Offshore sediment mathematical model optimization method, device, electronic equipment and storage medium

Abstract

The present application relates to the technical field of electronic digital data processing for sediment simulation, and provides a method, device, electronic device and storage medium for optimizing a mathematical model of offshore sediment, the method comprising: obtaining a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and obtaining multispectral remote sensing image data of the target sea area at at least one sampling time; based on the multispectral remote sensing image data, determining the sediment content inversion data of the surface layer of the water body of the target sea area; based on the sediment content inversion data, determining the first sediment content contour line corresponding to the target sea area at at least one sampling time; based on the first sediment content contour line and the first time series corresponding to a plurality of observation points, verifying the pre-constructed mathematical model of offshore sediment in the target sea area, and optimizing the parameters of the mathematical model of offshore sediment. The present application can improve the simulation accuracy of the mathematical model of offshore sediment.

Description

Technical Field

[0001] The present application belongs to the technical field of electric digital data processing for sediment volume simulation, and in particular, relates to a method, device, electronic equipment and storage medium for optimizing a mathematical model of offshore sediment. Background Art

[0002] Improving the simulation accuracy of offshore sediment mathematical models has always been a major problem in the industry. On the one hand, this is because the sediment movement process in the offshore is complex and some theoretical and mathematical processes are not yet accurate. On the other hand, it is costly to obtain a large amount of measured data. In practical applications, only very limited measured data can be used to calibrate the model, resulting in low simulation accuracy of offshore sediment mathematical models, which affects the prediction effect of offshore sand content. Summary of the invention

[0003] The embodiments of the present application provide a method, device, electronic device and storage medium for optimizing a mathematical model of offshore sediment to solve the problems of low simulation accuracy of the mathematical model of offshore sediment and unsatisfactory prediction effect of offshore sediment content.

[0004] This application is implemented through the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing a mathematical model of offshore sediment, comprising:

[0006] Acquire a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and acquire multispectral remote sensing image data of the target sea area at at least one sampling time;

[0007] Based on the multispectral remote sensing image data, determining the sediment content inversion data of the surface layer of the water body of the target sea area; based on the sediment content inversion data, determining the first sediment content contour line corresponding to the target sea area at the at least one sampling time;

[0008] Based on the first sediment content contour lines and the first time series respectively corresponding to the multiple observation points, the pre-constructed offshore sediment mathematical model of the target sea area is verified, and the parameters of the offshore sediment mathematical model are optimized.

[0009] In combination with the first aspect, in some embodiments, the pre-constructed offshore sediment mathematical model of the target sea area is verified based on the first sediment content contour line and the first time series respectively corresponding to the multiple observation points, and the parameters of the offshore sediment mathematical model are optimized, including:

[0010] Based on the offshore sediment mathematical model, obtaining first sediment content simulation data of the surface layer of the water body of the target sea area at the at least one sampling time, and obtaining second sediment content simulation data of the surface layer of the water body of the target sea area within the sampling period;

[0011] Based on the first sediment content simulation data and the first sediment content contour line, verify the spatial dimension of the offshore sediment mathematical model, and optimize the parameters of the offshore sediment mathematical model;

[0012] Based on the second sediment content simulation data and the first time series corresponding to the multiple observation points, the nearshore sediment mathematical model that has completed the spatial dimension verification is verified in terms of time dimension, and the parameters of the nearshore sediment mathematical model that has completed the spatial dimension verification are optimized.

[0013] In combination with the first aspect, in some embodiments, verifying the spatial dimension of the offshore sediment mathematical model based on the first sediment content simulation data and the first sediment content contour line, and optimizing the parameters of the offshore sediment mathematical model, includes:

[0014] Based on the first sediment content simulation data, extracting a second sediment content contour line corresponding to the target sea area at the at least one sampling time; wherein the second sediment content contour line and the first sediment content contour line have the same contour interval;

[0015] Based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line, analyzing the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time;

[0016] Based on the spatial distribution similarity, the offshore sediment mathematical model is verified, and based on the verification result, the controllable parameters of the offshore sediment mathematical model are adjusted; wherein the controllable parameters include roughness, siltation rate and shear stress coefficient.

[0017] In combination with the first aspect, in some embodiments, analyzing the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line includes:

[0018] Extracting the spatial features of the contour lines corresponding to each value in the first sediment content contour line at a certain moment, and extracting the spatial features of the contour lines corresponding to each value in the second sediment content contour line at the same moment; wherein the spatial features include curvature, curvature radius, length, starting point and end point;

[0019] Calculate the curvature similarity, curvature radius similarity, length similarity, starting point similarity and end point similarity corresponding to the contour lines with the same value in the first sediment content contour line and the second sediment content contour line corresponding to the certain moment;

[0020] Based on a preset weight vector, the curvature similarity, the curvature radius similarity, the length similarity, the starting point similarity and the end point similarity, the spatial feature similarity of each contour line of the same value corresponding to the certain moment is determined;

[0021] Based on the similarity of spatial characteristics of the contour lines of the same value corresponding to the certain moment, the similarity of spatial distribution of the first sediment content contour line and the second sediment content contour line at the certain moment is determined.

[0022] In combination with the first aspect, in some embodiments, the verifying the offshore sediment mathematical model based on the spatial distribution similarity, and adjusting the controllable parameters of the offshore sediment mathematical model based on the verification result, includes:

[0023] Based on the spatial distribution similarity, determining a target approximation function;

[0024] According to the target approximation function, the offshore sediment mathematical model is verified to obtain a verification result;

[0025] According to the verification results, based on the target approximation function, the controllable parameters of the offshore sediment mathematical model are adjusted through an optimization algorithm.

[0026] In combination with the first aspect, in some embodiments, based on the second sediment content simulation data and the first time series corresponding to the multiple observation points, the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification is verified, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are optimized, including:

[0027] Based on the second simulated sediment content data, extracting a second time series of simulated sediment content corresponding to each of the plurality of observation points in the target sea area during the sampling period;

[0028] Based on the first time series and the second time series corresponding to the multiple observation points, the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification is verified, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are adjusted with the similarity of the time series data curve as the optimization goal.

[0029] In combination with the first aspect, in some embodiments, the multispectral remote sensing image data includes data corresponding to the red and green bands of each pixel; and determining the inversion data of the sediment content of the surface layer of the water body of the target sea area based on the multispectral remote sensing image data includes:

[0030] Determine the red-green band ratio corresponding to each pixel in the multispectral remote sensing image data based on the data corresponding to the red and green bands of each pixel in the multispectral image data;

[0031] For pixels whose red-green band ratio is less than or equal to the first ratio, based on the first sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0032] For pixels whose red-green band ratio is greater than or equal to the second ratio, based on the second sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0033] For a pixel whose red-green band ratio is greater than the first ratio and less than the second ratio, based on the sediment content inversion formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0034] Wherein, the first ratio is less than the second ratio, and the first ratio and the second ratio are preset values ​​determined based on the probability density distribution of the red-green band ratio of the surface layer of the water body of the target sea area; the first sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple first positions and the red-green band ratio of the pixels corresponding to the multiple first positions, and the first position is the sea area position corresponding to the pixel whose red-green band ratio is less than or equal to the first ratio; the second sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple second positions and the red-green band ratio of the pixels corresponding to the multiple second positions, and the second position is the sea area position corresponding to the pixel whose red-green band ratio is greater than or equal to the second ratio;

[0035] The sediment content inversion formula is:

[0036]

[0037] in, is the inversion value of sediment content; It is the red band DN value (Digital Number, remote sensing image pixel brightness value) / water body reflection value, It is the green wave DN value / water body reflection value. That is, the ratio of the red and green bands; a and b are calibration parameters.

[0038] In a second aspect, the present application embodiment provides a device for optimizing a mathematical model of offshore sediment, including:

[0039] An acquisition module is used to acquire a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and to acquire multispectral remote sensing image data of the target sea area at at least one sampling time;

[0040] A processing module, used to determine the sediment content inversion data of the surface layer of the water body of the target sea area based on the multispectral remote sensing image data; based on the sediment content inversion data, determine the first sediment content contour line corresponding to the target sea area at the at least one sampling time;

[0041] The optimization module is used to verify the pre-constructed offshore sediment mathematical model of the target sea area based on the first sediment content contour line and the first time series corresponding to the multiple observation points, and optimize the parameters of the offshore sediment mathematical model.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the mathematical model of offshore sediment as described in any one of the first aspects is implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing the mathematical model of offshore sediment as described in any one of the first aspects is implemented.

[0044] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the offshore sediment mathematical model optimization method described in any one of the first aspects above.

[0045] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0046] The embodiment of the present application provides a method, device, electronic device and storage medium for optimizing a mathematical model of offshore sediment, which verifies the pre-constructed mathematical model of offshore sediment in the target sea area through the measured sediment content collected at the observation point and the sediment content contour line determined by the multispectral remote sensing image data, and optimizes the parameters of the mathematical model of offshore sediment. In this way, a large amount of verification data is provided by multispectral remote sensing image data, and the model is verified by using multispectral remote sensing image data and the measured data of sediment content, which can improve the simulation accuracy of the mathematical model of offshore sediment to a certain extent. In addition, the sediment content contour line is determined by multispectral remote sensing data. Since the sediment content contour line can reflect the distribution trend of the sediment content, the present application verifies the mathematical model of offshore sediment through the sediment content contour line, and can also optimize the simulation effect of the model from the spatial distribution trend, thereby further improving the simulation accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 This is a schematic diagram of an application scenario of a method for optimizing a mathematical model of offshore sediment provided in an embodiment of the present application;

[0049] Figure 2 It is a flow chart of a method for optimizing a mathematical model of offshore sediment provided in one embodiment of the present application;

[0050] Figure 3 It is a distribution diagram of the first sediment content contour line provided in an embodiment of the present application;

[0051] Figure 4 It is a distribution diagram of the second sediment content contour line provided in one embodiment of the present application;

[0052] Figure 5 It is a structural schematic diagram of a device for optimizing a mathematical model of offshore sediment provided in an embodiment of the present application;

[0053] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0055] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0056] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0057] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0058] The embodiment of the present application provides a method for optimizing a mathematical model of offshore sediment, firstly obtaining a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and obtaining multispectral remote sensing image data of the target sea area at at least one sampling time; then, based on the multispectral remote sensing image data, determining the sediment content inversion data of the surface layer of the water body of the target sea area, and based on the sediment content inversion data, determining the first sediment content contour line corresponding to the target sea area at at least one sampling time; finally, based on the first sediment content contour line and the first time series corresponding to a plurality of observation points, verifying the pre-constructed mathematical model of offshore sediment in the target sea area, and optimizing the parameters of the mathematical model of offshore sediment. In this way, the measured sediment content is combined with the multispectral remote sensing image data to jointly verify the mathematical model of offshore sediment, and a large amount of verification data is provided by the multispectral remote sensing image data, which can improve the simulation accuracy of the mathematical model of offshore sediment to a certain extent. Moreover, the present application also determines the sediment content contour lines through multi-spectral remote sensing data. Since the sediment content contour lines can reflect the distribution trend of the sediment content, the present application verifies the mathematical model of offshore sediment through the sediment content contour lines, and can also optimize the simulation effect of the model from the perspective of spatial distribution trend, thereby further improving the simulation accuracy of the model.

[0059] Figure 1 Schematic diagram of an application scenario of the offshore sediment mathematical model optimization method provided by an embodiment of the present application. Figure 1 In this scenario, the target sea area is a sea area close to the land. In order to predict the sediment content of the surface layer of the water body in the target sea area, a corresponding offshore sediment mathematical model is established. When verifying the model, on the one hand, the measured sediment content of the target sea area is obtained, and the first time series is determined based on the measured sediment content, and then the offshore sediment mathematical model is verified using the first time series; on the other hand, the multispectral remote sensing image of the target sea area is obtained, and the first sediment content contour line is determined based on the multispectral remote sensing image, and then the offshore sediment mathematical model is verified using the first sediment content contour line. In this way, the model is verified in the time dimension through the first time series of the measured sediment content, and the model is verified in the spatial dimension through the first sediment content contour line, thereby effectively improving the simulation accuracy of the offshore sediment mathematical model. Using the offshore sediment mathematical model verified in the above application scenario, the sediment content of the surface layer of the water body in the target sea area can be predicted more accurately.

[0060] In practical applications, the nearshore sediment mathematical model in the embodiment of the present application can be a sediment mathematical model of the sea area near a coastal waterway. Through the above-mentioned nearshore sediment mathematical model optimization method, the sediment mathematical model can more accurately simulate the sand content distribution and change characteristics of the sea area near the coastal waterway, so as to more accurately understand the sediment movement laws in the sea area where the coastal waterway is located, which is helpful for engineering design and construction, especially in aspects such as channel dredging, port construction and maintenance.

[0061] At present, there are many open-source data of multispectral remote sensing images, which are easy to obtain, and the accuracy and quality are getting higher and higher. Therefore, it is possible to consider using multispectral remote sensing image inversion technology to obtain the sediment content of the surface layer of the water body, thereby enriching the verification data of the offshore sediment mathematical model. It should be noted that the offshore sediment mathematical model and the offshore sediment mathematical model optimization method of the embodiment of the present application are both suitable for waters with obvious offshore sediment movement characteristics. The offshore sediment movement characteristics are mainly affected by the following factors:

[0062] 1. Hydrodynamic conditions: including tides, wind and waves, river input, etc. These factors determine the transportation and distribution patterns of sediment.

[0063] 2. Characteristics of sediment itself: The physical characteristics of sediment, such as particle size, density and composition, affect its suspension, sedimentation and transportation behavior in water bodies.

[0064] 3. Topography: The undulating changes in the seabed topography affect the water flow, which in turn affects the movement of sediment.

[0065] 4. Biological action: Biological activities, such as bioturbation, can change the stability and distribution of sediment.

[0066] 5. Human activities: such as land reclamation and port construction, may change the original hydrodynamic conditions and sediment transport paths.

[0067] In waters with obvious characteristics of offshore sediment movement, the sediment content characteristics of the surface of the water body are more significant. By inverting multispectral remote sensing images, sufficient effective data can be obtained, so that these effective data can be used to verify the model. However, for general rivers, lakes or open seas, the sediment content characteristics of the surface of the water body are not significant, and the sediment content is even close to 0, so it is impossible to invert effective data from their multispectral remote sensing images.

[0068] In summary, the model optimization method provided in the embodiment of the present application is generally applicable to offshore waters, and is particularly applicable to waters near coastal waterways where sediment movement is active. In particular, offshore waters generally refer to waters that are relatively close to land. In one embodiment, waters where sediment movement is active may refer to waters where the average sediment content of the surface layer is greater than 0.01 kg / m 3In an embodiment of the present application, the offshore sea area may be a sea area whose outer boundary is less than a preset distance from the land, or the offshore sea area may be a coastal waters area whose surface sand content can be inverted through multispectral imaging. Since the distribution of sediment in different offshore waters may vary, the preset distance may be adjusted according to the actual geographical conditions. In one embodiment, the preset distance may be determined based on the sand content of the surface water body. For example, the preset distance may be 20 nautical miles, that is, the outer boundary of the current target sea area is 20 nautical miles away from the land, and the sand content of the surface water body is 0.01 kg / m 3 , and within the outer boundary of the target sea area, the sediment content of the surface water is higher than 0.01kg / m 3 For another example, the preset distance may also be 15 nautical miles, etc. Of course, for some non-offshore waters with significant offshore sediment movement characteristics, the offshore sediment mathematical model optimization method provided in the embodiment of the present application may also be used to optimize its sediment mathematical model.

[0069] Figure 2 is a flow chart of a method for optimizing a mathematical model of offshore sediment provided by an embodiment of the present application, with reference to Figure 2 , the optimization method of the offshore sediment mathematical model is described in detail as follows:

[0070] Step 101, obtaining a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and obtaining multispectral remote sensing image data of the target sea area at at least one sampling time.

[0071] The target sea area may be a sea area whose outer boundary is less than a preset distance from the land, that is, the target sea area may be a near-shore sea area.

[0072] In an embodiment of the present application, a suitable point can be selected in the target sea area to establish a sediment content observation station, that is, an observation point. Each observation point should at least observe the sediment content of the surface layer of the nearby water body, and the sediment content of other layers can be observed as needed. In practical applications, sediment content observation stations can be established based on offshore buoys and other marine observation platforms. Each sediment content observation station can be equipped with a communication module, such as a 4G, 5G communication module or a satellite communication module, etc., through which the observation data (measured sediment content) is transmitted back to the control center in real time, so as to record, analyze, and predict the sediment content of the target sea area.

[0073] For the above-mentioned multispectral remote sensing image data, the multispectral remote sensing image data should contain at least four bands: red, green, blue and near-infrared. In practical applications, appropriate remote sensing image data can be selected according to the scope of the established offshore sediment mathematical model and the model grid resolution. For models with large scope and large grid step, medium and low resolution remote sensing images can be selected; such as Gaofen Satellite No. 1 and No. 6 low-resolution series images, Landsat-8 / 9 series images, MODIS images, etc. For models with small scope and small grid step, medium and high resolution images can be selected, such as Gaofen No. 1, No. 2, No. 6 high-resolution series images, etc. When selecting a data source, the image update cycle should also be considered to improve the accuracy of verification.

[0074] In the embodiment of the present application, the multispectral remote sensing image data includes data corresponding to the red and green bands of each pixel. For example, the DN value of the green band and the DN value of the red band. The multispectral remote sensing image data also includes the water body reflectance value corresponding to each pixel. Here, the DN value is the brightness value of the remote sensing image pixel, which records the gray value of the object.

[0075] In one possible implementation, multispectral remote sensing image data of the target sea area at a sampling time can be obtained, so that the sediment content data of the sea area corresponding to each pixel at the sampling time can be inverted. In another possible implementation, multispectral remote sensing image data of the target sea area at multiple sampling times can also be obtained, so that the sediment content data of the sea area corresponding to each pixel at multiple sampling times can be inverted, and the model can be verified using the sediment content inversion data at multiple sampling times, which helps to improve the accuracy of the model in simulating long-term sediment content.

[0076] Step 102, based on the multispectral remote sensing image data, determine the sediment content inversion data of the surface layer of the water body of the target sea area; based on the sediment content inversion data, determine the first sediment content contour line corresponding to the target sea area at at least one sampling time.

[0077] In some embodiments, determining the sediment content inversion data of the surface layer of the water body of the target sea area based on the multispectral remote sensing image data in step 102 can be achieved through steps 201 to 204:

[0078] Step 201, based on the data corresponding to the red and green bands of each pixel in the multispectral image data, determine the red-green band ratio corresponding to each pixel in the multispectral remote sensing image data.

[0079] In one implementation, the data corresponding to the red band includes the DN value and water body reflection value of the red band, and the data corresponding to the green band includes the DN value and water body reflection value of the green band. The red-green band ratio may be the ratio of the red wave relative reflection value to the green wave relative reflection value; wherein the red wave relative reflection value is the DN value of the red band and the water body reflection value, and the green wave relative reflection value is the DN value of the green band and the water body reflection value.

[0080] Step 202, for pixels whose red-green band ratio is less than or equal to the first ratio, based on the first sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel.

[0081] Step 203, for pixels whose red-green band ratio is greater than or equal to the second ratio, based on the second sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel.

[0082] Step 204, for pixels whose red-green band ratio is greater than the first ratio and less than the second ratio, based on the sediment content inversion formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel.

[0083] Among them, the first ratio is less than the second ratio, and the first ratio and the second ratio are preset values ​​determined based on the probability density distribution of the red-green band ratio of the surface layer of the water body in the target sea area. The first sediment content fitting formula can be obtained by fitting based on the measured sediment content data of multiple first positions and the red-green band ratio of the pixels corresponding to multiple first positions; wherein the first position is the sea area position corresponding to the pixel whose red-green band ratio is less than or equal to the first ratio. Similarly, the second sediment content fitting formula can be obtained by fitting based on the measured sediment content data of multiple second positions and the red-green band ratio of the pixels corresponding to multiple second positions; wherein the second position is the sea area position corresponding to the pixel whose red-green band ratio is greater than or equal to the second ratio.

[0084] In the above embodiment, the inversion formula for sediment content is:

[0085]

[0086] in, is the inversion value of sediment content; is the red band DN value / water body reflection value, It is the green wave DN value / water body reflection value. That is, the ratio of the red and green bands; a and b are calibration parameters.

[0087] The above calibration parameters a and b can be calibrated by measuring the sediment content.

[0088] It should be noted that, in the above-mentioned embodiment, when inverting the sand content inversion data of the surface layer of the water body of the target sea area through multispectral remote sensing image data, the sand content of the surface layer of the water body of the target sea area is divided into three cases. This is because, in the offshore waters, the red-green band ratio of the surface sandy water body is roughly linearly related to the natural logarithm of the sand content, so it can be inverted by the above-mentioned sand content inversion formula. However, in the offshore waters, for the local waters along the coast, especially the coast of the estuary, the water body is relatively turbid, so the sand content of the surface layer of the water body is very high, and the above-mentioned sand content inversion formula is not applicable at this time. For the outer boundary of the target sea area, the seawater depth is large, and the surface layer of the water body is usually clearer, that is, the sand content is very low, and the above-mentioned sand content inversion formula is not applicable at this time.

[0089] Based on the above situation, the inventor of the present application has studied and analyzed the relationship between the red-green band ratio of the water body in the remote sensing image and the sediment content of the surface of the water body, and found that the red-green band ratio of the water body in the remote sensing image generally conforms to the normal distribution characteristics. Based on the probability density function of the red-green band ratio, the corresponding relationship between the red-green band ratio and the sediment content can be divided into sections. For example, the situation where the red-green band ratio is not greater than the first ratio is divided into the first section, the situation where the red-green band ratio is greater than the first ratio and less than the second ratio is divided into the second section, and the situation where the red-green band ratio is not less than the second ratio is divided into the third section. Among them, the first section corresponds to the situation where the sediment content of the surface of the water body is very low, and the third section corresponds to the situation where the sediment content of the surface of the water body is very high. Since the situations corresponding to the first and third sections are not suitable for using the sediment content inversion formula, for the situations of the first and third sections, the least squares method can be used to determine the corresponding sediment content fitting formula using the measured sediment content data and the red-green band ratio data.

[0090] The first ratio and the second ratio can be set specifically according to the actual scenario. For example, for a certain target sea area, when the value range of the red-green band ratio is 0 to the first ratio, the probability corresponding to the value range is 3%; when the value range of the red-green band ratio is the second ratio to the maximum value, the probability corresponding to the value range is 3%. That is, the first ratio and the second ratio can be determined by the probability density function and occurrence probability of the red-green band ratio.

[0091] In some embodiments, before using multispectral remote sensing images for quantitative inversion of sediment content, necessary data preprocessing should be performed, such as radiation correction, geometric correction, atmospheric correction, cropping, fusion and splicing, etc. The relevant processing methods are relatively mature and there are many mature software to provide support. If automated processing is required, ENVI IDL interface programming can be used for automated processing. After the automated processing is completed, the surface sediment content of the target sea area can be obtained by calculating the first sediment content fitting formula, the second sediment content fitting formula and the sediment content inversion formula.

[0092] In some embodiments, the implementation process of determining the first sediment content contour line corresponding to the target sea area at at least one sampling time based on the sediment content inversion data in the above step 102 can be: based on a preset isovalue interval, extracting the first sediment content contour line from the sediment content inversion data at the same sampling time.

[0093] It can be understood that, when the isovalue interval has been set, each isovalue line in the first sand content isovalue line can be determined in sequence according to the preset minimum sand content and the isovalue interval; each isovalue line in the first sand content isovalue line can also be determined in sequence according to the preset maximum sand content and the isovalue interval. Finally, the first sand content isovalue line can be represented by an image.

[0094] Step 103, based on the first sediment content contour line and the first time series corresponding to the plurality of observation points, the pre-constructed offshore sediment mathematical model of the target sea area is verified, and the parameters of the offshore sediment mathematical model are optimized.

[0095] In some embodiments, step 103 may be implemented through steps 301 to 303:

[0096] Step 301, based on the offshore sediment mathematical model, obtain the first sediment content simulation data of the surface layer of the water body of the target sea area at at least one sampling time, and obtain the second sediment content simulation data of the surface layer of the water body of the target sea area within the sampling period.

[0097] In the embodiment of the present application, a mathematical model covering the target sea area can be established based on the "Specifications for Numerical Simulation of Water Transport Engineering". It is recommended that the model grid adopts a triangulated network with higher precision. If there are key areas of concern such as port channels and cross-sea bridges in the target sea area, the simulation accuracy can be improved by encrypting the grid of the area.

[0098] Step 302: Based on the first sediment content simulation data and the first sediment content contour line, the spatial dimension of the offshore sediment mathematical model is verified, and the parameters of the offshore sediment mathematical model are optimized.

[0099] In this embodiment, step 302 can be implemented through steps 1 to 3:

[0100] Step 1: Based on the first sediment content simulation data, extract the second sediment content contour line corresponding to the target sea area at at least one sampling time; wherein the second sediment content contour line and the first sediment content contour line have the same contour interval.

[0101] Step 2: Based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line, analyze the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time.

[0102] In a possible implementation, step 2 may include: first, extracting the spatial characteristics of the contour lines corresponding to each value in the first sediment content contour line at a certain moment, and extracting the spatial characteristics of the contour lines corresponding to each value in the second sediment content contour line at the same moment; wherein the spatial characteristics include curvature, radius of curvature, length, starting point and end point. Then, calculating the curvature similarity, radius of curvature similarity, length similarity, starting point similarity and end point similarity corresponding to the contour lines of the same value in the first sediment content contour line and the second sediment content contour line corresponding to the certain moment. Afterwards, based on the preset weight vector, curvature similarity, radius of curvature similarity, length similarity, starting point similarity and end point similarity, determine the spatial characteristic similarity of the contour lines of the same value corresponding to the certain moment. Finally, based on the spatial characteristic similarity of the contour lines of the same value corresponding to the certain moment, determine the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the certain moment.

[0103] The above implementation method can determine the shape of each contour line by extracting the curvature, curvature radius, length, starting point and end point of each contour line in the first sediment content contour line and the second sediment content contour line. By comparing the shapes of contour lines with the same values ​​in the sediment content inversion data and the sediment content simulation data, the sediment content simulation accuracy of the offshore sediment mathematical model can be judged from the perspective of spatial distribution trend. Figure 3 is a distribution diagram of the first sediment content contour line provided in an embodiment of the present application, Figure 4 is a schematic diagram of the distribution of the second sediment content contour line provided in one embodiment of the present application. Figure 3 and Figure 4 , the first sediment content contour line and the second sediment content contour line both contain three closed contour lines (sand content is x1, x2, x3) and two open contour lines (sand content is x4, x5). It can be seen that the first sediment content contour line and the second sediment content contour line are not exactly the same, but the shapes of the contour lines with the same sediment content value are very similar, that is, the model's prediction results of the target sea area sediment content are closer to the actual situation in terms of distribution trend. On this basis, continuing to adjust the model parameters can make the spatial distribution of the second sediment content contour line closer and closer to the actual situation.

[0104] In another possible implementation, step 2 may include: extracting a first contour line and a second contour line of a certain value from the first sediment content contour line and the second sediment content contour line at the same time; calculating the centroids of the first contour line and the second contour line respectively; taking a preset number of first points on the first contour line according to a preset arc length interval, and taking a preset number of second points on the second contour line according to a preset arc length interval; calculating the Euclidean distance from each first point to the centroid of the first contour line, and taking the Euclidean distances from all first points to the centroid of the first contour line as a first set; calculating the Euclidean distance from each second point to the centroid of the second contour line, and taking the Euclidean distances from all second points to the centroid of the second contour line as a second set; calculating the land movement distance (Earth Mover's Distance, EMD, or bulldozer distance) of the first set and the second set; and taking the inverse of the sum of the land movement distances corresponding to the first contour line and the second contour line of all values ​​as the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time.

[0105] In the above implementation, the calculation formula of the centroid can be:

[0106]

[0107] The centroid is a three-dimensional point ( x 0 , y 0 , z 0 ), including three-dimensional information of longitude, latitude and altitude, or it can also be self-made three-dimensional coordinate data; n is the preset quantity, x i is the first contour line or the second contour line extracted from i Points x Axis coordinates, y i For the i The y-axis coordinate of the point, z i For the i The z-coordinate of a point.

[0108] In the above implementation, the curve characteristics of the corresponding contour line are represented by the set of Euclidean distances from multiple points on each contour line to the centroid; the similarity between the two contour lines is described by the land movement distance between the sets corresponding to the simulated contour line and the inversion contour line of the same value. The first sediment content contour line and the second sediment content contour line are both spatial curves. The calculation of the distance from the point on the contour line to the centroid is also a three-dimensional space calculation. Therefore, the land movement distance can better represent the spatial distribution similarity between the two contour lines. It should be noted that the smaller the land movement distance, the higher the similarity. Because the first contour line and the second contour line both include multiple contour lines, the spatial distribution similarity of all contour lines is summed, and then the inverse of the sum is determined. In the parameter optimization process, the maximum inverse number can be used as the optimization target, so as to maximize the spatial distribution similarity between the first sediment content contour line and the second sediment content contour line, and improve the simulation accuracy of the model.

[0109] It should be noted that the inventor of the present application has considered that the absolute value obtained by remote sensing inversion data may have errors. If the sediment content inversion data and the sediment content simulation data are directly compared numerically, the simulation accuracy of the model obtained in this way is unreliable. Or if the simulation accuracy of the model is determined by the numerical offset of the first sediment content contour line and the second sediment content contour line, the error of the absolute value in the inversion data will also cause a large error in the judgment of the simulation accuracy. And in the embodiment of the present application, the mode of verifying the model based on the similar spatial distribution of the first sediment content contour line and the second sediment content contour line is to verify the distribution trend of the contour line, so that the reliability of the simulation accuracy determined in the verification process can be improved. In addition, the embodiment of the present application is then further verified and calibrated by time series, which can not only improve the simulation accuracy of the model from the time dimension, but also improve the accuracy of the predicted value to a certain extent, and make up for the error defect caused by the absolute value of the sediment content inversion data.

[0110] Step 3: Based on the spatial distribution similarity, the mathematical model of offshore sediment is verified, and the controllable parameters of the mathematical model of offshore sediment are adjusted based on the verification results; wherein the controllable parameters include roughness, siltation rate and shear stress coefficient.

[0111] The above step 3 uses the spatial distribution similarity to verify and calibrate the mathematical model of offshore sediment. The verification and calibration method can be: based on the spatial distribution similarity, determine the target approximation function; based on the target approximation function, verify the mathematical model of offshore sediment to obtain the verification result; based on the verification result, based on the target approximation function, adjust the controllable parameters of the mathematical model of offshore sediment through the optimization algorithm.

[0112] It can be understood that the control variable of the target approximation function is the above-mentioned controllable parameter, and the dependent variable of the target approximation function is the spatial distribution similarity. Since the second sediment content contour line is determined by the controllable parameter, when determining the spatial similarity of the first sediment content contour line and the second sediment content contour line, the controllable parameter is still the control variable. The approximation target of the above-mentioned target approximation function is to take the maximum value of the spatial distribution similarity. In this embodiment, the verification process of the model is the process of calculating the target approximation function, and the calibration process of the model is the process of making the value of the target approximation function as large as possible by adjusting the parameters.

[0113] In the above embodiment, the inversion data of the surface sediment content of the water body obtained by remote sensing inversion is used to verify the offshore sediment mathematical model, which can realize the verification of the surface sediment content field at a single moment and over a large range, and the accuracy of the model is tested and optimized in the spatial dimension, thereby effectively improving the simulation accuracy of the model. If the inversion data of the sediment content at multiple acquisition moments are obtained, the first sediment content contour lines at multiple moments can be used for synchronous verification.

[0114] Step 303, based on the second sediment content simulation data and the first time series corresponding to the plurality of observation points, the offshore sediment mathematical model that has completed the spatial dimension verification is verified in terms of time dimension, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are optimized.

[0115] In this implementation, step 303 may include: based on the second sediment content simulation data, extracting the second time series of simulated sediment content corresponding to multiple observation points in the target sea area during the sampling period; based on the first time series and the second time series corresponding to the multiple observation points, verifying the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification, and adjusting the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification with the similarity of the time series data curve as the optimization goal.

[0116] In the above embodiment, after the spatial dimension verification is completed, the model is verified by using the measured sediment content of the time series, and the simulation value with the same observation point and observation time is extracted. The similarity is made as close as possible by adjusting the model parameters according to the principle of similarity of the time series data curve. Here, the least square method can be used as an error assessment method. It should be noted that the spatial dimension verification result should be ensured to be accurate during the model parameter adjustment process, that is, the parameter adjustment process of the time dimension should be constrained by the spatial dimension verification result.

[0117] The above offshore sediment mathematical model optimization method uses the measured data of sediment content and the surface sediment content data of water bodies obtained by remote sensing inversion (sediment content inversion data) to verify the offshore sediment mathematical model, and adjusts the parameters of the model to make the comparison and verification results more consistent. This verification process also includes the time series value verification based on the measured sediment content data and the single-time large-scale surface sediment content verification based on the sediment content inversion data, that is, the offshore sediment mathematical model is tested and optimized in both the time dimension and the space dimension, which can effectively improve the simulation accuracy of the model and improve the accuracy of sediment content prediction.

[0118] The offshore sediment mathematical model obtained by the above offshore sediment mathematical model optimization method can be used for short-term prediction of sediment movement and scouring and silting conditions, and then help predict water depth changes. By continuously observing the sediment content data in real time, the simulation accuracy of the model can be continuously optimized and long-term prediction can be achieved. In addition, if the sediment content observation point is located near the waterway, the offshore sediment mathematical model can also be used to predict the movement of sediment and scouring and silting changes in the waterway. Furthermore, when the quantitative inversion accuracy of remote sensing images is stable, the surface observation of sediment content can be cancelled, and the existing inversion mode and parameters can be used to save measurement costs.

[0119] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0120] Corresponding to the offshore sediment mathematical model optimization method described in the above embodiment, Figure 5 A schematic diagram of the structure of a nearshore sediment mathematical model optimization device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0121] See also Figure 5 The offshore sediment mathematical model optimization device 500 in the embodiment of the present application may include an acquisition module 510 , a processing module 520 and an optimization module 530 .

[0122] The acquisition module 510 is used to acquire the first time series of the measured sediment content corresponding to the multiple observation points in the target sea area during the sampling period, and to acquire the multispectral remote sensing image data of the target sea area at at least one sampling time;

[0123] The processing module 520 is used to determine the sediment content inversion data of the surface layer of the water body of the target sea area based on the multispectral remote sensing image data; based on the sediment content inversion data, determine the first sediment content contour line corresponding to the target sea area at at least one sampling time;

[0124] The optimization module 530 is used to verify the pre-constructed offshore sediment mathematical model of the target sea area based on the first sediment content contour line and the first time series corresponding to the multiple observation points, and optimize the parameters of the offshore sediment mathematical model.

[0125] Optionally, the processing module 520 may be specifically used for:

[0126] Based on the data corresponding to the red and green bands of each pixel in the multispectral image data, the red-green band ratio corresponding to each pixel in the multispectral remote sensing image data is determined;

[0127] For pixels whose red-green band ratio is less than or equal to the first ratio, based on the first sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0128] For pixels whose red-green band ratio is greater than or equal to the second ratio, based on the second sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0129] For pixels whose red-green band ratio is greater than the first ratio and less than the second ratio, based on the sediment content inversion formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel;

[0130] Among them, the first ratio is less than the second ratio, and the first ratio and the second ratio are preset values ​​determined based on the probability density distribution of the red-green band ratio of the surface layer of the water body in the target sea area; the first sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple first positions and the red-green band ratio of the pixels corresponding to the multiple first positions, and the first position is the sea area position corresponding to the pixel with a red-green band ratio less than or equal to the first ratio; the second sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple second positions and the red-green band ratio of the pixels corresponding to the multiple second positions, and the second position is the sea area position corresponding to the pixel with a red-green band ratio greater than or equal to the second ratio;

[0131] The inversion formula of sediment content is:

[0132]

[0133] in, is the inversion value of sediment content; is the red band DN value / water body reflection value, It is the green wave DN value / water body reflection value. That is, the ratio of the red and green bands; a and b are calibration parameters.

[0134] Optionally, the optimization module 530 may be specifically used for:

[0135] Based on the offshore sediment mathematical model, first simulated sediment content data of the surface layer of the target sea area at at least one sampling time is obtained, and second simulated sediment content data of the surface layer of the target sea area during the sampling period is obtained;

[0136] Based on the first sediment content simulation data and the first sediment content contour line, the spatial dimension of the offshore sediment mathematical model is verified, and the parameters of the offshore sediment mathematical model are optimized;

[0137] Based on the second sediment content simulation data and the first time series corresponding to multiple observation points, the nearshore sediment mathematical model that has completed the spatial dimension verification is verified in terms of time dimension, and the parameters of the nearshore sediment mathematical model that has completed the spatial dimension verification are optimized.

[0138] Optionally, the optimization module 530 may be specifically used for:

[0139] Based on the first sediment content simulation data, extract the second sediment content contour line corresponding to the target sea area at at least one sampling time; wherein the second sediment content contour line and the first sediment content contour line have the same contour interval;

[0140] Based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line, the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time is analyzed;

[0141] Based on the similarity of spatial distribution, the mathematical model of offshore sediment is verified, and the controllable parameters of the mathematical model of offshore sediment are adjusted based on the verification results; among them, the controllable parameters include roughness, siltation rate and shear stress coefficient.

[0142] Optionally, the optimization module 530 may be specifically used for:

[0143] Extract the spatial characteristics of the contour lines corresponding to each value in the first sediment content contour line at a certain moment, and extract the spatial characteristics of the contour lines corresponding to each value in the second sediment content contour line at the same moment; wherein the spatial characteristics include curvature, curvature radius, length, starting point and end point;

[0144] Calculate the curvature similarity, curvature radius similarity, length similarity, starting point similarity and end point similarity corresponding to the contour lines with the same value in the first sediment content contour line and the second sediment content contour line corresponding to the certain moment;

[0145] Based on the preset weight vector and curvature similarity, curvature radius similarity, length similarity, starting point similarity and end point similarity, the spatial feature similarity of each contour line with the same value corresponding to the certain moment is determined;

[0146] Based on the similarity of spatial characteristics of the contour lines of the same value corresponding to the certain moment, the similarity of spatial distribution of the first sediment content contour line and the second sediment content contour line at the certain moment is determined.

[0147] Optionally, the optimization module 530 may be specifically used for:

[0148] Based on the spatial distribution similarity, the target approximation function is determined;

[0149] Based on the target approximation function, the controllable parameters of the offshore sediment mathematical model are adjusted through the optimization algorithm.

[0150] Optionally, the optimization module 530 may be specifically used for:

[0151] Based on the second simulated sediment content data, extract the second time series of simulated sediment content corresponding to multiple observation points in the target sea area during the sampling period;

[0152] Based on the first time series and the second time series corresponding to multiple observation points, the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification is verified, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are adjusted with the similarity of the time series data curve as the optimization goal.

[0153] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0154] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0155] The present application also provides an electronic device, see Figure 6The electronic device 600 may include: at least one processor 610, a memory 620, and a computer program stored in the memory 620 and executable on the at least one processor 610. When the processor 610 executes the computer program, the steps in any of the above-mentioned method embodiments are implemented, for example: Figure 2 Steps 101 to 103 in the illustrated embodiment. Alternatively, when the processor 610 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 5 The functions of the acquisition module 510, the processing module 520 and the optimization module 530 are shown.

[0156] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 620, and executed by the processor 610 to complete the present application. The one or more modules / units may be a series of computer program segments capable of completing specific functions, and the program segments are used to describe the execution process of the computer program in the electronic device 600.

[0157] Those skilled in the art will understand that Figure 6 These are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0158] The processor 610 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0159] The memory 620 may be an internal storage unit of the electronic device, or an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 620 is used to store computer programs and other programs and data required by the electronic device. The memory 620 may also be used to temporarily store data that has been output or is to be output.

[0160] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0161] The offshore sediment mathematical model optimization method provided in the embodiment of the present application can be applied to computers, wearable devices, vehicle-mounted devices, tablet computers, laptop computers, netbooks, personal digital assistants (PDA), augmented reality (AR) / virtual reality (VR) devices, mobile phones and other electronic devices. The embodiment of the present application does not impose any restrictions on the specific type of electronic devices.

[0162] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each embodiment of the above-mentioned offshore sediment mathematical model optimization method can be implemented.

[0163] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned offshore sediment mathematical model optimization method when executing the computer program product.

[0164] 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 present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0165] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0166] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and 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.

[0168] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing a mathematical model of offshore sediment, characterized in that: include: Acquire a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and acquire multispectral remote sensing image data of the target sea area at at least one sampling time; Based on the multispectral remote sensing image data, determining the sediment content inversion data of the surface layer of the water body of the target sea area; based on the sediment content inversion data, determining the first sediment content contour line corresponding to the target sea area at the at least one sampling time; Based on the offshore sediment mathematical model, obtaining first sediment content simulation data of the surface layer of the water body of the target sea area at the at least one sampling time, and obtaining second sediment content simulation data of the surface layer of the water body of the target sea area within the sampling period; Based on the first sediment content simulation data and the first sediment content contour line, verify the spatial dimension of the offshore sediment mathematical model, and optimize the parameters of the offshore sediment mathematical model; Based on the second sediment content simulation data and the first time series respectively corresponding to the multiple observation points, the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification is verified, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are optimized; The method of verifying the spatial dimension of the offshore sediment mathematical model based on the first sediment content simulation data and the first sediment content contour line, and optimizing the parameters of the offshore sediment mathematical model, includes: extracting the second sediment content contour line corresponding to the target sea area at the at least one sampling time based on the first sediment content simulation data; wherein the second sediment content contour line and the first sediment content contour line have the same contour interval; analyzing the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line; verifying the offshore sediment mathematical model based on the spatial distribution similarity, and adjusting the controllable parameters of the offshore sediment mathematical model based on the verification result.

2. The offshore sediment mathematical model optimization method according to claim 1, characterized in that: The controllable parameters include roughness, siltation rate and shear stress coefficient.

3. The offshore sediment mathematical model optimization method according to claim 2, characterized in that: The analyzing the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line includes: Extracting the spatial features of the contour lines corresponding to each value in the first sediment content contour line at a certain moment, and extracting the spatial features of the contour lines corresponding to each value in the second sediment content contour line at the same moment; wherein the spatial features include curvature, curvature radius, length, starting point and end point; Calculate the curvature similarity, curvature radius similarity, length similarity, starting point similarity and end point similarity corresponding to the contour lines with the same value in the first sediment content contour line and the second sediment content contour line corresponding to the certain moment; Based on a preset weight vector, the curvature similarity, the curvature radius similarity, the length similarity, the starting point similarity and the end point similarity, the spatial feature similarity of each contour line of the same value corresponding to the certain moment is determined; Based on the similarity of spatial characteristics of the contour lines of the same value corresponding to the certain moment, the similarity of spatial distribution of the first sediment content contour line and the second sediment content contour line at the certain moment is determined.

4. The offshore sediment mathematical model optimization method according to claim 2, characterized in that: The method of verifying the offshore sediment mathematical model based on the spatial distribution similarity and adjusting the controllable parameters of the offshore sediment mathematical model based on the verification result includes: Based on the spatial distribution similarity, determining a target approximation function; According to the target approximation function, the offshore sediment mathematical model is verified to obtain a verification result; According to the verification results, the controllable parameters of the offshore sediment mathematical model are adjusted.

5. The offshore sediment mathematical model optimization method according to claim 1, characterized in that: The method of verifying the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification based on the second sediment content simulation data and the first time series corresponding to the multiple observation points, and optimizing the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification, includes: Based on the second simulated sediment content data, extracting a second time series of simulated sediment content corresponding to each of the plurality of observation points in the target sea area during the sampling period; Based on the first time series and the second time series corresponding to the multiple observation points, the time dimension of the offshore sediment mathematical model that has completed the spatial dimension verification is verified, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are adjusted with the similarity of the time series data curve as the optimization goal.

6. The offshore sediment mathematical model optimization method according to claim 1, characterized in that: The multispectral remote sensing image data includes data corresponding to the red and green bands of each pixel; the inversion data of the sediment content of the surface layer of the water body in the target sea area is determined based on the multispectral remote sensing image data, including: Determine the red-green band ratio corresponding to each pixel in the multispectral remote sensing image data based on the data corresponding to the red and green bands of each pixel in the multispectral image data; For pixels whose red-green band ratio is less than or equal to the first ratio, based on the first sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel; For pixels whose red-green band ratio is greater than or equal to the second ratio, based on the second sediment content fitting formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel; For a pixel whose red-green band ratio is greater than the first ratio and less than the second ratio, based on the sediment content inversion formula and the red-green band ratio corresponding to the pixel, determine the sediment content inversion value of the sea area location corresponding to the pixel; Wherein, the first ratio is less than the second ratio, and the first ratio and the second ratio are preset values ​​determined based on the probability density distribution of the red-green band ratio of the surface layer of the water body of the target sea area; the first sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple first positions and the red-green band ratio of the pixels corresponding to the multiple first positions, and the first position is the sea area position corresponding to the pixel whose red-green band ratio is less than or equal to the first ratio; the second sediment content fitting formula is obtained by fitting based on the measured sediment content data of multiple second positions and the red-green band ratio of the pixels corresponding to the multiple second positions, and the second position is the sea area position corresponding to the pixel whose red-green band ratio is greater than or equal to the second ratio; The sediment content inversion formula is: in, is the inversion value of sediment content; is the red band DN value / water body reflection value, It is the green wave DN value / water body reflection value. That is, the ratio of the red and green bands; a and b are calibration parameters.

7. A device for optimizing offshore sediment mathematical model, characterized in that: include: An acquisition module is used to acquire a first time series of measured sediment contents corresponding to a plurality of observation points in a target sea area during a sampling period, and to acquire multispectral remote sensing image data of the target sea area at at least one sampling time; A processing module, used to determine the sediment content inversion data of the surface layer of the water body of the target sea area based on the multispectral remote sensing image data; based on the sediment content inversion data, determine the first sediment content contour line corresponding to the target sea area at the at least one sampling time; An optimization module, for obtaining, based on the offshore sediment mathematical model, first simulated sediment content data of the surface layer of the water body of the target sea area at the at least one sampling time, and second simulated sediment content data of the surface layer of the water body of the target sea area within the sampling period; Based on the first sediment content simulation data and the first sediment content contour line, the offshore sediment mathematical model is verified in terms of spatial dimension, and the parameters of the offshore sediment mathematical model are optimized; based on the second sediment content simulation data and the first time series corresponding to the multiple observation points, the offshore sediment mathematical model that has completed the spatial dimension verification is verified in terms of time dimension, and the parameters of the offshore sediment mathematical model that has completed the spatial dimension verification are optimized; The optimization module is specifically used for: Based on the first sediment content simulation data, extract the second sediment content contour line corresponding to the target sea area at the at least one sampling time; wherein the second sediment content contour line and the first sediment content contour line have the same contour interval; based on the distribution of the first sediment content contour line and the distribution of the second sediment content contour line, analyze the spatial distribution similarity of the first sediment content contour line and the second sediment content contour line at the same time; Based on the spatial distribution similarity, the offshore sediment mathematical model is verified, and based on the verification result, the controllable parameters of the offshore sediment mathematical model are adjusted.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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