CN115424134B - Pollution sea flux prediction method and device based on remote sensing image - Google Patents
Pollution sea flux prediction method and device based on remote sensing image Download PDFInfo
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Abstract
The invention relates to the technical field of geographic information, in particular to a pollution sea flux prediction method based on remote sensing images, which comprises the following steps: obtaining an orthographic image of a cultivation area, removing a non-water body area in the orthographic image, obtaining a water body image of the cultivation area, and obtaining water quality data corresponding to each sampling point in the water body image of the cultivation area according to the preset number of sampling points; acquiring wave band data corresponding to each sampling point in a water body image of a culture area, combining water quality data corresponding to the same sampling point and the wave band data, and acquiring training data sets corresponding to a plurality of sampling points; inputting training data sets corresponding to a plurality of sampling points into a random forest neural network model for training to obtain a water quality prediction model; inputting the orthographic image of the cultivation area into a water quality prediction model, obtaining water quality prediction data of a water body area of the cultivation area, and obtaining a pollution source area detection result of the cultivation area according to the water quality prediction data of the water body area of the cultivation area.
Description
Technical Field
The invention relates to the technical field of geographic information, in particular to a pollution sea flux prediction method, device and equipment based on remote sensing images and a storage medium.
Background
In recent years, with the rapid development of economy and society, the demand of human beings for aquatic products is increasing, and the cultivation amount is also increasing. The problem of water eutrophication caused by the direct discharge of the culture tail water to rivers, lakes and seas is also increasingly prominent, and the safety of drinking water for human beings is seriously affected. Natural water has certain self-cleaning capability, but when overload eutrophication water is discharged, namely the polluted sea flux exceeds the self-cleaning capability of the water, the water is eutrophicated, and a series of water environment ecological problems are caused.
However, the purification technology and the management cost of sewage discharge are high, and sewage treatment is not completely popularized, so that the situation that sewage is directly discharged into rivers, lakes and seas occurs in partial areas, particularly in areas with high supervision difficulty. However, the current technical scheme cannot comprehensively and accurately calculate the pollutant sea flux in a large area, and causes a great difficulty for water pollution supervision.
Disclosure of Invention
Based on the above, the invention aims to provide a pollution sea flux prediction method, a device, equipment and a storage medium based on remote sensing images, which are used for constructing a corresponding water quality prediction model and a corresponding water depth prediction model by acquiring water quality data, water depth data and wave band data in the remote sensing images of a cultivation area and adopting a deep learning method, accurately and rapidly acquiring the water quality prediction data and the water depth prediction data of the cultivation area and acquiring the pollution sea flux prediction data of the water body area of the cultivation area according to the water quality prediction data and the water depth prediction data.
In a first aspect, an embodiment of the present application provides a method for predicting a contaminated sea flux based on a remote sensing image, including the following steps:
acquiring a plurality of remote sensing images of a cultivation area, and splicing the plurality of remote sensing images to obtain an orthographic image of the cultivation area, wherein the orthographic image comprises a water body area to be a non-water body area;
removing a non-water body region in the orthographic image, acquiring a water body image of the culture region, and acquiring water quality data corresponding to each sampling point in the water body image of the culture region according to the preset number of sampling points;
Acquiring wave band data and water depth data corresponding to all sampling points in a water body image of the culture area, combining water quality data and wave band data corresponding to the same sampling point to acquire a plurality of first training data sets corresponding to the sampling points, and combining the water depth data and the wave band data corresponding to the same sampling point to acquire a plurality of second training data sets corresponding to the sampling points;
taking the wave band data as independent variables, taking the water quality data as the dependent variables, constructing a first random forest neural network model, inputting a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model;
taking the wave band data as independent variables, taking the water depth data as the dependent variables, constructing a second random forest neural network model, inputting a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, and obtaining a water depth prediction model;
and respectively inputting the orthographic image of the cultivation area into the water quality prediction model and the water depth prediction model, acquiring water quality prediction data and water depth prediction data of the water body area of the cultivation area, and acquiring the sea flux prediction data of the cultivation area according to the water quality prediction data and the water depth prediction data of the water body area of the cultivation area and the water body area of the cultivation area.
In a second aspect, an embodiment of the present application provides a pollution sea flux prediction device based on remote sensing images, including:
the system comprises an orthographic image acquisition module, a display module and a display module, wherein the orthographic image acquisition module is used for acquiring a plurality of remote sensing images of a cultivation area, and splicing the plurality of remote sensing images to obtain an orthographic image of the cultivation area, wherein the orthographic image comprises a water body area and is a non-water body area;
the water quality data acquisition module is used for eliminating a non-water body area in the orthographic image, acquiring a water body image of the culture area, and acquiring water quality data corresponding to each sampling point in the water body image of the culture area according to the preset number of sampling points;
the training data acquisition module is used for acquiring wave band data and water depth data corresponding to all sampling points in the water body image of the culture area, combining the water quality data and the wave band data corresponding to the same sampling point to acquire a plurality of first training data sets corresponding to the sampling points, and combining the water depth data and the wave band data corresponding to the same sampling point to acquire a plurality of second training data sets corresponding to the sampling points;
the first model training module is used for taking the wave band data as an independent variable, taking the water quality data as a dependent variable, constructing a first random forest neural network model, inputting a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model;
The second model training module is used for taking the wave band data as an independent variable, taking the water depth data as a dependent variable, constructing a second random forest neural network model, inputting a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, and obtaining a water depth prediction model;
the pollution sea flux prediction module is used for respectively inputting the orthographic images of the cultivation area into the water quality prediction model and the water depth prediction model, obtaining water quality prediction data and water depth prediction data of the water body area of the cultivation area, and obtaining pollution sea flux prediction data of the water body area of the cultivation area according to the water body area of the cultivation area, the water quality prediction data and the water depth prediction data of the water body area of the cultivation area.
In a third aspect, embodiments of the present application provide a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program when executed by the processor performs the steps of the remote sensing image based method for predicting the incoming sea flux of pollution as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium storing a computer program, where the computer program when executed by a processor implements the steps of the remote sensing image based pollution into sea flux prediction method according to the first aspect.
In the embodiment of the application, a pollution sea flux prediction method, device, equipment and storage medium based on remote sensing images are provided, by acquiring water quality data, water depth data and wave band data in the remote sensing images of a cultivation area, a deep learning method is adopted to construct a corresponding water quality prediction model and a corresponding water depth prediction model, the water quality prediction data and the water depth prediction data of the cultivation area are accurately and rapidly acquired, the pollution sea flux prediction data of the water body area of the cultivation area is acquired according to the water quality prediction data and the water depth prediction data, and the pollution sea flux exceeding area is timely detected and early warned.
For a better understanding and implementation, the present invention is described in detail below with reference to the drawings.
Drawings
Fig. 1 is a schematic flow chart of a pollution sea flux prediction method based on remote sensing images according to a first embodiment of the present application;
Fig. 2 is a schematic flow chart of a pollution sea flux prediction method based on remote sensing images according to a second embodiment of the present application;
fig. 3 is a schematic flow chart of S2 in the method for predicting the incoming sea flux of pollution based on remote sensing images according to the first embodiment of the present application;
fig. 4 is a schematic flow chart of S6 in the method for predicting the incoming sea flux of pollution based on remote sensing image according to the first embodiment of the present application;
fig. 5 is a schematic flow chart of S62 in the method for predicting the incoming sea flux of pollution based on remote sensing image according to the first embodiment of the present application;
fig. 6 is a schematic flow chart of a pollution sea flux prediction method based on remote sensing images according to a third embodiment of the present application;
fig. 7 is a schematic structural diagram of a pollution sea flux prediction device based on remote sensing images according to a fourth embodiment of the present application;
fig. 8 is a schematic structural diagram of a computer device according to a fifth embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first message may also be referred to as a second message, and similarly, a second message may also be referred to as a first message, without departing from the scope of the present application. The word "if"/"if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
Referring to fig. 1, fig. 1 is a flow chart of a pollution sea flux prediction method based on remote sensing images according to an embodiment of the present application, where the method includes the following steps:
S1: and acquiring a plurality of remote sensing images of the cultivation area, and splicing the plurality of remote sensing images to obtain an orthographic image of the cultivation area.
The execution subject of the pollution entering sea flux prediction method based on the remote sensing image is a prediction device (hereinafter referred to as a prediction device) of the pollution entering sea flux prediction method based on the remote sensing image, and in an optional embodiment, the prediction device may be a computer device or a server, or a server cluster formed by combining multiple computer devices.
The aquaculture area is an area related to the aquaculture industry and can comprise an aquaculture pond. In daily operation, the aquaculture industry directly discharges the cultivation tail water into rivers, lakes and seas, and natural water bodies have certain self-cleaning capability, but when once the overload cultivation tail water is discharged, the pollution sea flux exceeds the self-cleaning capability of the water bodies, the water bodies are eutrophicated, and a series of water environment ecological problems are caused.
In this embodiment, the prediction device may obtain a plurality of remote sensing images of a cultivation area input by a user, or may obtain a plurality of remote sensing images of the cultivation area through an unmanned aerial vehicle, and splice the plurality of remote sensing images to obtain an orthographic image of the cultivation area, where the orthographic image includes a water area and is a non-water area.
S2: removing a non-water body region in the orthographic image, acquiring a water body image of the culture region, and acquiring water quality data corresponding to each sampling point in the water body image of the culture region according to the preset number of sampling points.
In order to reduce the complexity of the operation and improve the efficiency of acquiring the band data, in this embodiment, the prediction device eliminates the non-water area in the multispectral image and acquires the water image of the sample area.
According to the preset sampling point number, each sampling point in the water body image of the culture area is obtained, specifically, the prediction equipment lays 10 sampling points in the culture area according to a convenience principle and a uniformity principle, and water quality data corresponding to each sampling area in the water body image of the sample area is obtained, wherein the water quality data comprises chemical oxygen demand data, total nitrogen data, total phosphorus data and chlorophyll data.
Referring to fig. 2, fig. 2 is a flow chart of a method for predicting a polluted sea flux based on remote sensing images according to another embodiment of the present application, including step S7, wherein step S7 is performed before step S2, and the method specifically includes the following steps:
s7: preprocessing the orthographic image to obtain a preprocessed orthographic image, wherein the preprocessing comprises radiometric calibration and atmospheric correction.
In this embodiment, the prediction device performs radiation calibration and atmospheric correction on the orthographic image, acquires the preprocessed orthographic image, and improves accuracy of band data measurement on the orthographic image.
Referring to fig. 3, fig. 3 is a schematic flow chart of step S2 in the remote sensing image-based pollution sea flux prediction method according to an embodiment of the present application, including steps S21 to S22, specifically including the following steps:
s21: acquiring a water body image corresponding to a sample area, a green light wave band value and a near infrared wave band value of each pixel in the water body image, and calculating the normalized water index value of each pixel in the water body image corresponding to the sample area according to a preset normalized water index calculation algorithm to obtain a minimum normalized water index value serving as a water body pixel distinguishing threshold.
The normalized water index calculation algorithm is as follows:
wherein B is green For the green band value, B NIR For the near infrared band value, NDWI is normalized moisture index;
in this embodiment, the prediction device obtains a water body image corresponding to a sample area, and green light band values and near infrared band values of each pixel in the water body image, calculates normalized water index values of each pixel in the water body image corresponding to the sample area according to a preset normalized water index calculation algorithm, and obtains a minimum normalized water index value as a water body pixel distinguishing threshold.
S22: the green light wave band value and the near infrared wave band value of each pixel of the orthographic image are obtained, the normalized moisture index value of each pixel in the orthographic image is obtained according to the normalized moisture index calculation algorithm, a plurality of water body pixels in the orthographic image are obtained according to the normalized moisture index value of each pixel in the orthographic image and the water body pixel distinguishing threshold value, and the plurality of water body pixels are combined to obtain the water body image of the cultivation area.
The water body image is an orthographic image including a water body region, in this embodiment, the prediction device compares normalized water indexes corresponding to each pixel in the orthographic image with the water body segmentation threshold, sets the pixel as a water body pixel when the normalized water index corresponding to the pixel is greater than the water body segmentation threshold, sets the pixel as a land pixel when the normalized water index corresponding to the pixel is less than or equal to the water body segmentation threshold, eliminates the land pixel in the orthographic image, and obtains the water body image corresponding to the water body region of the orthographic image as the water body image of the cultivation region.
S3: the method comprises the steps of obtaining wave band data and water depth data corresponding to all sampling points in a water body image of a culture area, combining water quality data and wave band data corresponding to the same sampling point, obtaining a plurality of first training data sets corresponding to the sampling points, combining the water depth data and the wave band data corresponding to the same sampling point, and obtaining a plurality of second training data sets corresponding to the sampling points.
The band data includes a blue Duan Fanshe band reflectance, a green band reflectance, a red band reflectance, and a near infrared band reflectance. In this embodiment, the prediction device obtains band data and water depth data corresponding to each sampling point in the water body image of the cultivation area, combines water quality data and band data corresponding to the same sampling point to obtain a plurality of first training data sets corresponding to the sampling points, and combines water depth data and band data corresponding to the same sampling point to obtain a plurality of second training data sets corresponding to the sampling points.
Specifically, the prediction device may acquire coordinate data of each sampling point in the water body image of the cultivation area, acquire a sampling area corresponding to each sampling point according to a preset radius by using the coordinate data of each sampling point as a circle center, acquire band data and water depth data corresponding to each pixel in the sampling area corresponding to each sampling point, and perform average processing on the band data and the water depth data respectively to acquire average band data and average water depth data corresponding to the sampling area corresponding to each sampling point, which are used as band data and water depth data corresponding to each sampling point in the water body image of the cultivation area.
S4: and taking the wave band data as independent variables, taking the water quality data as the dependent variables, constructing a first random forest neural network model, inputting a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model.
The random forest neural network model is a classifier comprising a plurality of decision trees, and its output class is a mode of the class output by the individual trees.
In this embodiment, the prediction device uses the band data as an independent variable, uses the water quality data as a dependent variable, constructs a first random forest neural network model, and inputs a plurality of first training data sets corresponding to the sampling points into the random forest neural network model for training, so as to obtain a water quality prediction model.
Specifically, since the water quality data comprises total nitrogen data, total phosphorus data, chemical oxygen demand data and chlorophyll data, the prediction device sets a characteristic variable data set corresponding to the water quality data corresponding to the sampling point, wherein the characteristic variable data set comprises characteristic variables, and the characteristic variables comprise total nitrogen characteristic variables, total phosphorus characteristic variables, chemical oxygen demand characteristic variables and chlorophyll characteristic variables;
According to the first training data set corresponding to the sampling points, the prediction equipment randomly extracts a plurality of n samples from the characteristic variable data set corresponding to the water quality data corresponding to the sampling points, randomly selects m characteristic variables for each sample of the n samples, generates a decision tree model corresponding to the sample, randomly selects one characteristic variable from the m characteristic variables of each decision tree model as a node for splitting, stops splitting when the coefficient of the foundation is minimum, constructs a random forest neural network model, and inputs the first training data set corresponding to the sampling points into the random forest neural network model for training to obtain a water quality prediction model.
S5: and taking the wave band data as independent variables, taking the water depth data as the dependent variables, constructing a second random forest neural network model, inputting a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, and obtaining a water depth prediction model.
In this embodiment, the prediction device uses the band data as an independent variable, uses the water depth data as an independent variable, constructs a second random forest neural network model, and inputs a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, so as to obtain a water depth prediction model.
S6: and respectively inputting the orthographic image of the cultivation area into the water quality prediction model and the water depth prediction model, acquiring water quality prediction data and water depth prediction data of the water body area of the cultivation area, and acquiring pollution sea flux prediction data of the water body area of the cultivation area according to the water quality prediction data and the water depth prediction data of the water body area of the cultivation area and the water quality prediction data and the water depth prediction data of the water body area of the cultivation area.
In this embodiment, the prediction device inputs the orthographic image of the cultivation area to the water quality prediction model and the water depth prediction model, obtains the water quality prediction data and the water depth prediction data of the water body area of the cultivation area, and obtains the polluted sea flux prediction data of the water body area of the cultivation area according to the water quality prediction data and the water depth prediction data of the water body area of the cultivation area and the water body area of the cultivation area.
Referring to fig. 4, fig. 4 is a schematic flow chart of step S6 in the remote sensing image-based pollution sea flux prediction method according to an embodiment of the present application, including steps S61 to S62, specifically including the following steps:
S61: and acquiring area data of the water body region of the culture region, and acquiring volume prediction data of the water body region of the culture region according to the area data and the water depth prediction data.
The area data is used for indicating the area of the water body area of each pixel, and the volume prediction data is used for indicating the volume of the water body area of each pixel;
in this embodiment, the prediction device obtains the area data of the water body region of the cultivation region, and obtains the volume prediction data of the water body region of the cultivation region according to the area data and the water depth prediction data, specifically as follows:
W v ＝W d ×W s
in which W is v For the volume prediction data, W d For the water depth prediction data, W s Is the area data.
S62: and obtaining the pollution sea flux prediction data of the water body region of the culture region according to the volume prediction data, the water quality prediction data and the corresponding pollution sea flux calculation algorithm of the water body region of the culture region.
In this embodiment, the prediction device obtains pollution sea flux prediction data of the water body region of the cultivation region according to the volume prediction data, the water quality prediction data and the corresponding pollution sea flux calculation algorithm of the water body region of the cultivation region, wherein the pollution sea flux prediction data includes total nitrogen sea flux prediction data, total phosphorus sea flux prediction data, chemical oxygen demand sea flux prediction data and chlorophyll sea flux prediction data.
Referring to fig. 5, fig. 5 is a schematic flow chart of step S62 in the remote sensing image-based pollution sea flux prediction method according to an embodiment of the present application, including steps S621 to S624, specifically as follows:
s621: and acquiring the total nitrogen sea flux prediction data of the culture area according to the volume prediction data, the total nitrogen prediction data and a preset total nitrogen sea flux prediction data calculation algorithm of the water body area of the culture area.
The total nitrogen sea flux prediction data calculation algorithm is as follows:
TN＝∑W v ×C TN
wherein TN is the total nitrogen sea flux prediction data, W v For the volume prediction data, C TN Predicting data for the total nitrogen;
in this embodiment, the prediction device calculates, according to the volume prediction data, the total nitrogen prediction data, and the preset total nitrogen sea-going amount prediction data calculation algorithm of the water body region of the cultivation region, total nitrogen sea-going amount prediction data corresponding to each pixel of the water body region of the cultivation region, and obtains the total nitrogen sea-going amount prediction data of the water body region of the cultivation region as the total nitrogen sea-going amount prediction data of the cultivation region.
S622: and acquiring the total phosphorus sea flux prediction data of the culture area according to the volume prediction data, the total phosphorus prediction data and a preset total phosphorus sea flux prediction data calculation algorithm of the water body area of the culture area.
The calculation algorithm of the total phosphorus sea flux prediction data is as follows:
TP＝∑W v ×C TP
wherein TP is the total phosphorus sea flux prediction data, C TP Predicting data for the total phosphorus;
in this embodiment, the prediction device calculates, according to the volume prediction data, the total nitrogen prediction data, and the preset total phosphorus sea-intake prediction data calculation algorithm of the water body region of the cultivation region, total phosphorus sea-intake prediction data corresponding to each pixel of the water body region of the cultivation region, to obtain total phosphorus sea-intake prediction data of the water body region of the cultivation region, as total phosphorus sea-intake prediction data of the cultivation region.
S623: and acquiring the chemical oxygen demand sea flux prediction data of the culture area according to the volume prediction data, the chemical oxygen demand prediction data and a preset chemical oxygen demand sea flux prediction data calculation algorithm of the water body area of the culture area.
The calculation algorithm of the chemical oxygen demand sea flux prediction data is as follows:
COD＝∑W v ×C COD wherein COD is the predicted data of the chemical oxygen demand sea flux, C COD Predictive data for said chemical oxygen demand;
in this embodiment, the prediction device calculates the chemical oxygen demand sea flux prediction data corresponding to each pixel of the water body region of the cultivation region according to the volume prediction data, the chemical oxygen demand prediction data and a preset chemical oxygen demand sea flux prediction data calculation algorithm of the water body region of the cultivation region, so as to obtain the chemical oxygen demand sea flux prediction data of the water body region of the cultivation region, as the chemical oxygen demand sea flux prediction data of the cultivation region.
S621: and acquiring chlorophyll sea-entering flux prediction data of the culture area according to the volume prediction data and chlorophyll prediction data of the water body area of the culture area and a preset chlorophyll sea-entering flux prediction data calculation algorithm.
The chlorophyll sea flux prediction data calculation algorithm is as follows:
TN＝∑W v ×C TN
wherein CHI is the chlorophyll-in-sea flux prediction data, C CHI Predictive data for the chlorophyll.
In this embodiment, the prediction device calculates, according to the volume prediction data, the chlorophyll prediction data, and a preset chlorophyll-in-sea-volume prediction data calculation algorithm of the water body region of the cultivation region, chlorophyll-in-sea-volume prediction data corresponding to each pixel of the water body region of the cultivation region, to obtain chlorophyll-in-sea-volume prediction data of the water body region of the cultivation region, as chlorophyll-in-sea-volume prediction data of the cultivation region.
Referring to fig. 6, fig. 6 is a flow chart of a pollution sea flux prediction method based on remote sensing images according to a third embodiment of the present application, and further includes step S8, which specifically includes:
s8: acquiring electronic map data corresponding to the cultivation area, acquiring pollution early warning identification of the water area of the cultivation area according to pollution sea-entering flux prediction data of the water area of the cultivation area and a preset pollution sea-entering flux threshold value, and displaying the corresponding pollution early warning identification and marking the pollution sea-entering flux prediction data on the electronic map data according to the pollution early warning identification of the water area of the cultivation area.
In this embodiment, the prediction device obtains electronic map data corresponding to the cultivation area, and displays an electronic map reflecting the cultivation area on a preset display interface.
According to the pollution sea flux prediction data of the water body area of the cultivation area and a preset pollution sea flux threshold value, pollution early warning identification of the water body area of the cultivation area is obtained, specifically, the pollution sea flux threshold value comprises a total nitrogen sea flux threshold value, a total phosphorus sea flux threshold value, a chemical oxygen demand sea flux threshold value and a chlorophyll sea flux threshold value, the pollution sea flux prediction data are respectively compared with the corresponding pollution sea flux threshold value by prediction equipment, and when the pollution sea flux prediction data are larger than or equal to the corresponding pollution sea flux threshold value, the pollution early warning identification is obtained.
And according to the pollution early warning identification of the water body area of the cultivation area, displaying the corresponding pollution early warning identification and marking the pollution sea flux prediction data on the electronic map data. Therefore, the area with the possibility of exceeding the standard of the tail water emission of the cultivation is timely detected and early warned.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a remote sensing image-based pollution sea flux prediction device according to an embodiment of the present application, where the device may implement all or a part of the remote sensing image-based pollution sea flux prediction device through software, hardware or a combination of both, and the device 7 includes:
an orthographic image obtaining module 71, configured to obtain a plurality of remote sensing images of a cultivation area, and splice the plurality of remote sensing images to obtain an orthographic image of the cultivation area, where the orthographic image includes a water area to be a non-water area;
the water quality data acquisition module 72 is configured to reject a non-water body region in the orthographic image, acquire a water body image of the culture region, and acquire water quality data corresponding to each sampling point in the water body image of the culture region according to a preset number of sampling points;
the training data obtaining module 73 is configured to obtain band data and water depth data corresponding to each sampling point in a water body image of the cultivation area, combine water quality data and band data corresponding to the same sampling point to obtain a plurality of first training data sets corresponding to the sampling points, and combine the water depth data and the band data corresponding to the same sampling point to obtain a plurality of second training data sets corresponding to the sampling points;
The first model training module 74 is configured to construct a first random forest neural network model by using the band data as an independent variable and the water quality data as a dependent variable, and input a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, so as to obtain a water quality prediction model;
a second model training module 75, configured to construct a second random forest neural network model by using the band data as an independent variable and the water depth data as an independent variable, and input a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, so as to obtain a water depth prediction model;
the pollution sea-going flux prediction module 76 is configured to input the orthographic image of the cultivation area into the water quality prediction model and the water depth prediction model, obtain water quality prediction data and water depth prediction data of the water body area of the cultivation area, and obtain pollution sea-going flux prediction data of the water body area of the cultivation area according to the water quality prediction data and water depth prediction data of the water body area of the cultivation area and the water body area of the cultivation area.
In the embodiment of the application, a plurality of remote sensing images of a cultivation area are acquired through an orthographic image acquisition module, and spliced to obtain the orthographic image of the cultivation area, wherein the orthographic image comprises a water area to be a non-water area; removing a non-water body region in the orthographic image through a water quality data acquisition module, acquiring a water body image of the culture region, and acquiring water quality data corresponding to each sampling point in the water body image of the culture region according to the preset number of sampling points; acquiring wave band data and water depth data corresponding to all sampling points in a water body image of the culture area through a training data acquisition module, combining water quality data and wave band data corresponding to the same sampling point to acquire a plurality of first training data sets corresponding to the sampling points, and combining the water depth data and the wave band data corresponding to the same sampling point to acquire a plurality of second training data sets corresponding to the sampling points; the method comprises the steps of constructing a first random forest neural network model by taking the wave band data as an independent variable and the water quality data as a dependent variable through a first model training module, inputting a plurality of first training data sets corresponding to sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model; the wave band data are used as independent variables, the water depth data are used as dependent variables, a second random forest neural network model is built, a plurality of second training data sets corresponding to the sampling points are input into the second random forest neural network model for training, and a water depth prediction model is obtained; the method comprises the steps of respectively inputting an orthographic image of a cultivation area into a water quality prediction model and a water depth prediction model through a pollution sea flux prediction module, obtaining water quality prediction data and water depth prediction data of the water body area of the cultivation area, obtaining the pollution sea flux prediction data of the water body area of the cultivation area according to the water quality prediction data and the water depth prediction data of the water body area of the cultivation area and the water quality prediction data and the water depth prediction data of the water body area of the cultivation area, constructing a corresponding water quality prediction model and a corresponding water depth prediction model through a deep learning method, and obtaining the water quality prediction data and the water depth prediction data of the cultivation area accurately and rapidly according to the water quality prediction data and the water depth prediction data.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application, where the computer device 7 includes: a processor 81, a memory 82, and a computer program 83 stored on the memory 82 and executable on the processor 81; the computer device may store a plurality of instructions adapted to be loaded by the processor 81 and to execute the steps of the method according to the embodiment shown in fig. 1 to 6, and the specific execution process may be referred to in the specific description of the embodiment shown in fig. 1 to 6, which is not repeated here.
Wherein processor 81 may include one or more processing cores. The processor 81 performs various functions of the remote sensing image based pollution seaport prediction device 7 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 82 and invoking data in the memory 82 using various interfaces and various parts within the wired connection server, alternatively the processor 81 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programble Logic Array, PLA). The processor 81 may integrate one or a combination of several of a central processor 81 (Central Processing Unit, CPU), an image processor 81 (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the touch display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 81 and may be implemented by a single chip.
The Memory 82 may include a random access Memory 82 (Random Access Memory, RAM) or a Read-Only Memory 82 (Read-Only Memory). Optionally, the memory 82 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 82 may be used to store instructions, programs, code sets, or instruction sets. The memory 82 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 82 may also optionally be at least one memory device located remotely from the aforementioned processor 81.
The embodiment of the present application further provides a storage medium, where the storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executed by the processor, and the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to 6, and details are not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc.
The present invention is not limited to the above-described embodiments, but, if various modifications or variations of the present invention are not departing from the spirit and scope of the present invention, the present invention is intended to include such modifications and variations as fall within the scope of the claims and the equivalents thereof.
Claims (10)
1. The pollution sea flux prediction method based on the remote sensing image is characterized by comprising the following steps of:
acquiring a plurality of remote sensing images of a cultivation area, and splicing the plurality of remote sensing images to obtain an orthographic image of the cultivation area, wherein the orthographic image comprises a water body area to be a non-water body area;
removing a non-water body region in the orthographic image, acquiring a water body image of the culture region, and acquiring water quality data corresponding to each sampling point in the water body image of the culture region according to the preset number of sampling points;
acquiring wave band data and water depth data corresponding to all sampling points in a water body image of the culture area, combining water quality data and wave band data corresponding to the same sampling points to acquire a plurality of first training data sets corresponding to the sampling points, and combining the water depth data and the wave band data corresponding to the same sampling points to acquire a plurality of second training data sets corresponding to the sampling points;
Taking the wave band data as independent variables, taking the water quality data as the dependent variables, constructing a first random forest neural network model, inputting a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model;
taking the wave band data as independent variables, taking the water depth data as the dependent variables, constructing a second random forest neural network model, inputting a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, and obtaining a water depth prediction model;
respectively inputting the orthographic image of the cultivation area into the water quality prediction model and the water depth prediction model, obtaining water quality prediction data and water depth prediction data of the water body area of the cultivation area, obtaining area data of the water body area of the cultivation area, and obtaining volume prediction data of the water body area of the cultivation area according to the area data and the water depth prediction data, wherein the area data is used for indicating the area of the water body area of each pixel, and the volume prediction data is used for indicating the volume of the water body area of each pixel;
And obtaining the pollution sea flux prediction data of the water body region of the culture region according to the volume prediction data, the water quality prediction data and the corresponding pollution sea flux calculation algorithm of the water body region of the culture region.
2. The remote sensing image-based pollution sea flux prediction method according to claim 1, wherein: the water quality data comprises total nitrogen data, total phosphorus data, chemical oxygen demand data and chlorophyll data; the water quality prediction data includes total nitrogen prediction data, total phosphorus prediction data, chemical oxygen demand prediction data, and chlorophyll prediction data.
3. The pollution sea flux prediction method based on remote sensing images according to claim 2, wherein the pollution sea flux prediction data comprises total nitrogen sea flux prediction data, total phosphorus sea flux prediction data, chemical oxygen demand sea flux prediction data and chlorophyll sea flux prediction data.
4. The method for predicting the polluted sea flux based on the remote sensing image as set forth in claim 3, wherein the step of obtaining the predicted data of the polluted sea flux of the water body region of the cultivation region according to the predicted data of the volume, the predicted data of the water body region of the cultivation region and the corresponding calculation algorithm of the sea flux comprises the steps of:
According to the volume prediction data, the total nitrogen prediction data and a preset total nitrogen sea flux prediction data calculation algorithm of the water body region of the culture region, the total nitrogen sea flux prediction data of the culture region is obtained, wherein the total nitrogen sea flux prediction data calculation algorithm is as follows:
TN＝ΣW v ×C TN
wherein TN is the total nitrogen sea flux prediction data, W v For the volume prediction data, C TN Predicting data for the total nitrogen;
according to the volume prediction data, the total phosphorus prediction data and a preset total phosphorus sea flux prediction data calculation algorithm of the water body region of the culture region, the total phosphorus sea flux prediction data of the culture region is obtained, wherein the total phosphorus sea flux prediction data calculation algorithm is as follows:
TP＝ΣW v ×C TP
wherein TP is the total phosphorus sea flux prediction data, C TP Predicting data for the total phosphorus;
according to the volume prediction data, the chemical oxygen demand prediction data and a preset chemical oxygen demand sea flux prediction data calculation algorithm of the water body region of the culture region, the chemical oxygen demand sea flux prediction data of the culture region is obtained, wherein the chemical oxygen demand sea flux prediction data calculation algorithm is as follows:
COD＝ΣW v ×C COD
Wherein COD is the predicted data of the chemical oxygen demand sea flux, C COD Predictive data for said chemical oxygen demand;
and acquiring chlorophyll sea-entering flux prediction data of the culture area according to the volume prediction data and chlorophyll prediction data of the water body area of the culture area and a preset chlorophyll sea-entering flux prediction data calculation algorithm.
5. The method for predicting the incoming sea flux of pollution based on remote sensing images according to claim 1, wherein before removing the non-water body region in the orthographic image and obtaining the water body image of the cultivation region, the method comprises the steps of:
preprocessing the orthographic image to obtain a preprocessed orthographic image, wherein the preprocessing comprises radiometric calibration and atmospheric correction.
6. The method for predicting the incoming sea flux of pollution based on remote sensing images according to claim 1, wherein said removing the non-water area from the orthographic image and obtaining the water image of the cultivation area comprises the steps of:
acquiring a water body image corresponding to a sample area, a green light wave band value and a near infrared wave band value of each pixel in the water body image, and calculating the normalized water index value of each pixel in the water body image corresponding to the sample area according to a preset normalized water index calculation algorithm to obtain a minimum normalized water index value as a water body pixel distinguishing threshold, wherein the normalized water index calculation algorithm is as follows:
Wherein B is green For the green band value, B NIR For the near infrared band value, NDWI is normalized moisture index;
the green light wave band value and the near infrared wave band value of each pixel of the orthographic image are obtained, the normalized moisture index value of each pixel in the orthographic image is obtained according to the normalized moisture index calculation algorithm, a plurality of water body pixels in the orthographic image are obtained according to the normalized moisture index value of each pixel in the orthographic image and the water body pixel distinguishing threshold value, and the plurality of water body pixels are combined to obtain the water body image of the cultivation area.
7. The method for predicting the incoming sea flux of pollution based on remote sensing images according to claim 1, further comprising the steps of:
acquiring electronic map data corresponding to the cultivation area, acquiring pollution early warning identification of the water area of the cultivation area according to pollution sea-entering flux prediction data of the water area of the cultivation area and a preset pollution sea-entering flux threshold value, and displaying the corresponding pollution early warning identification and marking the pollution sea-entering flux prediction data on the electronic map data according to the pollution early warning identification of the water area of the cultivation area.
8. The utility model provides a pollution is gone into sea flux prediction unit based on remote sensing image which characterized in that includes:
the system comprises an orthographic image acquisition module, a display module and a display module, wherein the orthographic image acquisition module is used for acquiring a plurality of remote sensing images of a cultivation area, and splicing the plurality of remote sensing images to obtain an orthographic image of the cultivation area, wherein the orthographic image comprises a water body area and is a non-water body area;
the water quality data acquisition module is used for eliminating a non-water body area in the orthographic image, acquiring a water body image of the culture area, and acquiring water quality data corresponding to each sampling point in the water body image of the culture area according to the preset number of sampling points;
the training data acquisition module is used for acquiring wave band data and water depth data corresponding to all sampling points in the water body image of the culture area, combining water quality data and wave band data corresponding to the same sampling point to acquire a plurality of first training data sets corresponding to the sampling points, and combining the water depth data and the wave band data corresponding to the same sampling point to acquire a plurality of second training data sets corresponding to the sampling points;
the first model training module is used for taking the wave band data as an independent variable, taking the water quality data as a dependent variable, constructing a first random forest neural network model, inputting a plurality of first training data sets corresponding to the sampling points into the first random forest neural network model for training, and obtaining a water quality prediction model;
The second model training module is used for taking the wave band data as an independent variable, taking the water depth data as a dependent variable, constructing a second random forest neural network model, inputting a plurality of second training data sets corresponding to the sampling points into the second random forest neural network model for training, and obtaining a water depth prediction model;
the pollution sea flux prediction module is used for respectively inputting the orthographic images of the cultivation area into the water quality prediction model and the water depth prediction model, obtaining water body area of the cultivation area, water quality prediction data and water depth prediction data of the water body area of the cultivation area, obtaining area data of the water body area of the cultivation area, and obtaining volume prediction data of the water body area of the cultivation area according to the area data and the water depth prediction data, wherein the area data is used for indicating the area of the water body area of each pixel, and the volume prediction data is used for indicating the volume of the water body area of each pixel;
and obtaining the pollution sea flux prediction data of the water body region of the culture region according to the volume prediction data, the water quality prediction data and the corresponding pollution sea flux calculation algorithm of the water body region of the culture region.
9. A computer device, comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the remote sensing image based pollution seavolume prediction method as defined in any one of claims 1 to 7.
10. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, implements the steps of the remote sensing image based pollution searate prediction method as defined in any one of claims 1 to 7.
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