Method, device and equipment for identifying dominant bottleneck unit of drainage network

Abstract

The present application provides a method, device and equipment for identifying the dominant bottleneck unit of a drainage network, which is applied to the field of sewage treatment technology. The method analyzes and processes the drainage network based on the drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network. The drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water delivery capacity parameters of multiple drainage facilities in the drainage network; for any potential bottleneck unit among the multiple potential bottleneck units, at least one transformation strategy corresponding to the potential bottleneck unit is determined; based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, the target sensitivity index of each potential bottleneck unit is determined, and the dominant bottleneck unit is identified from the multiple potential bottleneck units according to the multiple target sensitivity indices, thereby improving the pollution control efficiency of the drainage network and reducing the transformation cost.

Description

Technical Field

[0001] The present application belongs to the technical field of sewage treatment, and in particular, relates to a method, device and equipment for identifying a dominant bottleneck unit in a drainage network. Background Art

[0002] The drainage system is an important infrastructure for collecting and treating urban sewage and runoff rainwater. During rainfall, the insufficient transfer capacity of bottleneck units often leads to large-scale blockage and sewage overflow, which poses a risk to the health of the water environment and public health safety. Accurately identifying bottleneck units and implementing optimization and transformation are important measures to reduce overflow pollution.

[0003] In the prior art, the bottleneck units in the drainage network are often identified based on explicit parameters such as pipeline full load rate, conveying capacity, and manhole overflow conditions.

[0004] However, the bottleneck unit identification method in the prior art does not take into account the interaction between the drainage units in the drainage network, resulting in some non-critical drainage units being mistakenly identified as bottleneck units, thereby reducing the pollution control efficiency of the drainage network and increasing the transformation cost. Summary of the invention

[0005] The present application provides a method, device and equipment for identifying the dominant bottleneck unit of a drainage network, so as to solve the defects of low efficiency and large error of the method for identifying the dominant bottleneck unit of a drainage network in the prior art.

[0006] In a first aspect, the present application provides a method for identifying a dominant bottleneck unit in a drainage network, the method comprising:

[0007] Based on a drainage system mathematical model corresponding to the drainage network, the drainage network is analyzed and processed to obtain multiple potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water transfer capacity parameters of multiple drainage facilities in the drainage network;

[0008] For any potential bottleneck unit among the multiple potential bottleneck units, determining at least one transformation strategy corresponding to the potential bottleneck unit;

[0009] Based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, a target sensitivity index of each potential bottleneck unit is determined, and a dominant bottleneck unit is identified from the multiple potential bottleneck units according to the multiple target sensitivity indices.

[0010] Optionally, the drainage network is analyzed and processed based on the drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network, including:

[0011] Determine historical rainfall measured data of multiple rainfall events, wherein the historical rainfall measured data includes: historical water dynamics and historical water quality data of multiple drainage facilities in the drainage network;

[0012] Using the historical hydrodynamic and water quality data of the plurality of drainage facilities, the initial model is trained to obtain a digital model of the drainage system, wherein the initial model is used to indicate the spatial positions of the plurality of drainage facilities in the drainage network and the corresponding connection relationships;

[0013] Controlling the operation of the drainage system digital model and determining output parameters of the drainage system digital model, wherein the output parameters are used to indicate water delivery capacity parameters of multiple drainage facilities in the drainage network;

[0014] Based on the water transfer capacity parameters of the plurality of drainage facilities, a plurality of potential bottleneck units of the drainage network are determined, wherein the potential bottleneck unit is a drainage facility among the plurality of drainage facilities.

[0015] Optionally, the number of the multiple rainfall events is three, and the historical hydrodynamic and water quality data of the multiple drainage facilities are used to train the initial model to obtain the digital model of the drainage system, including:

[0016] Inputting the first historical rainfall measured data into the initial model to simulate the hydrodynamics and water quality, and obtaining a first simulation value corresponding to the first historical rainfall measured data output by the initial model;

[0017] Determining a first degree of fit of the initial model according to the first simulated value and the first historical rainfall measured data;

[0018] When the first degree of fit is greater than a first threshold, determining that the parameter calibration of the initial model is completed, updating the parameters of the initial model to calibration parameters, and obtaining the initial model after parameter calibration;

[0019] Inputting the measured data of the second historical rainfall into the initial model after parameter calibration to simulate the hydrodynamics and water quality, and obtaining a second simulation value corresponding to the measured data of the second historical rainfall output by the initial model after parameter calibration;

[0020] Determine a second degree of fit of the initial model after parameter calibration according to the second simulated value and the second historical rainfall measured data;

[0021] When the second degree of fit is greater than a second threshold, determining that the model parameters of the initial model after parameter calibration pass the verification, and obtaining the initial model after parameter verification;

[0022] Based on the measured data of the third historical rainfall and the initial model after the parameters are verified, a digital model of the drainage system is obtained.

[0023] Optionally, there are multiple water delivery capacity parameters, and determining at least one transformation strategy corresponding to the potential bottleneck unit includes:

[0024] At least one transformation strategy corresponding to the potential bottleneck unit is determined based on a plurality of water delivery capacity parameters corresponding to the potential bottleneck unit.

[0025] Optionally, determining a target sensitivity index of each potential bottleneck unit based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units includes:

[0026] Based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy, determining the total amount of overflow pollution of the drainage system corresponding to each transformation strategy;

[0027] Determine a first-order sensitivity index and a second-order sensitivity index of each potential bottleneck unit according to the multiple transformation strategies and the total amount of drainage system overflow pollution corresponding to each transformation strategy;

[0028] The sum of the first-order sensitivity index and the second-order sensitivity index is taken as the target sensitivity index of the corresponding potential bottleneck unit.

[0029] Optionally, the step of identifying a dominant bottleneck unit from the plurality of potential bottleneck units according to the plurality of target sensitivity indexes comprises:

[0030] For any potential bottleneck unit among the multiple potential bottleneck units, determine whether a first-order sensitivity index of the potential bottleneck unit is less than a corresponding target sensitivity index;

[0031] When the first-order sensitivity index is less than the target sensitivity index, determining whether the first-order sensitivity index is greater than a third threshold;

[0032] If so, the potential bottleneck unit is determined to be a dominant bottleneck unit.

[0033] Optionally, the method further includes:

[0034] When the first-order sensitivity index is not greater than the third threshold, determining whether the first-order sensitivity index is within a first threshold range;

[0035] If the first-order sensitivity index is within a first threshold range, determining that the potential bottleneck unit is a secondary dominant bottleneck unit;

[0036] If the first-order sensitivity index is outside the first threshold range, determining the potential bottleneck unit as a non-dominant bottleneck unit;

[0037] According to the total amount of overflow pollution of the drainage system corresponding to each transformation strategy of the multiple potential bottleneck units, determining the transformation strategy with the minimum total amount of overflow pollution of the drainage system as the optimal transformation strategy;

[0038] The sub-dominant bottleneck units and non-dominant bottleneck units in the optimal transformation strategy are removed from the optimal transformation strategy to obtain an updated optimal transformation strategy, and the updated optimal transformation strategy is used as a target strategy to optimize the transformation of the drainage network.

[0039] In a second aspect, the present application provides a device for identifying a dominant bottleneck unit in a drainage network, the device comprising:

[0040] A processing module, configured to analyze and process the drainage network based on a drainage system mathematical model corresponding to the drainage network, so as to obtain a plurality of potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on a plurality of historical rainfall measured data and water transfer capacity parameters of a plurality of drainage facilities in the drainage network;

[0041] A determination module, configured to determine, for any one of the plurality of potential bottleneck units, at least one transformation strategy corresponding to the potential bottleneck unit;

[0042] The determination module is further used to determine a target sensitivity index of each potential bottleneck unit based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units;

[0043] The processing module is further configured to identify a dominant bottleneck unit from the multiple potential bottleneck units according to multiple target sensitivity indexes.

[0044] Optionally, the determination module is further used to determine historical rainfall measured data of multiple rainfall events, wherein the historical rainfall measured data includes: historical hydrodynamic and historical water quality data of multiple drainage facilities in the drainage network;

[0045] The processing module is further used to train the initial model using the historical hydrodynamic and water quality data of the plurality of drainage facilities to obtain a digital model of the drainage system, wherein the initial model is used to indicate the spatial positions of the plurality of drainage facilities in the drainage network and the corresponding connection relationships;

[0046] The processing module is also used to control the operation of the drainage system digital model;

[0047] The determination module is further used to determine output parameters of the drainage system digital model, wherein the output parameters are used to indicate water transfer capacity parameters of multiple drainage facilities in the drainage network;

[0048] The determination module is further used to determine multiple potential bottleneck units of the drainage network based on the water transfer capacity parameters of the multiple drainage facilities, wherein the potential bottleneck unit is a drainage facility among the multiple drainage facilities.

[0049] Optionally, the processing module is further used to input the first historical rainfall measured data into the initial model to perform hydrodynamic and water quality simulation processing, and obtain a first simulation value corresponding to the first historical rainfall measured data output by the initial model;

[0050] The determination module is further used to determine a first degree of fit of the initial model according to the first simulated value and the first historical rainfall measured data;

[0051] The determination module is further configured to determine that the calibration of the parameters of the initial model is completed when the first degree of fit is greater than a first threshold, and update the parameters of the initial model to calibration parameters to obtain the initial model after parameter calibration;

[0052] The processing module is further used to input the measured data of the second historical rainfall into the initial model after parameter calibration to perform simulation processing of hydrodynamics and water quality, and obtain a second simulation value corresponding to the measured data of the second historical rainfall output by the initial model after parameter calibration;

[0053] The determination module is further used to determine a second degree of fit of the initial model after parameter calibration according to the second simulated value and the second historical rainfall measured data;

[0054] The determination module is further configured to determine, when the second degree of fit is greater than a second threshold, that the model parameters of the initial model after parameter calibration have passed verification, thereby obtaining the initial model after parameter verification;

[0055] The processing module is also used to obtain a digital model of the drainage system based on the measured data of the third historical rainfall and the initial model after parameter verification.

[0056] Optionally, the determination module is further used to determine at least one transformation strategy corresponding to the potential bottleneck unit based on multiple water transfer capacity parameters corresponding to the potential bottleneck unit.

[0057] Optionally, the determination module is further used to determine the total amount of drainage system overflow pollution corresponding to each transformation strategy based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy;

[0058] The determination module is further used to determine the first-order sensitivity index and the second-order sensitivity index of each potential bottleneck unit according to the multiple transformation strategies and the total amount of overflow pollution of the drainage system corresponding to each transformation strategy;

[0059] The processing module is further configured to use the sum of the first-order sensitivity index and the second-order sensitivity index as the target sensitivity index of the corresponding potential bottleneck unit.

[0060] Optionally, the drainage network dominant bottleneck unit identification device further includes: a judgment module;

[0061] The judging module is used to judge, for any one of the multiple potential bottleneck units, whether the first-order sensitivity index of the potential bottleneck unit is less than the corresponding target sensitivity index;

[0062] The judging module is further configured to judge whether the first-order sensitivity index is greater than a third threshold value when the first-order sensitivity index is less than the target sensitivity index;

[0063] The determination module is further configured to determine that the potential bottleneck unit is a dominant bottleneck unit when the first-order sensitivity index is greater than a third threshold.

[0064] Optionally, the judging module is further configured to judge whether the first-order sensitivity index is within a first threshold range when the first-order sensitivity index is not greater than the third threshold;

[0065] The determination module is further configured to determine, when the first-order sensitivity index is within a first threshold range, that the potential bottleneck unit is a secondary dominant bottleneck unit;

[0066] The determination module is further configured to determine that the potential bottleneck unit is a non-dominant bottleneck unit when the first-order sensitivity index is outside a first threshold range;

[0067] The determination module is further used to determine, based on the total amount of overflow pollution of the drainage system corresponding to each transformation strategy of the multiple potential bottleneck units, that the transformation strategy with the minimum total amount of overflow pollution of the drainage system is the optimal transformation strategy;

[0068] The processing module is also used to remove the sub-dominant bottleneck units and non-dominant bottleneck units in the optimal transformation strategy from the optimal transformation strategy to obtain an updated optimal transformation strategy, and the updated optimal transformation strategy is used as a target strategy to optimize the transformation of the drainage network.

[0069] In a third aspect, the present application provides a device for identifying a dominant bottleneck unit of a drainage network, comprising:

[0070] Memory;

[0071] processor;

[0072] Wherein, the memory stores computer-executable instructions;

[0073] The processor executes the computer-executable instructions stored in the memory to implement the method for identifying the dominant bottleneck unit of the drainage network as described in the first aspect and various possible implementations of the first aspect.

[0074] In a fourth aspect, the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method for identifying the dominant bottleneck unit of a drainage network as described in the first aspect and various possible implementations of the first aspect.

[0075] The present application provides a method, device and equipment for identifying the dominant bottleneck unit of a drainage network. The method analyzes and processes the drainage network based on the drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water delivery capacity parameters of multiple drainage facilities in the drainage network; for any potential bottleneck unit among the multiple potential bottleneck units, at least one transformation strategy corresponding to the potential bottleneck unit is determined; based on multiple transformation strategies corresponding to the multiple potential bottleneck units, the target sensitivity index of each potential bottleneck unit is determined, and the dominant bottleneck unit is identified from the multiple potential bottleneck units according to the multiple target sensitivity indices, thereby improving the pollution control efficiency of the drainage network and reducing the transformation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0077] Figure 1 Schematic diagram of the process of identifying the dominant bottleneck unit of the drainage network provided in this application Figure 1 ;

[0078] Figure 2 Schematic diagram of the process of identifying the dominant bottleneck unit of the drainage network provided in this application Figure 2 ;

[0079] Figure 3 Schematic diagram of the process of identifying the dominant bottleneck unit of the drainage network provided in this application Figure 3 ;

[0080] Figure 4A schematic diagram of the structure of the dominant bottleneck unit identification device for the drainage network provided in this application;

[0081] Figure 5 A schematic diagram of the structure of the drainage network dominant bottleneck unit identification device provided in this application.

[0082] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0084] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0085] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0086] Urban drainage systems are responsible for collecting and treating sewage and rainwater, and are the core of urban infrastructure. During rainfall, the insufficient transport capacity of certain key units often causes drainage system blockage and sewage overflow, which not only threatens the health of water bodies, but also endangers public safety. Therefore, accurately identifying and improving these bottleneck units is critical to reducing overflow pollution.

[0087] In existing technical methods, some intuitive parameters are usually relied on to identify bottleneck units in the drainage network, such as the full load rate of the pipeline, the conveying capacity, and the overflow of the inspection well. These explicit parameters can directly reflect the load status and efficiency problems of the pipeline during the transmission process, and help technicians locate the key parts that may cause poor system operation or overflow. By analyzing these parameters, it is possible to preliminarily determine which parts need to be optimized or modified to improve the efficiency of the entire drainage system.

[0088] However, the existing methods for identifying the dominant bottleneck units in drainage networks often ignore the interactions between different drainage units when identifying the dominant bottleneck units in drainage networks, which leads to some non-critical drainage units being mistakenly identified as the main bottleneck units. This misjudgment not only reduces the efficiency of pollution control, but also increases unnecessary transformation costs in the subsequent transformation of the dominant bottleneck units in the drainage network. Therefore, a more accurate method is needed to distinguish the real dominant bottleneck units in order to improve the effect of pollution control and reasonably control the transformation investment.

[0089] In response to the above problems, this application proposes a method for identifying the dominant bottleneck units in the drainage network. This method analyzes the drainage network and identifies multiple potential bottleneck units by constructing a drainage system mathematical model based on historical rainfall data and drainage facility water transfer capacity parameters. Then, at least one transformation strategy is determined for each potential bottleneck unit, and the target sensitivity index of each unit is calculated. Finally, the dominant bottleneck units are identified based on these indices, thereby improving pollution control efficiency and reducing costs.

[0090] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0091] Figure 1 Schematic diagram of a method for identifying the dominant bottleneck unit of a drainage network provided in an application embodiment Figure 1 .like Figure 1 As shown, the method for identifying the dominant bottleneck unit of a drainage network provided in this embodiment includes:

[0092] S101. Based on a drainage system mathematical model corresponding to the drainage network, the drainage network is analyzed and processed to obtain a plurality of potential bottleneck units of the drainage network.

[0093] Among them, the data include historical rainfall measured data and water transfer capacity parameters of multiple drainage facilities in the drainage network, which will serve as the training basis for the mathematical model of the drainage system; the model is trained using the collected historical rainfall measured data and the water transfer capacity parameters of multiple drainage facilities in the drainage network, and the model parameters are adjusted until the model can accurately simulate known rainfall events to obtain the mathematical model of the drainage system; the mathematical model of the drainage system can simulate the changes in hydrodynamics and water quality after rainfall events.

[0094] Run the drainage system mathematical model to analyze the drainage network, identify potential dominant bottleneck units through the model output, and analyze the working status of each part of the network through the model output, such as: inspection well overflow, pipeline full load rate and other indicators. Identify drainage facilities that frequently experience high load or overflow under different conditions and mark them as potential bottleneck units.

[0095] By simulating the behavior of the real drainage network through mathematical models, potential bottleneck units can be identified more accurately. When optimizing the drainage network later, renovation resources can be allocated more specifically, avoiding unnecessary renovations and saving costs.

[0096] S102: For any potential bottleneck unit among the multiple potential bottleneck units, determine at least one transformation strategy corresponding to the potential bottleneck unit.

[0097] Among them, for any potential bottleneck unit among multiple potential bottleneck units, the load condition of the potential bottleneck unit under different rainfall conditions is evaluated, such as: full load rate, flow rate, etc., the overflow frequency and degree of the potential bottleneck unit under extreme weather conditions are analyzed, and the impact of the potential bottleneck unit on downstream water quality is evaluated.

[0098] Conduct a status assessment of each potential bottleneck unit to identify its main problems. Based on the problem diagnosis, develop at least one transformation strategy for each potential bottleneck unit, such as expanding the pipe diameter, optimizing the pipe slope, increasing the capacity of the pump station, etc.

[0099] By analyzing the characteristics of each potential bottleneck unit in detail, the most suitable transformation strategy can be formulated for each potential bottleneck unit to improve the pertinence and effectiveness of the transformation.

[0100] S103. Based on multiple transformation strategies corresponding to multiple potential bottleneck units, determine the target sensitivity index of each potential bottleneck unit, and identify the dominant bottleneck unit from the multiple potential bottleneck units according to the multiple target sensitivity indexes.

[0101] Among them, based on multiple transformation strategies corresponding to multiple potential bottleneck units, the transformation strategies are applied to the mathematical model to simulate the operation of the drainage system after the transformation; the impact of the transformation strategies on the system performance is evaluated, such as overflow reduction, water quality improvement, etc.; the target sensitivity index of each potential bottleneck unit under each transformation strategy is calculated, and the dominant bottleneck unit is identified from multiple potential bottleneck units based on multiple target sensitivity indices.

[0102] By identifying the dominant bottleneck unit from multiple potential bottleneck units, in the subsequent renovation of the drainage network, the renovation resources can be reasonably allocated, blind investment can be avoided, and the rational use of funds can be ensured.

[0103] This embodiment proposes a method for identifying a dominant bottleneck unit of a drainage network. The method analyzes and processes the drainage network based on a drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network. The drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water transfer capacity parameters of multiple drainage facilities in the drainage network. For any potential bottleneck unit among the multiple potential bottleneck units, at least one transformation strategy corresponding to the potential bottleneck unit is determined; based on multiple transformation strategies corresponding to the multiple potential bottleneck units, a target sensitivity index of each potential bottleneck unit is determined, and a dominant bottleneck unit is identified from the multiple potential bottleneck units based on the multiple target sensitivity indices, thereby improving the pollution control efficiency of the drainage network and reducing the transformation cost.

[0104] Figure 2 This is a schematic diagram of a method for identifying a dominant bottleneck unit in a drainage network provided in an embodiment of the present application. Figure 2 This embodiment is in Figure 1 Based on the embodiment, a possible implementation method of analyzing and processing the drainage network based on the drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network is described in detail. Figure 2 As shown, the method comprises:

[0105] S201. Determine the historical rainfall measured data of three rainfall events, where the historical rainfall measured data include: historical hydrodynamics and historical water quality data of multiple drainage facilities in the drainage network.

[0106] Among them, determine the historical rainfall measured data of multiple rainfall events, including but not limited to rainfall amount, rainfall intensity, rainfall duration, etc.; obtain the historical hydrodynamic and historical water quality data of multiple drainage facilities in the drainage network from the drainage network management system or monitoring equipment; match the rainfall data with the hydrodynamic and water quality data of the drainage facilities in time to ensure that the timestamps of the data are consistent.

[0107] Select three representative rainfall events from the historical rainfall data of multiple rainfall events, such as light rain, moderate rain, and heavy rain, to cover different rainfall scenarios. Make sure that the selected rainfall events have sufficient differences to provide diverse inputs in subsequent model training and validation.

[0108] By selecting representative rainfall events and high-quality data, the training quality and prediction accuracy of the mathematical model can be improved. The analysis results of the mathematical model can provide a scientific basis for decision makers to make more reasonable decisions in drainage system management and reconstruction.

[0109] S202: Input the first historical rainfall measured data into the initial model to simulate the hydrodynamics and water quality, and obtain a first simulation value corresponding to the first historical rainfall measured data output by the initial model.

[0110] The measured rainfall data during the first rainfall event, such as rainfall amount, rainfall duration, rainfall intensity, etc., are input into the initial model. The initial model is used to indicate the spatial positions of multiple drainage facilities in the drainage network and the corresponding connection relationships. The drainage facilities are various types of drainage facilities, such as drainage pipes, drainage pumps, drainage valves, etc. The initial model cleans and formats the collected data to meet the input requirements of the model, and sets the initial parameters of the model, such as permeability, runoff coefficient, geometric characteristics of the watershed, etc. The initial model simulates the water flow movement and water quality changes of multiple drainage facilities during the rainfall according to the input historical rainfall measured data and parameter settings, and obtains the first simulation value corresponding to the first historical rainfall measured data output by the initial model.

[0111] Through simulation processing, the performance of the model under specific historical rainfall conditions can be verified, so as to adjust and optimize the model parameters and improve the accuracy and reliability of the model. The simulation results can provide a scientific basis for the management and optimization of the drainage system and help decision makers make more reasonable planning and transformation plans.

[0112] S203: Determine a first degree of fit of the initial model according to the first simulation value and the first historical rainfall measured data.

[0113] The first simulation value is compared with the first historical rainfall measured data to determine the first degree of fit of the initial model, and the first degree of fit indicates the accuracy and reliability of the model.

[0114] By simulating the measured data, the prediction accuracy of the model's response to rainfall events can be improved, and the simulation results can be used to verify and calibrate the initial model to improve the prediction ability of the initial model.

[0115] S204. When the first degree of fit is greater than a first threshold, determine that parameter calibration of the initial model is completed, update the parameters of the initial model to calibration parameters, and obtain the initial model after parameter calibration.

[0116] Among them, the first degree of fit of the model is calculated by comparing the output of the initial model with the first historical rainfall measured data; if the first degree of fit exceeds the preset first threshold, it means that the prediction of the model is highly consistent with the actual data and can well reflect the actual situation, that is, the parameter calibration of the initial model is determined to be completed; the calibrated parameters are updated to the initial model to complete the parameter calibration of the initial model.

[0117] In one possible implementation, the original model uses the first historical rainfall measured data, the historical hydrodynamic and historical water quality data of each drainage facility in the drainage network, and the hydrodynamic measured data and water quality measured data of each drainage facility in the drainage network to calibrate the parameters of the initial model. In the process, the determination coefficient is used to evaluate the first fit between the simulated value output by the initial model and the historical rainfall measured value. Specifically, when the first fit is greater than or equal to 0.8, it is considered that the fitting effect is good, that is, the model parameters currently set for the initial model are valid.

[0118] S205, inputting the measured data of the second historical rainfall into the initial model after parameter calibration to simulate the hydrodynamics and water quality, and obtaining a second simulation value corresponding to the measured data of the second historical rainfall output by the initial model after parameter calibration.

[0119] Among them, the measured rainfall data during the second rainfall event, such as rainfall amount, rainfall duration, rainfall intensity and other measured data, are input into the initial model after parameter calibration, and the initial model after parameter calibration is started to simulate the water flow movement and water quality changes of multiple drainage facilities during the second rainfall, so as to obtain the second simulation value corresponding to the second historical rainfall measured data output by the initial model after parameter calibration.

[0120] By using measured data for simulation, the model's response prediction accuracy to rainfall events can be improved, helping decision makers better understand the impact of rainfall on drainage systems, which helps optimize drainage system design and improve drainage efficiency.

[0121] S206. Determine a second degree of fit of the initial model after parameter calibration based on the second simulation value and the second historical rainfall measured data.

[0122] The second simulation value is compared with the second historical rainfall measured data to determine the second degree of fit of the initial model after parameter calibration, and the second degree of fit indicates the accuracy and reliability of the model parameters.

[0123] By simulating the measured data, the prediction accuracy of the model's response to rainfall events can be improved. The simulation results can be used to verify and calibrate the initial model after parameter calibration to improve the prediction ability of the initial model.

[0124] S207: When the second degree of fit is greater than the second threshold, determine that the model parameters of the initial model after parameter calibration pass the verification, and obtain the initial model after parameter verification.

[0125] Among them, the second degree of fit of the model is calculated by comparing the output of the initial model with the measured data of the second historical rainfall. If the second degree of fit exceeds the preset second threshold, it means that the prediction of the model is highly consistent with the actual data and can well reflect the actual situation, that is, the model parameters of the initial model after parameter calibration are verified, and the initial model after parameter verification is obtained.

[0126] In one possible implementation, the original model uses the measured data of the second historical rainfall, the historical hydrodynamics and historical water quality data of each drainage facility in the drainage network, and the measured hydrodynamics and water quality data of each drainage facility in the drainage network to verify the parameters of the initial model after parameter calibration. In the process, the determination coefficient is used to evaluate the second degree of fit between the simulated value output by the initial model after parameter calibration and the measured value of historical rainfall. Specifically, when the second degree of fit is greater than or equal to 0.6, it is considered that the fitting effect is good, that is, the model parameters currently set in the initial model after parameter calibration have passed the verification.

[0127] S208. Obtain a digital model of the drainage system based on the measured data of the third historical rainfall and the initial model after parameter verification.

[0128] S209, controlling the operation of the digital model of the drainage system, and determining output parameters of the digital model of the drainage system, where the output parameters are used to indicate water transfer capacity parameters of multiple drainage facilities in the drainage network.

[0129] S210. Determine multiple potential bottleneck units of the drainage network based on the water delivery capacity parameters of multiple drainage facilities.

[0130] The measured data of the third rainfall were input into the initial model after parameter verification, and the model was started for simulation to calculate the hydrodynamic process and water quality changes. Based on the measured data of the third historical rainfall and the initial model after parameter verification, the digital model of the drainage system was obtained.

[0131] Control the operation of the digital model of the drainage system and determine the output parameters of the digital model of the drainage system. The output parameters are used to indicate the water delivery capacity parameters of multiple drainage facilities in the drainage network. Based on the water delivery capacity parameters of multiple drainage facilities, analyze the performance of each facility under different rainfall conditions. By analyzing the performance of multiple drainage facilities under different conditions, identify drainage facilities that are prone to problems under high load conditions, that is, potential bottleneck units.

[0132] By controlling the simulation analysis of the digital model of the drainage system, the bottleneck units in the drainage system can be identified more accurately, so that the bottleneck units can be transformed in a targeted manner, unnecessary investment can be avoided, and transformation resources can be reasonably allocated.

[0133] This embodiment proposes a method for identifying the dominant bottleneck unit of a drainage network. The method collects historical rainfall measured data of three rainfalls, uses the first historical rainfall data to simulate the hydrodynamics and water quality of the initial model, and evaluates the model's fit. When the fit reaches a preset threshold, it is determined that the model parameters have been calibrated and updated to the calibration parameters; the calibrated model is further verified using the second rainfall data, and the fit is evaluated again. When the fit meets the requirements, the model parameters are confirmed to be valid, and a verified model is obtained; the third rainfall data and the verified model are combined to construct a digital model of the drainage system, run the digital model of the drainage system, obtain the water transfer capacity parameters of the drainage facilities, and identify multiple potential bottleneck units based on this, and effectively identify potential bottleneck units in the drainage system through simulation analysis, providing a scientific basis for subsequent optimization and transformation.

[0134] Figure 3 This is a schematic diagram of a method for identifying a dominant bottleneck unit in a drainage network provided in an embodiment of the present application. Figure 3 This embodiment is in Figure 1 Based on the embodiment, the method for identifying the dominant bottleneck unit of the drainage network is described in detail. Figure 3 As shown, the method comprises:

[0135] S301. Based on the drainage system mathematical model corresponding to the drainage network, the drainage network is analyzed and processed to obtain multiple potential bottleneck units of the drainage network.

[0136] Step S301 is the same as step S101 and will not be described again.

[0137] S302: for any potential bottleneck unit among the multiple potential bottleneck units, determine at least one transformation strategy corresponding to the potential bottleneck unit based on multiple water delivery capacity parameters corresponding to the potential bottleneck unit.

[0138] Among them, the historical water delivery capacity parameters of each potential bottleneck unit are sorted out, including but not limited to full load rate, flow rate, flow rate, water level height, etc., and the performance of each potential bottleneck unit under different rainfall conditions is analyzed to determine its specific problems under high load conditions, such as overflow, blockage, etc. For any potential bottleneck unit among multiple potential bottleneck units, based on the multiple water delivery capacity parameters corresponding to the potential bottleneck unit, the characteristics of each bottleneck unit are evaluated, such as pipeline size, material, slope, length, etc., to determine the main problems of each potential bottleneck unit and form a problem list. For the problem list of each potential bottleneck unit, formulate at least one transformation strategy corresponding to the potential bottleneck unit, such as expanding the pipeline diameter, optimizing the pipeline slope, increasing the capacity of the pump station, desilting, etc.

[0139] By analyzing the characteristics of each potential bottleneck unit in detail, the most suitable transformation strategy can be formulated for each unit to improve the targetedness and effectiveness of the transformation.

[0140] S303. Based on multiple transformation strategies corresponding to multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy, determine the total amount of drainage system overflow pollution corresponding to each transformation strategy.

[0141] Among them, based on multiple transformation strategies corresponding to multiple potential bottleneck units, the transformation strategies are applied to the mathematical model of the drainage system. When each potential bottleneck unit is in at least one corresponding transformation strategy, the total amount of overflow pollution of the drainage system under the transformation strategy is simulated, and the impact of the transformation strategy on the performance of the drainage system is evaluated, including overflow reduction, water quality improvement, etc.

[0142] Through simulation calculation, we can scientifically evaluate the impact of each transformation strategy on the total amount of overflow pollution, so as to select the most effective transformation plan. Based on the total amount of overflow pollution corresponding to each transformation strategy, we can reasonably allocate transformation resources and avoid unnecessary investment.

[0143] S304. Determine a first-order sensitivity index and a second-order sensitivity index of each potential bottleneck unit according to multiple transformation strategies and the total amount of drainage system overflow pollution corresponding to each transformation strategy.

[0144] Among them, the first-order sensitivity index represents the influence of a single bottleneck unit on the total amount of overflow pollution, and the second-order sensitivity index represents the influence of the interaction between two bottleneck units on the total amount of overflow pollution.

[0145] According to multiple transformation strategies and the total amount of overflow pollution of the drainage system corresponding to each transformation strategy, for each transformation strategy of each potential bottleneck unit, its impact on the total amount of overflow pollution is calculated, and the first-order sensitivity index of each potential bottleneck unit is determined; for any combination of two transformation strategies of each potential bottleneck unit, the impact of their joint action on the total amount of overflow pollution is calculated, and the second-order sensitivity index of each potential bottleneck unit is determined.

[0146] By calculating the sensitivity index, we can scientifically evaluate the impact of each transformation strategy on the total amount of overflow pollution, and thus select the most effective transformation plan. Based on the total amount of overflow pollution corresponding to each transformation strategy, we can reasonably allocate transformation resources and avoid unnecessary investment.

[0147] S305 . Taking the sum of the first-order sensitivity index and the second-order sensitivity index as the target sensitivity index of the corresponding potential bottleneck unit.

[0148] The first-order sensitivity index is added to the second-order sensitivity index to obtain the target sensitivity index of each potential bottleneck unit.

[0149] S306 . For any potential bottleneck unit among the multiple potential bottleneck units, determine whether the first-order sensitivity index of the potential bottleneck unit is less than the corresponding target sensitivity index.

[0150] S307: When the first-order sensitivity index is less than the target sensitivity index, determine whether the first-order sensitivity index is greater than a third threshold.

[0151] S308: When the first-order sensitivity index is not greater than the third threshold, determine the potential bottleneck unit as the dominant bottleneck unit.

[0152] For any potential bottleneck unit among the multiple potential bottleneck units, the first-order sensitivity index of each potential bottleneck unit is compared with the target sensitivity index. If the first-order sensitivity index is less than the target sensitivity index, it is further determined whether the first-order sensitivity index is greater than a third threshold. If the first-order sensitivity index is not greater than the third threshold, the potential bottleneck unit is determined to be a dominant bottleneck unit.

[0153] A possible implementation method is to determine, for any potential bottleneck unit among multiple potential bottleneck units, whether the first-order sensitivity index of the potential bottleneck unit is less than the corresponding target sensitivity index. When the first-order sensitivity index is less than the target sensitivity index, determine whether the first-order sensitivity index is greater than a third threshold. Specifically, determine whether the first-order sensitivity index is greater than 0.1. When the first-order sensitivity index is greater than 0.1, determine that the potential bottleneck unit is a dominant bottleneck unit.

[0154] Optionally, when the first-order sensitivity index is not greater than the third threshold, determining whether the first-order sensitivity index is within the first threshold range;

[0155] If the first-order sensitivity index is within the first threshold range, the potential bottleneck unit is determined to be a secondary dominant bottleneck unit;

[0156] If the first-order sensitivity index is outside the first threshold range, the potential bottleneck unit is determined to be a non-dominant bottleneck unit;

[0157] In a possible implementation, when the first-order sensitivity index is not greater than the third threshold, it is determined whether the first-order sensitivity index is within the first threshold range. Specifically, when the first-order sensitivity index is not greater than 0.1, it is determined whether the first-order sensitivity index is greater than 0.01 and less than 0.1; when the first-order sensitivity index is greater than 0.01 and less than 0.1, the potential bottleneck unit is determined to be a secondary dominant bottleneck unit; when the first-order sensitivity index is less than 0.01, the potential bottleneck unit is determined to be a non-dominant bottleneck unit.

[0158] According to the total amount of overflow pollution of the drainage system corresponding to each transformation strategy of multiple potential bottleneck units, the transformation strategy with the smallest total amount of overflow pollution of the drainage system is determined as the best transformation strategy;

[0159] The sub-dominant bottleneck units and non-dominant bottleneck units in the optimal transformation strategy are removed from the optimal transformation strategy to obtain an updated optimal transformation strategy, and the updated optimal transformation strategy is used as a target strategy to optimize the transformation of the drainage network.

[0160] Among them, according to the total amount of overflow pollution of the drainage system corresponding to each transformation strategy of multiple potential bottleneck units, the strategy that minimizes the total amount of overflow pollution is selected as the best transformation strategy among all transformation strategies. Sub-dominant bottleneck units and non-dominant bottleneck units are removed from the best transformation strategy, and the best transformation strategy is updated according to the remaining dominant bottleneck units. The updated best transformation strategy is used as the target strategy for optimizing the transformation of the drainage network. The target strategy is implemented and its effect is continuously monitored to ensure that the transformation achieves the expected effect of reducing overflow pollution.

[0161] This embodiment proposes a method for identifying dominant bottleneck units in a drainage network. The method analyzes the drainage network based on a mathematical model of the drainage system to identify multiple potential bottleneck units; for each potential bottleneck unit, at least one possible transformation strategy is determined based on its water transmission capacity parameter; when all potential bottleneck units are transformed according to one or more transformation strategies, the total amount of overflow pollution of the drainage system corresponding to each transformation strategy is calculated; based on various transformation strategies and their corresponding total amount of overflow pollution, the first-order sensitivity index and the second-order sensitivity index of each potential bottleneck unit are calculated, and the sum of the first-order sensitivity index and the second-order sensitivity index is used as the target sensitivity index; based on the first-order sensitivity index and the target sensitivity index, the dominant bottleneck unit in the drainage network is determined to improve the accuracy of the dominant bottleneck unit identification and reduce the cost of subsequent dominant bottleneck unit transformation.

[0162] Figure 4 A schematic diagram of the structure of a drainage network dominant bottleneck unit identification device provided in this application, such as Figure 4 As shown, the drainage network dominant bottleneck unit identification device 400 provided in this embodiment includes:

[0163] A processing module 401 is used to analyze and process the drainage network based on a drainage system mathematical model corresponding to the drainage network to obtain multiple potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water transfer capacity parameters of multiple drainage facilities in the drainage network;

[0164] A determination module 402 is used to determine at least one transformation strategy corresponding to any potential bottleneck unit among the multiple potential bottleneck units;

[0165] The determination module 402 is further configured to determine a target sensitivity index of each potential bottleneck unit based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units;

[0166] The processing module 401 is further configured to identify a dominant bottleneck unit from the multiple potential bottleneck units according to multiple target sensitivity indexes.

[0167] Optionally, the determination module 402 is further used to determine historical rainfall measured data of multiple rainfall events, wherein the historical rainfall measured data includes: historical hydrodynamic and historical water quality data of multiple drainage facilities in the drainage network;

[0168] The processing module 401 is further used to train the initial model using the historical hydrodynamic and water quality data of the plurality of drainage facilities to obtain a digital model of the drainage system, wherein the initial model is used to indicate the spatial positions of the plurality of drainage facilities in the drainage network and the corresponding connection relationships;

[0169] The processing module 401 is also used to control the operation of the drainage system digital model;

[0170] The determination module 402 is further used to determine output parameters of the drainage system digital model, wherein the output parameters are used to indicate water transfer capacity parameters of multiple drainage facilities in the drainage network;

[0171] The determination module 402 is further used to determine a plurality of potential bottleneck units of the drainage network based on the water transfer capacity parameters of the plurality of drainage facilities, wherein the potential bottleneck unit is a drainage facility among the plurality of drainage facilities.

[0172] Optionally, the processing module 401 is further used to input the first historical rainfall measured data into the initial model to perform hydrodynamic and water quality simulation processing, and obtain a first simulation value corresponding to the first historical rainfall measured data output by the initial model;

[0173] The determination module 402 is further configured to determine a first degree of fit of the initial model according to the first simulated value and the first historical rainfall measured data;

[0174] The determination module 402 is further configured to determine that the calibration of the parameters of the initial model is completed when the first degree of fit is greater than a first threshold, and update the parameters of the initial model to calibration parameters to obtain the initial model after parameter calibration;

[0175] The processing module 401 is further used to input the measured data of the second historical rainfall into the initial model after parameter calibration to simulate the hydrodynamics and water quality, and obtain a second simulation value corresponding to the measured data of the second historical rainfall output by the initial model after parameter calibration;

[0176] The determination module 402 is further used to determine a second degree of fit of the initial model after parameter calibration according to the second simulated value and the second historical rainfall measured data;

[0177] The determination module 402 is further configured to determine, when the second degree of fit is greater than a second threshold value, that the model parameters of the initial model after parameter calibration have passed verification, thereby obtaining the initial model after parameter verification;

[0178] The processing module 401 is further used to obtain a digital model of the drainage system according to the measured data of the third historical rainfall and the initial model after parameter verification.

[0179] Optionally, the determination module 402 is further used to determine at least one transformation strategy corresponding to the potential bottleneck unit based on a plurality of water delivery capacity parameters corresponding to the potential bottleneck unit.

[0180] Optionally, the determination module 402 is further configured to determine the total amount of drainage system overflow pollution corresponding to each transformation strategy based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy;

[0181] The determination module 402 is further used to determine the first-order sensitivity index and the second-order sensitivity index of each potential bottleneck unit according to the multiple transformation strategies and the total amount of drainage system overflow pollution corresponding to each transformation strategy;

[0182] The processing module 401 is further configured to use the sum of the first-order sensitivity index and the second-order sensitivity index as the target sensitivity index of the corresponding potential bottleneck unit.

[0183] Optionally, the drainage network dominant bottleneck unit identification device further includes: a judgment module;

[0184] The judging module is used to judge, for any one of the multiple potential bottleneck units, whether the first-order sensitivity index of the potential bottleneck unit is less than the corresponding target sensitivity index;

[0185] The judging module is further configured to judge whether the first-order sensitivity index is greater than a third threshold value when the first-order sensitivity index is less than the target sensitivity index;

[0186] The determination module 402 is further configured to determine that the potential bottleneck unit is a dominant bottleneck unit when the first-order sensitivity index is greater than a third threshold.

[0187] Optionally, the judging module is further configured to judge whether the first-order sensitivity index is within a first threshold range when the first-order sensitivity index is not greater than the third threshold;

[0188] The determination module 402 is further configured to determine, when the first-order sensitivity index is within a first threshold range, that the potential bottleneck unit is a secondary dominant bottleneck unit;

[0189] The determination module 402 is further configured to determine that the potential bottleneck unit is a non-dominant bottleneck unit when the first-order sensitivity index is outside a first threshold range;

[0190] The determination module 402 is further configured to determine, based on the total amount of drainage system overflow pollution corresponding to each of the multiple potential bottleneck units in each of the transformation strategies, that the transformation strategy with the minimum total amount of drainage system overflow pollution is the optimal transformation strategy;

[0191] The processing module 401 is also used to remove the sub-dominant bottleneck units and non-dominant bottleneck units in the optimal transformation strategy from the optimal transformation strategy to obtain an updated optimal transformation strategy, and the updated optimal transformation strategy is used as a target strategy to optimize the transformation of the drainage network.

[0192] The present embodiment provides a drainage network dominant bottleneck unit identification device, which can execute the drainage network dominant bottleneck unit identification method provided by the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail in this embodiment.

[0193] Figure 5 This is a schematic diagram of the structure of a drainage network dominant bottleneck unit identification device provided in this application. Figure 5 As shown, the present application provides a drainage network dominant bottleneck unit identification device, and the drainage network dominant bottleneck unit identification device 500 includes: a receiver 501, a transmitter 502, a processor 503 and a memory 504.

[0194] Receiver 501, used for receiving instructions and data;

[0195] A transmitter 502, used for sending instructions and data;

[0196] Memory 504, used to store computer-executable instructions;

[0197] The processor 503 is used to execute the computer-executable instructions stored in the memory 504 to implement the various steps executed by the method for identifying the dominant bottleneck unit of the drainage network in the above embodiment. For details, please refer to the relevant description in the above embodiment of the method for identifying the dominant bottleneck unit of the drainage network.

[0198] Optionally, the memory 504 may be independent or integrated with the processor 503 .

[0199] When the memory 504 is independently provided, the electronic device further includes a bus for connecting the memory 504 and the processor 503 .

[0200] The present embodiment provides a drainage network dominant bottleneck unit identification device, which can execute the drainage network dominant bottleneck unit identification method provided by the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail in this embodiment.

[0201] The present application also provides a computer storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, a method for identifying a dominant bottleneck unit in a drainage network as performed by the above-mentioned device for identifying a dominant bottleneck unit in a drainage network is implemented.

[0202] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0203] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0204] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for identifying the dominant bottleneck unit of a drainage network, characterized in that: The method comprises: Based on a drainage system mathematical model corresponding to the drainage network, the drainage network is analyzed and processed to obtain multiple potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on multiple historical rainfall measured data and water transfer capacity parameters of multiple drainage facilities in the drainage network; For any potential bottleneck unit among the multiple potential bottleneck units, determining at least one transformation strategy corresponding to the potential bottleneck unit; Based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units, determine a target sensitivity index of each potential bottleneck unit, and identify a dominant bottleneck unit from the plurality of potential bottleneck units according to the plurality of target sensitivity indexes; The step of determining a target sensitivity index of each potential bottleneck unit based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units includes: Based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy, determining the total amount of overflow pollution of the drainage system corresponding to each transformation strategy; According to the multiple transformation strategies and the total amount of overflow pollution of the drainage system corresponding to each transformation strategy, determine the first-order sensitivity index and the second-order sensitivity index of each potential bottleneck unit; wherein the first-order sensitivity index represents the influence degree of the total amount of overflow pollution of a single bottleneck unit, and the second-order sensitivity index represents the influence degree of the total amount of overflow pollution when two bottleneck units interact; The sum of the first-order sensitivity index and the second-order sensitivity index is taken as the target sensitivity index of the corresponding potential bottleneck unit; The step of identifying a dominant bottleneck unit from the plurality of potential bottleneck units according to the plurality of target sensitivity indexes comprises: For any potential bottleneck unit among the multiple potential bottleneck units, determine whether a first-order sensitivity index of the potential bottleneck unit is less than a corresponding target sensitivity index; When the first-order sensitivity index is less than the target sensitivity index, determining whether the first-order sensitivity index is greater than a third threshold; If so, the potential bottleneck unit is determined to be a dominant bottleneck unit.

2. The method according to claim 1, characterized in that The drainage system mathematical model corresponding to the drainage network is used to analyze and process the drainage network to obtain multiple potential bottleneck units of the drainage network, including: Determine historical rainfall measured data of multiple rainfall events, wherein the historical rainfall measured data includes: historical water dynamics and historical water quality data of multiple drainage facilities in the drainage network; Using the historical hydrodynamic and water quality data of the plurality of drainage facilities, the initial model is trained to obtain a digital model of the drainage system, wherein the initial model is used to indicate the spatial positions of the plurality of drainage facilities in the drainage network and the corresponding connection relationships; Controlling the operation of the drainage system digital model and determining output parameters of the drainage system digital model, wherein the output parameters are used to indicate water delivery capacity parameters of multiple drainage facilities in the drainage network; Based on the water transfer capacity parameters of the plurality of drainage facilities, a plurality of potential bottleneck units of the drainage network are determined, wherein the potential bottleneck unit is a drainage facility among the plurality of drainage facilities.

3. The method according to claim 2, characterized in that The number of the multiple rainfall events is three. The historical hydrodynamic and water quality data of the multiple drainage facilities are used to train the initial model to obtain a digital model of the drainage system, including: Inputting the first historical rainfall measured data into the initial model to simulate the hydrodynamics and water quality, and obtaining a first simulation value corresponding to the first historical rainfall measured data output by the initial model; Determining a first degree of fit of the initial model according to the first simulated value and the first historical rainfall measured data; When the first degree of fit is greater than a first threshold, determining that the parameter calibration of the initial model is completed, updating the parameters of the initial model to calibration parameters, and obtaining the initial model after parameter calibration; Inputting the measured data of the second historical rainfall into the initial model after parameter calibration to simulate the hydrodynamics and water quality, and obtaining a second simulation value corresponding to the measured data of the second historical rainfall output by the initial model after parameter calibration; Determine a second degree of fit of the initial model after parameter calibration according to the second simulated value and the second historical rainfall measured data; When the second degree of fit is greater than a second threshold, determining that the model parameters of the initial model after parameter calibration pass the verification, and obtaining the initial model after parameter verification; Based on the measured data of the third historical rainfall and the initial model after the parameters are verified, a digital model of the drainage system is obtained.

4. The method according to claim 2, characterized in that: There are multiple water delivery capacity parameters, and determining at least one transformation strategy corresponding to the potential bottleneck unit includes: At least one transformation strategy corresponding to the potential bottleneck unit is determined based on a plurality of water delivery capacity parameters corresponding to the potential bottleneck unit.

5. The method according to claim 1, characterized in that The method further comprises: When the first-order sensitivity index is not greater than a third threshold, determining whether the first-order sensitivity index is within a first threshold range; If the first-order sensitivity index is within a first threshold range, determining that the potential bottleneck unit is a secondary dominant bottleneck unit; If the first-order sensitivity index is outside the first threshold range, determining the potential bottleneck unit as a non-dominant bottleneck unit; According to the total amount of overflow pollution of the drainage system corresponding to each transformation strategy of the multiple potential bottleneck units, determining the transformation strategy with the smallest total amount of overflow pollution of the drainage system as the optimal transformation strategy; The sub-dominant bottleneck units and non-dominant bottleneck units in the optimal transformation strategy are removed from the optimal transformation strategy to obtain an updated optimal transformation strategy, and the updated optimal transformation strategy is used as a target strategy to optimize the transformation of the drainage network.

6. A device for identifying the dominant bottleneck unit of a drainage network, characterized in that: The device comprises: A processing module, configured to analyze and process the drainage network based on a drainage system mathematical model corresponding to the drainage network, so as to obtain a plurality of potential bottleneck units of the drainage network, wherein the drainage system mathematical model is obtained by training based on a plurality of historical rainfall measured data and water transfer capacity parameters of a plurality of drainage facilities in the drainage network; A determination module, configured to determine, for any one of the plurality of potential bottleneck units, at least one transformation strategy corresponding to the potential bottleneck unit; The determination module is further used to determine a target sensitivity index of each potential bottleneck unit based on a plurality of transformation strategies corresponding to the plurality of potential bottleneck units; The processing module is further used to identify a dominant bottleneck unit from the multiple potential bottleneck units according to multiple target sensitivity indexes; The determination module is further configured to determine the total amount of drainage system overflow pollution corresponding to each transformation strategy based on the multiple transformation strategies corresponding to the multiple potential bottleneck units, when each potential bottleneck unit is in at least one corresponding transformation strategy; The determination module is further used to determine the first-order sensitivity index and the second-order sensitivity index of each potential bottleneck unit according to the multiple transformation strategies and the total amount of overflow pollution of the drainage system corresponding to each transformation strategy; wherein the first-order sensitivity index represents the influence degree of the total amount of overflow pollution of a single bottleneck unit, and the second-order sensitivity index represents the influence degree of the total amount of overflow pollution when two bottleneck units interact; The processing module is further used to use the sum of the first-order sensitivity index and the second-order sensitivity index as the target sensitivity index of the corresponding potential bottleneck unit; The drainage network dominant bottleneck unit identification device further includes: a judgment module; The judging module is used to judge, for any one of the multiple potential bottleneck units, whether the first-order sensitivity index of the potential bottleneck unit is less than the corresponding target sensitivity index; The judging module is further configured to judge whether the first-order sensitivity index is greater than a third threshold value when the first-order sensitivity index is less than the target sensitivity index; The determination module is further configured to determine that the potential bottleneck unit is a dominant bottleneck unit when the first-order sensitivity index is greater than a third threshold.

7. A device for identifying the dominant bottleneck unit of a drainage network, characterized in that: include: Memory; processor; Wherein, the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for identifying the dominant bottleneck unit of a drainage network according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that The computer storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the method for identifying the dominant bottleneck unit of a drainage network according to any one of claims 1 to 5.

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