Water depth inversion method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a water depth inversion method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring historical remote sensing image data and historical ground water level gauge data in a target area; inputting the second historical radiation brightness value into a physical model to obtain predicted water depth information output by the physical model; correcting the predicted water depth information by using the second historical real water depth information, and updating the physical model according to a correction result to obtain a target physical model; and inputting the current radiation brightness value into the target physical model to obtain target predicted water depth information output by the target physical model. According to the method and the device, the technical problem that the precision of an inversion result is relatively low due to the influence of factors such as a cloud layer, atmosphere and an earth surface type when an existing satellite remote sensing technology is used for performing large-range water depth inversion is solved.

Description

Technical Field

[0001] The present application relates to the technical field of water depth quantitative inversion, and in particular, to a water depth inversion method and device, a non-volatile storage medium, and an electronic device. Background Art

[0002] In the field of power grid security monitoring, the impact of floods on transmission lines has always been a focus of research and attention. Since transmission lines cover a wide range and most of them are located in areas with complex terrain, traditional ground monitoring methods are difficult to achieve real-time monitoring of large-scale flooding. Therefore, remote sensing technology, especially satellite remote sensing technology, has been widely used in power grid flood monitoring. Satellite remote sensing technology has the advantages of wide coverage, periodic observation, and no need for on-site operations. It can obtain a wide range of surface water information in a short time.

[0003] For the monitoring of flood depth in power grids, existing technologies mainly rely on satellite remote sensing images for water depth inversion. These methods usually include visible light band reflectivity models based on optical remote sensing images, synthetic aperture radar interferometry based on radar remote sensing images and other technologies. These technologies can provide water depth information of flood disasters to a certain extent, but the accuracy for complex terrain and urban areas still needs to be improved. Specifically, when the existing satellite remote sensing technology is used for large-scale water depth inversion, the accuracy of the inversion results is not high due to the influence of factors such as clouds, atmosphere, and surface type, especially in complex terrain and urban areas, the error is large.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a water depth inversion method and device, a non-volatile storage medium, and an electronic device to at least solve the technical problem that when the existing satellite remote sensing technology performs large-scale water depth inversion, the inversion results have low accuracy due to the influence of factors such as clouds, atmosphere, and surface type.

[0006] According to one aspect of an embodiment of the present application, a water depth inversion method is provided, comprising: acquiring historical remote sensing image data and historical ground water level gauge data in a target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level gauge data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; determining a physical model for representing the correlation between the first historical radiation brightness value and the first historical real water depth information; inputting the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; correcting the predicted water depth information using the second historical real water depth information, and updating the physical model according to the correction result to obtain a target physical model; inputting the current radiation brightness value into the target physical model to obtain target predicted water depth information output by the target physical model.

[0007] Optionally, determining a physical model for representing the correlation between a first historical radiation brightness value and first historical real water depth information includes: obtaining the average daily change value of solar radiation intensity in the target area within a preset historical time period; if the average daily change value of solar radiation intensity is within a first preset interval, using a single-band model to determine the correlation relationship, wherein the single-band model is used to determine the linear or nonlinear relationship between water depth and the reflectivity of a preset band and the radiation brightness value; if the average daily change value of solar radiation intensity is within a second preset interval, using a dual-band model to determine the correlation relationship, wherein the dual-band model is used to determine the linear or nonlinear relationship between water depth and the ratio or logarithm of the difference between the reflectivity of two different bands and the radiation brightness value. If the daily average change in solar radiation intensity is within the third preset interval, the logarithmic ratio model is used to determine the linear or nonlinear relationship between the natural logarithm of the water depth and the reflectivity ratio and the radiation brightness value, and the minimum value of the third preset interval is greater than the maximum value of the second preset interval; if the daily average change in solar radiation intensity is within the fourth preset interval, the multi-band model is used to determine the linear or nonlinear relationship between the water depth and the reflectivity and radiation brightness values ​​of multiple preset bands, and the minimum value of the fourth preset interval is greater than the maximum value of the third preset interval.

[0008] Optionally, the first preset interval is 45W / m 2 Up to 55W / m 2 ; The second preset interval is 100W / m 2 Up to 180W / m 2 ; The third preset interval is 200W / m 2 Up to 220W / m 2 ; The fourth preset interval is 300W / m2 Up to 350W / m 2 .

[0009] Optionally, determining the association relationship using a dual-band model includes: determining the association relationship using the following formula: Z = (1 / fk i )(lnr Bi -X i ) where Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water body, r Bi is the bottom reflectivity at water depth Z, L i is the radiation brightness value at water depth Z, L si is the radiation brightness value at the preset water depth, R i It is a function that represents the relationship between solar radiation, the transmission flux at the interface between the atmosphere and water, and the refractive index of the water surface.

[0010] Optionally, the predicted water depth information is corrected using the second historical real water depth information, including: dividing the second historical real water depth information into training set data and verification set data; using the training set data to train a polynomial regression model, and using the verification set data to verify the mean square error between the output value of the polynomial regression model and the true value, and when the mean square error is less than a preset threshold, a trained polynomial regression model is obtained; and the predicted water depth information is input into the trained polynomial regression model to obtain a correction result for the predicted water depth information.

[0011] Optionally, the target predicted water depth information includes: multiple discrete water depth measurement points and water depth values ​​corresponding to the water depth measurement points, wherein each water depth measurement point has a geographic coordinate identifier; after obtaining the target predicted water depth information output by the target physical model, the method also includes: converting the geographic coordinate identifier of the water depth measurement point into a plane coordinate suitable for visual display; constructing a visual graphic based on the converted plane coordinates and the water depth values ​​corresponding to the water depth measurement point; setting different color mapping rules according to different ranges of water depth values ​​to perform color rendering on the constructed visual graphic; and displaying the rendered visual graphic on a display device.

[0012] Optionally, historical remote sensing image data and historical ground water level gauge data in the target area are obtained, including: determining an area whose distance from a power transmission line of a preset length meets a preset distance as the target area; downloading initial remote sensing image data of the target area within a preset time window from a satellite image database, selecting remote sensing image data with a resolution greater than a first preset threshold and a cloud coverage percentage less than a second preset threshold from the initial remote sensing image data, performing radiation correction, geometric correction and atmospheric correction on the selected remote sensing image data to obtain historical remote sensing image data; determining the target acquisition time corresponding to the initial remote sensing image data, and obtaining historical ground water level gauge data corresponding to the target acquisition time.

[0013] According to another aspect of the embodiment of the present application, a water depth inversion device is also provided, including: an acquisition module, used to acquire historical remote sensing image data and historical ground water level meter data in a target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level meter data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; a determination module, used to determine a physical model used to represent the correlation relationship between the first historical radiation brightness value and the first historical real water depth information; a first processing module, used to input the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; a correction module, used to correct the predicted water depth information using the second historical real water depth information, and update the physical model according to the correction result to obtain a target physical model; a second processing module, used to input the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model.

[0014] According to another aspect of the embodiment of the present application, a non-volatile storage medium is further provided, the storage medium including a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above water depth inversion method.

[0015] According to another aspect of the embodiments of the present application, there is further provided an electronic device, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the above water depth inversion method is executed when the program is run.

[0016] According to yet another aspect of the embodiments of the present application, a computer program is further provided, wherein the above water depth inversion method is implemented when the computer program is executed by a processor.

[0017] According to another aspect of the embodiment of the present application, a computer program product is also provided, the computer program product comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above water depth inversion method is implemented.

[0018] In an embodiment of the present application, historical remote sensing image data and historical ground water level meter data in the target area are obtained, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level meter data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; a physical model for representing the correlation between the first historical radiation brightness value and the first historical real water depth information is determined; the second historical radiation brightness value is input into the physical model, and a The method comprises the following steps: obtaining the predicted water depth information output by the physical model; correcting the predicted water depth information using the second historical real water depth information, and updating the physical model according to the correction result to obtain the target physical model; inputting the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model, thereby achieving the purpose of improving the accuracy of the water depth inversion result, thereby realizing the technical effect of providing more accurate data support for flood disaster warning and emergency disposal, and further solving the technical problem that the existing satellite remote sensing technology has low accuracy of the inversion result when performing large-scale water depth inversion due to the influence of factors such as cloud layer, atmosphere and surface type. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a flow chart of a water depth inversion method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of another water depth inversion method according to an embodiment of the present application;

[0022] Figure 3 is a structural diagram of a water depth inversion device according to an embodiment of the present application;

[0023] Figure 4 It is a hardware structure block diagram of a computer terminal of a water depth inversion method according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present application, a method embodiment of a water depth inversion method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of a water depth inversion method according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step S101, obtaining historical remote sensing image data and historical ground water level gauge data in the target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level gauge data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number.

[0029] The historical remote sensing image data of the target area are downloaded by logging into the public satellite image database (such as NASA Earthdata and ESA Copernicus OpenAccess Hub). At the same time, the historical ground water level gauge data in the buffer zone along the transmission line in the target area are collected.

[0030] The historical remote sensing image data includes a first number of first historical radiance values, and in order to improve the efficiency of model training and avoid data redundancy, a second number of second historical radiance values ​​are selected from this part of the data for model training, wherein the second number is less than the first number. The historical ground water level gauge data includes a third number of first historical real water depth information, which is used for model calibration and verification. Similarly, in order to optimize model training, a fourth number of second historical real water depth information is selected from these data, wherein the fourth number is less than the third number.

[0031] Step S102: determine a physical model for representing the association relationship between the first historical radiance value and the first historical true water depth information.

[0032] Based on the water quality, bottom type, and light conditions of the target area, a physical model is determined to represent the relationship between the first historical radiance value and the first historical real water depth information. For example, if the water quality in the target area is clear, a dual-band model or a logarithmic ratio model may be used first; if the water quality is turbid, a multi-band model may be used.

[0033] Step S103: input the second historical radiation brightness value into the physical model to obtain the predicted water depth information output by the physical model.

[0034] The second historical radiation brightness value is input into the selected physical model, and the model is used to calculate the predicted water depth information. This process is part of the model training, which aims to preliminarily evaluate the inversion ability of the model through limited and good quality historical data.

[0035] Step S104: Correct the predicted water depth information using the second historical real water depth information, and update the physical model according to the correction result to obtain the target physical model.

[0036] The fourth number of second historical real water depth information is compared with the predicted water depth information obtained in step S103, and the error index (such as RMSE, MAE) is calculated to evaluate the prediction accuracy of the model. Based on the second historical real water depth information, the preliminary inversion result is corrected. By constructing a regression model, the predicted water depth information is used as an independent variable, the actual water depth information is used as a dependent variable, and the model parameters are fitted to reduce the prediction error. The result of the model update is to obtain a more accurate target physical model.

[0037] Step S105, inputting the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model.

[0038] The current remote sensing image data of the target area is obtained, and preprocessed by radiation calibration, geometric correction and atmospheric correction to obtain the current radiation brightness value. The current radiation brightness value is input into the target physical model, and the model is used to perform real-time water depth inversion to obtain the target predicted water depth information. This result will more accurately reflect the current water depth conditions of the target area.

[0039] Through the above steps, not only can a physical model based on historical data be established, but the model parameters can also be continuously optimized through the correction process, ultimately achieving accurate prediction of the real-time water depth conditions in the target area. This method combines the wide-area coverage capability of satellite remote sensing and the high-precision measurement of ground water level gauges, effectively improving the accuracy and real-time performance of water depth inversion, which is of great significance for power grid security monitoring, disaster warning and emergency response.

[0040] The following Figure 1 The steps shown are exemplary and explanatory.

[0041] According to some optional embodiments of the present application, determining a physical model for representing the correlation between a first historical radiation brightness value and a first historical real water depth information can be achieved by the following method: obtaining the daily average change value of the solar radiation intensity in the target area within a preset historical time period; if the daily average change value of the solar radiation intensity is within a first preset interval, using a single-band model to determine the correlation relationship, wherein the single-band model is used to determine the linear or nonlinear relationship between the water depth and the reflectivity of the preset band and the radiation brightness value; if the daily average change value of the solar radiation intensity is within a second preset interval, using a dual-band model to determine the correlation relationship, wherein the dual-band model is used to determine the logarithm of the ratio or difference between the water depth and the reflectivity of two different bands, and the radiation brightness value. The linear or nonlinear relationship between the values ​​of water depth and reflectivity is determined, and the minimum value of the second preset interval is greater than the maximum value of the first preset interval; if the daily average change value of solar radiation intensity is within the third preset interval, the logarithmic ratio model is used to determine the association relationship, wherein the logarithmic ratio model is used to determine the linear or nonlinear relationship between the natural logarithm of the water depth and reflectivity ratio and the radiation brightness value, and the minimum value of the third preset interval is greater than the maximum value of the second preset interval; if the daily average change value of solar radiation intensity is within the fourth preset interval, the multi-band model is used to determine the association relationship, wherein the multi-band model is used to determine the linear or nonlinear relationship between water depth and the reflectivity and radiation brightness values ​​of multiple preset bands, and the minimum value of the fourth preset interval is greater than the maximum value of the third preset interval.

[0042] Preferably, the first preset interval is 45W / m 2 Up to 55W / m 2 ; The second preset interval is 100W / m 2 Up to 180W / m 2 ; The third preset interval is 200W / m 2Up to 220W / m 2 ; The fourth preset interval is 300W / m 2 Up to 350W / m 2 .

[0043] Specifically, before analyzing the remote sensing image data of the target area, it is first necessary to obtain the average daily change in solar radiation intensity in the target area within a preset historical period. The average daily change in solar radiation intensity can be estimated by collecting metadata from historical remote sensing data, especially solar altitude angle and atmospheric condition data, combined with a solar radiation transfer model (such as the MODTRAN model). The selection of historical duration needs to take into account factors such as seasonal changes and weather conditions to ensure that the collected data can reflect the typical lighting conditions of the target area.

[0044] Determine which physical model to use for water depth inversion based on the daily average change in solar radiation intensity. Changes in solar radiation intensity will directly affect the reflectivity of remote sensing images, and thus affect the accuracy of water depth inversion. Therefore, it is necessary to use different physical models for different lighting conditions. When the daily average change in solar radiation intensity is within the first preset interval (45W / m 2 Up to 55W / m 2 ), a single-band model is used. Single-band models are usually based on the linear or nonlinear relationship between the reflectivity of a specific band and the water depth, and are suitable for situations where the lighting conditions are relatively stable. When the daily average change in solar radiation intensity is within the second preset interval (100W / m 2 Up to 180W / m 2 ), the dual-band model is used. The dual-band model uses the relationship between the logarithm of the ratio or difference of the reflectance of the two bands and the water depth, which can better eliminate the impact of light changes on the inversion results and is suitable for environments with medium light changes. When the daily average change value of solar radiation intensity is within the third preset interval (200W / m 2 Up to 220W / m 2 ), select the logarithmic ratio model. The logarithmic ratio model further improves the ability to handle light changes by calculating the relationship between the natural logarithm of the reflectivity ratio and the water depth. It is suitable for situations where light conditions vary greatly. When the average daily change in solar radiation intensity is within the fourth preset interval (300W / m 2 Up to 350W / m 2 ), a multi-band model is used. The multi-band model integrates the information of multiple bands to establish a more complex water depth inversion model, which can effectively cope with the environment with extreme changes in lighting conditions and improve the robustness of the inversion.

[0045] After determining which physical model to use, it is necessary to set the corresponding parameters according to the model characteristics, such as reflectivity, water optical properties, etc., and perform preliminary water depth inversion calculations. By inputting the radiation brightness values ​​in the historical remote sensing data into the model, a preliminary water depth estimate is obtained. Next, the actual water depth data of the historical ground water level gauge is used to correct the preliminary inversion results. By constructing a regression model, fitting the relationship between the predicted water depth and the actual water depth, optimizing and adjusting the parameters of the physical model, the accuracy and reliability of the inversion results are ensured.

[0046] According to some other optional embodiments of the present application, the association relationship can be determined by using the dual-band model by the following method: The association relationship is determined by using the following formula: Z = (1 / fk i )(lnr Bi -X i ) where Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water body, r Bi is the bottom reflectivity at water depth Z, L i is the radiation brightness value at water depth Z, L si is the radiation brightness value at the preset water depth, R i It is a function that represents the relationship between solar radiation, the transmission flux at the interface between the atmosphere and water, and the refractive index of the water surface.

[0047] In some optional embodiments of the present application, the use of the second historical real water depth information to correct the predicted water depth information can be achieved by the following method: dividing the second historical real water depth information into training set data and verification set data; using the training set data to train a polynomial regression model, and using the verification set data to verify the mean square error between the output value of the polynomial regression model and the true value, and when the mean square error is less than a preset threshold, obtaining a trained polynomial regression model; inputting the predicted water depth information into the trained polynomial regression model to obtain a correction result for the predicted water depth information.

[0048] The second historical real water depth information is randomly divided into training set data and validation set data. Usually, the training set contains most of the data (e.g., 70%-80%) for model training; the validation set retains a small part of the data (e.g., 20%-30%) for evaluating model performance. Use the training set data to build a polynomial regression model, which can be in the form of a linear or higher-order polynomial. The goal of the training is to find the optimal parameters of the polynomial model so that the difference between the model prediction value and the real water depth value in the training set data is minimized. The specific operation can use the polynomial regression function in the statistical software to set the model order and adjust the model parameters until the model training is completed. Use the validation set data to evaluate the performance of the polynomial regression model. Input the predicted water depth information in the validation set data into the model, compare it with the real water depth information in the same data set, and calculate the mean square error. Once the polynomial regression model passes the verification, that is, when the MSE is less than the preset threshold, the predicted water depth information can be input into this model for correction. Through the correction results output by the model, a predicted value closer to the real water depth can be obtained, thereby improving the accuracy of water depth inversion.

[0049] The above polynomial regression model trained based on historical data can not only improve the accuracy of the inverted water depth, but also ensure the stability and generalization ability of the model, so that it can be effectively applied under different lighting and environmental conditions. This model correction method provides an important data processing and accuracy improvement method for the quantitative inversion of flood depth in satellite-ground coordinated power grid.

[0050] As some optional embodiments of the present application, the target predicted water depth information includes: a plurality of discrete water depth measurement points and water depth values ​​corresponding to the water depth measurement points, wherein each water depth measurement point has a geographic coordinate identifier.

[0051] Furthermore, after obtaining the target predicted water depth information output by the target physical model, the following steps can also be performed: converting the geographic coordinate identification of the water depth measurement point into plane coordinates suitable for visual display; constructing a visual graphic based on the converted plane coordinates and the water depth values ​​corresponding to the water depth measurement points; setting different color mapping rules according to different ranges of water depth values ​​to perform color rendering on the constructed visual graphic; and displaying the rendered visual graphic on a display device.

[0052] Specifically, the geographic coordinates (usually longitude and latitude coordinates) of the water depth measurement points are first converted into plane coordinates suitable for two-dimensional map display. This can be achieved by using map projection technology, such as using UTM (Universal Transverse Mercator) projection or Web Mercator projection, to convert the three-dimensional coordinates on the earth's surface into plane coordinates on a two-dimensional map for subsequent processing in GIS software.

[0053] Secondly, based on the converted plane coordinates and the water depth values ​​corresponding to the water depth measurement points, a visualization graphic is constructed using GIS software or programming languages ​​(such as Python's Matplotlib and Seaborn libraries). This usually involves creating a scatter plot or contour map, in which the location of each water depth measurement point is represented by its plane coordinates, and the water depth value is displayed by the size, color or value of the contour line.

[0054] Third, to make the visualization easier to interpret, color mapping rules are set according to different ranges of water depth values. For example, shallow water areas may be represented by light blue, while deep water areas may be represented by dark blue or black. The choice of color mapping rules should ensure the continuity and contrast of depth changes, so that the observer can quickly identify the pattern of water depth changes. This can be achieved by defining a color gradient table (Color Map), which maps the value range to a series of colors.

[0055] Finally, the constructed visualization graphics are rendered in color, and the color of each water depth measurement point or grid point is set according to the color mapping rules to generate a colored water depth distribution map. Then, using the interface of the GIS software or developing a visualization application, the rendered graphics are displayed on a display device, which can be a computer screen, tablet computer or any other device that supports graphic display. When displaying, additional map elements such as scale, compass, legend, etc. can also be added to enhance the readability of the map and the completeness of the information.

[0056] Through the above steps, not only can the water depth distribution of the target area be visually displayed, but it can also be color-coded according to the numerical range of the water depth, so that observers can quickly understand the changing trend of the water depth. This has important practical value for flood depth monitoring and disaster warning of power grids, and can help decision makers and emergency response teams more effectively assess flood risks and formulate response strategies. In addition, through visual display, potential deviations or abnormal areas in model predictions can be further identified, providing a basis for model optimization and iteration.

[0057] In some optional embodiments of the present application, obtaining historical remote sensing image data and historical ground water level gauge data in the target area can be achieved by the following method: determining an area whose distance from a transmission line of a preset length meets a preset distance as the target area; downloading initial remote sensing image data of the target area within a preset time window from a satellite image database, selecting remote sensing image data with a resolution greater than a first preset threshold and a cloud coverage percentage less than a second preset threshold from the initial remote sensing image data, performing radiation correction, geometric correction and atmospheric correction on the selected remote sensing image data, and obtaining historical remote sensing image data; determining the target acquisition time corresponding to the initial remote sensing image data, and obtaining the historical ground water level gauge data corresponding to the target acquisition time.

[0058] Specifically, first, based on the location coordinates of the transmission line, a buffer zone with a preset length from the transmission line is determined as the target area. The selection of the preset length should take into account the width of the transmission line that may be affected by floods, ensuring that the target area can cover all potentially affected parts of the transmission line. For example, if the preset length is 500 meters, then the target area will be the area within 500 meters on both sides of the transmission line.

[0059] Secondly, log in to satellite image databases such as NASA Earthdata and ESA Copernicus Open Access Hub, and download initial remote sensing image data according to the latitude and longitude range of the target area and the preset time window. The preset time window should include a period of time before and after the flood event to ensure that the complete process of water body changes can be captured.

[0060] Again, from the downloaded initial remote sensing image data, filter out the data that meets the following conditions: Resolution is greater than the first preset threshold: The choice of resolution directly affects the accuracy of water depth inversion. The setting of the preset threshold should ensure that the data can distinguish the details of the transmission line and the surrounding area. For example, the first preset threshold can be set to 30 meters to ensure that the image can clearly display the surface features. The cloud coverage percentage is less than the second preset threshold: Clouds will block the surface and affect the quality of remote sensing data. The setting of the preset threshold should exclude images that are severely interfered by clouds. For example, the second preset threshold can be set to 10% to ensure the availability and accuracy of the data. And pre-process the screened remote sensing image data, including: Radiation correction: Convert digital numbers (DN) to surface reflectivity to eliminate the influence of sensors and atmospheric effects. Geometric correction: Adjust the geometric deformation of the image to ensure the consistency of image pixels and ground coordinates. Atmospheric correction: Reduce the impact of the atmosphere on the image and improve the accuracy of the data.

[0061] Then, determine the shooting time of the historical remote sensing image data, that is, the target acquisition time. Match the target acquisition time with the recording time of the ground water level meter, and select the water level meter data with the same time or time difference within the allowable range. Ensure the synchronization of the data to achieve the effective combination of remote sensing data and ground data.

[0062] Finally, the historical remote sensing image data and the historical ground water level gauge data are integrated to provide basic data for building the physical model. Next, according to the previous technical briefing, the steps of physical model construction, model training and verification, result correction and inversion can be carried out to finally achieve accurate inversion of the water depth in the target area.

[0063] Through the above steps, it can be ensured that the remote sensing data and ground water level gauge data used for water depth inversion meet the high-precision requirements in time, space and quality, providing a solid data foundation for the quantitative inversion of flood depth in the satellite-ground coordinated power grid.

[0064] Figure 2 is a flow chart of another water depth inversion method according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0065] Step 201, data collection and preprocessing, including the acquisition and organization of remote sensing image data and ground water level meter data. A series of preprocessing steps such as radiation correction, geometric correction, and atmospheric correction are performed on the downloaded remote sensing data to eliminate the influence of external factors such as atmosphere and illumination to ensure the quality and accuracy of the data; collect the data of the ground water level meter, including its location coordinates and real-time water level information, which will be used for subsequent model calibration and verification. Data collection and preprocessing are the basic steps of the remote sensing water depth inversion project. The core is to ensure the quality of the data so that subsequent analysis and modeling can be based on accurate and reliable original data.

[0066] The data collection and preprocessing process in this embodiment mainly consists of the following four sub-steps:

[0067] Step 211, remote sensing data acquisition, determine the buffer zone of a certain distance along the transmission line as the study area, determine the time range of the required data, and download the remote sensing image data of the target area from the satellite image database. Remote sensing data acquisition is to collect electromagnetic radiation signals from the earth's surface through satellite platforms. These signals contain physical and chemical information of the surface. The sensors carried by the satellite can receive and record radiation in different wavelength ranges to form remote sensing images, which are then used for analysis and monitoring of surface features. According to the location of the transmission line, a buffer zone of a certain width on both sides of the line is delineated as the study area to ensure that all parts of the transmission line that may be affected by floods are covered; select the time window for data collection, usually before and after the expected or actual flood event, to capture the changes in the water body. Log in to public satellite image databases such as NASA Earthdata and ESACopernicus Open Access Hub, select appropriate satellites (such as Landsat, Sentinel series) and bands, and download the corresponding remote sensing image data according to the limitations of the study area and time range. In addition, select a file containing all necessary bands and metadata when downloading.

[0068] Step 2012: Data screening, through metadata inspection and image quality assessment, select cloudless or low cloud cover, high-definition images, and exclude data affected by weather conditions. Data screening is based on image quality and time series consistency to ensure that the data used can meet the accuracy requirements of inverted water depth. The judgment of image quality is mainly based on factors such as cloud coverage, lighting conditions and sensor performance. Specific practices include: checking the metadata of each image, confirming information such as shooting time, cloud coverage percentage, sensor type, etc. Use image processing software (such as ENVI, GDAL, QGIS) to open the image, check the image clarity, and ensure that there are no clouds or the cloud cover is less than 10%. Eliminate images with severe cloud cover, poor lighting conditions or obvious sensor stripes to ensure data quality.

[0069] Step 2013, image preprocessing, implementation of radiation correction, converting pixel values ​​into real surface reflectance; applying geometric correction to ensure the accuracy of image spatial position; performing atmospheric correction to reduce image distortion caused by atmospheric effects. Radiation calibration is an important step in remote sensing data processing, which refers to converting the digital quantization value (DN) of remote sensing images into physical quantities, such as radiation brightness, reflectance or surface temperature. Radiation calibration can eliminate the errors generated by the sensor itself and improve the accuracy and comparability of remote sensing data. Remote sensing image geometric correction refers to the process of eliminating the spatial distortion of remote sensing images caused by factors such as sensor attitude, platform motion, earth curvature and atmospheric refraction, so that the objects in the image match the actual position on the ground. The remote sensing image after geometric correction can be used to make accurate geographic coordinate system maps and perform overlay analysis with other geographic spatial data. The principle of geometric correction is to convert the pixel coordinates in the image into ground coordinates by establishing a transformation relationship between the image coordinate system and the ground coordinate system. Atmospheric correction refers to the process of eliminating the influence of the atmosphere on remote sensing images so that the reflectance or radiation brightness of the objects in the remote sensing images is close to their true values. Atmospheric correction can improve the accuracy and comparability of remote sensing data, making it more suitable for quantitative analysis and application.

[0070] Step 2014: collect water level gauge data in the target area, ensure data time synchronization, that is, the water level data is consistent with the shooting time of the remote sensing image, and conduct a preliminary check on the water level data to remove abnormal values ​​to ensure data accuracy and reliability. Ensure that the collection time of the water level gauge data matches the shooting time of the remote sensing image, and generally require that the time difference does not exceed a few hours. Use statistical methods to remove abnormal values ​​in the water level gauge data to ensure data continuity and consistency.

[0071] Step 202, construct a physical model for preliminary water depth inversion, that is, use physical optics principles and remote sensing data to estimate water depth. This involves establishing the relationship between the radiation brightness value received by the sensor and the bottom reflectivity, water depth and water optical properties. Through the radiation transfer equation, the reflectivity of the remote sensing image is converted into water depth information, providing a basis for subsequent correction.

[0072] The physical model of water depth inversion is mainly based on the relationship between the optical properties of water bodies and remote sensing data. The radiation transmission theory is used to analyze the relationship between the radiation brightness value received by the sensor and the water depth, bottom reflectivity and optical properties of the water body. The radiation transmission equation describes the propagation, absorption, scattering and reflection process of light in the water body. Among them, the optical properties of the water body (such as absorption coefficient, scattering coefficient) and the bottom reflectivity have a direct impact on the radiation brightness value received by the remote sensor.

[0073] Step 202 specifically includes the following steps:

[0074] Step 221, water depth inversion model selection, select or customize the water depth inversion physical model according to regional characteristics, such as single-band model, dual-band (ratio logarithm) model, logarithmic ratio model and multi-band model. When selecting a water depth inversion model, factors such as water quality, bottom type, and light conditions in the study area need to be considered. Different models are based on different physical principles and assumptions, for example:

[0075] Single-band model: simply uses the reflectivity of a specific band to establish a linear or nonlinear relationship with water depth.

[0076]

[0077] In the formula, Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water body, r Bi is the bottom reflectivity at water depth Z, L i is the radiation brightness value at water depth Z, L si is the radiation brightness value of deep water, R i It is a function of solar radiation, transmission at the air-water interface, and refraction at the water surface.

[0078] Dual-band model: The water depth is inverted through the ratio or logarithm of the difference between the reflectances of two different bands. It is suitable for clearer waters.

[0079]

[0080] In the formula, (i=1,2, representing the 1st and 2nd bands respectively), Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water in the i-th band, r Bi is the bottom reflectivity at water depth Z in the i-th band, L i is the radiation brightness value at the water depth Z in the i-th band, L si is the radiation brightness value of deep water, R i It is a function of solar radiation, transmission at the air-water interface, and refraction at the water surface.

[0081] Logarithmic ratio model: Using the relationship between the natural logarithm of the reflectivity ratio and the water depth can reduce water surface fluctuations and atmospheric effects.

[0082]

[0083] Where m1 is the ratio to water depth scale adjustment constant, n is a fixed value for the entire area, and λ i is the blue band, λ j is the green band, R w(λ) is the water reflectivity in the λ band at depth Z, and m0 is the offset relative to the water depth of 0 m.

[0084] Multi-band model: Integrates information from multiple bands to improve inversion accuracy, suitable for complex water environments.

[0085]

[0086] In the formula, (i=1,2, representing the 1st and 2nd bands respectively), Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water in the i-th band, r Bi is the bottom reflectivity at water depth Z in the i-th band, L i is the radiation brightness value at the water depth Z in the i-th band, L si is the radiation brightness value of deep water, R i It is a function of solar radiation, transmission at the air-water interface, and refraction at the water surface.

[0087] Analyze the characteristics of the target area, evaluate the applicability and accuracy of each model, test multiple models and compare the results, and select the most appropriate model for subsequent analysis.

[0088] Step 2022, parameter setting, set the parameters required for the model. Determine the input parameters required for the inversion model, such as spectral reflectance, terrain data, etc. Collect and process the required parameter data to ensure its accuracy and reliability. Set the initial parameter values ​​of the model, perform parameter sensitivity analysis, and determine the best parameter combination. Model parameters include spectral reflectance, water optical properties (such as absorption and scattering coefficients), bottom reflectance, etc. The accuracy and reliability of the parameters are crucial to model performance.

[0089] Specifically, collect and process remote sensing images, terrain data, and possible field observation data. Use existing databases or literature to determine initial parameter values, perform parameter sensitivity analysis, identify which parameters have the greatest impact on model output, adjust these parameters to optimize model performance, and use statistical methods or empirical formulas to estimate model parameters, such as calibrating bottom reflectance and water optical properties through field measurement data.

[0090] Step 2023, preliminary inversion execution, using remote sensing data and physical model to perform preliminary water depth inversion calculation. Input the pre-processed remote sensing image data into the physical model, run the model, calculate the water depth inversion result of the transmission line buffer zone, and output the calculation result as an image file for subsequent analysis. Use the pre-processed remote sensing data as input, combined with the selected physical model and parameter settings, to perform water depth inversion calculation.

[0091] Specifically, the preprocessed image data is imported into the water depth inversion model, and the model calculation is performed to obtain preliminary water depth inversion results, and the inversion results are output in a geospatial data format, such as GeoTIFF, so as to be read by GIS software.

[0092] Step 2024, visualize the results, present the preliminary inversion results in the form of a map, to facilitate subsequent analysis and correction. Use GIS software (such as ArcGIS, QGIS, etc.) to load the inversion results, visualize the inversion results, and generate a water depth distribution map.

[0093] Step 203, regression model construction and correction, use the actual measurement data of the surface water level gauge to correct the preliminary inversion results to improve their accuracy. By constructing a regression model, the water depth estimate of the preliminary inversion is used as the independent variable, and the true value of the water level gauge is used as the dependent variable, and the model parameters are fitted by minimizing the difference between the predicted value and the actual value. The regression model can capture the relationship between the preliminary inversion results and the actual water depth, so as to systematically correct the preliminary inversion results on a global scale and improve the overall accuracy of the water depth inversion. Regression is used to analyze the relationship between one or more independent variables (explanatory variables) and dependent variables (response variables). In this context, we use a polynomial regression model to correct the results of the water depth inversion to make it closer to the actual measurement data of the surface water level gauge.

[0094] The step 203 specifically includes the following steps:

[0095] Step 2031, data pairing, pairing the preliminary inversion results with the true data of the water level gauge. Determine the corresponding positions of the preliminary inversion results and the water level gauge data, match the two, ensure consistency in time and space, check the paired data, and remove data points that do not meet the requirements. Data pairing is to ensure the consistency in time and space between the preliminary inversion results and the measured data of the ground water level gauge, which is the prerequisite for model construction and correction. The specific operation is: Use GIS software or programming language to match the location coordinates of the water level gauge with the pixels on the remote sensing image. Ensure that the measurement time of the water level gauge matches the shooting time of the remote sensing image, usually allowing a small time window (such as ±1 hour). Eliminate data points containing outliers or missing values ​​to ensure the integrity and accuracy of the data.

[0096] Step 2032, model training, using paired data to build a regression model and estimate model parameters. The paired data is divided into a training set and a validation set, and the training set data is used to build a regression model, estimate the intercept and slope parameters, and evaluate the model fitting effect, such as R 2 Value, residual analysis, etc. The purpose of regression model training is to estimate the model parameters by minimizing the difference between the predicted value and the actual value, so as to establish the relationship between the preliminary inversion results and the measured water depth.

[0097] Specifically, the paired data are randomly divided into a training set (about 70%-80%) and a validation set (about 20%-30%). Using the training set data, a linear regression or polynomial regression model is applied to estimate model parameters (such as intercept, slope, polynomial coefficients). The fitting effect of the model on the training set is evaluated by calculating statistics such as the coefficient of determination R2 and the standard deviation of the residual.

[0098] The polynomial regression model is: y = β0 + β1x + β2x 2 +β3x 3 +...+β n x n +∈

[0099] Among them, y is the dependent variable (ground measured water depth), x is the independent variable (preliminary inversion water depth), β0, β1,…,β n are model parameters, and ∈ is the error term.

[0100] Step 2033, model validation, verifies the prediction ability and generalization ability of the model. Use the validation set data to evaluate the prediction effect of the model, calculate the prediction error of the model, such as the mean square error, mean absolute error, etc., and optimize the model to ensure its generalization ability and robustness. Model validation verifies the generalization ability and robustness of the model by evaluating the performance of the model on unseen validation set data. The specific operation is: using the validation set data, calculate the mean square error, mean absolute error and other indicators predicted by the model. Check the residual distribution between the predicted value and the actual value to ensure that it conforms to the normal distribution and has no obvious deviation or heteroscedasticity. According to the verification results, adjust the model parameters or try different model structures to improve the prediction accuracy.

[0101] Error calculation is mainly used to quantify the difference between the model prediction value and the actual observation value. Commonly used error indicators include mean square error, mean absolute error, etc.

[0102] The mean squared error measures the average of the squares of the differences between the predicted values ​​and the actual values, and its formula is:

[0103] Among them, y i is the actual value of the ith sample, is the model's predicted value for the sample, and N is the total number of samples.

[0104] The mean absolute error measures the average of the absolute differences between the predicted values ​​and the actual values, and its formula is:

[0105]

[0106] Residual analysis is a further diagnosis of the model prediction results to check whether the model assumptions are valid and whether there are systematic deviations. The residual is the difference between the actual observation value and the model prediction value, that is:

[0107] Check whether the residuals follow a normal distribution. Common methods include drawing a histogram of the residuals, a QQ plot (QuantileQuantile Plot), and applying a normality test (such as the ShapiroWilk test). Heteroskedasticity means that the variance of the residuals changes with changes in the predicted value or an explanatory variable. Common test methods include the BreuschPagan test and the White test. Check whether there is a systematic deviation in the residuals, that is, whether the mean of the residuals deviates significantly from zero. This can be observed by calculating the mean of the residuals or drawing a scatter plot of the residuals against the predicted values.

[0108] Step 2034, result correction, use the trained regression model to correct the preliminary inversion results. Input the preliminary inversion results into the regression model, perform error correction, output the corrected water depth inversion results, and perform a preliminary check on the correction results to ensure their rationality and accuracy. Use the trained regression model, take the preliminary inversion results as input, and output the corrected water depth estimation value to improve the accuracy of the inversion results.

[0109] Step 204: Result verification and output, check the corrected inversion results to ensure their quality and reliability. This includes comparing with the measured values ​​of the water level gauge and calculating statistical indicators (RMSE, coefficient of determination R 2 The inversion results are visualized and output in the form of high-resolution maps to intuitively display the water depth distribution.

[0110] Comparing the inverted data with ground-truth data (e.g., measurements from a water level gauge) is a direct way to verify the accuracy of the inverted results. The ground-truth data provides a real-world reference point for evaluating the accuracy of the model predictions. To quantitatively evaluate the model performance, a series of statistical indices can be calculated, such as the root mean square error, the coefficient of determination (R 2 )wait.

[0111] The root mean square error measures the root mean square of the difference between the predicted value and the actual value, and its formula is:

[0112] Among them, y i is the actual value of the ith sample, is the model's predicted value for the sample, and N is the total number of samples.

[0113] Coefficient of Determination (R 2 ): R 2 It reflects the proportion of the variation explained by the model to the total variation. Its value ranges from 0 to 1. The closer the value is to 1, the better the model fit is. 2 The formula is:

[0114]

[0115] in, is the average of all actual observations.

[0116] Displaying the inversion results in the form of high-resolution maps can intuitively understand the distribution of water depth. Specifically, it involves the application of geographic information system (GIS) software to convert numerical data into spatial distribution maps. For water depth data, color gradients can be used to represent water bodies of different depths, allowing observers to quickly identify depth changes in water bodies.

[0117] The inversion process is essentially solving the inverse problem, that is, inferring unknown physical parameters or states from indirect observational data. In hydrology, this may mean inferring water depth distribution from satellite imagery or sonar data. The inversion algorithm attempts to find an optimal solution that minimizes the difference between the model prediction and the observed data. This difference is usually measured by a loss function (such as MSE), and the optimization process may use various mathematical methods such as least squares, maximum likelihood estimation, or Bayesian inference. Finally, the verification and output steps can ensure that the inversion results are not only mathematically reasonable, but also accurate and reliable in real-world application scenarios.

[0118] The step 204 specifically includes the following steps:

[0119] Step 2041, result comparison, compare the corrected inversion result with the true value of the water level gauge again. Compare the corrected water depth inversion result with the water level gauge data, calculate the difference between the two, evaluate the correction effect, compare and analyze the results, and find out the possible sources of error and improvement directions. True value acquisition: First, it is necessary to ensure that the measurement value of the water level gauge is accurate as the "true value". Water level gauges are usually deployed in fixed positions and can continuously record water level changes and provide accurate water depth data in a time series. These data can be used as a benchmark to verify the inversion results.

[0120] Data registration: Since the water level gauge data is a point measurement, and the inversion results are usually spatially distributed, it is necessary to match the location of the water level gauge with the inversion grid and extract the inverted water depth value at the corresponding location. Error calculation: Calculate the difference between the inversion result and the water level gauge data. Common error indicators include the root mean square error, which can reflect the square root of the average deviation between the predicted value and the observed value, and gives the absolute size of the error; in addition, there is the mean absolute error, which reflects the average absolute deviation between the predicted value and the observed value, regardless of the positive or negative sign. Correction effect evaluation: By comparing the RMSE or MAE before and after correction, the effectiveness of the correction method can be evaluated. If the error after correction is significantly reduced, it means that the correction method has effectively improved the accuracy of the inversion result.

[0121] Step 2042, error analysis, calculate the statistical error of the inversion result after correction. Calculate statistical error indicators such as root mean square error and mean absolute error. Analyze the error distribution, find out the areas and causes of large errors, and put forward suggestions for further improving the model and method. Calculation of statistical error indicators:

[0122] Produce an error distribution map to visualize the error value of each grid point, which helps to identify the spatial pattern of the error. Analyze the error distribution map to identify areas with higher errors and explore possible sources of error, such as inaccuracy of model assumptions, quality issues of input data, changes in environmental conditions, etc. Based on the results of the error analysis, propose targeted improvement measures, such as optimizing model parameters, adding calibration sites, and improving data preprocessing procedures.

[0123] Step 2043, visualize the output, and generate a high-resolution map with the final corrected depth inversion result. Use GIS software to generate a high-resolution depth distribution map, add necessary map elements and annotations, and ensure the clarity and readability of the map.

[0124] Through the above steps, it can be seen that the advantages of this application are:

[0125] 1. Satellite-ground collaboration significantly improves the accuracy and reliability of water depth inversion. Traditional water depth inversion methods rely on single remote sensing data and are easily affected by atmospheric conditions, illumination changes, and sensor errors, resulting in uncertainty and deviation in the inversion results. Through satellite-ground collaboration, this application combines the wide-area coverage advantages of satellite remote sensing and the high-precision measurement of ground-based water level meters to form a complementary observation system. Satellite remote sensing data provides a macroscopic perspective of water distribution and preliminary water depth estimates, while ground-based water level meters provide key control points and correction standards. The combination of the two significantly improves the accuracy and reliability of water depth inversion, especially in complex water conditions.

[0126] 2. Realize large-scale, high-timeliness water depth monitoring. This application uses the real-time monitoring capability of satellite remote sensing to cover a large area in a short period of time, achieving rapid response and continuous monitoring. This is especially important for flood warning, disaster assessment and emergency management. Compared with traditional manual measurement or local monitoring methods, this application can provide more comprehensive and timely water depth information, which helps to quickly take response measures and reduce losses.

[0127] 3. Reduced uncertainty in remote sensing data. Remote sensing data is subject to a variety of factors, including cloud cover, sensor noise, atmospheric effects, etc., which will affect the accuracy of water depth inversion. By integrating the true value data of the ground water level gauge, this application can effectively correct and verify the preliminary inversion results, significantly reducing the inherent uncertainty of remote sensing data and improving the credibility of the inversion results. Especially in complex environmental conditions, this correction mechanism is particularly important, which can ensure that the inversion results maintain a high degree of accuracy under any circumstances.

[0128] 4. Refined processing of transmission lines improves the accuracy of water depth assessment in specific areas. Transmission lines and their surrounding environment are the key areas of focus of this application because they play a vital role in power supply and safe operation. Through water depth inversion specifically for transmission line buffers, this application is able to provide high-resolution water depth information, which is of great significance for assessing the impact of floods on power grid facilities, formulating emergency response plans, and optimizing power grid design. Refined processing methods ensure that reliable and detailed water depth data can be obtained even in narrow or complex terrain.

[0129] In summary, this application not only greatly improves the accuracy and practicality of water depth inversion through satellite-ground coordination, high-timeliness monitoring, uncertainty reduction, and refined processing of key areas, but also opens up new possibilities for research and application in related fields. It is expected to become an important tool for future hydrological monitoring and disaster management.

[0130] Figure 3 is a structural diagram of a water depth inversion device according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0131] The acquisition module 31 is used to acquire historical remote sensing image data and historical ground water level meter data in the target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values; the historical ground water level meter data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number.

[0132] The determination module 32 is used to determine a physical model for representing the correlation relationship between the first historical radiation brightness value and the first historical real water depth information.

[0133] The first processing module 33 is used to input the second historical radiation brightness value into the physical model to obtain the predicted water depth information output by the physical model.

[0134] The correction module 34 is used to correct the predicted water depth information using the second historical real water depth information, and update the physical model according to the correction result to obtain the target physical model.

[0135] The second processing module 35 is used to input the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model.

[0136] Optionally, the determination module 32 is further used to perform the following steps: obtaining the average daily change value of the solar radiation intensity in the target area within a preset historical time length; if the average daily change value of the solar radiation intensity is within a first preset interval, using a single-band model to determine the correlation relationship, wherein the single-band model is used to determine the linear or nonlinear relationship between the water depth and the reflectivity of the preset band and the radiation brightness value; if the average daily change value of the solar radiation intensity is within a second preset interval, using a dual-band model to determine the correlation relationship, wherein the dual-band model is used to determine the linear or nonlinear relationship between the water depth and the ratio or logarithm of the difference of the reflectivity of two different bands and the radiation brightness value, and the maximum value of the second preset interval is used. The minimum value is greater than the maximum value of the first preset interval; if the daily average change value of the solar radiation intensity is within the third preset interval, the logarithmic ratio model is used to determine the association relationship, wherein the logarithmic ratio model is used to determine the linear or nonlinear relationship between the natural logarithm of the water depth and the reflectivity ratio and the radiation brightness value, and the minimum value of the third preset interval is greater than the maximum value of the second preset interval; if the daily average change value of the solar radiation intensity is within the fourth preset interval, the multi-band model is used to determine the association relationship, wherein the multi-band model is used to determine the linear or nonlinear relationship between the water depth and the reflectivity and radiation brightness values ​​of multiple preset bands, and the minimum value of the fourth preset interval is greater than the maximum value of the third preset interval.

[0137] Optionally, the first preset interval is 45W / m 2 Up to 55W / m 2 ; The second preset interval is 100W / m 2 Up to 180W / m 2 ; The third preset interval is 200W / m 2 Up to 220W / m 2 ; The fourth preset interval is 300W / m 2 Up to 350W / m 2 .

[0138] Optionally, determining the association relationship using a dual-band model includes: determining the association relationship using the following formula: Z = (1 / fk i )(lnr Bi -X i ) where Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water body, r Bi is the bottom reflectivity at water depth Z, L i is the radiation brightness value at water depth Z, L si is the radiation brightness value at the preset water depth, R i It is a function that represents the relationship between solar radiation, the transmission flux at the interface between the atmosphere and water, and the refractive index of the water surface.

[0139] Optionally, the correction module 34 is also used to perform the following steps: divide the second historical real water depth information into training set data and verification set data; use the training set data to train the polynomial regression model, and use the verification set data to verify the mean square error between the output value of the polynomial regression model and the true value, and when the mean square error is less than a preset threshold, obtain the trained polynomial regression model; input the predicted water depth information into the trained polynomial regression model to obtain a correction result for the predicted water depth information.

[0140] Optionally, the target predicted water depth information includes: a plurality of discrete water depth measurement points and water depth values ​​corresponding to the water depth measurement points, wherein each water depth measurement point has a geographic coordinate identifier. The water depth inversion device is also used to perform the following steps after obtaining the target predicted water depth information output by the target physical model: converting the geographic coordinate identifier of the water depth measurement point into a plane coordinate suitable for visual display; constructing a visual graphic according to the converted plane coordinates and the water depth values ​​corresponding to the water depth measurement point; setting different color mapping rules according to different ranges of water depth values ​​to color render the constructed visual graphic; and displaying the rendered visual graphic on a display device.

[0141] Optionally, the acquisition module 31 is also used to perform the following steps: determine an area whose distance from a transmission line of a preset length meets a preset distance as a target area; download initial remote sensing image data of the target area within a preset time window from a satellite image database, select remote sensing image data with a resolution greater than a first preset threshold and a cloud coverage percentage less than a second preset threshold from the initial remote sensing image data, perform radiation correction, geometric correction and atmospheric correction on the selected remote sensing image data, and obtain historical remote sensing image data; determine the target acquisition time corresponding to the initial remote sensing image data, and obtain historical ground water level gauge data corresponding to the target acquisition time.

[0142] It should be noted that the above Figure 3The modules in the embodiment may be program modules (e.g., a set of program instructions for implementing a specific function) or hardware modules. For the latter, they may be expressed in the following forms, but are not limited thereto: the expression form of the above modules is a processor, or the functions of the above modules are implemented by a processor.

[0143] It should be noted that Figure 3 The preferred implementation of the illustrated embodiment can be found in Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0144] Figure 4 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a water depth inversion method. Figure 4 As shown, the computer terminal 40 may include one or more (402a, 402b, ..., 402n are used to illustrate) processors 402 (the processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 404 for storing data, and a transmission module 406 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0145] It should be noted that the one or more processors 402 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 40. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0146] The memory 404 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the water depth inversion method in the embodiment of the present application. The processor 402 executes various functional applications and data processing by running the software programs and modules stored in the memory 404, that is, realizing the above-mentioned water depth inversion method. The memory 404 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 404 may further include a memory remotely arranged relative to the processor 402, and these remote memories may be connected to the computer terminal 40 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0147] The transmission module 406 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 40. In one example, the transmission module 406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0148] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 40 .

[0149] It should be noted that, in some optional embodiments, the above Figure 4 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 4 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0150] It should be noted that Figure 4 The computer terminal shown is used to execute Figure 1 The water depth inversion method shown, therefore the relevant explanations in the execution method of the above commands are also applicable to the electronic device and will not be repeated here.

[0151] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above water depth inversion method.

[0152] A program for a non-volatile storage medium to perform the following functions: obtaining historical remote sensing image data and historical ground water level gauge data in a target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level gauge data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; determining a physical model for representing the correlation between the first historical radiation brightness value and the first historical real water depth information; inputting the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; correcting the predicted water depth information using the second historical real water depth information, and updating the physical model according to the correction result to obtain a target physical model; inputting the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model.

[0153] An embodiment of the present application further provides an electronic device, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the above water depth inversion method is executed when the program is run.

[0154] The processor is used to run a program that performs the following functions: obtaining historical remote sensing image data and historical ground water level meter data in a target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level meter data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; determining a physical model for representing the correlation between the first historical radiation brightness value and the first historical real water depth information; inputting the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; correcting the predicted water depth information using the second historical real water depth information, and updating the physical model according to the correction result to obtain a target physical model; inputting the current radiation brightness value into the target physical model to obtain target predicted water depth information output by the target physical model.

[0155] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0156] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0157] In the above-mentioned embodiments of the present application, the collected information is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary protection measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

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

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

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

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0162] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A water depth inversion method, characterized in that: include: Acquire historical remote sensing image data and historical ground water level gauge data in the target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level gauge data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; Determining a physical model for representing the association relationship between the first historical radiance value and the first historical true water depth information; Inputting the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; Correcting the predicted water depth information using the second historical real water depth information, and updating the physical model according to the correction result to obtain a target physical model; The current radiation brightness value is input into the target physical model to obtain the target predicted water depth information output by the target physical model.

2. The method according to claim 1, characterized in that Determining a physical model for representing the association relationship between the first historical radiance value and the first historical real water depth information includes: Obtain the average daily change value of the solar radiation intensity of the target area within a preset historical period; If the daily average change value of the solar radiation intensity is within a first preset interval, the correlation relationship is determined using a single-band model, wherein the single-band model is used to determine a linear or nonlinear relationship between water depth and the reflectivity and radiation brightness value of a preset band; If the daily average change value of the solar radiation intensity is within a second preset interval, the correlation relationship is determined using a dual-band model, wherein the dual-band model is used to determine the linear or nonlinear relationship between the water depth and the ratio or logarithm of the difference of the reflectivity of two different bands and the radiation brightness value, and the minimum value of the second preset interval is greater than the maximum value of the first preset interval; If the daily average change value of the solar radiation intensity is within a third preset interval, the correlation relationship is determined using a logarithmic ratio model, wherein the logarithmic ratio model is used to determine a linear or nonlinear relationship between the natural logarithm of the water depth and reflectivity ratio and the radiation brightness value, and the minimum value of the third preset interval is greater than the maximum value of the second preset interval; If the average daily change in solar radiation intensity is within a fourth preset interval, the correlation relationship is determined using a multi-band model, wherein the multi-band model is used to determine the linear or nonlinear relationship between water depth and the reflectivity and radiation brightness values ​​of multiple preset bands, and the minimum value of the fourth preset interval is greater than the maximum value of the third preset interval.

3. The method according to claim 2, characterized in that The first preset interval is 45W / m 2 Up to 55W / m 2 The second preset interval is 100W / m 2 Up to 180W / m 2 ; The third preset interval is 200W / m 2 Up to 220W / m 2 ; The fourth preset interval is 300W / m 2 Up to 350W / m 2 .

4. The method according to claim 1, characterized in that: Determining the association relationship using a dual-band model includes: determining the association relationship using the following formula: Z=(1 / fk i )(lnr Bi -X i ) Among them, Z is the relative water depth, f is the length of the water path, k i is the effective attenuation coefficient of water body, r Bi is the bottom reflectivity at water depth Z, L i is the radiation brightness value at water depth Z, L si is the radiation brightness value at the preset water depth, R i It is a function that represents the relationship between solar radiation, the transmission flux at the interface between the atmosphere and water, and the refractive index of the water surface.

5. The method according to claim 1, characterized in that Correcting the predicted water depth information using the second historical real water depth information includes: Dividing the second historical real water depth information into training set data and verification set data; The training set data is used to train the polynomial regression model, and the validation set data is used to validate the mean square error between the output value of the polynomial regression model and the true value, and when the mean square error is less than a preset threshold, a polynomial regression model that has completed the training is obtained; The predicted water depth information is input into the trained polynomial regression model to obtain a correction result of the predicted water depth information.

6. The method according to claim 1, characterized in that The target predicted water depth information includes: a plurality of discrete water depth measurement points and water depth values ​​corresponding to the water depth measurement points, wherein each of the water depth measurement points has a geographic coordinate identifier; After obtaining the target predicted water depth information output by the target physical model, the method further includes: Converting the geographic coordinates of the depth measurement points into plane coordinates suitable for visual display; Constructing a visualization graph according to the converted plane coordinates and the water depth values ​​corresponding to the water depth measurement points; Different color mapping rules are set according to different ranges of water depth values ​​to render the constructed visualization graphics; The rendered visualization graphics are displayed on the display device.

7. The method according to claim 1, characterized in that Obtain historical remote sensing image data and historical ground water level gauge data in the target area, including: Determine an area whose distance from a transmission line of a preset length meets a preset distance as the target area; Downloading the initial remote sensing image data of the target area within a preset time window from a satellite image database, selecting remote sensing image data with a resolution greater than a first preset threshold and a cloud coverage percentage less than a second preset threshold from the initial remote sensing image data, performing radiation correction, geometric correction and atmospheric correction on the selected remote sensing image data, and obtaining historical remote sensing image data; The target acquisition time corresponding to the initial remote sensing image data is determined, and the historical ground water level gauge data corresponding to the target acquisition time is obtained.

8. A water depth inversion device, characterized in that: include: An acquisition module, used for acquiring historical remote sensing image data and historical ground water level meter data in a target area, wherein the historical remote sensing image data includes: a first number of first historical radiation brightness values ​​and a second number of second historical radiation brightness values, and the historical ground water level meter data includes: a third number of first historical real water depth information and a fourth number of second historical real water depth information, wherein the second number is smaller than the first number, and the fourth number is smaller than the third number; A determination module, used to determine a physical model for representing the association relationship between the first historical radiance value and the first historical real water depth information; A first processing module, used for inputting the second historical radiation brightness value into the physical model to obtain predicted water depth information output by the physical model; A correction module, used to correct the predicted water depth information using the second historical real water depth information, and update the physical model according to the correction result to obtain a target physical model; The second processing module is used to input the current radiation brightness value into the target physical model to obtain the target predicted water depth information output by the target physical model.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the water depth inversion method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the program executes the water depth inversion method described in any one of claims 1 to 7 when running.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the water depth inversion method described in any one of claims 1 to 7 is implemented.

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