, this scheme adheres to the principle of "maximum objectivity" by employing equal weighting to ensure the universality of the assessment results. The system's instantaneous overall dimensionless comprehensive health performance function... Under ideal operating conditions (i.e.) Based on the baseline, it is defined by deducting the weighted functional loss caused by pipeline defects and water accumulation. The calculation formula is as follows: The function's range of values ​​is... This method can comprehensively depict the dynamic time-history characteristics of system performance throughout the entire process of a rainstorm, from "absorption of disturbances to performance degradation to post-disaster recovery." In practical applications, if a more refined assessment is needed for specific engineering objectives, the analytic hierarchy process (AHP) or economic loss curves based on historical disaster data can be introduced to adjust the weights of each physical loss indicator.

[0082] In this embodiment, node overload loss, surface inundation loss, and surface water volume loss are treated with equal weights, i.e., each has a weight of 1 / 3. In other embodiments, if the assessment objective focuses more on traffic safety, building flooding risk, or underground pipeline operation safety, differentiated weights can be set for the three types of loss items. For example, when the assessment object is a densely populated area of ​​main roads, the weight of the surface inundation loss item can be increased; when the assessment object is an area with a high risk of underground pipeline pressure, the weight of the node overload loss item can be increased; and when the assessment object is a low-lying flood detention area, the weight of the surface water volume loss item can be increased.

[0083] At this point, the comprehensive health performance function can be expanded to:

[0084] in:

[0085] In the formula, , , These represent the weights for node overload loss, surface inundation loss, and surface water volume loss, respectively. These weights can be obtained through expert weighting, the analytic hierarchy process (AHP), or fitting historical disaster economic loss data.

[0086] Furthermore, the quantitative assessment results of the drainage system's resilience are expressed as follows: based on the dimensionless comprehensive health performance function, a comprehensive resilience index is defined for the study period. for: In the formula, This is the moment when adverse boundary effects such as rainfall begin. This represents the point at which the drainage system essentially returns to a stable operating state; this comprehensive resilience index The range of values ​​is The moment when the system basically recovers to a stable operating state. The criterion for judgment is: comprehensive health performance function. Rebound and stabilize at The moment when the total surface water volume falls below 5% of the baseline maximum value is considered the critical point. Its physical significance lies in quantifying the system's ability to maintain functionality and recover after disasters throughout the entire process of extreme rainfall impact by integrating the time-history performance curve. A higher value indicates stronger system resilience and recovery efficiency. By comparing the comprehensive resilience index under two comparative conditions—ideal pipe network and existing defects—the reduction effect of existing defects on system resilience can be quantitatively identified, providing decision-making support for prioritizing the renovation and upgrading of high-density coastal urban areas.

[0087] In some specific embodiments, the method further includes: after obtaining the quantitative assessment results of the drainage system resilience, identifying areas with a comprehensive resilience index below a threshold as waterlogging-vulnerable nodes according to preset quantitative screening rules; and identifying the dominant internal causes of the resilience decay of waterlogging-vulnerable nodes through sensitivity analysis of different classification parameters. Specifically, the quantitative screening rules are: extracting nodes whose overflow volume ranks in the top 10% of the study area under the current defect conditions, and whose surface water depth is greater than 0.15m (i.e., the preset depth threshold) for more than 1 hour (i.e., the preset time threshold). The logic for identifying the dominant internal causes of disasters is as follows: if the ratio of the dry-day basic liquid level to the pipe diameter of the node before rainfall is greater than the preset threshold, then the dominant internal cause is diagnosed as the initial space occupation caused by the misconnection of rainwater and sewage; if the rate of rise of the operating water level exceeds the set threshold and there is siltation in the corresponding downstream pipe section, then the dominant internal cause is diagnosed as hydraulic obstruction caused by pipe siltation.

[0088] Example Typical coastal high-density built-up areas were selected as assessment targets, such as an island (land area of ​​approximately 141.09 km², built-up area of ​​approximately 110 km²). The typical "high in the middle and low around the perimeter" topographical pattern of this area was identified, particularly for key topographical features such as historically reclaimed land with low-lying terrain and limited self-drainage conditions (ground elevation only 2-3 m). By identifying the superimposed relationships between highly solidified underlying surfaces and complex vertical elevation boundaries, a composite baseline constraint was constructed to characterize the physical characteristics of rapid runoff accumulation, earlier peak times, and the continuous encroachment of existing defects (such as mixed stormwater and sewage connections, and pipe siltation) on the flow cross-section and storage space.

[0089] refer to Figure 2 , Figure 2 The overall technical flow of a drainage system resilience assessment method based on pipeline defect parameterization according to an embodiment of the present invention is shown. Figure 2 As shown, this embodiment takes a high-density coastal urban area as the object, and its quantitative evaluation process includes the following stages: The first phase is the multi-source data fusion and data foundation construction phase. First, data on underground pipeline GIS ledgers, manhole and pipe section elevations, diameters, slopes, materials, digital elevation models, road elevation control points, land use status data, design storm events, and pipeline endoscopic inspection results are collected for the study area. Then, data preprocessing is performed to unify the coordinate system with the elevation benchmark and to verify the pipeline network topology logic, thereby constructing a basic data foundation capable of supporting model derivation. The pipeline endoscopic inspection data is interpreted to identify and extract operational defects, including pipeline siltation and scale buildup; structural defects, including pipeline deformation, misalignment, collapse, or local reverse slope; and defects describing the system's normalized baseflow, such as mixed stormwater and sewage connections. These defects are then spatially associated with corresponding pipeline network nodes or pipe sections to generate a categorized defect database for subsequent parametric correction.

[0090] The second stage is the hydrodynamic scenario simulation and response analysis stage. In this stage, a coupled hydrodynamic model of a one-dimensional underground pipe network and a two-dimensional surface runoff is established using InfoWorks ICM software. The one-dimensional model is used to solve for changes in water level and flow rate in the underground pipes, while the two-dimensional model is used to solve for the confluence, runoff, and water accumulation evolution of surface runoff under the influence of rainstorms. Two-way coupling is achieved by utilizing the water exchange relationship between manhole nodes and the surface grid. During the parameterization of defects, operational and structural defects are identified based on the results of pipe endoscopy inspections; for pipe sections with siltation, the siltation rate is extracted. And according to the formula The effective cross-sectional area for water flow is corrected. Simultaneously, hydraulic resistance parameters such as the Manning roughness of the pipe section are adjusted based on the degree of siltation, scale buildup, and structural defects. For pipe sections with deformation, misalignment, collapse, or local reverse slope, their effective flow capacity is further reduced according to the severity of the defects. Based on this, ideal network conditions and existing defect conditions are set up separately. The ideal network condition uses the design cross-section and resistance parameters, while the existing defect condition uses the effective cross-sectional area and resistance parameters after defect parameterization correction. Both conditions are simulated time-series under the same typical design rainstorm scenario.

[0091] The third stage is the dynamic quantification and resilience assessment of system performance. In this stage, the number of nodes overloaded at each moment in the simulation process is extracted and statistically analyzed in real time. Two-dimensional grid area where surface water depth exceeds 0.15m. and the instantaneous total volume of water accumulation on the ground surface The maximum number of overloaded nodes under the baseline rainstorm scenario is selected. Maximum inundation area and maximum water volume As a normalization benchmark, the nodal overload loss function is calculated respectively. Surface inundation over-limit loss function and the system volume over-limit loss function Subsequently, a comprehensive health performance function was constructed using an equal-weighting approach. The comprehensive toughness index was then calculated based on the performance curve. , In the formula, This is the moment when adverse boundary effects such as rainfall begin. This represents the point at which the drainage system essentially returns to a stable operating state; this comprehensive resilience index The range of values ​​is Among them, the moment when the system recovers to a stable operating state. It can be determined by combining the simulation output timing, for example, when each loss term continues to fall and tends to stabilize over several consecutive output periods.

[0092] The fourth stage is the disaster-causing mechanism diagnosis and engineering targeting stage. By comparing the simulation output results under two conditions—ideal pipe network and existing defects—the impact deviation of existing defects on drainage resilience is accurately characterized from two dimensions: overall system performance evolution and local spatial risk. This stage first implements diagnostic methods, comparing the performance curves and spatial water accumulation distribution under the two conditions—ideal pipe network and existing defects—to quantitatively identify waterlogging-vulnerable nodes from two dimensions: overall system performance evolution and local spatial risk. Further, the stage traces the internal causes of the disaster, using the upstream flow process line output from the simulation and combining it with the categorized defect database obtained in the first stage to deeply analyze the dominant factors leading to the decline in drainage performance in local areas. The upstream flow process line refers to the original time-series data of flow and water level, characterized by the real-time operating status of the underground pipe network, output from the simulation operation in step S3. This data is used to compare the difference with the synchronous data under ideal conditions, thereby identifying the dominant disaster-causing factors. Ultimately, based on the above quantitative assessment and retrospective diagnosis results, targeted engineering basis is provided for pipeline network renovation and upgrading, thereby providing scientific quantitative support for prioritizing the dredging plan, defect repair and local renovation and upgrading schemes of the drainage pipeline network in the study area, realizing the leap from intuitive experience judgment to rational quantitative diagnosis.

[0093] By performing hydrodynamic simulations on ideal pipe networks and existing defective pipe networks under different return period rainfall scenarios under free discharge boundary conditions, the performance calculation results of the drainage system in the study area under different operating conditions were obtained, as shown in Table 1.

[0094] Table 1. Performance Calculation Table of Drainage System under Different Operating Conditions on a Certain Island

[0095] For ideal pipeline network operating conditions, the dynamic evolution response results of system performance indicators with rainfall duration under 1-year, 5-year, and 50-year rainfall scenarios are shown in Tables 2, 3, and 4, respectively. As can be seen from the tables, with the increase of the rainfall recurrence period, both the magnitude and duration of the drop in the system's comprehensive health performance function show an increasing trend.

[0096] Table 2. Time-series evolution response of drainage system performance under ideal pipe network conditions in a certain island under a 1a-year rainfall scenario.

[0097] Table 3. Time-series evolution response of drainage system performance under ideal pipe network conditions in a certain island under a 5a return period rainfall scenario.

[0098] Table 4. Time-series evolution response of drainage system performance under ideal pipe network conditions in a certain island under a 50-year return period rainfall scenario.

[0099] Table 1-4 shows that by performing hydrodynamic simulations on the existing pipe network under different return period rainfall scenarios, the basic drainage capacity of the system was quantitatively evaluated. The simulation results show that under a 1-year low-intensity rainfall condition, the drainage system in the study area already exhibits obvious early pressure characteristics. Of the 17,758 main pipe nodes in the one-dimensional model of the entire area, approximately 2,073 pipe nodes (about 12% of the total number of nodes) experienced varying degrees of overload. The maximum surface water accumulation area reached 179.79 ha, and the maximum total water volume was 350,400 m³. At this point, the system's comprehensive resilience index... The value was 0.92; when the rainfall intensity increased to a 5-year return period, the system performance significantly degraded, with approximately 3,661 pipeline nodes (about 21% of the total number of nodes) becoming overloaded, the maximum waterlogged area expanding to 383.09 ha, and the maximum total water volume increasing to 760,400 m³. The water quality index (BMI) dropped to 0.84. Meanwhile, spatial simulations based on coupled one-dimensional and two-dimensional hydrodynamic models revealed that the maximum water depth in some vulnerable areas, such as older neighborhoods, could reach 1.0 m, and some roads and low-lying areas exhibited persistent waterlogging responses with significant lag in drainage. These spatial simulation results indicate that the overall disturbance resistance level of the existing drainage system in the study area is low, only able to relatively stably cope with rainfall scenarios occurring once every 1–5 years, and there is a significant deviation between the actual drainage capacity and the nominal design standard. This assessment conclusion corroborates the existing defects in the pipe network revealed by the first-stage CCTV inspection, indicating that the decline in the baseline health of the pipe network has systematically weakened the system's drainage performance, rather than being merely a localized, occasional problem.

[0100] Table 5. Time-series evolution response of drainage system performance under current defect conditions in a certain island under a 5a return period rainfall scenario.

[0101] Furthermore, in conjunction with references Figure 3 Tables 1, 3, and 5 Figure 3 The diagram shows a comparison of the dynamic evolution curves of the comprehensive health performance function under the ideal pipeline network condition and the existing defect condition under a 5-year return period design storm according to this embodiment. To quantitatively assess the reduction effect of existing defects in the aging pipeline network on the system's flood control capacity, this embodiment, while maintaining complete consistency between external rainfall and discharge boundary conditions, selects a typical 5-year return period design storm with a rainfall duration of 2 hours as the test scenario, constructing two comparative conditions: Scenario A (ideal pipeline network state) and Scenario B (existing defect state). Scenario A excludes inflow from dry weather with incorrect connections and restores the effective cross-sectional area and roughness parameters of the pipeline to their original design values; Scenario B retains the actual baseflow from incorrect connections and incorporates the effective cross-sectional area and corrected Manning roughness parameters calculated in step S2 based on CCTV detection results. Simulation results (see Table 5) show that the reduction effect of pipeline background defects on system resilience is extremely significant. Under Scenario A, the pipeline network can fully utilize its original design capacity for water storage and discharge, the overall surface overflow range is limited, and the comprehensive resilience index is [not specified in the original text]. The value is 0.84; however, in scenario B, due to the long-term occupation of the effective flow section by the mixed-flow base flow during dry weather, coupled with the surge in hydraulic resistance caused by local pipe siltation, the number of overflow nodes increases to 5943, resulting in a dimensionless comprehensive health performance function. A sharp drop occurred during the peak rainfall period. The index decreased to 0.66, a decrease of 0.18 compared to scenario A. By introducing the 50-year return period standard condition shown in Table 1, number 3, as a performance reference benchmark, the comparison shows that the comprehensive resilience index of the current defective condition (number 4) under 5-year return period rainfall is completely consistent with the ideal 50-year return period extreme condition (both are 0.66), thus quantitatively revealing that the inherent defects in the pipeline network have led to a serious and substantial degradation of the system's defense standards. The shaded area between the two curves represents the resilience attenuation loss caused by pipeline operational defects, structural defects, and rainwater and sewage mixing defects. This loss area completely covers the system's lifecycle from performance impairment to post-disaster recovery. From Figure 3 The performance curves shown indicate that the performance degradation in scenario B begins significantly earlier than in scenario A, with a deeper trough and a longer recovery period. This quantitatively reveals that the continuous encroachment of the misconnected base current on the initial storage space is the direct cause of the system's premature instability. Based on the above comparative evaluation results, this can be further combined with the fourth-stage disaster mechanism diagnosis to provide scientific quantitative decision-making support for prioritizing pipeline dredging, defect repair, and local modification schemes within the study area.

[0102] Under a typical design storm scenario of a 5-year return period and a duration of 2 hours, simulation results show that the comprehensive resilience index of the existing defective condition drops from 0.84 in the ideal pipeline network condition to 0.66, a decrease of 0.18, accompanied by a significant increase in the proportion of overflow nodes. Using the ideal pipeline network condition of a 50-year return period as a reference, its comprehensive resilience index is also 0.66, indicating that the system resilience level under the 5-year existing defective condition is close to the performance degradation level under the 50-year ideal pipeline network condition, thus revealing that pipeline defects may lead to a substantial reduction in nominal flood control capacity. These research results indicate that existing pipeline defects, by compressing the effective storage volume in the pre-rainfall period, amplify the chain reaction of node overload and surface runoff during storms. This 0.18 difference quantitatively reveals the extent to which latent degradation hinders the system's flood control effectiveness. The significant gap between the current performance degradation path of the pipeline network and the ideal state further confirms that the key factors restricting flood control safety in high-density old coastal urban areas are no longer limited to rainfall intensity itself, but depend more on the physical health of the pipeline network facilities and their long-term degradation. Regarding the identification of spatially vulnerable nodes, spatial overlay analysis based on simulation results shows that vulnerable nodes in the study area exhibit a clear clustering pattern, mainly distributed in low-lying old residential areas near the coast, bottleneck sections of downstream main pipelines, and high-consequence functional nodes such as road low points and underpasses. The failure mechanisms of the above three types of nodes have inherent consistency and can all be attributed to the spatial coupling of three unfavorable conditions: "high-load confluence—low-redundancy discharge—unfavorable boundary constraints." A causal analysis combining the upstream flow process lines output by each node in step S3, the pipeline operating water level, and the CCTV detection defect data obtained in the first stage reveals that mixed stormwater and sewage connections and pipeline siltation are the dominant internal causes inducing early system instability. Analysis reveals that the combined effects of these two types of defects in the early stages of rainfall easily trigger a cascading failure response of "local pipe blockage—upstream backflow—node overflow" when the inflow increases, leading to persistent waterlogging in low-lying areas with significantly delayed drainage. Based on the above-mentioned disaster-causing mechanism diagnosis, the assessment method proposed in this invention can effectively avoid overestimation of robustness and engineering decision-making biases caused by deviating from the actual health status of the pipe network. In terms of governance strategies, this embodiment suggests that a four-in-one comprehensive governance path of "source peak shaving—process repair—end-of-pipe intensive drainage—intelligent scheduling" is an effective direction for improving system resilience. Accordingly, it is recommended that the drainage governance paradigm of high-density old urban areas in coastal areas shift from the traditional "scale-oriented" to "resilience-oriented." In subsequent drainage-specific planning, urban renewal, and operation management, the comprehensive resilience index and the waterlogging control level at key nodes should be incorporated into the binding evaluation index system, thereby promoting the transformation of drainage governance towards a system model aimed at maintaining full-process functionality and rapid recovery capabilities, providing scientific support for precise governance in the study area.

[0103] Continue to refer to Figure 4As an implementation of the above method, this application provides an embodiment of a framework diagram of a drainage system resilience assessment system 400 based on pipeline defect parameterization. This system embodiment is similar to... Figure 1 Corresponding to the illustrated method embodiment, this system can be specifically applied to various electronic devices. The system 400 includes a digital infrastructure construction module 401, a physical parameter conversion module 402, a timing simulation module 403, and a resilience quantification assessment module 404, all interconnected, wherein: The digital infrastructure module 401 is configured to acquire multi-source basic data, including pipeline endoscopic inspection data, for the study area. After preprocessing and topological logic verification of the multi-source basic data, a spatial mapping relationship between pipeline facilities and surface grid is established. A classified defect database is constructed based on the pipeline endoscopic inspection data to form a basic data foundation for model deduction. The classified defect database includes operational defects and structural defects. The physical parameter conversion module 402 is configured to convert operational defects and structural defects into physical parameters of the hydrodynamic model that characterize effective flow capacity and hydraulic resistance based on a classified disease database and using preset parameterized mapping rules. The time-series simulation module 403 is configured to construct a one-dimensional and two-dimensional coupled hydrodynamic model based on the model extrapolation base data and the physical parameters of the hydrodynamic model. It sets two comparative conditions: ideal pipeline network conditions and current defect conditions, and performs time-series simulation under typical design rainstorm scenarios. The resilience quantification assessment module 404 is configured to construct a multidimensional normalized loss function based on time-series simulation results, encompassing underground pipe network overload, surface inundation area, and surface water volume. Based on this multidimensional normalized loss function, a dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance is calculated and integrated over the entire lifecycle to obtain the comprehensive resilience index. By comparing the difference in comprehensive resilience indices under ideal pipe network conditions and current defective conditions, the quantitative assessment result of the drainage system's resilience is obtained. The dimensionless comprehensive health performance function... The calculation formula is:

[0104] In the formula, , , These are the overload loss functions for underground pipe network nodes, the overload loss function for surface inundation, and the overload loss function for system volume, respectively, all normalized to [value missing]. Dimensional interval; , , These are the weight coefficients of the corresponding loss functions.

[0105] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for assessing the resilience of drainage systems based on pipeline defect parameterization, characterized in that, include: S1. Acquire multi-source basic data of the study area, including pipeline endoscopic inspection data. After preprocessing and topological logic verification of the multi-source basic data, establish a spatial mapping relationship between pipeline facilities and surface grid. Construct a classification disease database based on the pipeline endoscopic inspection data to form a basic data foundation for model deduction. The classification disease database includes operational defects and structural defects. S2, Based on the classified disease database, using the preset parameterized mapping rules, the operational defects and structural defects are transformed into physical parameters of the hydrodynamic model that characterize the effective flow capacity and hydraulic resistance. S3. Based on the model derivation base data and the physical parameters of the hydrodynamic model, construct a one-dimensional and two-dimensional coupled hydrodynamic model, set up two comparison conditions: ideal pipeline network condition and current defect condition, and conduct time-series simulation under a typical design rainstorm scenario. S4. Based on the time-series simulation results, a multidimensional normalized loss function is constructed, encompassing underground pipe network overload, surface inundation area, and surface water volume. A dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance is calculated based on this multidimensional normalized loss function, and a full-cycle integral calculation is performed to obtain the comprehensive resilience index. By comparing the difference in comprehensive resilience index between the ideal pipe network operating condition and the current defective operating condition, a quantitative assessment result of the drainage system's resilience is obtained. The dimensionless comprehensive health performance function... The calculation formula is: In the formula, , , These are the overload loss functions for underground pipe network nodes, the overload loss function for surface inundation, and the overload loss function for system volume, respectively, all normalized to [value missing]. Dimensional interval; , , These are the weight coefficients of the corresponding loss functions.

2. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 1, characterized in that, The multi-source basic data also includes drainage network attribute data, topographic data, and meteorological and hydrological data; wherein, the drainage network attribute data includes the network topology connection relationship, the spatial coordinates of manholes and pipe sections, the bottom elevation of the pipe, the pipe diameter, the material, and the slope; the topographic data includes digital elevation models, measured road control points, and land use status maps; the meteorological and hydrological data includes designed rainstorm processes or measured minute-level rainfall sequences.

3. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 2, characterized in that, Step S1 includes: S11, Obtain the geographic information system (GIS) ledger data of underground pipelines in the study area, and extract the pipeline network topology connection relationship, spatial coordinates of manholes and pipe sections, pipe bottom elevation, pipe diameter, material and slope attributes; Obtain topographic data, including digital elevation model, measured road control points and land use status map; Obtain meteorological and hydrological data, including minute-level rainfall sequence process data; Obtain pipeline endoscopic inspection data, including closed-circuit television endoscopic inspection or periscope inspection results; S12, unify the coordinate system and elevation benchmark of the multi-source basic data, and fill in the missing values ​​of key fields in the order of measured value first, interpolation of adjacent pipe segments, and design ledger value as a backup; construct a directed topology graph and verify hydraulic connectivity using a topology logic verification algorithm. The topology logic verification includes: checking the correlation between pipe segment endpoints and inspection wells, checking the consistency between pipe segment flow direction and elevation, eliminating isolated / suspended / repeated / self-loop pipe segments, identifying slope anomalies, and merging nodes with a distance of less than 0.5m to 2.0m with a preset tolerance to generate a one-dimensional pipe network skeleton with complete hydraulic connectivity. S13. In the geographic information system environment, establish the spatial mapping relationship between one-dimensional pipeline facility nodes and two-dimensional surface runoff calculation grid to establish the hydraulic correlation describing the exchange of groundwater and surface water. S14, interpret the pipeline endoscopic inspection data, identify and extract the operational defects including pipeline siltation and scale buildup, and the structural defects including pipeline deformation, misalignment, collapse or local reverse slope, and associate the operational defects and structural defects with the corresponding pipeline network facility nodes or pipe sections in terms of spatial attributes to generate the classified disease database.

4. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 1, characterized in that, The physical parameters of the hydrodynamic model include: the effective cross-sectional area corresponding to the operational defects and the structural defects, and used to characterize the flow capacity; and the corrected Manning roughness corresponding to the operational defects and used to characterize the hydraulic resistance.

5. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 4, characterized in that, The effective water passage cross-section is reduced and corrected using the siltation rate in the aforementioned operational defects to obtain the effective water passage area. The formula for the reduction and correction is as follows: In the formula, This is the corrected effective cross-sectional area of ​​the water passage; This represents the original cross-sectional area; This refers to the siltation rate.

6. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 4, characterized in that, The correction formula for the Manning roughness is as follows: In the formula, This is the corrected Manning roughness. The roughness is the same as the original design Manning roughness. This is the drag amplification factor caused by sedimentation; The siltation rate; This is a correction term for additional resistance caused by adhesion, scaling, or rough pipe walls.

7. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 1, characterized in that, In the coupled one-dimensional and two-dimensional hydrodynamic model, the one-dimensional pipe network model solves the Saint-Venant equations based on the dynamic wave module to obtain the changes in water level and flow in underground pipes, while the two-dimensional surface runoff model uses shallow water equations to perform gridded calculations to obtain the evolution process of surface runoff. The one-dimensional pipe network model and the two-dimensional surface runoff model exchange water bidirectionally through inspection wells or rainwater inlet nodes.

8. The method for assessing the resilience of drainage systems based on pipeline defect parameterization according to claim 1, characterized in that, The underground pipeline network node overload loss function for The proportion of the number of inspection well nodes that are constantly experiencing full flow, backflow, or overflow to the maximum number of nodes under the baseline rainstorm scenario; The surface inundation excess loss function for The proportion of the two-dimensional grid area where the water depth exceeds the disaster control threshold at any given moment to the maximum flooded area under the baseline rainstorm scenario; The system volume excess loss function for The proportion of the instantaneous total volume of water remaining on the urban surface to the maximum volume of water under the baseline rainstorm scenario.

9. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 1, characterized in that, The comprehensive resilience index is obtained by integral of the following formula: In the formula, As a comprehensive resilience index; This is the moment when rainfall begins; This is the moment when the system recovers to a stable operating state; This is a comprehensive health performance function.

10. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 1, characterized in that, After obtaining the quantitative assessment results of the drainage system's resilience, the following is also included: According to the preset quantitative screening rules, areas where the evaluation indicators under the current defect conditions exceed the set threshold are identified as waterlogging-vulnerable nodes; combined with the time series simulation results and the classified disease database, the dominant internal factors causing the decline in the resilience of the waterlogging-vulnerable nodes are identified through the disaster-causing internal cause tracing logic.

11. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 10, characterized in that, The quantitative screening rule is as follows: under the current defect conditions, high-risk nodes with overflow water volume greater than a preset water volume threshold, and surrounding surface water depth greater than a preset depth threshold and duration exceeding a preset time threshold are extracted as the waterlogging-vulnerable nodes.

12. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 10, characterized in that, The logic for tracing the internal causes of the disaster includes: If the proportion of the basic liquid level in the dry weather at the waterlogged vulnerable node before rainfall to the pipe diameter is greater than the preset space occupancy threshold, then the primary cause of the disaster is diagnosed as the initial storage space encroachment caused by the misconnection of rainwater and sewage. If the rate of rise of the operating water level at the waterlogged vulnerable node exceeds the preset backlog threshold during rainfall, and the corresponding downstream pipe section has a siltation record in the classification disease database, then the primary cause of the disaster is diagnosed as hydraulic obstruction caused by pipe siltation.

13. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 7, characterized in that, The dynamic wave module solves the Saint-Venant equations by: assuming the coordinates along the pipe axis are... The time is The pipeline flow rate is The cross-sectional area of ​​the water passage is The water depth is The bottom elevation of the pipe is Total head is The friction gradient along the route is The slope of the pipe bottom is Lateral inflow is Then the continuity equation and the momentum equation are respectively: The continuity equation is: The momentum equation is: Among them, friction slope This can be expressed by Manning's formula as follows: In the formula: For pipeline flow rate; This refers to the cross-sectional area of ​​the water passage. The lateral inflow per unit length of the pipe is expressed in m² / s. It is the acceleration due to gravity; This refers to the total head within the pipe. This refers to the elevation of the bottom of the pipe. The depth of the water inside the pipe; The slope of frictional resistance along the route; The hydraulic radius; The roughness is the Manning roughness after parameterization correction for disease.

14. The method for assessing the resilience of a drainage system based on pipeline defect parameterization according to claim 7, characterized in that, The construction of the two-dimensional surface runoff model includes: assuming the water depth in the two-dimensional surface grid is... ,along direction and The average flow velocities in the directions are respectively and The surface elevation is Rainfall intensity is Infiltration loss is The exchange flow per unit area between a one-dimensional node and a two-dimensional mesh is Then the two-dimensional shallow water equations satisfy: Continuity equation: Directional momentum equation: Directional momentum equation: In the formula: Water depth is represented by a two-dimensional surface grid. , Two-dimensional surface grids in , Average flow velocity in the direction; For calculating time; , Calculate the plane coordinates of the two-dimensional Earth surface; It is the acceleration due to gravity; This refers to the surface elevation. Rainfall intensity; The strength is reduced due to infiltration or initial damage; This refers to the flow rate per unit area exchanged between a one-dimensional pipeline node and a two-dimensional surface grid. The roughness of the two-dimensional surface is the Manning roughness.

15. A drainage system resilience assessment system based on pipeline defect parameterization, characterized in that, include: The digital infrastructure module is configured to acquire multi-source basic data, including pipeline endoscopic inspection data, for the study area. After preprocessing and topological logic verification of the multi-source basic data, a spatial mapping relationship between pipeline facilities and surface grid is established. A classification disease database is constructed based on the pipeline endoscopic inspection data to form a basic data foundation for model deduction. The classification disease database includes operational defects and structural defects. The physical parameter conversion module is configured to convert the operational defects and structural defects into physical parameters of the hydrodynamic model that characterize the effective flow capacity and hydraulic resistance, based on the classified disease database and using preset parameterized mapping rules. The time-series simulation module is configured to construct a one-dimensional and two-dimensional coupled hydrodynamic model based on the model derivation base data and the physical parameters of the hydrodynamic model, set two comparison conditions: ideal pipeline network condition and current defect condition, and perform time-series simulation under typical design rainstorm scenarios. The resilience quantification assessment module is configured to construct a multidimensional normalized loss function based on time-series simulation results, encompassing underground pipe network overload, surface inundation area, and surface water volume. Based on this multidimensional normalized loss function, a dimensionless comprehensive health performance function reflecting the dynamic evolution of system performance is calculated and integrated over the entire lifecycle to obtain a comprehensive resilience index. By comparing the difference in comprehensive resilience indices under ideal pipe network conditions and existing defect conditions, the quantitative assessment result of the drainage system's resilience is obtained. The dimensionless comprehensive health performance function... The calculation formula is: In the formula, , , These are the overload loss functions for underground pipe network nodes, the overload loss function for surface inundation, and the overload loss function for system volume, respectively, all normalized to [value missing]. Dimensional interval; , , These are the weight coefficients of the corresponding loss functions.

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