A highly efficient total runoff control device and dispatching method for civil airports in highly built-up areas

Abstract

The present invention relates to the technical field of sponge city planning, and in particular to an efficient total runoff control device and scheduling method for civil airports in highly built-up areas. The method comprises the following steps: performing airport runoff total control rate index processing on civil airports to generate target airport runoff control indexes; constructing a rainfall runoff hydrological model; performing rainfall runoff optimization on the rainfall runoff hydrological model using the target airport runoff control index to obtain an optimized rainfall runoff hydrological model; performing real-time rainfall dynamic runoff simulation processing according to the optimized rainfall runoff hydrological model to generate total runoff prediction data; performing adaptive regulation and storage optimization processing on the total runoff prediction data to generate adaptive regulation and storage control optimization data; performing intelligent drainage collaborative regulation according to the adaptive regulation and storage control optimization data to generate drainage volume regulation state data. The present invention realizes intelligent rainfall regulation and storage control through total runoff prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of sponge city planning, and in particular to a highly efficient total runoff control device and a dispatching method for a civil airport in a highly built-up area. Background Art

[0002] In line with the current requirements of developing green aviation and promoting the construction of green airports, many new and expanded airports in China have proposed to build "ecological sponge airports" to achieve sustainable development of airports. The construction of sponge airports is based on rain and flood control, and then builds an ecological green airport. Through the comprehensive management and utilization of rainwater, the total amount of runoff and runoff pollution discharged from the airport are controlled, and the drainage and flood prevention safety of the airport are ensured. At the same time, water resources are saved, and airports with the ability to absorb, store, purify and release water naturally are built. However, traditional civil airports usually rely on fixed drainage schemes, which lack flexibility and real-time performance. Under extreme weather conditions such as heavy rain, these drainage schemes cannot be quickly adjusted in the face of sudden rainfall, resulting in the inability to effectively control rainwater, increasing the risk of flooding. Summary of the invention

[0003] Based on this, the present invention provides a highly efficient total runoff control device and scheduling method for civil airports in highly built-up areas to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an efficient total runoff scheduling method for civil airports in highly built-up areas includes the following steps:

[0005] Step S1: Analyze historical rainfall events of civil airports to generate effective rainfall event data; process the airport runoff total control rate index according to the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model according to the effective rainfall event data; adjust the storage volume scale of the regulation and storage unit using the target airport runoff control index to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model by using the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model;

[0006] Step S2: using a rainfall monitoring unit to perform real-time rainfall monitoring to generate original rainfall monitoring data; performing rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; using an optimized rainfall-runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data;

[0007] Step S3: Perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate a storage control strategy based on the runoff control threshold data to generate storage control strategy data; perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data;

[0008] Step S4: Generate drainage flow control instructions according to the adaptive regulation and control optimization data to generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data to generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data to generate drainage overflow warning data.

[0009] The present invention analyzes the historical rainfall events of civil airports and extracts effective rainfall event data therefrom so as to more accurately evaluate the airport runoff control requirements. The effective rainfall event data are used to process the airport runoff total control rate index to ensure that the airport can effectively control the runoff under various rainfall conditions and ensure the normal operation of the airport. A rainfall runoff hydrological model is constructed according to the effective rainfall event data, which helps to understand the impact of rainfall on airport runoff. The runoff control index of the target airport is used to adjust the storage volume scale of the storage unit to obtain a more suitable storage volume scale, which can effectively improve the utilization rate of the storage unit and ensure that there is enough capacity to store runoff water when a rainfall event occurs. The rainfall runoff hydrological model is optimized by adjusting the storage volume scale data to obtain a more accurate rainfall runoff hydrological model. The current rainfall situation can be obtained in time by using the rainfall monitoring unit to perform real-time rainfall monitoring. By performing rainfall prediction processing on the original rainfall monitoring data, the rainfall can be predicted before the flood occurs, and countermeasures can be prepared in advance to reduce the losses caused. The total runoff forecast data obtained through model simulation enables the airport to adjust drainage measures according to actual conditions and reduce the adverse effects of runoff on the airport. Comprehensive runoff risk analysis based on the total runoff forecast data can comprehensively evaluate the runoff situation and determine the runoff risk level under different scenarios. According to the actual risk situation, the critical value of runoff control is formulated so that the corresponding control measures can be initiated in time to prevent potential runoff risks. Different storage strategies are formulated according to actual needs to ensure effective control of water flow under different runoff conditions and reduce flood risks. Dynamic optimization of storage strategies is carried out according to actual conditions so that they can better adapt to different rainfall scenarios, improve storage efficiency, and reduce flood risks. According to the real-time rainfall conditions and the runoff conditions around the airport, appropriate drainage flow control instruction data is generated in time to ensure that the drainage system can effectively respond to different runoff conditions, adjust the drainage flow in time to reduce the load of the drainage system and reduce flood risks. Intelligent pump station drainage adjustment is carried out according to the drainage flow control instruction data to ensure that each part of the drainage system can be adjusted according to the instructions, ensure the stable operation of the drainage system, and effectively control the water level rise. According to the drainage volume adjustment state data, drainage overflow warning processing is performed, and the operation state of the drainage system can be monitored in time. Once the drainage system overflows, warning data is generated in time to remind relevant departments to take countermeasures, minimize the losses caused, and ensure the safety of the airport and surrounding areas. Therefore, an efficient total runoff scheduling method for civil airports in highly built-up areas of the present invention adjusts the water storage scale through the historical rainfall data of the civil airport area, and at the same time constructs an optimized rainfall runoff hydrological model.Through the refined monitoring of the airport's real-time rainfall, the total rainfall of the rainfall event can be predicted, and the total runoff volume can be simulated by optimizing the rainfall-runoff hydrological model to obtain runoff prediction data. Adaptive storage control and intelligent drainage regulation are carried out based on the runoff prediction data, and the airport's rainwater runoff discharge is managed in a refined manner to ensure the overall drainage safety of the airport.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: collecting historical regional rainfall data for civil airports to generate historical rainfall data for airport areas;

[0012] Step S12: Perform rainfall event analysis on the historical rainfall data of the airport area to generate effective rainfall event data; perform airport runoff total control rate index processing based on the effective rainfall event data to generate the target airport runoff control index;

[0013] Step S13: Perform site rainfall characteristic analysis based on the effective rainfall event data to generate airport rainfall event characteristic data; evaluate the surface runoff coefficient of the civil airport to generate surface runoff coefficient evaluation data;

[0014] Step S14: constructing a rainfall runoff hydrological model through surface runoff coefficient evaluation data, using the rainfall runoff hydrological model to simulate the rainfall runoff peak value of the airport rainfall event characteristic data, and generating historical runoff peak value data;

[0015] Step S15: performing runoff control index optimization processing on the historical runoff peak data and the runoff control index of the target airport to generate runoff control index optimization and runoff control index optimization data;

[0016] Step S16: Analyzing the efficiency of the regulating and storing facilities of the regulating and storing units to generate regulating and storing facility efficiency data;

[0017] Step S17: performing water balance processing on the regulation and storage facility efficiency data through the runoff control index optimization data, and adjusting the regulation and storage capacity scale of the regulation and storage unit to obtain regulation and storage capacity scale adjustment data;

[0018] Step S18: Optimizing the rainfall-runoff hydrological model by adjusting the storage volume scale data to obtain an optimized rainfall-runoff hydrological model.

[0019] The present invention collects historical regional rainfall data for civil airports, and can collect rainfall data in the area where the airport is located, which is helpful for understanding the rainfall conditions in the area where the airport is located. Rainfall event analysis of historical rainfall data in the airport area can identify effective rainfall events, which is helpful for determining the main rainfall patterns and frequencies faced by the airport. The airport runoff total control rate index is processed according to the effective rainfall event data to ensure that the runoff flow can be effectively controlled under different rainfall events and the normal operation of airport facilities is guaranteed. The site rainfall characteristic analysis can deeply understand the refined rainfall characteristic parameters of the airport area, and the surface runoff coefficient evaluation of the civil airport can evaluate the surface runoff coefficient. The rainfall runoff peak value simulation of the airport rainfall event characteristic data using the rainfall runoff hydrological model is helpful for simulating and predicting the runoff peak value under the airport rainfall event. According to the historical runoff data and the target control index, the runoff control strategy is optimized to minimize the adverse effects of runoff on the airport facilities and the surrounding environment. The efficiency analysis of the regulation and storage facilities on the regulation and storage unit can evaluate the efficiency of the regulation and storage facilities. The volume of the regulation and storage facilities is adjusted according to the actual water balance to cope with different rainfall scenarios and improve the utilization efficiency of water resources. The rainfall-runoff hydrological model is optimized by adjusting the storage volume scale data, so that it can more accurately simulate the impact of rainfall events on runoff and provide more precise data support for the airport's water resources management and flood prevention work.

[0020] Preferably, step S12 comprises the following steps:

[0021] Step S121: preprocessing the historical rainfall data of the airport area, dividing the rainfall events, and generating historical rainfall event data;

[0022] Step S122: based on the historical rainfall event data, rainfall events equal to or less than 2 mm are eliminated to generate valid rainfall event data;

[0023] Step S123: sorting the rainfall in ascending order according to the valid rainfall event data to generate sorted rainfall event data;

[0024] Step S124: Calculate the total rainfall control rate according to the sorted rainfall event data to generate rainfall event control rate data;

[0025] Step S125: Draw a rainfall relationship curve according to the rainfall event control rate data to generate a rainfall-control rate curve graph;

[0026] Step S126: matching the rainfall area type of the civil airport to generate rainfall area type data; formulating the runoff control index range according to the rainfall area type data to generate rainwater runoff control index range data;

[0027] Step S127: The rainfall-control rate curve is first screened for rainfall that meets the standard range using the rainwater runoff control index range data to obtain the target airport runoff control index.

[0028] The present invention performs data preprocessing and rainfall event division on the historical rainfall data of the airport area, can clean and organize the original data, ensure the quality and availability of the data, and divide the historical rainfall data into different rainfall events. Based on the historical rainfall event data, rainfall events equal to or less than 2 mm are eliminated. Such processing helps to filter out tiny rainfall events, making the analysis more accurate and the obtained effective rainfall event data more practical. The rainfall is sorted in ascending order according to the effective rainfall event data, and such sorting helps to observe and analyze the rainfall events more clearly. The total rainfall control rate is calculated according to the sorted rainfall event data, and the control rate of the rainfall event can be quantified. The rainfall relationship curve is drawn according to the rainfall event control rate data, and such a chart can intuitively show the relationship between the rainfall event and the control rate. The rainfall area type matching of the civil airport can be carried out, and a suitable control index range can be formulated according to the rainfall characteristics and terrain conditions of the area where the airport is located to ensure the effectiveness of the control measures. By screening the rainfall that meets the standard in the first range on the rainfall-control rate curve, the control index of the target airport under different rainfall scenarios is determined.

[0029] Preferably, step S14 comprises the following steps:

[0030] Step S141: obtaining a digital elevation model of a civil airport;

[0031] Step S142: using the civil airport digital elevation model to perform comprehensive runoff curve number processing on the surface runoff coefficient assessment data to generate a comprehensive runoff curve coefficient;

[0032] Step S143: Calculate the rainwater retention amount according to the airport rainfall event characteristic data to generate rainfall retention amount data;

[0033] Step S144: Calculate effective rainfall according to the airport rainfall event characteristic data to generate effective rainfall data;

[0034] Step S145: constructing a runoff simulation physical model for the rainfall retention data and the effective rainfall data based on the preset runoff curve model by using the comprehensive runoff curve coefficient to generate a runoff simulation physical model;

[0035] Step S146: performing physical parameter fitting processing on the civil airport digital elevation model using the runoff simulation physical model, thereby obtaining a rainfall runoff hydrological model;

[0036] Step S147: using a rainfall-runoff hydrological model to perform historical runoff hydrological simulation on the airport rainfall event characteristic data to generate historical rainfall-runoff simulation data;

[0037] Step S148: Perform historical runoff peak analysis based on historical rainfall runoff simulation data to generate historical runoff peak data.

[0038] The invention obtains the digital elevation model of a civil airport and can provide detailed information of the airport terrain. By using the digital elevation model of a civil airport to process the surface runoff coefficient evaluation data with the integrated runoff curve number, the characteristics of the airport surface runoff are accurately evaluated. The rainwater retention calculation based on the characteristic data of the airport rainfall event can quantify the retention of rainwater by the surface and vegetation in the rainfall event. The effective rainfall calculation based on the characteristic data of the airport rainfall event is helpful to determine the actual flow rate in the airport rainfall event. Based on the preset runoff curve model, the runoff simulation physical model is constructed for the rainfall retention data and the effective rainfall data through the integrated runoff curve coefficient. The runoff simulation physical model is used to fit the physical parameters of the digital elevation model of the civil airport to establish the hydrological model of the airport, which can more accurately simulate the runoff process under the airport rainfall event. The rainfall runoff hydrological model is used to perform historical runoff hydrological simulation on the characteristic data of the airport rainfall event, which helps to understand the runoff situation under the historical rainfall event of the airport. The historical runoff peak value analysis is performed based on the historical rainfall runoff simulation data to determine the runoff peak value in the historical rainfall event of the airport.

[0039] Preferably, step S2 comprises the following steps:

[0040] Step S21: Obtaining real-time airport weather forecast data;

[0041] Step S22: performing emptying preprocessing of the storage unit according to the airport weather forecast data to generate storage preprocessing data;

[0042] Step S23: Based on the storage pre-processing data, a rainfall monitoring unit is used to perform real-time rainfall monitoring on a civil airport to generate original rainfall monitoring data;

[0043] Step S24: performing spatiotemporal rainfall analysis based on the original rainfall monitoring data to generate spatiotemporal rainfall monitoring matrix data; performing rainfall forecasting processing on the spatiotemporal rainfall monitoring matrix data using real-time airport weather forecast data to generate forecast rainfall data;

[0044] Step S25: Using the optimized rainfall-runoff hydrological model, the predicted rainfall data is subjected to dynamic runoff simulation processing to generate total runoff prediction data.

[0045] The present invention obtains real-time airport weather forecast data, which helps to timely understand future rainfall conditions, prepare countermeasures in advance, and accurately predict the time, intensity, and duration of rainfall events. By performing emptying pretreatment of the storage unit according to the airport weather forecast data, the storage facilities can be emptied in advance according to the predicted rainfall conditions, providing a larger capacity for receiving rainfall, reducing the pressure on the drainage system, and slowing down the occurrence of floods. Based on the storage pretreatment data, a rainfall monitoring unit is used to perform real-time rainfall monitoring of civil airports, and such monitoring can obtain rainfall data in a timely manner. Spatiotemporal rainfall analysis is performed based on the original rainfall monitoring data, and the spatiotemporal distribution and rainfall of rainfall events can be more accurately predicted. The predicted rainfall data is subjected to dynamic runoff simulation processing using an optimized rainfall runoff hydrological model, which helps to simulate the runoff generation under the predicted rainfall event.

[0046] Preferably, step S24 comprises the following steps:

[0047] Step S241: performing time stamp processing on the original rainfall monitoring data to generate time series rainfall monitoring data;

[0048] Step S242: performing distributed grid matching according to the time series rainfall monitoring data to obtain grid rainfall monitoring data;

[0049] Step S243: performing rainfall variation calculation according to the grid rainfall monitoring data to generate grid rainfall variation data;

[0050] Step S244: performing rainfall correlation analysis based on the grid rainfall variation data to generate rainfall correlation matrix data;

[0051] Step S245: performing optimal spatiotemporal interpolation processing on the grid rainfall monitoring data through the rainfall correlation matrix data to generate spatiotemporal rainfall monitoring matrix data;

[0052] Step S246: Use the real-time airport weather forecast data to perform rainfall prediction processing on the spatiotemporal rainfall monitoring matrix data to generate predicted rainfall data.

[0053] The present invention performs timestamp processing on the original rainfall monitoring data, and generates time-series rainfall monitoring data, which can organize and sort the rainfall data in chronological order, thereby ensuring the time sequence of the data. Distributed grid matching is performed according to the time-series rainfall monitoring data, and such matching processing can correspond the monitoring data to the spatial grid, thereby better describing the distribution characteristics of rainfall. Rainfall variation calculation is performed according to the grid rainfall monitoring data, which helps to understand the spatial variation of rainfall. Rainfall correlation analysis is performed according to the grid rainfall variation data, which helps to evaluate the rainfall correlation between different regions, and provides more accurate weight distribution for interpolation processing. Optimal spatiotemporal interpolation processing is performed on the grid rainfall monitoring data through rainfall correlation matrix data, and such interpolation processing can fill the blank areas between the monitoring data, thereby improving the spatial resolution and accuracy of the monitoring data. Rainfall prediction processing is performed on the spatiotemporal rainfall monitoring matrix data using real-time airport weather forecast data, which helps to predict the spatiotemporal distribution and intensity of future rainfall events.

[0054] Preferably, step S246 includes the following steps:

[0055] Step S2461: performing spatiotemporal rainfall clustering processing on the spatiotemporal rainfall monitoring matrix data to generate spatiotemporal rainfall clustering data;

[0056] Step S2462: Draw a dynamic rainfall heat map based on the spatiotemporal rainfall clustering data to generate a dynamic rainfall heat map;

[0057] Step S2463: performing rainfall trend analysis according to the dynamic rainfall heat map to generate rainfall trend data;

[0058] Step S2464: performing rainfall evolution vector propagation evaluation on the dynamic rainfall heat map through rainfall trend data to generate rainfall evolution evaluation data;

[0059] Step S2465: extracting real-time weather numerical values ​​according to the real-time airport weather forecast data to generate real-time weather numerical data; performing forecast field correction processing on the rainfall evolution assessment data using the real-time weather numerical data to obtain corrected rainfall forecast field data;

[0060] Step S2466: Retrieve similar historical rainfall fields based on the revised rainfall forecast field data, perform rainfall forecast processing, and generate predicted rainfall data.

[0061] The present invention performs spatiotemporal rainfall clustering processing on spatiotemporal rainfall monitoring matrix data, which can classify and aggregate rainfall events with similar characteristics, thereby forming a more representative rainfall pattern, which helps to identify and understand different types of rainfall events. Dynamic rainfall heat map drawing is performed based on spatiotemporal rainfall clustering data, and the generation of dynamic rainfall heat map can intuitively display the distribution and change trend of different spatiotemporal rainfall patterns. Rainfall trend analysis based on dynamic rainfall heat map can discover the periodicity, trend and law of rainfall events, which helps to warn rainfall changes in advance. Dynamic rainfall heat map is evaluated for rainfall evolution vector propagation through rainfall trend data, which helps to analyze the diffusion and evolution process of rainfall events. Real-time weather numerical extraction is performed based on real-time airport weather forecast data, and such data can provide the latest meteorological information. Historical similar rainfall field retrieval is performed based on modified rainfall forecast field data, which helps to find rainfall scenes similar to the current situation based on historical data, thereby improving the accuracy and reliability of rainfall prediction.

[0062] Preferably, step S3 comprises the following steps:

[0063] Step S31: evaluating the storage capacity of the storage unit to generate storage capacity evaluation data; performing real-time drainage efficiency processing according to the drainage unit to generate drainage efficiency data;

[0064] Step S32: Calculate the waterlogging risk index of the storage capacity assessment data using the total runoff forecast data to generate waterlogging risk index data; Calculate the external discharge risk index of the external discharge efficiency data using the total runoff forecast data to generate external discharge risk index data;

[0065] Step S33: Perform comprehensive risk analysis based on the waterlogging risk index data and the external drainage risk index data to generate comprehensive risk index data; set the runoff control threshold value according to the comprehensive risk index data to generate the runoff control threshold value data;

[0066] Step S34: formulating a storage control strategy based on the runoff control threshold data, and executing the control strategy using a control unit to generate storage control strategy data;

[0067] Step S35: monitoring the state of the regulating and storing unit through the regulating and storing unit to generate regulating and storing unit state data, wherein the regulating and storing unit state data includes water level state data, water inflow state data and water outflow state data;

[0068] Step S36: optimizing the storage capacity according to the storage unit status data to generate storage capacity optimization data;

[0069] Step S37: using the storage capacity optimization data to perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data.

[0070] The present invention can accurately evaluate the capacity of each storage unit by evaluating the storage capacity of the storage unit and determine its maximum acceptable water storage capacity. At the same time, when performing real-time external discharge efficiency processing, generating external discharge efficiency data can timely understand the external discharge capacity of the drainage unit, and adjust the external discharge efficiency according to the actual situation to ensure the effectiveness and stability of the drainage system operation. The waterlogging risk index calculation of the storage capacity evaluation data through the total runoff prediction data can evaluate the waterlogging risk of the storage unit under different rainfall conditions. At the same time, the external discharge risk index calculation of the external discharge efficiency data through the total runoff prediction data can evaluate the external discharge capacity of the drainage system and identify the drainage risk in advance. The runoff control threshold setting based on the comprehensive risk index data can set a reasonable control threshold according to the actual situation of the system, adjust the control strategy in time, and ensure the safe and stable operation of the system. According to the preset control target and threshold, a reasonable storage control strategy is formulated to ensure the effective control of the system in rainfall events. The control strategy execution using the control unit can monitor and control the operating state of the storage unit in real time to ensure the effective implementation of the control strategy. The storage unit status monitoring through the storage unit can fully understand the operating state of the storage unit. Optimizing the storage capacity based on the storage unit status data can dynamically adjust the storage capacity according to the actual operation conditions to improve the storage efficiency and reduce the risk of waterlogging. Dynamically adjust the control strategy according to the real-time storage capacity and rainfall conditions to minimize the risk of waterlogging and improve the utilization efficiency of water resources and the operation efficiency of the drainage system.

[0071] Preferably, step S4 comprises the following steps:

[0072] Step S41: Performing real-time status monitoring on the drainage unit to generate real-time drainage status monitoring data; wherein the real-time drainage status monitoring data includes real-time pump station status data, real-time drainage flow data and real-time drainage pressure data;

[0073] Step S42: generating drainage flow control instruction data by adaptively adjusting and optimizing the control data based on the real-time drainage flow data;

[0074] Step S43: performing intelligent pump station load balancing distribution on the drainage flow control instruction data through the real-time pump station status data to generate pump station load distribution data;

[0075] Step S44: dynamically adjusting the pump speed based on the pump station load distribution data to generate the discharge volume adjustment state data;

[0076] Step S45: Perform drainage overflow warning processing according to the real-time drainage pressure data and the drainage volume adjustment status data to generate drainage overflow warning data.

[0077] The present invention performs real-time status monitoring on the drainage unit, generates real-time drainage status monitoring data, and can timely understand the operation status of the drainage system. These real-time monitoring data include real-time pump station status data, real-time drainage flow data, and real-time drainage pressure data, which can help monitor whether the system is operating normally and whether there are abnormal conditions or potential faults. The drainage flow is dynamically adjusted according to the real-time storage optimization data to meet the actual water level changes and rainfall conditions, and to ensure the efficient operation of the drainage system. Through the real-time pump station status data, the drainage flow control instruction data is intelligently distributed to the pump station load, and the drainage tasks can be reasonably distributed according to the real-time status and working conditions of the pump station, the utilization rate and operation efficiency of the pump station can be improved, and the energy consumption and operation and maintenance costs can be reduced. The running speed of the pump is dynamically adjusted according to the load of the pump station to ensure the stable operation of the drainage system and avoid problems caused by too fast or too slow drainage speed. Drainage overflow early warning processing is performed according to the real-time drainage pressure data and the drainage volume adjustment status data, and the drainage overflow early warning data is generated to monitor the drainage pressure in time, and predict whether overflow occurs according to the adjustment of the drainage volume, and take measures in time to prevent overflow of the drainage system to ensure drainage safety.

[0078] Preferably, the present invention further provides an efficient total runoff control device for civil airports in highly built-up areas, the efficient total runoff control device for civil airports in highly built-up areas comprising a rainfall monitoring unit, a control unit, a storage unit, a drainage unit and a scheduling decision unit, the drainage unit being arranged at the outlet of the storage unit, the control unit being arranged beside the storage unit, the scheduling decision unit being used to execute the efficient total runoff scheduling method for civil airports in highly built-up areas as described above, the scheduling decision unit comprising:

[0079] The storage scale adjustment module is used to analyze historical rainfall events at civil airports and generate effective rainfall event data; process the airport runoff total control rate index based on the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model based on the effective rainfall event data; use the target airport runoff control index to adjust the storage volume scale of the storage unit to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model through the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model;

[0080] The total runoff prediction module is used to use the rainfall monitoring unit to perform real-time rainfall monitoring and generate original rainfall monitoring data; perform rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; use the optimized rainfall runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data;

[0081] The adaptive regulation and storage control module is used to perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate regulation and storage control strategies based on the runoff control threshold data to generate regulation and storage control strategy data; perform adaptive regulation and storage optimization processing on the regulation and storage control strategy data to generate adaptive regulation and storage control optimization data;

[0082] The intelligent drainage coordination module is used to generate drainage flow control instructions according to the adaptive storage control optimization data, and generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data, and generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data, and generate drainage overflow warning data. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the steps of a method for efficiently dispatching the total amount of runoff for a civil airport in a highly built-up area according to the present invention;

[0084] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0085] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0086] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0087] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0088] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0089] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0090] To achieve this, please refer to Figures 1 to 3 The present invention provides an efficient total runoff scheduling method for a civil airport in a highly built-up area, comprising the following steps:

[0091] Step S1: Analyze historical rainfall events of civil airports to generate effective rainfall event data; process the airport runoff total control rate index according to the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model according to the effective rainfall event data; adjust the storage volume scale of the regulation and storage unit using the target airport runoff control index to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model by using the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model;

[0092] Step S2: using a rainfall monitoring unit to perform real-time rainfall monitoring to generate original rainfall monitoring data; performing rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; using an optimized rainfall-runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data;

[0093] Step S3: Perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate a storage control strategy based on the runoff control threshold data to generate storage control strategy data; perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data;

[0094] Step S4: Generate drainage flow control instructions according to the adaptive regulation and control optimization data to generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data to generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data to generate drainage overflow warning data.

[0095] The present invention analyzes the historical rainfall events of civil airports and extracts effective rainfall event data therefrom so as to more accurately evaluate the airport runoff control requirements. The effective rainfall event data are used to process the airport runoff total control rate index to ensure that the airport can effectively control the runoff under various rainfall conditions and ensure the normal operation of the airport. A rainfall runoff hydrological model is constructed according to the effective rainfall event data, which helps to understand the impact of rainfall on airport runoff. The runoff control index of the target airport is used to adjust the storage volume scale of the storage unit to obtain a more suitable storage volume scale, which can effectively improve the utilization rate of the storage unit and ensure that there is enough capacity to store runoff water when a rainfall event occurs. The rainfall runoff hydrological model is optimized by adjusting the storage volume scale data to obtain a more accurate rainfall runoff hydrological model. The current rainfall situation can be obtained in time by using the rainfall monitoring unit to perform real-time rainfall monitoring. By performing rainfall prediction processing on the original rainfall monitoring data, the rainfall can be predicted before flooding occurs, and countermeasures can be prepared in advance to reduce the losses caused. The total runoff forecast data obtained through model simulation enables the airport to adjust drainage measures according to actual conditions and reduce the adverse effects of runoff on the airport. Comprehensive runoff risk analysis based on the total runoff forecast data can comprehensively evaluate the runoff situation and determine the runoff risk level under different scenarios. According to the actual risk situation, the critical value of runoff control is formulated so that the corresponding control measures can be initiated in time to prevent potential runoff risks. Different storage strategies are formulated according to actual needs to ensure effective control of water flow under different runoff conditions and reduce flood risks. Dynamic optimization of storage strategies is carried out according to actual conditions so that they can better adapt to different rainfall scenarios, improve storage efficiency, and reduce flood risks. According to the real-time rainfall conditions and the runoff conditions around the airport, appropriate drainage flow control instruction data is generated in time to ensure that the drainage system can effectively respond to different runoff conditions, adjust the drainage flow in time to reduce the load of the drainage system and reduce flood risks. Intelligent pump station drainage adjustment is carried out according to the drainage flow control instruction data to ensure that each part of the drainage system can be adjusted according to the instructions, ensure the stable operation of the drainage system, and effectively control the water level rise. According to the drainage volume adjustment state data, drainage overflow warning processing is performed, and the operation state of the drainage system can be monitored in time. Once the drainage system overflows, warning data is generated in time to remind relevant departments to take countermeasures, minimize the losses caused, and ensure the safety of the airport and surrounding areas. Therefore, an efficient total runoff scheduling method for civil airports in highly built-up areas of the present invention adjusts the water storage scale through the historical rainfall data of the civil airport area, and at the same time constructs an optimized rainfall runoff hydrological model.Through the refined monitoring of the airport's real-time rainfall, the total rainfall of the rainfall event can be predicted, and the total runoff volume can be simulated by optimizing the rainfall-runoff hydrological model to obtain runoff prediction data. Adaptive storage control and intelligent drainage regulation are carried out based on the runoff prediction data, and the airport's rainwater runoff discharge is managed in a refined manner to ensure the overall drainage safety of the airport.

[0096] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for efficiently scheduling the total amount of runoff for a civil airport in a highly built-up area according to the present invention. In this embodiment, the method for efficiently scheduling the total amount of runoff for a civil airport in a highly built-up area includes the following steps:

[0097] Step S1: Analyze historical rainfall events of civil airports to generate effective rainfall event data; process the airport runoff total control rate index according to the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model according to the effective rainfall event data; adjust the storage volume scale of the regulation and storage unit using the target airport runoff control index to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model by using the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model;

[0098] In an embodiment of the present invention, historical rainfall data of a civil airport over the past 30 years or more is analyzed to determine the criteria for effective rainfall events, such as rainfall or duration thresholds. Statistical tools or algorithms are used to screen the historical rainfall data to identify rainfall events that meet the conditions. Based on these effective rainfall event data, the total runoff control rate index of the airport is calculated to evaluate the runoff control capability of the airport. A rainfall runoff hydrological model is constructed based on the effective rainfall event data to simulate the generation process of surface runoff around the airport. The volume of the regulation and storage unit is adjusted according to the simulation results to optimize the rainfall runoff hydrological model. The data is adjusted according to the scale of the regulation and storage volume, and rainfall runoff is optimized to achieve runoff control of the target airport.

[0099] Step S2: using a rainfall monitoring unit to perform real-time rainfall monitoring to generate original rainfall monitoring data; performing rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; using an optimized rainfall-runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data;

[0100] In the embodiment of the present invention, a rainfall monitoring unit is used for real-time monitoring to obtain data such as rainfall amount, intensity and duration at each time point. These raw data are pre-processed, including format conversion, noise filtering and verification, to generate accurate rainfall monitoring data. Then, the monitoring data is processed using statistical and analytical techniques to predict the rainfall in the future time period, taking into account the optimization of model parameters to improve accuracy. Subsequently, the optimized rainfall-runoff hydrological model is activated, and the predicted rainfall data is used as input to simulate the dynamic changes of runoff. After the model is run, hourly runoff data is generated, and the total runoff in the simulation period is accumulated and calculated.

[0101] Step S3: Perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate a storage control strategy based on the runoff control threshold data to generate storage control strategy data; perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data;

[0102] In an embodiment of the present invention, the capacity of each regulation and storage facility, including reservoirs, flood storage areas, etc., is evaluated, and the water level-storage capacity curve and runoff into the reservoir are calculated. According to the actual operation of the drainage unit, the real-time discharge efficiency and the degree of matching with the demand are evaluated. Taking into account the risk factors involved, such as rainfall, regulation and storage capacity, drainage capacity, etc., the waterlogging and external discharge risk indexes are calculated. The runoff control threshold is set, and corresponding control measures are formulated according to the comprehensive risk indicators. Sensors are used to monitor the status of the regulation and storage unit in real time, including water level, water inlet and outlet, etc., to schedule and optimize the regulation and storage capacity. The regulation and storage control strategy is adjusted to achieve more effective rainwater regulation and discharge.

[0103] Step S4: Generate drainage flow control instructions according to the adaptive regulation and control optimization data to generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data to generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data to generate drainage overflow warning data.

[0104] In an embodiment of the present invention, sensors are deployed in the pump station to monitor the operating status and parameters, including the start status, operating time, current, voltage, etc. A flow meter or water level sensor is installed at the drainage pipe or water body outlet to monitor the change of drainage flow in real time. Pressure sensors are installed at key locations to monitor the change of pipeline pressure in real time. According to the storage control optimization data, the real-time drainage flow is analyzed, the best regulation scheme is determined, and drainage flow regulation instructions are generated, including pump station start and stop commands, valve opening adjustment, etc. The scheduling decision unit intelligently handles the load balancing of the pump station, dynamically adjusts the pump speed according to the load distribution of the pump station, and maintains normal operation. Analyze the real-time drainage pressure data, detect abnormal conditions and trigger overflow warning processing. The warning data is sent to relevant personnel to take timely measures.

[0105] Preferably, step S1 comprises the following steps:

[0106] Step S11: collecting historical regional rainfall data for civil airports to generate historical rainfall data for airport areas;

[0107] Step S12: Perform rainfall event analysis on the historical rainfall data of the airport area to generate effective rainfall event data; perform airport runoff total control rate index processing based on the effective rainfall event data to generate the target airport runoff control index;

[0108] Step S13: Perform site rainfall characteristic analysis based on the effective rainfall event data to generate airport rainfall event characteristic data; evaluate the surface runoff coefficient of the civil airport to generate surface runoff coefficient evaluation data;

[0109] Step S14: constructing a rainfall runoff hydrological model through surface runoff coefficient evaluation data, using the rainfall runoff hydrological model to simulate the rainfall runoff peak value of the airport rainfall event characteristic data, and generating historical runoff peak value data;

[0110] Step S15: performing runoff control index optimization processing on the historical runoff peak data and the runoff control index of the target airport to generate runoff control index optimization and runoff control index optimization data;

[0111] Step S16: Analyzing the efficiency of the regulating and storing facilities of the regulating and storing units to generate regulating and storing facility efficiency data;

[0112] Step S17: performing water balance processing on the regulation and storage facility efficiency data through the runoff control index optimization data, and adjusting the regulation and storage capacity scale of the regulation and storage unit to obtain regulation and storage capacity scale adjustment data;

[0113] Step S18: Optimizing the rainfall-runoff hydrological model by adjusting the storage volume scale data to obtain an optimized rainfall-runoff hydrological model.

[0114] In the embodiment of the present invention, the historical rainfall data of the past few years in the area where the airport is located are legally collected, and the rainfall data of at least 30 years or more are collected, and these data include information such as rainfall, rainfall duration, and rainfall intensity. The historical rainfall data of the airport area is analyzed to identify effective rainfall events. First, the standards for effective rainfall events are defined, such as rainfall exceeding a certain threshold or duration exceeding a certain duration. Then, according to these standards, the historical rainfall data are screened and classified using statistical analysis tools or algorithms to identify rainfall events that meet the conditions. The airport runoff total control rate index is processed for each effective rainfall event to determine the runoff control capacity under different rainfall events. The effective rainfall event data is analyzed for site rainfall characteristics. This includes statistics and analysis of the spatiotemporal distribution, rainfall intensity, duration, etc. of rainfall events to understand the characteristics and laws of rainfall in the airport area. For example, a soil hydrological model is used to simulate the generation process of surface runoff around the airport, and the size of the surface runoff coefficient is evaluated based on the simulation results to obtain surface runoff coefficient evaluation data. The preset runoff curve model is fitted with the airport digital elevation model to obtain the rainfall runoff hydrological model, and the rainfall runoff peak value is simulated for the airport rainfall event characteristic data, including the runoff generation and runoff peak formation process during the simulation rainfall event. Finally, the historical runoff peak data is generated based on the simulation results.

[0115] Preferably, step S12 comprises the following steps:

[0116] Step S121: preprocessing the historical rainfall data of the airport area, dividing the rainfall events, and generating historical rainfall event data;

[0117] Step S122: based on the historical rainfall event data, rainfall events equal to or less than 2 mm are eliminated to generate valid rainfall event data;

[0118] Step S123: sorting the rainfall in ascending order according to the valid rainfall event data to generate sorted rainfall event data;

[0119] Step S124: Calculate the total rainfall control rate according to the sorted rainfall event data to generate rainfall event control rate data;

[0120] Step S125: Draw a rainfall relationship curve according to the rainfall event control rate data to generate a rainfall-control rate curve graph;

[0121] Step S126: matching the rainfall area type of the civil airport to generate rainfall area type data; formulating the runoff control index range according to the rainfall area type data to generate rainwater runoff control index range data;

[0122] Step S127: The rainfall-control rate curve is first screened for rainfall that meets the standard range using the rainwater runoff control index range data to obtain the target airport runoff control index.

[0123] In an embodiment of the present invention, historical rainfall data of meteorological stations in the airport area are obtained, covering a time range of several years. Incomplete or obviously erroneous records are deleted, and missing data is supplemented. Rainfall data is arranged in chronological order to ensure the continuity and integrity of the data. The standard of rainfall events is defined, for example, 12 hours of no rainfall is used as the interval for event division, and continuous rainfall data is divided into events. The start time, end time, duration and total rainfall of each rainfall event are recorded to generate historical rainfall event data. Rainfall events with a single rainfall equal to or less than 2 mm are defined as invalid rainfall events. All events with a rainfall greater than 2 mm are screened out from the historical rainfall event data. All valid rainfall events are arranged in ascending order from small to large according to the total rainfall. The sorted rainfall event data is recorded, including the start time, end time, duration and total rainfall of the event. For example, the control rate is calculated by using the percentage of cumulative rainfall to total rainfall. According to the sorted rainfall event data, the rainfall of each rainfall event is gradually accumulated, and its percentage of total rainfall is calculated. In the drawing tool, the rainfall-control rate curve is drawn with the cumulative rainfall of the rainfall event as the horizontal coordinate and the corresponding control rate as the vertical coordinate. Determine the characteristics of the rainfall area type according to the area where the civil airport is located, and generate rainfall area type data. According to the rainfall area type data, combined with the relevant sponge planning requirements of the area, formulate a rainwater runoff control index range that meets the rainfall characteristics of the area. The rainwater runoff control index range determines the screening control index range parameters, namely the maximum allowable runoff peak and the annual runoff control rate. In the rainfall-control rate curve, search from left to right to find the first data point that meets the control index range. Record the rainfall and control rate corresponding to the found data point as the runoff control index of the target airport.

[0124] Preferably, step S14 comprises the following steps:

[0125] Step S141: obtaining a digital elevation model of a civil airport;

[0126] Step S142: using the civil airport digital elevation model to perform comprehensive runoff curve number processing on the surface runoff coefficient assessment data to generate a comprehensive runoff curve coefficient;

[0127] Step S143: Calculate the rainwater retention amount according to the airport rainfall event characteristic data to generate rainfall retention amount data;

[0128] Step S144: Calculate effective rainfall according to the airport rainfall event characteristic data to generate effective rainfall data;

[0129] Step S145: constructing a runoff simulation physical model for the rainfall retention data and the effective rainfall data based on the preset runoff curve model by using the comprehensive runoff curve coefficient to generate a runoff simulation physical model;

[0130] Step S146: performing physical parameter fitting processing on the civil airport digital elevation model using the runoff simulation physical model, thereby obtaining a rainfall runoff hydrological model;

[0131] Step S147: using a rainfall-runoff hydrological model to perform historical runoff hydrological simulation on the airport rainfall event characteristic data to generate historical rainfall-runoff simulation data;

[0132] Step S148: Perform historical runoff peak analysis based on historical rainfall runoff simulation data to generate historical runoff peak data.

[0133] In an embodiment of the present invention, for example, the digital elevation model data of the airport and its surroundings are legally obtained. After preprocessing the data and removing noise and erroneous data, the processed elevation data is imported into ArcGIS software to ensure that the data covers the entire airport area. ArcGIS software is used for comprehensive processing to analyze the terrain characteristics and runoff coefficients of various areas of the airport. The comprehensive runoff curve number of each area is calculated, for example, 0.7 for the runway area and 0.5 for the apron area. A suitable rainwater retention calculation model is selected, such as the SCS-CN method or other empirical models, and the rainwater retention of each rainfall event is calculated one by one using the rainfall event characteristic data and the selected calculation model. For example, the effective rainfall is calculated using the direct runoff method. For a rainfall event with a rainfall intensity of 60 mm and a duration of 3 hours, the effective rainfall obtained by calculation is 800 cubic meters. The SCS-CN runoff curve model is selected, and the model is constructed using the previously calculated comprehensive runoff curve coefficient, rainfall retention data and effective rainfall data. By inputting these data, a physical model that can simulate the runoff process under different rainfall events at the airport is constructed. The constructed runoff simulation physical model was matched with the digital elevation model of the airport, and the parameters of the physical model were adjusted using a fitting algorithm to make it more accurately reflect the topographic characteristics of the airport. A complete rainfall-runoff hydrological model was generated. The generated rainfall-runoff hydrological model was used to simulate the runoff hydrology of each historical rainfall event. The runoff process data of each simulation was recorded, including runoff volume, peak flow, duration, etc. The peak runoff data of all rainfall events were screened out from the historical rainfall-runoff simulation data set. The distribution and frequency of these peak flows were obtained through statistical analysis.

[0134] Preferably, step S2 comprises the following steps:

[0135] Step S21: Obtaining real-time airport weather forecast data;

[0136] Step S22: performing emptying preprocessing of the storage unit according to the airport weather forecast data to generate storage preprocessing data;

[0137] Step S23: Based on the storage pre-processing data, a rainfall monitoring unit is used to perform real-time rainfall monitoring on a civil airport to generate original rainfall monitoring data;

[0138] Step S24: performing spatiotemporal rainfall analysis based on the original rainfall monitoring data to generate spatiotemporal rainfall monitoring matrix data; performing rainfall forecasting processing on the spatiotemporal rainfall monitoring matrix data using real-time airport weather forecast data to generate forecast rainfall data;

[0139] Step S25: Using the optimized rainfall-runoff hydrological model, the predicted rainfall data is subjected to dynamic runoff simulation processing to generate total runoff prediction data.

[0140] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0141] Step S21: Obtaining real-time airport weather forecast data;

[0142] In an embodiment of the present invention, an API interface is provided according to the weather forecast data source, corresponding access rights and keys are obtained, and a script or program is written to access the API. According to the data format (such as JSON, XML) provided by the weather forecast data source, a program is written to convert the data into the required internal data format. For example, through the API interface of Weather.com, access rights and keys are obtained, and a Python script is written to request the API regularly to obtain real-time weather forecast data. The data in JSON format is converted into the CSV format used internally, and parameters such as rainfall, rainfall intensity, wind speed, wind direction, temperature, and humidity are extracted. These parameters are stored in a MySQL database and set to be automatically updated once an hour.

[0143] Step S22: performing emptying preprocessing of the storage unit according to the airport weather forecast data to generate storage preprocessing data;

[0144] In an embodiment of the present invention, the collected weather forecast data is analyzed to determine whether a set threshold is reached. If the rainfall, rainfall intensity or duration in the forecast data exceeds a preset threshold, the emptying preprocessing program of the regulating and storing unit is started. According to the threshold judgment result, an emptying plan for the regulating and storing unit is formulated, including specific parameters such as emptying time, emptying rate and emptying volume. For example, when the weather forecast predicts light rain within the next 24 hours, the regulating and storing pool will not be discharged, and all rainwater will be stored during the rainfall period. When the weather forecast predicts moderate rain within the next 24 hours, each regulating and storing pool will empty its storage capacity in advance, and all rainwater will be stored during the rainfall period. When the weather forecast predicts heavy rain and above (including heavy rain, heavy rainstorm and extremely heavy rainstorm) within the next 24 hours, each regulating and storing pool will empty its storage capacity in advance, and the drainage scheduling plan will be combined for scheduling during the rainfall period to ensure the safety of airport drainage.

[0145] Step S23: Based on the storage pre-processing data, a rainfall monitoring unit is used to perform real-time rainfall monitoring on a civil airport to generate original rainfall monitoring data;

[0146] In an embodiment of the present invention, a rainfall monitoring unit is deployed at a key location in the airport, including a rain gauge, a rainfall sensor, and a data acquisition device. Ensure that the monitoring equipment covers the entire airport area, connect the rainfall monitoring unit to the scheduling decision unit, and be able to transmit data in real time. According to the situation of the storage and pre-processing data, set the monitoring parameters of the rainfall monitoring unit in advance, including the monitoring frequency (such as collecting data every 5 minutes), data transmission frequency, and data storage format, and start the rainfall monitoring unit deployed in the airport to start real-time monitoring of rainfall conditions. Perform preliminary processing on the received raw rainfall data, including data format conversion, noise filtering, and data verification, to generate raw rainfall monitoring data.

[0147] Step S24: performing spatiotemporal rainfall analysis based on the original rainfall monitoring data to generate spatiotemporal rainfall monitoring matrix data; performing rainfall forecasting processing on the spatiotemporal rainfall monitoring matrix data using real-time airport weather forecast data to generate forecast rainfall data;

[0148] In the embodiment of the present invention, the original rainfall monitoring data of each rainfall monitoring unit of the airport is collected, including information such as rainfall amount, rainfall intensity and rainfall duration at different time points. The data is segmented according to the time and space dimensions to establish a spatiotemporal rainfall monitoring matrix. The rows of the matrix represent different time points, and the columns represent different spatial positions (monitoring units). The rainfall amount and rainfall intensity at each time point and spatial position are calculated to generate the spatiotemporal rainfall monitoring matrix data. The integrated data is subjected to rainfall prediction processing. The predicted rainfall data is compared with the actual observed data, the error range is calculated, and the model parameters are optimized.

[0149] Step S25: Using the optimized rainfall-runoff hydrological model, the predicted rainfall data is subjected to dynamic runoff simulation processing to generate total runoff prediction data.

[0150] In an embodiment of the present invention, a rainfall-runoff hydrological model previously constructed and optimized is called, and the generated predicted rainfall data is imported into the model, including rainfall data per hour in the future time period. According to the predicted rainfall data, the time step and simulation period of the model operation are set. For example, one hour is set as a time step to simulate the runoff situation in the next 24 hours. The initial conditions of the model are set, including parameters such as soil moisture content and surface water level, so that the model can accurately perform dynamic simulation. The optimized rainfall-runoff hydrological model is started to perform dynamic runoff simulation processing. The model uses the predicted rainfall data as input to calculate the surface runoff in each time step. After the model is completed, the runoff data in each time step is output to generate the total runoff prediction data. The output total runoff data is summarized and counted to calculate the cumulative total runoff in the simulation period.

[0151] Preferably, step S24 includes the following steps:

[0152] Step S241: performing time stamp processing on the original rainfall monitoring data to generate time series rainfall monitoring data;

[0153] Step S242: performing distributed grid matching according to the time series rainfall monitoring data to obtain grid rainfall monitoring data;

[0154] Step S243: performing rainfall variation calculation according to the grid rainfall monitoring data to generate grid rainfall variation data;

[0155] Step S244: performing rainfall correlation analysis based on the grid rainfall variation data to generate rainfall correlation matrix data;

[0156] Step S245: performing optimal spatiotemporal interpolation processing on the grid rainfall monitoring data through the rainfall correlation matrix data to generate spatiotemporal rainfall monitoring matrix data;

[0157] Step S246: Use the real-time airport weather forecast data to perform rainfall prediction processing on the spatiotemporal rainfall monitoring matrix data to generate predicted rainfall data.

[0158] In an embodiment of the present invention, a timestamp is added to each piece of original rainfall monitoring data to record the specific time of data collection. The data is sorted in chronological order to generate time-series rainfall monitoring data. The airport area is divided into multiple grid cells, each grid cell represents a certain spatial range. According to the location of the rainfall monitoring unit, the time-series rainfall monitoring data is matched to the corresponding grid cell. The rainfall data in each grid cell is summarized and the average rainfall of each grid cell is calculated. Grid rainfall monitoring data is generated to represent the rainfall conditions of each grid cell at each time point. The rainfall data of each grid cell is varied to calculate the degree of change of rainfall in time and space. The rainfall variation of each grid cell is calculated using statistical methods such as variance or standard deviation. Statistical analysis methods such as Pearson correlation coefficient are used to calculate the rainfall correlation between each grid cell. The similarity of rainfall between different grid cells is evaluated to generate a correlation coefficient. The calculated correlation coefficient is organized into a matrix form to generate rainfall correlation matrix data. The rows and columns of the matrix represent each grid cell respectively, and the values ​​in the matrix represent the correlation between grid cells. According to the rainfall correlation matrix data, select an appropriate spatiotemporal interpolation method, such as Kriging interpolation or inverse distance weighted interpolation. Use the interpolation method to perform spatiotemporal interpolation on the grid rainfall monitoring data to fill in the missing data points. Generate rainfall data for each time point and spatial location to form a complete spatiotemporal rainfall monitoring matrix. Use real-time weather forecast data, combined with the spatiotemporal rainfall monitoring matrix data, to establish a time series prediction model, predict the rainfall in the next 24 hours, and generate predicted rainfall data.

[0159] Preferably, step S246 includes the following steps:

[0160] Step S2461: performing spatiotemporal rainfall clustering processing on the spatiotemporal rainfall monitoring matrix data to generate spatiotemporal rainfall clustering data;

[0161] Step S2462: Draw a dynamic rainfall heat map based on the spatiotemporal rainfall clustering data to generate a dynamic rainfall heat map;

[0162] Step S2463: performing rainfall trend analysis according to the dynamic rainfall heat map to generate rainfall trend data;

[0163] Step S2464: performing rainfall evolution vector propagation evaluation on the dynamic rainfall heat map through rainfall trend data to generate rainfall evolution evaluation data;

[0164] Step S2465: extracting real-time weather numerical values ​​according to the real-time airport weather forecast data to generate real-time weather numerical data; performing forecast field correction processing on the rainfall evolution assessment data using the real-time weather numerical data to obtain corrected rainfall forecast field data;

[0165] Step S2466: Retrieve similar historical rainfall fields based on the revised rainfall forecast field data, perform rainfall forecast processing, and generate predicted rainfall data.

[0166] In the embodiment of the present invention, an appropriate clustering algorithm is selected, such as K-means clustering, hierarchical clustering or density clustering. The spatiotemporal rainfall monitoring matrix data is standardized to ensure the scale consistency between the various features. The selected clustering algorithm is applied to the standardized data for clustering, and similar spatiotemporal rainfall patterns are classified into the same category. The generated spatiotemporal rainfall clustering data is used to sort out the rainfall pattern classification of each grid unit at different time points. Using geographic information system (GIS) software or data visualization tools, the rainfall pattern classification of each grid unit is represented by different colors or densities to form a dynamic rainfall heat map. The distribution and change trend of rainfall in different regions in the dynamic rainfall heat map are analyzed to detect the trend of increasing or decreasing rainfall and the change of rainfall spatial distribution. Using rainfall trend data, the evolution process of rainfall is evaluated, including the evolution in time and space, and the spatial propagation path and rate of rainfall increase and decrease are analyzed. According to the evaluation results, a rainfall evolution vector diagram is drawn to indicate the propagation direction and intensity of rainfall increase and decrease. The acquired weather forecast data is parsed and extracted to extract the required real-time weather numerical data. Correlate the real-time weather numerical data with the previously generated rainfall evolution assessment data to determine the impact of current weather conditions on rainfall evolution. According to the real-time weather numerical data, modify the rainfall evolution assessment data to adjust the predicted rainfall or change trend. Use the real-time weather numerical data to retrieve historically similar rainfall scene data, that is, historical rainfall data similar to the current weather conditions. Use linear regression models or machine learning algorithms to predict future rainfall and generate predicted rainfall data.

[0167] Preferably, step S3 comprises the following steps:

[0168] Step S31: evaluating the storage capacity of the storage unit to generate storage capacity evaluation data; performing real-time drainage efficiency processing according to the drainage unit to generate drainage efficiency data;

[0169] Step S32: Calculate the waterlogging risk index of the storage capacity assessment data using the total runoff forecast data to generate waterlogging risk index data; Calculate the external discharge risk index of the external discharge efficiency data using the total runoff forecast data to generate external discharge risk index data;

[0170] Step S33: Perform comprehensive risk analysis based on the waterlogging risk index data and the external drainage risk index data to generate comprehensive risk index data; set the runoff control threshold value according to the comprehensive risk index data to generate the runoff control threshold value data;

[0171] Step S34: formulating a storage control strategy based on the runoff control threshold data, and executing the control strategy using a control unit to generate storage control strategy data;

[0172] Step S35: monitoring the state of the regulating and storing unit through the regulating and storing unit to generate regulating and storing unit state data, wherein the regulating and storing unit state data includes water level state data, water inflow state data and water outflow state data;

[0173] Step S36: optimizing the storage capacity according to the storage unit status data to generate storage capacity optimization data;

[0174] Step S37: using the storage capacity optimization data to perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data.

[0175] In the embodiment of the present invention, the storage capacity of each storage facility of the storage unit is evaluated, including reservoirs, flood storage areas, etc. The capacity and water level-storage capacity curve of each facility, as well as the runoff storage of the controlled basin are calculated. The real-time external discharge efficiency is calculated according to the design parameters and actual operation of the drainage unit. The degree of matching between the external discharge capacity of the drainage unit and the demand is analyzed, and the change of external discharge efficiency under rainfall conditions is considered. The waterlogging risk index is usually determined by the risk factors involved, such as the amount of rainwater entering the reservoir, the storage capacity, the drainage capacity, etc. The calculation of the waterlogging risk index requires comprehensive consideration of these factors, weighting or scoring each risk factor, and obtaining the waterlogging risk index value of each storage unit. Consider the design parameters and actual operation of the drainage unit, such as drainage capacity, drainage efficiency and other factors. By establishing an external discharge risk assessment model or indicator system, the various risk factors are comprehensively evaluated, and the external discharge risk index value of each drainage unit is obtained. The waterlogging risk index data and the external discharge risk index data are integrated, and their weights and interrelationships are considered to perform a comprehensive risk analysis. A variety of analytical methods, such as weighted average, expert scoring method or mathematical model, can be used to integrate waterlogging risk and drainage risk into a comprehensive risk index. According to the value of the comprehensive risk index, the corresponding runoff control threshold is formulated. The setting of the threshold should take into account the impact of different levels of comprehensive risk on the regulation and storage system. For example, the comprehensive risk index is 0.72. According to historical data and expert advice, it is determined that when the comprehensive risk index exceeds 0.7, corresponding control measures should be taken. Therefore, the runoff control threshold is set to 0.7, which means that when the comprehensive risk index reaches or exceeds 0.7, the control strategy of the regulation and storage system needs to be activated. Use sensors or monitoring equipment to monitor the regulation and storage unit in real time to obtain key status data, including water level, water inflow and water outflow. According to the status data of the regulation and storage unit, the regulation and storage capacity is dispatched, and the status data of the regulation and storage unit is used for analysis and evaluation to determine whether the regulation and storage capacity needs to be optimized. If the regulation and storage unit frequently reaches saturation or cannot meet the rainwater regulation and storage needs, it is necessary to increase or adjust the real-time water storage capacity, and coordinate optimization with the drainage unit to generate regulation and storage capacity optimization data. For example, when the rainfall is less than the designed rainfall for runoff control, the control unit controls the rainwater to enter the lower layer of the storage unit, and the drainage unit discharges the stored initial rainwater to the sewage treatment system to achieve the control of the total runoff and initial rain surface source pollution; when the rainfall exceeds the total runoff control requirements, the control unit controls the rainwater to enter the upper layer of the storage unit, and the drainage unit discharges the stored rainwater to the external waters to ensure the safety of airport drainage. According to the storage capacity optimization data and real-time monitoring data, the storage control strategy is adjusted and optimized to achieve more effective rainwater storage and discharge. Adaptive storage optimization can dynamically adjust the storage strategy according to real-time monitoring data and storage capacity to make it more adaptable to different rainfall conditions and hydrological conditions.

[0176] Preferably, step S4 comprises the following steps:

[0177] Step S41: Performing real-time status monitoring on the drainage unit to generate real-time drainage status monitoring data; wherein the real-time drainage status monitoring data includes real-time pump station status data, real-time drainage flow data and real-time drainage pressure data;

[0178] Step S42: generating drainage flow control instruction data by adaptively adjusting and optimizing the control data based on the real-time drainage flow data;

[0179] Step S43: performing intelligent pump station load balancing distribution on the drainage flow control instruction data through the real-time pump station status data to generate pump station load distribution data;

[0180] Step S44: dynamically adjusting the pump speed based on the pump station load distribution data to generate the water discharge adjustment state data;

[0181] Step S45: Perform drainage overflow warning processing according to the real-time drainage pressure data and the drainage volume adjustment status data to generate drainage overflow warning data.

[0182] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0183] Step S41: Performing real-time status monitoring on the drainage unit to generate real-time drainage status monitoring data; wherein the real-time drainage status monitoring data includes real-time pump station status data, real-time drainage flow data and real-time drainage pressure data;

[0184] In an embodiment of the present invention, sensors or monitoring equipment are deployed in the drainage pump station to monitor the operating status and working parameters of the pump station. The sensor can monitor the on or off status of the pump station, the operating time of the pump, the current and voltage and other parameters. Real-time pump station status data can reflect the operating status of the pump station, including information such as the operating status of the pump, fault alarm and operating efficiency. A flow meter or water level sensor is installed at the outlet of the drainage pipe or water body to monitor changes in the drainage flow. The flow meter can monitor the drainage flow in real time by measuring the amount of water passing through the pipe per unit time. A pressure sensor is installed at a key position of the drainage pipe or at the pump station to monitor the pressure changes in the drainage pipe. The pressure sensor can measure the water pressure in the pipe in real time, reflecting the flow state and pressure conditions of the drainage pipe.

[0185] Step S42: generating drainage flow control instruction data by adaptively adjusting and optimizing the control data based on the real-time drainage flow data;

[0186] In the embodiment of the present invention, the scheduling decision unit analyzes and processes the real-time drainage flow data according to the adaptive storage control optimization data, including storage capacity optimization, storage control strategy, etc. The algorithm and strategy in the optimization data are used in combination with the real-time flow data to determine the best drainage flow control scheme. The optimized drainage flow control scheme is converted into an actual control instruction. The drainage flow control instruction data is generated, including the start and stop commands of the pump station, the opening degree of the regulating valve, the speed of the pump, etc., to adjust the operation of the drainage system and ensure the rationality and efficiency of water discharge. For example, when rainfall occurs at a civil airport, the rainfall process is monitored by the rainfall monitoring facilities arranged at various locations in the airport, and the control unit is transmitted back in real time as a scheduling basis; when the rainfall is less than the runoff control design rainfall, the control unit controls the rainwater to enter the lower layer of the storage unit, and when the water level reaches the pump start water level, the sewage pump station is turned on to discharge the stored initial rain to the sewage treatment system. When the rainfall exceeds the total runoff control requirement, the control unit controls the rainwater to enter the upper layer of the storage unit and starts the drainage scheduling mode: 1. When the water level in the storage tank is higher than the external water level, the gate is opened to drain water; 2. When the water level in the storage tank is lower than the external water level, the gate is closed: a. When the water level inside is lower than the pump start level, no water is drained; b. When the water level inside is higher than the pump start level, the first pump is started; c. When the water level inside is higher than the limit water level, the pump station is fully opened; d. When the water level inside is lower than the pump start level, the pump is turned off.

[0187] Step S43: performing intelligent pump station load balancing distribution on the drainage flow control instruction data through the real-time pump station status data to generate pump station load distribution data;

[0188] In the embodiment of the present invention, intelligent algorithm processing is performed based on the real-time pump station status data to achieve load balancing of the pump station. According to the operating conditions and performance characteristics of each pump, the load distribution of the pump is dynamically adjusted so that each pump in the pump station works in the best state. Based on the real-time monitored pump station status data, the intelligent algorithm analyzes the operating conditions and working performance of each pump. For example, if a pump is running at a high load or has a fault, the system will automatically adjust the working status of other pumps to balance the load of each pump in the pump station. For example, if a pump is overloaded or has a fault, the system will automatically adjust the workload of other pumps to ensure the normal operation of the pump station while improving drainage efficiency.

[0189] Step S44: dynamically adjusting the pump speed based on the pump station load distribution data to generate the discharge volume adjustment state data;

[0190] In an embodiment of the present invention, according to the load distribution data of the pump station, the system performs dynamic pump speed regulation to adjust the speed of each pump to meet the needs of the drainage system and maintain load balance. According to the load distribution situation, the scheduling decision unit calculates the optimal speed of each pump, and then sends an adjustment instruction to the pump station to achieve dynamic adjustment of the pump speed. For example, a PID controller, a fuzzy logic controller or an algorithm based on machine learning calculates the optimal speed of each pump based on the collected pump station load distribution data. The adjustment instruction can be sent to the pump station controller via a network or a dedicated control bus, instructing each pump to adjust to the corresponding speed. The pump station controller adjusts the motor speed or frequency of the pump according to the received instruction so that the pump discharge meets the actual demand and maintains load balance.

[0191] Step S45: Perform drainage overflow warning processing according to the real-time drainage pressure data and the drainage volume adjustment status data to generate drainage overflow warning data.

[0192] In an embodiment of the present invention, the collected real-time drainage pressure data is analyzed to check whether there are abnormal fluctuations or the pressure exceeds the set threshold. Combined with the drainage volume regulation status data, the drainage load of the current pump station is analyzed to determine whether the drainage system is operating normally and whether there is a risk of overflow. When the drainage pressure rises abnormally or the drainage volume regulation status indicates that the system is overloaded, the drainage overflow warning process is triggered. According to a preset overflow warning algorithm, such as an overflow detection algorithm based on the pressure change rate or an overflow prediction model based on the drainage volume regulation status, it is determined whether there is a possibility of drainage overflow. If the warning process determines that there is a risk of drainage overflow, the corresponding drainage overflow warning data is generated. The generated drainage overflow warning data is sent to the relevant management personnel or monitoring system so that timely countermeasures can be taken. The warning notification can be sent to the relevant responsible person via SMS, email, telephone, etc., or the alarm information can be directly displayed in the monitoring system.

[0193] Preferably, the present invention further provides an efficient total runoff control device for civil airports in highly built-up areas, the efficient total runoff control device for civil airports in highly built-up areas comprising a rainfall monitoring unit, a control unit, a storage unit, a drainage unit and a scheduling decision unit, the drainage unit being arranged at the outlet of the storage unit, the control unit being arranged beside the storage unit, the scheduling decision unit being used to execute the efficient total runoff scheduling method for civil airports in highly built-up areas as described above, the scheduling decision unit comprising:

[0194] The storage scale adjustment module is used to analyze historical rainfall events at civil airports and generate effective rainfall event data; process the airport runoff total control rate index based on the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model based on the effective rainfall event data; use the target airport runoff control index to adjust the storage volume scale of the storage unit to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model through the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model;

[0195] The total runoff prediction module is used to use the rainfall monitoring unit to perform real-time rainfall monitoring and generate original rainfall monitoring data; perform rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; use the optimized rainfall runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data;

[0196] The adaptive regulation and storage control module is used to perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate regulation and storage control strategies based on the runoff control threshold data to generate regulation and storage control strategy data; perform adaptive regulation and storage optimization processing on the regulation and storage control strategy data to generate adaptive regulation and storage control optimization data;

[0197] The intelligent drainage coordination module is used to generate drainage flow control instructions according to the adaptive storage control optimization data, and generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data, and generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data, and generate drainage overflow warning data.

[0198] The beneficial effect of the present invention is that the historical analysis of rainfall events at civil airports is to extract effective data to more accurately evaluate the airport runoff control requirements. By constructing a rainfall runoff hydrological model, the impact of rainfall on airport runoff can be understood, and the storage unit can be adjusted according to the airport's runoff control index to ensure that there is enough capacity to store runoff water when a rainfall event occurs. According to the total runoff forecast data obtained by model simulation, the airport can adjust drainage measures to reduce the adverse impact of runoff on the airport, and conduct a comprehensive runoff risk analysis to determine the runoff risk level under different scenarios to formulate corresponding control measures. Different storage strategies are formulated according to actual needs to ensure that water flow is effectively controlled under different runoff conditions, and different storage strategies are formulated according to actual needs to ensure that water flow is effectively controlled under different runoff conditions, reduce flood risks, and dynamically optimize storage strategies to improve storage efficiency and reduce flood risks. Appropriate drainage flow control instruction data is generated through real-time rainfall conditions and runoff conditions around the airport, which can ensure that the drainage system effectively responds to different runoff conditions and adjusts the drainage flow in time to reduce flood risks. By utilizing the airport's existing storage and regulation facilities for renovation, reducing investment and eliminating the need to repeatedly build low-impact storage and regulation facilities, the demand for civil airport construction land can be reduced, alleviating the pressure of airport land shortage. While ensuring the safety of airport drainage, the common problem of total runoff control difficulties in civil airport sponge construction can be solved, and the airport sponge city construction can be achieved.

[0199] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0200] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An efficient total runoff scheduling method for civil airports in highly built-up areas, characterized in that: The invention is applied to an efficient total runoff control device for civil airports in highly built-up areas, wherein the efficient total runoff control device for civil airports in highly built-up areas comprises a rainfall monitoring unit, a control unit, a storage unit, a drainage unit and a scheduling decision unit, wherein the drainage unit is arranged at the outlet of the storage unit, and the control unit is arranged beside the storage unit. The efficient total runoff scheduling method for civil airports in highly built-up areas comprises the following steps: Step S1: Analyze historical rainfall events of civil airports to generate effective rainfall event data; process the airport runoff total control rate index according to the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model according to the effective rainfall event data; adjust the storage volume scale of the regulation unit using the target airport runoff control index to obtain the regulation volume scale adjustment data; optimize the rainfall runoff hydrological model by using the regulation volume scale adjustment data to obtain the optimized rainfall runoff hydrological model; wherein the regulation unit is a two-layer regulation storage tank, and the upper and lower layers are connected by pipes and valves. Step S1 is specifically as follows: Step S11: collecting historical regional rainfall data for civil airports to generate historical rainfall data for airport areas; Step S12: Perform rainfall event analysis on the historical rainfall data of the airport area to generate effective rainfall event data; perform airport runoff total control rate index processing based on the effective rainfall event data to generate the target airport runoff control index; Step S13: Perform site rainfall characteristic analysis based on the effective rainfall event data to generate airport rainfall event characteristic data; evaluate the surface runoff coefficient of the civil airport to generate surface runoff coefficient evaluation data; Step S14: constructing a rainfall runoff hydrological model through surface runoff coefficient evaluation data, using the rainfall runoff hydrological model to simulate the rainfall runoff peak value of the airport rainfall event characteristic data, and generating historical runoff peak value data; Step S15: performing runoff control index optimization processing on the historical runoff peak data and the runoff control index of the target airport to generate runoff control index optimization and runoff control index optimization data; Step S16: Analyzing the efficiency of the regulating and storing facilities of the regulating and storing units to generate regulating and storing facility efficiency data; Step S17: performing water balance processing on the regulation and storage facility efficiency data through the runoff control index optimization data, and adjusting the regulation and storage capacity scale of the regulation and storage unit to obtain regulation and storage capacity scale adjustment data; Step S18: optimizing the rainfall-runoff hydrological model by adjusting the storage volume scale data to obtain an optimized rainfall-runoff hydrological model; Step S2: using a rainfall monitoring unit to perform real-time rainfall monitoring to generate original rainfall monitoring data; performing rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; using an optimized rainfall runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data; wherein the rainfall monitoring unit includes multiple rainfall monitoring stations deployed inside the airport, and step S2 is specifically as follows: Step S21: Obtaining real-time airport weather forecast data; Step S22: performing emptying preprocessing of the storage unit according to the airport weather forecast data to generate storage preprocessing data; Step S23: Based on the storage pre-processing data, a rainfall monitoring unit is used to perform real-time rainfall monitoring on a civil airport to generate original rainfall monitoring data; Step S24: performing spatiotemporal rainfall analysis based on the original rainfall monitoring data to generate spatiotemporal rainfall monitoring matrix data; performing rainfall prediction processing on the spatiotemporal rainfall monitoring matrix data using real-time airport weather forecast data to generate predicted rainfall data; wherein step S24 is specifically as follows: Step S241: performing time stamp processing on the original rainfall monitoring data to generate time series rainfall monitoring data; Step S242: performing distributed grid matching according to the time series rainfall monitoring data to obtain grid rainfall monitoring data; Step S243: performing rainfall variation calculation according to the grid rainfall monitoring data to generate grid rainfall variation data; Step S244: performing rainfall correlation analysis based on the grid rainfall variation data to generate rainfall correlation matrix data; Step S245: performing optimal spatiotemporal interpolation processing on the grid rainfall monitoring data through the rainfall correlation matrix data to generate spatiotemporal rainfall monitoring matrix data; Step S246: using the real-time airport weather forecast data to perform rainfall forecasting processing on the spatiotemporal rainfall monitoring matrix data to generate forecast rainfall data; wherein step S246 is specifically as follows: Step S2461: performing spatiotemporal rainfall clustering processing on the spatiotemporal rainfall monitoring matrix data to generate spatiotemporal rainfall clustering data; Step S2462: Draw a dynamic rainfall heat map based on the spatiotemporal rainfall clustering data to generate a dynamic rainfall heat map; Step S2463: performing rainfall trend analysis according to the dynamic rainfall heat map to generate rainfall trend data; Step S2464: performing rainfall evolution vector propagation evaluation on the dynamic rainfall heat map through rainfall trend data to generate rainfall evolution evaluation data; Step S2465: extracting real-time weather numerical values ​​according to the real-time airport weather forecast data to generate real-time weather numerical data; performing forecast field correction processing on the rainfall evolution assessment data using the real-time weather numerical data to obtain corrected rainfall forecast field data; Step S2466: searching for historical similar rainfall fields based on the revised rainfall forecast field data, and performing rainfall forecast processing to generate forecast rainfall data; Step S25: using the optimized rainfall-runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data; Step S3: Perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate a storage control strategy based on the runoff control threshold data to generate storage control strategy data; perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data; wherein, step S3 is specifically as follows: Step S31: evaluating the storage capacity of the storage unit to generate storage capacity evaluation data; performing real-time drainage efficiency processing according to the drainage unit to generate drainage efficiency data; Step S32: Calculate the waterlogging risk index of the storage capacity assessment data using the total runoff forecast data to generate waterlogging risk index data; Calculate the external discharge risk index of the external discharge efficiency data using the total runoff forecast data to generate external discharge risk index data; Step S33: Perform comprehensive risk analysis based on the waterlogging risk index data and the external drainage risk index data to generate comprehensive risk index data; set the runoff control threshold value according to the comprehensive risk index data to generate the runoff control threshold value data; Step S34: formulating a storage control strategy based on the runoff control threshold data, and executing the control strategy using a control unit to generate storage control strategy data; Step S35: monitoring the state of the regulating and storing unit through the regulating and storing unit to generate regulating and storing unit state data, wherein the regulating and storing unit state data includes water level state data, water inflow state data and water outflow state data; Step S36: optimizing the storage capacity according to the storage unit status data to generate storage capacity optimization data; Step S37: using the storage capacity optimization data to perform adaptive storage optimization processing on the storage control strategy data to generate adaptive storage control optimization data; Step S4: Generate drainage flow control instructions according to the adaptive storage control optimization data to generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data to generate drainage volume regulation state data; perform drainage overflow warning processing according to the drainage volume regulation state data to generate drainage overflow warning data; wherein, the drainage unit is provided with a plurality of pump stations, and step S4 is specifically as follows: Step S41: Performing real-time status monitoring on the drainage unit to generate real-time drainage status monitoring data; wherein the real-time drainage status monitoring data includes real-time pump station status data, real-time drainage flow data and real-time drainage pressure data; Step S42: generating drainage flow control instruction data by adaptively adjusting and optimizing the control data based on the real-time drainage flow data; Step S43: performing intelligent pump station load balancing distribution on the drainage flow control instruction data through the real-time pump station status data to generate pump station load distribution data; Step S44: dynamically adjusting the pump speed based on the pump station load distribution data to generate the water discharge adjustment state data; Step S45: Perform drainage overflow warning processing according to the real-time drainage pressure data and the drainage volume adjustment status data to generate drainage overflow warning data.

2. The efficient total runoff dispatching method for civil airports in highly built-up areas according to claim 1 is characterized in that: Step S12 includes the following steps: Step S121: preprocessing the historical rainfall data of the airport area, dividing the rainfall events, and generating historical rainfall event data; Step S122: based on the historical rainfall event data, rainfall events equal to or less than 2 mm are eliminated to generate valid rainfall event data; Step S123: sorting the rainfall in ascending order according to the valid rainfall event data to generate sorted rainfall event data; Step S124: Calculate the total rainfall control rate according to the sorted rainfall event data to generate rainfall event control rate data; Step S125: Draw a rainfall relationship curve according to the rainfall event control rate data to generate a rainfall-control rate curve graph; Step S126: matching the rainfall area type of the civil airport to generate rainfall area type data; formulating the runoff control index range according to the rainfall area type data to generate rainwater runoff control index range data; Step S127: The rainfall-control rate curve is first screened for rainfall that meets the standard range using the rainwater runoff control index range data to obtain the target airport runoff control index.

3. The efficient total runoff dispatching method for civil airports in highly built-up areas according to claim 1 is characterized in that: Step S14 includes the following steps: Step S141: obtaining a digital elevation model of a civil airport; Step S142: using the civil airport digital elevation model to perform comprehensive runoff curve number processing on the surface runoff coefficient assessment data to generate a comprehensive runoff curve coefficient; Step S143: Calculate the rainwater retention amount according to the airport rainfall event characteristic data to generate rainfall retention amount data; Step S144: Calculate effective rainfall according to the airport rainfall event characteristic data to generate effective rainfall data; Step S145: constructing a runoff simulation physical model for the rainfall retention data and the effective rainfall data based on the preset runoff curve model by using the comprehensive runoff curve coefficient to generate a runoff simulation physical model; Step S146: performing physical parameter fitting processing on the civil airport digital elevation model using the runoff simulation physical model, thereby obtaining a rainfall runoff hydrological model; Step S147: using a rainfall-runoff hydrological model to perform historical runoff hydrological simulation on the airport rainfall event characteristic data to generate historical rainfall-runoff simulation data; Step S148: Perform historical runoff peak analysis based on historical rainfall runoff simulation data to generate historical runoff peak data.

4. An efficient total runoff control device for civil airports in highly built-up areas, characterized in that: The highly efficient total runoff control device for a civil airport in a highly built-up area comprises a rainfall monitoring unit, a control unit, a storage unit, a drainage unit and a scheduling decision unit, wherein the drainage unit is arranged at the outlet of the storage unit, and the control unit is arranged beside the storage unit. The scheduling decision unit is used to execute the efficient total runoff scheduling method for a civil airport in a highly built-up area according to claim 1, and the scheduling decision unit comprises: The storage scale adjustment module is used to analyze historical rainfall events at civil airports and generate effective rainfall event data; process the airport runoff total control rate index based on the effective rainfall event data to generate the target airport runoff control index; construct a rainfall runoff hydrological model based on the effective rainfall event data; use the target airport runoff control index to adjust the storage volume scale of the storage unit to obtain the storage volume scale adjustment data; optimize the rainfall runoff hydrological model through the storage volume scale adjustment data to obtain the optimized rainfall runoff hydrological model; The total runoff prediction module is used to use the rainfall monitoring unit to perform real-time rainfall monitoring and generate original rainfall monitoring data; perform rainfall prediction processing based on the original rainfall monitoring data to generate predicted rainfall data; use the optimized rainfall runoff hydrological model to perform dynamic runoff simulation processing on the predicted rainfall data to generate total runoff prediction data; The adaptive regulation and storage control module is used to perform comprehensive runoff risk analysis based on the total runoff forecast data to generate comprehensive risk index data; set the runoff control threshold based on the comprehensive risk index data to generate runoff control threshold data; formulate regulation and storage control strategies based on the runoff control threshold data to generate regulation and storage control strategy data; perform adaptive regulation and storage optimization processing on the regulation and storage control strategy data to generate adaptive regulation and storage control optimization data; The intelligent drainage coordination module is used to generate drainage flow control instructions according to the adaptive storage control optimization data, and generate drainage flow control instruction data; perform intelligent pump station drainage regulation according to the drainage flow control instruction data, and generate drainage volume regulation status data; perform drainage overflow warning processing according to the drainage volume regulation status data, and generate drainage overflow warning data.

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