Intelligent closure method and equipment for rainfall runoff, storage medium and program product

Abstract

The invention provides an intelligent closure method and equipment for rainfall runoff, a storage medium and a program product. The method comprises the following steps: acquiring rainfall forecast data of a target area, and calling a rainfall pattern discrimination module to discriminate the rainfall forecast data to generate target rainfall pattern data; and determining a target time group of the rainfall forecast data, calling a water quality simulation database, performing matching processing on the target rainfall pattern data and the target time group with the water quality simulation database so as to obtain target interception time and target interception water quantity, and performing intelligent interception on the rainfall runoff according to the target interception time and the target interception water quantity. According to the method, the closure efficiency of the urban drainage system is improved, accurate closure of the rainfall runoff with the high pollution value is achieved, the closure response capacity of the urban drainage system is improved by continuously optimizing the closure strategy, the method is suitable for making the closure strategy under different rainfall conditions and time changes, and the water pollution risk is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of rainfall runoff control, and in particular to an intelligent interception method, device, storage medium and program product for rainfall runoff. Background Art

[0002] With the effective control of point source pollution, the pollution impact of non-point source pollution caused by rainfall on the urban water environment has become increasingly prominent. In the urban diversion production area, rainfall runoff can carry a large amount of surface accumulated pollutants and discharge them into urban water bodies through underground rainwater pipes, causing the water quality to deteriorate.

[0003] At present, in order to deal with water environment problems caused by rainfall runoff, most of the methods adopted are to intercept and store the initial runoff, intercept the initial runoff with poor water quality, and regulate and store it before discharging it. When accurately intercepting the initial runoff, most of the existing technologies use the reference rainfall corresponding to the initial runoff as the basis for interception.

[0004] However, when intercepting water according to a fixed reference rainfall, the impact of external environmental factors such as rainfall conditions on the water quality of rainfall runoff is ignored, which can easily lead to unnecessary interception of clean rainwater and may also miss some heavily polluted initial runoff, causing it to be directly discharged into rivers, exacerbating water pollution. Summary of the invention

[0005] The present application provides an intelligent interception method, equipment, storage medium and program product for rainfall runoff, which are used to solve the technical problem of unnecessary interception of clean rainwater and omission of some seriously polluted initial runoff due to ignoring the impact of external environmental factors such as rainfall conditions on the water quality of rainfall runoff, thereby aggravating water pollution.

[0006] In a first aspect, the present application provides an intelligent interception method for rainfall runoff, comprising:

[0007] Obtain rainfall forecast data for the target area and call the rainfall type discrimination module;

[0008] Controlling the rainfall type discrimination module to discriminate the rainfall forecast data to obtain target rainfall type data, wherein the target rainfall type data is used to indicate the rainfall change corresponding to the rainfall forecast data;

[0009] Determine the target time grouping of the rainfall forecast data, and call a water quality simulation database, wherein the water quality simulation database is used to store interception information corresponding to various rainfall types in different time periods;

[0010] The target rainfall pattern data and the target time are grouped and matched with the water quality simulation database to obtain the target interception time and the target intercepted water volume, and the rainfall runoff is intelligently intercepted according to the target interception time and the target intercepted water volume.

[0011] Optionally, the controlling the rainfall type discrimination module to discriminate the rainfall forecast data to obtain target rainfall type data includes:

[0012] Acquire a rain pattern database of the target area, wherein the rain pattern database is used to store information of various rain patterns in the target area;

[0013] Controlling the rainfall type discrimination module to perform normalization processing on the rainfall forecast data to obtain rain type data to be discriminated;

[0014] The rain type data to be identified is matched with the rain type database to obtain the target rain type data.

[0015] Optionally, matching the rain type data to be identified with the rain type database to obtain the target rain type data includes:

[0016] Determine a plurality of basic rain type data and a plurality of extended rain type data according to the rain type database, wherein the basic rain type data is used to characterize the normalized rain type distribution of different rainfall events in the target area, and the extended rain type data is used to indicate the rain type distribution under different rainfall amounts and different rainfall durations;

[0017] Controlling the rainfall rain type discrimination module to respectively calculate the rain type similarity between the rain type data to be discriminated and the plurality of basic rain type data, wherein the rain type similarity is used to indicate the degree of difference between the rain type data to be discriminated and any one of the basic rain type data;

[0018] Acquire a preset screening condition, and perform screening processing on multiple rain type similarities according to the preset screening condition, wherein the preset screening condition is used to indicate to screen out basic rain type data having the smallest degree of difference with the rain type data to be determined from multiple basic rain type data;

[0019] The target rain type data is determined according to the basic rain type data corresponding to the screening result, the plurality of extended rain type data and the rainfall amount and rainfall duration corresponding to the rainfall forecast data.

[0020] Optionally, the method further includes:

[0021] Acquire historical rainfall time series data of the target area;

[0022] Standardizing the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data for the target area;

[0023] A rain type database and a rainfall type discrimination module are generated according to a plurality of basic rain type data and a plurality of extended rain type data.

[0024] Optionally, the step of performing standardization processing on the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data of the target area includes:

[0025] Normalizing the historical rainfall time series data to obtain a plurality of basic rainfall pattern data of the target area;

[0026] Acquiring preset rainfall parameters, wherein the preset rainfall parameters include: a preset rainfall amount and a preset rainfall duration;

[0027] Determining a plurality of rainfall conditions according to the preset rainfall amount and the preset rainfall duration;

[0028] The plurality of basic rain pattern data are respectively extended and processed according to the plurality of rainfall conditions to obtain a plurality of extended rain pattern data.

[0029] Optionally, the method further includes:

[0030] Calling a rainfall runoff simulation model and a constant flow water database, and determining a plurality of constant flow water parameters according to the constant flow water database, wherein the rainfall runoff simulation model is used to predict the water quality and water quantity change curve at the water inlet of the regulating reservoir under different rainfall conditions, and the constant flow water parameters are used to indicate the water quality and water quantity information at the water inlet of the regulating reservoir on sunny days under different time groups;

[0031] Based on the multiple extended rain type data and the multiple constant flow water parameters, controlling the rainfall runoff simulation model to perform simulation processing to obtain multiple water quality and water quantity change curves;

[0032] Determine the interception water quality limit of the target area, and determine the interception time and intercepted water volume corresponding to multiple extended rainfall type data in different time groups according to the interception water quality limit and the multiple water quality and water volume change curves;

[0033] A water quality simulation database is constructed based on the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data under different time groups.

[0034] Optionally, the method further includes:

[0035] Acquire historical hydrological data at the water inlet of the sunny day regulating reservoir, wherein the historical hydrological data is used to indicate the water quality and water quantity data of the rainwater pipe network at the water inlet of the sunny day regulating reservoir during the historical period;

[0036] Dividing the historical hydrological data into time periods to obtain constant flow data under multiple time groups;

[0037] Calculate the mean of the constant flow data under different time groups respectively, and determine the calculation results as the constant flow parameters under the corresponding time groups respectively;

[0038] Based on the constant flow water parameters under multiple different time groups, a constant flow water database is constructed.

[0039] In a second aspect, the present application provides an intelligent interception device for rainfall runoff, comprising:

[0040] The acquisition module is used to obtain the rainfall forecast data of the target area and call the rainfall type discrimination module.

[0041] The processing module is used to control the rainfall type discrimination module to perform discrimination processing on the rainfall forecast data to obtain target rainfall type data, wherein the target rainfall type data is used to indicate the rainfall change corresponding to the rainfall forecast data.

[0042] A determination module is used to determine the target time grouping of the rainfall forecast data.

[0043] The processing module is also used to call a water quality simulation database, and the water quality simulation database is used to store interception information corresponding to various rainfall types in different time periods.

[0044] The processing module is also used to group the target rainfall type data and the target time, match them with the water quality simulation database, obtain the target interception time and the target intercepted water volume, and intelligently intercept the rainfall runoff according to the target interception time and the target intercepted water volume.

[0045] Optionally, the acquisition module is further used to acquire a rain pattern database of the target area, and the rain pattern database is used to store information of various rain patterns in the target area.

[0046] The processing module is also used to control the rainfall type discrimination module to perform normalization processing on the rainfall forecast data to obtain rain type data to be discriminated.

[0047] The processing module is further used to match the rain type data to be identified with the rain type database to obtain the target rain type data.

[0048] Optionally, the determination module is also used to determine multiple basic rain type data and multiple extended rain type data based on the rain type database, the basic rain type data are used to characterize the normalized rain type distribution of different rainfall events in the target area, and the extended rain type data are used to indicate the rain type distribution under different rainfall amounts and different rainfall durations.

[0049] The processing module is further used to control the rainfall rain type discrimination module to respectively calculate the rain type similarity between the rain type data to be discriminated and the multiple basic rain type data, wherein the rain type similarity is used to indicate the degree of difference between the rain type data to be discriminated and any one of the basic rain type data.

[0050] The acquisition module is further used to acquire a preset screening condition, wherein the preset screening condition is used to indicate to screen out basic rain type data having the smallest difference with the rain type data to be determined from a plurality of basic rain type data.

[0051] The processing module is further used to perform screening processing on multiple rain type similarities according to the preset screening conditions.

[0052] The determination module is further used to determine the target rain type data according to the basic rain type data corresponding to the screening result, the plurality of extended rain type data and the rainfall amount and rainfall duration corresponding to the rainfall forecast data.

[0053] Optionally, the acquisition module is also used to acquire historical rainfall time series data of the target area.

[0054] The processing module is also used to perform standardization processing on the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data for the target area.

[0055] The processing module is also used to generate a rain type database and a rainfall type discrimination module according to a plurality of basic rain type data and a plurality of extended rain type data.

[0056] Optionally, the processing module is further used to normalize the historical rainfall time series data to obtain multiple basic rainfall type data of the target area.

[0057] The acquisition module is further used to acquire preset rainfall parameters, which include: preset rainfall amount and preset rainfall duration.

[0058] The determination module is further used to determine a plurality of rainfall conditions according to the preset rainfall amount and the preset rainfall duration.

[0059] The processing module is further used to perform expansion processing on the multiple basic rain type data respectively according to the multiple rainfall conditions to obtain multiple expanded rain type data.

[0060] Optionally, the processing module is also used to call a rainfall-runoff simulation model and a constant flow water database, and the rainfall-runoff simulation model is used to predict the water quality and quantity change curve at the water inlet of the storage tank under different rainfall conditions.

[0061] The determination module is also used to determine multiple constant flow water parameters based on the constant flow water database. The constant flow water parameters are used to indicate water quality and water quantity information at the water inlet of the sunny storage tank under different time groups.

[0062] The processing module is also used to control the rainfall runoff simulation model to perform simulation processing based on the multiple extended rainfall type data and the multiple constant flow water parameters to obtain multiple water quality and water quantity change curves.

[0063] The determination module is also used to determine the interception water quality limit of the target area, and determine the interception time and intercepted water volume corresponding to multiple extended rainfall type data in different time groups based on the interception water quality limit and the multiple water quality and water volume change curves.

[0064] The processing module is also used to construct a water quality simulation database according to the interception time and intercepted water volume corresponding to a plurality of extended rainfall type data in different time groups.

[0065] Optionally, the acquisition module is also used to obtain historical hydrological data at the water inlet of the sunny day regulating reservoir, and the historical hydrological data is used to indicate the water quality and water quantity data of the rainwater pipe network at the water inlet of the sunny day regulating reservoir during a historical period.

[0066] The processing module is also used to divide the historical hydrological data into time periods to obtain constant water flow data under multiple time groups.

[0067] The processing module is also used to calculate the mean of the constant flow data in different time groups respectively.

[0068] The determination module is further used to determine the calculation results as the constant flow water parameters under the corresponding time groups.

[0069] The processing module is further used to construct a constant flow water database based on the constant flow water parameters in multiple different time groups.

[0070] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0071] The memory stores computer-executable instructions;

[0072] The processor executes the computer-executable instructions stored in the memory to implement the intelligent interception method for rainfall runoff as described in the above-mentioned first aspect and various possible implementations of the first aspect.

[0073] In a fourth aspect, the present application provides a computer-readable storage medium having computer execution instructions stored thereon, which, when executed by a processor, are used to implement the intelligent interception method for rainfall runoff as described in the first aspect and various possible implementations of the first aspect.

[0074] In a fifth aspect, the present application provides a program product, including a computer program, which, when executed by a processor, implements the intelligent interception method for rainfall runoff as described above.

[0075] The intelligent interception method, device, storage medium and program product of rainfall runoff provided by the present application obtain the rainfall forecast data and rain type database of the target area, call the rainfall rain type discrimination module, normalize the rainfall forecast data, obtain the rain type data to be discriminated, and match the rain type data to be discriminated with the rain type database to obtain the target rain type data; determine the target time grouping of the rainfall forecast data, call the water quality simulation database, match the target rain type data and the target time grouping with the water quality simulation database, so as to obtain the target interception time and the target intercepted water volume, and intelligently intercept the rainfall runoff according to the target interception time and the target intercepted water volume. This method improves the interception efficiency of the urban drainage system, realizes the precise interception of rainfall runoff with high pollution values, improves the interception response capability of the urban drainage system by continuously optimizing the interception strategy, is suitable for the formulation of interception strategies under different rainfall conditions and time changes, improves the flexibility and reliability of interception strategy formulation, and reduces the risk of water pollution. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0077] Figure 1 Schematic diagram of the intelligent interception method for rainfall runoff provided in this application Figure 1 ;

[0078] Figure 2 Schematic diagram of the intelligent interception method for rainfall runoff provided in this application Figure 2 ;

[0079] Figure 3 A schematic diagram of the water quality change curve at the inlet of the storage pond on rainy days in January provided for this application;

[0080] Figure 4 A schematic diagram of the flow rate change curve at the water inlet of the storage tank on rainy days in January provided for this application;

[0081] Figure 5 A schematic diagram of the structure of the intelligent interception device for rainfall runoff provided in this application;

[0082] Figure 6 A schematic diagram of the structure of the intelligent interception device for rainfall runoff provided in this application.

[0083] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0084] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0085] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0086] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0088] With the effective control of point source pollution, the pollution impact of non-point source pollution caused by rainfall on the urban water environment has become increasingly prominent. In the urban diversion production area, rainfall runoff can carry a large amount of surface accumulated pollutants and discharge them into urban water bodies through underground rainwater pipes, causing the water quality to deteriorate.

[0089] At present, in response to water environment problems caused by rainfall runoff, various places mostly adopt the method of intercepting and regulating the initial runoff, intercepting the initial runoff with poor water quality, regulating and reducing its pollution before discharging it.

[0090] However, in actual operation, accurate interception of initial runoff still faces many challenges, as follows:

[0091] First, when accurately intercepting initial runoff, most existing technologies use the reference rainfall corresponding to the initial runoff as the basis for interception, but ignore the impact of dynamic changes in external environmental factors such as rainfall conditions, soil type, vegetation cover, and land use type on the laws of runoff water quality. Therefore, mechanically intercepting according to a fixed reference rainfall is often difficult to achieve the purpose of both accurate and efficient interception. On the one hand, it may lead to unnecessary interception of clean rainwater, which not only wastes the storage capacity, but also easily leads to too low influent concentration in downstream sewage treatment plants. On the other hand, some severely polluted initial runoff may be missed, causing it to be directly discharged into rivers, exacerbating water pollution.

[0092] Second, in view of the situation that guest water often infiltrates into the rainwater pipe network in river network areas and there is constant flowing water on sunny days, the water quality change pattern of rainfall runoff in the rainwater pipe network is more complicated during rainfall, and the traditional interception judgment method is difficult to effectively deal with the complex and changeable rainfall runoff pollution problem.

[0093] The intelligent interception method of rainfall runoff provided in this application is intended to solve the above technical problems of the prior art.

[0094] First, the implementation scenarios involved in this application are described.

[0095] Rainfall runoff is an important component of urban water resources. Through interception measures, rainwater can be collected and stored for subsequent urban greening, road cleaning, landscape water replenishment and other purposes, thereby realizing the rational use of water resources. The urban drainage system can intercept rainfall runoff, and the rational interception of rainfall runoff can alleviate urban waterlogging, rationally utilize and protect water resources, protect water quality safety and improve the urban ecological environment.

[0096] Therefore, the present application proposes an urban drainage system provided with an interception control module, which can realize water quality prediction and intelligent interception of rainfall runoff in a target area, and the target area can be, for example, an urban area corresponding to the urban drainage system, or a joint area of ​​any one or several areas in the urban drainage system. The present application does not impose any special restrictions on the target area.

[0097] For example, an urban drainage system equipped with an interception control module can obtain rainfall forecast information for the target area in real time. The interception control module is also provided with a water quality simulation database and a rain type database, and the water quality simulation database stores interception control times corresponding to different rainfall types and different times; through the rain type database, the rainfall type corresponding to the current rainfall forecast information can be determined, and then combined with the time of the current rainfall and the water quality simulation database, the corresponding interception time and intercepted water volume can be determined. Accurate interception control can effectively alleviate the pressure of rainfall runoff on the urban drainage system, so that the urban drainage system can dynamically adjust the interception strategy according to actual conditions, ensure that the intercepted water quality meets the preset standards, and help improve the overall water quality of urban water bodies and reduce pollution.

[0098] The present application provides an intelligent interception method for rainfall runoff, by collecting the annual water quality and water volume data of the rainwater pipe network at the water inlet of the sunny day storage tank, constructing a historical constant flow water information database; for the catchment range of the storage tank, collecting urban data information and rainfall information such as the rainwater pipe network, geographic space, land use, etc. within the range, combining the historical constant flow water information database, constructing a hydrological-hydrodynamic model; combining the historical rainfall sequence data within the range, generating multiple basic rain types and extended rain types, and then using the hydrological-hydrodynamic model to construct a water quality simulation database, and obtaining the interception time under different rain types and different time groups; by obtaining the rainfall forecast information of the target area in real time, combining the interception time and intercepted water volume stored in the water quality simulation database, realizing the intelligent interception of rainfall runoff. This method improves the interception efficiency of the urban drainage system, realizes the precise interception of rainfall runoff with high pollution values, and by continuously optimizing the interception strategy, not only improves the interception response capability of the urban drainage system, but also ensures that the intercepted water quality meets the preset standards, reduces pollutants from entering natural water bodies, and protects the ecological environment.

[0099] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0100] Figure 1 Schematic diagram of the process of the intelligent interception method of rainfall runoff provided in the embodiment of the present application Figure 1 The execution subject of this embodiment may be, for example, an urban drainage system provided with a interception control module. Figure 1 As shown, the intelligent interception method of rainfall runoff provided in this embodiment includes:

[0101] S101: Obtain rainfall forecast data for the target area and call a rainfall type discrimination module.

[0102] The rainfall forecast data is used to indicate the rainfall conditions in the target area at that time, and the rainfall type discrimination module is used to determine the rainfall type information corresponding to the rainfall forecast data.

[0103] It can be understood that the execution subject of this step is the urban drainage system, which can obtain the rainfall forecast data within the corresponding range in real time, and the rainfall forecast data refers to the data on the rainfall conditions in a specific area in a certain time period in the future obtained through meteorological monitoring equipment (such as meteorological satellites, radars, ground meteorological stations, etc.) and meteorological models, and the rainfall forecast data usually includes information such as rainfall amount, rainfall intensity, rainfall duration, rainfall start and end time, etc.; the urban drainage system is provided with an interception control module, and the interception control module is also provided with a rainfall type discrimination module, which is a lower module of the interception control module; the rainfall type discrimination module can be used to determine the rain type information corresponding to the current rainfall forecast data, thereby obtaining the corresponding rain type data, wherein the rain type information can be, for example, the intensity distribution, duration, peak time, etc. of the rainfall. The present application does not impose any special restrictions on the acquisition of rainfall forecast data.

[0104] For example, if the target area is city A, the city drainage system can obtain rainfall forecast data within the geographical scope of city A. The rainfall forecast data can be: the change of rainfall corresponding to the rainfall event occurring in city A within the expected time period with the rainfall duration, as shown in Table 1:

[0105] Table 1

[0106]

[0107] Where t(min) represents the duration of the rainfall event in city A, and P(t)(mm) represents the rainfall at time t of the rainfall event.

[0108] S102: Control the rainfall type discrimination module to discriminate the rainfall forecast data to obtain target rainfall type data.

[0109] Among them, the target rain type data is used to indicate the rainfall changes corresponding to the rainfall forecast data.

[0110] It can be understood that a rain pattern database is stored in the urban drainage system, and the rain pattern database stores historical rain pattern data corresponding to different historical time periods under different rainfall conditions in the target area.

[0111] The rainfall type discrimination module is controlled to analyze and process the rainfall forecast data to obtain normalized rainfall type information corresponding to the rainfall forecast data; the rainfall type discrimination module is then controlled to call the historical rainfall type data in the rainfall type database, and the normalized rainfall type information corresponding to the rainfall forecast data is discriminated with a plurality of historical rainfall type data in combination with the rainfall amount and rainfall duration in the rainfall forecast data, thereby determining the historical rainfall type data that is closest to the rainfall type information corresponding to the rainfall forecast data, and determining the historical rainfall type data as the target rainfall type data.

[0112] For example, if the rain type database includes: historical rain type data A, historical rain type data B, historical rain type data C and historical rain type data D, the rainfall type discrimination module is controlled to perform normalization processing on the rainfall forecast data to obtain rain type information corresponding to the rainfall forecast data; then, in combination with the rainfall amount and rainfall duration in the rainfall forecast data, the rain type information is respectively compared with multiple historical rain type data in the rain type database to determine the historical rain type data that is closest to the rain type information; if the result of the comparison processing is historical rain type data B, the historical rain type data B is determined as the target rain type data.

[0113] S103: Determine the target time grouping of the rainfall forecast data, and call a water quality simulation database.

[0114] The time grouping may be divided according to natural months, for example.

[0115] Understandably, in the rainwater pipe network in the river network area, there is often infiltration of guest water and frequent water flow on sunny days, which makes the water quality change pattern of rainfall runoff in the rainwater pipe network during rainfall more complicated; in order to better deal with the above-mentioned complex situations, the water quality and water quantity data of the flowing water in the rainwater pipe network at the water inlet of the sunny storage tank are collected, and the corresponding water quality and water quantity data under different time groups are different. Therefore, when determining the corresponding interception strategy for different rainfall events, the time grouping of the rainfall event is combined to obtain the corresponding water quality and water quantity data of the flowing water in the rainwater pipe network at the water inlet of the sunny storage tank, thereby helping to optimize the interception judgment and strategy.

[0116] According to the time information of the rainfall forecast data, the time grouping of the rainfall forecast data can be determined, and the time grouping is determined as the target time grouping, and the water quality simulation database stored in the urban drainage system is called.

[0117] For example, if the time grouping stored in the interception control module is a natural month grouping, the specific time grouping includes: January, February, March, April, May, June, July, August, September, October, November and December; the time node of the rainfall forecast data obtained at that time is 18:00 on January 12, 2024, then the time grouping of the rainfall forecast data is January, and January is used as the target time grouping determined by the interception strategy at that time; the interception control module also stores a water quality simulation database, which is called to determine the interception strategy for the rainfall event at that time. This application does not impose any special restrictions on the division of time groups.

[0118] S104: Grouping the target rainfall pattern data and the target time, matching them with the water quality simulation database to obtain the target interception time and the target intercepted water volume, and intelligently intercepting the rainfall runoff according to the target interception time and the target intercepted water volume.

[0119] Among them, the interception time is used to indicate the valve opening and closing time of the interception equipment of the urban drainage system during rainfall, and the intercepted water volume is used to indicate the runoff volume intercepted by the interception equipment during rainfall.

[0120] It can be understood that a water quality simulation database is provided in the interception control module, and the water quality simulation database stores the interception control time and intercepted water volume corresponding to different time groups under different rainfall conditions, and the interception control time includes the valve opening time and valve closing time of the interception device; wherein, different interception control times and intercepted water volumes correspond to different rainfall type data and time groups, and the combination of different rainfall type data and time groups has a unique corresponding interception control time and intercepted water volume.

[0121] According to the target rainfall type data determined at that time, matching processing is performed with multiple rainfall type data stored in the water quality simulation database to obtain rainfall type data of multiple different time groups under the same rainfall conditions, and then the time grouping consistent with the target time grouping is screened out from the multiple time groups, so as to determine the target interception time and target intercepted water volume corresponding to the target rainfall type data and the target time grouping, and generate an interception strategy according to the target interception time and target intercepted water volume, and send the interception strategy to the interception device, and the interception device intelligently intercepts the rainfall runoff according to the received interception strategy.

[0122] For example, if the rainfall type data stored in the water quality simulation database include: historical rainfall type data A, historical rainfall type data B, historical rainfall type data C and historical rainfall type data D, the corresponding time groups include: January, February, March, April, May, June, July, August, September, October, November and December, and the rainfall type data and time groups stored in the water quality simulation database can be combined arbitrarily, and different time groups correspond to different sunny day constant flow water data; according to the target rainfall type data determined at that time, the rainfall type data corresponding to different time groups are screened out from the water quality simulation database, specifically including: January - historical rainfall type data B, February - historical rainfall type data B, March - historical rainfall type data B, April - historical rainfall type data B Historical rainfall type data B, May - historical rainfall type data B, June - historical rainfall type data B, July - historical rainfall type data B, August - historical rainfall type data B, September - historical rainfall type data B, October - historical rainfall type data B, November - historical rainfall type data B, December - historical rainfall type data B; if the target time group determined this time is January, then the valve opening time and valve closing time of the interception device corresponding to "January - historical rainfall type data B", and the intercepted water volume are respectively determined as the target interception time and the target intercepted water volume, and an interception strategy is generated according to the target interception time and the target intercepted water volume, and the interception strategy is sent to the interception device, and the interception device performs intelligent interception of rainfall runoff according to the received interception strategy.

[0123] The intelligent interception method of rainfall runoff provided in this embodiment obtains rainfall forecast data of the target area, and calls the rainfall type discrimination module to discriminate the rainfall forecast data and generate target rainfall type data; determines the target time grouping of the rainfall forecast data, and calls the water quality simulation database, matches the target rainfall type data and the target time grouping with the water quality simulation database, thereby obtaining the target interception time and the target intercepted water volume, and intelligently intercepts the rainfall runoff according to the target interception time and the target intercepted water volume. This method improves the interception efficiency of the urban drainage system, realizes the precise interception of rainfall runoff with high pollution values, is suitable for the formulation of interception strategies under different rainfall conditions and time changes, improves the flexibility and reliability of interception strategy formulation, and reduces the risk of water pollution.

[0124] Figure 2 Schematic diagram of the process of the intelligent interception method of rainfall runoff provided in the embodiment of the present application Figure 2 .like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, the intelligent interception method of rainfall runoff is described in detail. The intelligent interception method of rainfall runoff shown in this embodiment includes:

[0125] S201: Obtain rainfall forecast data for the target area and call a rainfall type discrimination module.

[0126] Step S201 is similar to the above step S101 and will not be described again.

[0127] S202: Acquire a rainfall pattern database of the target area.

[0128] S203: Control the rainfall type discrimination module to perform normalization processing on the rainfall forecast data to obtain rain type data to be discriminated.

[0129] The rain type data to be identified is used to indicate the rainfall change corresponding to the rainfall forecast data.

[0130] It is understandable that the rainfall types in different geographical regions are determined by multiple factors such as climate conditions, topographical features, atmospheric circulation patterns and water body distribution, and these multiple factors affect the formation mechanism, intensity, duration and spatial distribution of rainfall, resulting in different geographical regions having their own unique rainfall characteristics and types; therefore, constructing different rain type databases for different geographical regions will help to more accurately predict the runoff generated by rainfall events.

[0131] The rain type database of the target area is obtained, and the rainfall type discrimination module is controlled to normalize the rainfall forecast data so that the rainfall forecast data and the rainfall type data corresponding to different rainfall type data in the rainfall type database are within the same magnitude range; based on the normalized rainfall type data, the rain type data to be discriminated in the target area is determined.

[0132] For example, the rainfall type discrimination module is controlled to perform normalization processing on the rainfall forecast data, and the normalized data obtained is the rain type data to be discriminated. The rain type data before and after the normalization processing are shown in Table 2:

[0133] Table 2

[0134]

[0135] Among them, t(min) represents the rainfall duration of the rainfall event in city A, P(t)(mm) represents the rainfall at time t of the rainfall event, t′(min) represents the normalized rainfall duration of the rainfall event in city A, and P(t′)(mm) represents the normalized rainfall at time t of the rainfall event; the normalized rainfall forecast data is also the rain type data to be identified.

[0136] Preferably, the method further comprises:

[0137] Acquire historical rainfall time series data of the target area; standardize the historical rainfall time series data to obtain multiple basic rain type data and multiple extended rain type data of the target area; generate a rain type database and a rainfall type discrimination module based on the multiple basic rain type data and the multiple extended rain type data.

[0138] Among them, the basic rain pattern data is used to characterize the normalized rain pattern distribution of different rainfall events in the target area, and the extended rain pattern data is used to indicate the rain pattern distribution under different rainfall amounts and different rainfall durations.

[0139] It can be understood that rainfall events in any area have different rain type characteristics, and there are events with the same rain type characteristics among multiple rainfall events. Therefore, the rainfall events in the current target area are screened according to the rain type characteristics, and rainfall events with repeated rain type characteristics are eliminated; that is, the historical rainfall time series data in the target area are screened according to the rain type characteristics, and repeated rain type data are eliminated; in addition, the rain type characteristics refer to the characteristics of rainfall distribution within a certain period of time, usually including the intensity, duration and distribution pattern of rainfall, for example, the peak value of rainfall intensity in the historical rainfall time series data is identified, the number of peaks in each rainfall event, and the time when each peak occurs in each rainfall event are calculated, so as to determine the rain type characteristics corresponding to the historical rainfall time series data.

[0140] The historical rainfall time series data of the target area is obtained, and the rain type features corresponding to the historical rainfall time series data are extracted. According to the multiple rain type features obtained at that time, the data with repeated rain type features in the historical rainfall time series data are screened out; the historical rainfall time series data after the data screening is standardized to obtain multiple basic rain type data and multiple extended rain type data, and the obtained multiple basic rain type data and multiple extended rain type data are stored in the same database to generate a rain type database for the target area; based on the rain type database of the target area, combined with the method for standardizing the rainfall forecast data and the logic of performing similarity comparison between the rain type data in the rain type database and the rain type data in the rainfall forecast data, a rainfall type discrimination module is generated.

[0141] It can be understood that the rain type database not only stores basic rain type data that characterize the rainfall characteristics of the target area, but also stores extended rain type data corresponding to different rainfall amounts and different rainfall durations. There is a correlation between the basic rain type data and the extended rain type data. Each basic rain type data can be expanded according to different rainfall amounts and different rainfall durations to obtain multiple extended rain type data.

[0142] Specifically, the historical rainfall time series data are standardized to obtain multiple basic rainfall type data and multiple extended rainfall type data in the target area, including:

[0143] The historical rainfall time series data are normalized to obtain multiple basic rainfall pattern data of the target area; the preset rainfall parameters are obtained; multiple rainfall conditions are determined according to the preset rainfall amount and the preset rainfall duration; the multiple basic rainfall pattern data are expanded according to the multiple rainfall conditions to obtain multiple expanded rainfall pattern data.

[0144] The preset rainfall parameters include: preset rainfall amount and preset rainfall duration; the rainfall conditions may be, for example: rainfall amount, rainfall intensity, rainfall duration and rainfall distribution pattern.

[0145] Since the basic rain type data is the result of characteristic summary of the data of different rainfall events recorded in the target area, in order to better adapt to the changes in different rainfall amounts and rainfall durations, arbitrary numerical combinations of rainfall amounts and rainfall durations are performed, and based on the obtained different numerical combinations and multiple basic rain type data, the changes in rain type data under different rainfall amounts and rainfall durations are simulated to obtain multiple extended rain type data.

[0146] According to the number and peak time of rainfall peaks, repeated rainfall pattern data in the historical rainfall time series data of the target area are screened out, and the historical rainfall time series data after screening out the repeated rainfall pattern data are normalized to obtain multiple basic rainfall pattern data of the target area; preset rainfall parameters, that is, preset rainfall amount and preset rainfall duration, are obtained, and the number of preset rainfall amounts and preset rainfall durations is respectively multiple, and the multiple preset rainfall amounts and preset rainfall durations are freely combined to obtain multiple rainfall conditions; according to the multiple rainfall conditions, the multiple basic rainfall pattern data are respectively expanded to obtain multiple extended rainfall pattern data.

[0147] For example, the historical rainfall time series data of the city A area for several decades are collected. According to the characteristic values ​​of rainfall types, the data can be: the number of rainfall peaks N, the time when the rainfall peaks occur R, and the repeated rainfall types are eliminated. Then, the historical rainfall time series data without repeated rainfall types are normalized to obtain multiple basic rainfall types, and the basic rainfall types are expanded into rainfall type data with different rainfall amounts and rainfall durations. The specific process of normalizing the rainfall forecast data and the historical rainfall time series data includes the normalization of rainfall amount and the normalization of rainfall duration. The following formula can be used for normalizing the rainfall amount:

[0148]

[0149] Among them, P(t′) normal represents the normalized rainfall at time t, P(t) represents the rainfall at time t, P total Indicates the total rainfall amount;

[0150] The following formula can be used to normalize rainfall duration:

[0151]

[0152] Among them, t′ is the normalized time, t total is the total duration of rainfall;

[0153] The specific process of expanding the basic rainfall pattern into rainfall pattern data with different rainfall amounts and rainfall durations can be achieved using the following formula:

[0154] P ′ (t″)=P(t′) normal ×P t ' otal (3)

[0155] t″=t′×t t ' otal (4)

[0156] Among them, P t ' otal represents the total rainfall amount set when expanding the rain pattern, P′(t″) represents the rainfall at time t″ after expansion, t t ' otal Indicates the rainfall duration set when expanding the rain type;

[0157] When expanding the rain type, the expanded rainfall amount may include: 5mm, 10mm, 15mm, ... 40mm, 45mm, 50mm, etc., and the expanded rainfall duration may include: 0.5h, 1h, 1.5h, ... 4h, 4.5h, 5h, etc., and multiple rainfall amounts and multiple rainfall durations are used to calculate and process with multiple basic rain types respectively to obtain multiple expanded rain type data, and the multiple basic rain type data and the multiple expanded rain type data are respectively saved in the database, that is, a rain type database is generated; based on the rain type database of area A in the city, a rainfall type discrimination module is generated, and the rainfall type discrimination module can perform standardization processing on each rainfall forecast data, and perform similarity comparison between the basic rain type data stored in the rain type database and the rain type data of the rainfall forecast data, and then determine the target rain type data according to the actual rainfall conditions. This application does not impose any special restrictions on the time range of historical rainfall time series data.

[0158] S204: matching the rain type data to be identified with the rain type database to obtain the target rain type data.

[0159] The rainfall type discrimination module calls multiple basic rainfall type data in the rainfall type database of the target area, and matches the rain type data to be discriminated corresponding to the rainfall forecast data with multiple basic rainfall type data respectively, and obtains the matching degree between the rain type data to be discriminated and the basic rainfall type data respectively. At this time, the basic rainfall type with the highest matching degree is determined as the target rainfall type data.

[0160] Preferably, matching the rain type data to be identified with the rain type database to obtain target rain type data includes:

[0161] According to the rain type database, multiple basic rain type data and multiple extended rain type data are determined; the rainfall rain type discrimination module is controlled to respectively calculate the rain type similarities between the rain type data to be discriminated and the multiple basic rain type data; the preset screening conditions are obtained, and the multiple rain type similarities are screened according to the preset screening conditions; the target rain type data is determined according to the rainfall amount and rainfall duration corresponding to the basic rain type data, the multiple extended rain type data and the rainfall forecast data corresponding to the screening results.

[0162] The rain type similarity is used to indicate the difference between the rain type data to be identified and any basic rain type data, and the preset screening condition is used to indicate screening out the basic rain type data with the smallest difference from the rain type data to be identified from multiple basic rain type data.

[0163] It can be understood that in the process of matching the rain type data to be identified with the rain type database, the rain type characteristics of the rain type data to be identified are used to quickly locate the basic rain type data that matches it, and then according to the rainfall and rainfall duration corresponding to the rainfall forecast data at that time, the only corresponding rain type data can be determined from the multiple extended rain type data corresponding to the basic rain type data determined at that time, and the extended rain type data can be determined as the target rain type data.

[0164] The rainfall type identification module calls multiple basic rainfall type data in the rainfall type database of the target area, and based on the multiple basic rainfall type data, respectively calculates the rainfall type similarities between the rainfall type data to be identified and the multiple basic rainfall type data; obtains preset screening conditions, and screens and processes the multiple rainfall type similarities according to the preset screening conditions to obtain the basic rainfall type data with the smallest rainfall type similarity to the rainfall type data to be identified; since in the rainfall type database, each basic rainfall type data corresponds to multiple extended rainfall type data with different rainfall amounts and rainfall durations, according to the forecast rainfall amount and rainfall duration in the current rainfall forecast data, the multiple extended rainfall type data corresponding to the basic rainfall type data are screened and processed, and the extended rainfall type data that best matches the current forecast rainfall amount and rainfall duration are screened out, and the extended rainfall type data is determined as the target rainfall type data.

[0165] It can be understood that the extended rain pattern data is to expand the basic rain pattern data according to any combination of different rainfall amounts and rainfall durations, and the expansion process of the basic rain pattern data is based on the actual changes in the target area, that is, the actual rainfall amount and rainfall duration. Therefore, the rainfall amount and rainfall duration of the most matching extended rain pattern data screened out at this time are the same as the specific values ​​of the rainfall amount and rainfall duration forecasted at that time. This application does not impose any special restrictions on any combination of rainfall amount and rainfall duration.

[0166] For example, the following formula can be used to determine the rainfall type similarity between the basic rainfall type data and the rainfall type data to be identified:

[0167]

[0168] Among them, P(t′) normal,A represents the normalized rainfall of rain type A at time t, P(t′) normal,B represents the normalized rainfall of rain type B at time t, D A,B represents the difference between rain type A and rain type B, that is, the rain type similarity between rain type data A and rain type data B; when D A,B When the value is the smallest, it can be considered that the rain type data A and the rain type data B are the closest. According to the normalized rainfall forecast data in Table 2, and using the calculation formula of rain type similarity (Formula 5), ​​the rain type similarities between the rain type data to be identified corresponding to the normalized data and the multiple basic rain type data in the rain type database are calculated respectively, and the closest basic rain type is found, that is, D A,B The basic rain type data corresponding to the minimum; if the multiple extended rain type data corresponding to the basic rain type data determined at that time are respectively: extended rain type data A-rainfall A and rainfall duration A, extended rain type data B-rainfall B and rainfall duration B, extended rain type data C-rainfall C and rainfall duration C, and the forecast rainfall and rainfall duration in the rainfall forecast data at that time are respectively: rainfall D and rainfall duration D, the rainfall and rainfall duration corresponding to the rainfall forecast data are matched with the rainfall and rainfall duration corresponding to the extended rain type data, and the rainfall and rainfall duration with the highest degree of matching with the rainfall forecast data are determined, that is, the specific values ​​of the rainfall and rainfall duration corresponding to the rainfall forecast data at that time and the extended rain type data with the highest degree of matching are completely consistent; if the rainfall and rainfall duration with the highest degree of matching are: rainfall A and rainfall duration A, the extended rain type data A is determined as the target rain type data; wherein, the rainfall data corresponding to the target rain type data after the extended processing is shown in Table 3, as follows:

[0169] Table 3

[0170] t″(min) P′(t″)(mm) 0 0.5 5 1 10 1 15 0.6 20 0.5 25 0.2 30 0.2 35 0.2 40 0.2 45 0.2 50 0.5 55 0.5 60 0.5 65 0.2 70 0.2 75 0.2 80 0.2 85 0.1 90 0.1

[0171] Where t″ represents the duration of rainfall after expansion, and P′(t″) represents the rainfall at time t″ after expansion.

[0172] S205: Determine the target time grouping of the rainfall forecast data, and call the water quality simulation database.

[0173] According to the time information of the rainfall forecast data, the time grouping of the rainfall forecast data can be determined, and the time grouping is determined as the target time grouping, and the water quality simulation database stored in the urban drainage system is called.

[0174] Preferably, the construction of the water quality simulation database specifically includes:

[0175] Call the rainfall-runoff simulation model and the constant flow water database, and determine multiple constant flow water parameters based on the constant flow water database; control the rainfall-runoff simulation model to perform simulation processing based on multiple extended rainfall pattern data and multiple constant flow water parameters to obtain multiple water quality and water quantity change curves; determine the interception water quality limit of the target area, and determine the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data in different time groups based on the interception water quality limit and multiple water quality and water quantity change curves; construct a water quality simulation database based on the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data in different time groups.

[0176] Among them, the rainfall runoff simulation model is used to predict the water quality and quantity change curves at the water inlet of the regulating reservoir under different rainfall conditions. The constant flow water parameters are used to indicate the water quality and water quantity information at the water inlet of the regulating reservoir on sunny days under different time groups. The interception water quality limit is used to indicate the pollutant concentration of the interception equipment for effective interception of rainfall runoff. The water quality and quantity change curves include: water quality change curve and flow change curve.

[0177] It is understandable that different geographical areas correspond to different interception water quality limits, and the interception water quality limits can be independently set according to the water environment pollution control requirements of the target area; in addition, the interception water quality limit can be for any water quality index in the rainfall runoff water quality. Since the water quality indexes corresponding to different geographical areas are different, different interception water quality limits can be determined according to the water quality characteristics of different geographical areas, and the interception water quality limit can be a single water quality index or a combination of multiple water quality indexes; a single water quality index can be, for example, a COD concentration of 50 mg / L as the interception water quality limit, and a combination of multiple water quality indexes can be, for example, COD controlled within 50 mg / L, and TP controlled within 0.5 mg / L, as the interception water quality limit for the target area. This application does not impose any special restrictions on the interception water quality limit.

[0178] Determine multiple extended rainfall pattern data from the rainfall pattern database, and then determine the constant flow water quality and water quantity data under multiple different time groups according to the time grouping in the constant flow water database, and import the multiple extended rainfall pattern data and the constant flow water quality and water quantity data under multiple different time groups into the rainfall runoff simulation model, and simulate the water quality and water quantity change curve at the water inlet of the regulating reservoir on rainy days under different time groups and different rainfall conditions; determine the interception time and intercepted water volume of rainfall runoff under different time groups and different rainfall conditions according to the interception water quality limit corresponding to the target area and the water quality and water quantity change curve of the simulation, and save the obtained results to the database to generate a water quality simulation database, and the interception time and intercepted water volume form a corresponding relationship with the rainfall pattern data.

[0179] For example, Figure 3 This is a schematic diagram of the water quality change curve at the water inlet of the storage pond on rainy days in January provided by this application, such as Figure 3 As shown, if the interception water quality limit of city A is C limit , then the corresponding cut-off time is T s and T e , and T s is the valve opening time of the shut-off device, T e The valve closing time of the shut-off device; Figure 4 This is a schematic diagram of the flow change curve at the water inlet of the storage tank on rainy days in January provided by this application, and Figure 4 T s and T e and Figure 3 T s and T e Consistent, such as Figure 4 As shown, T s To T e The change curve of the flow rate Q at the water inlet of the regulating reservoir corresponding to the time period, combined with the valve opening time T s and valve closing time T e , the amount of water intercepted at that time can be calculated, and Figure 4 The shaded part is the amount of water intercepted by the interception device at that time; Figure 3 The water quality change curve at the inlet of the regulating reservoir on a rainy day in January shown has only one corresponding extended rainfall type data and time grouping. Figure 4 This is only an example of the change in flow at the water inlet of the storage tank on rainy days, and this application does not impose any special restrictions on the change in the water flow.

[0180] Specifically, the construction of the constant flow database includes:

[0181] Obtain historical hydrological data at the water inlet of the sunny day regulating reservoir; divide the historical hydrological data into time periods to obtain constant flow water data under multiple time groups; calculate the mean of the constant flow water data under different time groups, and determine the calculation results as the constant flow water parameters under the corresponding time groups; construct a constant flow water database based on the constant flow water parameters under multiple different time groups.

[0182] Among them, historical hydrological data are used to indicate the water quality and water quantity data of the rainwater pipe network at the water inlet of the storage tank on sunny days during the historical period.

[0183] Collect the annual water quality of the rainwater pipe network at the inlet of the sunny storage tank (C constant ) and water volume (V constant) data, and build a historical constant flow water information database; specifically: divide the annual data into time periods according to the data volume and data value distribution characteristics, for example: divide the annual constant flow water quality and quantity data according to natural months, according to four quarters, according to flood season and non-flood season, etc., so that the values ​​of each group of data are as concentrated as possible and meet the normal distribution, so as to facilitate the determination of interception time and intercepted water volume; calculate the mean of each time group data, and obtain the water quality (C average ) and water volume (V average ) mean data, which will be used as the constant flow water parameter in the rainwater pipe network under the corresponding time grouping, and can be used for the training of rainfall runoff simulation model and the construction of water quality simulation database.

[0184] For example, the water quality and water quantity of the running water are grouped according to the natural month. The Kolmogorov-Smirnov test is performed on the water quantity data and water quality index data of the running water in each group using the SciPy library of Python. The results show that each group of data conforms to the normal distribution under this classification condition. Therefore, the method of grouping by natural month is adopted; further, the mean of the water quality and water quantity data of each group of running water is calculated, and finally the water quality (C average ) and water volume (V average ) and the mean data will be used as the constant flow water parameter in the rainwater pipe network under the corresponding time grouping.

[0185] Specifically, the construction of rainfall runoff simulation model includes:

[0186] According to the catchment area of ​​the regulating and storage pond in the target area, urban data such as rainwater pipe network, geographic space, land use, etc. within the range are collected to build a hydrological-hydrodynamic model; rainfall information under several rainfall conditions in the historical time period within the range, and the measured data of water volume and water quality at the water inlet of the regulating and storage pond are collected, and combined with the water quality and water volume data of sunny day constant flow water in the constant flow water database in the target area, a rainfall-runoff simulation model is constructed using the hydrological-hydrodynamic model.

[0187] For example, for the catchment area of ​​the regulating reservoir in the drainage system of city A, urban data such as rainwater pipe network, geographic space, and land use within the area are collected to build a hydrological-hydrodynamic model; rainfall information under several rainfall conditions, water volume and water quality measured data at the water inlet of the regulating reservoir are collected, and combined with the parameters of the regular flow water on sunny days, the SWMM simulation software is used to build a rainy day hydrological-hydrodynamic-water quality coupling model, thereby obtaining a rainfall runoff simulation model; among them, the hydrological-hydrodynamic model is a mathematical model used to simulate hydrological processes and hydrodynamic behaviors. This type of model is usually used to study the behavior of surface water and groundwater such as rivers, lakes, reservoirs, groundwater systems, and urban drainage systems. SWMM (Stormwater Management Model) is mainly used to simulate the hydrological process of urban watersheds, with special attention to rainwater runoff and pollutant transport.

[0188] S206: Grouping the target rainfall pattern data and the target time, matching them with the water quality simulation database to obtain the target interception time and the target intercepted water volume, and intelligently intercepting the rainfall runoff according to the target interception time and the target intercepted water volume.

[0189] Step S206 is similar to the above step S104 and will not be described in detail here.

[0190] The intelligent interception method of rainfall runoff provided in this implementation obtains the rainfall forecast data and the rain type database of the target area, and calls the rainfall type discrimination module to normalize the rainfall forecast data to obtain the rain type data to be discriminated, and matches the rain type data to be discriminated with the rain type database to obtain the target rain type data; determines the target time grouping of the rainfall forecast data, and calls the water quality simulation database to match the target rain type data and the target time grouping with the water quality simulation database, thereby obtaining the target interception time and the target intercepted water volume, and intelligently intercepts the rainfall runoff according to the target interception time and the target intercepted water volume. This method improves the interception efficiency of the urban drainage system, realizes the precise interception of rainfall runoff with high pollution values, and improves the interception response capability of the urban drainage system by continuously optimizing the interception strategy. It is suitable for the formulation of interception strategies under different rainfall conditions and time changes, improves the flexibility and reliability of interception strategy formulation, and reduces the risk of water pollution.

[0191] Figure 5 This is a schematic diagram of the structure of the intelligent interception device for rainfall runoff provided in this application. Figure 5 As shown, the present application provides an intelligent interception device for rainfall runoff, and the intelligent interception device 500 for rainfall runoff includes:

[0192] The acquisition module 501 is used to acquire the rainfall forecast data of the target area and call the rainfall type discrimination module.

[0193] The processing module 502 is used to control the rainfall type discrimination module to perform discrimination processing on the rainfall forecast data to obtain target rainfall type data, wherein the target rainfall type data is used to indicate the rainfall change corresponding to the rainfall forecast data.

[0194] The determination module 503 is used to determine the target time grouping of the rainfall forecast data.

[0195] The processing module 502 is further used to call a water quality simulation database, and the water quality simulation database is used to store interception information corresponding to various rainfall types in different time periods.

[0196] The processing module 502 is also used to group the target rainfall type data and the target time, match them with the water quality simulation database, obtain the target interception time and the target retained water volume, and intelligently intercept the rainfall runoff according to the target interception time and the target retained water volume.

[0197] Optionally, the acquisition module 501 is further used to acquire a rain pattern database of the target area, and the rain pattern database is used to store information of various rain patterns in the target area.

[0198] The processing module 502 is further used to control the rainfall type discrimination module to perform normalization processing on the rainfall forecast data to obtain rain type data to be discriminated.

[0199] The processing module 502 is further used to match the rain type data to be identified with the rain type database to obtain the target rain type data.

[0200] Optionally, the determination module 503 is also used to determine multiple basic rain type data and multiple extended rain type data based on the rain type database, the basic rain type data are used to characterize the normalized rain type distribution of different rainfall events in the target area, and the extended rain type data are used to indicate the rain type distribution under different rainfall amounts and different rainfall durations.

[0201] The processing module 502 is further used to control the rainfall rain type discrimination module to respectively calculate the rain type similarity between the rain type data to be discriminated and the multiple basic rain type data, wherein the rain type similarity is used to indicate the difference between the rain type data to be discriminated and any basic rain type data.

[0202] The acquisition module 501 is further used to acquire a preset screening condition, wherein the preset screening condition is used to indicate to screen out basic rain type data having the smallest difference with the rain type data to be determined from a plurality of basic rain type data.

[0203] The processing module 502 is further used to filter multiple rain pattern similarities according to the preset filtering condition.

[0204] The determination module 503 is further used to determine the target rainfall type data according to the basic rainfall type data corresponding to the screening result, the plurality of extended rainfall type data and the rainfall amount and rainfall duration corresponding to the rainfall forecast data.

[0205] Optionally, the acquisition module 501 is further used to acquire historical rainfall time series data of the target area.

[0206] The processing module 502 is further used to perform standardization processing on the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data of the target area.

[0207] The processing module 502 is further used to generate a rain type database and a rainfall type identification module according to a plurality of basic rain type data and a plurality of extended rain type data.

[0208] Optionally, the processing module 502 is further used to normalize the historical rainfall time series data to obtain a plurality of basic rainfall pattern data of the target area.

[0209] The acquisition module 501 is further used to acquire preset rainfall parameters, which include: preset rainfall amount and preset rainfall duration.

[0210] The determination module 503 is further configured to determine a plurality of rainfall conditions according to the preset rainfall amount and the preset rainfall duration.

[0211] The processing module 502 is further used to perform expansion processing on the multiple basic rain type data according to the multiple rainfall conditions to obtain multiple expanded rain type data.

[0212] Optionally, the processing module 502 is further used to call a rainfall-runoff simulation model and a constant flow water database, wherein the rainfall-runoff simulation model is used to predict the water quality and quantity change curve at the water inlet of the regulating reservoir under different rainfall conditions.

[0213] The determination module 503 is further used to determine a plurality of constant flow water parameters according to the constant flow water database. The constant flow water parameters are used to indicate water quality and water quantity information at the water inlet of the sunny storage tank under different time groups.

[0214] The processing module 502 is further used to control the rainfall runoff simulation model to perform simulation processing based on the multiple extended rainfall pattern data and the multiple constant flow water parameters to obtain multiple water quality and water quantity change curves.

[0215] The determination module 503 is further used to determine the interception water quality limit of the target area, and determine the interception time and intercepted water volume corresponding to multiple extended rainfall type data in different time groups according to the interception water quality limit and the multiple water quality and water volume change curves.

[0216] The processing module 502 is further used to construct a water quality simulation database according to the interception time and intercepted water volume corresponding to a plurality of extended rainfall type data in different time groups.

[0217] Optionally, the acquisition module 501 is also used to obtain historical hydrological data at the water inlet of the sunny day regulating reservoir, and the historical hydrological data is used to indicate the water quality and water quantity data of the rainwater pipe network at the water inlet of the sunny day regulating reservoir during a historical period.

[0218] The processing module 502 is further used to divide the historical hydrological data into time periods to obtain constant flow data under multiple time groups.

[0219] The processing module 502 is further used to calculate the mean of the constant flow data in different time groups.

[0220] The determination module 503 is further used to determine the calculation results as the constant flow parameters under the corresponding time groups.

[0221] The processing module 502 is further used to construct a constant flow water database based on the constant flow water parameters in multiple different time groups.

[0222] Figure 6 This is a schematic diagram of the structure of the intelligent interception device for rainfall runoff provided in this application. Figure 6 As shown, the present application provides an intelligent interception device for rainfall runoff, and the intelligent interception device 600 for rainfall runoff includes: a receiver 601 , a transmitter 602 , a processor 603 and a memory 604 .

[0223] Receiver 601, used for receiving instructions and data;

[0224] A transmitter 602, used for sending instructions and data;

[0225] Memory 604, used to store computer-executable instructions;

[0226] The processor 603 is used to execute the computer-executable instructions stored in the memory 604 to implement the various steps executed by the intelligent interception method for rainfall runoff in the above embodiment. For details, please refer to the relevant description in the above embodiment of the intelligent interception method for rainfall runoff.

[0227] Optionally, the memory 604 may be independent or integrated with the processor 603 .

[0228] When the memory 604 is independently provided, the electronic device further includes a bus for connecting the memory 604 and the processor 603 .

[0229] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, an intelligent interception method for rainfall runoff as performed by the intelligent interception device for rainfall runoff described above is implemented.

[0230] The present application also provides a program product, including a computer program, which, when executed by a processor, implements the intelligent interception method for rainfall runoff as described above.

[0231] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0232] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0233] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0234] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0235] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0236] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0237] In the above embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0238] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0239] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent interception method for rainfall runoff, characterized in that: The method comprises: Obtain rainfall forecast data for the target area and call the rainfall type discrimination module; Controlling the rainfall type discrimination module to discriminate the rainfall forecast data to obtain target rainfall type data, wherein the target rainfall type data is used to indicate the rainfall change corresponding to the rainfall forecast data; Determine the target time grouping of the rainfall forecast data, and call a water quality simulation database, wherein the water quality simulation database is used to store interception information corresponding to various rainfall types in different time periods; The target rainfall pattern data and the target time are grouped and matched with the water quality simulation database to obtain the target interception time and the target intercepted water volume, and the rainfall runoff is intelligently intercepted according to the target interception time and the target intercepted water volume.

2. The method according to claim 1, characterized in that The controlling the rainfall type discrimination module to discriminate the rainfall forecast data to obtain target rainfall type data includes: Acquire a rain pattern database of the target area, wherein the rain pattern database is used to store information of various rain patterns in the target area; Controlling the rainfall type discrimination module to perform normalization processing on the rainfall forecast data to obtain rain type data to be discriminated; The rain type data to be identified is matched with the rain type database to obtain the target rain type data.

3. The method according to claim 2, characterized in that The matching process of the rain type data to be identified with the rain type database to obtain the target rain type data includes: Determine a plurality of basic rain type data and a plurality of extended rain type data according to the rain type database, wherein the basic rain type data is used to characterize the normalized rain type distribution of different rainfall events in the target area, and the extended rain type data is used to indicate the rain type distribution under different rainfall amounts and different rainfall durations; Controlling the rainfall rain type discrimination module to respectively calculate the rain type similarity between the rain type data to be discriminated and the plurality of basic rain type data, wherein the rain type similarity is used to indicate the degree of difference between the rain type data to be discriminated and any one of the basic rain type data; Acquire a preset screening condition, and perform screening processing on multiple rain type similarities according to the preset screening condition, wherein the preset screening condition is used to indicate to screen out basic rain type data having the smallest degree of difference with the rain type data to be determined from multiple basic rain type data; The target rain type data is determined according to the basic rain type data corresponding to the screening result, the plurality of extended rain type data and the rainfall amount and rainfall duration corresponding to the rainfall forecast data.

4. The method according to claim 1, characterized in that The method further comprises: Acquire historical rainfall time series data of the target area; Standardizing the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data for the target area; A rain type database and a rainfall type discrimination module are generated according to a plurality of basic rain type data and a plurality of extended rain type data.

5. The method according to claim 4, characterized in that The standardization process of the historical rainfall time series data to obtain a plurality of basic rainfall type data and a plurality of extended rainfall type data of the target area includes: Normalizing the historical rainfall time series data to obtain a plurality of basic rainfall pattern data of the target area; Acquiring preset rainfall parameters, wherein the preset rainfall parameters include: a preset rainfall amount and a preset rainfall duration; Determining a plurality of rainfall conditions according to the preset rainfall amount and the preset rainfall duration; The plurality of basic rain pattern data are respectively extended and processed according to the plurality of rainfall conditions to obtain a plurality of extended rain pattern data.

6. The method according to claim 3 or 4, characterized in that: The method further comprises: Calling a rainfall runoff simulation model and a constant flow water database, and determining a plurality of constant flow water parameters according to the constant flow water database, wherein the rainfall runoff simulation model is used to predict the water quality and water quantity change curve at the water inlet of the regulating reservoir under different rainfall conditions, and the constant flow water parameters are used to indicate the water quality and water quantity information at the water inlet of the regulating reservoir on sunny days under different time groups; Based on the multiple extended rain type data and the multiple constant flow water parameters, controlling the rainfall runoff simulation model to perform simulation processing to obtain multiple water quality and water quantity change curves; Determine the interception water quality limit of the target area, and determine the interception time and intercepted water volume corresponding to multiple extended rainfall type data in different time groups according to the interception water quality limit and the multiple water quality and water volume change curves; A water quality simulation database is constructed based on the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data under different time groups.

7. The method according to claim 6, characterized in that The method further comprises: Acquire historical hydrological data at the water inlet of the sunny day regulating reservoir, wherein the historical hydrological data is used to indicate the water quality and water quantity data of the rainwater pipe network at the water inlet of the sunny day regulating reservoir during the historical period; Dividing the historical hydrological data into time periods to obtain constant flow data under multiple time groups; Calculate the mean of the constant flow data under different time groups respectively, and determine the calculation results as the constant flow parameters under the corresponding time groups respectively; Based on the constant flow water parameters under multiple different time groups, a constant flow water database is constructed.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

10. A program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when being executed by a processor.

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