Intelligent interception method, device, storage medium and program product for rainfall runoff

Abstract

The application provides an intelligent interception method, device, storage medium and program product for rainfall runoff. The method comprises the following steps: obtaining rainfall forecast data of a target area, calling a rainfall type discrimination module, discriminating and processing the rainfall forecast data, and generating target rainfall type data; determining a target time grouping of the rainfall forecast data, calling a water quality simulation database, and matching the target rainfall type data and the target time grouping with the water quality simulation database, so as to obtain a target interception time and a target interception water volume, and intelligently intercepting the rainfall runoff according to the target interception time and the target interception water volume. The method improves the interception efficiency of the urban drainage system, realizes accurate interception of the rainfall runoff with high pollution value, improves the interception response capability of the urban drainage system by continuously optimizing the interception strategy, is suitable for the formulation of the interception strategy under different rainfall conditions and time changes, and reduces the water pollution risk.

Description

Technical Field

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

[0002] With the effective control of point source pollution, the impact of non-point source pollution caused by rainfall on the urban water environment is becoming increasingly prominent. In urban sewer systems, rainwater runoff can carry a large amount of surface-accumulated pollutants, which are discharged into urban water bodies through underground stormwater pipe networks, leading to a decline in water quality.

[0003] Currently, most methods for addressing water environment problems caused by rainfall runoff involve intercepting and regulating the initial runoff. This involves blocking the initial runoff with poor water quality, regulating and reducing its pollution before discharging it. When accurately intercepting the initial runoff, existing technologies mostly use the reference rainfall corresponding to the initial runoff as the basis for interception.

[0004] However, when intercepting runoff based on a fixed reference rainfall, the impact of external environmental factors such as rainfall conditions on the water quality of rainwater runoff is ignored. This can easily lead to the unnecessary interception of clean rainwater and may also cause some heavily polluted initial runoff to be missed, resulting in its direct discharge into rivers and exacerbating water pollution. Summary of the Invention

[0005] This application provides an intelligent interception method, device, storage medium, and program product for rainfall runoff, which solves the technical problem of unnecessary interception of clean rainwater and omission of some heavily polluted initial runoff due to ignoring the influence of external environmental factors such as rainfall conditions on the water quality of rainfall runoff, thereby aggravating water pollution.

[0006] In a first aspect, this application provides a smart interception method for rainfall runoff, comprising:

[0007] Obtain rainfall forecast data for the target area and call the rainfall pattern identification module;

[0008] The rainfall pattern discrimination module is controlled to process the rainfall forecast data to obtain target rainfall pattern data, which is used to indicate the rainfall changes corresponding to the rainfall forecast data.

[0009] The target time grouping of the rainfall forecast data is determined, and the water quality simulation database is called. The water quality simulation database is used to store the 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. The rainfall runoff is then intelligently intercepted according to the target interception time and the target intercepted water volume.

[0011] Optionally, the control of the rainfall pattern discrimination module to process the rainfall forecast data to obtain target rainfall pattern data includes:

[0012] Obtain a rain pattern database for the target area, wherein the rain pattern database is used to store various rain pattern information within the target area;

[0013] The rainfall pattern discrimination module is controlled to normalize the rainfall forecast data to obtain the rainfall pattern data to be discriminated.

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

[0015] Optionally, the step of matching the rainfall pattern data to be determined with the rainfall pattern database to obtain the target rainfall pattern data includes:

[0016] Based on the rainfall pattern database, multiple basic rainfall pattern data and multiple extended rainfall pattern data are determined. The basic rainfall pattern data are used to characterize the normalized rainfall pattern distribution of different rainfall events in the target area, and the extended rainfall pattern data are used to indicate the rainfall pattern distribution under different rainfall amounts and different rainfall durations.

[0017] The rainfall pattern discrimination module is controlled to calculate the rainfall pattern similarity between the rainfall pattern data to be discerned and the multiple basic rainfall pattern data respectively. The rainfall pattern similarity is used to indicate the degree of difference between the rainfall pattern data to be discerned and any one of the basic rainfall pattern data.

[0018] Obtain preset filtering conditions and filter multiple rain pattern similarities according to the preset filtering conditions. The preset filtering conditions are used to indicate the basic rain pattern data with the smallest difference from the rain pattern data to be judged from multiple basic rain pattern data.

[0019] The target rainfall pattern data is determined based on the basic rainfall pattern data corresponding to the screening results, the multiple extended rainfall pattern data, and the rainfall amount and duration corresponding to the rainfall forecast data.

[0020] Optionally, the method further includes:

[0021] Obtain historical rainfall time-series data for the target area;

[0022] The historical rainfall time series data are standardized to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area.

[0023] Based on multiple basic rainfall pattern data and multiple extended rainfall pattern data, a rainfall pattern database and a rainfall pattern discrimination module are generated.

[0024] Optionally, the standardization process of the historical rainfall time-series data to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area includes:

[0025] The historical rainfall time series data are normalized to obtain multiple basic rainfall pattern data for the target area;

[0026] Obtain preset rainfall parameters, which include: preset rainfall amount and preset rainfall duration;

[0027] Based on the preset rainfall amount and the preset rainfall duration, multiple rainfall conditions are determined;

[0028] The multiple basic rainfall pattern data are extended according to the multiple rainfall conditions to obtain multiple extended rainfall pattern data.

[0029] Optionally, the method further includes:

[0030] The rainfall-runoff simulation model and the constant flow water database are invoked, and multiple constant flow water parameters are determined based on the constant flow water database. The rainfall-runoff simulation model is used to predict the water quality and quantity change curves at the inlet of the storage tank under different rainfall conditions, and the constant flow water parameters are used to indicate the water quality and quantity information at the inlet of the storage tank on sunny days under different time groups.

[0031] Based on the multiple extended rainfall pattern data and the multiple constant flow parameters, the rainfall-runoff simulation model is controlled to perform simulation processing to obtain multiple water quality and quantity change curves.

[0032] Determine the interception water quality limit for the target area, and based on the interception water quality limit and the multiple water quality and quantity change curves, determine the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups;

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

[0034] Optionally, the method further includes:

[0035] Historical hydrological data of the intake of the reservoir during sunny weather is obtained. The historical hydrological data is used to indicate the water quality and quantity of the constant flow of rainwater in the stormwater pipe network at the intake of the reservoir during historical periods.

[0036] The historical hydrological data is divided into time periods to obtain constant flow data under multiple time groups;

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

[0038] A constant flow database is constructed based on constant flow parameters under multiple different time groups.

[0039] Secondly, this application provides an intelligent interception device for rainfall runoff, comprising:

[0040] The acquisition module is used to acquire rainfall forecast data for the target area and call the rainfall pattern identification module.

[0041] The processing module is used to control the rainfall pattern discrimination module to process the rainfall forecast data to obtain target rainfall pattern data, which is used to indicate the rainfall changes corresponding to the rainfall forecast data.

[0042] The 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, which stores interception information corresponding to various rainfall patterns in different time periods.

[0044] The processing module is further configured to group the target rainfall pattern data and the target time, match them with the water quality simulation database 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.

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

[0046] The processing module is also used to control the rainfall pattern discrimination module to normalize the rainfall forecast data to obtain the rainfall pattern data to be discriminated.

[0047] The processing module is further configured to match the rain pattern data to be determined with the rain pattern database to obtain the target rain pattern data.

[0048] Optionally, the determining module is further configured to determine multiple basic rainfall pattern data and multiple extended rainfall pattern data based on the rainfall pattern database. The basic rainfall pattern data is used to characterize the normalized rainfall pattern distribution of different rainfall events within the target area, and the extended rainfall pattern data is used to indicate the rainfall pattern distribution under different rainfall amounts and different rainfall durations.

[0049] The processing module is further configured to control the rainfall pattern discrimination module to calculate the rainfall pattern similarity between the rainfall pattern data to be discerned and the multiple basic rainfall pattern data respectively. The rainfall pattern similarity is used to indicate the degree of difference between the rainfall pattern data to be discerned and any one of the basic rainfall pattern data.

[0050] The acquisition module is also used to acquire preset filtering conditions, which are used to indicate the selection of basic rainfall pattern data with the smallest difference from the rainfall pattern data to be judged from multiple basic rainfall pattern data.

[0051] The processing module is also used to filter multiple rain pattern similarities according to the preset filtering conditions.

[0052] The determining module is further configured to determine the target rainfall pattern data based on the basic rainfall pattern data corresponding to the filtering results, the multiple extended rainfall pattern data, and the rainfall amount and duration corresponding to the rainfall forecast data.

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

[0054] The processing module is also used to standardize the historical rainfall time series data to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area.

[0055] The processing module is also used to generate a rain pattern database and a rainfall pattern discrimination module based on multiple basic rainfall pattern data and multiple extended rainfall pattern data.

[0056] Optionally, the processing module is further configured to normalize the historical rainfall time series data to obtain multiple basic rainfall pattern data for the target area.

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

[0058] The determining module is further configured to determine multiple rainfall conditions based on the preset rainfall amount and the preset rainfall duration.

[0059] The processing module is further configured to perform extended processing on the multiple basic rainfall pattern data according to the multiple rainfall conditions, so as to obtain multiple extended rainfall pattern data.

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

[0061] The determining module is also used to determine multiple constant flow parameters based on the constant flow water database. The constant flow water parameters are used to indicate the water quality and quantity information at the inlet of the storage tank on sunny days 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 pattern data and the multiple constant flow parameters, so as to obtain multiple water quality and quantity change curves.

[0063] The determining module is also used to determine the interception water quality limit for the target area, and to determine the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups based on the interception water quality limit and the multiple water quality and quantity change curves.

[0064] The processing module is also used to construct a water quality simulation database based on the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups.

[0065] Optionally, the acquisition module is further configured to acquire historical hydrological data of the inlet of the reservoir during sunny weather, wherein the historical hydrological data is used to indicate the water quality and quantity data of the constant flow of rainwater in the stormwater pipe network at the inlet of the reservoir during historical periods.

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

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

[0068] The determining module is also used to determine the calculation results as constant flow parameters under the corresponding time group.

[0069] The processing module is also used to construct a constant flow database based on constant flow parameters under multiple different time groups.

[0070] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0071] The memory stores computer-executed instructions;

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

[0073] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions 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] Fifthly, this 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 for rainfall runoff provided in this application acquires rainfall forecast data and a rainfall pattern database for the target area. It then calls a rainfall pattern discrimination module to normalize the rainfall forecast data, obtaining rainfall pattern data to be discriminated. This data is then matched with the rainfall pattern database to obtain target rainfall pattern data. Target time groups of the rainfall forecast data are determined, and a water quality simulation database is invoked to match the target rainfall pattern data and target time groups with the database, thereby obtaining the target interception time and target interception volume. The rainfall runoff is then intelligently intercepted according to the target interception time and target interception volume. This method improves the interception efficiency of urban drainage systems, achieves precise interception of high-pollution rainfall runoff, and enhances the interception response capability of urban drainage systems through continuous optimization of the interception strategy. It is applicable to the formulation of interception strategies under different rainfall conditions and temporal variations, improving the flexibility and reliability of interception strategy formulation and reducing the risk of water pollution. Attached Figure Description

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

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

[0078] Figure 2 Flowchart 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 rainwater storage tank during January, provided for this application;

[0080] Figure 4 A schematic diagram of the flow rate change curve at the inlet of the rainwater storage tank during January, provided for this application;

[0081] Figure 5 A schematic diagram 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 accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0084] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

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

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

[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, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0088] With the effective control of point source pollution, the impact of non-point source pollution caused by rainfall on the urban water environment is becoming increasingly prominent. In urban sewer systems, rainwater runoff can carry a large amount of surface-accumulated pollutants, which are discharged into urban water bodies through underground stormwater pipe networks, leading to a decline in water quality.

[0089] Currently, in response to water environment problems caused by rainfall runoff, most regions adopt the method of intercepting and regulating the initial runoff, intercepting the initial runoff with poor water quality, regulating and reducing pollution before discharging it.

[0090] However, in practice, the precise interception of initial runoff still faces many challenges, as follows:

[0091] First, when conducting precise interception of initial runoff, most existing technologies use 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 runoff water quality patterns. Therefore, mechanically intercepting runoff according to a fixed reference rainfall often fails to achieve both precision and efficiency. On the one hand, it may lead to the unnecessary interception of clean rainwater, wasting storage capacity and easily resulting in excessively low influent concentrations at downstream sewage treatment plants. On the other hand, it may also miss some heavily polluted initial runoff, causing it to be directly discharged into rivers and exacerbating water pollution.

[0092] Secondly, given that rainwater infiltration into the stormwater pipe network in river network areas is common and there is constant flow of water on sunny days, the water quality changes of rainfall runoff in the stormwater pipe network are more complex during rainfall, and traditional interception judgment methods are difficult to effectively deal with the complex and ever-changing rainfall runoff pollution problem.

[0093] The intelligent interception method for rainfall runoff provided in this application aims to solve the above-mentioned technical problems of the prior art.

[0094] First, the implementation scenarios involved in this application will be explained.

[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, thus achieving the rational use of water resources. Urban drainage systems can intercept rainfall runoff, and the rational interception of rainfall runoff can alleviate urban flooding, rationally utilize and protect water resources, protect water quality safety, and improve the urban ecological environment.

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

[0097] For example, an urban drainage system equipped with a flow interception control module can obtain real-time rainfall forecast information for the target area. This flow interception control module also includes a water quality simulation database and a rainfall pattern database. The water quality simulation database stores the flow interception control times corresponding to different rainfall patterns and times. By using the rainfall pattern database, the rainfall pattern corresponding to the current rainfall forecast can be determined. Combined with the time of the current rainfall and the water quality simulation database, the corresponding flow interception time and the amount of water to be intercepted can be determined. Precise flow interception control can effectively alleviate the pressure of rainfall runoff on the urban drainage system, enabling the urban drainage system to dynamically adjust the flow interception strategy according to the actual situation, ensuring that the intercepted water quality meets the preset standards, which helps to improve the overall water quality of urban water bodies and reduce pollution.

[0098] This application provides an intelligent interception method for rainfall runoff. It constructs a historical constant flow information database by collecting annual water quality and quantity data of the stormwater pipe network at the inlet of a stormwater storage tank during sunny days. For the catchment area of ​​the storage tank, it collects urban data information such as stormwater pipe network, geospatial data, land use, and rainfall data within that area. Combining this with the historical constant flow information database, it constructs a hydrological-hydrodynamic model. Based on historical rainfall sequence data within this area, it generates multiple basic and extended rainfall patterns. Then, using the hydrological-hydrodynamic model, it constructs a water quality simulation database to obtain the interception time under different rainfall patterns and time groups. By acquiring real-time rainfall forecast information for the target area and combining it with the interception time and intercepted water volume stored in the water quality simulation database, it achieves intelligent interception of rainfall runoff. This method improves the interception efficiency of urban drainage systems, achieves precise interception of high-pollution rainfall runoff, and by continuously optimizing the interception strategy, it not only improves the interception response capability of urban drainage systems but also ensures that the intercepted water quality meets preset standards, reduces pollutants entering natural water bodies, and protects the ecological environment.

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

[0100] Figure 1 A flowchart illustrating the intelligent interception method for rainfall runoff provided in this application embodiment. Figure 1 The implementing entity in this embodiment can be, for example, an urban drainage system equipped with a flow interception control module. Figure 1 As shown, the intelligent interception method for rainfall runoff provided in this embodiment includes:

[0101] S101: Obtain rainfall forecast data for the target area and call the rainfall pattern identification module.

[0102] Among them, the rainfall forecast data is used to indicate the rainfall situation in the target area at that time, and the rainfall pattern discrimination module is used to determine the rainfall pattern information corresponding to the rainfall forecast data.

[0103] Understandably, the entity executing this step is the urban drainage system. The urban drainage system can acquire real-time rainfall forecast data within its corresponding range. This rainfall forecast data refers to data about rainfall in a specific area within a future time period, predicted by meteorological monitoring equipment (such as meteorological satellites, radar, and ground meteorological stations) and meteorological models. Rainfall forecast data typically includes information such as rainfall amount, rainfall intensity, rainfall duration, and the start and end times of the rainfall. The urban drainage system is equipped with a flow interception control module, which also includes a rainfall pattern identification module. This rainfall pattern identification module is a subordinate module of the flow interception control module. Using this rainfall pattern identification module, the rainfall pattern information corresponding to the current rainfall forecast data can be determined, thereby obtaining the corresponding rainfall pattern data. The rainfall pattern information can include, for example, the intensity distribution, duration, and peak time of the rainfall. This 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's drainage system can obtain rainfall forecast data within the geographical area of ​​city A. This rainfall forecast data can be: the variation of rainfall amount corresponding to rainfall events occurring in city A within the expected time period as a function of rainfall duration, as shown in Table 1.

[0105] Table 1

[0106]

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

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

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

[0110] Understandably, urban drainage systems store rainfall pattern databases, which contain historical rainfall pattern data corresponding to different rainfall conditions and historical time periods within the target area.

[0111] The rainfall pattern discrimination module is controlled to analyze and process the rainfall forecast data to obtain the normalized rainfall pattern information corresponding to the rainfall forecast data. Then, the rainfall pattern discrimination module is controlled to call up historical rainfall pattern data in the rainfall pattern database. Combining the rainfall amount and rainfall duration in the rainfall forecast data, the normalized rainfall pattern information corresponding to the rainfall forecast data is compared with multiple historical rainfall pattern data. The historical rainfall pattern data that is closest to the rainfall pattern information corresponding to the rainfall forecast data is determined and identified as the target rainfall pattern data.

[0112] For example, if the rainfall pattern database includes historical rainfall pattern data A, historical rainfall pattern data B, historical rainfall pattern data C, and historical rainfall pattern data D, the rainfall pattern discrimination module is controlled to normalize the rainfall forecast data to obtain the rainfall pattern information corresponding to the rainfall forecast data. Then, combined with the rainfall amount and rainfall duration in the rainfall forecast data, the rainfall pattern information is compared with multiple historical rainfall pattern data in the rainfall pattern database to determine the historical rainfall pattern data that is closest to the rainfall pattern information. If the comparison result is historical rainfall pattern data B, then historical rainfall pattern data B is determined as the target rainfall pattern data.

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

[0114] For example, time groups can be divided according to calendar months.

[0115] Understandably, in river network areas, stormwater drainage systems often experience infiltration of external water and continuous runoff during sunny days, making the water quality variation patterns of rainfall runoff in the stormwater drainage system more complex during rainfall. To better address these complexities, water quality and quantity data of the runoff in the stormwater drainage system at the inlet of the stormwater storage tank are collected during sunny days. Since the water quality and quantity data differ across different time groups, when determining corresponding interception strategies for different rainfall events, the water quality and quantity data of the runoff in the stormwater drainage system at the inlet of the stormwater storage tank during sunny days are obtained by combining the time grouping of the rainfall event. This helps to optimize interception judgments and strategies.

[0116] Based on the time information of the rainfall forecast data, the time group to which the rainfall forecast data belongs can be determined, and this time group can be identified as the target time group. The water quality simulation database stored in the urban drainage system can then be accessed.

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

[0118] S104: Group the target rainfall pattern data and the target time, match them with the water quality simulation database 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.

[0119] Among them, the interception time is used to indicate the opening and closing time of the interception equipment in 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] Understandably, the interception control module is equipped with a water quality simulation database, which stores the interception control time and intercepted water volume corresponding to different rainfall conditions and different time groups. The interception control time includes the valve opening time and valve closing time of the interception equipment. Different interception control times and intercepted water volumes correspond to different rainfall patterns and time groups, and there is a unique corresponding interception control time and intercepted water volume for different combinations of rainfall patterns and time groups.

[0121] Based on the target rainfall pattern data determined for this time, it is matched with multiple rainfall pattern data stored in the water quality simulation database to obtain multiple rainfall pattern data in different time groups under the same rainfall conditions. Then, the time group that matches the target time group is selected from the multiple time groups, thereby determining the target interception time and target interception volume corresponding to the target rainfall pattern data and target time group. An interception strategy is generated according to the target interception time and target interception volume, and the interception strategy is sent to the interception equipment. The interception equipment intelligently intercepts the rainfall runoff according to the received interception strategy.

[0122] For example, if the rainfall pattern data stored in the water quality simulation database includes: historical rainfall pattern data A, historical rainfall pattern data B, historical rainfall pattern data C, and historical rainfall pattern data D, and the corresponding time groups include: January, February, March, April, May, June, July, August, September, October, November, and December, and the rainfall pattern data and time groups stored in the water quality simulation database can be arbitrarily combined, with different time groups corresponding to different clear-day constant flow data; according to the target rainfall pattern data determined at this time, the rainfall pattern data corresponding to different time groups are filtered from the water quality simulation database, specifically including: January - historical rainfall pattern data B, February - historical rainfall pattern data B, March - historical rainfall pattern data B, April - historical rainfall pattern data D, etc. Historical rainfall data B, May-historical rainfall data B, June-historical rainfall data B, July-historical rainfall data B, August-historical rainfall data B, September-historical rainfall data B, October-historical rainfall data B, November-historical rainfall data B, December-historical rainfall data B; if the target time group determined in this instance is January, then the valve opening time and closing time of the interception device corresponding to "January-historical rainfall data B", as well as the intercepted water volume, are determined as the target interception time and target intercepted water volume, respectively. An interception strategy is generated according to the target interception time and target intercepted water volume, and the interception strategy is sent to the interception device. The interception device then intelligently intercepts the rainfall runoff according to the received interception strategy.

[0123] The intelligent interception method for rainfall runoff provided in this embodiment acquires rainfall forecast data for the target area and calls a rainfall pattern discrimination module to process the rainfall forecast data and generate target rainfall pattern data. It then determines the target time grouping of the rainfall forecast data and calls a water quality simulation database to match the target rainfall pattern data and target time grouping with the water quality simulation database, thereby obtaining the target interception time and target intercepted water volume. The method then intelligently intercepts the rainfall runoff according to the target interception time and target intercepted water volume. This method improves the interception efficiency of urban drainage systems, achieves precise interception of high-pollution rainfall runoff, and is applicable to the formulation of interception strategies under different rainfall conditions and temporal variations. It improves the flexibility and reliability of interception strategy formulation and reduces the risk of water pollution.

[0124] Figure 2 A flowchart illustrating the intelligent interception method for rainfall runoff provided in this application embodiment. Figure 2 .like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, a detailed description of the intelligent interception method for rainfall runoff is provided. The intelligent interception method for rainfall runoff shown in this embodiment includes:

[0125] S201: Obtain rainfall forecast data for the target area and call the rainfall pattern identification module.

[0126] Step S201 is similar to step S101 above, and will not be repeated here.

[0127] S202: Obtain the rain pattern database for the target area.

[0128] S203: Control the rainfall pattern discrimination module to normalize the rainfall forecast data to obtain the rainfall pattern data to be discriminated.

[0129] Among them, the rainfall pattern data to be identified is used to indicate the rainfall changes corresponding to the rainfall forecast data.

[0130] Understandably, the rainfall types in different geographical regions are determined by a variety of factors, including climate conditions, topographic features, atmospheric circulation patterns, and water distribution. These factors influence the formation mechanism, intensity, duration, and spatial distribution of rainfall, resulting in unique rainfall characteristics and types in different geographical regions. Therefore, constructing different rainfall pattern databases for different geographical regions can help to more accurately predict the runoff generated by rainfall events.

[0131] The system acquires the rainfall pattern database for the target area and controls the rainfall pattern discrimination module to normalize the rainfall forecast data, ensuring that the rainfall forecast data and the rainfall pattern data corresponding to different rainfall patterns in the rainfall pattern database are within the same order of magnitude. Based on the normalized rainfall pattern data, the system determines the rainfall pattern data to be discriminated for the target area.

[0132] For example, the rainfall pattern discrimination module normalizes the rainfall forecast data, and the normalized data is the rainfall pattern data to be discriminated. The rainfall pattern data before and after normalization are shown in Table 2:

[0133] Table 2

[0134]

[0135] Where t(min) represents the duration of rainfall event A in city A, P(t)(mm) represents the rainfall amount at time t of the rainfall event, t′(min) represents the normalized duration of rainfall event A in city A, and P(t′)(mm) represents the normalized rainfall amount at time t of the rainfall event; the normalized rainfall forecast data is also the rainfall pattern data to be determined.

[0136] Preferably, the method further includes:

[0137] The system acquires historical rainfall time-series data for the target area; it standardizes the historical rainfall time-series data to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area; and it generates a rainfall pattern database and a rainfall pattern discrimination module based on the multiple basic rainfall pattern data and multiple extended rainfall pattern data.

[0138] The basic rainfall pattern data is used to characterize the normalized rainfall pattern distribution of different rainfall events within the target area, while the extended rainfall pattern data is used to indicate the rainfall pattern distribution under different rainfall amounts and durations.

[0139] It is understandable that rainfall events within any region exhibit different rainfall pattern characteristics, and that some rainfall events share the same rainfall pattern characteristics. Therefore, rainfall events within the current target region are filtered according to rainfall pattern characteristics to eliminate those with duplicate rainfall pattern characteristics. In other words, historical rainfall time series data within the target region is filtered according to rainfall pattern characteristics to eliminate duplicate rainfall pattern data. Furthermore, rainfall pattern characteristics refer to the characteristics of rainfall distribution over a certain period of time, typically including rainfall intensity, duration, and distribution pattern. For example, identifying the peak values ​​of rainfall intensity in historical rainfall time series data, calculating the number of peak values ​​in each rainfall event, and the time of occurrence of each peak value in each rainfall event, can help determine the rainfall pattern characteristics corresponding to the historical rainfall time series data.

[0140] Historical rainfall time-series data for the target area is acquired, and the corresponding rainfall pattern features are extracted. Data with duplicate rainfall pattern features are filtered out based on the acquired rainfall pattern features. The filtered historical rainfall time-series data is then standardized to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data. These data are stored in the same database to generate a rainfall pattern database for the target area. Based on this rainfall pattern database, a rainfall pattern discrimination module is generated by combining the standardization method for rainfall forecast data with the logic of comparing the similarity between the rainfall pattern data in the rainfall pattern database and the rainfall pattern data in the rainfall forecast data.

[0141] Understandably, the rainfall pattern database not only stores basic rainfall pattern data that characterizes the rainfall characteristics of the target area, but also stores extended rainfall pattern data that characterize different rainfall amounts and durations. Furthermore, there is a correlation between the basic rainfall pattern data and the extended rainfall pattern data. Each basic rainfall pattern data can be extended according to different rainfall amounts and durations to obtain multiple extended rainfall pattern data.

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

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

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

[0145] Since basic rainfall pattern data is the result of summarizing the characteristics of different rainfall events recorded in the target area, in order to better adapt to the changes in different rainfall amounts and durations, the rainfall amounts and durations are arbitrarily combined numerically. Based on the different numerical combinations and multiple basic rainfall pattern data, the changes in rainfall pattern data under different rainfall amounts and durations are simulated to obtain multiple extended rainfall pattern data.

[0146] Based on the number and timing of peak rainfall, duplicate rainfall patterns in the historical rainfall time series data of the target area are filtered out. The historical rainfall time series data after filtering out duplicate rainfall patterns are then normalized to obtain multiple basic rainfall pattern data for the target area. Preset rainfall parameters, namely preset rainfall amount and preset rainfall duration, are obtained, and multiple preset rainfall amounts and preset rainfall durations are each obtained. Multiple preset rainfall amounts and preset rainfall durations are freely combined to obtain multiple rainfall conditions. The multiple basic rainfall pattern data are then extended according to these multiple rainfall conditions to obtain multiple extended rainfall pattern data.

[0147] For example, collecting decades of historical rainfall time-series data within area A of city, and based on rainfall pattern characteristics (specifically, the number of rainfall peaks N and the time of occurrence R of the rainfall peaks), duplicate rainfall patterns are eliminated. Then, the historical rainfall time-series data after eliminating duplicate patterns is normalized to obtain multiple basic rainfall patterns. These basic rainfall patterns are then expanded into rainfall patterns with different rainfall amounts and durations. The specific process of normalizing rainfall forecast data and historical rainfall time-series data includes normalizing rainfall amount and normalizing rainfall duration. The following formula can be used when normalizing rainfall amount:

[0148]

[0149] Where, P(t′) normal Let P(t) represent the normalized rainfall at time t, and let P(t) represent the rainfall at time t. total This indicates the total rainfall amount for all events.

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

[0151]

[0152] Where t′ is the normalized time, t total This represents the total duration of rainfall.

[0153] The specific process of expanding a basic rainfall pattern into rainfall pattern data with different rainfall amounts and 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 P'(t″) represents the total rainfall amount set when expanding the rainfall pattern, and P′(t″) represents the rainfall amount at time t″ after expansion. t ′ otal This indicates the duration of rainfall when the extended rainfall pattern is set.

[0157] When expanding rainfall patterns, the expanded rainfall amounts can include 5mm, 10mm, 15mm, ... 40mm, 45mm, 50mm, etc., and the expanded rainfall durations can include 0.5h, 1h, 1.5h, ... 4h, 4.5h, 5h, etc. Multiple rainfall amounts and durations are calculated and processed with multiple basic rainfall patterns to obtain multiple expanded rainfall pattern data. These multiple basic rainfall pattern data and multiple expanded rainfall pattern data are then saved to a database, thus generating a rainfall pattern database. Based on the rainfall pattern database for area A of city A, a rainfall pattern discrimination module is generated. This module can standardize each rainfall forecast data and compare the similarity between the basic rainfall pattern data stored in the rainfall pattern database and the rainfall pattern data of the rainfall forecast data. Finally, it determines the target rainfall pattern 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: Match the rain pattern data to be determined with the rain pattern database to obtain the target rain pattern data.

[0159] The rainfall pattern identification module calls up multiple basic rainfall pattern data from the rainfall pattern database of the target area, and matches the rainfall pattern data to be identified corresponding to the rainfall forecast data with the multiple basic rainfall pattern data respectively, and obtains the matching degree between the rainfall pattern data to be identified and the basic rainfall pattern data. At this time, the basic rainfall pattern with the highest matching degree is determined as the target rainfall pattern data.

[0160] Preferably, the rainfall pattern data to be identified is matched with a rainfall pattern database to obtain target rainfall pattern data, including:

[0161] Based on the rainfall pattern database, multiple basic rainfall pattern data and multiple extended rainfall pattern data are determined; the rainfall pattern discrimination module is controlled to calculate the rainfall pattern similarity between the rainfall pattern data to be discriminated and the multiple basic rainfall pattern data; preset filtering conditions are obtained, and multiple rainfall pattern similarities are filtered according to the preset filtering conditions; the target rainfall pattern data is determined based on the rainfall amount and rainfall duration corresponding to the basic rainfall pattern data, multiple extended rainfall pattern data and rainfall forecast data corresponding to the filtering results.

[0162] Among them, the rain pattern similarity is used to indicate the degree of difference between the rain pattern data to be judged and any basic rain pattern data, and the preset filtering conditions are used to indicate the basic rain pattern data with the smallest degree of difference from the rain pattern data to be judged from multiple basic rain pattern data.

[0163] Understandably, during the matching process between the rainfall pattern data to be identified and the rainfall pattern database, the rainfall pattern characteristics of the rainfall pattern data to be identified are used to quickly locate the matching basic rainfall pattern data. Then, according to the rainfall amount and duration corresponding to the current rainfall forecast data, the unique corresponding rainfall pattern data can be determined from multiple extended rainfall pattern data corresponding to the current determined basic rainfall pattern data, and the extended rainfall pattern data is determined as the target rainfall pattern data.

[0164] The rainfall pattern identification module calls multiple basic rainfall pattern data from the rainfall pattern database of the target area. Based on these basic rainfall pattern data, it calculates the rainfall pattern similarity between the rainfall pattern data to be identified and these basic rainfall pattern data. It obtains preset filtering conditions and filters the multiple rainfall pattern similarities according to the preset filtering conditions to obtain the basic rainfall pattern data with the lowest rainfall pattern similarity to the rainfall pattern data to be identified. Since each basic rainfall pattern data in the rainfall pattern database corresponds to multiple extended rainfall pattern data with different rainfall amounts and durations, it filters the multiple extended rainfall pattern data corresponding to the basic rainfall pattern data according to the forecast rainfall amount and duration in the current rainfall forecast data. It then selects the extended rainfall pattern data that best matches the current forecast rainfall amount and duration and identifies this extended rainfall pattern data as the target rainfall pattern data.

[0165] Understandably, extended rainfall pattern data expands upon basic rainfall pattern data by any combination of different rainfall amounts and durations. Furthermore, the expansion process of the basic rainfall pattern data is based on the actual changes in the target area, i.e., the actual existing rainfall amounts and durations. Therefore, the rainfall amounts and durations of the most matching extended rainfall pattern data selected in this instance are the same as the specific values ​​of the rainfall amounts and durations predicted in this instance. This application does not impose any special restrictions on the arbitrary combinations of rainfall amounts and durations.

[0166] For example, the similarity between basic rainfall pattern data and rainfall pattern data to be identified can be determined using the following formula:

[0167]

[0168] Where, P(t′) normal,A Let P(t′) represent the normalized rainfall of rainfall pattern A at time t. normal,B D represents the normalized rainfall of rainfall pattern B at time t. A,B This represents the difference between rainfall pattern A and rainfall pattern B, that is, the similarity between rainfall pattern data A and rainfall pattern data B; when D A,B When the similarity is minimized, rainfall pattern data A and rainfall pattern data B can be considered the closest. Based on the normalized rainfall forecast data in Table 2, and using the rainfall pattern similarity calculation formula (Formula 5), ​​the rainfall pattern similarity between the normalized data and the rainfall pattern data to be judged and multiple basic rainfall pattern data in the rainfall pattern database is calculated. The closest basic rainfall pattern is found, that is, D. A,B The minimum rainfall pattern data corresponds to the basic rainfall pattern data. If the multiple extended rainfall pattern data corresponding to the basic rainfall pattern data determined in this instance are: extended rainfall pattern data A - rainfall A and rainfall duration A, extended rainfall pattern data B - rainfall B and rainfall duration B, extended rainfall pattern data C - rainfall C and rainfall duration C, and the forecast rainfall and rainfall duration in the current rainfall forecast data are: rainfall D and rainfall duration D, the rainfall and rainfall duration corresponding to the current rainfall forecast data are matched with the rainfall and rainfall duration corresponding to the extended rainfall pattern data, and the rainfall and rainfall duration with the highest matching degree with the current rainfall forecast data are determined. That is, the specific values ​​of rainfall and rainfall duration corresponding to the current rainfall forecast data and the extended rainfall pattern data with the highest matching degree are completely consistent. If the rainfall and rainfall duration with the highest matching degree are: rainfall A and rainfall duration A, then the extended rainfall pattern data A is determined as the target rainfall pattern data. The rainfall data corresponding to the target rainfall pattern 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 extended rainfall duration, and P′(t″) represents the rainfall amount at time t″ after the extension.

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

[0173] Based on the time information of the rainfall forecast data, the time group to which the rainfall forecast data belongs can be determined, and this time group can be identified as the target time group. The water quality simulation database stored in the urban drainage system can then be accessed.

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

[0175] The rainfall-runoff simulation model and the constant flow water database were invoked, and multiple constant flow water parameters were determined based on the database. The rainfall-runoff simulation model was controlled to perform simulation processing based on multiple extended rainfall pattern data and multiple constant flow water parameters, resulting in multiple water quality and quantity change curves. The interception water quality limit for the target area was determined, and based on the interception water quality limit and multiple water quality and quantity change curves, the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data under different time groups were determined. A water quality simulation database was constructed based on the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data under different time groups.

[0176] Among them, the rainfall-runoff simulation model is used to predict the water quality and quantity change curves at the inlet of the storage tank under different rainfall conditions, the constant flow parameters are used to indicate the water quality and quantity information at the inlet of the storage tank 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, and the water quality and quantity change curves include: water quality change curve and flow rate change curve.

[0177] It is understandable that different geographical regions have different interception water quality limits, and these limits can be set independently based on the water environment pollution control requirements of the target area. Furthermore, the interception water quality limits can target any water quality indicator in rainfall runoff. Since the water quality indicators differ across geographical regions, different interception water quality limits can be determined based on the water quality characteristics of different geographical regions. These limits can be a single water quality indicator or a combination of multiple indicators. For example, a single water quality indicator could be a COD concentration of 50 mg / L as the interception water quality limit, while a combination of multiple indicators could be, for example, controlling COD below 50 mg / L and TP below 0.5 mg / L, thus serving as the interception water quality limit for the target area. This application does not impose any special restrictions on the interception water quality limits.

[0178] Multiple extended rainfall pattern data were determined from the rainfall pattern database. Then, based on the time grouping in the constant flow database, the constant flow water quality and quantity data under multiple different time groups were determined. These multiple extended rainfall pattern data and the constant flow water quality and quantity data under multiple different time groups were imported into the rainfall runoff simulation model to simulate the water quality and quantity change curves at the inlet of the rainwater storage tank under different time groups and different rainfall conditions. Based on the interception water quality limit corresponding to the target area and combined with the water quality and quantity change curves of the current simulation, the interception time and intercepted water volume of rainfall runoff under different time groups and different rainfall conditions were determined. The results were saved to the database to generate a water quality simulation database, and the interception time and intercepted water volume correspond to the rainfall pattern data.

[0179] For example, Figure 3 This is a schematic diagram of the water quality change curve at the inlet of the storage tank during January's rainy weather, as provided in this application. Figure 3 As shown, if the interception water quality limit for city A is C limit The corresponding interception time is T. s and T e And T s T is the valve opening time of the shut-off device. e This refers to the valve closing time of the flow-stopping device; Figure 4 This is a schematic diagram of the flow rate change curve at the inlet of the stormwater storage tank during January, provided in this application. Figure 4 T in s and T e and Figure 3 T in s and T e Consistent, such as Figure 4 As shown, T s To T e The curve showing the change in flow rate Q at the inlet of the regulating reservoir during the corresponding time period, combined with the valve opening time T. s Valve closing time T e The amount of water intercepted in that instance can be calculated, and Figure 4 The shaded area shown represents the amount of water intercepted by the interception device in that particular flow; among which, Figure 3 The water quality change curve at the inlet of the storage tank during January rainy days shown has only one corresponding extended rainfall pattern data and time grouping. Figure 4 This is merely an example of the flow rate change at the inlet of the rainwater storage tank during rainy days, and this application does not impose any special restrictions on the flow rate change.

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

[0181] Historical hydrological data of the intake of the reservoir on sunny days were obtained; the historical hydrological data were divided into time periods to obtain constant flow data under multiple time groups; the mean of constant flow data under different time groups was calculated, and the calculation results were determined as constant flow parameters under the corresponding time groups; a constant flow database was constructed based on the constant flow parameters under multiple different time groups.

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

[0183] Collect the annual water quality (C) of the rainwater pipe network at the inlet of the storage tank during sunny days. constant ) and water volume (V) constantThe data is used to construct a historical database of normal flow water information. Specifically, the annual data is divided into time periods based on the distribution characteristics of the data volume and values. For example, the annual normal flow water quality and quantity data can be divided by natural month, by the four quarters, or by the flood season and non-flood season, so that the values ​​of each group of data are as concentrated as possible and meet the normal distribution, which facilitates the determination of interception time and intercepted water volume. The mean value of the data in each time group is calculated to obtain the water quality (C) of the normal flow water in the rainwater pipe network on sunny days under different time groups. average ) and water volume (V) average The mean data will be used as the constant flow parameters in the rainwater pipe network for the corresponding time group, and can be used for training the rainfall-runoff simulation model and building the water quality simulation database.

[0184] For example, the water quality and quantity of the constant flow water were grouped according to natural months. Using Python's SciPy library, Kolmogorov-Smirnov tests were performed on the water quantity data and various water quality indicators for each group. The results showed that the data in each group conformed to a normal distribution under this classification condition. Therefore, grouping by natural month was adopted. Furthermore, the mean of the water quality and quantity data for each group of constant flow water was calculated, ultimately yielding the water quality (C) of the constant flow water in the rainwater drainage network under different groups on sunny days. average ) and water volume (V) average The mean data will be used as the constant flow parameters in the rainwater pipe network for the corresponding time group.

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

[0186] For the catchment area of ​​the stormwater storage tank in the target area, urban data such as stormwater pipe network, geospatial data, and land use are collected within this area to build a hydrological-hydrodynamic model. Rainfall information under several rainfall conditions during historical periods, as well as measured data on water volume and quality at the inlet of the stormwater storage tank, are collected within this area. Combined with the data on the quality and quantity of normal runoff on sunny days in the normal runoff database of the target area, a rainfall-runoff simulation model is constructed using the hydrological-hydrodynamic model.

[0187] For example, focusing on the catchment area of ​​a stormwater storage tank in city A's drainage system, urban data such as stormwater pipe network, geospatial data, and land use within this area are collected to build a hydrological-hydrodynamic model. Rainfall information under several rainfall conditions, measured water volume and quality data at the stormwater storage tank inlet are collected, and combined with normal runoff parameters during sunny days, a coupled hydrological-hydrodynamic-water quality model for rainy days is built using SWMM simulation software, thus obtaining a rainfall-runoff simulation model. A hydrological-hydrodynamic model is a mathematical model used to simulate hydrological processes and hydrodynamic behavior. These models are typically used to study the behavior of surface water and groundwater in rivers, lakes, reservoirs, groundwater systems, and urban drainage systems. SWMM (Stormwater Management Model) is primarily used to simulate hydrological processes in urban watersheds, with a particular focus on stormwater runoff and pollutant transport.

[0188] S206: Group the target rainfall pattern data and the target time, match them with the water quality simulation database 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.

[0189] Step S206 is similar to step S104 above, and will not be described again here.

[0190] The intelligent interception method for rainfall runoff provided in this implementation acquires rainfall forecast data and a rainfall pattern database for the target area. It then calls a rainfall pattern discrimination module to normalize the rainfall forecast data, obtaining rainfall pattern data to be discriminated. This data is then matched with the rainfall pattern database to obtain target rainfall pattern data. Target time groups of the rainfall forecast data are determined, and a water quality simulation database is invoked to match the target rainfall pattern data and target time groups with the database, thus obtaining the target interception time and target interception volume. Intelligent interception of rainfall runoff is then performed according to the target interception time and target interception volume. This method improves the interception efficiency of urban drainage systems, achieves precise interception of high-pollution rainfall runoff, and enhances the interception response capability of urban drainage systems through continuous optimization of the interception strategy. It is applicable to the formulation of interception strategies under different rainfall conditions and temporal variations, improving the flexibility and reliability of interception strategy formulation and reducing the risk of water pollution.

[0191] Figure 5 A schematic diagram of the intelligent interception device for rainfall runoff provided in this application. Figure 5 As shown, this application provides an intelligent interception device for rainfall runoff, the intelligent interception device 500 for rainfall runoff comprising:

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

[0193] The processing module 502 is used to control the rainfall pattern discrimination module to process the rainfall forecast data to obtain target rainfall pattern data, which 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 also used to call the water quality simulation database, which is used to store interception information corresponding to various rainfall patterns in different time periods.

[0196] The processing module 502 is further configured to group the target rainfall pattern data and the target time, match them with the water quality simulation database 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.

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

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

[0199] The processing module 502 is further configured to match the rain pattern data to be determined with the rain pattern database to obtain the target rain pattern data.

[0200] Optionally, the determining module 503 is further configured to determine multiple basic rainfall pattern data and multiple extended rainfall pattern data based on the rainfall pattern database. The basic rainfall pattern data is used to characterize the normalized rainfall pattern distribution of different rainfall events in the target area, and the extended rainfall pattern data is used to indicate the rainfall pattern distribution under different rainfall amounts and different rainfall durations.

[0201] The processing module 502 is further configured to control the rainfall pattern discrimination module to calculate the rainfall pattern similarity between the rainfall pattern data to be discerned and the multiple basic rainfall pattern data respectively, wherein the rainfall pattern similarity is used to indicate the degree of difference between the rainfall pattern data to be discerned and any one of the basic rainfall pattern data.

[0202] The acquisition module 501 is further configured to acquire preset filtering conditions, which are used to indicate the selection of basic rainfall pattern data with the smallest difference from the rainfall pattern data to be judged from multiple basic rainfall pattern data.

[0203] The processing module 502 is also used to perform filtering processing on multiple rain pattern similarities according to the preset filtering conditions.

[0204] The determining module 503 is further configured to determine the target rainfall pattern data based on the basic rainfall pattern data corresponding to the screening results, the multiple extended rainfall pattern data, and the rainfall amount and duration corresponding to the rainfall forecast data.

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

[0206] The processing module 502 is also used to standardize the historical rainfall time series data to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area.

[0207] The processing module 502 is also used to generate a rain pattern database and a rainfall pattern discrimination module based on multiple basic rainfall pattern data and multiple extended rainfall pattern data.

[0208] Optionally, the processing module 502 is further configured to normalize the historical rainfall time series data to obtain multiple basic rainfall pattern data for the target area.

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

[0210] The determining module 503 is further configured to determine multiple rainfall conditions based on the preset rainfall amount and the preset rainfall duration.

[0211] The processing module 502 is further configured to perform extended processing on the multiple basic rainfall pattern data according to the multiple rainfall conditions to obtain multiple extended rainfall pattern data.

[0212] Optionally, the processing module 502 is also used to call the rainfall-runoff simulation model and the constant flow water database. The rainfall-runoff simulation model is used to predict the water quality and quantity change curves at the inlet of the storage tank under different rainfall conditions.

[0213] The determining module 503 is further configured to determine multiple constant flow parameters based on the constant flow water database. The constant flow water parameters are used to indicate the water quality and quantity information at the inlet of the storage tank on sunny days under different time groups.

[0214] The processing module 502 is also 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 parameters, so as to obtain multiple water quality and quantity change curves.

[0215] The determining module 503 is also used to determine the interception water quality limit of the target area, and to determine the interception time and intercepted water volume corresponding to multiple extended rainfall pattern data under different time groups based on the interception water quality limit and the multiple water quality and quantity change curves.

[0216] The processing module 502 is also used to construct a water quality simulation database based on the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups.

[0217] Optionally, the acquisition module 501 is further configured to acquire historical hydrological data of the inlet of the reservoir during sunny weather, wherein the historical hydrological data is used to indicate the water quality and quantity data of the constant flow of rainwater in the stormwater pipe network at the inlet of the reservoir during historical periods.

[0218] The processing module 502 is also 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 also used to calculate the mean of the constant flow data under different time groups.

[0220] The determining module 503 is also used to determine the calculation results as constant flow parameters under the corresponding time group.

[0221] The processing module 502 is also used to construct a constant flow database based on constant flow parameters under multiple different time groups.

[0222] Figure 6 A schematic diagram of the intelligent interception device for rainfall runoff provided in this application. Figure 6 As shown, this application provides an intelligent interception device for rainfall runoff. 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 is used to receive instructions and data;

[0224] Transmitter 602 is used to send commands and data;

[0225] Memory 604 is used to store instructions executed by the computer;

[0226] The processor 603 is used to execute computer execution instructions stored in the memory 604 to implement the various steps of the intelligent interception method for rainfall runoff in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the intelligent interception method for rainfall runoff.

[0227] Optionally, the memory 604 can be either standalone or integrated with the processor 603.

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

[0229] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the intelligent interception method for rainfall runoff performed by the intelligent interception device for rainfall runoff described above.

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

[0231] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0232] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

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

[0234] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0235] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0236] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0237] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as these combinations of technical features do not contradict each other, they should be considered within the scope of this specification.

[0238] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0239] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A smart interception method for rainfall runoff, characterized in that, The method includes: Obtain rainfall forecast data for the target area and call the rainfall pattern identification module; The rainfall pattern discrimination module is controlled to process the rainfall forecast data to obtain target rainfall pattern data, which is used to indicate the rainfall changes corresponding to the rainfall forecast data. The target time grouping of the rainfall forecast data is determined, and the water quality simulation database is called. The water quality simulation database is used to store the 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 interception volume. The rainfall runoff is then intelligently intercepted according to the target interception time and the target interception volume. The control module for determining rainfall patterns processes the rainfall forecast data to obtain target rainfall pattern data, including: Obtain a rain pattern database for the target area, wherein the rain pattern database is used to store various rain pattern information within the target area; The rainfall pattern discrimination module is controlled to normalize the rainfall forecast data to obtain the rainfall pattern data to be discriminated. Based on the rainfall pattern database, multiple basic rainfall pattern data and multiple extended rainfall pattern data are determined. The basic rainfall pattern data are used to characterize the normalized rainfall pattern distribution of different rainfall events in the target area, and the extended rainfall pattern data are used to indicate the rainfall pattern distribution under different rainfall amounts and different rainfall durations. The rainfall pattern discrimination module is controlled to calculate the rainfall pattern similarity between the rainfall pattern data to be discerned and the multiple basic rainfall pattern data respectively. The rainfall pattern similarity is used to indicate the degree of difference between the rainfall pattern data to be discerned and any one of the basic rainfall pattern data. Obtain preset filtering conditions and filter multiple rain pattern similarities according to the preset filtering conditions. The preset filtering conditions are used to indicate the basic rain pattern data with the smallest difference from the rain pattern data to be judged from multiple basic rain pattern data. The target rainfall type data is determined based on the basic rainfall type data corresponding to the screening results, the multiple extended rainfall type data, and the rainfall amount and duration corresponding to the rainfall forecast data; The method further includes: Obtain historical rainfall time-series data for the target area; The historical rainfall time series data are standardized to obtain multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area. Based on multiple basic rainfall pattern data and multiple extended rainfall pattern data, a rainfall pattern database and a rainfall pattern discrimination module are generated. The rainfall-runoff simulation model and the constant flow water database are invoked, and multiple constant flow water parameters are determined based on the constant flow water database. The rainfall-runoff simulation model is used to predict the water quality and quantity change curves at the inlet of the storage tank under different rainfall conditions, and the constant flow water parameters are used to indicate the water quality and quantity information at the inlet of the storage tank on sunny days under different time groups. Based on the multiple extended rainfall pattern data and the multiple constant flow parameters, the rainfall-runoff simulation model is controlled to perform simulation processing to obtain multiple water quality and quantity change curves. Determine the interception water quality limit for the target area, and based on the interception water quality limit and the multiple water quality and quantity change curves, determine the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups; A water quality simulation database is constructed based on the interception time and intercepted water volume corresponding to multiple extended rainfall patterns under different time groups.

2. The method according to claim 1, characterized in that, The standardization process of the historical rainfall time-series data yields multiple basic rainfall pattern data and multiple extended rainfall pattern data for the target area, including: The historical rainfall time series data are normalized to obtain multiple basic rainfall pattern data for the target area; Obtain preset rainfall parameters, which include: preset rainfall amount and preset rainfall duration; Based on the preset rainfall amount and the preset rainfall duration, multiple rainfall conditions are determined; The multiple basic rainfall pattern data are extended according to the multiple rainfall conditions to obtain multiple extended rainfall pattern data.

3. The method according to claim 1, characterized in that, The method further includes: Historical hydrological data of the intake of the reservoir during sunny weather is obtained. The historical hydrological data is used to indicate the water quality and quantity of the constant flow of rainwater in the stormwater pipe network at the intake of the reservoir during historical periods. The historical hydrological data is divided into time periods to obtain constant flow data under multiple time groups; Calculate the mean values ​​of the constant flow data under different time groups, and determine the calculation results as the constant flow parameters under the corresponding time groups; A constant flow database is constructed based on constant flow parameters under multiple different time groups.

4. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 3.

6. A program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 3.

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