A method and system for monitoring land-based pollution in an estuarine and coastal environment

Abstract

The application provides a kind of estuary coast land source pollution monitoring method and system, comprising: obtaining multispectral remote sensing image and digital surface model of target monitoring area in different seasons, based on preset water body parameter inversion model and pollution target identification model, respectively obtain water quality parameter data and land pollution source information in different seasons;Analysis of surface elevation information, and real-time receiving data of hydrological observation equipment in target monitoring area in different seasons, obtain land pollution source diffusion information in different seasons;Respectively, the water quality parameter data, land pollution source information and land pollution source diffusion information are weighted and summed, to obtain the estuary coast land source pollution risk evaluation index in different seasons, and the estuary coast land source pollution risk evaluation index in each season is accumulated to obtain the annual estuary coast land source pollution risk comprehensive index of the target monitoring area. It can improve the efficiency and accuracy of estuary coast land source pollution monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information, in particular to a river estuary and coastal land source pollution monitoring method and system. BACKGROUND

[0002] The river estuary and coastal area is a transitional zone connecting land and sea, and the health of its ecological environment is crucial to maintaining the balance of the entire marine ecosystem. However, with the acceleration of industrialization and urbanization, the river estuary and coastal area is facing increasingly serious land source pollution into the sea, which poses a serious threat to the ecological environment of the area.

[0003] The existing traditional monitoring means mainly rely on manual on-site sampling and monitoring, which is not only time-consuming and laborious, but also has a long sampling cycle, and cannot realize real-time monitoring and rapid response of pollutants, resulting in low monitoring efficiency. In addition, due to the limitations of traditional monitoring methods, the influence of different seasons on pollution diffusion and migration trajectory is not fully considered, resulting in deviation of monitoring results, and further affecting the accuracy of risk assessment. SUMMARY

[0004] The present application aims to solve the problems in the prior art, and provides a river estuary and coastal land source pollution monitoring method and system which can improve the monitoring efficiency and accuracy of river estuary and coastal land source pollution by analyzing remote sensing images and considering the influence of different seasons on pollution diffusion.

[0005] A river estuary and coastal land source pollution monitoring method, comprising:

[0006] Obtaining multispectral remote sensing images and digital surface models of the target monitoring area in different seasons;

[0007] According to the spectral reflectance of different bands in the multispectral remote sensing images, the water quality parameter data in different seasons is obtained based on a preset water body parameter inversion model;

[0008] According to the digital surface model, pollution identification is performed based on a preset pollution target identification model to obtain land pollution source information in different seasons;

[0009] Extracting the elevation information of the ground surface from the digital surface model to obtain a digital elevation model;

[0010] Real-time receiving data of hydrological observation equipment in the target monitoring area in different seasons, and combining the digital elevation model to analyze the terrain of the target monitoring area to obtain land pollution source diffusion information in different seasons;

[0011] The water quality parameter data, the land pollution source information and the land pollution source diffusion information are respectively weighted and summed to obtain a river estuary and coast land source pollution risk evaluation index in different seasons;

[0012] The river estuary and coast land source pollution risk evaluation indexes in different seasons are accumulated to obtain a comprehensive index of the river estuary and coast land source pollution risk in the whole year in the target monitoring area;

[0013] When the comprehensive index of the river estuary and coast land source pollution risk in the whole year is greater than a preset comprehensive index threshold, a warning instruction is sent to a river estuary and coast land source pollution warning device.

[0014] The application also provides a river estuary and coast land source pollution monitoring system, comprising:

[0015] A monitoring data acquisition module is configured to acquire multispectral remote sensing images and a digital surface model of a target monitoring area in different seasons;

[0016] A water quality parameter inversion module is configured to obtain water quality parameter data in different seasons based on a preset water body parameter inversion model according to spectral reflectivity of different bands in the multispectral remote sensing images;

[0017] A land pollution source information acquisition module is configured to obtain land pollution source information in different seasons based on a preset pollution target identification model by performing pollution identification according to the digital surface model;

[0018] A digital elevation model acquisition module is configured to extract elevation information of a ground surface from the digital surface model to obtain a digital elevation model;

[0019] A land pollution source diffusion information acquisition module is configured to receive data of hydrological observation equipment in the target monitoring area in different seasons in real time, and obtain land pollution source diffusion information in different seasons by analyzing a topography of the target monitoring area in combination with the digital elevation model;

[0020] A river estuary and coast land source pollution risk evaluation index calculation module is configured to obtain a river estuary and coast land source pollution risk evaluation index in different seasons by weighting and summing the water quality parameter data, the land pollution source information and the land pollution source diffusion information;

[0021] A comprehensive index of the river estuary and coast land source pollution risk in the whole year calculation module is configured to obtain a comprehensive index of the river estuary and coast land source pollution risk in the whole year in the target monitoring area by accumulating the river estuary and coast land source pollution risk evaluation indexes in different seasons;

[0022] A warning module is configured to send a warning instruction to a river estuary and coast land source pollution warning device when the comprehensive index of the river estuary and coast land source pollution risk in the whole year is greater than a preset comprehensive index threshold.

[0023] With respect to the prior art, the present application obtains multispectral remote sensing images and digital surface models in different seasons, uses water body parameter inversion models and pollution target identification models to quickly and accurately obtain water quality parameter data and land pollution source information. At the same time, combined with the digital elevation model and the real-time received hydrological observation data, the diffusion of the land pollution source is further analyzed, the real-time dynamic monitoring of the pollutants can be realized, and the monitoring efficiency is significantly improved. Moreover, the present application fully considers the influence of seasonal change on pollution diffusion and migration track, and through weighted summation and accumulation processing of data in different seasons, a more accurate and comprehensive annual estuary and coastal land source pollution risk comprehensive index is obtained, and the accuracy of the estuary and coastal land source pollution monitoring is effectively improved.

[0024] In order to more clearly understand the present application, the specific embodiments of the present application will be described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A flowchart of an estuary and coastal land source pollution monitoring method of the present application;

[0026] Figure 2 A method flowchart for constructing a water body parameter inversion model in an estuary and coastal land source pollution monitoring method of the present application;

[0027] Figure 3 A method flowchart for obtaining land pollution source information in an estuary and coastal land source pollution monitoring method of the present application;

[0028] Figure 4 A method flowchart for constructing a pollution target identification model in an estuary and coastal land source pollution monitoring method of the present application;

[0029] Figure 5 A method flowchart for obtaining a digital elevation model in an estuary and coastal land source pollution monitoring method of the present application;

[0030] Figure 6 A method flowchart for obtaining land pollution source diffusion information in an estuary and coastal land source pollution monitoring method of the present application;

[0031] Figure 7 A method flowchart for obtaining an estuary and coastal land source pollution risk evaluation index in an estuary and coastal land source pollution monitoring method of the present application;

[0032] Figure 8 A method flowchart for constructing a weight index set in an estuary and coastal land source pollution monitoring method of the present application;

[0033] Figure 9A method flow chart for constructing a standard value set in a method for monitoring land source pollution into an estuary and coastal sea according to the present application;

[0034] Figure 10 A schematic diagram of a system for monitoring land source pollution into an estuary and coastal sea according to the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work are within the scope of protection of the present application.

[0036] It should be understood that the schematic drawings are not drawn to scale. The flowchart used in the present application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented in no order, the steps without logical context relationship can be reversed in order or implemented simultaneously. In addition, a person skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0037] In this document, the phrase "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it independent or alternative to other embodiments. A person skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0038] Embodiment 1

[0039] Please refer to Figure 1 , Figure 1 A flowchart of a method for monitoring land source pollution into an estuary and coastal sea according to the present application.

[0040] The present application provides a method for monitoring land source pollution into an estuary and coastal sea, which specifically comprises the following steps:

[0041] S1: Obtain multispectral remote sensing images and digital surface models of a target monitoring area in different seasons;

[0042] S2: Obtain water quality parameter data in different seasons based on a preset water body parameter inversion model according to the spectral reflectance of different bands in the multispectral remote sensing images;

[0043] S3: According to the digital surface model, pollution identification is performed based on a preset pollution target identification model to obtain land pollution source information in different seasons;

[0044] S4: Elevation information of the ground is extracted from the digital surface model to obtain a digital elevation model;

[0045] S5: Real-time data of hydrological observation equipment in the target monitoring area in different seasons are received, and the terrain of the target monitoring area is analyzed in combination with the digital elevation model to obtain land pollution source diffusion information in different seasons;

[0046] S6: The water quality parameter data, land pollution source information and land pollution source diffusion information are weighted and summed respectively to obtain a river estuary and coastal land source pollution risk evaluation index in different seasons;

[0047] S7: The river estuary and coastal land source pollution risk evaluation indexes in different seasons are accumulated to obtain an annual river estuary and coastal land source pollution risk comprehensive index of the target monitoring area;

[0048] S8: When the annual river estuary and coastal land source pollution risk comprehensive index is greater than a preset risk comprehensive index threshold, a warning instruction is sent to a river estuary and coastal land source pollution warning device.

[0049] Compared with the prior art, the scheme quickly and accurately obtains water quality parameter data and land pollution source information by obtaining multispectral remote sensing images and digital surface models in different seasons, using a water body parameter inversion model and a pollution target identification model. At the same time, the diffusion of the land pollution source is further analyzed by combining the digital elevation model and the real-time received hydrological observation data, which can realize real-time dynamic monitoring of the pollutants and significantly improve the monitoring efficiency. Moreover, the scheme fully considers the influence of seasonal changes on pollution diffusion and migration trajectory, and obtains a more accurate and comprehensive annual river estuary and coastal land source pollution risk comprehensive index by weighted summing and accumulating the data in different seasons, which effectively improves the accuracy of river estuary and coastal land source pollution monitoring.

[0050] The estuary coast land source pollution monitoring method of the present application can be executed by a computer system comprising a pollution monitoring database server, a data acquisition server, a pollution monitoring server and an estuary coast sea entry warning device. The pollution monitoring database server is used to store the multispectral remote sensing images, digital surface models and data of hydrological observation devices of the target monitoring area in different seasons, and to construct the pollution monitoring database. The data acquisition server is used to acquire the multispectral remote sensing images, digital surface models and data of hydrological observation devices of the target monitoring area from the pollution monitoring database server, and to send them to the pollution monitoring server for processing. The pollution monitoring server executes the estuary coast land source pollution monitoring method of the present application, acquires the water quality parameter data, land pollution source information and land pollution source diffusion information of the target monitoring area in different seasons according to the multispectral remote sensing images and digital surface models, performs weighted summation to obtain the estuary coast land source pollution risk evaluation index in different seasons, and thus obtains the annual estuary coast land source pollution risk comprehensive index. When the annual estuary coast land source pollution risk comprehensive index is greater than a preset risk comprehensive index threshold, a warning instruction is sent to the estuary coast sea entry warning device and pushed to the corresponding estuary coast sea entry warning device. The estuary coast sea entry warning device can log in to the pollution monitoring server, select the target area as needed, and thus obtain accurate estuary coast land source pollution warning information in the target area, or perform a similar subscription operation to automatically push the relevant estuary coast land source pollution warning information by the pollution monitoring server.

[0051] For step S1, in the present embodiment, the multispectral remote sensing images and digital surface models are acquired from the pollution monitoring database server in response to a calling instruction of the pollution monitoring server to the pollution monitoring database server. The multispectral remote sensing images refer to the imaging of the earth's surface in multiple spectral bands by using a multispectral sensor to obtain the reflection or emission information of the earth's surface objects in different spectral ranges. The spectral reflectance of different bands in the multispectral remote sensing images includes the spectral reflectance of blue light, green light, red light, ultraviolet light, visible light and near-infrared light bands. The digital surface model (DSM) is a three-dimensional model representing the terrain of the earth's surface and the height information of all objects on the ground (such as buildings, trees, vehicles, etc.).

[0052] In response to a collection instruction of the user to the pollution monitoring database server, the pollution monitoring database server acquires the multispectral remote sensing images from a satellite remote sensing platform, an aerial remote sensing platform or a drone remote sensing platform. The digital surface model is acquired from a geographic information department or a scientific research institution, thereby constructing the pollution monitoring database.

[0053] The different seasons include spring, summer, autumn and winter.

[0054] For step S2, the water quality parameter data includes total nitrogen, total phosphorus and chemical oxygen demand. In response to a first analysis instruction of a user to the pollution monitoring server, the pollution monitoring server obtains multi-spectral remote sensing images from the pollution monitoring database server, and obtains water quality parameter data in different seasons based on a preset water body parameter inversion model according to spectral reflectance of different bands in the multi-spectral remote sensing images, and stores the water quality parameter data in the pollution monitoring database server.

[0055] In spring, the air temperature rises and the rainfall increases, and the river runoff increases, resulting in high total nitrogen, total phosphorus and chemical oxygen demand entering the sea, and high risk of land source pollution entering the sea at the estuary and coast; in summer, heavy rain runoff carries a large amount of total nitrogen, total phosphorus and chemical oxygen demand into rivers and oceans, resulting in high concentration of total nitrogen, total phosphorus and chemical oxygen demand entering the sea, and high risk of land source pollution entering the sea at the estuary and coast; in autumn, a large amount of phosphorus fertilizer and nitrogen fertilizer remains in farmland and enters rivers and oceans during rainfall or irrigation, resulting in high concentration of total nitrogen, total phosphorus and chemical oxygen demand entering the sea, and high risk of land source pollution entering the sea at the estuary and coast; in winter, the air temperature is low, the rainfall is less, the river runoff decreases, the concentration of total nitrogen, total phosphorus and chemical oxygen demand entering the sea is low, and the risk of land source pollution entering the sea at the estuary and coast is low.

[0056] Of course, according to the actual monitoring demand, the water quality parameter data can also include heavy metal content and other parameters.

[0057] Please refer to Figure 2 , Figure 2 A method flowchart for constructing a water body parameter inversion model in an estuary and coast land source pollution monitoring method of the application. The preset water body parameter inversion model is constructed, including:

[0058] S21: obtaining the ground measurement data, wherein the ground measurement data at least includes historical water quality parameter concentrations of total nitrogen, total phosphorus and chemical oxygen demand;

[0059] S22: selecting a plurality of multi-spectral remote sensing images and historical water quality parameter concentrations, and dividing them into a water quality parameter training set and a water quality parameter verification set according to a 7:3 ratio;

[0060] S23: taking spectral reflectance of different bands in the multi-spectral remote sensing images of the water quality parameter training set as input data, and taking historical water quality parameter concentrations of the water quality parameter training set as output data, training a machine learning model to obtain the water body parameter inversion model;

[0061] S24: Taking the spectral reflectance of different bands in the multispectral remote sensing image of the water quality parameter verification set as input data, obtaining the water quality parameter concentration prediction result corresponding to the water quality parameter verification set based on the water body parameter inversion model;

[0062] S25: Using the first loss function, calculating the error between the water quality parameter concentration prediction result and the historical water quality parameter concentration of the water quality parameter verification set as the first evaluation result;

[0063] S26: According to the first evaluation result, adjusting the network parameters of the water body parameter inversion model through the first optimization algorithm to obtain the trained water body parameter inversion model.

[0064] For step S21, the ground measurement data can be obtained from environmental protection department website, water quality monitoring station or environmental database, etc. and stored in the pollution monitoring database.

[0065] For step S22, the water quality parameter training set and the water quality parameter verification set both include the multispectral remote sensing image and the historical water quality parameter concentration corresponding to the multispectral remote sensing image.

[0066] For step S23, the machine learning model preferentially selects support vector machine, neural network or random forest. By determining and selecting the bands sensitive to total nitrogen, total phosphorus and chemical oxygen demand in the multispectral remote sensing image as input data, the historical water quality parameter concentration of total nitrogen, total phosphorus and chemical oxygen demand corresponding to the multispectral remote sensing image as output data, the machine learning model is trained to obtain the water body parameter inversion model. Wherein, the bands sensitive to total nitrogen and total phosphorus in the multispectral remote sensing image include blue light, green light and red light; the band sensitive to the chemical oxygen demand includes ultraviolet, visible light and near infrared band.

[0067] For steps S24-S26, the first loss function can select mean square error function, and the first optimization algorithm can select Bayesian optimization method. Of course, in other embodiments, according to actual needs, the first loss function can also select other loss functions such as mean absolute error, and the first optimization algorithm can also select other optimization algorithms such as stochastic gradient descent algorithm and small batch gradient descent.

[0068] For step S3, in one embodiment, the land pollution source information includes: the number of land pollution sources, the type of land pollution sources, and the number of sea discharge outlets. The number of land pollution sources refers to the total number of sources (including facilities, sites, etc.) that discharge pollutants from land to sea in the target monitoring area, including various industrial discharge points, agricultural discharge points, domestic sewage discharge points, etc. The type of land pollution sources refers to different source types of pollutants discharged from land to sea, including industrial pollution sources, agricultural pollution sources, domestic pollution sources, and other pollution sources. The industrial pollution sources refer to pollutants such as wastewater, waste gas, and waste residue generated during industrial production. The agricultural pollution sources refer to agricultural chemicals such as fertilizers and pesticides used in agricultural production, as well as manure and the like generated by livestock and poultry breeding. The domestic pollution sources refer to garbage and sewage generated in urban and rural life. The other pollution sources include but are not limited to ship port pollution, marine engineering pollution, and pollution caused by accidents, disasters, and other emergencies.

[0069] Please refer to Figure 3 , Figure 3 The present application is a method flow chart for obtaining land pollution source information in a river estuary and coastal sea pollution monitoring method. According to the digital surface model, pollution identification is performed based on a pre-set pollution target identification model to obtain land pollution source information in different seasons, including:

[0070] S31: inputting the digital surface model into the pollution target identification model to identify the pollution source, generating a pollution source identification frame, a pollution source type probability, and a sea discharge outlet identification frame;

[0071] S32: selecting the pollution source type with the highest pollution source type probability as the actual monitoring value of the land pollution source type;

[0072] S33: counting the number of pollution source identification frames and sea discharge outlet identification frames to obtain the actual monitoring values of the number of land pollution sources and the number of sea discharge outlets.

[0073] Of course, in other embodiments, according to actual monitoring needs, the land pollution source information can also include pollution source emission intensity and other information.

[0074] Please refer to 4, Figure 4A method flowchart for constructing a pollution target identification model in an estuary and coastal sea source pollution monitoring method. In this embodiment, the ground measurement data described in step S31 also includes topographic map, land use type and population density data. The topographic map is a graphical representation of the topography of the target monitoring area, which can be obtained from official agencies or professional map platforms. The land use type includes industrial land, agricultural land and construction land, etc. Industrial land is more likely to produce industrial wastewater discharge, while agricultural land is related to pesticide and fertilizer pollution. The land use type can be obtained from geographic spatial data cloud or resource environment science and data center. The population density data refers to the population number of the target monitoring area. High population density areas are more likely to be affected by pollution. The population density data can be obtained from official statistical data.

[0075] Constructing the preset pollution target identification model includes:

[0076] S311: superimposing and displaying the digital surface model and the topographic map, and combining the land use type and population density data to frame and label the pollution source and the sea discharge outlet. The frame and label information at least includes the type label of the pollution source;

[0077] S312: selecting a plurality of digital surface models with frame and label information corresponding thereto, and dividing them into a pollution identification training set and a pollution identification verification set according to a 7:3 ratio;

[0078] S313: taking the digital surface model with frame and label information corresponding thereto in the pollution identification training set as input data, and taking the frame and label information corresponding thereto in the pollution identification training set as output data, training a deep learning network model to obtain the pollution target identification model;

[0079] S314: taking the digital surface model with frame and label information corresponding thereto in the pollution identification verification set as input data, and obtaining the water quality parameter concentration prediction result corresponding to the pollution identification verification set based on the water body parameter inversion model;

[0080] S315: using a second loss function to calculate the error between the frame and label information corresponding to the pollution identification verification set and the water quality parameter concentration prediction result corresponding to the pollution identification verification set as a second evaluation result;

[0081] S316: adjusting the network parameters of the pollution target identification model through a second optimization algorithm according to the second evaluation result to obtain the trained pollution target identification model.

[0082] For step S311, the terrain map is taken as a background layer by using a geographic information system or a remote sensing technology application such as ArcGIS software, and the digital surface model is presented in three-dimensional form, so as to realize superimposed display of the digital surface model and the terrain map. Meanwhile, the land use type data and the population density data are fused with the DSM and the terrain map by using the spatial analysis function of the ArcGIS software, such as superimposed analysis and buffer zone analysis. The land pollution source region and the sea discharge outlet are framed on the superimposed display map, and the land pollution source type and the sea discharge outlet type are labeled.

[0083] In other embodiments, the digital surface model can also be converted into two-dimensional information and then superimposed on the terrain map to realize superimposed display of the digital surface model and the terrain map.

[0084] For step S312, the pollution identification training set and the pollution identification verification set each include the digital surface model with completed frame labeling and the information corresponding to the frame labeling.

[0085] For step S313, in the embodiment, the DeepLab V3+ model is preferentially selected, the digital surface model with completed frame labeling in the pollution identification training set is taken as input data, the information corresponding to the frame labeling in the pollution identification training set is taken as output data, the DeepLab V3+ model is back propagated, and the pollution target identification model is obtained. Of course, according to actual requirements, other deep learning models can also be selected.

[0086] For steps S314-316, in an embodiment, the second loss function preferentially selects the intersection over union loss function, and the second optimization algorithm preferentially selects the stochastic gradient descent algorithm. Of course, in other embodiments, according to actual requirements, the second loss function can also select other loss functions such as the mean absolute error, and the second optimization algorithm can also select other optimization algorithms such as the mini-batch gradient descent.

[0087] For step S4, in the embodiment, please refer to Figure 5 , Figure 5 A method flowchart for acquiring a digital elevation model in the estuary and coastal sea land source pollution monitoring method of the application. The elevation information of the ground surface is extracted from the digital surface model to obtain a digital elevation model, including the following steps:

[0088] S41: Marking ground points on the digital elevation model;

[0089] S42: Extracting elevation information from the ground points to obtain a ground surface elevation information matrix;

[0090] S43: According to the ground elevation information matrix, the elevation information is interpolated by using an interpolation method to generate a continuous elevation surface.

[0091] S44: The continuous elevation surface is gridded to generate the digital elevation model with regular grids.

[0092] For steps S41-S44, the ground points refer to feature points close to ground objects and feature points protruding from the bottom of ground objects. The elevation information refers to the height or altitude data of the earth's surface, specifically the Z value of the ground points. The interpolation method can choose linear interpolation, bilinear interpolation, cubic convolution interpolation, etc. Gridding the continuous elevation surface refers to determining the size and shape of the grid cells. Generally, the grid cells can be square, rectangular or other shapes, and the continuous elevation surface is divided into a series of regular grid cells, each of which contains the elevation information of one or more ground points.

[0093] For step S5, the land pollution source diffusion information includes topographic conditions, hydrodynamic characteristics and hydrological tide characteristics. Of course, according to the actual monitoring needs, the land pollution source diffusion information can also include wind information, etc.

[0094] Please refer to Figure 6 , Figure 6 is a flow chart of a method for obtaining land pollution source diffusion information in an estuary and coastal land source pollution monitoring method. The method comprises the following steps: S51: analyzing the topography of the target monitoring area by using the digital elevation model to obtain the actual monitoring value of the topographic condition. S52: Real-time receiving data of hydrological observation equipment in the target monitoring area in different seasons to obtain the actual monitoring value of the hydrodynamic characteristics and hydrological tide characteristics.

[0095] For S51, in the present embodiment, the topographic conditions include at least terrain and slope. The terrain is the overall trend of the ups and downs of the ground surface. The greater the terrain, the greater the speed of surface runoff and the diffusion speed of pollutants, and the lower the risk of estuary and coastal land source pollution. The slope is the degree of inclination of the terrain surface. The greater the slope, the greater the flow rate of surface runoff and the diffusion speed of pollutants, and the lower the risk of estuary and coastal land source pollution.

[0096] The actual monitoring values of the terrain and slope are obtained by importing the digital elevation model into a geographic information system software such as ArcGIS software, processing and analyzing the DEM data by using terrain analysis tools in the GIS software, including slope analysis, slope direction analysis, terrain relief analysis, terrain analysis and surface curvature analysis, and the like.

[0097] In other embodiments, the terrain conditions can further include the degree of ground relief, etc.[1]

[0098] For S52, the hydrodynamic characteristics refer to the dynamic characteristics exhibited by the water flow in the target monitoring area, including flow velocity and turbulence intensity in the water body, etc. The faster the flow velocity of the water flow, the faster the diffusion speed of the pollutants, and the lower the risk of the estuary and coastal land source pollution into the sea. The turbulence intensity in the water body can enhance the mixing effect of the water body. The greater the turbulence intensity in the water body, the faster the diffusion speed of the pollutants, and the lower the risk of the estuary and coastal land source pollution into the sea.

[0099] In spring and summer, the rainfall is abundant, the flow velocity of the river increases, the diffusion speed of the pollutants is fast, and the risk of the estuary and coastal land source pollution into the sea is low. In autumn and winter, the rainfall decreases, the flow velocity of the river slows down, the diffusion speed of the pollutants is slow, and the risk of the estuary and coastal land source pollution into the sea is high. In spring and summer, heavy rain and flood cause the turbulence intensity in the water body to increase, accelerating the diffusion of the pollutants, and the risk of the estuary and coastal land source pollution into the sea is low. In autumn and winter, the flow is relatively stable, the turbulence intensity weakens, the diffusion speed of the pollutants is slow, and the risk of the estuary and coastal land source pollution into the sea is high.

[0100] The hydrological tidal characteristics refer to the periodic rising and falling phenomenon of seawater on the earth under the action of gravity, including the tidal period and the tidal range. The greater the tidal range, the faster the diffusion speed of the pollutants, and the lower the risk of the estuary and coastal land source pollution into the sea. The shorter the tidal period, the more frequent the rising and falling of seawater, the faster the flow velocity, the faster the diffusion speed of the pollutants, and the lower the risk of the estuary and coastal land source pollution into the sea.

[0101] In spring, especially around the vernal equinox, due to the relatively strong gravitational action of the sun and the moon, combined with the influence of meteorological factors such as spring storm surges, the tidal range is relatively large, and the risk of the estuary and coastal land source pollution into the sea is relatively low. In summer, due to the northward migration of the summer sun, the gravitational action of the sun is enhanced, but the evaporation of seawater is large, the tidal range is relatively small compared with spring, and the risk of the estuary and coastal land source pollution into the sea is relatively high compared with spring. In autumn, similar to spring, due to the relatively strong gravitational action of the sun and the moon, the tidal range is relatively large, and the risk of the estuary and coastal land source pollution into the sea is relatively low. In winter, due to the southward migration of the winter sun, the gravitational action of the sun is weakened, combined with the low winter temperature and the contraction of the seawater volume, the tidal range is relatively small compared with spring, summer and autumn, and the risk of the estuary and coastal land source pollution into the sea is relatively high compared with spring, summer and autumn.[2]

[0102] For step S6, please refer to Figure 7 , Figure 7 A method flow chart for obtaining a river estuary land source pollution risk evaluation index in a river estuary land source pollution monitoring method of the application. The water quality parameter data, land pollution source information and land pollution source diffusion information are weighted and summed to obtain a river estuary land source pollution risk evaluation index in different seasons, which comprises:

[0103] S61: calculating the weight index corresponding to the number of land pollution sources, the type of land pollution sources, the number of sea outfall, the hydrodynamic characteristics, the hydrological tide characteristics, the terrain conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in each season, and constructing a weight index set;

[0104] S62: calculating the standard value corresponding to the number of land pollution sources, the type of land pollution sources, the number of sea outfall, the hydrodynamic characteristics, the hydrological tide characteristics, the terrain conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in each season, and constructing a standard value set;

[0105] S63: according to the weight index set and the standard value set, calculating the river estuary land source pollution risk evaluation index in each season according to the following formula:

[0106]

[0107] In the formula, is the river estuary land source pollution risk evaluation index in the i th season, is the j th element in the weight index set in the i th season, is the j th element in the standard value set in the i th season, and a is the size of the weight index set.

[0108] Please also refer to Figure 8 , Figure 8 A method flow chart for constructing a weight index set in a river estuary land source pollution monitoring method of the application. For step S61, the weight index corresponding to the number of land pollution sources, the type of land pollution sources, the number of sea outfall, the hydrodynamic characteristics, the hydrological tide characteristics, the terrain conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in each season is calculated to construct a weight index set, which comprises:

[0109] S611: obtaining a plurality of historical monitoring values of the number of land pollution sources, the type of land pollution sources, the number of sea outfall, the hydrodynamic characteristics, the hydrological tide characteristics, the terrain conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in each season, respectively;

[0110] S612: Count the occurrence number of any historical monitoring value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in each season, and accumulate to obtain the total number in different seasons;

[0111] S613: Divide the occurrence number of any historical monitoring value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in different seasons by the total number in the corresponding season to obtain the probability of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand corresponding to each historical monitoring value in different seasons;

[0112] S614: According to the probability of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand corresponding to each historical monitoring value in different seasons, the information entropy of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand is calculated respectively according to the following formula:

[0113]

[0114] In the formula, is the information entropy of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, is the probability of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand corresponding to each historical monitoring value, and n is the number of corresponding historical monitoring values;

[0115] S615: Add the information entropy of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in different seasons to obtain the total sum of information entropy in different seasons;

[0116] S616: Divide the information entropy of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in different seasons by the sum of the information entropy in the corresponding seasons, respectively, to obtain the weight index of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in different seasons, and construct the weight index set.

[0117] Wherein, for steps S611-S616, a number of historical monitoring values of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in different seasons can be obtained from the ecological environment monitoring agencies or local ecological environment bureau websites. The different seasons include spring, summer, autumn and winter.

[0118] Wherein, in this embodiment, the historical monitoring values of the hydrodynamic characteristics are specifically the historical monitoring values of the flow velocity and the turbulent intensity in the water body; the historical monitoring values of the hydrological tidal characteristics are specifically the historical monitoring values of the tidal period and the tidal range; and the historical monitoring values of the topographic conditions are specifically the historical monitoring values of the terrain and the slope.

[0119] For the numerical data of the number of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand, data cleaning and normalization and other preprocessing operations are also performed. For the non-numerical data of the type of land pollution sources, the type of land pollution sources is also numerically processed, and then data cleaning and normalization preprocessing operations are performed.

[0120] Please refer to Figure 9 , Figure 9 It is a method flowchart for constructing a standard value set in an estuary and coastal land source pollution monitoring method. For step S62, the standard values corresponding to the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in each season are calculated to construct a standard value set, which includes:

[0121] S621: According to the historical monitoring values of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand in different seasons, respectively, to obtain the maximum historical monitoring value and the minimum historical monitoring value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, the hydrodynamic characteristics, the hydrological tidal characteristics, the topographic conditions, the total nitrogen, the total phosphorus and the chemical oxygen demand;

[0122] S622: According to the preset estuary and coastal land source pollution risk correlation evaluation rules, the correlation of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand and the estuary and coastal land source pollution risk is determined in turn, wherein the correlation includes positive correlation and negative correlation.

[0123] S623: Obtain the actual monitoring values of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in each season.

[0124] S624: According to the maximum historical monitoring value, the minimum historical monitoring value and the actual monitoring value, the standard values of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in each season are calculated according to the following formula, and the standard value set is constructed:

[0125]

[0126] In the formula, is the standard value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, is the actual monitoring value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, and are the maximum historical monitoring value and the minimum historical monitoring value of the number of land pollution sources, the type of land pollution sources, the number of sea discharge outlets, hydrodynamic characteristics, hydrological tide characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, respectively.

[0127] For steps S621-S624, the respective seasons include: spring, summer, autumn, and winter. The preset estuary and coastal in-sea land source pollution risk correlation evaluation rule is specifically: the number of land pollution sources, the type of land pollution sources, the number of in-sea sewage outlets, total nitrogen, total phosphorus, and chemical oxygen demand are positively correlated with the estuary and coastal in-sea land source pollution risk. The terrain condition and the hydrodynamic characteristic are negatively correlated with the estuary and coastal in-sea land source pollution risk. For the terrain condition and the hydrodynamic characteristic, specifically, the elevation, the slope, the water flow velocity, the turbulence intensity of the water body, and the tidal range are negatively correlated with the estuary and coastal in-sea land source pollution risk. For the hydrological tidal characteristic, specifically, the tidal range is negatively correlated with the estuary and coastal in-sea land source pollution risk, and the tidal period is positively correlated with the estuary and coastal in-sea land source pollution risk. Of course, in other embodiments, according to the actual monitoring needs, the estuary and coastal in-sea land source pollution risk correlation evaluation rule can be adaptively modified.[3]

[0128] The actual monitoring values of the number of land pollution sources, the type of land pollution sources, and the number of in-sea sewage outlets in each season can refer to steps S31-S33 described above; the actual monitoring values of the hydrodynamic characteristic, the hydrological tidal characteristic, and the terrain condition in each season can refer to steps S51-S52 described above; and the actual monitoring values of total nitrogen, total phosphorus, and chemical oxygen demand in each season can refer to step S2 described above, which will not be repeated here.

[0129] In this embodiment, the actual monitoring values of the hydrodynamic characteristic are specifically actual monitoring values of the water flow velocity and the turbulence intensity in the water body; the actual monitoring values of the hydrological tidal characteristic are specifically actual monitoring values of the tidal period and the tidal range; and the actual monitoring values of the terrain condition are specifically actual monitoring values of the elevation and the slope.

[0130] For the numerical data of the number of land pollution sources, the number of in-sea sewage outlets, the hydrodynamic characteristic, the hydrological tidal characteristic, the terrain condition, total nitrogen, total phosphorus, and chemical oxygen demand, data cleaning and normalization and other preprocessing operations are also performed. For the non-numerical data of the type of land pollution sources, the type of land pollution sources is also numerically processed, and then data cleaning and normalization preprocessing operations are performed.

[0131] For step S63, the size of the weight index set is equal to the size of the standard value, and a is preferably 9. The estuary and coastal in-sea land source pollution risk evaluation index in each season includes the estuary and coastal in-sea land source pollution risk evaluation index in spring, summer, autumn, and winter.

[0132] For steps S7 and S8, in the present embodiment, after obtaining the estuary and coastal sea source pollution risk evaluation indexes in spring, summer, autumn and winter respectively, the indexes are added to obtain the annual estuary and coastal sea source pollution risk comprehensive index of the target monitoring area.

[0133] The preset risk comprehensive index threshold value can be set according to the estuary and coastal sea source pollution risk evaluation indexes in different seasons, and the maximum value of the threshold values or the weighted average value according to the weights can be taken as the preset risk comprehensive index threshold value. Of course, according to actual needs, the risk comprehensive index threshold value can also be set based on statistical analysis of historical risk comprehensive indexes, for example, analyzing statistical quantities such as the highest value, average value, median and the like of historical risk comprehensive indexes.

[0134] When the annual estuary and coastal sea source pollution risk comprehensive index is greater than the preset risk comprehensive index threshold value, a warning instruction is sent to the estuary and coastal sea source pollution monitoring device. The warning instruction includes a warning level, a risk description, pollution source information and suggested measures, and according to the degree that the annual estuary and coastal sea source pollution risk comprehensive index is greater than the preset risk comprehensive index threshold value, the warning instruction can be set to different warning levels, such as a first-level warning (the highest level), a second-level warning, a third-level warning and the like; the risk description is a detailed description of the annual estuary and coastal sea source pollution risk comprehensive index exceeding the threshold value, for example, the degree of exceeding the threshold value and the like; the pollution source information includes the main types and quantities of land pollution sources that cause the pollution risk to rise; and the suggested measures are a series of suggested measures provided according to the warning level and the risk description, such as strengthening monitoring and limiting emission of the main types of land pollution sources that cause the pollution risk to rise, and starting an emergency plan and the like.

[0135] Embodiment 2

[0136] Please refer to Figure 10 , Figure 10 is a schematic view of an estuary and coastal sea source pollution monitoring system according to the present application.

[0137] The present application also provides an estuary and coastal sea source pollution monitoring system, which comprises:

[0138] A monitoring data acquisition module 1 is configured to acquire multispectral remote sensing images and digital surface models of a target monitoring area in different seasons.

[0139] A water quality parameter inversion module 2 is configured to obtain water quality parameter data in different seasons based on a preset water body parameter inversion model according to spectral reflectance in different bands of the multispectral remote sensing images.

[0140] The land pollution source information acquisition module 3 is configured to perform pollution identification based on a preset pollution target identification model according to the digital surface model, to obtain land pollution source information in different seasons.

[0141] The digital elevation model acquisition module 4 is configured to extract elevation information of the ground from the digital surface model, to obtain a digital elevation model.

[0142] The land pollution source diffusion information acquisition module 5 is configured to receive data of hydrological observation equipment in the target monitoring area in different seasons in real time, and analyze the terrain of the target monitoring area in combination with the digital elevation model, to obtain land pollution source diffusion information in different seasons.

[0143] The estuary and coastal land source pollution risk evaluation index calculation module 6 is configured to perform weighted summation on the water quality parameter data, the land pollution source information and the land pollution source diffusion information, to obtain an estuary and coastal land source pollution risk evaluation index in different seasons.

[0144] The annual estuary and coastal land source pollution risk comprehensive index calculation module 7 is configured to accumulate the estuary and coastal land source pollution risk evaluation indexes in different seasons, to obtain an annual estuary and coastal land source pollution risk comprehensive index of the target monitoring area.

[0145] The warning module 8 is configured to send a warning instruction to an estuary and coastal land source pollution warning device when the annual estuary and coastal land source pollution risk comprehensive index is greater than a preset risk comprehensive index threshold.

[0146] It should be noted that the data obtained by the estuary and coastal land source pollution monitoring system provided in the present application when implementing the estuary and coastal land source pollution monitoring method are saved in the storage of the system in a one-to-one correspondence, and the data required for calculation can be directly obtained from the storage when related calculation is needed.

[0147] It should be further noted that the estuary and coastal land source pollution monitoring system provided in the above embodiments when implementing the estuary and coastal land source pollution monitoring method is only used as an example for the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the estuary and coastal land source pollution monitoring system provided in the above embodiments and the estuary and coastal land source pollution monitoring method of embodiment 1 belong to the same concept, and the implementation process is described in detail in the method embodiment, which will not be repeated here.

[0148] Based on the same inventive concept, the present application also provides an electronic device, which can be a server, a desktop computing device or a mobile computing device (e.g., a laptop computer, a handheld computing device, a tablet computer, a netbook, etc.), and the like. The device includes one or more processors and a memory, wherein the processor is configured to execute a program to implement the method for monitoring the estuary coast into the sea pollution, and the memory is configured to store the computer program executable by the processor.

[0149] The present application is not limited to the above-described embodiments, and various modifications or alterations of the present application can be made without departing from the spirit and scope of the present application, and it is intended that such modifications and alterations be included within the scope of the claims and the equivalent thereof of the present application.

Claims

1. A method for monitoring land-based pollution entering the sea from estuaries and coastlines, characterized in that, Includes the following steps: Acquire multispectral remote sensing images and digital surface models of the target monitoring area under different seasons; Based on the spectral reflectance of different bands in the multispectral remote sensing image, and based on a preset water body parameter inversion model, water quality parameter data for different seasons are obtained; wherein, the water quality parameter data includes at least total nitrogen, total phosphorus, and chemical oxygen demand; Based on the digital surface model, pollution is identified using a preset pollution target identification model to obtain land pollution source information for different seasons; wherein, the land pollution source information includes at least: the number of land pollution sources, the types of land pollution sources, and the number of sewage outlets into the sea; The elevation information of the land surface is extracted from the digital surface model to obtain a digital elevation model; The system receives data from hydrological observation equipment in the target monitoring area in real time under different seasons, and combines it with the digital elevation model to analyze the topography of the target monitoring area to obtain land pollution source diffusion information under different seasons; wherein, the land pollution source diffusion information includes at least: hydrodynamic characteristics, hydrotidal characteristics and topographic conditions; The water quality parameter data, land-based pollution source information, and land-based pollution source diffusion information are weighted and summed respectively to obtain the risk assessment index of land-based pollution entering the sea from the estuary and coastline under different seasons, including: Calculate the weighted indices corresponding to the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand for each season, and construct a set of weighted indices, including: For each season, obtain several historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand. The number of occurrences of any historical monitoring value for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in each season were counted and summed to obtain the total number of occurrences in different seasons. Divide the number of occurrences of any of the historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons by the total number of occurrences in the corresponding season to obtain the probability of each of the historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons. Based on the probability of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand corresponding to each of the historical monitoring values ​​under different seasons, the information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand are calculated according to the following formulas: In the formula, The information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand. The probability of each historical monitoring value corresponding to the number of land pollution sources, the type of land pollution sources, the number of sewage outlets into the sea, hydrodynamic characteristics, hydrological and tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, where n is the number of the corresponding historical monitoring values; The information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in different seasons are added together to obtain the total information entropy in different seasons; By dividing the information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons by the sum of the information entropy in the corresponding seasons, the weight indices of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons are obtained respectively. Calculate the standard values ​​corresponding to the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in each season, and construct a set of standard values; Based on the weighted index set and the standard value set, the risk assessment index of land-based pollution entering the sea at the estuary and coastline for each season is calculated according to the following formula: In the formula, This is the risk assessment index for land-based pollution entering the sea from the estuary and coastline in the i-th season. Let j be the j-th element in the set of weight indices for the i-th season. Let j be the j-th element in the set of standard values ​​for the i-th season, and a be the size of the set of weight indices or the set of standard values. The risk assessment indices of land-based pollution sources entering the sea from the estuary and coastline under different seasons are summed to obtain the comprehensive risk index of land-based pollution sources entering the sea from the estuary and coastline of the target monitoring area throughout the year. When the annual comprehensive risk index of land-based pollution entering the sea from the estuary and coast exceeds the preset comprehensive risk index threshold, an early warning command is sent to the early warning equipment entering the sea from the estuary and coast.

2. The method for monitoring land-based pollution entering the sea from estuaries and coastlines according to claim 1, characterized in that, The calculation of the standard values ​​corresponding to the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand for each season includes: Based on the historical monitoring values ​​of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand for each season, the maximum and minimum historical monitoring values ​​of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand are obtained respectively. According to the preset correlation assessment rules for land-based pollution risk at estuaries and coastlines, the correlation between the number of land-based pollution sources, the types of land-based pollution sources, the number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand and the risk of land-based pollution at estuaries and coastlines is determined in sequence. The correlation includes positive correlation and negative correlation. Obtain actual monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in each season; Based on the maximum historical monitoring value, minimum historical monitoring value, and actual monitoring value, the standard values ​​of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand are calculated for each season according to the following formulas: In the formula, The standard values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand are provided. The values ​​refer to the actual monitoring values ​​of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand. and These are the maximum and minimum historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydrological and tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand, respectively.

3. The method for monitoring land-based pollution entering the sea from estuaries and coastlines according to claim 2, characterized in that, The step of identifying pollution based on the digital surface model and a preset pollution target identification model to obtain land-based pollution source information for different seasons, and obtaining actual monitoring values ​​of the number of land-based pollution sources, types of land-based pollution sources, and number of sewage outlets into the sea for each season, includes: The digital surface model is input into the pollution target identification model to identify pollution sources and generate pollution source identification boxes, pollution source type probabilities, and sewage outlet identification boxes. The pollution source type with the highest probability of being selected is used as the actual monitoring value of the land-based pollution source type. The actual monitoring values ​​of the number of land-based pollution sources and the number of sewage outlets into the sea are obtained by counting the number of pollution source identification boxes and sewage outlet identification boxes into the sea.

4. A method for monitoring land-based pollution entering the sea from estuaries and coastlines according to claim 2, characterized in that, The system receives data from hydrological observation equipment in the target monitoring area in real time under different seasons, and combines this data with the digital elevation model to analyze the topography of the target monitoring area, obtaining information on the diffusion of land-based pollution sources under different seasons, and acquiring actual monitoring values ​​of hydrodynamic characteristics, hydrotidal characteristics, and topographic conditions for each season, including: Using the digital elevation model, the terrain of the target monitoring area is analyzed to obtain the actual monitoring values ​​of the terrain conditions; Data from hydrological observation equipment in the target monitoring area is received in real time under different seasons to obtain the actual monitoring values ​​of the hydrodynamic characteristics and hydrological tidal characteristics.

5. A method for monitoring land-based pollution entering the sea from estuaries and coastlines according to claim 1, characterized in that, Constructing the preset water body parameter inversion model includes: Acquire ground measurement data, wherein the ground measurement data includes at least the historical water quality parameter concentrations of total nitrogen, total phosphorus, and chemical oxygen demand; Several multispectral remote sensing images and historical water quality parameter concentrations were selected and divided into a water quality parameter training set and a water quality parameter validation set in a 7:3 ratio. The spectral reflectance of different bands in the multispectral remote sensing images of the water quality parameter training set is used as input data, and the historical water quality parameter concentrations of the water quality parameter training set are used as output data to train the machine learning model, thereby obtaining the water body parameter inversion model. Using the spectral reflectance of different bands in the multispectral remote sensing images of the water quality parameter validation set as input data, and based on the water body parameter inversion model, the predicted concentration of water quality parameters corresponding to the water quality parameter validation set is obtained. Using the first loss function, the error between the predicted water quality parameter concentration and the historical water quality parameter concentration of the water quality parameter validation set is calculated as the first evaluation result; Based on the first evaluation result, the network parameters of the water body parameter inversion model are adjusted using the first optimization algorithm to obtain the water body parameter inversion model after training.

6. A method for monitoring land-based pollution entering the sea from estuaries and coastlines according to claim 5, characterized in that, The ground measurement data also includes: topographic maps, land use types, and population density data; Constructing the preset pollution target identification model includes: The digital surface model is overlaid on the topographic map, and the pollution sources and sewage outlets into the sea are selected and marked by combining the land use type and population density data. The information of the selected and marked areas includes at least the type label of the pollution source. Several digital surface models with completed bounding box annotations and their corresponding bounding box annotation information are selected and divided into a pollution identification training set and a pollution identification verification set in a 7:3 ratio. The pollution target identification model is obtained by using the digital surface model with completed bounding box annotations in the pollution identification training set as input data and the information of the bounding box annotations in the pollution identification training set as output data to train the deep learning network model. Using the digital surface model with completed bounding box annotations in the pollution identification verification set as input data, and based on the water body parameter inversion model, the water quality parameter concentration prediction results corresponding to the pollution identification verification set are obtained. Using the second loss function, the error between the information of the box-selected annotation corresponding to the pollution identification verification set and the water quality parameter concentration prediction result corresponding to the pollution identification verification set is calculated as the second evaluation result; Based on the second evaluation result, the network parameters of the pollution target recognition model are adjusted using the second optimization algorithm to obtain the pollution target recognition model that has been trained.

7. A monitoring system for land-based pollution entering the sea from river estuaries and coastlines, characterized in that, include: Monitoring data acquisition module: used to acquire multispectral remote sensing images and digital surface models of the target monitoring area under different seasons; Water quality parameter inversion module: used to obtain water quality parameter data for different seasons based on the spectral reflectance of different bands in the multispectral remote sensing image and a preset water body parameter inversion model; wherein, the water quality parameter data includes at least total nitrogen, total phosphorus and chemical oxygen demand; Land pollution source information acquisition module: used to identify pollution based on the digital surface model and a preset pollution target identification model to obtain land pollution source information under different seasons; wherein, the land pollution source information includes at least: the number of land pollution sources, the types of land pollution sources, and the number of sewage outlets into the sea; Digital elevation model acquisition module: Extracts the elevation information of the ground surface from the digital surface model to obtain the digital elevation model; Land-based pollution source diffusion information acquisition module: receives data from hydrological observation equipment in the target monitoring area in real time under different seasons, and analyzes the topography of the target monitoring area in combination with the digital elevation model to obtain land-based pollution source diffusion information under different seasons; wherein, the land-based pollution source diffusion information includes at least: hydrodynamic characteristics, hydrotidal characteristics and topographic conditions; The estuary-coastal land-based pollution risk assessment index calculation module calculates the weighted sum of the water quality parameter data, land-based pollution source information, and land-based pollution source diffusion information to obtain the estuary-coastal land-based pollution risk assessment index for different seasons, including: Calculate the weighted indices corresponding to the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand for each season, and construct a set of weighted indices, including: For each season, obtain several historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand. The number of occurrences of any historical monitoring value for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in each season were counted and summed to obtain the total number of occurrences in different seasons. Divide the number of occurrences of any of the historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons by the total number of occurrences in the corresponding season to obtain the probability of each of the historical monitoring values ​​for the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons. Based on the probability of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand corresponding to each of the historical monitoring values ​​under different seasons, the information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outfalls into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand are calculated according to the following formulas: In the formula, The information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand. The probability of each historical monitoring value corresponding to the number of land pollution sources, the type of land pollution sources, the number of sewage outlets into the sea, hydrodynamic characteristics, hydrological and tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand, where n is the number of the corresponding historical monitoring values; The information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in different seasons are added together to obtain the total information entropy in different seasons; By dividing the information entropy of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons by the sum of the information entropy in the corresponding seasons, the weight indices of the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus, and chemical oxygen demand in different seasons are obtained respectively. Calculate the standard values ​​corresponding to the number of land-based pollution sources, types of land-based pollution sources, number of sewage outlets into the sea, hydrodynamic characteristics, hydro-tidal characteristics, topographic conditions, total nitrogen, total phosphorus and chemical oxygen demand in each season, and construct a set of standard values; Based on the weighted index set and the standard value set, the risk assessment index of land-based pollution entering the sea at the estuary and coastline for each season is calculated according to the following formula: In the formula, This is the risk assessment index for land-based pollution entering the sea from the estuary and coastline in the i-th season. Let j be the j-th element in the set of weight indices for the i-th season. Let j be the j-th element in the set of standard values ​​for the i-th season, and a be the size of the set of weight indices or the set of standard values. The module for calculating the comprehensive risk index of land-based pollution from estuaries and coastlines entering the sea throughout the year is used to accumulate the risk assessment indices of land-based pollution from estuaries and coastlines entering the sea in each season to obtain the comprehensive risk index of land-based pollution from estuaries and coastlines entering the sea throughout the year for the target monitoring area. Early warning module: When the annual comprehensive risk index of land-based pollution entering the sea from the estuary and coast exceeds the preset comprehensive risk index threshold, an early warning command is sent to the early warning equipment entering the sea from the estuary and coast.

8. 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, it implements a method for monitoring land-based pollution entering the sea from estuaries and coastlines as described in any one of claims 1 to 6.

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