A monitoring, early warning and regulation system applied to urban rainwater pollution prevention and control

Abstract

The application relates to the technical field of environmental data processing, and discloses a monitoring, early warning and regulation system applied to urban rainwater pollution prevention and control, which comprises an online monitoring system, a river-inlet discharge port monitoring unit and a receiving water body monitoring unit, a rainfall monitoring unit, a discharge port monitoring and early warning model and a river-inlet discharge port interception and scheduling system. According to the classified sampling parameters of drainage in a rainfall event, rainwater can be finely classified into initial rainwater, mid-term rainwater and late rainwater; through intelligent regulation and control of the river-inlet discharge port interception gate and fine processing, the rainwater pollution prevention and control cost can be reduced and the efficiency can be improved, and scientific and intelligent rainwater pollution diversion can be realized.

Description

Technical Field

[0001] This invention relates to the field of environmental data processing technology, specifically to a monitoring, early warning and control system for urban rainwater pollution prevention and control. Background Technology

[0002] While urban point source pollution has been effectively controlled, urban non-point source pollution from rainwater runoff is becoming increasingly serious due to rapid urbanization and the increase in impermeable surfaces. In the initial stages of rainfall, rainwater dissolves pollutants and particulate matter in the air. After settling, it washes over roofs, roads, and storm drains, resulting in initial rainwater carrying large amounts of suspended solids (SS), organic matter, pathogens, heavy metals, and other pollutants. Therefore, initial rainwater has a high pollution load. Many rainwater runoff pollution control technologies exist, generally involving rainwater diversion followed by rainwater storage, constructed wetlands, cyclone separators, and intercepting storm drains for treatment.

[0003] Existing methods for initial rainwater diversion mainly rely on water level information and rainfall time to define the separation boundary between initial and subsequent rainwater. Chinese patent publication number CN114293634A, entitled "An Initial Rainwater Pollution Control System," discloses a technical solution that uses water level information to determine the initial rainwater inflow and outflow status, and controls valve actions based on this information to achieve separate collection and discharge of initial rainwater. Similarly, Chinese patent publication number CN111549881A, entitled "An Initial Rainwater Diversion Method," also discloses a method where rainwater within the catchment area flows into an initial rainwater diversion device through a factory rainwater ditch. The first 15 minutes of rainwater flow through the initial rainwater ditch into an initial rainwater collection pool, while subsequent rainwater flows through the initial rainwater diversion device from the subsequent rainwater ditch. After passing through a screen to filter larger impurities, the rainwater enters the factory pond, thus achieving diversion.

[0004] While this method can separate rainwater and sewage, it relies solely on rainfall time and water level, failing to consider rainfall volume, initial rainwater concentration, and the environmental capacity of the receiving water body for pollution control. This can lead to incomplete initial rainwater diversion, leaving a significant possibility that some initial rainwater non-point source pollution will enter the river, exceeding the river's environmental capacity and causing water quality deterioration. Alternatively, it can result in excessive initial rainwater diversion, with the water fully meeting the receiving water body's standards before being discharged into the river. This results in a large volume of diverted rainwater, leading to a large and difficult subsequent treatment process. A large amount of low-concentration later rainwater enters the sewage network, causing low water quality concentration and low operational efficiency, and causing sewage treatment plants to operate under overload during rainy days. Moreover, the current sophisticated treatment of rainwater pollution is insufficient. Rainwater pollution from a single rainfall event is usually divided into initial rainwater and subsequent rainwater. Initial rainwater is intercepted and purified using artificial wetlands, ecological ponds, or diverted to sewage treatment plants. Among these methods, the artificial wetland and ecological pond purification methods require large areas of initial rainwater storage tanks and ecological treatment land, as well as significant investment, making it difficult to promote on a large scale. As a result, initial rainwater non-point source pollution remains a pain point and challenge affecting water quality. Summary of the Invention

[0005] The purpose of this invention is to provide a monitoring, early warning, and control system for urban stormwater pollution prevention and control, and to solve the following technical problems:

[0006] How to provide a monitoring, early warning, and control system that can perform refined separation and treatment of rainwater and sewage.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A monitoring, early warning, and control system for urban stormwater pollution prevention and control includes:

[0009] The online monitoring system includes a river outfall monitoring unit, a receiving water body monitoring unit, and a rainfall monitoring unit;

[0010] The river discharge outlet monitoring unit is used to obtain drainage classification sampling parameters at the target river discharge outlet; the receiving water body monitoring unit is used to obtain water quality parameters of the assessment section of the target receiving water body; and the rainfall monitoring unit is used to obtain real-time rainfall.

[0011] The discharge outlet monitoring and early warning model is used to classify the type of drainage sent to the target river discharge outlet into initial rainwater, intermediate rainwater and late rainwater according to the drainage classification sampling parameters.

[0012] The river outfall interception and scheduling system is used to intercept, regulate, and schedule different types of drainage based on the drainage type classification results of the outfall monitoring and early warning model.

[0013] The drainage classification sampling parameters include water quality data, water quantity data, rainfall data, catchment area, runoff coefficient, and land type for the time period ΔT prior to the current time.

[0014] As a further aspect of the present invention: the discharge outlet monitoring and early warning model includes:

[0015] A generation module, connected to the online monitoring system, is used to generate corresponding historical data images based on the drainage classification sampling parameters.

[0016] The identification module is data-connected to the generation module and is used to output the corresponding drainage type judgment result based on the historical data images.

[0017] The recognition module is a trained neural network model.

[0018] As a further aspect of the present invention: after the identification module completes the judgment of the drainage completion type of the current target river discharge outlet, the river discharge outlet monitoring unit moves to the next target river discharge outlet to collect the corresponding drainage classification sampling parameters.

[0019] As a further aspect of the present invention: the method for generating corresponding historical data images based on the drainage classification sampling parameters includes:

[0020] The curves of the various parameters included in the drainage classification sampling parameters changing over time are arranged in a preset order and generated on a blank standard image.

[0021] Each data parameter corresponds to a curve with a set coordinate system.

[0022] The curve color corresponding to each data parameter is different.

[0023] As a further aspect of the present invention: the coordinate system colors corresponding to different curves are different, the color depth of the vertical axis of the coordinate system gradually changes from bottom to top, the rate of change is related to the degree of vertical compression of the corresponding curve, and the color depth value of the vertical axis color depth is related to the vertical value associated with the corresponding curve.

[0024] As a further aspect of the present invention: the color depth of the horizontal axis of the coordinate system remains unchanged, and the color depth of the horizontal axis is correlated with the length of the time period ΔT.

[0025] As a further aspect of the present invention, it also includes a target watershed digital twin system, used to simulate and analyze the changes in water quality and quantity at the target river discharge outlet under different rainfall events, and to analyze the environmental capacity of the receiving water body and the total amount of pollutants allocated to the target river discharge outlet.

[0026] The rainwater pollution diversion water quality analysis module determines the diversion boundaries of initial rainwater, intermediate rainwater, and late rainwater for each target river outlet based on the water quality and quantity of the receiving water body assessment section, the environmental capacity of the receiving water body, and the total amount of pollutants allocated to the target river outlets.

[0027] The river discharge interception and scheduling system intercepts, regulates, and schedules the drainage of each target river discharge outlet according to the diversion boundary.

[0028] The diversion limits include the initial, intermediate, and late-stage rainfall capacity of the receiving water body corresponding to the target river outlet discharge.

[0029] As a further aspect of the present invention, it also includes a dynamic limit adjustment module, comprising:

[0030] A cross-sectional light emitting unit is used to emit a transmitted light beam at the cross-section of the target assessment section;

[0031] A cross-sectional light receiving unit is used to receive the transmitted light column and acquire a sampled image at the target assessment cross-section.

[0032] The cross-section analysis unit is used to output the corresponding cross-section water layer score based on the sampled image;

[0033] An adjustment processing unit is used to dynamically adjust the diversion boundary based on the cross-sectional water layer score.

[0034] The cross-section analysis unit is a trained YOLOv5 neural network model.

[0035] The beneficial effects of this invention are as follows: Based on the drainage classification sampling parameters of a single rainfall event, rainwater and sewage can be finely divided into initial rainwater, intermediate rainwater, and late rainwater. Initial rainwater is intercepted by a river outlet interception gate and directed to the sewage network for unified treatment at a sewage treatment plant. This effectively addresses initial rainwater pollution without affecting the operational efficiency of the sewage treatment plant. Through initial rainwater diversion, the concentration and volume of intermediate rainwater are effectively reduced. Interception at the river outlet interception gate directs it to ecological ponds and constructed wetlands, effectively reducing the area of ​​ecological ponds and constructed wetlands required for treatment and the complexity of the process, thus achieving cost reduction and efficiency improvement in intermediate rainwater pollution treatment. Late rainwater, after assessment and analysis, has no impact on the receiving water quality and is directly discharged into the river. Intelligent control and refined treatment through the river outlet interception gate achieve cost reduction and efficiency improvement in rainwater pollution prevention and control, realizing scientific and intelligent diversion of rainwater pollution. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1This is a schematic diagram of the module connections of the monitoring, early warning and control system in this invention;

[0038] Figure 2 This is a diagram illustrating the overall principle of the monitoring, early warning, and control system in this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 and Figure 2 As shown, this invention is a monitoring, early warning, and control system for urban stormwater pollution prevention and control, comprising:

[0041] The online monitoring system includes a river outfall monitoring unit, a receiving water body monitoring unit, and a rainfall monitoring unit;

[0042] The river discharge outlet monitoring unit is used to obtain drainage classification sampling parameters at the target river discharge outlet; the receiving water body monitoring unit is used to obtain water quality parameters of the assessment section of the target receiving water body; and the rainfall monitoring unit is used to obtain real-time rainfall.

[0043] The discharge outlet monitoring and early warning model is used to classify the type of discharge sent to the target river discharge outlet into initial rainwater, intermediate rainwater and late rainwater based on the drainage classification sampling parameters;

[0044] The river outfall interception and scheduling system is used to intercept, regulate, and schedule different types of drainage based on the drainage type classification results of the outfall monitoring and early warning model.

[0045] Among them, the drainage classification sampling parameters include water quality data, water quantity data, rainfall data, catchment area, runoff coefficient and land type, etc., which affect water quality during the time period ΔT before the current time. Water quality data generally consider parameters such as COD, ammonia nitrogen, total phosphorus, total nitrogen, conductivity, SS, pH, and dissolved oxygen.

[0046] In this invention, rainwater and sewage can be finely divided into initial rainwater, intermediate rainwater, and late rainwater based on the drainage classification sampling parameters of a single rainfall event. Initial rainwater is intercepted by a river outfall interception gate and directed to the sewage network for unified treatment at a sewage treatment plant, effectively addressing initial rainwater pollution without affecting the operational efficiency of the sewage treatment plant. After initial rainwater diversion, the concentration and volume of intermediate rainwater are effectively reduced, and it is then intercepted by a river outfall interception gate and directed to ecological ponds and constructed wetlands, effectively reducing the area of ​​ecological ponds and constructed wetlands required for treatment and the complexity of the process, thus achieving cost reduction and efficiency improvement in intermediate rainwater pollution treatment. Late rainwater, after assessment and analysis, has no impact on the water quality of the receiving water body and is directly discharged into the river. Intelligent control and refined treatment through the river outfall interception gate achieve cost reduction and efficiency improvement in rainwater pollution prevention and control, realizing scientific and intelligent diversion of rainwater pollution.

[0047] As a further aspect of the present invention: the discharge outlet monitoring and early warning model includes:

[0048] The generation module, connected to the online monitoring system, is used to generate corresponding historical data images based on drainage classification sampling parameters;

[0049] The identification module is connected to the generation module and is used to output the corresponding drainage type judgment result based on historical data images.

[0050] The recognition module is a trained neural network model.

[0051] In this way, data analysis of drainage to be discharged into the river can be initiated at the start of rainfall, and various data in the drainage classification sampling parameters can be used to form relevant historical data images. The recognition module can use an existing CNN model, whose training samples are generated in the same way as the historical data images, the difference being the addition of a manual annotation step. This allows for the evaluation of drainage types from a large amount of different types of data, providing more dimensions for judgment, no longer simply considering the duration of rainfall and water level, and enabling more refined and effective treatment of rainwater pollution.

[0052] In addition, depending on the different manual annotations during the training phase, the drainage type in the future can be predicted based on existing historical data images. For example, when the rainfall is less than 5mm, it can be predicted how many hours the water quality at the river outlet will reach the initial rainwater quality, how many hours it will reach the mid-rainwater quality, and how many hours it will reach the late-rainwater quality, thus guiding the subsequent regulation of the interception gate at the river outlet.

[0053] As a further aspect of the present invention: after the identification module completes the judgment of the drainage completion type of the current target river discharge outlet, the river discharge outlet monitoring unit moves to the next target river discharge outlet to collect the corresponding drainage classification sampling parameters.

[0054] By using the above technical solution, a mobile identification module can be used to collect drainage classification sampling parameters of the current target river discharge outlet. After the collection is completed and the corresponding drainage type is determined, the system can proceed to the next target river discharge outlet, thus significantly reducing costs.

[0055] As a further aspect of the present invention: the method for generating corresponding historical data images based on drainage classification sampling parameters includes:

[0056] The curves of the various parameters included in the drainage classification sampling parameters changing over time are arranged in a preset order and generated on a blank standard image.

[0057] Each data parameter corresponds to a curve with a set coordinate system.

[0058] The curve color corresponding to each data parameter is different.

[0059] Through the above technical solution, the trained recognition module can distinguish the data parameter types represented by different curves by color, and can quickly and accurately determine the drainage type by the trend of multiple curves in the same image.

[0060] As a further aspect of the present invention: the coordinate system colors corresponding to different curves are different, the color depth of the vertical axis of the coordinate system gradually changes from bottom to top, the rate of change is related to the degree of vertical compression of the corresponding curve, and the color depth value of the vertical axis is related to the vertical value associated with the corresponding curve.

[0061] As a further aspect of the present invention: the color depth of the horizontal axis of the coordinate system remains unchanged, and the color depth of the horizontal axis is correlated with the length of the time interval ΔT.

[0062] The above technical solution is insufficient to guarantee accurate display of various data parameters simply by showing the curve's trend. Furthermore, to ensure uniformity in recognition standards, the size of historical data images is generally kept consistent. The selection of the ΔT time period length and the magnitude of each curve's peak value affect the size of the historical data images. Therefore, to ensure that all curves can be accurately represented by images and quantified by the recognition model, during training, the scaling of the curve in the vertical direction can be quantified based on the gradient rate of the color depth of the vertical axis, and the length of the ΔT time period in the horizontal direction can be quantified based on the color depth of the horizontal axis. The recognition model trained in this way can obtain the timestamp and vertical axis value represented by any point on the curve based on the color depth of the coordinate axes and its changes, thereby ensuring accurate judgment and recognition of drainage types.

[0063] As a further aspect of the present invention, it also includes a target watershed digital twin system, used to simulate and analyze the changes in water quality and quantity at the target river discharge outlet under different rainfall events, and to analyze the environmental capacity of the receiving water body and the total amount of pollutants allocated to the target river discharge outlet.

[0064] The rainwater pollution diversion water quality analysis module determines the diversion boundaries of initial rainwater, intermediate rainwater, and late rainwater for each target river outlet based on the water quality and quantity of the receiving water body assessment section, the environmental capacity of the receiving water body, and the total amount of pollutants allocated to the target river outlets.

[0065] The river outfall interception and scheduling system intercepts, regulates, and schedules the drainage of each target river outfall according to the diversion boundary;

[0066] Among them, the target watershed digital twin system is built based on GIS+BIM+IoT, and the diversion boundary includes the initial, medium and late rainfall capacity of the receiving water body that is matched with the discharge of the corresponding target river outlet.

[0067] As a further aspect of the present invention, it also includes a dynamic limit adjustment module, comprising:

[0068] The cross-sectional light emitting unit is used to emit a transmitted light beam at the cross-section of the target assessment section;

[0069] The cross-sectional light receiving unit is used to receive the transmitted light column and acquire the sampled image at the target assessment cross-section;

[0070] The cross-section analysis unit is used to output the corresponding cross-section water layer score based on the sampled images;

[0071] The adjustment processing unit is used to dynamically adjust the diversion boundary based on the cross-sectional water layer score;

[0072] The cross-sectional analysis unit is a trained YOLOv5 neural network model.

[0073] In this embodiment of the invention, the cross-sectional light emitting unit can be driven by a hull that can float on the water surface. The hull is equipped with a telescopic rod that can extend and retract. The cross-sectional light emitting unit and the cross-sectional light receiving unit are respectively fixed at the far end of the telescopic rod relative to the hull. The extension of the telescopic rod changes the target assessment cross-sectional area. The distance between the emitting and receiving surfaces of the cross-sectional light emitting unit and the cross-sectional light receiving unit is adjustable, and the distance is controlled within 0.5-1mm. This can prevent large particles of debris from blocking the light beam and affecting the acquisition of the sampling image. In this way, the water clarity of the target assessment cross-sectional area can be reflected by the cross-sectional water layer score. If the cross-sectional water layer score is lower than a preset threshold, it means that the initial rainwater tolerance in the corresponding diversion boundary needs to be reduced. If the cross-sectional water layer score is higher than the preset threshold, the initial rainwater tolerance in the diversion boundary can be increased. This can ensure the health of the receiving water body as much as possible.

[0074] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A monitoring, early warning, and control system for urban stormwater pollution prevention and control, characterized in that, include: The online monitoring system includes a river outfall monitoring unit, a receiving water body monitoring unit, and a rainfall monitoring unit; The river discharge outlet monitoring unit is used to obtain drainage classification sampling parameters at the target river discharge outlet; the receiving water body monitoring unit is used to obtain water quality parameters of the assessment section of the target receiving water body; and the rainfall monitoring unit is used to obtain real-time rainfall. The discharge outlet monitoring and early warning model is used to classify the type of drainage sent to the target river discharge outlet into initial rainwater, intermediate rainwater and late rainwater according to the drainage classification sampling parameters. The river outfall interception and scheduling system is used to intercept, regulate, and schedule different types of drainage based on the drainage type classification results of the outfall monitoring and early warning model. A digital twin system for the target watershed is used to simulate and analyze the changes in water quality and quantity at the target river outlets under different rainfall events, and to analyze the environmental capacity of the receiving water body and the total amount of pollutants allocated to the target river outlets. The rainwater pollution diversion water quality analysis module determines the diversion boundaries of initial rainwater, intermediate rainwater, and late rainwater for each target river outlet based on the water quality and quantity of the receiving water body assessment section, the environmental capacity of the receiving water body, and the total amount of pollutants allocated to the target river outlets. The river discharge interception and scheduling system intercepts, regulates, and schedules the drainage of each target river discharge outlet according to the diversion boundary. The drainage classification sampling parameters include those prior to the current time. Water quality data, water quantity data, rainfall data, catchment area, runoff coefficient, and land type within the time period; the diversion boundary includes the initial, medium, and late rainfall capacity of the receiving water body corresponding to the target river outlet. The dynamic limit adjustment module includes: A cross-sectional light emitting unit is used to emit a transmitted light beam at the test cross-section of the receiving water body. A cross-sectional light receiving unit is used to receive the transmitted light column and acquire a sampling image at the cross-section of the receiving water body for assessment. The cross-section analysis unit is used to output the corresponding cross-section water layer score based on the sampled image; An adjustment processing unit is used to dynamically adjust the diversion boundary based on the cross-sectional water layer score. The cross-sectional analysis unit is a trained Yolov5 neural network model.

2. The monitoring, early warning, and control system for urban stormwater pollution prevention and control according to claim 1, characterized in that, The discharge outlet monitoring and early warning model includes: A generation module, connected to the online monitoring system, is used to generate corresponding historical data images based on the drainage classification sampling parameters. The identification module is data-connected to the generation module and is used to output the corresponding drainage type judgment result based on the historical data images. The recognition module is a trained neural network model.

3. The monitoring, early warning, and control system for urban stormwater pollution prevention and control according to claim 2, characterized in that, Once the identification module has determined the drainage completion type of the current target river outlet, the river outlet monitoring unit moves to the next target river outlet to collect the corresponding drainage classification sampling parameters.

4. The monitoring, early warning, and control system for urban stormwater pollution prevention and control according to claim 2, characterized in that, The method for generating corresponding historical data images based on the drainage classification sampling parameters includes: The curves of the various parameters included in the drainage classification sampling parameters changing over time are arranged in a preset order and generated on a blank standard image. Each data parameter corresponds to a curve with a set coordinate system. The curve color corresponding to each data parameter is different.

5. The monitoring, early warning, and control system for urban stormwater pollution prevention and control according to claim 4, characterized in that, The coordinate system colors corresponding to different curves are different. The color depth of the vertical axis of the coordinate system gradually changes from bottom to top. The rate of change is related to the vertical compression degree of the corresponding curve. The color depth value of the vertical axis is related to the vertical value associated with the corresponding curve.

6. The monitoring, early warning, and control system for urban stormwater pollution prevention and control according to claim 5, characterized in that, The color depth of the horizontal axis of the coordinate system remains unchanged, and the color depth of the horizontal axis is related to... The length of the time period is associated with the corresponding time period.

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