A sewer network model joint correction method and system based on topology reconfiguration and multi-dimensional error decoupling

Abstract

The application discloses a kind of based on topological reconfiguration and multi-dimensional error decoupling sewer network model joint correction method, comprising: constructing pipe network space topological model, and local sub-basin pipe network model;Based on the physical parameter input to local sub-basin pipe network model generates initial simulation sequence and carries out comparison inversion, to determine time phase shift and static datum elevation compensation amount;Time phase shift and static datum elevation compensation amount are used as boundary constraint, based on nonlinear optimization algorithm is carried out joint optimization rating, to obtain best water flow resistance and runoff mechanism parameter;Based on best water flow resistance and runoff mechanism parameter as input, drive local sub-basin pipe network model carries out calculus, to generate final high-fidelity water depth prediction sequence.The application also provides a kind of sewer network model joint correction system.The method provided by the application can improve the numerical simulation accuracy and calculation robustness of complex underground sewer network under extreme rainfall conditions.

Description

Technical Field

[0001] This invention belongs to the field of urban flood control and disaster reduction and hydrological and hydrodynamic numerical simulation technology, and in particular relates to a joint correction method and system for drainage network models based on topology reconstruction and multidimensional error decoupling. Background Technology

[0002] With rapid urbanization and frequent extreme rainstorms, urban flooding is becoming increasingly severe, making the safe operation of urban water systems and the construction of flood control resilience crucial. In urban flood control and drainage practices, constructing accurate hydrological and hydrodynamic models of underground drainage networks is a prerequisite for risk assessment, network renovation, and emergency early warning. Accurately reproducing and predicting the hydrodynamic evolution under complex network conditions can provide reliable physical data support for underground water situation monitoring and disaster prevention and mitigation.

[0003] However, the current construction and calibration of large-scale urban drainage network numerical models face the dual challenges of poor quality of underlying physical space data and high coupling of errors from multi-source observation data. Traditional physical mechanism modeling and parameter calibration have significant limitations in practical engineering applications, making it difficult to meet the engineering requirements for model computational accuracy, robustness, and rigor of physical mechanisms. Currently, the main shortcomings of numerical simulation and model correction for urban drainage networks are as follows: 1. Existing hydrological and hydrodynamic mechanism models are highly dependent on the accuracy of front-end spatial mapping data.

[0004] 2. In the traditional process of data assimilation and model parameter calibration, there are common problems of different parameters having the same effect and compensatory errors.

[0005] Patent document CN121525220A discloses a hybrid driving modeling method and device for urban drainage pipe networks. The method includes: constructing control equations for the surface runoff generation stage and control equations for the pipe network transportation stage; constructing a mechanism-driven model based on the control equations for the surface runoff generation stage and the pipe network transportation stage, and obtaining the prediction error of the mechanism-driven model; establishing a data-driven model, and inputting the error into the data-driven model to calculate the residual correction amount of the mechanism-driven model, determining the dynamic compensation value, and fusing the dynamic compensation value and the prediction result based on the corresponding weights to construct a hybrid driving model for urban drainage pipe networks.

[0006] Patent document CN120671577A discloses an intelligent health analysis system for urban drainage pipe networks based on multimodal twin simulation. It constructs an analysis system consisting of multiple modules, including 3D modeling, topology relationship construction, feature parameter extraction, coupling accuracy assessment, flow pattern misjudgment risk prediction and dynamic adjustment. By extracting the residual height gradient of the wellhead topography and the elevation difference offset index of historical waterlogging points, and combining local hydraulic simulation and error sensitivity index analysis, it can classify and dynamically correct the deviation of the wellhead modeling, effectively improving the elevation coupling accuracy and hydraulic simulation reliability of the drainage pipe network model. Summary of the Invention

[0007] The purpose of this invention is to provide a joint correction method and system for drainage network models based on topology reconstruction and multidimensional error decoupling. This method can strictly decouple and gradually assimilate spatiotemporal system observation errors, mechanism parameter calibration errors, and flood peak amplitude attenuation errors, thereby significantly improving the numerical simulation accuracy and computational robustness of complex underground drainage networks under extreme rainfall conditions.

[0008] To achieve the first objective of this invention, the following technical solution is provided: a joint correction method for a drainage network model based on topology reconstruction and multidimensional error decoupling, comprising: Acquire underground pipeline data within the target area and construct the corresponding pipeline spatial topology model based on the solution conditions of the hydrodynamic continuity equation; In the pipeline network spatial topology model, a local sub-basin pipeline network containing the target monitoring node is extracted. The downstream node of the cut-off node is used as a fixed water level discharge outlet and dynamic backwater level parameters are set, while the upstream node of the cut-off node is set with dynamic dry weather baseflow parameters, so as to construct the corresponding local sub-basin pipeline network model. An initial simulation sequence is generated by inputting preset physical parameters into the local sub-basin network model, and the initial simulation sequence is compared and inverted with the measured sequence to determine the time phase shift and static reference elevation compensation. The time phase shift and static benchmark elevation compensation are used as boundary constraints. Based on a nonlinear optimization algorithm, the basin parameters, dynamic backwater level parameters, and dynamic dry-day baseflow parameters of the local sub-basin are jointly optimized and calibrated to obtain the optimal flow resistance and runoff generation mechanism parameters. Based on the optimal water flow resistance and runoff generation mechanism parameters as input, the local sub-basin network model is driven to perform calculations and output the corresponding physical reference sequence. A segmented dynamic stretching algorithm is introduced to assimilate the sequence in order to generate the final high-fidelity water depth prediction sequence.

[0009] Specifically, the construction process of the pipeline network spatial topology model is as follows: For underground pipeline data, graph theory-based pathfinding algorithms are used to correct erroneous pipe segments that violate gravity-based flow characteristics. Perform absolute elevation convergence and dynamic reduction of pipe bottom offset parameters to eliminate reverse slope and local water stagnation nodes in underground pipeline data; Based on the corrected underground pipeline data, the elevation difference anomalies are corrected through geometric constraint verification in order to construct the corresponding pipeline spatial topology model.

[0010] Specifically, the graph theory pathfinding algorithm process is as follows: Using the final outlet of the pipeline network as the calculation endpoint, the priority queue algorithm is used to deduce the physical shortest convergence path distance from all nodes of the pipeline network to the outlet. If the path distance of the upstream node of the current pipe segment is less than that of the downstream node, it is determined that it does not conform to the law of gravity flow, and the start and end topology of the current pipe segment is automatically reversed and the bias parameters are interchanged.

[0011] Specifically, the process of eliminating the localized water stagnation points is as follows: Extract the minimum absolute bottom elevation of all pipe sections connected to each inspection well node, and subtract the safety buffer water depth to obtain the theoretical bottom elevation of the corresponding node; Forced lifting calculations are performed on nodes whose actual elevation is lower than the preset theoretical bottom elevation, and the offset parameters of the connected pipe sections are reduced proportionally at the same time to completely eliminate local stagnant water zones that hinder water flow calculation.

[0012] Specifically, the process of eliminating the back slope of the pipeline network is as follows: Calculate the absolute vertical drop of the pipe segment. If the vertical drop is greater than or equal to the horizontal length of the pipe segment, the length of the pipe segment is automatically extended based on the vertical drop. The absolute elevation of the highest pipe top connected to the node is calculated, and the maximum depth attribute of the node is automatically overwritten based on the required calculated safe water depth to eliminate hydraulic geometric boundary anomalies.

[0013] Specifically, the watershed parameters include catchment width and impermeability.

[0014] Specifically, the process of joint optimization is as follows: An objective function for evaluating hydrodynamic mechanisms is established. In each iteration, a simulated water level curve is generated based on the current watershed parameters, dynamic backwater level parameters, and dynamic dry-day baseflow parameters. The time sequence is aligned according to the time phase shift, and a static reference elevation compensation is added to obtain the simulation sequence. Subsequently, the fitness scores of the simulated and measured sequences are calculated, and the nonlinear optimization algorithm is guided to approximate the rise and fall trend and receding hysteresis characteristics of the real water flow within the preset value range, thereby obtaining the optimal water flow resistance and runoff generation and confluence mechanism parameters.

[0015] To achieve the second objective of this invention, the following technical solution is provided: a joint correction system for a drainage network model, used to execute the steps of the above-mentioned joint correction method for a drainage network model based on topology reconstruction and multidimensional error decoupling.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: An adaptive reconstruction mechanism for the spatial topology of the pipeline network based on the physical pathfinding characteristics of water flow is constructed to eliminate the numerical divergence problem of the hydrodynamic solver caused by missing survey data and elevation errors, and to generate a physical mechanism model that satisfies the solution conditions of the water flow continuity equation. A local hydraulic equivalent boundary interception and a three-level sequential multidimensional data assimilation architecture are established to strictly decouple and assimilate spatiotemporal system observation errors, mechanism parameter calibration errors and flood peak amplitude attenuation errors, thereby significantly improving the numerical simulation accuracy and computational robustness of complex underground drainage networks under extreme rainfall conditions. Attached Figure Description

[0017] Figure 1 This is a flowchart of the joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling provided in this embodiment; Figure 2 This is a comparison diagram of adaptive topology correction and elevation reconstruction of the drainage network provided in this embodiment. Figure 2 (a) in the diagram is a schematic diagram of the drainage network before adaptive correction of spatial topology and elevation reconstruction. Figure 2 (b) is a schematic diagram of the drainage network after adaptive correction of spatial topology and elevation reconstruction; Figure 3 This is a framework diagram showing the coupling between urban flooding environmental characteristics and runoff generation and confluence algorithm attributes provided in this embodiment; Figure 4 This is a comparison chart of water depth simulation and nonlinear compensation before and after joint correction of pipeline nodes under extreme rainfall conditions provided in this embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown in this embodiment, a joint correction method for a drainage network model based on topology reconstruction and multidimensional error decoupling is provided, including the following steps: (1) Pipeline data analysis and hydraulic topology reconstruction: Underground pipeline data of the target area is obtained through GIS, and erroneous pipe sections that violate gravity confluence characteristics are corrected through graph theory pathfinding algorithm. Absolute elevation convergence and dynamic reduction of pipe bottom offset parameters are performed to automatically eliminate pipeline reverse slope and local water stagnant nodes. Geometric constraint verification is performed to repair elevation difference anomalies and generate a pipeline spatial topology model that satisfies the solution conditions of the hydrodynamic continuity equation.

[0020] (2) Subnetting and hydraulic equivalent boundary construction: The local sub-basin network model containing the target monitoring node is extracted from the spatial topology model of the whole network. The downstream node of the cut-off is converted into a fixed water level discharge outlet and the dynamic top water level parameter is preset. The dynamic dry weather base flow parameter is preset at the upstream node of the cut-off to replace the external hydraulic boundary effect of the cut-off network.

[0021] (3) First stage systematic spatiotemporal error stripping: The initial simulation sequence is generated by driving the local sub-basin network model with the initial physical mechanism parameters. The initial simulation sequence and the measured water level sequence are compared by grid search. The optimal time phase shift and static reference elevation compensation are inverted and locked to eliminate the systematic errors caused by meteorological monitoring time difference and liquid level sensor zero drift.

[0022] (4) Joint inversion of hydrodynamic mechanism parameters in the second stage: Under the constraints of the time phase shift and the static reference elevation compensation, the local sub-basin catchment width, impermeability, dynamic top backwater level parameters and dynamic dry weather base flow parameters are jointly optimized and calibrated based on the nonlinear optimization algorithm to obtain the optimal flow resistance and runoff generation mechanism parameters.

[0023] (5) Nonlinear compensation for flood peak attenuation error in the third stage: The physical reference sequence is output by driving the model using the optimal water flow resistance and runoff generation mechanism parameters. For the residual attenuation of extreme flood peak amplitude caused by the physical generalization of the catchment area, a segmented dynamic stretching algorithm is introduced to assimilate the sequence.

[0024] (6) Joint correction output and flood control mapping: Combining spatiotemporal system error stripping parameters, physical mechanism inversion parameters and nonlinear compensation parameters, the final high-fidelity water depth prediction sequence is output and simultaneously fed back to the urban flood control twin early warning platform.

[0025] Specifically, the construction process of the pipeline network spatial topology model is as follows: For underground pipeline data, graph theory-based pathfinding algorithms are used to correct erroneous pipe segments that violate gravity-based flow characteristics. Perform absolute elevation convergence and dynamic reduction of pipe bottom offset parameters to eliminate reverse slope and local water stagnation nodes in underground pipeline data; Based on the corrected underground pipeline data, the elevation difference anomalies are corrected through geometric constraint verification in order to construct the corresponding pipeline spatial topology model.

[0026] Specifically, the graph theory pathfinding algorithm process is as follows: Using the final outlet of the pipeline network as the calculation endpoint, the priority queue algorithm is used to deduce the physical shortest convergence path distance from all nodes of the pipeline network to the outlet. If the path distance of the upstream node of the current pipe segment is less than that of the downstream node, it is determined that it does not conform to the law of gravity flow, and the start and end topology of the current pipe segment is automatically reversed and the bias parameters are interchanged.

[0027] Specifically, the process of eliminating the localized water stagnation points is as follows: Extract the minimum absolute bottom elevation of all pipe sections connected to each inspection well node, and subtract the safety buffer water depth to obtain the theoretical bottom elevation of the corresponding node; Forced lifting calculations are performed on nodes whose actual elevation is lower than the preset theoretical bottom elevation, and the offset parameters of the connected pipe sections are reduced proportionally at the same time to completely eliminate local stagnant water zones that hinder water flow calculation.

[0028] Specifically, the process of eliminating the back slope of the pipeline network is as follows: Calculate the absolute vertical drop of the pipe segment. If the vertical drop is greater than or equal to the horizontal length of the pipe segment, the length of the pipe segment is automatically extended based on the vertical drop. The absolute elevation of the highest pipe top connected to the node is calculated, and the maximum depth attribute of the node is automatically overwritten based on the required calculated safe water depth to eliminate hydraulic geometric boundary anomalies.

[0029] Specifically, the first stage of systematic spatiotemporal error stripping includes: Within a set time sliding window, the initial simulation sequence is shifted by time steps to obtain a phase-aligned sequence; The mean difference between the measured sequence and the phase-aligned sequence is calculated and extracted as the static reference elevation compensation amount. Taking the maximization of the Nash efficiency coefficient as the objective function, the optimal solution parameter combination that makes the coefficient reach its extreme value is locked. This combination is the desired time phase shift and static reference elevation compensation amount.

[0030] Specifically, the process of determining the joint optimization rate is as follows: An objective function for evaluating hydrodynamic mechanisms is established. In each iteration, the evaluation engine first aligns the simulated water level curve generated by the current physical parameters with the time phase shift locked in the previous step, and then superimposes the static reference elevation compensation. The fitness scores of the corrected simulated sequence and the measured sequence are calculated to guide the nonlinear optimization algorithm to iteratively update the parameters within the set physical feasible domain, approximating the rise and fall trend and receding hysteresis characteristics of the real water flow.

[0031] Specifically, the third stage of nonlinear compensation for flood peak attenuation error includes: Set a dynamic segmented water depth threshold and a flood peak stretching factor; for the physical reference sequence output by the hydrodynamic model, when the water depth value at the corresponding time is less than the dynamic segmented water depth threshold, only the static reference elevation compensation is superimposed; when the water depth value is greater than or equal to the threshold, on the basis of superimposing the dynamic segmented water depth threshold and the reference elevation compensation, the flood peak stretching factor is introduced to perform differential amplification calculation on the water depth exceeding the limit. During the calculation process, a non-negative truncation boundary condition is added to ensure that the corrected water depth prediction sequence is not less than zero. The optimal combination of dynamic segmented water depth threshold and flood peak stretching ratio is obtained through optimization algorithm. Under the premise of keeping the dry base flow and the smooth receding process unchanged, extreme value compensation is performed on the water volume reduction residual of the extreme flood peak during the rainstorm period.

[0032] This embodiment also provides a joint correction system for drainage network models, used to execute the steps of the joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling provided in the above embodiments.

[0033] To better illustrate the effects of the technical solution in this application, the following specific examples are provided.

[0034] like Figure 2 As shown in (a), the core drainage zone of a certain city covers a vast area and has an extremely complex underground pipe network topology. The original GIS data often has problems such as reverse slope, disordered flow direction and missing elevation.

[0035] With the increasing frequency of extreme rainstorms, traditional uncorrected pipe network models are unable to accurately depict the true direction of gravity flow and the propagation of dynamic waves, resulting in severe numerical oscillations and extremely large errors in flood warnings. The pressure of flood prevention and disaster reduction is becoming increasingly prominent. In the figure, this is mainly reflected in the presence of reverse slope and disordered flow direction in the original data, which prevents water from flowing smoothly into the channel.

[0036] To address the above problems, a joint correction is performed using the solution provided in this embodiment, the process of which is as follows: Multi-source heterogeneous data of the study area were acquired. For meteorological rainfall-driven data, 10-minute high-frequency measured rainfall time series from automatic weather stations were extracted. Spatial interpolation was performed using the inverse distance weighting (IDW) method to generate high-precision gridded rainfall data with a spatial resolution of 0.05°. Based on spatial topological mapping relationships, the gridded rainfall was accurately allocated to the corresponding pipe network sub-catchments as model input. Simultaneously, pipe network GIS data (including preliminary physical topology and node elevations, which contain measurement errors) and measured liquid level time series data from target monitoring points were acquired.

[0037] To address the issues of disordered pipeline flow direction and reverse slope caused by surveying errors in the original GIS data, the following solution is adopted: A. Construction of Topological Distance Field: Abstracting the urban drainage network into an undirected graph. ,in It is a set of nodes (including inspection wells, discharge outlets, etc.). Let this be the set of pipelines. Define all fixed-level discharge outlets as the root node set and initialize their topological distance to 0; set the initial topological distance of all other nodes in the graph to infinity.

[0038] B. Global Shortest Path Distance Measurement: A priority queue data structure is introduced, and Dijkstra's shortest path algorithm is used to perform topology traversal of the entire pipeline network. Using the actual pipeline length as the edge weight, the shortest topological distance from each node to the nearest discharge outlet is iteratively updated. .

[0039] C. Physical flow direction adaptive correction: Traversing all pipelines Let the starting node of a certain pipeline be... (Corresponding topological distance is) The endpoint is (Corresponding topological distance is) ).

[0040] If detected < ,and If it is not infinite, it indicates the original starting point of the pipeline in the physical topology. Compared to the finish line Closer to the emission outlet, the original data shows a reversed flow direction and misalignment.

[0041] At this point, the algorithm automatically reverses the topology of the pipeline (interchanges) and ), and simultaneously reverse the inlet and outlet offset parameters of its associated pipelines, forcing the water flow throughout the region to conform to topological gravity and converge into the discharge outlet, such as Figure 2 As shown in (b) in the figure, the pipeline topology flow direction after correction is shown.

[0042] D. Absolute elevation smoothing and adaptive correction of node bottom elevation: After flow direction correction, elevation verification is performed from bottom to top along the connected tree.

[0043] The actual pipe bottom elevation is calculated based on the physical offset of the pipeline. If the elevation of the upstream node is found to be lower than the critical elevation required by the downstream, the elevation of the upstream node is forcibly raised to eliminate the reverse slope. Simultaneously, the lowest elevation of all pipelines connected to each manhole is extracted, and redundant ultra-deep nodes below this elevation threshold are corrected by elevation lifting. The inlet and outlet offsets of the connecting pipelines are also reduced proportionally, thereby completely eliminating localized invalid water-stagnant zones caused by errors in the original spatial data and the resulting numerical calculation oscillations. After the above joint correction of topology and elevation, the final result is as follows: Figure 2 The drainage network model shown in (b) has a real physical evolution law, which lays a stable topological and elevation foundation for subsequent hydrodynamic simulation.

[0044] To improve the computational efficiency of the hydrodynamic model and eliminate the propagation of global errors caused by the external pipe network, a local sub-basin pipe network interception and boundary equivalence decoupling mechanism was designed. With the target monitoring node as the core, based on the reconstructed connected graph topology, the mechanism automatically traces upwards and downwards to intercept local pipe networks, applying parameterized equivalent boundary conditions at the interception points. A schematic diagram of the local sub-basin pipe network interception is shown below. Figure 3 As shown, the process is as follows: A. Reconstruction of Equivalent Boundaries for Upstream and Tributaries: For the truncated upstream main pipe and lateral tributaries, the original physical simulation of the vast catchment area is stripped away, and it is transformed into an equivalent hydraulic input boundary. Dry-weather flow parameters are dynamically injected at the truncated nodes to compensate for external external water and groundwater infiltration that are not explicitly calculated by the model.

[0045] B. Downstream Hydraulic Equivalent Boundary Reconstruction: For discharge outlets at downstream pipeline cutoff points, they are forcibly converted from free discharge to fixed-level discharge outlets. To simulate the backwater effect on the current local pipeline network caused by full downstream main pipelines or sudden surges in river levels under extreme rainfall conditions, a dynamic backwater parameter is introduced. The foundation pipe bottom elevation is superimposed with this backwater parameter to form a new absolute backwater level boundary.

[0046] C. Preservation of Internal Mechanisms and Joint Optimization of Boundaries: Within the captured local pipe network, the full-scale, high-precision gridded real rainfall-driven and runoff-generating physical mechanisms are preserved. A mathematical optimization function is constructed with the goal of maximizing the Nash efficiency coefficient. A simplex search algorithm is introduced to conduct multi-dimensional joint iterative exploration of the surface physical parameters of the internal sub-catchment (including the catchment width adjustment factor and the impermeability adjustment factor) and the external boundary parameters (dry-day base flow rate and outlet backwater height) until the objective function converges. This step, through the combination of boundary parameterization and pure mathematical optimization, perfectly equivalences the complex backwater and runoff interference of the external global pipe network while maintaining the rigor of the local hydrodynamic mechanism.

[0047] Furthermore, based on the reconstructed local pipe network, continuous numerical simulations were performed using a one-dimensional pipe network hydrodynamic simulation engine. Using the Nash efficiency coefficient or root mean square error as the optimization objective function, a swarm intelligence heuristic algorithm was employed for multi-parameter joint calibration. Physical parameters such as the Manning coefficient of the pipes and the characteristic width of the sub-sumption areas were automatically and iteratively updated, enabling the simulated curves to initially match the overall fluctuation trend of the measured liquid level in terms of shape.

[0048] To address the persistent residuals between the physical mechanism model and real-world observations, an advanced mathematical assimilation mechanism is introduced for multidimensional error decoupling. In this embodiment, six pipeline level monitoring points (node ​​numbers: Y12810, NID_07556, Y43559, NID_02118, Y34773, and Y12098) distributed across various core locations within the study area were selected as the real observation data source. Before assimilation, an abnormal sensor rejection mechanism was established to filter invalid data from damaged stations to ensure the quality of the assimilated sample. Subsequently, the following nonlinear assimilation steps were performed: A. Phase Alignment and Gridded Time Shift: To eliminate time differences caused by spatial heterogeneity of rainfall or system data transmission delays, a gridded time series was searched within a time window of ±60 minutes, with a step size of 10 minutes. For missing data resulting from the shift, backward and forward nearest-neighbor values ​​were used to fill the gaps, maintaining temporal continuity.

[0049] B. Piecewise Threshold Determination and Baseline Compensation: Define the piecewise optimization objective function and set the baseline compensation amount. Flood peak magnification factor and segmentation threshold As a variable to be optimized, extract the simulated water level after translation. ,when When determining whether the model is in a dry baseflow or regular rainfall state, only the overall reference plane translation is applied. Linear compensation is performed to eliminate sensor elevation zero-point drift error.

[0050] C. Nonlinear stretching of extreme flood peaks: when At this time, it is determined that the pipeline network has entered a state of full pipe backflow or extreme flood peak. At this time, at the threshold point Compensation amount with reference Based on this, an amplification factor is applied to the water level surplus portion exceeding the threshold. Nonlinear stretching is applied. The assimilated water level transport... The piecewise mathematical expression is:

[0051] At the same time, in order to conform to the laws of reality, a non-negative constraint is imposed on the assimilated water level, which is mandatory. .

[0052] D. Simplex Optimization and Degradation Prevention Mechanism: Aiming to maximize the Nash efficiency coefficient, the simplex search algorithm solves for the optimal translation time and piecewise parameter combination within multidimensional boundaries (such as limited baseline compensation range and peak stretching ratio range). The algorithm incorporates degradation prevention evaluation logic; if the assimilated Nash efficiency coefficient is lower than the original simulation value, it automatically rolls back to the original physical simulation parameters, ensuring that the error stripping process strictly and positively improves model accuracy.

[0053] Finally, the nodal water level time series, after dual correction through physical topology reconstruction and mathematical error assimilation, is output as a standard data report, and a high-resolution simulation fitting comparison graph is generated. For example... Figure 4The comparison chart of water level correction verification for typical flood-prone monitoring nodes shows that the graph simultaneously maps four types of core data features: the actual observed curve (solid line), the original simulated curve (dotted line), the adaptive piecewise optimization correction curve (short dashed line), and clearly marks the "segmentation point" water level threshold line (dotted line) used to distinguish different hydrodynamic flow states. The verification results show that the original simulated curve output by the original physical model often has a low baseline water level and is severely inadequate in depicting extreme flood peaks (e.g., the gray dotted line remains at a low level for a long time and fails to capture significant peak values). The joint correction method introduced in this invention implements a differentiated assimilation strategy based on the "segmentation point": below the segmentation point, it effectively compensates for and aligns with the normal baseline water level; above the segmentation point, it not only accurately corrects the peak misalignment of actual rainfall through time phase synchronization but also effectively compensates for the original physical model's deficiency in predicting low flood peak water levels through a nonlinear stretching algorithm. After adaptive piecewise optimization processing, the waveform trends of the correction curve (blue short dashed line) and the actual observed curve (orange solid line) achieve a high degree of consistency. From the quantitative evaluation indicators of each subgraph, the Nash efficiency coefficient (NSE) of all typical flood-prone monitoring nodes reached a stable and significantly positive range (ranging from approximately 0.35 to 0.75, for example, the NSE of station 230727011 is as high as 0.7474). The method proposed in this invention can generate a refined urban drainage network hydrodynamic model with clear output error, stable operation, and conformity to the real physical evolution law, which can be directly applied to real-time flood prevention early warning and digital twin intelligent scheduling system.

[0054] Furthermore, the terms "upper," "lower," "inner," "outer," "front," and "rear" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0055] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

[0056] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling, characterized in that, include: Data on underground pipelines within the target area is acquired, and a corresponding spatial topology model of the pipeline network is constructed based on the solution conditions of the hydrodynamic continuity equation. The construction process of the spatial topology model of the pipeline network is as follows: For underground pipeline data, a graph theory pathfinding algorithm is used to correct erroneous pipe segments that violate gravity flow characteristics. The process of the graph theory pathfinding algorithm is as follows: Using the final outlet of the pipeline network as the calculation endpoint, the priority queue algorithm is used to deduce the physical shortest convergence path distance from all nodes of the pipeline network to the outlet. If the path distance of the upstream node of the current pipe segment is less than that of the downstream node, it is determined that it does not conform to the law of gravity flow, and the start and end topology of the current pipe segment is automatically reversed and the bias parameters are interchanged. Absolute elevation convergence and dynamic reduction of pipe bottom offset parameters are performed to eliminate pipeline network reverse slope and local water stagnation nodes in underground pipeline data. The process of eliminating the pipeline network reverse slope is as follows: Calculate the absolute vertical drop of the pipe segment. If the vertical drop is greater than or equal to the horizontal length of the pipe segment, the length of the pipe segment is automatically extended based on the vertical drop. Calculate the absolute elevation of the highest pipe top connected to the node, and automatically overwrite the maximum depth attribute of the node based on the required calculated safe water depth to eliminate hydraulic geometric boundary anomalies; The process of eliminating the localized water stagnation points is as follows: Extract the minimum absolute bottom elevation of all pipe sections connected to each inspection well node, and subtract the safety buffer water depth to obtain the theoretical bottom elevation of the corresponding node; Forced lifting calculations are performed on nodes whose actual elevation is lower than the preset theoretical bottom elevation, and the offset parameters of the connected pipe sections are reduced proportionally at the same time to completely eliminate local stagnant water zones that hinder water flow calculations. Based on the corrected underground pipeline data, the elevation difference anomalies are corrected through geometric constraint verification in order to construct the corresponding pipeline spatial topology model. In the pipeline network spatial topology model, a local sub-basin pipeline network containing the target monitoring node is extracted. The downstream node of the cut-off node is used as a fixed water level discharge outlet and dynamic backwater level parameters are set, while the upstream node of the cut-off node is set with dynamic dry weather baseflow parameters, so as to construct the corresponding local sub-basin pipeline network model. An initial simulation sequence is generated by inputting preset physical parameters into the local sub-basin network model, and the initial simulation sequence is compared and inverted with the measured sequence to determine the time phase shift and static reference elevation compensation. The time phase shift and static benchmark elevation compensation are used as boundary constraints. Based on a nonlinear optimization algorithm, the basin parameters, dynamic backwater level parameters, and dynamic dry-day baseflow parameters of the local sub-basin are jointly optimized and calibrated to obtain the optimal flow resistance and runoff generation mechanism parameters. Based on the optimal water flow resistance and runoff generation and confluence mechanism parameters as input, the local sub-basin network model is driven to perform calculations and output the corresponding physical reference sequence. A segmented dynamic stretching algorithm is introduced to assimilate the sequence in order to generate the final high-fidelity water depth prediction sequence. The specific process of the segmented dynamic stretching algorithm is as follows: Set dynamic segmented water depth thresholds and flood peak stretching ratios; For the physical reference sequence output by the local sub-basin network model, when the water depth value at the corresponding time is less than the dynamic segmented water depth threshold, only the static reference elevation compensation is added. When the water depth value is greater than or equal to the dynamic segmented water depth threshold, the difference amplification calculation is performed on the part of the water depth exceeding the limit by introducing the flood peak stretching factor on the basis of superimposing the dynamic segmented water depth threshold and the benchmark elevation compensation. A non-negative truncation boundary condition is added during the calculation process to ensure that the corrected water depth prediction sequence is not less than zero. By optimizing the algorithm to find the optimal combination of dynamic segmented water depth threshold and flood peak stretching ratio, extreme value compensation is performed on the water volume reduction residual of extreme flood peaks during rainstorms, while keeping the dry base flow and smooth receding process unchanged.

2. The joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling as described in claim 1, characterized in that, The watershed parameters include catchment width and impermeability.

3. The joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling as described in claim 1, characterized in that, The process of joint optimization is as follows: An objective function for evaluating hydrodynamic mechanisms is established. In each iteration, a simulated water level curve is generated based on the current watershed parameters, dynamic backwater level parameters, and dynamic dry-day baseflow parameters. The time sequence is aligned according to the time phase shift, and a static reference elevation compensation is added to obtain the simulation sequence. Subsequently, the fitness scores of the simulated and measured sequences are calculated, and the nonlinear optimization algorithm is guided to approximate the rise and fall trend and receding hysteresis characteristics of the real water flow within the preset value range, thereby obtaining the optimal water flow resistance and runoff generation and confluence mechanism parameters.

4. A joint correction system for a drainage pipe network model, characterized in that, The steps are for performing the joint correction method for drainage network models based on topology reconstruction and multidimensional error decoupling as described in any one of claims 1 to 3.

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