CN115618769B - Drainage system evaluation method and system based on hydraulic model - Google Patents
Drainage system evaluation method and system based on hydraulic model Download PDFInfo
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Abstract
The embodiment of the application discloses a drainage system evaluation method and a drainage system evaluation system based on a hydraulic model, which belong to the technical field of drainage systems, wherein the drainage system evaluation method based on the hydraulic model comprises the following steps: acquiring related information of a drainage system, and establishing a hydraulic model of the drainage system based on the related information of the drainage system, wherein the hydraulic model of the drainage system is used for simulating the running states of the drainage system under different drainage requirement scenes; determining drainage efficiency of the drainage system in a target drainage demand scene based on a drainage system hydraulic model; the drainage system optimization scheme is determined based on the drainage system hydraulic model and the drainage efficiency of the drainage system in the target drainage demand scene, and the drainage system optimization method has the advantages of being more efficient, comprehensive and intelligent in evaluating and optimizing the drainage system.
Description
Technical Field
The application mainly relates to the technical field of drainage systems, in particular to a drainage system evaluation method and system based on a hydraulic model.
Background
The urban drainage system is generally composed of all or part of elements such as pipe networks, pump stations, sewage treatment plants, storage/purification facilities, receiving water bodies and the like, and the facilities are communicated with each other and run in a linkage way to jointly execute the tasks of urban waterlogging prevention and treatment and sewage treatment. However, urban drainage systems are generally built gradually along with urban development, concepts and technologies in various stages are developed, and defects in value construction and management result in insufficient matching among facilities, so that a series of problems are brought, such as direct drainage of sewage in dry days, combined overflow pollution in rainy days, high-water-level operation of a pipe network and the like. The existing drainage management technology only realizes the visualization of drainage pipe network data, and lacks a professional analysis function; still further, the old set of modes of paper management and experience management are still in use.
Accordingly, there is a need for a drainage system assessment method and system based on a hydraulic model for more efficient, comprehensive, and intelligent assessment and optimization of drainage systems.
Disclosure of Invention
In order to solve the technical problems that the existing drainage management technology only realizes the visualization of drainage pipe network data, lacks a professional analysis function and cannot realize systematic evaluation and optimization of a drainage system, one embodiment of the specification provides a drainage system evaluation method based on a hydraulic model, which comprises the following steps: acquiring related information of a drainage system, and establishing a hydraulic model of the drainage system based on the related information of the drainage system, wherein the hydraulic model of the drainage system is used for simulating the running states of the drainage system under different drainage requirement scenes; determining drainage efficiency of the drainage system in a target drainage demand scene based on the drainage system hydraulic model; and determining an optimization scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene.
In some embodiments, the acquiring drainage system related information, building a drainage system hydraulic model based on the drainage system related information, comprises: acquiring the drainage system related information, wherein the drainage system related information comprises shallow drainage system related information and deep tunnel drainage system related information; establishing a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model based on the related information of the shallow drainage system; establishing a deep tunnel drainage system model based on the related information of the deep tunnel drainage system; and connecting the rainwater shallow drainage system hydraulic model and the sewage shallow drainage system hydraulic model with the deep tunnel drainage system model to establish the drainage system hydraulic model.
In some embodiments, the method further comprises: acquiring drainage data of the drainage system in a historical drainage demand scene, wherein the drainage data comprises real water flow data of a plurality of monitoring nodes of the drainage system; acquiring simulated water flow data of a plurality of virtual nodes corresponding to the drainage system hydraulic model on the drainage system hydraulic model under the historical drainage demand scene; and determining the reality of the hydraulic model of the drainage system based on the real water flow data and the virtual water flow data.
In some embodiments, said adjusting the drainage system hydraulic model based on the fidelity comprises: and when the reality is smaller than a preset threshold, adjusting model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise surface production confluence parameters, pipeline roughness coefficients and pipeline sediments.
In some embodiments, the determining drainage effectiveness of the drainage system in the target drainage demand scenario based on the drainage system hydraulic model comprises: determining the negative pressure condition of a sewage pipeline of the sewage shallow layer drainage system hydraulic model in a dry-land scene based on the drainage system hydraulic model; determining the water level condition of a canal and the waterlogging risk level of the hydraulic model of the rainwater shallow drainage system under a target rainfall intensity scene based on the hydraulic model of the drainage system; and determining the drainage efficiency of the drainage system based on the sewage pipeline negative pressure condition, the canal water level condition and the waterlogging risk level.
One of the embodiments of the present specification provides a drainage system evaluation system based on a hydraulic model, the system comprising: the system comprises an information acquisition module, a water drainage system hydraulic model and a water drainage system control module, wherein the information acquisition module is used for acquiring related information of a water drainage system and establishing the water drainage system hydraulic model based on the related information of the water drainage system, and the water drainage system hydraulic model is used for simulating the running state of the water drainage system under different water drainage requirement scenes; the system evaluation module is used for determining the drainage efficiency of the drainage system in a target drainage demand scene based on the drainage system hydraulic model; the system optimization module is used for determining an optimization scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene.
In some embodiments, the information acquisition module is further to: acquiring the drainage system related information, wherein the drainage system related information comprises shallow drainage system related information and deep tunnel drainage system related information; establishing a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model based on the related information of the shallow drainage system; establishing a deep tunnel drainage system model based on the related information of the deep tunnel drainage system; and connecting the rainwater shallow drainage system hydraulic model and the sewage shallow drainage system hydraulic model with the deep tunnel drainage system model to establish the drainage system hydraulic model.
In some embodiments, the information acquisition module is further to: acquiring drainage data of the drainage system in a historical drainage demand scene, wherein the drainage data comprises real water flow data of a plurality of monitoring nodes of the drainage system; acquiring simulated water flow data of a plurality of virtual nodes corresponding to the drainage system hydraulic model on the drainage system hydraulic model under the historical drainage demand scene; determining the reality of the hydraulic model of the drainage system based on the real water flow data and the virtual water flow data; and adjusting the hydraulic model of the drainage system based on the reality degree until the reality degree of the adjusted hydraulic model of the drainage system meets the preset condition.
In some embodiments, the information acquisition module is further to: and when the reality is smaller than a preset threshold, adjusting model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise a runoff coefficient, a confluence parameter, a pipeline roughness coefficient and pipeline sediments.
In some embodiments, the system evaluation module is further to: determining the negative pressure condition of a sewage pipeline of the sewage shallow layer drainage system hydraulic model in a dry-land scene based on the drainage system hydraulic model; determining the water level condition of a canal and the waterlogging risk level of the hydraulic model of the rainwater shallow drainage system under a target rainfall intensity scene based on the hydraulic model of the drainage system; and determining the drainage efficiency of the drainage system based on the sewage pipeline negative pressure condition, the canal water level condition and the waterlogging risk level.
Drawings
The application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a drainage system evaluation system based on a hydraulic model according to some embodiments of the present application;
FIG. 2 is a block diagram of a drainage system evaluation system based on a hydraulic model according to some embodiments of the application;
FIG. 3 is an exemplary flow chart of a drainage system assessment method based on a hydraulic model, according to some embodiments of the application;
FIG. 4 is an exemplary flow chart for creating a hydraulic model of a drainage system according to some embodiments of the application;
FIG. 5 is an exemplary flow chart for adjusting a hydraulic model of a drainage system according to some embodiments of the application.
In the figure, 100, an application scene; 110. a processing device; 120. a network; 130. a user terminal; 140. a storage device.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is apparent to those of ordinary skill in the art that the present application may be applied to other similar situations according to the drawings without inventive effort. It should be understood that these exemplary embodiments are presented merely to enable those skilled in the relevant art to better understand and practice the application and are not intended to limit the scope of the application in any way. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on clients and/or servers. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic diagram of an application scenario 100 of a drainage system evaluation system 200 based on a hydraulic model according to some embodiments of the present application.
As shown in fig. 1, the application scenario 100 may include a processing device 110, a network 120, a user terminal 130, and a storage device 140.
In some embodiments, the processing device 110 may be used to process information and/or data related to drainage system assessment and optimization. For example, the processing device 110 may be configured to obtain drainage system related information, and build a drainage system hydraulic model based on the drainage system related information, where the drainage system hydraulic model is configured to simulate an operational state of the drainage system in different drainage demand scenarios; determining drainage efficiency of the drainage system in a target drainage demand scene based on a drainage system hydraulic model; and determining an optimization scheme of the drainage system based on the drainage efficiency of the drainage system in the target drainage demand scene.
In some embodiments, the processing device 110 may be regional or remote. For example, the processing device 110 may access information and/or material stored in the user terminal 130 and the storage device 140 via the network 120. In some embodiments, processing device 110 may be directly connected to user terminal 130 and storage device 140 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. For example, the cloud platform may include one of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, or the like, or any combination thereof. In some embodiments, the processing device 110 may comprise a processor, which may comprise one or more sub-processors (e.g., a single core processing device or a multi-core processing device). By way of example only, a processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an editable logic circuit (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like, or any combination thereof.
The network 120 may facilitate the exchange of data and/or information in the application scenario 100. In some embodiments, one or more components in the application scenario 100 (e.g., the processing device 110, the user terminal 130, and the storage device 140) may send data and/or information to other components in the application scenario 100 through the network 120. For example, the processing device 110 may obtain drainage system related information from the storage device 140 via the network 120. In some embodiments, network 120 may be any type of wired or wireless network. For example, the network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an internal network, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, and the like, or any combination thereof.
The user terminal 130 may acquire information or data in the application scenario 100, and the user (e.g., a manager of the drainage system) may be a user of the user terminal 130. For example, the user terminal 130 may obtain the drainage performance of the drainage system in the target drainage requirement scenario from the processing device 110 through the network 120. In some embodiments, the user terminal 130 may include one or any combination of a mobile device, a tablet, a notebook, etc. In some embodiments, the mobile device may include a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof.
In some embodiments, the storage device 140 may be connected to the network 120 to enable communication with one or more components of the application scenario 100 (e.g., the processing device 110, the user terminal 130, etc.). One or more components of the application scenario 100 may access materials or instructions stored in the storage device 140 through the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components (e.g., the processing device 110, the user terminal 130) in the application scenario 100. In some embodiments, the storage device 140 may be part of the processing device 110.
It should be noted that the foregoing description is provided for the purpose of illustration only and is not intended to limit the scope of the present application. Many variations and modifications will be apparent to those of ordinary skill in the art, given the benefit of this disclosure. The features, structures, methods, and other features of the described exemplary embodiments of the application may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the storage device 140 may be a data storage device including a cloud computing platform, such as a public cloud, a private cloud, a community, a hybrid cloud, and the like. However, such changes and modifications do not depart from the scope of the present application.
FIG. 2 is a block diagram of a drainage system evaluation system based on a hydraulic model, according to some embodiments of the application.
As shown in fig. 2, the drainage system evaluation system based on the hydraulic model may include an information acquisition module, a system evaluation module, and a system optimization module.
The information acquisition module can be used for acquiring related information of the drainage system and establishing a hydraulic model of the drainage system based on the related information of the drainage system, wherein the hydraulic model of the drainage system is used for simulating the running states of the drainage system under different drainage requirement scenes.
The system evaluation module may be configured to determine drainage effectiveness of the drainage system in a target drainage demand scenario based on the drainage system hydraulic model.
The system optimization module can be used for determining an optimization scheme of the drainage system based on the drainage efficiency of the drainage system under the target drainage demand scene.
For more description of the information acquisition module, the system evaluation module, and the system optimization module, refer to fig. 3 and related descriptions thereof, and are not repeated here.
FIG. 3 is an exemplary flow chart of a drainage system assessment method based on a hydraulic model, according to some embodiments of the application. In some embodiments, the hydraulic model-based drainage system evaluation method may be performed by a hydraulic model-based drainage system evaluation system. As shown in fig. 3, the drainage system evaluation method based on the hydraulic model may include the following steps.
Step 310, acquiring drainage system related information, and establishing a drainage system hydraulic model based on the drainage system related information. In some embodiments, step 310 may be performed by an information acquisition module.
The drainage system related information may be information related to the establishment of the drainage system. In some embodiments, the drainage system related information may include shallow drainage system related information and deep tunnel drainage system related information, where the shallow drainage system related information may be information related to a shallow drainage system in a drainage system, for example, information related to facilities such as a shallow drainage pipe network, an inspection well, a pipe channel, a drain, a gate, a weir, a pump station, and the like; the deep tunnel drainage system related information may be information related to the deep tunnel drainage system in the drainage system, for example, planning design data (e.g., drawing, etc.) of the deep tunnel drainage system.
In some embodiments, the information acquisition module may acquire drainage system related information from the user terminal 130, the storage device 140, and/or an external data source.
In some embodiments, in conjunction with fig. 4, obtaining drainage system related information, building a drainage system hydraulic model based on the drainage system related information, comprises:
Acquiring related information of a drainage system;
establishing a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model based on the related information of the shallow drainage system;
establishing a deep tunnel drainage system model based on the related information of the deep tunnel drainage system;
and connecting the rainwater shallow drainage system hydraulic model and the sewage shallow drainage system hydraulic model with the deep tunnel drainage system model to establish the drainage system hydraulic model.
For example, the information acquisition module can input information related to facilities such as an inner shallow drainage pipe network, an inspection well, a pipe canal, a drain outlet, a gate, a weir, a pump station and the like into a Infooks model platform to complete topology structure carding and sub-water area division of the drainage system, and construct a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model corresponding to the drainage system; the information acquisition module can establish a deep tunnel drainage system model by using a large-caliber pipe channel according to planning and design data of the deep tunnel drainage system, and the route, pipe diameter scale and hydraulic factors in the deep tunnel drainage system model are kept consistent with the planning data, and are connected and coupled with the rainwater shallow drainage system hydraulic model and the sewage shallow drainage system hydraulic model through inflow facilities, so that the construction of the drainage system hydraulic model is completed.
In some embodiments, after the hydraulic model of the drainage system is built, the information acquisition module may perform initial setting of model parameters of the hydraulic model of the drainage system according to characteristics of the drainage pipeline and the water collecting area, reference specifications, guidelines, and related research results. The model parameters may include, among others, runoff coefficients, confluence parameters, pipe roughness coefficients, pipe deposits.
In some embodiments, as shown in table 1, the initial set value of the pipe roughness coefficient may be determined according to the class of the canal.
TABLE 1
| Channel class | Coefficient of pipe roughness |
| UPVC pipe, PE pipe and glass fiber reinforced plastic pipe | 0.009~0.011 |
| Asbestos cement pipe and steel pipe | 0.012 |
| Ceramic pipe and cast iron pipe | 0.013 |
| Concrete pipe, reinforced concrete pipe and cement mortar plastering channel | 0.013~0.014 |
| Slurry brick channel | 0.015 |
| Stone block channel | 0.017 |
| Dry masonry channel | 0.020~0.025 |
| Soil open channel | 0.025~0.030 |
In some embodiments, the initial set-up value of the pipe deposit may be determined by spot check and/or human evaluation of the pipe.
It can be understood that the types of the urban under-ground mat can be divided into five types of vegetation, water systems, pavement, houses and roads, wherein the houses, the roads and the water systems are non-permeable ground surfaces, and the ground surface production confluence parameters corresponding to different types of mat are different, for example, the ground surface production confluence parameters of the houses are 0.85-0.095, the ground surface production confluence parameters of the roads are 0.85-0.095, and the ground surface production confluence parameters of the water systems are 1. The information acquisition module can determine the surface production confluence parameter according to the pad surface type.
In some embodiments, in conjunction with fig. 5, to make the simulation results of the drainage system hydraulic model for evaluation more reliable, the information acquisition module may also be used to:
acquiring drainage data of a drainage system in a historical drainage demand scene, wherein the drainage data comprises real water flow data of a plurality of monitoring nodes of the drainage system;
acquiring simulated water flow data of a plurality of monitoring nodes corresponding to a plurality of virtual nodes on the drainage system hydraulic model under a historical drainage demand scene;
determining the reality of a hydraulic model of the drainage system based on the real water flow data and the virtual water flow data;
and adjusting the hydraulic model of the drainage system based on the reality degree until the reality degree of the adjusted hydraulic model of the drainage system meets the preset condition, wherein the preset condition can be that the reality degree of the hydraulic model of the drainage system is larger than a preset threshold value.
In some embodiments, the monitoring nodes may be disposed in the drain main, the sub-partition junction pipeline, and key nodes that can reflect the upstream drain condition, and an on-line monitoring device may be installed at the monitoring nodes to obtain real water flow data, where the on-line monitoring device may include a flowmeter, a level meter, and the like. The on-line monitoring equipment is set to monitor the water quantity information every 15 minutes in a normal state of the monitoring frequency, and the monitoring time is on-line continuous monitoring. The real water flow data can be calculated flow, water level, accumulated water depth and the like of the monitoring node at a plurality of real time points.
In some embodiments, the information obtaining module may determine a rainfall and a sewage treatment capacity corresponding to the hydraulic model of the drainage system based on the historical drainage demand scene, where the hydraulic model of the drainage system operates to obtain simulated water flow data of a plurality of virtual nodes corresponding to the plurality of monitoring nodes on the hydraulic model of the drainage system. The simulated water flow data may include calculated flow, water level, water accumulation depth, etc. of the monitoring node at a plurality of virtual time points corresponding to the plurality of real time points at a corresponding virtual node on the hydraulic model of the drainage system.
In some embodiments, the information acquisition module may determine the similarity of the real water flow data to the simulated water flow data based on a similarity algorithm (e.g., a jaccard similarity coefficient algorithm, a cosine similarity algorithm, etc.). It can be appreciated that the higher the similarity, the higher the realism of the hydraulic model of the drainage system. For example, the deviation amplitude of the simulated value and the true value of the water quantity and the water level of the monitoring node is not large, and the deviation rate is basically smaller than +/-15%, which indicates that the hydraulic model of the drainage system is high in reality and high in precision.
It can be understood that the information acquisition module can determine the reality of the hydraulic model of the drainage system by using the real water flow data and the simulated water flow data corresponding to the plurality of historical drainage demand scenes. Specifically, in some embodiments, the information obtaining module may determine, based on a similarity algorithm (e.g., a jaccard similarity coefficient algorithm, a cosine similarity algorithm, etc.), a similarity between real water flow data and simulated water flow data corresponding to a plurality of historical water drainage demand scenarios, and determine, based on a similarity mean corresponding to the plurality of historical water drainage demand scenarios, a true degree of the hydraulic model of the water drainage system.
The actual water flow data of the monitoring node at the simulation calculation 14 under the rainfall condition A is matched with the simulated water flow data, the water level and flow peak value deviation of the simulated water flow data and the actual water flow data are within +/-10%, and the peak value time deviation is less than 1h; the accumulated water of the simulation node of the monitoring node at the ground surface 39 under the rainfall condition B is identical with the accumulated water of the monitoring node, the coincidence degree is 83%, the simulation water flow data and the real water flow data are basically consistent, and the reality degree of the hydraulic model of the drainage system meets the preset condition.
In some embodiments, the information acquisition module may also be configured to:
and when the reality is smaller than a preset threshold, adjusting model parameters of the hydraulic model of the drainage system.
It can be understood that the simulated water flow data of a plurality of virtual nodes corresponding to the plurality of monitoring nodes on the drainage system hydraulic model can be obtained under the historical drainage demand scene of the adjusted drainage system hydraulic model, the authenticity of the drainage system hydraulic model is determined, and if the authenticity of the drainage system hydraulic model still does not meet the preset condition, the model parameters of the drainage system hydraulic model can be repeatedly adjusted until the authenticity of the drainage system hydraulic model after adjustment meets the preset condition.
Step 320, determining drainage efficiency of the drainage system in the target drainage demand scene based on the drainage system hydraulic model. In some embodiments, step 320 may be performed by a system evaluation module.
The drainage effectiveness may be used to characterize the drainage performance of the drainage system. The target drainage demand scenario may correspond to rainfall intensity and sewage treatment capacity.
In some embodiments, determining drainage effectiveness of the drainage system in the target drainage demand scenario based on the drainage system hydraulic model may include:
determining the negative pressure condition of a sewage pipeline of the hydraulic model of the sewage shallow layer drainage system under a dry-day scene based on the hydraulic model of the drainage system, wherein the dry-day scene is a scene when the rainfall is 0;
determining the water level condition of a canal and the waterlogging risk level of the hydraulic model of the rainwater shallow layer drainage system under a target rainfall intensity scene based on the hydraulic model of the drainage system;
and determining the drainage efficiency of the drainage system based on the sewage pipeline negative pressure condition, the canal water level condition and the waterlogging risk level.
In some embodiments, the sewer line negative pressure condition may include a negative pressure pipe diameter ratio and distribution. For example, for city A, the drainage system hydraulic model simulates drought The total length of a main pipeline (DN is more than or equal to 700) of the negative pressure (water head line is higher than the pipe top) of the sewage system under a sky scene is 243.39km, the total length of the main pipeline of the sewage is 475.3km, the negative pressure pipe diameter accounts for 51.2%, the distribution of the negative pressure pipe diameters is subjected to regional statistical analysis, the negative pressure pipeline is mainly distributed in a sixth drainage partition and a seventh drainage partition, the proportion of the negative pressure pipeline in the region is as high as 92.6 percent and 64.36 percent, the negative pressure pipeline is mainly positioned under a main road within a three-ring, and the total area of the negative pressure region is 241km 2 。
The canal level conditions may be used to characterize the drainage capacity of the canal in a target rainfall intensity scenario.
The waterlogging risk level can be used for representing the waterlogging risk degree of the drainage system under the target rainfall intensity scene, and in combination with table 2, the system evaluation module can determine the waterlogging risk level based on the ponding depth and the ponding time.
TABLE 2
| Waterlogging risk level | Depth of accumulated water | Water accumulation time |
| Slightly water accumulation | ≤0.15m | ＜1h |
| Slight inland inundation | 0.15-0.4m | 1-2h |
| Severe inland inundation | ＞0.4 m | ＞2h |
In some embodiments, the system evaluation module may determine the drainage effectiveness of the drainage system based on the sewer line negative pressure condition, the tank water level condition, and the waterlogging risk level through the effectiveness determination model. The efficiency determining model may be a machine learning model for determining drainage efficiency of the drainage system, the input of the efficiency determining model may be a sewage pipeline negative pressure condition, a canal water level condition and a waterlogging risk level, and the output of the efficiency determining model may be the drainage efficiency of the drainage system. In some embodiments, the area obtaining module 210 may update the parameters of the initial performance determining model based on the first training sample until the trained initial performance determining model meets the preset condition, to obtain the trained performance determining model. The first training sample may include a sample sewer line negative pressure condition, a sample trench tank water level condition, and a sample waterlogging risk level, and the label of the first training sample is a drainage efficiency of the drainage system. The preset condition may be that the loss function converges, the loss function value is smaller than a preset value, or the iteration number is larger than a preset number, etc. In some embodiments, the tag of the first training sample may be obtained in any manner, e.g., by manually determining the tag of the first training sample, and, e.g., by an external data source. Types of machine learning models include, but are not limited to, neural Networks (NNs), convolutional Neural Networks (CNNs), deep Neural Networks (DNNs), recurrent Neural Networks (RNNs), etc., or any combination thereof, for example, the performance determination model may be a model formed by a combination of convolutional and deep neural networks.
In some embodiments, the system evaluation module may generate a virtual sample sewage pipeline negative pressure condition, a sample canal tank water level condition, and a sample waterlogging risk level by the countermeasure network based on the real sample sewage pipeline negative pressure condition, the sample canal tank water level condition, and the sample waterlogging risk level, and update parameters of the initial performance determination model by using the virtual sample sewage pipeline negative pressure condition, the sample canal tank water level condition, and the sample waterlogging risk level until the trained initial performance determination model meets a preset condition, thereby obtaining a trained performance determination model.
Step 330, determining an optimization scheme of the drainage system based on the drainage system hydraulic model and the drainage efficiency of the drainage system in the target drainage demand scene. In some embodiments, step 330 may be performed by a system optimization module.
When the drainage efficiency of the drainage system does not meet the preset efficiency threshold, the system optimization module can determine an optimization scheme of the drainage system based on the drainage system hydraulic model and the drainage efficiency of the drainage system in the target drainage demand scene. Wherein, the optimization scheme can comprise: (1) According to the current situation of a drainage system, intercepting the primary rain in a region with high implementation difficulty of intercepting the green sponge into a deep tunnel drainage system for treatment; (2) According to the design functions of the deep tunnel, including sewage regulation, initial rain regulation and waterlogging regulation, inflow control conditions of the deep tunnel shaft and the shallow drainage pipeline and end outflow control conditions are determined; (3) The deep tunnel drainage system can provide emergency regulation capacity for the sewage system, and can be accessed into the deep tunnel system through a sewage main pipe through a two-three-level tunnel when a sewage accident occurs, so that sewage emergency dispatch and accident excessive regulation are realized, and guarantee and support are provided for safe operation of sewage plants and drainage pipe networks; (4) Considering the current situation of the construction strength, the difficulty of shallow water logging treatment, the distance from the river water body and other factors, and comprehensively considering the water logging amount of a plurality of current situation easy-to-waterlogging areas to be intercepted and enter a deep tunneling drainage system for treatment.
In some embodiments, the system optimization module may determine the optimization scheme of the drainage system based on the drainage system hydraulic model and the drainage efficiency of the drainage system in the target drainage demand scenario by hand.
In some embodiments, the system optimization module may determine an optimization scheme of the drainage system based on the drainage system hydraulic model and the drainage effectiveness of the drainage system in the target drainage demand scenario through the system optimization model. The system optimization model can be a machine learning model for determining an optimization scheme of the drainage system, the input of the system optimization model can be information for representing a hydraulic model of the drainage system and drainage efficiency of the drainage system in a target drainage demand scene, and the output of the system optimization model can be the optimization scheme of the drainage system. In some embodiments, the area obtaining module 210 may update the parameters of the initial system optimization model based on the second training sample until the trained initial system optimization model meets the preset condition, to obtain the trained system optimization model. Types of system optimization models include, but are not limited to, neural Networks (NNs), convolutional Neural Networks (CNNs), deep Neural Networks (DNNs), recurrent Neural Networks (RNNs), etc., or any combination thereof, for example, the machine learning model may be a model formed by a combination of convolutional and deep neural networks.
In further embodiments of the present application, there is provided a drainage system assessment device based on a hydraulic model, comprising at least one treatment apparatus and at least one storage apparatus; at least one memory device is configured to store computer instructions and at least one processing device is configured to execute at least some of the computer instructions to implement a hydraulic model-based drainage system assessment method as described above.
In still other embodiments of the present application, a computer readable storage medium is provided, the storage medium storing computer instructions that when executed by a processing device implement a hydraulic model-based drainage system assessment method as above.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are required by the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if the description, definition, and/or use of the term in the appended claims does not conform to or conflict with the present disclosure.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly described and depicted herein.
Claims (10)
1. A drainage system evaluation method based on a hydraulic model, comprising:
acquiring related information of a drainage system, and establishing a hydraulic model of the drainage system based on the related information of the drainage system, wherein the hydraulic model of the drainage system is used for simulating the running states of the drainage system under different drainage requirement scenes;
determining drainage efficiency of the drainage system in a target drainage demand scene based on the drainage system hydraulic model;
determining an optimization scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene;
the obtaining drainage system related information, establishing a drainage system hydraulic model based on the drainage system related information, includes:
Information related to inner shallow drainage pipe networks, inspection wells, pipe channels, discharge ports, gates, weirs and pump station facilities is input into a Inoworks model platform, topology structure carding and sub-water area division of a drainage system are completed, and a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model corresponding to the drainage system are constructed;
establishing a deep tunnel drainage system model by using a large-caliber pipe channel according to planning and design data of the deep tunnel drainage system, wherein routing, pipe diameter scale and hydraulic factors in the deep tunnel drainage system model are kept consistent with the planning data;
the construction of the hydraulic model of the drainage system is completed through the connection and coupling of the inflow facility with the hydraulic model of the rainwater shallow drainage system and the hydraulic model of the sewage shallow drainage system;
performing initial setting of model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise a runoff coefficient, a converging parameter, a pipeline roughness coefficient and pipeline sediments, the initial setting value of the pipeline roughness coefficient is determined according to the type of a filling channel, and the converging parameter is determined based on the type of a pad surface;
the determining drainage efficiency of the drainage system in the target drainage demand scene based on the drainage system hydraulic model comprises the following steps:
Determining the negative pressure condition of a sewage pipeline of the sewage shallow layer drainage system hydraulic model in a dry-land scene based on the drainage system hydraulic model;
determining the water level condition of a canal and the waterlogging risk level of the hydraulic model of the rainwater shallow drainage system under a target rainfall intensity scene based on the hydraulic model of the drainage system;
determining drainage efficiency of a drainage system based on the sewage pipeline negative pressure condition, the canal tank water level condition and the waterlogging risk level;
the method for determining the optimal scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene comprises the following steps:
when the drainage efficiency of the drainage system in a target drainage demand scene, generating an optimization scheme of the drainage system based on an optimization condition and a hydraulic model of the drainage system, wherein the optimization condition at least comprises: according to the current situation of a drainage system, intercepting the primary rain in a region with high implementation difficulty of intercepting the green sponge into a deep tunnel drainage system for treatment; according to the design functions of the deep tunnel, including sewage regulation, initial rain regulation and waterlogging regulation, inflow control conditions of the deep tunnel shaft and the shallow drainage pipeline and end outflow control conditions are determined; the deep tunnel drainage system provides emergency storage capacity for the sewage system, and the sewage system is accessed through a sewage main pipe through a two-level tunnel and a three-level tunnel when an accident occurs; considering the current situation of construction strength, difficulty of shallow water logging control and distance from river water body, the waterlogging amount of a plurality of current situation easy waterlogging areas is intercepted and enters a deep tunneling drainage system for treatment.
2. The drainage system evaluation method based on a hydraulic model according to claim 1, further comprising:
acquiring drainage data of the drainage system in a historical drainage demand scene, wherein the drainage data comprises real water flow data of a plurality of monitoring nodes of the drainage system;
acquiring simulated water flow data of a plurality of virtual nodes corresponding to the drainage system hydraulic model on the drainage system hydraulic model under the historical drainage demand scene;
determining the reality of the hydraulic model of the drainage system based on the real water flow data and the simulated water flow data;
and adjusting the hydraulic model of the drainage system based on the reality degree until the reality degree of the adjusted hydraulic model of the drainage system meets the preset condition.
3. The drainage system evaluation method based on a hydraulic model according to claim 2, wherein the adjusting the drainage system hydraulic model based on the degree of realism comprises:
and when the reality is smaller than a preset threshold, adjusting model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise surface production confluence parameters, pipeline roughness coefficients and pipeline sediments.
4. A drainage system assessment method based on a hydraulic model according to any one of claims 1-3, wherein determining the channel water level condition and the waterlogging risk level of the hydraulic model of the shallow-rain drainage system in a target rainfall intensity scene based on the hydraulic model of the drainage system comprises:
and determining the waterlogging risk level based on the ponding depth and the ponding time.
5. A drainage system assessment method based on a hydraulic model according to any one of claims 1-3, further comprising:
generating a virtual sample sewage pipeline negative pressure condition, a sample canal tank water level condition and a sample waterlogging risk level based on a real sample sewage pipeline negative pressure condition, a sample canal tank water level condition and the sample waterlogging risk level by an countermeasure network, and updating parameters of an initial efficiency determination model by using the virtual sample sewage pipeline negative pressure condition, the sample canal tank water level condition and the sample waterlogging risk level until the trained initial efficiency determination model meets preset conditions, so as to obtain a trained efficiency determination model.
6. A drainage system evaluation system based on a hydraulic model, comprising:
The system comprises an information acquisition module, a water drainage system hydraulic model and a water drainage system control module, wherein the information acquisition module is used for acquiring related information of a water drainage system and establishing the water drainage system hydraulic model based on the related information of the water drainage system, and the water drainage system hydraulic model is used for simulating the running state of the water drainage system under different water drainage requirement scenes;
the information acquisition module is further configured to:
information related to inner shallow drainage pipe networks, inspection wells, pipe channels, discharge ports, gates, weirs and pump station facilities is input into a Inoworks model platform, topology structure carding and sub-water area division of a drainage system are completed, and a rainwater shallow drainage system hydraulic model and a sewage shallow drainage system hydraulic model corresponding to the drainage system are constructed;
establishing a deep tunnel drainage system model by using a large-caliber pipe channel according to planning and design data of the deep tunnel drainage system, wherein routing, pipe diameter scale and hydraulic factors in the deep tunnel drainage system model are kept consistent with the planning data;
the construction of the hydraulic model of the drainage system is completed through the connection and coupling of the inflow facility with the hydraulic model of the rainwater shallow drainage system and the hydraulic model of the sewage shallow drainage system;
performing initial setting of model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise a runoff coefficient, a converging parameter, a pipeline roughness coefficient and pipeline sediments, the initial setting value of the pipeline roughness coefficient is determined according to the type of a filling channel, and the converging parameter is determined based on the type of a pad surface;
The system evaluation module is used for determining the drainage efficiency of the drainage system in a target drainage demand scene based on the drainage system hydraulic model;
the system optimization module is used for determining an optimization scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene;
the determining drainage efficiency of the drainage system in the target drainage demand scene based on the drainage system hydraulic model comprises the following steps:
determining the negative pressure condition of a sewage pipeline of the sewage shallow layer drainage system hydraulic model in a dry-land scene based on the drainage system hydraulic model;
determining the water level condition of a canal and the waterlogging risk level of the hydraulic model of the rainwater shallow drainage system under a target rainfall intensity scene based on the hydraulic model of the drainage system;
determining drainage efficiency of a drainage system based on the sewage pipeline negative pressure condition, the canal tank water level condition and the waterlogging risk level;
the method for determining the optimal scheme of the drainage system based on the hydraulic model of the drainage system and the drainage efficiency of the drainage system in a target drainage demand scene comprises the following steps:
When the drainage efficiency of the drainage system in a target drainage demand scene, generating an optimization scheme of the drainage system based on an optimization condition and a hydraulic model of the drainage system, wherein the optimization condition at least comprises: according to the current situation of a drainage system, intercepting the primary rain in a region with high implementation difficulty of intercepting the green sponge into a deep tunnel drainage system for treatment; according to the design functions of the deep tunnel, including sewage regulation, initial rain regulation and waterlogging regulation, inflow control conditions of the deep tunnel shaft and the shallow drainage pipeline and end outflow control conditions are determined; the deep tunnel drainage system provides emergency storage capacity for the sewage system, and the sewage system is accessed through a sewage main pipe through a two-level tunnel and a three-level tunnel when an accident occurs; considering the current situation of construction strength, difficulty of shallow water logging control and comprehensive consideration of factors of distance from river water bodies, the water logging amounts of a plurality of current situation easy-to-waterlogging areas are intercepted and enter a deep tunneling drainage system for treatment.
7. The drainage system evaluation system based on hydraulic model of claim 6, wherein the information acquisition module is further configured to:
acquiring drainage data of the drainage system in a historical drainage demand scene, wherein the drainage data comprises real water flow data of a plurality of monitoring nodes of the drainage system;
Acquiring simulated water flow data of a plurality of virtual nodes corresponding to the drainage system hydraulic model on the drainage system hydraulic model under the historical drainage demand scene;
determining the reality of the hydraulic model of the drainage system based on the real water flow data and the simulated water flow data;
and adjusting the hydraulic model of the drainage system based on the reality degree until the reality degree of the adjusted hydraulic model of the drainage system meets the preset condition.
8. The drainage system evaluation system based on hydraulic model of claim 7, wherein the information acquisition module is further configured to:
and when the reality is smaller than a preset threshold, adjusting model parameters of the hydraulic model of the drainage system, wherein the model parameters comprise a runoff coefficient, a confluence parameter, a pipeline roughness coefficient and pipeline sediments.
9. The drainage system evaluation system based on a hydraulic model according to any one of claims 6 to 8, wherein determining the water level condition of a canal and the risk level of waterlogging of the hydraulic model of the shallow-rain drainage system in a target rainfall intensity scene based on the hydraulic model of the drainage system comprises:
And determining the waterlogging risk level based on the ponding depth and the ponding time.
10. The drainage system evaluation system based on hydraulic model according to any one of claims 6-8, wherein the system evaluation module is further configured to:
generating a virtual sample sewage pipeline negative pressure condition, a sample canal tank water level condition and a sample waterlogging risk level based on a real sample sewage pipeline negative pressure condition, a sample canal tank water level condition and the sample waterlogging risk level by an countermeasure network, and updating parameters of an initial efficiency determination model by using the virtual sample sewage pipeline negative pressure condition, the sample canal tank water level condition and the sample waterlogging risk level until the trained initial efficiency determination model meets preset conditions, so as to obtain a trained efficiency determination model.
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