Airport Roof Rainwater Treatment Monitoring System and Method Based on Cloud Computing

Abstract

The present invention discloses an airport rooftop rainwater treatment and monitoring system and method based on cloud computing, which relates to the technical field of rainwater monitoring, and solves the technical problem that in extreme weather conditions such as heavy rain, the traditional drainage system is difficult to quickly drain the accumulated water on the rooftop, which may lead to waterlogging and affect the normal operation of the airport. The present invention predicts the rainfall in the target area; determines the drainage pipe combination according to the rainfall in the target area; obtains the content of each element in the rainwater and analyzes the water quality index of the rainwater; evaluates the pollution level of the rainwater based on the water quality index; transports different water qualities to the corresponding storage pools based on the pollution level of the rainwater; and transports different water qualities to the corresponding filter pools based on the pollution level of the rainwater. The present invention can quickly and effectively disperse and drain the accumulated water on the rooftop, thereby reducing the pressure on the airport building structure caused by the accumulated water and helping to extend the service life of the building.

Description

Technical Field

[0001] The present invention belongs to the field of rainwater monitoring, and specifically relates to an airport roof rainwater treatment and monitoring system and method based on cloud computing. Background Art

[0002] With the intensification of global climate change and the rapid advancement of urbanization, airports, as important transportation hubs connecting the world, the safety and sustainability of their infrastructure are facing unprecedented challenges. In particular, the roof drainage and rainwater treatment systems of airports, as key links to ensure the normal operation of airports, the optimization and upgrading of their performance are particularly important.

[0003] Traditional rainwater treatment systems, limited by technical levels and design concepts, often have problems of low drainage efficiency. Under extreme weather conditions such as heavy rain, traditional drainage systems are difficult to quickly drain the accumulated water on the roof, which may lead to waterlogging and flooding, thereby affecting the normal operation of the airport and even causing damage to the airport building structure. In addition, due to limited monitoring means, traditional systems often cannot promptly detect abnormal situations during the rainwater treatment process, such as water quality deterioration and pipeline blockage, which further increases the risk of airport operation.

[0004] Therefore, the present invention provides an airport roof rainwater treatment and monitoring system and method based on cloud computing. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes an airport roof rainwater treatment and monitoring system and method based on cloud computing, which is used to solve the technical problem that under extreme weather conditions such as heavy rain, traditional drainage systems are difficult to quickly drain the accumulated water on the roof, which may lead to waterlogging and flooding, thereby affecting the normal operation of the airport.

[0006] To achieve the above object, the first aspect of the present invention provides an airport roof rainwater treatment and monitoring system and method based on cloud computing, including a rainwater quality analysis module, and a rainfall prediction module and a rainwater treatment module connected thereto;

[0007] Rainfall prediction module: used to predict the rainfall in the target area; determine the drainage pipe combination according to the rainfall in the target area; wherein, the target area is the airport area where rainwater monitoring is carried out;

[0008] Rainwater quality analysis module: used to obtain the content of each element in the rainwater, analyze the water quality index of the rainwater; evaluate the pollution level of the rainwater based on the water quality index;

[0009] Rainwater treatment module: used to transport different water qualities to the corresponding reservoirs based on the pollution level of the rainwater; and transport different water qualities to the corresponding filter ponds based on the pollution level of the rainwater.

[0010] Preferably, the airport rainfall situation in the predicted target area includes:

[0011] Extract the meteorological data and corresponding actual rainfall amounts at several different times in the target area from historical data, and establish a rainfall database for the target area; wherein, the meteorological data is the input data of the neural network model, and the actual rainfall amount corresponding to the meteorological data is the output data of the neural network model;

[0012] Train the neural network model with the input data and output data to obtain a rainfall prediction model;

[0013] Collect the future meteorological data of the target area, and input the future meteorological data into the rainfall prediction model to output the rainfall prediction value within the future time period of the target area.

[0014] The present invention collects and organizes the past meteorological data and rainfall amounts of the target area to establish a rich database, which provides a solid foundation for the training of the neural network model, helps the model learn the complex relationship between meteorological data and rainfall amounts, thereby improving the prediction accuracy; and the neural network model has a strong non-linear fitting ability. By learning and training the input data, the model can capture the non-linear relationship between meteorological data and rainfall amounts, and then achieve accurate prediction of future rainfall amounts.

[0015] Preferably, construct the rainfall prediction value output by the neural network model and the corresponding actual rainfall amount into two sequences with the same length, and through the formula Calculate the mean square error MSE between the two sequences; judge whether the mean square error is greater than a preset threshold; if yes, optimize the neural network model; if not, the training of the neural network model ends; wherein, j is the element serial number of the sequence, j = 0, 1,..., M, M is a positive integer, y j is the actual rainfall amount, is the predicted rainfall prediction value.

[0016] The present invention measures the gap between the actual data and the predicted data through the mean square error metric, that is, the overall deviation degree of the model predicted data relative to the actual data as a whole. Comparing the MSE with the preset threshold can be used as an effective model optimization strategy; when the MSE is greater than the threshold, it indicates that the prediction performance of the model is not good and needs further optimization; the feedback mechanism based on MSE helps to ensure the accuracy and reliability of the model.

[0017] Preferably, the determining the drainage pipe combination according to the rainfall amount in the target area includes:

[0018] Based on the rainfall prediction value in the future time period of the target area, calculate the rainfall prediction value per unit time;

[0019] Obtain the cross-sectional area and maximum flow velocity of the drainage pipe, and calculate the maximum flow rate of the pipe;

[0020] Perform a ratio process on the standard conveying flow rate of the drainage pipe and the predicted rainfall value per unit time to obtain the required number of drainage pipes, denoted as G; wherein, the standard conveying flow rate of the drainage pipe is the product of the standard opening of the drainage pipe and the maximum conveying flow rate of the drainage pipe; the maximum conveying flow rate of the drainage pipe is the product of the cross-sectional area and the maximum flow velocity of the drainage pipe;

[0021] Through the formula PL = min[∑( Rki / STi) / N] calculate the average usage frequency PL of N drainage pipes using a greater-than-standard opening; the drainage pipe combination with the lowest usage frequency is the drainage pipe combination to be opened; where k is the standard opening; i is the pipe number, i = 0, 1,..., N, N is a positive integer; STi is the usage duration of pipe i from the start of use to the current; Rki is the number of times pipe i uses the standard opening within the STi period.

[0022] The present invention can achieve a reasonable allocation of drainage resources by calculating the predicted rainfall value per unit time and determining the required number of drainage pipes accordingly. This method can ensure that there are sufficient drainage pipes put into operation during heavy rainfall, and avoid unnecessary resource waste during light rainfall. This dynamic allocation strategy helps to improve the overall efficiency and economic benefits of the drainage system. And it also considers the usage frequency and lifespan of the drainage pipes; by calculating the average usage frequency of the drainage pipes using a greater-than-standard opening, it is possible to identify the pipe combination with the lowest usage frequency and a longer lifespan as the drainage pipe combination to be preferentially opened. This strategy helps to extend the service life of the drainage pipes, reduce maintenance and replacement costs, and ensure the stability and reliability of the drainage system.

[0023] Preferably, several drainage pipes of the same specification are provided on each roof of the airport, and the number of drainage pipes on each roof of the airport is set based on the standard conveying flow rate of the drainage pipes and the maximum rainfall in the historical data of the airport.

[0024] Preferably, the standard opening of the drainage pipe includes:

[0025] Establish a digital twin model of the airport drainage pipe, and set several groups of different rainfall amounts. Each group of rainfall amounts is conveyed by drainage pipes with different openings. After repeated experiments, obtain the usage duration of the drainage pipe at different rainfall amounts and different openings, and perform an average process on the usage durations at the same opening and different rainfall amounts to obtain the average usage duration of the drainage pipe at this opening;

[0026] Compare the average usage duration of each opening degree, and the opening degree corresponding to the maximum average usage duration is the standard opening degree of the drainage pipe; among them, the usage duration of the drainage pipe at different rainfall amounts and different opening degrees is the duration from the start of work until it cannot work properly.

[0027] By constructing a digital twin model that corresponds one-to-one with the physical drainage pipe system, the present invention can accurately simulate the working state of the drainage pipe under different rainfall amounts and opening degrees, which helps to understand the actual working conditions of the drainage pipe and can also provide strong support for subsequent optimization decisions; and through repeated experiments and data analysis, the usage duration of the drainage pipe under different conditions can be obtained, and then the standard opening degree can be determined. This process fully considers the influence of rainfall amount and pipe opening degree on the working state of the drainage pipe, ensures the rationality of the standard opening degree, and enables the drainage pipe to work longer.

[0028] Preferably, the obtaining the content of each element in the rainwater and analyzing the water quality index of the rainwater includes:

[0029] Collect a number of rainwater samples, detect the content of each element in the number of rainwater samples, and perform average processing to obtain the content of each element in the rainwater of the target area;

[0030] Perform difference processing on the content of each element contained in the rainwater and the preset standard threshold of the corresponding element content, and perform ratio processing on the difference and the corresponding preset threshold to obtain the deviation rate of the element relative to the preset threshold, denoted as Yr; through the formula SZ = e ∑(Yr-Br) Calculate the water quality index SZ of the rainwater; where Br is the preset threshold, r is the number of each element contained in the rainwater, and Σ sums over r.

[0031] The present invention compares the content of each element in the rainwater with the corresponding preset standard threshold, which can intuitively reflect the exceeding standard situation or compliance degree of the content of each element; through difference processing and ratio processing, the deviation rate of each element relative to the preset threshold is obtained. This index can quantitatively represent the deviation degree of the content of each element, and through the analysis of the deviation rate of each element, the comprehensive influence of the water quality index is obtained, so that the water quality index can comprehensively reflect the overall water quality status of the rainwater.

[0032] Preferably, the evaluating the pollution level of the rainwater based on the water quality index includes:

[0033] Compare the water quality index of the rainwater with the preset range; if the water quality index is greater than the preset range, the pollution level of the rainwater is level one; if the water quality index is within the preset range, the pollution level of the rainwater is level two; if the water quality index is less than the preset range, the pollution level of the rainwater is level three; where the pollution level level one > level two > level three.

[0034] Preferably, the pollution levels based on rainwater are used to convey different water qualities to corresponding filter ponds, including:

[0035] Open the inlet valve of the corresponding filter pond according to the pollution level of the rainwater, and the filter pond adjusts the filter medium and the corresponding ratio in real time according to the content of each element in the rainwater.

[0036] Preferably, in the second aspect of the present invention, a monitoring method for rainwater treatment on the airport roof based on cloud computing is proposed, including the following steps:

[0037] Step 1: Predict the rainfall in the target area; determine the drainage pipeline combination according to the rainfall in the target area;

[0038] Step 2: Obtain the content of each element in the rainwater, analyze the water quality index of the rainwater; evaluate the pollution level of the rainwater based on the water quality index;

[0039] Step 3: Convey different water qualities to corresponding reservoirs based on the pollution level of the rainwater; and convey different water qualities to corresponding filter ponds based on the pollution level of the rainwater.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. By accurately predicting the rainfall per unit time, the present invention rationally allocates drainage resources to ensure that sufficient pipelines are put into operation during the peak rainfall period, effectively meeting the drainage demand; while in the case of less rainfall, it can avoid overusing pipelines, achieving resource conservation and optimization; and considering the service life of the drainage pipelines, identifying those pipeline combinations with relatively low usage frequency and longer remaining life, and being used as the priority to be opened, which not only helps to balance the usage load of each pipeline and extend the service life of the overall pipeline system;

[0042] 2. By simulating the operating conditions of the drainage pipelines under different rainfall intensities and opening settings, through multiple experiments and detailed data analysis processes, the present invention can accurately measure the operating duration of the drainage pipelines under different combinations of rainfall intensities and opening degrees, and accordingly determine an optimal pipeline opening degree, that is, the standard opening degree; the present invention comprehensively considers the changes in rainfall and the direct impact of the pipeline opening state on the drainage efficiency, thereby ensuring the scientificity and rationality of the selected standard opening degree; by adopting this standard opening degree, the drainage pipelines can maintain a longer stable operating time under various conditions, effectively improving their working efficiency and service life;

[0043] 3. The present invention predicts the rainfall in the target area; determines the drainage pipe combination according to the rainfall in the target area; obtains the content of each element in the rainwater and analyzes the water quality index of the rainwater; evaluates the pollution level of the rainwater based on the water quality index; transports rainwater of different water qualities to the corresponding reservoirs according to the pollution level of the rainwater; and analyzes the filtration scheme of the filter tank according to the pollution level of the rainwater. The present invention not only improves the efficiency and response speed of the drainage system, effectively disperses and quickly discharges the roof ponding, reduces the structural load caused by the ponding, thereby extending the service life of the airport building, but also realizes the reasonable utilization and pollution control of rainwater. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic structural diagram of the system of the present invention;

[0046] Figure 2 It is a schematic diagram of the method for determining the drainage pipe combination according to the rainfall in the target area of the present invention;

[0047] Figure 3 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0049] Please refer to Figure 1 , the first aspect embodiment of the present invention provides an airport roof rainwater treatment and monitoring system and method based on cloud computing, including a rainwater quality analysis module, and a rainfall prediction module and a rainwater treatment module connected thereto;

[0050] The rainfall prediction module predicts the rainfall in the target area; wherein, the target area is the airport area where rainwater monitoring is carried out;

[0051] Specifically, a number of meteorological data and corresponding actual rainfall amounts at different times in the target area are extracted from historical data to establish a rainfall database in the target area; wherein, the meteorological data is the input data of the neural network model, and the corresponding actual rainfall amount of the meteorological data is the output data of the neural network model;

[0052] Train the neural network model with input data and output data to obtain a rainfall prediction model;

[0053] Collect future meteorological data of the target area and input the future meteorological data into the rainfall prediction model to output the predicted rainfall value in the future time period of the target area.

[0054] For example: Obtain the historical meteorological data of an airport from the meteorological department, including date, temperature, humidity, wind speed, wind direction, air pressure, cloud cover, etc.; at the same time, obtain the actual rainfall data corresponding to these dates; match the meteorological data and rainfall data according to the date to ensure that each meteorological data point has a corresponding rainfall; fill in (such as using interpolation method) or delete the missing values to ensure the integrity of the data.

[0055] Select a neural network model, such as a multi-layer perceptron (MLP); Input layer: the dimension of the meteorological data, that is, the number of features such as temperature, humidity, wind speed, wind direction, air pressure, cloud cover, etc.; Hidden layer: set one or more layers according to needs, each layer contains a certain number of neurons, and use activation functions such as ReLU; Output layer: a neuron for outputting the predicted rainfall; Train the neural network model with historical data to obtain a rainfall prediction model;

[0056] After the model training is successful, obtain future meteorological data: Obtain the meteorological data of the next week in Beijing from the weather forecast, including temperature, humidity, wind speed, wind direction, air pressure, cloud cover, etc. Rainfall prediction: Input the meteorological data of the next week into the trained rainfall prediction model; The model outputs the predicted rainfall value for each day of the next week.

[0057] Among them, construct two sequences with the same length from the predicted rainfall value output by the neural network model and the corresponding actual rainfall, and use the formula Calculate the mean squared error MSE between the two sequences; Judge whether the mean squared error is greater than the preset threshold; If yes, optimize the neural network model; If no, the training of the neural network model ends; where j is the element serial number of the sequence, j = 0, 1,..., M, M is a positive integer, y j is the actual rainfall, is the predicted rainfall value.

[0058] For example: Suppose the actual rainfall data (unit: mm) of an airport for a period of time: [10, 15, 20, 25, 30, 35, 40], and its corresponding predicted rainfall data (unit: mm): [12, 13, 18, 27, 32, 36, 38];

[0059] MSE actual = (1 / 7) × [(10 - 12)² + (15 - 13)² + (20 - 18)² + (25 - 27)² + (30 - 32)² + (35 - 36)² + (40 - 38)²] ≈ 3.5;

[0060] Assume that the preset MSE threshold is 4 (this value can be adjusted according to the actual situation): Therefore, it is judged that the difference between the prediction result and the actual result is within the acceptable range, and the neural network model does not need to be optimized.

[0061] And determine the drainage pipe combination according to the rainfall in the target area;

[0062] Please refer to Figure 2 , specifically, based on the predicted rainfall value in the target area during the future time period, calculate the predicted rainfall value per unit time;

[0063] Obtain the cross-sectional area and maximum flow velocity of the drainage pipe, and calculate the maximum flow rate of the pipe; among them, the cross-sectional area and maximum flow velocity can be obtained by querying the specification of the drainage pipe;

[0064] Perform a ratio process on the standard delivery flow rate of the drainage pipe and the predicted rainfall value per unit time to obtain the required number of drainage pipes, denoted as N; among them, the standard delivery flow rate of the drainage pipe is the product of the standard opening of the drainage pipe and the maximum delivery flow rate of the drainage pipe; the maximum delivery flow rate of the drainage pipe is the product of the cross-sectional area and the maximum flow velocity of the drainage pipe;

[0065] Among them, establish a digital twin model of the airport drainage pipe, and set several groups of different rainfall amounts. Each group of rainfall amounts is transported by drainage pipes with different openings. Through repeated experiments, obtain the service life of the drainage pipe under different rainfall amounts and different openings, and perform an average process on the service life under the same opening and different rainfall amounts to obtain the average service life of the drainage pipe at this opening. Compare the average service life of each opening, and the opening corresponding to the maximum average service life is the standard opening of the drainage pipe; among them, the service life of the drainage pipe under different rainfall amounts and different openings is the time from the start of work until it cannot work properly.

[0066] The present invention installs sensors on the drainage pipe to collect real-time data such as the water level, flow rate, and water quality in the pipe. Integrate information from sensors, historical databases, and other relevant data sources to build a unified data platform. According to the characteristics of the drainage pipe and the collected data, build a high-precision three-dimensional model or simulation model. The model should be able to simulate the behavior and state of the water flow in the pipe under different rainfall amounts. By comparing the actual data with the model prediction results, continuously adjust and optimize the model parameters to improve the model accuracy;

[0067] Setting up experiments and collecting data: Set several groups of different rainfall amounts, with each group of rainfall amount representing a different rainfall intensity;

[0068] Adjusting the pipe opening: For each group of rainfall amounts, set different drainage pipe openings for transportation;

[0069] Select the average working duration under different rainfall amounts from them, and the opening that can work for the longest time is the standard opening of the drainage pipe, that is, the opening that can not only meet the drainage requirements but also cause less damage to the drainage pipe.

[0070] Through the formula PL = min[∑( Rki / STi) / N] calculate the average usage frequency PL of N drainage pipes using a greater-than-standard opening; The drainage pipe combination with the minimum usage frequency is the drainage pipe combination to be opened; where k is the standard opening; i is the pipe number, i = 0, 1,..., N, and N is a positive integer; STi is the usage duration of pipe i from the start of use to the current; Rki is the number of times pipe i uses the standard opening during the STi period.

[0071] Solve the optimal solution of the drainage pipe usage frequency through an iterative algorithm.

[0072] The present invention determines the number of drainage pipes to be opened according to the standard conveying flow of the drainage pipe and the predicted rainfall amount per unit time. Assume that X drainage pipes are required for a certain prediction; Randomly select X drainage pipes from N drainage pipes, and several drainage pipe combination schemes will be obtained; Analyze the usage frequency of each group of drainage pipe combinations; Count the number of times each drainage pipe in the combination uses the standard opening during the usage duration, and perform a mean process with the usage duration to obtain the usage frequency of each drainage pipe, and then divide by X to obtain the frequency of each pipe in the drainage pipe combination using the standard opening on average;

[0073] The smaller the average frequency of using the standard opening indicates that the group of drainage pipes usually has a lower frequency of using a greater-than-standard opening, less wear on the drainage pipes, so a longer service life and can continue to be used; The larger the average frequency of using the standard opening indicates that the group of drainage pipes usually has a higher frequency of using a less-than-standard opening, more serious wear on the drainage pipes, so a shorter service life and cannot be used frequently, and regular maintenance is required.

[0074] It should be noted that several drainage pipes of the same specification are set on each roof of the airport, and the number of drainage pipes on each roof of the airport is set based on the standard conveying flow of the drainage pipe and the maximum rainfall amount in the historical data of the airport.

[0075] The rainwater quality analysis module obtains the content of each element in the rainwater and analyzes the water quality index of the rainwater; Evaluate the pollution level of the rainwater based on the water quality index;

[0076] Among them, the elements of rainwater include pH value, trace elements (such as lead, copper, zinc, etc.), gas dissolutions (such as CO2, SO2, etc.), and so on.

[0077] Specifically, collect several rainwater samples, detect the content of each element in several rainwater samples, and perform mean processing to obtain the content of each element in the rainwater of the target area;

[0078] Perform difference processing on the content of each element contained in the rainwater and the preset standard threshold of the corresponding element content, and perform ratio processing on the difference and the corresponding preset threshold to obtain the deviation rate of the element relative to the preset threshold, denoted as Yr; Through the formula SZ = e ∑(Yr-Br) Calculate the water quality index SZ of the rainwater; where Br is the preset threshold, r is the number of each element contained in the rainwater, and Σ sums over r.

[0079] Compare the water quality index of the rainwater with the preset range; if the water quality index is greater than the preset range, the rainwater pollution level is grade one; if the water quality index is within the preset range, the rainwater pollution level is grade two; if the water quality index is less than the preset range, the rainwater pollution level is grade three; among them, the pollution level grade one > grade two > grade three.

[0080] The present invention evaluates the pollution degree of rainwater through the water quality index; the pollution degree of rainwater is mainly closely related to the deviation rate of the content of each element in the rainwater relative to the preset standard threshold. When there are large deviation rates of more elements in the rainwater, it indicates that there are a large number of elements exceeding the standard in the rainwater, the rainwater is polluted more seriously, the water quality is poor, that is, the water quality index of the rainwater is larger.

[0081] The rainwater treatment module transports different water qualities to the corresponding storage pools based on the pollution level of the rainwater; and transports different water qualities to the corresponding filter pools based on the pollution level of the rainwater;

[0082] Specifically, open the inlet valve of the corresponding filter pool according to the pollution level of the rainwater, and the filter pool adjusts the filter medium and the corresponding ratio of the filter pool in real time according to the content of each element in the rainwater.

[0083] During the filtration process, the system will automatically select the most suitable filter medium according to the real-time content of each element in the rainwater. These media may include activated carbon, sand, quartz sand, resin, special alloys, etc., and they each have different removal effects on different pollutants.

[0084] In addition to selecting the medium, the system will also adjust the ratio of the medium according to the specific composition of the rainwater. For example, if the heavy metal content in the rainwater is high, the system may increase the proportion of the medium with stronger adsorption capacity for heavy metals.

[0085] As the composition of rainwater changes, the types and proportions of the filtration medium will also change accordingly to ensure the best filtration effect.

[0086] Please refer to Figure 3 , the second aspect of the present invention provides a cloud computing-based monitoring method for rainwater treatment on the airport roof, including the following steps:

[0087] Step 1: Predict the rainfall in the target area; determine the drainage pipe combination according to the rainfall in the target area;

[0088] Step 2: Obtain the content of each element in the rainwater and analyze the water quality index of the rainwater; evaluate the pollution level of the rainwater based on the water quality index;

[0089] Step 3: Transport different water qualities to the corresponding storage tanks based on the pollution level of the rainwater; and transport different water qualities to the corresponding filtration tanks based on the pollution level of the rainwater.

[0090] Some of the data in the above formula are taken as numerical values after removing the dimension. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0091] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An airport rooftop rainwater treatment and monitoring system based on cloud computing, characterized in that, It includes a rainwater quality analysis module, as well as a rainfall prediction module and a rainwater treatment module connected thereto; Rainfall prediction module: used to predict the rainfall in the target area; determine the drainage pipe combination according to the rainfall in the target area; wherein, the target area is the airport area where rainwater monitoring is carried out; The determining the drainage pipe combination according to the rainfall in the target area includes: Based on the predicted rainfall value in the target area within the future time period, calculate the predicted rainfall value per unit time; Obtain the cross-sectional area and maximum flow velocity of the drainage pipe, and calculate the maximum flow rate of the pipe; Perform a ratio process on the standard delivery flow rate of the drainage pipe and the predicted rainfall value per unit time to obtain the required number of drainage pipes, marked as G; wherein, the standard delivery flow rate of the drainage pipe is the product of the standard opening of the drainage pipe and the maximum delivery flow rate of the drainage pipe; the maximum delivery flow rate of the drainage pipe is the product of the cross-sectional area and the maximum flow velocity of the drainage pipe; Calculate the average usage frequency PL of N drainage pipes using a greater than standard opening through the formula PL = min[∑(Rki / STi) / N]; the drainage pipe combination with the minimum usage frequency is the drainage pipe combination to be opened; wherein, k is the standard opening; i is the pipe number, i = 0, 1,..., N, and N is a positive integer; STi is the usage duration of pipe i from the start of use to the current time; Rki is the number of times pipe i uses the standard opening within the STi period; Among them, several drainage pipes of the same specification are set on each roof of the airport, and the number of drainage pipes on each roof of the airport is set based on the standard delivery flow rate of the drainage pipe and the maximum rainfall in the airport historical data; The standard opening of the drainage pipe includes: Establish a digital twin model of the airport drainage pipe, and set several groups of different rainfall amounts. Each group of rainfall amounts is transported by drainage pipes with different openings. After repeated experiments, obtain the usage duration of the drainage pipe at different rainfall amounts and different openings, and perform an average process on the usage durations at the same opening and different rainfall amounts to obtain the average usage duration of the drainage pipe at this opening; Compare the average usage durations of each opening, and the opening corresponding to the maximum average usage duration is the standard opening of the drainage pipe; wherein, the usage duration of the drainage pipe at different rainfall amounts and different openings is the duration from the start of work to the inability to work properly; Rainwater quality analysis module: used to obtain the content of each element in the rainwater, analyze the water quality index of the rainwater; evaluate the pollution level of the rainwater based on the water quality index; Rainwater treatment module: used to transport different water qualities to the corresponding reservoir based on the pollution level of the rainwater; and transport different water qualities to the corresponding filter tank based on the pollution level of the rainwater.

2. The cloud computing-based airport rooftop rainwater treatment monitoring system according to claim 1, wherein The predicting the airport rainfall situation in the target area includes: Extract the meteorological data and the corresponding actual rainfall at several different times in the target area from the historical data, and establish a rainfall database for the target area; wherein, the meteorological data is the input data of the neural network model, and the actual rainfall corresponding to the meteorological data is the output data of the neural network model; Train the neural network model through the input data and output data to obtain a rainfall prediction model; Collect future meteorological data of the target area, and input the future meteorological data into the rainfall prediction model to output the predicted rainfall value in the future time period of the target area.

3. The cloud computing-based airport roof rainwater treatment monitoring system according to claim 2, characterized in that, Construct the predicted rainfall values output by the neural network model and the corresponding actual rainfall into two sequences with the same length. Through the formula Calculate the mean square error MSE between the two sequences; Determine whether the mean square error is greater than a preset threshold; If yes, optimize the neural network model; If no, the training of the neural network model ends; where j is the element number of the sequence, j = 0, 1,..., M, M is a positive integer, y j is the actual rainfall,[[]] is the predicted rainfall value.

4. The cloud computing-based airport rooftop rainwater treatment monitoring system according to claim 1, characterized in that Obtain the content of each element in the rainwater and analyze the water quality index of the rainwater, including: Collect a number of rainwater samples, detect the content of each element in the number of rainwater samples, and perform mean processing to obtain the content of each element in the rainwater of the target area; The content of each element contained in the rainwater is subjected to a difference process with the preset standard threshold of the corresponding element content, and the difference is subjected to a ratio process with the corresponding preset threshold to obtain the deviation rate of the element relative to the preset threshold, denoted as Yr; through the formula SZ = e ∑(Yr-Br) The water quality index SZ of the rainwater is calculated; where Br is the preset threshold, r is the number of each element contained in the rainwater, and Σ sums over r.

5. The cloud computing-based airport rooftop rainwater treatment monitoring system according to claim 4, wherein Evaluate the pollution level of the rainwater based on the water quality index, including: Compare the water quality index of the rainwater with the preset range; if the water quality index is greater than the preset range, the rainwater pollution level is level one; if the water quality index is within the preset range, the rainwater pollution level is level two; if the water quality index is less than the preset range, the rainwater pollution level is level three; among them, the pollution level one > level two > level three.

6. The cloud computing-based airport roof rainwater treatment monitoring system according to claim 5, characterized in that, Transport different water qualities to the corresponding filter pools based on the pollution level of the rainwater, including: Open the inlet valve of the corresponding filter pool according to the pollution level of the rainwater, and the filter pool adjusts the filter medium and the corresponding ratio of the filter pool in real time according to the content of each element in the rainwater.

7. A method for monitoring the treatment of rainwater on the airport roof based on cloud computing, which operates based on the cloud-computing-based airport roof rainwater treatment monitoring system described in any one of claims 1-6, characterized in that, Include the following steps: Step 1: Predict the rainfall in the target area; determine the drainage pipe combination according to the rainfall in the target area; Step 2: Obtain the content of each element in the rainwater, analyze the water quality index of the rainwater; evaluate the pollution level of the rainwater based on the water quality index; Step 3: Transport different water qualities to the corresponding storage pools based on the pollution level of the rainwater; and transport different water qualities to the corresponding filter pools based on the pollution level of the rainwater.

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