Non-point source pollution regulation and rain flood stagnating storage method and system

Abstract

The invention relates to the technical field of environmental data processing, in particular to a non-point source pollution regulation and rain flood stagnant storage method and system. The method comprises the following steps: preprocessing the pollutant concentration, pollutant accessibility and runoff volume of an obtained target pixel to obtain target feature data; inputting the target feature data into a trained accessibility classification model for classification to obtain a pollutant accessibility category; and determining a corresponding rainwater retention and pollutant treatment strategy according to the land utilization type corresponding to the target pixel, the pollutant accessibility type and the rainfall and the rainwater pollution degree detected by the intelligent cut-off device. Therefore, the problems of low rainwater collection efficiency, poor treatment effect, low utilization rate and the like of existing measures can be effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental data processing, and in particular to a method and system for non-point source pollution remediation and rainwater detention. BACKGROUND

[0002] Due to the rapid expansion of cities, there are many built-up areas around the drinking water sources, and there are also many forest lands, grasslands and other different land use types, resulting in an increasingly complex environment. In the prior art, the prediction of non-point source pollution, rainwater detention and water resource utilization of the corresponding watershed of the environment mostly adopt the traditional piecemeal governance mode, and lack a solution that comprehensively considers watershed rainwater collection, purification and utilization, resulting in poor rainwater collection and governance effect and low utilization efficiency. SUMMARY

[0003] Therefore, the embodiments of the present application provide a method and system for non-point source pollution remediation and rainwater detention, which can effectively solve the problems of low rainwater collection efficiency, poor governance effect and low utilization rate of existing measures.

[0004] In a first aspect, the embodiments of the present application provide a method for non-point source pollution remediation and rainwater detention, comprising: Pretreating the obtained pollutant concentration, pollutant accessibility and runoff of the target pixel to obtain target feature data; inputting the target feature data into a trained accessibility classification model for classification to obtain a pollutant accessibility category; According to the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent intercepting device, a corresponding rainwater detention and pollutant treatment strategy is determined.

[0005] In some embodiments, the determining of the corresponding rainwater detention and pollutant treatment strategy according to the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent intercepting device comprises: Based on a preset division standard, the target area corresponding to the land use type is divided into catchment areas to obtain a plurality of target catchment areas; According to the pollutant accessibility category corresponding to each target catchment area, and the rainfall and rainwater pollution degree of the target catchment area predicted by the intelligent intercepting device using a regression tree model, a rainwater detention and pollutant treatment strategy is determined.

[0006] In a second aspect, the embodiments of the present application provide a non-point source pollution remediation and rainwater detention control system. The system controls each electrically controlled gate to implement the non-point source pollution remediation and rainwater detention method provided in the first aspect of the present application.

[0007] In some embodiments, the system comprises an intelligent interception device; the intelligent interception device comprises: a water inlet, a high-pollution rainwater outlet, a low-pollution rainwater outlet, a first well chamber, a second well chamber, a debris barrier, an anti-backflow plate, and a plurality of electrically controlled gates; The high-pollution rainwater outlet and the low-pollution rainwater outlet are arranged below the first well chamber, and each outlet is provided with an electrically controlled gate; The water inlet is in communication with the second well chamber; the debris barrier is arranged between the second well chamber and the first well chamber; and the anti-backflow plate is arranged below the debris barrier; The intelligent interception device controls the corresponding electrically controlled gates according to the rainfall and the pollution degree of the rainwater, and diverts the high-pollution rainwater through the high-pollution rainwater outlet and the low-pollution rainwater through the low-pollution rainwater outlet.

[0008] The embodiments of the present application have the following beneficial effects: The non-point source pollution remediation and rainwater detention method of the present application comprises: preprocessing the pollutant concentration, pollutant accessibility, and runoff of the obtained target pixel to obtain target feature data; inputting the target feature data into a trained accessibility classification model for classification to obtain a pollutant accessibility category; and determining a corresponding rainwater detention and pollutant treatment strategy according to the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent interception device. The present application can adapt to local conditions and implement classified measures according to different human activities and different accessibility conditions in the basin. Thus, the problems of low rainwater collection efficiency, poor treatment effect, and low utilization rate of existing measures can be effectively solved. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0010] Figure 1 An application scenario diagram of the non-point source pollution remediation and rainwater detention control system of the embodiments of the present application is shown; Figure 2 Another application scenario diagram of the non-point source pollution remediation and rainwater detention control system of the embodiments of the present application is shown; Figure 3 A flowchart of the non-point source pollution remediation and rainwater detention method of the embodiments of the present application is shown; Figure 4A schematic diagram of the classification tree in the method for treating non-point source pollution and storing rainwater flood of the embodiment of the present application is shown. Figure 5 A first kind of basin division involved in the method for identifying non-point source pollution of river is shown. Figure 6 A second kind of basin division involved in the method for identifying non-point source pollution of river is shown. Figure 7 A schematic diagram of the regression tree in the method for treating non-point source pollution and storing rainwater flood of the embodiment of the present application is shown. Figure 8 A first kind of side view of the intelligent intercepting well in the method for treating non-point source pollution and storing rainwater flood of the embodiment of the present application is shown. Figure 9 A second kind of side view of the intelligent intercepting well in the method for treating non-point source pollution and storing rainwater flood of the embodiment of the present application is shown. Figure 10 A third kind of side view of the intelligent intercepting well in the method for treating non-point source pollution and storing rainwater flood of the embodiment of the present application is shown.

[0011] Main element symbol explanation: 10-intelligent intercepting well; 20-ecological pool; 30-small lake pool; 40-reservoir; 50-water gate; 60-water supplement point; 70-water plant; 80-drainage area; 90-clean water ditch; 100-ecological ditch; 110-reservoir gate; 120-pipe network; 130-water supplement pipe network; 101-inlet; 102-highly-polluted rainwater outlet; 103-lowly-polluted rainwater outlet; 104-debris blocking net; 105-anti-backflow plate; 106-solar cell; 107-control room; 201-well cover; 202-first well chamber; 203-second well chamber; 204-electric control gate; 2041-fixed frame, 2042-mechanical arm, 2043-gate; 205-automatic water quality monitoring meter; 206-rainfall meter; 207-liquid level meter. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments.

[0013] The components of the application embodiments described and illustrated herein can be arranged and designed in a wide variety of different configurations. Therefore, the following detailed description of the application, read with reference to the accompanying drawings, is not intended to limit the scope of the application as claimed, but is merely representative of certain selected embodiments of the application. The resulting connection of all embodiments of the application encompasses all alternatives, modifications and equivalents falling within the scope of the application.

[0014] Hereinafter, the terms "include", "have", and their conjugates, used in the various embodiments of the present application, merely indicate the presence of the features, numbers, steps, operations, elements, components, or combinations thereof, and do not exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof. Also, the terms "first", "second", "third", and the like, are used only to distinguish the description, and are not to be construed as designating or implying an order or sequence in time.

[0015] Unless defined otherwise, all terms used herein (including technical terms and scientific terms) have the same meanings as those generally understood by those having ordinary knowledge in the field of the various embodiments of the present application. The terms (such as terms defined in a generally used dictionary) should be interpreted as having the same meanings as those in the context of related technology and should not be interpreted to have ideal or excessively formal meanings, unless clearly defined in the various embodiments of the present application.

[0016] Some embodiments of the present application will be described below in detail with reference to the accompanying drawings. The following embodiments and features of the embodiments can be combined with each other, without conflict.

[0017] The present application first provides a non-point source pollution remediation and rainwater detention control system, which implements the non-point source pollution remediation and rainwater detention method of the present application by controlling each electrically controlled gate. In other words, the non-point source pollution remediation and rainwater detention control system is used to achieve rainwater collection, detention, and reuse.

[0018] Exemplarily, the scenarios in which the non-point source pollution remediation and rainwater detention control system is applicable include Figure 1As shown, the scenario includes an ecological reservoir 20, a small lake reservoir 30, a reservoir 40, a water gate 50, a water replenishment point 60, a water plant 70, a drainage area 80, a clear water ditch 90, an ecological ditch 100, a pipe network 120, and a water replenishment pipe network 130, in addition to a plurality of intelligent intercepting wells 10, a plurality of water gates 50, and a plurality of reservoir water gates 110. Among them, the scenario is divided into a near-natural area, an upstream strong human activity area, and a downstream strong human activity area. The non-point source pollution remediation and rain flood detention control system controls the plurality of intelligent intercepting wells 10, the plurality of water gates 50, and the plurality of reservoir water gates 110 to implement the non-point source pollution remediation and rain flood detention method of the present application.

[0019] Among them, the near-natural area, the upstream strong human activity area, and the downstream strong human activity area are divided according to different land use types. The near-natural area is an area with less human disturbance and higher naturalness; the strong human activity area is a highly urbanized or industrialized area; the upstream is a water conservation and ecologically sensitive area; and the downstream is a densely populated and economically active area that receives pollutants.

[0020] As shown, Figure 2 The scenario obtains a plurality of pixels through rasterization based on a preset resolution; the pixels include elevation and land use type information.

[0021] The non-point source pollution remediation and rain flood detention method will be described below in conjunction with some specific embodiments.

[0022] Figure 3 A flowchart of the non-point source pollution remediation and rain flood detention method of the present application is shown. Exemplarily, the non-point source pollution remediation and rain flood detention method includes the following steps: S100, preprocessing the pollutant concentration, pollutant accessibility, and runoff of the obtained target pixel to obtain target feature data. Exemplarily, the target feature data includes classified pollutant accessibility, runoff, and pollutant data.

[0023] It can be understood that preprocessing includes classifying runoff, pollutant accessibility, and pollutant accessibility.

[0024] S200, inputting the target feature data into a trained accessibility classification model for classification to obtain a pollutant accessibility category.

[0025] Further, the accessibility classification model includes a classification tree model; the construction method of the classification tree model includes: S210, obtaining training data; the training data includes index attributes of a plurality of pixels; the index attributes include pollutant concentration, runoff, and pollutant accessibility.

[0026] S220, respectively, each indicator attribute is divided into grades according to the corresponding rules, and a plurality of sets of index feature data are obtained.

[0027] Exemplarily, the runoff and pollutant accessibility are classified: The runoff and pollutant accessibility are divided into 4 levels by quartiles: 0-0.25 (I level), 0.25-0.5 (II level), 0.5-0.75 (III level), 0.75-1 (IV level), and the division basis is percentage, for example, 0.25 is 25%.

[0028] The pollutant concentration is classified, and each pollutant is divided into three levels according to Table 1: COD: <100 mg / L (I level), 100-800 mg / L (II level), >800 mg / L (III level); TSS: <100 mg / L (I level), 100-1000 mg / L (II level), >1000 mg / L (III level); TP: <0.2 mg / L (I level), 0.2-1 mg / L (II level), >1 mg / L (III level); COD is the chemical oxygen demand; TSS is the total suspended solids, the mass concentration of suspended matter in water that cannot pass through the filter membrane (usually 0.45 micron pore size), and the unit is generally mg / L; TP: total phosphorus, which represents the total amount of all forms of phosphorus in water.

[0029] Table 1 Average values of pollutant indicators corresponding to different land use types and pollution levels

[0030] A plurality of sets of index feature data, for example, as shown in Table 2, include pixel number, pollutant concentration arranged according to the level, runoff, and pollutant accessibility.

[0031] Table 2 Index feature data

[0032] S230, a classification tree model is generated by using a plurality of sets of index feature data and a classification tree algorithm.

[0033] The method for generating the classification tree model includes: S231, input data preparation. The input data is the initially selected indicator attribute.

[0034] The index attributes include: pollutant concentration, runoff, pollutant accessibility, etc. Each pixel is regarded as a sample point, forming a data set containing multiple features.

[0035] S232, feature classification processing.

[0036] Pollutant concentration: divided into three levels according to the type of pollutant (such as COD, TSS, TP).

[0037] Runoff volume: divided into 4 levels (I ~ IV) using quartiles.

[0038] Pollutant accessibility: also divided into 4 levels (I to IV) and used as a target variable or decision basis.

[0039] S233, selecting the optimal segmentation feature and threshold.

[0040] The Gini index is used to measure the purity of a feature under a particular partitioning scheme. A smaller Gini index indicates a stronger ability of that partitioning scheme to distinguish between classes. Each time a node is split, the feature with the smallest Gini index and its optimal partitioning point are selected for the split. This means that the Gini index is calculated for all candidate features (e.g., pollutant concentration A1, runoff volume A2). The feature with the smallest Gini index is selected as the current node.

[0041] Among them, the Gini index is:

[0042] Among them, K means there are K categories, is the probability that the sample point belongs to the kth class.

[0043] For a given sample set D, its Gini index is:

[0044] in, is the subset of samples in D that belong to the kth class, and K is the number of classes.

[0045] In this embodiment, taking pollutant concentration as an example, the sample set D of regional pixels is divided into A1 (pollutant concentration) and A2 (runoff) according to different features. Among them, A1 is divided into three parts: I, II, and III. Under the pollutant concentration condition, the Gini index of feature A1 is:

[0046]

[0047]

[0048] The node with the minimum Gini index is selected as the optimal split point. Similarly, the Gini index of feature A2 is calculated as above.

[0049] S234, recursively constructing a tree structure. Starting from the root node, the current sample set is recursively divided until the stopping condition is met (e.g., reaching the maximum depth, the number of samples is less than a threshold, the Gini index is lower than a certain value, etc.).

[0050] where the root node includes the entire sample set, the first layer split: select the optimal feature (e.g., pollutant concentration) and its optimal split point (e.g., the boundary between levels I and II). The child nodes continue to split: for each subset, repeat the above process to select new optimal features and split points. The stopping condition is that all samples in the current node belong to the same class, the maximum depth is reached, the number of remaining samples is less than a certain threshold, or the Gini index change is less than a certain accuracy.

[0051] S235, output the classification result.

[0052] The classification result is four pollutant accessibility categories (1-4), for example, the accessibility levels include level one accessibility (accessibility 1), level two accessibility (accessibility 2), level two accessibility (accessibility 3), and level two accessibility (accessibility 4). Each level corresponds to different rainwater detention and pollutant control strategies. For example, the construction of the final classification tree is as shown in Figure 4 . Figure 4 The pollutant concentration, runoff, and accessibility data used in the classification tree are all limited classification data, such as levels I, II, and III. According to the pollutant concentration, runoff, and accessibility classification data of the whole watershed pixel, the classification tree is used to classify the accessibility, and then according to the characteristics of the pollutant concentration and runoff corresponding to the four accessibility classifications, the rainwater detention and pollutant control strategies are constructed.

[0053] Demonstratively, level one accessibility (accessibility 1): corresponding to low pollutant concentration and small runoff; level two accessibility (accessibility 2): corresponding to low pollutant concentration and large runoff; level three accessibility (accessibility 3): corresponding to high pollutant concentration and large runoff; level four accessibility (accessibility 4): corresponding to high pollutant concentration and small runoff.

[0054] S300, according to the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent interception device, determine the corresponding rainwater detention and pollutant control strategy.

[0055] The present application considers various factors, such as land use type, pollutant accessibility category, and rainwater pollution degree, and can give reasonable rainwater detention and pollutant control strategies.

[0056] In an implementation, in order to improve accuracy, according to the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent interception device, a corresponding rainwater storage and pollutant treatment strategy is determined, including: S310, for the target area corresponding to the land use type, based on a preset division standard, the catchment area is divided to obtain a plurality of target catchment areas; S320, according to the pollutant accessibility category corresponding to each target catchment area, and the rainfall and rainwater pollution degree of the target catchment area predicted by the intelligent interception device using the regression tree model, the rainwater storage and pollutant treatment strategy is determined. The pollutant accessibility category corresponding to the target catchment area is the pollutant accessibility category of the target pixel corresponding to the target catchment area.

[0057] Further, for the target area corresponding to the land use type, based on a preset division standard, the catchment area is divided to obtain a plurality of target catchment areas, including: S311, if the land use type is the near-natural area near the upstream drinking water source, the near-natural area is divided according to the preset unit watershed catchment area to obtain a plurality of target catchment areas; wherein the preset unit watershed is obtained by the following division method: The highest line between the adjacent two rivers is taken as the watershed of the unit watershed, and the target watershed (the watershed where the pixel is located) is divided into unit watersheds with rivers as units by manual vectorization. As shown in Figure 5 As shown in Figure 6 As shown in Figure 5 , Figure 6 The serial number in the above is the code of each watershed.

[0058] S312, if the land use type is the upstream strong human activity area near the upstream drinking water source, the upstream strong human activity area is divided according to the preset unit watershed catchment area to obtain a plurality of target catchment areas; S313, if the land use type is the downstream strong human activity area, the downstream strong human activity area is divided according to the drainage unit to obtain a plurality of target catchment areas; wherein the drainage unit division is a city drainage unit divided based on human facilities, which is suitable for city drainage system planning and management.

[0059] Further, according to the pollutant accessibility category corresponding to each target catchment area, and the rainfall and rainwater pollution degree of the target catchment area predicted by the intelligent interception device using the regression tree model, the rainwater storage and pollutant treatment strategy is determined, including: According to the pollutant accessibility category corresponding to the target catchment area, at least two target nodes of rainwater flow direction are determined, and the treatment mode at each target node is determined; wherein, if the at least two target nodes include an intelligent interception device; the intelligent interception device determines the rainwater flow direction in the intelligent interception device according to the rainfall and the rainwater pollution degree. Specifically, the following steps are included: S321, if the land use type is near-natural area near the upstream drinking water source, the target node includes a clean ditch, an ecological ditch, an intelligent interception device and an ecological reservoir; the treatment mode includes natural purification.

[0060] Exemplarily, as shown in Figure 1 in the case of land use type being near-natural area near the upstream drinking water source: (1) if the pollutant accessibility category corresponding to the target catchment area is level 1 accessibility (accessibility 1), the intelligent interception well 10 is used to control the rainwater to enter the clean ditch 90 directly through the ecological ditch 100.

[0061] (2) if the pollutant accessibility category corresponding to the target catchment area is accessibility 2 (corresponding to the case of low pollutant concentration and large runoff), the intelligent interception well 10 is used to control the rainwater to enter the clean ditch 90 directly through the ecological ditch 100.

[0062] (3) if the pollutant accessibility category corresponding to the target catchment area is accessibility 3 (corresponding to the case of high pollutant concentration and large runoff), the intelligent interception well 10 is used to control the rainwater to enter the intelligent interception well 10 through the ecological ditch 100, and the high-pollution rainwater in the intelligent interception well 10 is controlled to enter the ecological reservoir 2 for natural purification, and the low-pollution rainwater enters the clean ditch 90.

[0063] (4) if the pollutant accessibility category corresponding to the target catchment area is accessibility 4 (corresponding to the case of high pollutant concentration and small runoff), the intelligent interception well 10 is used to control the rainwater to enter the ecological reservoir 20 for natural purification directly through the ecological ditch 100.

[0064] In order to ensure reliability, in the case of land use type being near-natural area near the upstream drinking water source, if the water quality in the ecological reservoir meets the standard, if the reservoir capacity is less than the preset capacity, the purified water in the ecological reservoir is controlled to flow into the reservoir through the water gate; if the downstream river needs ecological water supplement, the water in the ecological reservoir is controlled to flow to the downstream river through the clean ditch for ecological water supplement. Specifically, after the water quality in the ecological reservoir 20 meets the standard, if the reservoir capacity is insufficient, the purified rainwater in the ecological reservoir can enter the reservoir; if the downstream river needs ecological water supplement, the water in the ecological reservoir 20 can be discharged to the downstream through the clean ditch 90 for ecological water supplement.

[0065] During flood discharge, the sluice gates are used to store upstream water in the ecological reservoir near the natural area until it reaches a preset maximum capacity. The water is then directed to flow down the Qingshui Ditch. For example, in extreme weather conditions where runoff is excessive and flood discharge is necessary, the upstream sluice gates can be used to fill the ecological reservoir 20 near the natural area, thereby retaining rainwater. The rainwater then flows down the Qingshui Ditch 90.

[0066] S322: If the land use type is an upstream intensive human activity area near an upstream drinking water source, the target nodes include small lakes and ponds, sewage treatment plants, clear water ditches, and intelligent interception devices; the treatment methods include natural purification, artificial purification, sedimentation, and storage.

[0067] For example, Figure 1 As shown in the figure, when the land use type is the upstream strong human activity area near the upstream drinking water source: (1) If the pollutant accessibility category corresponding to the target catchment area is accessibility 1, the rainwater is controlled to enter the intelligent interception well first, and the highly polluted rainwater is controlled by the intelligent interception well to be transported to the sewage treatment plant through the sewage pipe network, and the low-polluting rainwater is discharged into the clear water ditch through the rainwater pipe network. (2) If the pollutant accessibility category corresponding to the target catchment area is accessibility 2, the rainwater is controlled to enter the intelligent interception well first, and the highly polluted rainwater is controlled by the intelligent interception well to be precipitated in the sedimentation tank and then transported to the sewage treatment plant through the sewage pipe network, and the low-polluting rainwater is controlled to enter the small lake and pond for detention and storage before flowing into the clear water ditch. (3) If the pollutant accessibility category corresponding to the target catchment area is accessibility 3, the rainwater is controlled to enter the intelligent interception well first, and the highly polluted rainwater is controlled by the intelligent interception well to be transported to the sewage treatment plant through the sewage pipe network, and the low-polluting rainwater is controlled to enter the small lake and pond for detention and purification before flowing into the clear water ditch. (4) If the pollutant accessibility category corresponding to the target catchment area is accessibility 4, control the rainwater to enter the intelligent interception well first, and then control the highly polluted rainwater to be transported to the sewage treatment plant through the sewage pipe network through the intelligent interception well, and control the low-pollutant rainwater to enter the small lake and pond for purification before flowing into the clear water ditch.

[0068] Furthermore, if the land use type is an upstream strong human activity area near an upstream drinking water source, small lakes and ponds can be transformed between the upstream strong human activity area and the downstream river channel according to natural conditions; the sewage from the upstream strong human activity area can be introduced into the downstream sewage treatment plant for treatment by utilizing the elevation difference between the upstream strong human activity area and the downstream. Specifically, small lakes and ponds can be transformed between the upstream strong human activity area and the downstream river channel according to natural conditions to enhance the retention and purification of rainwater. Since this upstream strong human activity area is close to the drinking water source, sewage treatment plants should not be built in the area as much as possible. The elevation difference is used to introduce sewage and highly polluted rainwater into the downstream sewage treatment plant for treatment.

[0069] S323, if the land use type is a downstream strong human activity area, the target node includes a river, a sewage plant, an ecological lake / pond, an intelligent interception device, and a river; and the treatment method includes natural purification, artificial purification, and detention.

[0070] In the case of a land use type being a downstream strong human activity area: (1) If the pollutant accessibility category corresponding to the target catchment area is accessibility 1, high-pollution rainwater is collected to a sewage pipe network through an intelligent interception well, and low-pollution rainwater is discharged into a river. (2) If the pollutant accessibility category corresponding to the target catchment area is accessibility 2, control rainwater first enters an intelligent interception well, high-pollution rainwater is transported to a sewage plant through a sewage pipe network by controlling the intelligent interception well, and low-pollution rainwater enters an ecological lake / pond to be detained, and then is discharged into a downstream river from the ecological lake / pond. (3) If the pollutant accessibility category corresponding to the target catchment area is accessibility 3, under the condition of permission, high-pollution rainwater is collected to a sewage pipe network through an intelligent interception well, and low-pollution rainwater enters an ecological lake / pond, and then is connected to a downstream river from the ecological lake / pond. If an ecological lake / pond cannot be constructed in the region, low-pollution rainwater is directly connected to a downstream river. (4) If the pollutant accessibility category corresponding to the target catchment area is accessibility 4, high-pollution rainwater is collected to a sewage pipe network through an intelligent interception well, and low-pollution rainwater is discharged into a river.

[0071] Further, in the case of a land use type being a downstream strong human activity area, when there is a small lake / pond reservoir between an upstream strong human activity area and a downstream river, recycled water treated by a sewage treatment plant is supplemented into the small lake / pond reservoir through a water gate, and then is supplemented to the downstream river after purification by the small lake / pond reservoir; when there is no small lake / pond reservoir between the upstream strong human activity area and the downstream river, the recycled water is sent to the nearest water supplement point in the downstream strong human activity area through a water supplement pipe. Specifically, the small lake / pond reservoir constructed in the above process can purify the recycled water on non-rainy days. The recycled water treated by the sewage treatment plant is sent to the nearest water supplement point through a water supplement pipe, and is used for ecological water supplement to the river. In the case of an ecological lake / pond, the recycled water is preferentially supplemented into the ecological lake / pond, and is supplemented to the downstream river after further purification; in the case of no ecological lake / pond, the recycled water is directly supplemented through a water supplement point beside the river.

[0072] In an embodiment, the intelligent interception device determines the rainwater detention and pollutant treatment strategy by using the rainfall and the rainwater pollution degree of the target catchment area predicted by the regression tree model, including: The intelligent interception device predicts the rainfall and the rainwater pollution degree of the rainwater by using the generated regression tree model based on the rainwater detention time, the rain intensity, the rainfall, and the rainwater pollution degree in the target catchment area, and shunts the rainwater according to the rainwater pollution degree.

[0073] In the embodiments of the present application, multivariate regression analysis is utilized to predict the change of water quality over time under different rainfall based on the rainfall and water quality data measured by the rain gauge and the automatic water quality monitoring device of the female.

[0074] Exemplarily, the method of generating the regression tree model comprises: S410, each target variable in the training data set is sorted according to a preset rule to obtain a continuous variable training set; the training data set includes rainwater retention time, rainfall, rain intensity and rainwater pollution degree (the rainwater pollution degree is represented by pollutant concentration); the target variable includes rainfall and rainwater pollution degree.

[0075] Firstly, a training data set is prepared: a data set containing multiple samples is obtained, wherein each sample includes an input feature vector (multiple rainwater retention time points and corresponding rain intensity) and a corresponding target variable set (rainfall and rainwater pollution degree).

[0076] Table 3 Training data set

[0077] For each target variable (for example, rainfall and rainwater pollution degree), the values of the target variable in the training set are arranged in ascending or descending order according to the numerical value to obtain an ordered sequence. The ordered sequences corresponding to all target variables constitute a continuous variable training set.

[0078] S420, for each target variable, the corresponding candidate split point is calculated according to the adjacent target variables in the continuous variable training set.

[0079] For the ordered sequence of each target variable, the median value between the adjacent two samples is calculated as the candidate split point. For example, the candidate threshold of the pollutant concentration is determined, that is, (A1+A2) / 2, (A2+A3) / 2,..., (A(n-1)+An) / 2.

[0080] All candidate split points are saved as a candidate segmentation set.

[0081] S430, a regression tree node is constructed according to the continuous variable training set, and the square difference loss corresponding to each candidate split point is calculated by traversing all candidate split points, and the candidate split point corresponding to the minimum square difference loss is selected as the optimal split point.

[0082] Specifically, the root node is initialized to contain the entire training dataset. The optimal split point of each node is determined, for example, for the pollutant concentration, the loss function is calculated with (C1+C2) / 2, (C2+C3) / 2, …, (C(n-1)+Cn) / 2 as the node value respectively, and the node value with the minimum loss function is selected as the optimal split point; similarly, for the runoff, the loss function is calculated with (A1+A2) / 2, (A2+A3) / 2, …, (A(n-1)+An) / 2 as the node value respectively, and the node value with the minimum loss function is selected as the optimal split point.

[0083] In the specific calculation process, for the current node, all candidate split points are traversed, and the squared error loss under each split point is calculated using the following steps: (1) The current node dataset is divided into left and right subsets according to the split point; (2) the average target value of the left and right subsets is calculated; (3) the total squared error loss is calculated; (4) the split point that minimizes the loss is selected as the optimal split point of the current node.

[0084] S440, recursively divide the optimal split point until the termination condition is met, and output the final regression tree model.

[0085] According to the optimal split point, the current node is divided into left and right child nodes. Steps (1)-(4) are repeatedly executed for each child node until any of the following termination conditions is met: The number of samples in the current node is less than a set threshold, the loss reduction amplitude is less than a set threshold, the height of the tree reaches the maximum depth limit, or the variance of all target variables is below a certain threshold, indicating that the data has been sufficiently purified.

[0086] As shown in Figure 7 , the final regression tree model is output, obtaining a complete regression tree, where each leaf node stores the mean (or other statistical quantity) of the target variable of all samples under the node as the prediction output.

[0087] where the mathematical expression of the regression tree model is:

[0088] x: pollutant concentration / rainfall; R m : the time corresponding to the leaf node; m: the serial number of the leaf node, i.e. the number of the leaf node; M is the total serial number; c m : the predicted value corresponding to the leaf node after regression analysis; I: when the condition is met, the corresponding set under the condition is unique.

[0089] c m : the determination method is to minimize the loss function, and the loss function is as follows:

[0090]

[0091]

[0092] Optimization objective:

[0093] At this time, the loss function only contains one unknown parameter , directly derive the above formula and the derivative is 0, solve :

[0094]

[0095]

[0096] Let the leaf node contains samples , then the above formula =

[0097] Let the above formula = 0, we have:

[0098] Then:

[0099] That is, when the value of each leaf node is the average value of all samples of the node, the loss obtained is the smallest, that is, the optimal regression tree.

[0100] Exemplarily, Figure 7 is according to a node, that is, the real-time monitoring data in the interception well, the continuous data of rain intensity, residence time, rainfall, and pollutant concentration, according to multiple regression analysis, the classification of rainfall and pollutant concentration under different rain intensity and rainwater residence time is obtained. This classification helps the intelligent electric control device of the interception well to predict the opening and closing of the low-concentration rainwater outlet and the high-concentration rainwater outlet according to the measured data under the future rainfall conditions (rain intensity, time). When this prediction is accurate enough, future can reduce labor costs, and also can take out the water quality monitor, and the intelligent controller completely takes over the opening and closing of the intelligent electric control device according to the rain intensity and time.

[0101] In one embodiment, the intelligent interception device comprises: a water inlet, a high-pollution rainwater outlet, a low-pollution rainwater outlet, a first well chamber, a second well chamber, a plurality of electric control gates, a residue blocking net, an anti-backflow plate, a solar cell, a control chamber, and a well cover.

[0102] The first well chamber is provided with a high-pollution rainwater outlet and a low-pollution rainwater outlet, and each outlet is provided with an electrically-controlled gate; The water inlet is communicated with the second well chamber, and a debris barrier net is arranged between the second well chamber and the first well chamber, and an anti-backflow plate is arranged below the debris barrier net; The intelligent flow interception device controls the corresponding electrically-controlled gates according to the rainfall and the pollution degree of rainwater, and divides the high-pollution rainwater through the high-pollution rainwater outlet and divides the low-pollution rainwater through the low-pollution rainwater outlet.

[0103] Further, the intelligent flow interception device comprises a rain gauge, an automatic water quality monitoring meter and a liquid level meter; the rain gauge is arranged on one side above the first well chamber; and the automatic water quality monitoring meter is used to measure the measured water quality data required when training a regression tree.

[0104] The intelligent flow interception device is not limited to an intelligent flow interception well.

[0105] As shown in Figure 8 , Figure 9 , Figure 10 , the first well chamber 202 is a rectangular brick-laid cement-finished structure, the high-pollution rainwater outlet 102 and the low-pollution rainwater outlet 103 arranged below the well chamber are respectively provided with electrically-controlled gates 204, and the opening and closing of the high-pollution rainwater outlet and the low-pollution rainwater outlet are controlled. The automatic water quality monitoring meter 205, the rain gauge 206 and the liquid level meter 207 are arranged on one side above the first well chamber 202. The water inlet 101 is connected with the second well chamber 203 in the well chamber.

[0106] After the rainwater enters the second well chamber 203 through the water inlet 101, it first passes through the debris barrier net 104 to remove large-particle impurities, and the impurities are regularly taken out and cleaned by maintenance personnel, so as to avoid a large amount of garbage from being accumulated in the well chamber. The anti-backflow plate 105 is arranged below the debris barrier net 104, so as to avoid the rainwater in the first well chamber 202 from flowing back into the second well chamber 203 when the water volume is large, thereby causing secondary pollution or backflow of the water inlet rainwater pipe.

[0107] The electrically-controlled gate 204 is provided with electric energy by the solar cell 106, and is composed of a fixed frame 2041, a mechanical arm 2042 and a gate 2043. The fixed frame 2041 is arranged on the well wall below the first well chamber 202 and located directly above the two rainwater outlets. The mechanical arm 2042 is electrically controlled by the control chamber 107 and the liquid level meter 207, so as to drive the lifting of the gate. The gate is connected with the mechanical arm 2042 and is controlled to lift by the mechanical arm 2042, so as to realize the opening and closing of the two rainwater outlets.

[0108] The control room 107 is powered by the solar cell 106, and is composed of a water quality control module and a rainfall monitoring module. The water quality control module monitors the water quality below the low-pollution rainwater drainage outlet through the automatic water quality monitoring meter 205, and then determines the water quality control condition. The rainfall monitoring module monitors the rainfall and rain intensity through the rain gauge, and then determines the rainfall control condition. The control room 107 determines the opening and closing of the electrically-controlled gate 204 in combination with the water quality control module and the rainfall monitoring module.

[0109] The liquid level meter 207 is powered by the solar cell 106, and is arranged on one side above the first well chamber 202. When the first well chamber 202 reaches the early warning water level due to poor drainage in the case of sudden extreme heavy rainfall, the liquid level meter 207 is used to send an instruction to open all the gates, so as to open all the outlet pipes and realize rapid drainage, thereby avoiding waterlogging.

[0110] The solar cell 106 is arranged on the ground surface and above the well lid 201, and is used to supply power to the control room 107, the automatic water quality monitoring meter 205, the rain gauge 206, the liquid level meter 207 and the electrically-controlled gate 204 in the intelligent interception well.

[0111] The application comprehensively considers the non-point source pollution, rainwater storage and utilization of the basin, avoids the problem that the traditional treatment mode of zero-knock and fragmentation cannot comprehensively consider the rainwater collection, purification and utilization of the basin. The application can be adapted to the circumstances of different human activities and different accessibility in the basin, and has good treatment effect and high water resource utilization rate. Specifically, from the perspective of the upstream near-natural area: the application not only considers the collection and treatment of non-point source pollution under different accessibility in the upstream near-natural area, but also connects different treatment units, treatment units and reservoirs, and the entire upstream near-natural area and downstream river, to form an organic whole. In this way, the non-point source pollution can be effectively collected and purified, and the rainwater storage can also be used as an ecological water supplement source for the downstream. From the perspective of the upstream strong human activity area: although the area is a strong human activity area, its geographical location is in the middle transition area between the upstream and the downstream. In the collection and utilization of rainwater, it is not only necessary to be comprehensive and accurate, but also cannot cause additional pollution load to the near-natural area. The application uses the height difference to collect high-pollution rainwater to the downstream strong human activity area for unified treatment, and the small lake pond reservoir beside the clean water ditch for further storage and purification of low-pollution rainwater, to meet the demand in the area. From the perspective of the downstream strong human activity area: the pollution load of the area is high, and the river water quantity depends on the discharge of the upstream reservoir. Therefore, the scheme in the area not only treats the rainwater in the area according to the accessibility, but also uses the reclaimed water after collection to the sewage plant for water supplement, thereby saving the upstream water resources.

[0112] In addition, the intelligent intercepting well is an important unit in the rainwater collection system, which can accurately identify and separate high-pollution rainwater and low-pollution rainwater. Generally, the initial rainwater and the later rainwater of the intercepting well correspond to two high and low staggered outlets, which makes it unable to adjust the shunt time and shunt flow of high-pollution rainwater and low-pollution rainwater according to the actual situation. The intelligent intercepting well in the application can identify rainwater with different pollution concentrations based on the rain gauge and the automatic water quality monitor in the early stage, and can open and close the electric control gate. In the later stage, based on a large amount of measured data and multiple regression analysis, the rainwater quality and the rainfall under different rainfall intensity and rainfall time can be accurately predicted, so that the electric control gate can be intelligently opened and closed. The advantage of this is that the control of rainwater shunting can be flexibly adjusted. The intelligent intercepting well also includes the advantages of double separation well chamber, bar slag net and anti-backflow plate, which can ensure the separation efficiency of the intelligent intercepting well under different working conditions.

[0113] The pollutant concentration (corresponding to the average value of the pollutant concentration described below), pollutant accessibility and runoff of each pixel in the embodiments of the application will be described below in combination with some specific examples.

[0114] Exemplarily, the following method is used to calculate the pollutant concentration, pollutant accessibility (pollutant accessibility index) and runoff of each pixel: S510, based on the geographic information data packet corresponding to the target river basin, calculate a plurality of pollution influence factors of the target river basin grid (corresponding to the pixel) in the target river basin on the target river.

[0115] Exemplarily, the geographic information data packet includes the following data of the target river basin: 1) digital elevation model (DEM) data; 2) river basin grid data; 3) river network grid data; 4) land use type data; 5) runoff data.

[0116] 1) Digital Elevation Model (DEM) data includes: (1) Regular grid point elevation, regular grid point planar coordinates (X, Y) and corresponding elevation value (Z) stored in raster form, which constitutes the basic data set of terrain relief; (2) Terrain factors, such as slope, aspect, curvature. (3) Hydrological analysis parameters, such as flow direction, flow accumulation, watershed boundary. 2) Watershed raster data: Watershed raster data is a regular grid form of watershed geographic information data set, which divides the study area into a regular small grid (watershed grid), each grid contains specific geographic and attribute information. The following is the geographic and attribute information usually included in the watershed raster data: (1) Basic elevation raster, which stores the elevation value (Z) of regular grid unit in two-dimensional matrix form, each grid unit corresponds to planar coordinates (X, Y), which is a direct expression of the spatial distribution of terrain. (2) Hydrological characteristic raster, flow direction, flow accumulation. (3) Watershed boundary. 3) River network raster data, river network raster data represents rivers in the form of regular grid, which is convenient for superimposed analysis with other raster data (such as watershed raster data, DEM data, etc.). Main contents include: (1) River location information, (2) River level information, (3) Higher level, (4) River width and depth information, (5) Flow velocity information, (6) River connectivity information, (7) River elevation information, (8) River buffer zone information, (9) Hydrological characteristic information. 4) Land use type data, the purpose and cover type of land in the study area. Land use type data usually includes the following main contents: (1) Land use classification, according to certain standards (such as China land use status classification, Corine Land Cover, USGS land use classification, etc.), the study area is divided into different land use types. Common classifications include: Urban land: such as residential area, commercial area, industrial area, etc. Farmland: such as paddy field, dry land, etc. Forest: such as coniferous forest, broad-leaved forest, mixed forest, etc. Grassland: such as natural grassland, artificial grassland, etc. Water area: such as river, lake, reservoir, etc. Bare land: such as desert, gobi, sand land, etc. Wetland: such as marsh, mudflat, etc. Other special land: such as airport, port, mine, etc.

[0117] (2) Land use proportion; in the rasterized study area, each river network grid may contain a single or mixed land use type. If it is a mixed type, record the proportion (percentage) of each land use type. For example: a grid may have 60% farmland, 30% grassland, and 10% water area. (3) Land use change information; (4) Soil property information; (5) Human activity intensity, different land use types correspond to different human activity intensities, for example: urban land: dense construction, busy traffic, pollutants mainly from domestic sewage, industrial emissions, etc. Farmland: agricultural fertilization and pesticide use may cause nitrogen and phosphorus pollution. Industrial area: may discharge heavy metals, organic matter and other pollutants. (6) Pollutant emission characteristics. (7) Rainfall runoff characteristics.

[0118] 5) Runoff data, which describes the flow of surface water and groundwater in the study area, directly affecting the transport and diffusion of pollutants. The following are the main contents of the runoff data usually included: (1) Runoff volume; (2) Surface runoff and groundwater runoff; (3) Runoff depth; (4) Runoff coefficient; (5) Runoff time distribution; (6) Runoff spatial distribution; (7) Runoff path; (8) Runoff pollutant concentration; (9) Runoff model simulation data, runoff data simulated by hydrological models (such as SWAT, HSPF, HEC-HMS, etc.).

[0119] S520, calculating the pollutant accessibility index of the target river basin grid to the target river based on the plurality of pollution influence factors.

[0120] Exemplarily, the pollution influence factors include but are not limited to distance, relative height difference, runoff and average pollutant concentration, etc.

[0121] Wherein, the pollutant accessibility refers to the potential ability of the pollutant to enter the sensitive environment or organism through physical migration or chemical diffusion, and the sensitive environment in the present application refers to the target river.

[0122] The geographic information data packet in the present application can also be a geographic information data packet of a current time stage.

[0123] The pollutant accessibility index (Pollutant Accessibility Index, PAI) is used to quantify the possibility of the pollutant reaching the target river from a certain location (such as a river basin grid).

[0124] Exemplarily, the higher the pollutant accessibility index is, the more likely the target watershed grid is a potential non-point pollution source of the target river. For example, a threshold is set, and a critical PAI threshold is determined according to experience or statistical analysis. If the PAI value of the target watershed grid exceeds the PAI threshold, the target watershed grid is considered as a potential non-point pollution source of the target river. The position distribution of each potential non-point pollution source corresponding to the target watershed grid is counted to obtain the distribution of non-point pollution sources.

[0125] In an embodiment, based on the geographic information data packet corresponding to the target watershed, the plurality of pollution influence factors of the target watershed grid in the target watershed on the target river are calculated, including: based on the geographic information data packet corresponding to the target watershed, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river are calculated. The pollutant accessibility index of the target watershed grid on the target river is calculated based on the plurality of pollution influence factors, including: based on the distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river, the pollutant accessibility index of the target watershed grid on the target river is calculated.

[0126] Further, based on the geographic information data packet corresponding to the target watershed, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river are calculated, including: (1) According to the river network grid data and the watershed grid data of the target watershed in the geographic information data packet, the Euclidean distance from the center point of the target watershed grid to the center point of the target river network grid is calculated; wherein the target river network grid is the river network grid closest to the target river.

[0127] Exemplarily, in the embodiments of the present application, the watershed grid is one of the grids obtained by dividing the target watershed according to a set resolution; and the river network grid is one of the grids obtained by dividing the target river according to a set resolution. In other words, the watershed grid and the river network grid are both a small area obtained by dividing the target watershed according to a certain resolution.

[0128] Distance: the distance from the river affects the degree of pollution of the pollutant to the river water.

[0129] ArcGIS is a professional geographic information mapping software that provides users with a scalable and comprehensive GIS platform. It can help users quickly make maps, support single-user and multi-user editing, and also can perform complex automated workflows.

[0130] In the ArcGIS platform, the Euclidean distance between the target watershed grid and the river network grid in the target watershed is calculated by using the Euclidean distance tool set:

[0131] In the formula, 、 Xi, Yi represent the coordinates of the i-th river network grid center point; 、 Xj, Yj represent the coordinates of the j-th basin grid center point.

[0132] Further, the present application also needs to standardize the Euclidean distance (also known as proximity factor), which is specifically standardized by the following formula, wherein the farther the distance, the lower the pollutant accessibility:

[0133] In the formula, Xi, Yi represent the coordinates of the i-th river network grid center point; Xi, Yi represent the coordinates of the i-th river network grid center point; Xi, Yi represent the coordinates of the i-th river network grid center point.

[0134] In an embodiment, the present application first re-divides the target basin to obtain a plurality of unit basins, and the unit basins are divided by the following method: (1) The digital elevation model data included in the geographic information data packet is subjected to depression filling processing.

[0135] Illustratively, the DEM data (digital elevation model data) of the target basin is subjected to depression filling processing using the "Fill" tool. In the DEM data, local depressions or depressions may cause flow calculation to be interrupted or have unreasonable flow direction. Open ArcToolbox→Spatial Analyst Tools→Hydrology→Fill. Input the original DEM data, and run to generate a filled DEM data. After such processing, all unreasonable low points in the DEM data are filled, thereby ensuring the continuity of subsequent flow direction analysis.

[0136] (2) Based on the filled digital elevation model data, the flow direction of the target basin grid is calculated using a preset tool; The "Flow Direction" tool is called to calculate the flow direction, using the filled DEM data as input, to generate the flow direction information for each cell. Open ArcToolbox→Spatial Analyst Tools→Hydrology→FlowDirection. Input the filled DEM data. The tool calculates the steepest descent direction of water flow for each flow direction grid based on the D8 algorithm, and uses a code (usually 1, 2, 4, 8, 16, 32, 64, 128) to represent one of the eight directions. In this way, each flow direction grid has a corresponding flow direction value.

[0137] It should be noted that these river networks formed by runoff are not the actual river networks that we are actually flowing in the city. Therefore, in this application, the actual river network vector data is superimposed on the river network generated by the DEM data, and the water flow is forced to follow the true river direction.

[0138] (3) Connecting the points corresponding to the highest elevation between the two adjacent rivers as the divide of the unit watershed; based on each divide, the target watershed is segmented by a preset method to obtain each unit watershed.

[0139] Specifically, the highest line between the two adjacent rivers is connected as the divide of the unit watershed, and the target watershed is segmented into unit watersheds by manual vectorization, and each watershed grid cell in the segmented unit watershed (small watershed) is a potential non-point source pollution source of the river. Specifically, whether it is a non-point source pollution source is judged according to the pollutant accessibility index. As shown in Figure 5 As shown in Figure 6 As shown in the unit watershed division schematic diagram of the present application, Figure 5 , Figure 6 The serial number in the above table is the coding of each watershed. Among them, each watershed grid in the unit watershed can find a corresponding watershed grid in the existing watershed in terms of position and size.

[0140] Further, based on the geographic information data packet corresponding to the target watershed, the Euclidean distance of the target watershed grid to the target river, the relative height difference, the runoff and the average pollutant concentration are calculated, including: based on the elevation of the target watershed grid in the target unit watershed and the average elevation and the minimum elevation corresponding to all river network grids of the target river in the target unit watershed, the relative height difference of the target watershed grid is calculated.

[0141] Demonstratively, based on the DEM data, each unit watershed is coded, and the partition statistics tool is used to calculate the average elevation and the minimum elevation of all river network grids of the target river in the target unit watershed according to the coding field. Finally, the grid calculator is used to calculate the relative height difference of each watershed grid in the target unit watershed to the river (

[0142] wherein i∈1, 2,..., n, j∈1, 2,..., m, respectively represent the row and column of the watershed grid; k∈1, 2,..., z, represents the code serial number of the unit watershed; represents the relative height difference between the watershed grid in the ith row and the jth column and the river in the target unit watershed; represents the elevation of the watershed grid in the ith row and the jth column; represents the average elevation of all river network grids in the target river in the kth unit watershed; represents the minimum elevation of all river network grids in the target river in the kth unit watershed.

[0143] The relative height difference is normalized by the following formula, and the greater the relative height difference, the higher the pollutant accessibility:

[0144] wherein, represents the normalized component of the pollutant accessibility of the relative height difference factor in the kth unit watershed; represents the relative height difference between the watershed grid in the ith row and the jth column in the target unit watershed and the river in the target unit watershed; represents the maximum value of the relative height difference in all unit watersheds in the target watershed.

[0145] In an embodiment, based on the geographic information data packet corresponding to the target watershed, the Euclidean distance of the target watershed grid to the target river, the relative height difference, the runoff, and the average value of the pollutant concentration in the target watershed are calculated, including: based on the watershed runoff data and the land use type data of the target watershed in the geographic information data packet, the runoff of each watershed grid is estimated by using a preset model.

[0146] ​The amount of runoff is an important factor in determining the accessibility of pollutants. In order to quickly and accurately estimate the direct surface runoff under a specific rainfall event, especially in the absence of detailed hydrological observation data, in the embodiments of the present application, based on the runoff data and land use type data of the watershed, the direct surface runoff under a specific rainfall event is estimated by using ArcSWAT on the ArcGIS platform based on the SCS-CN (Soil Conservation Service Curve Number method) model. SCS-CN is an empirical model developed for estimating the direct surface runoff under a specific rainfall event. The curve number CN is the only key parameter of the model, which is determined by soil type, soil antecedent moisture condition and land use type. In the case of relatively stable regional soil type and moisture, the change of CN value is mainly caused by the change of land use type. The CN value usually ranges from 0 to 100, and the larger the value, the more runoff is generated. The specific steps for calculating runoff are, for example: input rainfall data and watershed characteristic parameters; obtain CN value according to soil type and land use type. Calculate the direct surface runoff using the formula and output the result.

[0147] In order to improve the accuracy, in the embodiments of the present application, the CN value is optimized by introducing a machine learning algorithm, or the CN value is dynamically adjusted in combination with remote sensing data. The machine learning algorithm for optimizing the CN value in the SCS-CN model specifically includes: input features: including but not limited to the following variables: rainfall, soil type, land use type, topographic features (slope, elevation), vegetation coverage, initial wetness condition.

[0148] Target variable: actual observed runoff or historical CN value data.

[0149] Exemplarily, a regression model is used to optimize the CN value, such as linear regression, random forest regression, gradient boosting regression (GBDT), which is suitable for predicting continuous CN value. Goal: to establish the mapping relationship between input features and CN value.

[0150] The steps include: collecting historical data (rainfall, runoff, soil type, etc.); using a regression model to fit the relationship between input features and actual CN value; verifying the model performance, adjusting the hyperparameters to improve the prediction accuracy.

[0151] Further, the runoff factor is standardized by the following formula, and the larger the runoff, the higher the accessibility of pollutants:

[0152] wherein, represents the normalized component of the accessibility of pollutants of the runoff factor in the i-th watershed grid; represents the runoff of the i-th watershed grid; represents the maximum value of the runoff of all watershed grids in the target watershed.

[0153] Further, based on the geographic information data packet corresponding to the target watershed, the Euclidean distance of the target watershed grid to the target river, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid are calculated, including: based on the total pollutant emission, the runoff, the area and the land use type or the percentage of the land use type in the target watershed grid in the geographic information data packet, the average pollutant concentration of the target watershed grid is calculated.

[0154] In an embodiment, the watershed grid is suitable for a single land use type, and the land use type in each watershed grid is only one. For example, the average pollutant concentration is calculated by the following formula:

[0155] wherein, represents the average value of the pollutant in the rainfall runoff; M represents the total emission of a certain pollutant in the rainfall runoff, in grams g; V represents the runoff of the i-th watershed grid, which is obtained according to the above calculation method of the runoff; represents the area of the i-th watershed grid; generally consistent with the precision in the DEM data, for example, if the precision of the DEM data is 30 m, then the watershed grid is 30*30 m 2 . represents the average value of the pollutant index corresponding to different land use types, and the average value of the pollutant index is, for example, any one of the average COD, the average TSS and the average TP. The land use type of the i-th watershed grid is determined from the land use type data.

[0156] The average COD (Chemical Oxygen Demand) represents the total amount of organic and inorganic substances that can be oxidized by a strong oxidizing agent in the water body, reflecting the degree of organic pollution of the water body. The average TSS (Total Suspended Solids) refers to the total mass of suspended particulate matter in a unit volume of water (usually in mg / L), which is used to measure the turbidity and particulate pollution of the water body. The average TP (Total Phosphorus) refers to the total amount of all forms of phosphorus in the water body (including dissolved and particulate forms), which is an important indicator for evaluating water eutrophication, as shown in Table 1.

[0157] In practical applications, many watershed grids may contain multiple land use types (for example, half of the watershed grid is urban impervious surface, and the other half is green land). If the entire watershed grid is simply classified as one type, it may cause errors. Therefore, in the embodiments of the present application, the contribution of each land use type is proportionally distributed.

[0158] In another embodiment, in order to improve the recognition accuracy and improve the use range, for example, to meet the application of mixed land use type of watershed grid, the following calculation formula is used to calculate the average value of pollutant concentration:

[0159] Wherein, N represents the total number of land use types; M represents the total emission of a certain pollutant in a rainfall runoff, in grams g; V represents the runoff of the i-th watershed grid, which is obtained according to the above calculation method of runoff; Sni represents the area of the n-th land use type in the i-th watershed grid; Sni represents the average value of the pollutant index corresponding to the n-th land use type.

[0160] Suppose that the impervious surface in the i-th watershed grid accounts for 50%, and the green land accounts for 50%, then in the formula S1 (impervious surface) x (impervious surface) + S2 (green land) x (green land).

[0161] Further, the method further comprises: (1) Based on the watershed grid data in the geographic information data package, a preset tool is used to create a regular fishing net (Grid Vector File) according to the set resolution creation rule, to generate a grid vector file which is the same (both position and size are the same) as each watershed grid. The set resolution is equal to the resolution used when the target watershed is divided into grids. The grid vector file includes data division and block management, and the grid vector file divides the target watershed into regular small units (a grid unit), which facilitates block processing of large-scale data. Each grid unit is an independent analysis object.

[0162] (2) Based on the grid vector file, a preset area tabulation tool is used to count the percentage of different land use types in the grid unit, and the area of different land use types is determined according to the percentage of different land use types.

[0163] Exemplarily, a fishing net is created in ArcGIS to generate a grid vector file which is the same size as the above-mentioned watershed grid. Specifically, 1) open the ArcGIS software. 2) Use the "Create Fishnet" tool. 3) Set the unit size of the fishing net to be the same as the size of the existing grid (for example, 10m x 10m). 4) Ensure that the fishing net covers the entire study area (target watershed). 5) Output the generated grid vector file.

[0164] Use the Area Table tool to calculate the percentage of each land use type in the grid. Determine the area of each land use type based on the percentage of each land use type. Specifically, 1) Load the land use type data in ArcGIS (usually in raster format). 2) Use the "Tabulate Area" tool. 3) Input the following parameters: Zone Layer: Select the fishnet vector file just generated (each grid cell represents a watershed raster). Class Layer: Select the land use type raster data. Output Table: Specify the path of the output table that will contain the statistical results.

[0165] 4) After running the tool, a table file will be generated, listing the area and percentage of each land use type in each watershed grid.

[0166] Result: A detailed statistical table is obtained, recording the proportion of various land use types in each watershed grid.

[0167] The average concentration of pollutants is standardized using the following formula. The higher the average concentration of pollutants, the higher the pollutant accessibility:

[0168] represents the standardized component of pollutant accessibility of the runoff factor in the ith watershed grid; represents the average value of the pollutant in the ith watershed grid; represents the maximum value of the average concentration of pollutants in all watershed grids within the target watershed.

[0169] In one embodiment, based on the Euclidean distance of the target watershed grid to the target river, the relative height difference, the runoff and the average concentration of pollutants in the target watershed within the target watershed, the pollutant accessibility index of the target watershed grid to the target river is calculated, including: Based on the corresponding maximum value in the target watershed, the Euclidean distance, the relative height difference, the runoff and the average concentration of pollutants are standardized.

[0170] Multiply the standardized Euclidean distance, relative height difference, runoff and average concentration of pollutants to obtain the pollutant accessibility.

[0171] The pollutant accessibility index A is used to evaluate the pollutant accessibility of each potential pollution source in the watershed, and its calculation formula is:

[0172] where, is the pollutant accessibility index of the xth watershed grid; a normalized component of the pollutant accessibility caused by each influencing factor; The number of influencing factors of the accessibility evaluation model is equal to the number of influencing factors n, for example, n=4 in the embodiment of the present application.

[0173] The present application also provides a terminal device, which exemplarily comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to make the terminal device perform the above-mentioned method for non-point source pollution control and rainwater storage.

[0174] The processor can be an integrated circuit chip with a processing capability of signals. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, a discrete gate or transistor logic device, a discrete hardware component, or at least one of the above. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, which can realize or execute the disclosed methods, steps, and logic block diagrams in the embodiments of the present application.

[0175] The memory can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and the like. The memory is used to store a computer program, and the processor can execute the computer program after receiving an execution instruction.

[0176] The present application also provides a computer-readable storage medium for storing the computer program used in the terminal device. For example, the computer-readable storage medium can include, but is not limited to, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0177] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other means. The apparatus embodiments described above are only illustrative, for example, the flowcharts and structural diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in alternative implementation, the functions noted in the block can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the structural diagram and / or flowchart, and the combination of blocks in the structural diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0178] In addition, each functional module or unit in the embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0179] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application.

[0180] The above describes only the specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for controlling non-point source pollution and storing rainwater and flood water, characterized in that: include: Preprocess the pollutant concentration, pollutant accessibility and runoff of the acquired target pixels to obtain target characteristic data; Inputting the target feature data into a trained accessibility classification model for classification to obtain a pollutant accessibility category; The corresponding rainwater retention and pollutant control strategies are determined based on the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution level detected by the intelligent interception device.

2. The method for non-point source pollution control and rainwater flood storage according to claim 1 is characterized in that: The determining of corresponding rainwater retention and pollutant control strategies based on the land use type corresponding to the target pixel, the pollutant accessibility category, and the rainfall and rainwater pollution degree detected by the intelligent interception device includes: For the target area corresponding to the land use type, watershed division is performed based on a preset division standard to obtain a plurality of target watershed areas; The rainwater retention and pollutant control strategies are determined based on the pollutant accessibility categories corresponding to the target catchment areas and the rainfall and rainwater pollution levels of the target catchment areas predicted by the intelligent interception device using a regression tree model.

3. The method for non-point source pollution control and rainwater flood storage according to claim 2 is characterized in that: The intelligent interception device uses the regression tree model to predict the rainfall and rainwater pollution level in the target catchment area to determine the rainwater retention and pollutant control strategy, including: The intelligent interception device predicts the amount of rain and the degree of rain pollution of the rain water based on the rain water retention time, rain intensity, amount of rain and the degree of rain water pollution in the target catchment area using the generated regression tree model, and diverts the rain water according to the degree of rain water pollution.

4. The method for non-point source pollution control and rainwater flood storage according to claim 2, characterized in that: The target area corresponding to the land use type is divided into watershed areas based on a preset division standard to obtain multiple target watershed areas, including: If the land use type is a near-natural area near an upstream drinking water source, the near-natural area is divided into watershed zones according to a preset unit watershed to obtain a plurality of target watershed areas; If the land use type is an upstream strong human activity area near an upstream drinking water source, the upstream strong human activity area is divided into multiple target watershed areas according to the preset unit watershed watershed division; If the land use type is a downstream strong human activity area, the downstream strong human activity area is divided into zones according to drainage units to obtain a plurality of target catchment areas.

5. The method for non-point source pollution control and rainwater flood storage according to claim 2, characterized in that: Determining rainwater retention and pollutant control strategies based on the pollutant accessibility categories corresponding to the target catchment areas and the rainfall and rainwater pollution levels of the target catchment areas predicted by the intelligent interception device using a regression tree model includes: Determining at least two target nodes for the rainwater flow and a treatment method at each target node based on the pollutant accessibility category corresponding to the target catchment area; wherein, if the at least two target nodes include the intelligent interception device; the intelligent interception device determines the rainwater flow direction within the intelligent interception device based on the rainfall and the degree of rainwater pollution; If the land use type is a near-natural area near an upstream drinking water source, the target nodes include clear water ditches, ecological ditches, intelligent interception devices, and ecological reservoirs; and the treatment method includes natural purification; If the land use type is an upstream intensive human activity area near an upstream drinking water source, the target nodes include small lakes and ponds, sewage treatment plants, clear water ditches, and intelligent interception devices; the treatment methods include natural purification, artificial purification, sedimentation, and storage; If the land use type is a downstream area with strong human activities, the target nodes include rivers, sewage treatment plants, ecological lakes / ponds, intelligent interception devices and rivers; the governance methods include natural purification, artificial purification and detention.

6. The method for non-point source pollution control and rainwater flood storage according to claim 5 is characterized in that: The stormwater retention and pollutant control strategies also include: In the case where the land use type is a near-natural area near an upstream drinking water source, if the water quality in the ecological reservoir meets the standards and the reservoir capacity is less than a preset capacity, the purified water in the ecological reservoir is directed to flow into the reservoir by controlling the sluice gate; if the downstream river channel requires ecological water replenishment, the water in the ecological reservoir is directed to the downstream river channel through the clear water ditch by controlling the sluice gate for ecological water replenishment; During flood discharge, the water from upstream is stored in the ecological reservoir in the near-natural area by controlling the sluice gate until the preset maximum water storage capacity is reached, and then the water from upstream is controlled to be discharged through the clear water ditch; And / or, if the land use type is an upstream intensive human activity area near an upstream drinking water source, small lakes and ponds are transformed between the upstream intensive human activity area and the downstream river channel according to natural conditions; and sewage from the upstream intensive human activity area is introduced into the downstream sewage treatment plant for treatment by utilizing the elevation difference between the upstream intensive human activity area and the downstream; And / or, if the land use type is a downstream area with strong human activity, when there is a small lake or pond between the upstream area with strong human activity and the downstream river channel, the recycled water treated by the sewage treatment plant is replenished into the small lake or pond by controlling the sluice gate, and then purified by the small lake or pond and replenished to the downstream river channel; when there is no small lake or pond between the upstream area with strong human activity and the downstream river channel, the recycled water is sent to the nearest water replenishment point in the downstream area with strong human activity through the water replenishment pipe.

7. The method for non-point source pollution control and rainwater flood storage according to claim 1, characterized in that: The reachability classification model includes a classification tree model; The method for constructing the classification tree model includes: Acquire training data; the training data includes indicator attributes of a plurality of pixels; the indicator attributes include: pollutant concentration, runoff, and pollutant accessibility; Each of the indicator attributes is graded according to corresponding rules to obtain multiple groups of indicator feature data; and the classification tree model is generated using a classification tree algorithm using the multiple groups of indicator feature data.

8. The method for non-point source pollution control and rainwater flood storage according to any one of claims 2 to 6, characterized in that: The method for generating the regression tree model includes: Each target variable in the training data set is sorted according to a preset rule to obtain a continuous variable training set; the training data set includes rainwater retention time, rain intensity, rainfall, and rainwater pollution level; the target variables include rainfall and rainwater pollution level; For each target variable, the corresponding candidate split point is calculated based on the adjacent target variables in the continuous variable training set; Constructing a regression tree node based on the continuous variable training set, and traversing all candidate split points to calculate the square difference loss corresponding to each candidate split point, and selecting the candidate split point corresponding to the minimum square difference loss as the optimal split point; The optimal split point is recursively divided until the termination condition is met, and the final regression tree model is output.

9. A non-point source pollution control and rainwater retention control system, characterized in that: The system implements the non-point source pollution control and rainwater flood storage method as described in any one of claims 1 to 8 by controlling each electric-controlled gate.

10. The non-point source pollution control and rainwater retention control system according to claim 9 is characterized in that: The system includes an intelligent intercepting device; the intelligent intercepting device includes: a water inlet, a high-pollution rainwater outlet, a low-pollution rainwater outlet, a first well chamber, a second well chamber, a slag fence, an anti-backflow plate and a plurality of electrically controlled gates; A high-pollution rainwater outlet and a low-pollution rainwater outlet are provided below the first well chamber, and each outlet is provided with an electric-controlled gate; The water inlet is connected to the second well chamber; a slag screen is provided between the second well chamber and the first well chamber; and a backflow prevention plate is provided below the slag screen; The intelligent intercepting device controls the corresponding electric-controlled gate according to the rainfall and the pollution degree of the rainwater, and diverts the highly polluted rainwater through the highly polluted rainwater outlet and diverts the low-pollution rainwater through the low-pollution rainwater outlet.

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