CN113988460A - Drainage pipe network drainage prediction method, device, equipment and storage medium - Google Patents
Drainage pipe network drainage prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a drainage pipe network drainage prediction method, device, equipment and storage medium, and relates to the technical field of urban hydraulic engineering. The method comprises the following steps: acquiring basic data of a target area; constructing a target rainstorm flood management model of the target area according to the basic data; using a target rainstorm flood management model to simulate and obtain accumulated water information and drainage information of each drainage pipe network node in a target area in each reappearance period; and determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period. According to the scheme, the waterlogging level of each drainage pipe network is comprehensively predicted by combining the ponding information and the drainage information of each drainage pipe network node in each recurrence period so as to obtain the drainage pipe network nodes which are easy to waterlog in each drainage pipe network, and the accuracy of the waterlogging level prediction of each drainage pipe network is improved.
Description
Technical Field
The application relates to the technical field of urban hydraulic engineering, in particular to a drainage pipe network drainage prediction method, device, equipment and storage medium.
Background
Along with the acceleration of urban progressiveness, the urban impervious area is greatly increased, the land utilization property is also remarkably changed, and in addition, global warming is caused, so that part of pipe networks laid in cities cannot bear loads caused by heavy rainfall and the increase of the impervious area, and the urban surface runoff is increased, and flood disasters are frequent. Therefore, how to evaluate the drainage capacity of the urban drainage pipe network and analyze factors influencing the drainage capacity of the pipe network is a technical problem which needs to be solved.
At present, a capability evaluation method for an urban drainage system mainly obtains an analysis result of a target area subjected to different intensity disasters based on mathematical statistics and analysis of historical disaster data.
However, the existing method only carries out statistical analysis based on historical disaster data, and has the problem of low accuracy of analysis results.
Disclosure of Invention
The present invention aims to provide a drainage pipe network drainage prediction method, device, equipment and storage medium for improving the accuracy of drainage level prediction of each drainage pipe network in a target area.
In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:
in a first aspect, an embodiment of the present application provides a drainage pipe network drainage prediction method, including:
acquiring basic data of a target area, wherein the basic data comprises: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period;
according to the basic data, a target rainstorm flood Management Model (Storm Water Management Model, SWMM for short) of the target area is constructed;
using the target SWMM model to obtain accumulated water information and drainage information of each drainage pipe network node in the target area in each reappearance period in a simulation mode;
and determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period.
Optionally, the constructing a target SWMM model of the target region according to the basic data includes:
based on the basic data, carrying out sub-catchment area generalization to obtain an initial SWMM model;
simulating to obtain the corresponding simulated runoff coefficients of the sub-catchment areas in each reappearance period by using the initial SWMM model;
and performing iterative correction on the initial SWMM model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient interval of the drainage network nodes to obtain the target rainstorm flood management model.
Optionally, the generalizing the sub-catchment area based on the basic data to obtain an initial SWMM model includes:
and according to the node position of each drainage pipe network, carrying out sub-catchment area generalization by using a Thiessen polygon algorithm to obtain an initial SWMM model.
Optionally, the iteratively correcting the initial SWMM model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient of each drainage pipe network node to obtain the target SWMM model includes:
carrying out weighted average on the simulated runoff coefficients of the sub catchment areas corresponding to the reproduction periods to obtain simulated runoff coefficient values;
and if the simulated runoff coefficient value is not the coefficient value in the total actual runoff coefficient interval, iteratively correcting the parameter to be calibrated in the initial SWMM model until the simulated runoff coefficient value obtained based on the corrected initial SWMM model is the coefficient value in the total actual runoff coefficient interval, and taking the corrected initial SWMM model as the target SWMM model.
Optionally, the generalizing the sub-catchment area based on the basic data to obtain an initial SWMM model includes:
according to the drainage pipe network vector data, extracting and obtaining pipe network data of each drainage pipe network, wherein the pipe network data comprise: length, node position, area of each catchment area and overflow length;
according to the digital elevation model, extracting the elevation data of each drainage pipe network, wherein the elevation data comprises: the elevation and the gradient of the nodes of each drainage pipe network;
and carrying out sub-catchment area generalization based on the pipe network data of each drainage pipe network and the elevation data of each drainage pipe network to obtain an initial SWMM model.
Optionally, the determining drainage pipe network nodes that are prone to waterlogging in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each recurrence period includes:
if the accumulated water information and the drainage information of the first drainage pipe network node in each reproduction period meet preset conditions, determining that the first drainage pipe network node is a drainage pipe network node easy to waterlog;
and the first drainage pipe network node is any one of the drainage pipe network nodes.
Optionally, the method further comprises:
adding preset marks for the drainage pipe network nodes which are easy to waterlog in each drainage pipe network in a geographic information system according to the position information of the drainage pipe network nodes which are easy to waterlog in each drainage pipe network to obtain a drainage distribution diagram;
and displaying the drainage distribution map.
In a second aspect, an embodiment of the present application further provides a drainage pipe network drainage prediction device, the device includes:
an obtaining module, configured to obtain basic data of a target area, where the basic data includes: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period;
the construction module is used for constructing a target SWMM model of the target area according to the basic data;
the simulation module is used for simulating and obtaining ponding information and drainage information of each drainage pipe network node in the target area in each reappearance period by using the target SWMM model;
and the determining module is used for determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period.
Optionally, the building module is further configured to:
based on the basic data, carrying out sub-catchment area generalization to obtain an initial SWMM model;
simulating to obtain the corresponding simulated runoff coefficients of the sub-catchment areas in each reappearance period by using the initial SWMM model;
and performing iterative correction on the initial SWMM model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient interval of the drainage network nodes to obtain the target SWMM model.
Optionally, the building module is further configured to:
and according to the node position of each drainage pipe network, carrying out sub-catchment area generalization by using a Thiessen polygon algorithm to obtain an initial SWMM model.
Optionally, the building module is further configured to:
carrying out weighted average on the simulated runoff coefficients of the sub catchment areas corresponding to the reproduction periods to obtain simulated runoff coefficient values;
if the simulated runoff coefficient value is not the coefficient value in the total actual runoff coefficient interval, iteratively correcting the parameter to be calibrated in the initial SWMM model until the simulated runoff coefficient value obtained based on the corrected initial SWMM model is the coefficient value in the total actual runoff coefficient interval, and taking the corrected initial SWMM model as the target SWMM model.
Optionally, the building module is further configured to:
according to the drainage pipe network vector data, extracting and obtaining pipe network data of each drainage pipe network, wherein the pipe network data comprise: length, node position, area of each catchment area and overflow length;
according to the digital elevation model, extracting the elevation data of each drainage pipe network, wherein the elevation data comprises: the elevation and the gradient of the nodes of each drainage pipe network;
and carrying out sub-catchment area generalization based on the pipe network data of each drainage pipe network and the elevation data of each drainage pipe network to obtain an initial SWMM model.
Optionally, the determining module is further configured to:
if the accumulated water information and the drainage information of the first drainage pipe network node in each reproduction period meet preset conditions, determining that the first drainage pipe network node is a drainage pipe network node easy to waterlog;
and the first drainage pipe network node is any one of the drainage pipe network nodes.
Optionally, the apparatus further comprises:
the increasing module is used for increasing preset marks for the drainage pipe network nodes which are easy to waterlog in each drainage pipe network in the geographic information system according to the position information of the drainage pipe network nodes which are easy to waterlog in each drainage pipe network to obtain a drainage distribution diagram;
and the display module is used for displaying the drainage distribution map.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method as provided by the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method as provided in the first aspect.
The beneficial effect of this application is:
the embodiment of the application provides a drainage pipe network drainage prediction side, device, equipment and storage medium, includes: acquiring basic data of a target area, wherein the basic data comprises: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period; constructing a target SWMM model of the target area according to the basic data; using a target SWMM model to obtain accumulated water information and drainage information of each drainage pipe network node in a target area in each reappearance period in a simulation mode; and determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period. According to the scheme, a target SWMM model of the target area is constructed and obtained mainly based on the obtained basic data of the target area, then the target SWMM model is operated to simulate and obtain water accumulation information and water drainage information of each drainage pipe network node in each recurrence period, and the water accumulation information and the water drainage information of each drainage pipe network node in each recurrence period are combined to comprehensively predict the water drainage level of each drainage pipe network so as to obtain drainage pipe network nodes prone to waterlogging in each drainage pipe network, so that the accuracy of predicting the water drainage capacity of each drainage pipe network is improved, improvement suggestions are provided for the drainage pipe network nodes prone to waterlogging according to prediction results, and analysis and prediction of the drainage pipe network nodes prone to waterlogging in each drainage pipe network are realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a drainage pipe network drainage prediction method according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of another drainage pipe network drainage prediction method according to an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of another drainage pipe network drainage prediction method according to an embodiment of the present disclosure;
fig. 5 is a schematic flow chart of another drainage pipe network drainage prediction method according to an embodiment of the present disclosure;
fig. 6 is a schematic flow chart of another drainage pipe network drainage prediction method according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a drainage pipe network drainage prediction device according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.
First, before the technical solutions provided in the present application are explained in detail, the related background related to the present application will be briefly explained.
At present, a capability evaluation method for an urban drainage system mainly obtains an analysis result of a target area subjected to different intensity disasters based on mathematical statistics and analysis of historical disaster data. However, the existing analysis method only carries out statistical analysis based on historical disaster data, and has the problem of low accuracy of analysis results.
In order to solve the technical problems in the prior art, the application provides a drainage pipe network drainage prediction method based on an SWMM model, and the constructed SWMM model is used for simulating the drainage capacity and node water accumulation conditions of each drainage pipe network in a target area at different reproduction periods; and then, comprehensively considering the two indexes of 'drainage capacity' and 'node water accumulation condition' of each drainage pipe network, comprehensively predicting and evaluating the drainage capacity of each drainage pipe network, so that a more accurate prediction result of the drainage capacity of the pipe network can be obtained, improvement suggestions can be provided for the pipe network with the easy waterlogging nodes and the poor drainage capacity according to the prediction result, and the analysis and prediction of the easy-waterlogging drainage pipe network nodes in each drainage pipe network are realized.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure; the electronic device can be a computer or a server or other processing devices, and is used for implementing the drainage pipe network drainage prediction method provided by the application. As shown in fig. 1, the electronic apparatus includes: a processor 101 and a memory 102.
The processor 101 and the memory 102 are electrically connected directly or indirectly to realize data transmission or interaction. For example, electrical connections may be made through one or more communication buses or signal lines.
The processor 101 may be an integrated circuit chip having signal processing capability. The Processor 101 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 102 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
It will be appreciated that the configuration depicted in FIG. 1 is merely illustrative and that electronic device 100 may include more or fewer components than shown in FIG. 1 or may have a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The memory 102 is used for storing a program, and the processor 101 calls the program stored in the memory 102 to execute the drainage network drainage prediction method provided by the following embodiment.
The following describes a drainage network drainage prediction method and corresponding beneficial effects provided by the present application through a plurality of embodiments.
Fig. 2 is a schematic flow chart of a drainage pipe network drainage prediction method according to an embodiment of the present application, and optionally, an execution subject of the method may be an electronic device such as a server or a computer, and has a data processing function. It should be understood that in other embodiments, the order of some steps in the drainage network drainage prediction method may be interchanged according to actual needs, or some steps may be omitted or deleted. As shown in fig. 2, the method includes:
s201, acquiring basic data of the target area.
Wherein the basic data includes: the digital elevation model, the drainage pipe network vector data and the rainfall data in each recurrence period provide reliable data sources for realizing drainage pipe network drainage prediction of a target area, so that a more accurate prediction result of the drainage capacity of the pipe network is obtained.
In order to facilitate description of the data type and attribute information of the obtained basic data, detailed information about the basic data may be obtained as shown in table 1 below.
TABLE 1 basic data Profile of target areas
In the present embodiment, for example, rainfall data of the target area in three types of low, medium, and high recurrence periods may be simulated based on a formula of chicago rain type and a formula of rainstorm intensity suitable for the target area. Wherein, the low, medium and high recurrence periods are 1 year-by-year rainstorm P ═ 1a, 2 year-by-year rainstorm P ═ 2a and 3 year-by-year rainstorm P ═ 3a respectively. The method comprises the following specific steps:
wherein the stormwater intensity formula for the target area is:
in formula (1), q is the rainstorm intensity (L/(s · hm2)) in the duration of t; a is a rainfall parameter (mm); c is a rainfall variation parameter; p is a rainfall recurrence period (a); t is rainfall duration (min); b is a rainfall duration correction parameter (min); and n is a rainstorm attenuation index.
For example, in this embodiment, P in the above formula (1) may respectively select 3 rainfall scenarios encountered in 2, 5, and 10 years, the time interval is set to 5min, the rainfall duration is 2h, and the peak ratio r is 0.475, so as to obtain rainfall data of each period within 24 hours under P-2 a, P-5 a, and P-10 a.
It should be understood that the obtained base data may also include: the land use type of the target area may be subdivided into residential areas, factories, farms, roads, greens, bodies of water, unused areas, parking lots, squares, etc.
Furthermore, all the basic data can adopt a WGS _1984_ Transverse _ Mercator projection coordinate system.
S202, constructing a target SWMM model of the target area according to the basic data.
The SWMM model comprises the following components and objects in 4 parts: 1. atmosphere part: the rainfall design of the rain gauge can be actual measurement rainfall data or rainfall data obtained by a rainstorm intensity formula of a target area and Chicago rain type simulation; secondly, the deposition of surface pollutants caused by rainfall; 2. a ground surface part: generalizing a research area, a pipe network, a pump station, an inspection well and nodes to obtain a generalized topographic map of a target area; 3. the underground water part: the method mainly receives the supply of part of rainfall to the underground water, and simultaneously part of the underground water enters the conveying part again, and the part is simulated by using an aquifer object; 4. a conveying part: consists of a conveying element pipe duct, a water pump, a regulator and a water storage treatment device. The portion of the inflow is from surface runoff, groundwater cross-flow, dry season sewage flow, or from a user-defined hydrological process line.
In this embodiment, the rainfall data in each recurrence period in the basic data acquired in step S201 may be converted into an SWMM model, and a rainfall sequence format may be input, where the first mode is to manually input the SWMM model, and the other mode is to open the rainfall data in each recurrence period in the SWMM model in a TXT document format.
Similarly, the digital elevation model and the drainage pipe network vector data in the basic data acquired in step S201 may also be converted into a TXT document form; and then inputting the conversion result into the SWMM model to construct a target SWMM model of the target area.
S203, using the target SWMM model to obtain accumulated water information and drainage information of each drainage pipe network node in the target area in each reappearance period in a simulation mode.
Wherein, ponding information includes: maximum overflow rate, overflow time, total overflow; the drainage information includes: the maximum full flow depth and the pipe network full flow duration.
Optionally, by operating the target SWMM model obtained by the above construction, node water accumulation conditions of each drainage pipe network node in the target area in the recovery periods P-2 a, P-5 a, and P-10 a, that is, the maximum overflow rate, the overflow time, and the total overflow amount of the node in different recovery periods can be obtained.
Meanwhile, the pipe network drainage capacity conditions of each drainage pipe network node in the target area in the recurrence period P of 2a, P of 5a and P of 10a, that is, the maximum full flow depth of the pipe network and the overload condition of the pipe network in different recurrence periods can also be obtained by using the target SWMM model simulation, wherein the overload condition of the pipe network is based on the full flow duration of the pipe network.
And S204, determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period.
On the basis of the above embodiment, the drainage pipe network nodes which are likely to cause flooding in each drainage pipe network can be obtained based on the above obtained accumulated water information and drainage information of each drainage pipe network node in the target area in each recurrence period.
Optionally, a plurality of indexes can be combined to determine drainage pipe network nodes prone to waterlogging in each drainage pipe network.
To sum up, the embodiment of the application provides a drainage pipe network drainage prediction method, including: acquiring basic data of a target area, wherein the basic data comprises: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period; constructing a target SWMM model of the target area according to the basic data; using a target SWMM model to obtain accumulated water information and drainage information of each drainage pipe network node in a target area in each reappearance period in a simulation mode; and determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period. According to the scheme, a target SWMM model of the target area is constructed and obtained mainly based on the obtained basic data of the target area, then the target SWMM model is operated to simulate and obtain water accumulation information and water drainage information of each drainage pipe network node in each recurrence period, and the water accumulation information and the water drainage information of each drainage pipe network node in each recurrence period are combined to comprehensively predict the water drainage level of each drainage pipe network so as to obtain drainage pipe network nodes prone to waterlogging in each drainage pipe network, so that the accuracy of predicting the water drainage capacity of each drainage pipe network is improved, improvement suggestions are provided for the drainage pipe network nodes prone to waterlogging according to prediction results, and analysis and prediction of the drainage pipe network nodes prone to waterlogging in each drainage pipe network are realized.
How to construct the target SWMM model of the target region from the basic data will be specifically explained by the following embodiments.
Alternatively, as shown in fig. 3, the step S202: according to the basic data, constructing a target SWMM model of the target area, comprising:
s301, carrying out sub-catchment area generalization based on basic data to obtain an initial SWMM model.
Optionally, in this embodiment, a taisen polygon algorithm may be used to perform sub-catchment area generalization according to the node position of each drainage pipe network, so as to obtain an initial SWMM model.
In addition, the sub-catchment area can be generalized based on the basic data in other ways to obtain the initial SWMM model.
And S302, simulating to obtain the corresponding simulated runoff coefficients of the sub-catchment areas in each reproduction period by using an initial SWMM model.
The runoff coefficient is used for representing the ratio of the surface runoff and the rainfall generated by rainfall in the catchment area.
It should be appreciated that various parameters in the initial SWMM model are set and calibrated to ensure the accuracy of the resulting target SWMM model simulation. The parameters in the SWMM model can be divided into measured parameters, geometric parameters and empirical parameters according to different determination methods.
Wherein, the actual measurement parameters mainly include: attribute information of the sub-catchment areas, the nodes, the pipe networks and the rain gauges can be obtained by data inquiry or field investigation; the geometrical parameters mainly include: the area, gradient, impermeability percentage, pipe network length, node coordinates and the like of the sub-catchment area can be obtained by preprocessing basic data of the target area by utilizing Geographic Information Systems (GIS for short). The empirical parameters mainly include: maximum/minimum infiltration rates, attenuation constants, Manning coefficients, impounded volume in impermeable areas, permeable puddles, and the like.
Optionally, the initial empirical parameter values of the initial SWMM model are set according to empirical values, for example, the empirical values of the empirical parameters in the SWMM user instruction manual may be referred to obtain the initial SWMM model, and the initial SWMM model is run to obtain simulated runoff coefficients of the sub-catchment areas corresponding to the recurrence period P2 a, P5 a, and P10 a.
And S303, carrying out iterative correction on the initial SWMM model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient interval of the drainage network nodes to obtain a target SWMM model.
For example, the total actual runoff coefficient interval of each drainage pipe network node may be an empirical interval set according to experience, or may be a preset test interval.
In this embodiment, the total actual runoff coefficient interval of each drainage pipe network node is related to the ratio of the waterproof coverage area of the target area. For example, if the waterproof coverage area of the target area is more than 70%, the total actual runoff coefficient interval number of each drainage pipe network node of the target area is 0.6-0.8; the impervious coverage area of the target area is 50% -70%, and the total actual runoff coefficient interval number of each drainage pipe network node of the target area is 0.5-0.7; and if the water-impermeable coverage area of the target area is 30-50%, the total actual runoff coefficient interval number of each drainage pipe network node of the target area is 0.4-0.6, and if the water-impermeable coverage area of the target area is less than 30%, the total actual runoff coefficient interval number of each drainage pipe network node of the target area is 0.3-0.5.
Optionally, referring to fig. 4, performing iterative correction on the initial SWMM model according to the simulated runoff coefficients of each sub-catchment area and the total actual runoff coefficient interval of each drainage network node to obtain a target SWMM model, which is specifically as follows:
s401, carrying out weighted average on the simulated runoff coefficients of the sub catchment areas corresponding to the reproduction periods to obtain the simulated runoff coefficient value.
S402, if the simulated runoff coefficient value is not the coefficient value in the total actual runoff coefficient interval, iteratively correcting the parameter to be calibrated in the initial SWMM model until the simulated runoff coefficient value obtained based on the corrected initial SWMM model is the coefficient value in the total actual runoff coefficient interval, and taking the corrected initial SWMM model as the target SWMM model.
For example, if the impermeable coverage area of the target area is greater than 70%, the total actual runoff coefficient interval of each drainage pipe network node of the target area may be determined to be 0.6-0.8.
In this embodiment, iterative correction is mainly performed on the empirical parameters in the SWMM model, so that the empirical parameters in the SWMM model can be used as parameters to be calibrated, and iterative correction is performed on the parameters to be calibrated for multiple times, and if the simulated runoff coefficient value obtained based on the corrected initial SWMM model is a coefficient value in the total actual runoff coefficient interval, the corrected initial SWMM model is used as the target SWMM model to obtain the target SWMM model conforming to the target area.
For convenience of explanation, an iterative correction process of the parameter to be calibrated in the initial SWMM model may be described with reference to table 2 below.
TABLE 2 iterative correction procedure for parameters to be calibrated in SWMM model
In this embodiment, for example, the simulated runoff coefficients of each sub-catchment area when the initial SWMM model simulated recurrence period P is 2a may be used to perform weighted averaging to obtain the simulated runoff coefficient value, compare with the total actual runoff coefficient interval in table 2, regard the empirical parameters of the sub-catchment area as the parameters to be calibrated, perform step-by-step iteration on the parameters to be calibrated, and finally obtain a "satisfactory solution", where the parameter iteration correction process is shown in table 2. Specifically, after iterative correction is performed on the parameter to be calibrated in the initial SWMM model for the 2 nd time, the simulated runoff coefficient value 0.625 obtained by the corrected initial SWMM model is a coefficient value in a total actual runoff coefficient interval (e.g., 0.6-0.8). However, in this embodiment, in order to ensure the accuracy of the finally obtained target SWMM model, 4 iterative corrections are further performed on the parameters to be calibrated in the initial SWMM model.
In addition, since the total actual runoff coefficient is an interval value, the parameter set to be calibrated which meets the requirement is not a unique solution, but a set comprising a plurality of sets of solutions, which is also called a "satisfactory solution".
In this embodiment, it is also necessary to verify the robustness of the parameters in the finally obtained target SWMM model. Specifically, simulated runoff coefficients of the sub-catchment areas in the reconstruction period P ═ 1a and P ═ 3a can be simulated by using the SWMM model, and the parameter set after iterative correction is verified, so that the simulated runoff coefficient under the reconstruction period P ═ 1a is 0.642, and the simulated runoff coefficient under the reconstruction period P ═ 3a is 0.707.
The end result of the empirical parameters for the target area that can also be obtained from table 2 above is: the water storage capacity of the non-permeable area depression is 0.012, the water storage capacity of the non-permeable area depression is 0.1, the water storage capacity of the permeable area depression is 2, the maximum infiltration rate is10, the minimum infiltration rate is 0.8, and the damping constant is 4.
The following examples will specifically teach how to generalize the sub-catchment area based on the basic data to obtain the initial SWMM model.
Alternatively, referring to fig. 5, the step S301: and (3) carrying out generalization on the sub-catchment areas based on basic data to obtain an initial SWMM model, wherein the generalization comprises the following steps:
s501, extracting pipe network data of each drainage pipe network according to the drainage pipe network vector data.
Wherein, pipe network data includes: length, node position, area of each catchment area and overflow length.
And S502, extracting and obtaining elevation data of each drainage pipe network according to the digital elevation model.
Wherein the elevation data comprises: the elevation and gradient of the nodes of each drainage pipe network.
In the embodiment, the vector data of the drainage pipe network, the digital elevation model and the like are preprocessed mainly based on ArcGIS10.2, so that information integration of a target area is realized. Vectorizing boundaries of each drainage pipe network, each drainage pipe network node, a target area and the like, and extracting information such as pipe network length, node positions (such as a starting node and a stopping node), node elevation, catchment area, gradient, overflowing length and the like by using drainage pipe network vector data and digital elevation model data.
Illustratively, for example, the pipe network attribute information input into the SWMM model mainly includes: the starting node and the terminating node numbers, and the length of the pipe network. If the range of the target area is small and the number of rainwater pipe networks and inspection wells (nodes) is small, the initial nodes and the termination nodes of the drainage pipe networks can be directly obtained through visual interpretation according to the node elevation, and the trend of each drainage pipe network flows from the nodes with higher elevation to the nodes with lower elevation.
The elevation data of each drainage pipe Network in the scheme is known data, DEM data can be generated through an Irregular triangular Network (TIN) method or a space interpolation method, vector data are converted into raster data, and finally node elevation and gradient of each drainage pipe Network are obtained by using Spatial analysis Tools in ArcToolbox > interaction- > interaction Values To Points.
The node coordinate information may be used to obtain X, Y coordinates via "coordinate Geometry". Firstly, X, Y fields and a data type Double are added in a node attribute table, and a calculation of a schedule Geometry is selected.
S503, carrying out sub-catchment area generalization based on pipe network data of each drainage pipe network and elevation data of each drainage pipe network to obtain an initial SWMM model.
It should be understood that there are two important concepts to distinguish when generalizing the sub-catchment areas: the system comprises a water Outlet (Outlet) and a discharge port (Outfall), wherein the water Outlet is a flow control device in an SWMM model and is used for controlling the flow of one node flowing to the other node, and the function of the system is similar to that of a pump station; the discharge port is the terminal node of the whole drainage system, and the discharge port has only an upstream node and no downstream node.
The information related to the catchment area to be acquired mainly includes the length, gradient, characteristic width, impermeability percentage, area, water outlet, inflection point coordinate of the sub-catchment area, etc.
In this embodiment, the taisen polygon method of ArcGIS10.2 may be used to perform sub-catchment area generalization according to pipe network data of each drainage pipe network and elevation data of each drainage pipe network, and then perform simple local adjustment to obtain an initial SWMM model.
The following embodiments specifically describe how to determine drainage pipe network nodes which are prone to waterlogging in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each recurrence period.
Optionally, if the accumulated water information and the drainage information of the first drainage pipe network node in each recurrence period meet preset conditions, determining that the first drainage pipe network node is a drainage pipe network node easy to waterlog; wherein, the first drainage pipe network node is any drainage pipe network node in each drainage pipe network node.
For example, the node water accumulation value of each drainage pipe network node in each regeneration period may be determined according to the maximum overflow rate, the overflow time, the total overflow amount and the like included in the water accumulation information of each drainage pipe network node in each regeneration period; determining the drainage flood value of each drainage pipe network node in each recurrence period according to the maximum full flow depth, the pipe network full flow duration and the like of the drainage information of each drainage pipe network node in each recurrence period; if the water accumulation value of the first drainage pipe network node in each reproduction period is far greater than the drainage value, the first drainage pipe network node can be determined to be a drainage pipe network node easy to waterlog.
In another realizable mode, for example, a target SWMM model is used for simulating to obtain the node maximum overflow rate, the overflow time and the total overflow quantity of each drainage pipe network node in each recurrence period; and the maximum overflow depth and the full flow length of each drainage pipe network are long, and the data are correlated to ArcGISI 10.2 for visual analysis to obtain drainage pipe network nodes which are easy to waterlog in a target area and drainage pipe networks with insufficient drainage capacity, so that the drainage capacity of the drainage pipe networks in a research area is evaluated in terms of node overflow conditions and pipe network drainage capacity.
Optionally, as shown in fig. 6, after determining that the first drainage network node is a waterlogging-prone drainage network node, the method further includes:
s601, adding preset marks for the drainage pipe network nodes prone to waterlogging in each drainage pipe network in a geographic information system according to the position information of the drainage pipe network nodes prone to waterlogging in each drainage pipe network to obtain a drainage distribution diagram.
And S602, displaying a drainage distribution map.
In this embodiment, for example, if the drainage pipe network nodes easy to flood in each drainage pipe network are J31, J32 and J33 according to the water accumulation information and the drainage information of each drainage pipe network node in each recurrence period, the position information of the drainage pipe network nodes easy to flood in each drainage pipe network can be J31, J32 and J33, the preset identifiers are added to the geographic information system of ArcGIS10.2 to obtain a drainage distribution diagram and display the drainage distribution diagram, so that the visual display of the drainage pipe network nodes easy to flood is realized, a user can more intuitively know which drainage pipe networks are the drainage pipe network nodes easy to flood from the drainage distribution diagram, and an improvement suggestion can be provided for the drainage pipe network nodes easy to flood to avoid the flooding condition in a target area.
The following description is provided for a device and a storage medium for performing the drainage prediction of the drainage pipe network, and the specific implementation process and technical effects thereof are referred to above, and will not be described again below.
Fig. 7 is a schematic structural diagram of a drainage pipe network drainage prediction device according to an embodiment of the present disclosure; as shown in fig. 7, the apparatus includes:
an obtaining module 701, configured to obtain basic data of a target area, where the basic data includes: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period;
a building module 702, configured to build a target SWMM model of the target region according to the basic data;
the simulation module 703 is configured to obtain, through simulation, accumulated water information and drainage information of each drainage pipe network node in the target area in each recurrence period using the target SWMM model;
and a determining module 704, configured to determine drainage pipe network nodes that are prone to waterlogging in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each recurrence period.
Optionally, the building module 702 is further configured to:
based on basic data, carrying out generalization on the sub-catchment areas to obtain an initial SWMM model;
simulating to obtain the corresponding simulated runoff coefficients of the sub catchment areas in each reappearance period by using an initial SWMM model;
and carrying out iterative correction on the initial SWMM model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient interval of the drainage pipe network nodes to obtain a target SWMM model.
Optionally, the building module 702 is further configured to:
and (4) carrying out sub-catchment area generalization by using a Thiessen polygon algorithm according to the node position of each drainage pipe network to obtain an initial SWMM model.
Optionally, the building module 702 is further configured to:
carrying out weighted average on the simulated runoff coefficients of the sub-catchment areas corresponding to the reproduction periods to obtain the simulated runoff coefficient value;
and if the simulated runoff coefficient value is not the coefficient value in the total actual runoff coefficient interval, iteratively correcting the parameter to be calibrated in the initial SWMM model until the simulated runoff coefficient value obtained based on the corrected initial SWMM model is the coefficient value in the total actual runoff coefficient interval, and taking the corrected initial SWMM model as the target SWMM model.
Optionally, the building module 702 is further configured to:
according to drainage pipe network vector data, draw the pipe network data that obtains each drainage pipe network, the pipe network data includes: length, node position, area of each catchment area and overflow length;
extracting and obtaining elevation data of each drainage pipe network according to the digital elevation model, wherein the elevation data comprises: the elevation and gradient of each drainage pipe network node;
and carrying out sub-catchment area generalization based on the pipe network data of each drainage pipe network and the elevation data of each drainage pipe network to obtain an initial SWMM model.
Optionally, the determining module 704 is further configured to:
if the accumulated water information and the drainage information of the first drainage pipe network node in each reproduction period meet preset conditions, determining the first drainage pipe network node as a drainage pipe network node easy to waterlog;
wherein, the first drainage pipe network node is any drainage pipe network node in each drainage pipe network node.
Optionally, the apparatus further comprises:
the increasing module is used for increasing preset marks for the drainage pipe network nodes which are easy to waterlog in each drainage pipe network in the geographic information system according to the position information of the drainage pipe network nodes which are easy to waterlog in each drainage pipe network to obtain a drainage distribution diagram;
and the display module is used for displaying the drainage distribution map.
The above-mentioned apparatus is used for executing the method provided by the foregoing embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
These above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Optionally, the invention also provides a program product, for example a computer-readable storage medium, comprising a program which, when being executed by a processor, is adapted to carry out the above-mentioned method embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (10)
1. A drainage pipe network drainage prediction method is characterized by comprising the following steps:
acquiring basic data of a target area, wherein the basic data comprises: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period;
according to the basic data, a target rainstorm flood management model of the target area is built;
using the target rainstorm flood management model to simulate and obtain accumulated water information and drainage information of each drainage pipe network node in the target area in each reappearance period;
and determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period.
2. The method of claim 1, wherein said constructing a target stormwater flood management model of the target area from the base data comprises:
based on the basic data, generalizing the sub-catchment areas to obtain an initial rainstorm flood management model;
simulating to obtain the corresponding simulated runoff coefficients of the sub-catchment areas in each reappearance period by using the initial rainstorm flood management model;
and carrying out iterative correction on the initial rainstorm flood management model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient intervals of the drainage pipe network nodes to obtain the target rainstorm flood management model.
3. The method of claim 2, wherein generalizing the sub-catchment areas based on the base data to obtain an initial storm flood management model comprises:
and according to the node position of each drainage pipe network, carrying out sub-catchment area generalization by using a Thiessen polygon algorithm to obtain an initial storm flood management model.
4. The method of claim 2, wherein iteratively modifying the initial storm flood management model according to the simulated runoff coefficients of the sub-catchment areas and the total actual runoff coefficient of the drainage network nodes to obtain the target storm flood management model comprises:
carrying out weighted average on the simulated runoff coefficients of the sub catchment areas corresponding to the reproduction periods to obtain simulated runoff coefficient values;
and if the simulation runoff coefficient value is not the coefficient value in the total actual runoff coefficient interval, iteratively correcting the parameter to be calibrated in the initial rainstorm flood management model until the simulation runoff coefficient value obtained based on the corrected initial rainstorm flood management model is the coefficient value in the total actual runoff coefficient interval, and taking the corrected initial rainstorm flood management model as the target rainstorm flood management model.
5. The method of claim 2, wherein generalizing the sub-catchment areas based on the base data to obtain an initial storm flood management model comprises:
according to the drainage pipe network vector data, extracting and obtaining pipe network data of each drainage pipe network, wherein the pipe network data comprise: length, node position, area of each catchment area and overflow length;
according to the digital elevation model, extracting the elevation data of each drainage pipe network, wherein the elevation data comprises: the elevation and the gradient of the nodes of each drainage pipe network;
and performing sub-catchment area generalization based on the pipe network data of each drainage pipe network and the elevation data of each drainage pipe network to obtain an initial storm flood management model.
6. The method of claim 1, wherein determining drainage pipe network nodes that are prone to flooding in each drainage pipe network according to the water accumulation information and the drainage information of each drainage pipe network node at each of the recurring periods comprises:
if the accumulated water information and the drainage information of the first drainage pipe network node in each reproduction period meet preset conditions, determining that the first drainage pipe network node is a drainage pipe network node easy to waterlog;
and the first drainage pipe network node is any one of the drainage pipe network nodes.
7. The method of claim 6, further comprising:
adding preset marks for the drainage pipe network nodes which are easy to waterlog in each drainage pipe network in a geographic information system according to the position information of the drainage pipe network nodes which are easy to waterlog in each drainage pipe network to obtain a drainage distribution diagram;
and displaying the drainage distribution map.
8. A drainage pipe network drainage prediction device, its characterized in that, the device includes:
an obtaining module, configured to obtain basic data of a target area, where the basic data includes: a digital elevation model, drainage pipe network vector data and rainfall data in each reappearance period;
the building module is used for building a target rainstorm flood management model of the target area according to the basic data;
the simulation module is used for simulating and obtaining ponding information and drainage information of each drainage pipe network node in the target area in each reappearance period by using the target rainstorm flood management model;
and the determining module is used for determining drainage pipe network nodes which are easy to waterlog in each drainage pipe network according to the ponding information and the drainage information of each drainage pipe network node in each reappearance period.
9. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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