Rainwater runoff non-point source pollution load prediction method and device based on drainage basin

Abstract

The invention is suitable for the technical field of pollution treatment, and provides a watershed-based rainwater runoff non-point source pollution load prediction method and device, and the method comprises the steps: obtaining the satellite remote sensing image data of each catchment subarea of a target watershed section, and the rainfall prediction information of each catchment subarea in a future rainfall period; dividing each catchment subarea into a plurality of functional areas; according to the satellite remote sensing image data, underlying surface distribution information of each functional area is determined; and according to the function type, rainfall prediction information and underlying surface distribution information of each functional area, predicting a rainwater runoff non-point source pollution load curve of each functional area in a future rainfall period, and finally determining a rainwater runoff non-point source pollution load prediction result of the cross section of the target drainage basin in the future rainfall period. The method can accurately predict the rainwater runoff non-point source pollution load.

Description

Technical Field

[0001] The invention belongs to the technical field of pollution control, and in particular relates to a method and device for predicting rainwater runoff surface source pollution load based on a watershed. Background Art

[0002] Urban non-point source pollution refers to the water environment pollution caused by various pollutants on the urban surface entering the receiving water body of urban rainfall runoff in the form of runoff or surface flow and large-scale under the scouring and leaching of rainwater. In the rapid urbanization process in my country, urban rainwater runoff carries a large amount of pollutants into the water body, which has become an important reason for the deterioration of urban water quality.

[0003] Rainwater runoff pollution prevention and control is the focus of current and future water quality improvement. To further reduce the impact of initial rain on the water quality of the assessment section and ensure the long-term clean water quality of the river, it is necessary to predict the pollution emissions from urban non-point sources and take corresponding regulation and treatment measures. However, there are many factors that affect urban non-point source pollution, and the causes are also complicated, which makes it difficult to accurately predict non-point source pollution. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for predicting rainwater runoff non-point source pollution load based on a watershed, so as to accurately predict the rainwater runoff non-point source pollution load.

[0005] A first aspect of an embodiment of the present invention provides a method for predicting rainwater runoff non-point source pollution load based on a watershed, comprising:

[0006] Obtain satellite remote sensing image data of each watershed subarea of ​​the target watershed section, as well as rainfall forecast information for each watershed subarea during future rainfall periods;

[0007] For each catchment area, multiple functional areas are divided;

[0008] Determining underlying surface distribution information of each functional area based on the satellite remote sensing image data;

[0009] According to the functional type, rainfall forecast information and underlying surface distribution information of each functional area, the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period is predicted;

[0010] Based on the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period, the rainwater runoff non-point source pollution load prediction result of the target watershed section in the future rainfall period is determined.

[0011] In combination with the first aspect, in a possible implementation manner of the first aspect, the rainfall prediction information includes rainfall levels, and the underlying surface distribution information includes an area ratio of each type of underlying surface;

[0012] The method of predicting the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area includes:

[0013] The functional type, rainfall level and area proportion of each underlying surface of each functional area are input into a pre-trained non-point source pollution prediction model to obtain the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period; wherein the non-point source pollution prediction model is a deep learning model.

[0014] In combination with the first aspect, in a possible implementation of the first aspect, the method further includes: pre-training the non-point source pollution prediction model;

[0015] The pre-training of the non-point source pollution prediction model comprises:

[0016] Obtain pollutant concentration time series and runoff discharge series monitored in multiple functional areas under different rainfall levels;

[0017] Determine the rainwater runoff non-point source pollution load curve for each functional area at different rainfall levels according to the pollutant concentration time series and the runoff discharge volume series;

[0018] The functional type, rainfall level and area proportion of each underlying surface of each functional area are used as the input of the initial model, and the corresponding rainwater runoff non-point source pollution load curve is used as the output of the initial model. The connection weights of the initial model are trained to obtain the non-point source pollution prediction model.

[0019] In combination with the first aspect, in a possible implementation of the first aspect, determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period includes:

[0020] The rainwater runoff non-point source pollution load curves of each functional area in each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of each catchment area;

[0021] The rainwater runoff non-point source pollution load curves of each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of the target watershed section;

[0022] Based on the rainwater runoff non-point source pollution load curve of the target watershed section, the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period is determined.

[0023] In combination with the first aspect, in a possible implementation of the first aspect, before fusing the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment zone, the method further includes:

[0024] Obtain air pollution data and / or pipeline pollutant deposition data for each functional area;

[0025] Determine the pollution load adjustment curve for each functional area during the future rainfall period based on the air pollution data and / or pipeline pollutant deposition data for each functional area;

[0026] According to the pollution load adjustment curve of each functional area, the rainwater runoff surface source pollution load curve of each functional area is adjusted.

[0027] In combination with the first aspect, in a possible implementation of the first aspect, after determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period, the method further includes:

[0028] Determine the maximum value of the pollution load of the target watershed section according to the rainwater runoff surface source pollution load curve of the target watershed section;

[0029] Obtaining the pollution absorption rate and water environment capacity of the target watershed section;

[0030] Determine the restoration time of the target watershed section according to the maximum value of the pollution load, the water environment capacity and the pollution absorption rate;

[0031] If the recovery time is greater than a preset time threshold, it is determined that a preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period.

[0032] In combination with the first aspect, in a possible implementation of the first aspect, after determining that a preset non-point source pollution reduction strategy needs to be executed during the future rainfall period, the method further includes:

[0033] According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the target period in the future rainfall period in which the slope is greater than a preset threshold;

[0034] The target period is determined as the period for implementing the non-point source pollution reduction strategy.

[0035] A second aspect of an embodiment of the present invention provides a rainwater runoff non-point source pollution load prediction device based on a watershed, comprising:

[0036] An acquisition module is used to obtain satellite remote sensing image data of each watershed subarea of ​​the target watershed section, as well as rainfall forecast information of each watershed subarea during future rainfall periods;

[0037] A division module is used to divide each catchment area into multiple functional areas;

[0038] A processing module, used to determine the underlying surface distribution information of each functional area based on the satellite remote sensing image data;

[0039] A prediction module is used to predict the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall prediction information and underlying surface distribution information of each functional area;

[0040] The calculation module is used to determine the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period.

[0041] In combination with the second aspect, in a possible implementation manner of the second aspect, the rainfall prediction information includes rainfall levels, and the underlying surface distribution information includes an area ratio of each type of underlying surface;

[0042] The prediction module is used to:

[0043] The functional type, rainfall level and area proportion of each underlying surface of each functional area are input into a pre-trained non-point source pollution prediction model to obtain the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period; wherein the non-point source pollution prediction model is a deep learning model.

[0044] In conjunction with the second aspect, in a possible implementation of the second aspect, the prediction module is further used to: pre-train the non-point source pollution prediction model;

[0045] The pre-training of the non-point source pollution prediction model comprises:

[0046] Obtain pollutant concentration time series and runoff discharge series monitored in multiple functional areas under different rainfall levels;

[0047] Determine the rainwater runoff non-point source pollution load curve for each functional area at different rainfall levels according to the pollutant concentration time series and the runoff discharge volume series;

[0048] The functional type, rainfall level and area proportion of each underlying surface of each functional area are used as the input of the initial model, and the corresponding rainwater runoff non-point source pollution load curve is used as the output of the initial model. The connection weights of the initial model are trained to obtain the non-point source pollution prediction model.

[0049] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the calculation module is used to:

[0050] The rainwater runoff non-point source pollution load curves of each functional area in each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of each catchment area;

[0051] The rainwater runoff non-point source pollution load curves of each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of the target watershed section;

[0052] Based on the rainwater runoff non-point source pollution load curve of the target watershed section, the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period is determined.

[0053] In conjunction with the second aspect, in a possible implementation of the second aspect, before fusing the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment zone, the calculation module is further used to:

[0054] Obtain air pollution data and / or pipeline pollutant deposition data for each functional area;

[0055] Determine the pollution load adjustment curve for each functional area during the future rainfall period based on the air pollution data and / or pipeline pollutant deposition data for each functional area;

[0056] According to the pollution load adjustment curve of each functional area, the rainwater runoff surface source pollution load curve of each functional area is adjusted.

[0057] In combination with the second aspect, in a possible implementation of the second aspect, after determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period, the calculation module is further used to:

[0058] Determine the maximum value of the pollution load of the target watershed section according to the rainwater runoff surface source pollution load curve of the target watershed section;

[0059] Obtaining the pollution absorption rate and water environment capacity of the target watershed section;

[0060] Determine the restoration time of the target watershed section according to the maximum value of the pollution load, the water environment capacity and the pollution absorption rate;

[0061] If the recovery time is greater than a preset time threshold, it is determined that a preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period.

[0062] In conjunction with the second aspect, in a possible implementation of the second aspect, after determining that a preset non-point source pollution reduction strategy needs to be executed during the future rainfall period, the calculation module is further used to:

[0063] According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the target period in the future rainfall period in which the slope is greater than a preset threshold;

[0064] The target period is determined as the period for implementing the non-point source pollution reduction strategy.

[0065] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method in the first aspect or any one of the implementations of the first aspect.

[0066] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method in the above-mentioned first aspect or any one of the implementation methods of the first aspect.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] The embodiment of the present invention takes into account that urban non-point source pollution is mainly affected by the coupling effect of the functional type, rainfall forecast information and underlying surface distribution information of the region. Therefore, the functional areas are divided by the watershed area of ​​the target basin section, and the underlying surface distribution information of the functional area is determined according to the satellite remote sensing image data. Then, according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area, the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period is predicted. The curve can reflect the change of non-point source pollution load over time. Finally, according to the rainwater runoff non-point source pollution load curve of each functional area, the rainwater runoff non-point source pollution load prediction result of the target basin section is obtained, which can help relevant personnel predict the non-point source pollution load, formulate corresponding non-point source pollution reduction strategies in advance, and help to weaken the impact of initial rain on the water quality of the assessment section, and ensure the long-term clean water quality of the river. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0070] Figure 1 Schematic diagram of an application scenario of a method for predicting rainwater runoff non-point source pollution load based on a watershed provided by an embodiment of the present invention;

[0071] Figure 2It is a schematic diagram of the implementation flow of the method for predicting rainwater runoff non-point source pollution load based on a watershed provided in an embodiment of the present invention;

[0072] Figure 3 It is a schematic diagram of the structure of a rainwater runoff non-point source pollution load prediction device based on a watershed provided by an embodiment of the present invention;

[0073] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0075] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0076] Figure 1 It is a schematic diagram of an application scenario of the method for predicting rainwater runoff non-point source pollution load based on a watershed provided in an embodiment of the present invention.

[0077] See also Figure 1 As shown in Figure 1, urban non-point sources are formed and discharged into the natural environment as urban runoff is formed and transferred. The urban ground is composed of various underlying surfaces. Urban runoff is first formed on different underlying surfaces and forms urban non-point sources through scouring. In a certain confluence area, runoff from different underlying surfaces eventually converges and enters the river basin.

[0078] Urban non-point source pollution is affected by many factors such as rainfall, underlying surface, and land use. In addition, rainfall is highly random, and the urban underlying surface and its land use have high spatial heterogeneity, resulting in a complex formation mechanism of urban non-point source pollution, high randomness, high uncertainty, and great difficulty in prediction and control.

[0079] To this end, the present invention proposes a method for predicting rainwater runoff surface source pollution load based on a watershed, see Figure 2 As shown, including:

[0080] Step S201, obtaining satellite remote sensing image data of each watershed subarea of ​​the target watershed section, and rainfall forecast information of each watershed subarea in the future rainfall period.

[0081] The watershed division of urban areas can be carried out in combination with the regional topographic data and the drainage network data. For example, the watershed boundaries of the main main streams in the basin are divided first on the basis of the basin boundaries; then, on the basis of the main main streams, the boundaries of the main tributaries are further refined; finally, on the basis of the division of the main tributary watershed units, the drainage network confluence characteristics of each area are further refined to delineate the final watershed division.

[0082] See also Figure 1 As shown in the figure, for a watershed, the target watershed section to be studied is set through the control section. The watershed sub-area flowing into the target watershed section is determined through the confluence of the divided watershed sub-areas, as the watershed sub-area corresponding to the target watershed section.

[0083] Satellite remote sensing images should be able to reflect the current status of the underlying surface of each catchment area as much as possible. First, the spatial resolution of the remote sensing data should be as high as possible, and second, the remote sensing data should be as cloud-free as possible.

[0084] Rainfall forecast information includes duration, rainfall level, etc. For example, rainfall levels can be divided into light rain, moderate rain, heavy rain, rainstorm, heavy rainstorm, and extremely heavy rainstorm, which are generally measured by daily rainfall. Light rain refers to daily rainfall below 10 mm; moderate rain refers to daily rainfall of 10 to 24.9 mm; heavy rain refers to daily rainfall of 25 to 49.9 mm; rainstorm refers to daily rainfall of 50 to 99.9 mm; heavy rainstorm refers to daily rainfall of 100 to 250 mm; extremely heavy rainstorm refers to daily rainfall of more than 250 mm.

[0085] Step S201, dividing each watershed area into multiple functional areas.

[0086] With the rapid development of urbanization, resources and elements continue to gather in cities. Urbanization and human socio-economic activities have significantly changed the composition, form, and structural pattern of urban landscapes, forming different types of functional areas (such as commercial areas, residential areas, industrial areas, etc.).

[0087] A larger water catchment unit is often composed of several different functional areas, and the pollution load characteristics of different functional areas are obviously different. In this embodiment, the land planning map of the city can be obtained, and the functional area composition of each water catchment unit can be determined according to the land planning map.

[0088] Step S203, determining the underlying surface distribution information of each functional area based on the satellite remote sensing image data.

[0089] The underlying surface is the interface between the atmosphere and the solid ground or liquid water surface below it. Through the pre-processing of satellite remote sensing images, including geometric correction, atmospheric correction and image fusion, combined with professional remote sensing interpretation software and manual interpretation, the remote sensing land use is refined and classified. According to the underlying surface composition type, the surface cover type can be divided into water bodies (reservoirs, canals, rivers, lakes), roofs, roads and green spaces (woodlands, grasslands, etc.).

[0090] Step S204, predicting the rainwater runoff surface source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area.

[0091] This embodiment pre-trains a deep learning non-point source pollution prediction model. By inputting the functional type, rainfall level and area proportion of each underlying surface of each functional area into the non-point source pollution prediction model, the rainwater runoff non-point source pollution load curve in the future rainfall period can be obtained.

[0092] There are obvious differences in the pollution loads of different underlying surfaces, so the underlying surface distribution information can be the area proportion of different underlying surfaces.

[0093] Rainfall forecast information can be rainfall levels. For example, during light rain, although the early runoff is small and the scouring capacity is limited, the pollutant concentration is very high during the light rain event. In moderate rain events, the pollutant concentration decreases due to the dilution effect of water. The larger the rain type, the greater the runoff, the greater the scouring capacity for surface pollutants. The scouring effect on the road surface is greater than the dilution effect of water. Therefore, the concentration level is the highest during the entire heavy rain process.

[0094] At the same time, usually 20% to 30% of the runoff carries more than 50% to 80% of the pollution load, and the initial scouring effect is obvious.

[0095] Through the non-point source pollution prediction model, the coupling effect of functional type, rainfall prediction information and underlying surface distribution information on pollution load can be considered to realize the prediction of pollution load changes.

[0096] Here, the nonpoint source pollution prediction model can be pre-trained in the following ways:

[0097] Obtain pollutant concentration time series and runoff discharge series monitored in multiple functional areas under different rainfall levels;

[0098] According to the pollutant concentration time series and runoff discharge series, the pollutant concentration multiplied by the runoff discharge at the same time is the pollution load generated, which can determine the rainwater runoff non-point source pollution load curve of each functional area under different rainfall levels;

[0099] The functional type, rainfall level and area proportion of each underlying surface in each functional area are taken as the input of the initial model, and the corresponding rainwater runoff non-point source pollution load curve is taken as the output of the initial model. The connection weights of the initial model are trained to obtain the non-point source pollution prediction model.

[0100] Step S205, determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period.

[0101] Here, the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment area can be fused to obtain the rainwater runoff non-point source pollution load curve of each catchment area; the rainwater runoff non-point source pollution load curves of each catchment area can be fused to obtain the rainwater runoff non-point source pollution load curve of the target watershed section; based on the rainwater runoff non-point source pollution load curve of the target watershed section, the rainwater runoff non-point source pollution load prediction result of the target watershed section in the future rainfall period is determined. Among them, curve fusion refers to the addition of the load values ​​of multiple curves at the same time.

[0102] As a possible implementation method, before merging the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment area, it can also include:

[0103] Obtain air pollution data and / or pipeline pollutant deposition data for each functional area;

[0104] Determine the pollution load adjustment curve for each functional area during the future rainfall period based on the air pollution data and / or pipeline pollutant deposition data for each functional area;

[0105] According to the pollution load adjustment curve of each functional area, the rainwater runoff surface source pollution load curve of each functional area is adjusted.

[0106] In this embodiment, atmospheric pollution and pipeline pollutant deposition are considered. Atmospheric pollution causes pollutants in rainwater, and pollutants deposited in pipelines will also flow into the river under the scouring of rainwater, both of which will have a great impact on the non-point source pollution load of rainwater runoff. Therefore, here, atmospheric pollution data for future periods can be obtained through weather forecast data. By sampling sediments in some typical sampling pipe sections, pipeline pollutant deposition data for future periods are obtained. Based on the two types of data, the pollution load adjustment curve for each functional area in the future rainfall period is determined by an intelligent algorithm model, and finally the rainwater runoff non-point source pollution load curve is adjusted according to the pollution load adjustment curve to improve accuracy.

[0107] The final rainwater runoff non-point source pollution load curve of the target watershed section can not only analyze the rainwater runoff non-point source pollution load of the target watershed section after rain, but also analyze the changes in the rainwater runoff non-point source pollution load during the rain, helping managers to formulate appropriate load reduction strategies.

[0108] The embodiment of the present invention takes into account that urban non-point source pollution is mainly affected by the coupling effect of the functional type, rainfall forecast information and underlying surface distribution information of the region. Therefore, the functional areas are divided by the watershed area of ​​the target basin section, and the underlying surface distribution information of the functional area is determined according to the satellite remote sensing image data. Then, according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area, the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period is predicted. The curve can reflect the change of non-point source pollution load over time. Finally, according to the rainwater runoff non-point source pollution load curve of each functional area, the rainwater runoff non-point source pollution load prediction result of the target basin section is obtained, which can help relevant personnel predict the non-point source pollution load, formulate corresponding non-point source pollution reduction strategies in advance, and help to weaken the impact of initial rain on the water quality of the assessment section, and ensure the long-term clean water quality of the river.

[0109] The above embodiments introduce how to predict the rainwater runoff non-point source pollution load of the target watershed section during the future rainfall period. In the following embodiment, based on the rainwater runoff non-point source pollution load prediction result, that is, the rainwater runoff non-point source pollution load curve of the target watershed section, the non-point source pollution reduction strategy of the target watershed section is analyzed, which is described in detail below:

[0110] After determining the prediction results of rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period, it can also include:

[0111] According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the maximum pollution load of the target watershed section;

[0112] Obtain the pollution absorption rate and water environment capacity of the target watershed section;

[0113] Determine the recovery time of the target watershed section based on the maximum pollution load, water environment capacity and pollution absorption rate;

[0114] If the recovery time is greater than the preset time threshold, it is determined that the preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period.

[0115] Water environment capacity refers to the maximum load of pollutants that a water body can accommodate while meeting the requirements of water environment quality, so it is also called water body load or pollution absorption capacity. Here, the maximum value of the rainwater runoff non-point source pollution load curve is the maximum pollution load of the target basin section. The maximum pollution load minus the water environment capacity and then divided by the pollution absorption rate is the recovery time of the target basin section.

[0116] For example, if the pollutants in the target watershed section can be restored to a level within the water environment capacity within 48 hours, there is no need to implement the non-point source pollution reduction strategy, and the river will absorb it by itself. Otherwise, the non-point source pollution reduction strategy needs to be implemented.

[0117] As a possible implementation method, the target period during the future rainfall period with a slope greater than the preset threshold can be determined based on the rainwater runoff non-point source pollution load curve of the target watershed section; the target period is determined as the period for implementing the non-point source pollution reduction strategy. The non-point source pollution reduction strategy can be to intercept the rainfall runoff and transport it to the sewage treatment plant for treatment or temporarily store it in the rainwater regulation facility to be input into the sewage system on sunny days or to be treated in a targeted manner. By implementing the non-point source pollution reduction strategy during the target period when the pollution load increases faster, the pollution load can be effectively reduced and the sewage system can be avoided from being overloaded.

[0118] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0119] Figure 3 : is a schematic diagram of the structure of a rainwater runoff non-point source pollution load prediction device based on a watershed provided by an embodiment of the present invention, comprising:

[0120] The acquisition module 31 is used to acquire satellite remote sensing image data of each watershed subarea of ​​the target watershed section, as well as rainfall forecast information of each watershed subarea in the future rainfall period.

[0121] The division module 32 is used to divide each watershed into multiple functional areas.

[0122] The processing module 33 is used to determine the underlying surface distribution information of each functional area based on the satellite remote sensing image data.

[0123] The prediction module 34 is used to predict the rainwater runoff surface source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall prediction information and underlying surface distribution information of each functional area.

[0124] The calculation module 35 is used to determine the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period.

[0125] As a possible implementation method, the rainfall prediction information includes rainfall levels, and the underlying surface distribution information includes the area proportion of each type of underlying surface;

[0126] The prediction module 34 is used to:

[0127] The functional type, rainfall level and area proportion of each underlying surface of each functional area are input into the pre-trained non-point source pollution prediction model to obtain the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period; among them, the non-point source pollution prediction model is a deep learning model.

[0128] As a possible implementation method, the prediction module 34 is also used to: pre-train a non-point source pollution prediction model;

[0129] Pre-trained nonpoint source pollution prediction models include:

[0130] Obtain pollutant concentration time series and runoff discharge series monitored in multiple functional areas under different rainfall levels;

[0131] According to the pollutant concentration time series and runoff discharge series, determine the rainwater runoff non-point source pollution load curve for each functional area under different rainfall levels;

[0132] The functional type, rainfall level and area proportion of each underlying surface in each functional area are taken as the input of the initial model, and the corresponding rainwater runoff non-point source pollution load curve is taken as the output of the initial model. The connection weights of the initial model are trained to obtain the non-point source pollution prediction model.

[0133] As a possible implementation manner, the calculation module 35 is used to:

[0134] The rainwater runoff non-point source pollution load curves of each functional area in each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of each catchment area;

[0135] The rainwater runoff non-point source pollution load curves of each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of the target watershed section;

[0136] Based on the rainwater runoff non-point source pollution load curve of the target watershed section, the prediction results of the rainwater runoff non-point source pollution load of the target watershed section during the future rainfall period are determined.

[0137] As a possible implementation method, before merging the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment zone, the calculation module 35 is further used to:

[0138] Obtain air pollution data and / or pipeline pollutant deposition data for each functional area;

[0139] Determine the pollution load adjustment curve for each functional area during the future rainfall period based on the air pollution data and / or pipeline pollutant deposition data for each functional area;

[0140] According to the pollution load adjustment curve of each functional area, the rainwater runoff surface source pollution load curve of each functional area is adjusted.

[0141] As a possible implementation method, after determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period, the calculation module 35 is further used to:

[0142] According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the maximum pollution load of the target watershed section;

[0143] Obtain the pollution absorption rate and water environment capacity of the target watershed section;

[0144] Determine the recovery time of the target watershed section based on the maximum pollution load, water environment capacity and pollution absorption rate;

[0145] If the recovery time is greater than the preset time threshold, it is determined that the preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period.

[0146] As a possible implementation, after determining that a preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period, the calculation module 35 is further used to:

[0147] According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the target period during the future rainfall period when the slope is greater than the preset threshold;

[0148] The target period is determined as the period for implementing the non-point source pollution reduction strategy.

[0149] The embodiment of the present invention takes into account that urban non-point source pollution is mainly affected by the coupling effect of the functional type, rainfall forecast information and underlying surface distribution information of the region. Therefore, the functional areas are divided by the watershed area of ​​the target basin section, and the underlying surface distribution information of the functional area is determined according to the satellite remote sensing image data. Then, according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area, the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period is predicted. The curve can reflect the change of non-point source pollution load over time. Finally, according to the rainwater runoff non-point source pollution load curve of each functional area, the rainwater runoff non-point source pollution load prediction result of the target basin section is obtained, which can help relevant personnel predict the non-point source pollution load, formulate corresponding non-point source pollution reduction strategies in advance, and help to weaken the impact of initial rain on the water quality of the assessment section, and ensure the long-term clean water quality of the river.

[0150] Figure 4 FIG. 4 is a schematic diagram of an electronic device 40 provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41, such as a rainwater runoff non-point source pollution load prediction program based on a watershed. When the processor 41 executes the computer program 43, the steps in the above-mentioned embodiments of the rainwater runoff non-point source pollution load prediction method based on a watershed are implemented, such as Figure 2 Alternatively, when the processor 41 executes the computer program 43, the functions of each module in the above-mentioned device embodiments are realized, for example, Figure 3 The functions of the modules 31 to 35 are shown.

[0151] Exemplarily, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 43 in the electronic device 40.

[0152] The electronic device 40 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 40 and does not constitute a limitation of the electronic device 40. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 40 may also include input and output devices, network access devices, buses, etc.

[0153] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0154] The memory 42 may be an internal storage unit of the electronic device 40, such as a hard disk or memory of the electronic device 40. The memory 42 may also be an external storage device of the electronic device 40, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 40. Further, the memory 42 may also include both an internal storage unit of the electronic device 40 and an external storage device. The memory 42 is used to store the computer program and other programs and data required by the electronic device 40. The memory 42 may also be used to temporarily store data that has been output or is to be output.

[0155] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0156] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0157] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0158] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0162] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for predicting rainwater runoff non-point source pollution load based on a watershed, characterized in that: include: Obtain satellite remote sensing image data of each watershed subarea of ​​the target watershed section, as well as rainfall forecast information for each watershed subarea during future rainfall periods; For each catchment area, multiple functional areas are divided; Determining underlying surface distribution information of each functional area based on the satellite remote sensing image data; According to the functional type, rainfall forecast information and underlying surface distribution information of each functional area, the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period is predicted; Based on the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period, the rainwater runoff non-point source pollution load prediction result of the target watershed section in the future rainfall period is determined.

2. The method for predicting rainwater runoff non-point source pollution load based on a watershed as claimed in claim 1, characterized in that: The rainfall prediction information includes rainfall levels, and the underlying surface distribution information includes the area ratio of each type of underlying surface; The method of predicting the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall forecast information and underlying surface distribution information of each functional area includes: The functional type, rainfall level and area proportion of each underlying surface of each functional area are input into a pre-trained non-point source pollution prediction model to obtain the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period; wherein the non-point source pollution prediction model is a deep learning model.

3. The method for predicting rainwater runoff non-point source pollution load based on a watershed as claimed in claim 2, characterized in that: The method further comprises: pre-training the non-point source pollution prediction model; The pre-training of the non-point source pollution prediction model comprises: Obtain pollutant concentration time series and runoff discharge series monitored in multiple functional areas under different rainfall levels; Determine the rainwater runoff non-point source pollution load curve for each functional area at different rainfall levels according to the pollutant concentration time series and the runoff discharge volume series; The functional type, rainfall level and area proportion of each underlying surface of each functional area are used as the input of the initial model, and the corresponding rainwater runoff non-point source pollution load curve is used as the output of the initial model. The connection weights of the initial model are trained to obtain the non-point source pollution prediction model.

4. The method for predicting rainwater runoff non-point source pollution load based on a watershed according to any one of claims 1 to 3, characterized in that: Determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period includes: The rainwater runoff non-point source pollution load curves of each functional area in each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of each catchment area; The rainwater runoff non-point source pollution load curves of each catchment area are merged to obtain the rainwater runoff non-point source pollution load curve of the target watershed section; Based on the rainwater runoff non-point source pollution load curve of the target watershed section, the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period is determined.

5. The method for predicting rainwater runoff non-point source pollution load based on a watershed as claimed in claim 4, characterized in that: Before the rainwater runoff non-point source pollution load curves of each functional area of ​​each catchment zone are merged, it also includes: Obtain air pollution data and / or pipeline pollutant deposition data for each functional area; Determine the pollution load adjustment curve for each functional area during the future rainfall period based on the air pollution data and / or pipeline pollutant deposition data for each functional area; According to the pollution load adjustment curve of each functional area, the rainwater runoff surface source pollution load curve of each functional area is adjusted.

6. The method for predicting rainwater runoff non-point source pollution load based on a watershed as claimed in claim 4, characterized in that: After determining the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period, it also includes: Determine the maximum value of the pollution load of the target watershed section according to the rainwater runoff surface source pollution load curve of the target watershed section; Obtaining the pollution absorption rate and water environment capacity of the target watershed section; Determine the restoration time of the target watershed section according to the maximum value of the pollution load, the water environment capacity and the pollution absorption rate; If the recovery time is greater than a preset time threshold, it is determined that a preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period.

7. The method for predicting rainwater runoff non-point source pollution load based on a watershed as claimed in claim 6, characterized in that: After determining that a preset non-point source pollution reduction strategy needs to be implemented during the future rainfall period, the method further includes: According to the rainwater runoff non-point source pollution load curve of the target watershed section, determine the target period in the future rainfall period in which the slope is greater than a preset threshold; The target period is determined as the period for implementing the non-point source pollution reduction strategy.

8. A rainwater runoff non-point source pollution load prediction device based on a watershed, characterized in that: include: An acquisition module is used to obtain satellite remote sensing image data of each watershed subarea of ​​the target watershed section, as well as rainfall forecast information of each watershed subarea during future rainfall periods; A division module is used to divide each catchment area into multiple functional areas; A processing module, used to determine the underlying surface distribution information of each functional area based on the satellite remote sensing image data; A prediction module is used to predict the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period according to the functional type, rainfall prediction information and underlying surface distribution information of each functional area; The calculation module is used to determine the prediction result of the rainwater runoff non-point source pollution load of the target watershed section in the future rainfall period according to the rainwater runoff non-point source pollution load curve of each functional area in the future rainfall period.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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