Method and device for monitoring distribution evolution characteristics of seabed sediment, electronic equipment and storage medium

Abstract

The invention discloses a method and a device for monitoring distribution evolution characteristics of seabed sediments, electronic equipment and a storage medium. The method comprises the following steps: obtaining initial planar pattern spot data based on sediment distribution area boundary data of a target sea area and seabed surface sampling analysis; carrying out vector change identification on the initial planar pattern spot data of the previous and later periods, and obtaining second planar pattern spot data according to a vector change identification result; and according to the pattern spot areas corresponding to the initial planar pattern spot data and the second planar pattern spot data, obtaining substrate distribution evolution characteristics of the target sea area through transfer matrix quantification. According to the embodiment of the invention, the efficiency, accuracy and scientificity of seabed sediment monitoring are remarkably improved in an automatic, quantitative and comprehensive coverage mode, and the method has important application value and popularization prospect and can be widely applied to the technical field of data evolution analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data evolution analysis, and in particular to a method, device, electronic equipment and storage medium for monitoring the evolution characteristics of seabed sediment distribution. Background Art

[0002] Seabed sediment refers to sediments on the seabed, including mud, rocks, reefs, etc. Through comprehensive investigation of various characteristics such as seabed topography, landforms, and sediments, analysis of sediments, and tracking and monitoring of seabed geographical environment and sediment characteristics, it has been studied in depth as a hot issue in seabed surveying and marine environmental monitoring. Using advanced monitoring and analysis methods, we can better understand the seabed geographical environment and sediment characteristics, and provide basic support information for marine resource exploration, marine scientific research, marine engineering construction, marine military applications, etc.

[0003] In the prior art, in order to identify the types and changes of unknown seabed sediments, commonly used methods of exploring the seabed include obtaining seabed samples through point operations such as drilling, grabbing, dragging boxes, and trawls. There are also surface operations using multi-beam acoustic systems with high coverage to obtain multi-beam backscatter echo data to map the type and distribution information of the entire seabed. However, the technical methods for monitoring changes in seabed sediment distribution still rely on the experience and analysis of professionals. At present, conventional methods mainly extract change areas through manual visual interpretation or human-computer interaction, which requires a large workload for interpretation, low work efficiency, and low accuracy. Summary of the invention

[0004] The present invention aims to solve the problem of related technical limitations at least to a certain extent. To this end, the present invention proposes a method, device, electronic device and storage medium for monitoring the distribution and evolution characteristics of seabed sediments, which can monitor the distribution and evolution characteristics of seabed sediments efficiently and accurately.

[0005] On the one hand, an embodiment of the present invention provides a method for monitoring the evolution characteristics of seabed sediment distribution, comprising the following steps: The initial surface patch data is obtained based on the boundary data of the bottom distribution area of ​​the target sea area and the seabed surface sampling analysis; Performing vector change recognition on the initial planar pattern data of the previous and next two periods, and obtaining the second planar pattern data according to the result of the vector change recognition; According to the patch areas corresponding to the initial surface patch data and the second surface patch data, the sediment distribution evolution characteristics of the target sea area are quantified through the transfer matrix.

[0006] Optionally, obtaining initial surface pattern data based on the bottom distribution area boundary data of the target sea area and the seabed surface sampling analysis includes the following steps: Obtain the backscattering intensity data and seabed sonar images of the target sea area through a multibeam sounding system, and then jointly delineate to obtain the quantitative bottom sediment distribution area boundary data of the target sea area based on acoustic reflection intensity; Obtain the seabed sediment sample data based on the seabed surface sampling of the target sea area; Conduct interpretation and judgment according to the bottom sediment distribution area boundary data combined with the seabed sediment sample data to obtain the initial planar patch data.

[0007] Optionally, obtaining the backscattering intensity data of the target sea area includes the following steps: Perform multi-dimensional error correction and gross error rejection processing on the shipborne beam sounding data of the target sea area to obtain preprocessed measurement data; the dimensions of error correction include sound speed, tide level, and draft; Convert the preprocessed measurement data from the depth datum to the elevation datum, and then use the interpolation method for grid interpolation; Resample the result of grid interpolation to obtain the backscattering intensity data.

[0008] Optionally, perform vector change recognition on the initial planar patch data of two consecutive periods, and obtain the second planar patch data according to the result of vector change recognition, including the following steps: Perform gray-scale image conversion on the initial planar patch data of two consecutive periods through the weighted average method to obtain the gray-scale images corresponding to the initial planar patch data of two consecutive periods; Perform division algebraic operation on the image pixel values of the gray-scale images of two consecutive periods to generate a difference map; Extract the change area from the difference map using a preset threshold to obtain the vectorized change patch range; determine the second planar patch data according to the vectorized change patch range.

[0009] Optionally, extracting the change area from the difference map using a preset threshold to obtain the vectorized change patch range includes the following steps: Calculate the overall variance based on the gray value of each pixel in the difference map and the average value of all pixels, and determine the preset threshold according to the overall variance; Based on the preset threshold, distinguish and assign values to the change area and the unchanged area of the difference map, and convert it into a binary map; determine the vectorized change patch range according to the binary map.

[0010] Optionally, according to the patch areas corresponding to the initial planar patch data and the second planar patch data, quantitatively obtain the bottom sediment distribution evolution characteristics of the target sea area through a transfer matrix, including the following steps: Use the seabed sediment surface area calculation model to obtain the patch areas corresponding to the patch data; the patch data includes the initial planar patch data and the second planar patch data; Based on the transition matrix, quantify and statistically analyze the dynamic process information of the mutual transformation between the areas of two patches, and obtain the evolution characteristics of the bottom sediment distribution in the target sea area.

[0011] Optionally, use the seabed sediment surface area calculation model to obtain the patch area corresponding to the patch data, including the following steps: Based on the patch data, calculate the projected area using the Albers equal-area conic projection method of the reference ellipsoid of the national geodetic coordinate system to obtain the true surface area of the patch; Obtain the digital elevation model data of the target sea area, and then determine the slope of the target sea area; Based on the true surface area of the patch and the slope, use the raster calculation model to process the raster surface area; determine the patch area according to the raster surface area.

[0012] On the other hand, an embodiment of the present invention provides a device for monitoring the evolution characteristics of the seabed sediment distribution, including: The first module is used to obtain the initial planar patch data based on the boundary data of the seabed sediment distribution area in the target sea area and the seabed surface sampling and analysis; The second module is used to perform vector change recognition on the initial planar patch data of two consecutive periods, and obtain the second planar patch data according to the result of the vector change recognition; The third module is used to quantify the evolution characteristics of the seabed sediment distribution in the target sea area through the transition matrix according to the patch areas corresponding to the initial planar patch data and the second planar patch data.

[0013] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above method for monitoring the evolution characteristics of the seabed sediment distribution.

[0014] On the other hand, an embodiment of the present invention provides a computer storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to implement the above method for monitoring the evolution characteristics of the seabed sediment distribution when executed by the processor.

[0015] In the embodiment of the present invention, the initial planar patch data is obtained based on the boundary data of the seabed sediment distribution area in the target sea area and the seabed surface sampling and analysis; vector change recognition is performed on the initial planar patch data of two consecutive periods, and the second planar patch data is obtained according to the result of the vector change recognition; the evolution characteristics of the seabed sediment distribution in the target sea area are quantified through the transition matrix according to the patch areas corresponding to the initial planar patch data and the second planar patch data. The embodiment of the present invention has the following beneficial effects: 1. Improve work efficiency: Traditional methods rely on manual visual interpretation or human-machine interaction to extract changed areas, resulting in a large amount of interpretation work and low efficiency. In contrast, the embodiments of the present invention significantly reduce manual intervention and remarkably improve work efficiency through automated vector change recognition and transfer matrix quantification.

[0016] 2. Enhance accuracy: Manual interpretation is easily affected by subjective factors, leading to low accuracy. The embodiments of the present invention reduce human error and improve the accuracy of the monitoring of the change in substrate distribution through data-based automated analysis.

[0017] 3. Provide comprehensive coverage: The embodiments of the present invention utilize the initial planar patch data and the high-coverage data of the multibeam acoustic system to comprehensively and continuously monitor the distribution and changes of the seabed substrate.

[0018] 4. Conduct quantitative analysis: By quantifying the evolution characteristics of substrate distribution through a transfer matrix, more accurate and scientific analysis results can be provided, helping researchers better understand the change rules of the seabed substrate.

[0019] In summary, the embodiments of the present invention significantly improve the efficiency, accuracy, and scientific nature of seabed substrate monitoring through automation, quantification, and comprehensive coverage, and have important application value and broad promotion prospects. Description of the Drawings

[0020] The drawings are used to provide a further understanding of the technical solutions of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation to the technical solutions of the present invention.

[0021] Figure 1 is a schematic diagram of an implementation environment for a method of monitoring the evolution characteristics of seabed substrate distribution provided by an embodiment of the present invention; Figure 2 is a schematic flowchart of a method for monitoring the evolution characteristics of seabed substrate distribution provided by an embodiment of the present invention; Figure 3 is an expanded flowchart of step S200 provided by an embodiment of the present invention; Figure 4 is a schematic diagram of an overall process example of a method for monitoring the evolution characteristics of seabed substrate distribution provided by an embodiment of the present invention; Figure 5 is a schematic diagram of the delineation of initial substrate classification planar patch data provided by an embodiment of the present invention; Figure 6 is a schematic diagram of an example of an initial substrate classification patch (M1) provided by an embodiment of the present invention; Figure 7 is a schematic diagram of an example of an updated substrate classification patch (M2) provided by an embodiment of the present invention; Figure 8 Schematic diagram of an example of the result of transfer matrix operation provided by an embodiment of the present invention; Figure 9 Schematic structural diagram of a device for monitoring the evolution characteristics of seabed sediment distribution provided by an embodiment of the present invention; Figure 10 Schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0022] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] It should be noted that although functional module division is performed in the system schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the sequence in the flowchart. Terms such as "first / S100" and "second / S200" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0024] Referring to "embodiment" in this article means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present invention. The appearance of this phrase at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0025] It can be understood that the method for monitoring the evolution characteristics of seabed sediment distribution provided by the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but it is not limited thereto.

[0026] For the convenience of understanding the technical solution of the present invention, first, the proprietary terms of technical features that may appear in the embodiments of the present invention are explained: As Figure 1 shown, it is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Referring to Figure 1 , this implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be network-connected by wireless or wired means to complete data transmission and exchange.

[0027] The server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0028] In addition, the server 101 can also be a node server in a blockchain network. Among them, the blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms.

[0029] The terminal 102 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 can be directly or indirectly connected through wired or wireless communication means, and the embodiments of the present invention do not make limitations here.

[0030] Exemplarily based on Figure 1 the implementation environment shown, an embodiment of the present invention provides a method for monitoring the evolution characteristics of seabed sediment distribution. Taking the application of this method for monitoring the evolution characteristics of seabed sediment distribution in the server 101 as an example for explanation, it can be understood that this method for monitoring the evolution characteristics of seabed sediment distribution can also be applied to the terminal 102.

[0031] Referring to Figure 2 Figure 2 is a flowchart of the method for monitoring the evolution characteristics of seabed sediment distribution applied to the server provided by an embodiment of the present invention. The execution subject of this method for monitoring the evolution characteristics of seabed sediment distribution can be any of the aforementioned computer devices (including the server or the terminal). Referring to Figure 2 this method includes the following steps: S100. Based on the boundary data of the sediment distribution area in the target sea area and the analysis of the seabed surface sampling, the initial planar patch data is obtained; ​It should be noted that in some embodiments, step S100 may include the following steps: obtaining the backscattering intensity data and the seabed sonar image of the target sea area through a multibeam sounding system, and then jointly delineating to obtain the quantitative seabed sediment distribution area boundary data based on the acoustic reflection intensity of the target sea area; obtaining the seabed sediment sample data based on the seabed surface sampling of the target sea area; and performing interpretation and judgment according to the seabed sediment distribution area boundary data in combination with the seabed sediment sample data to obtain the initial planar patch data.

[0032] Exemplarily, in some specific embodiments, first, based on the backscattering intensity data and the seabed sonar image obtained by the multibeam sounding system, the quantitative seabed sediment distribution area boundary data based on the acoustic reflection intensity can be jointly delineated, and supplemented with the seabed sediment sample data obtained by seabed surface sampling (2m) for interpretation and judgment to obtain the initial seabed sediment classification planar patch data (M1) of the target sea area.

[0033] Among them, in some embodiments, obtaining the backscattering intensity data of the target sea area may include the following steps: performing multi-dimensional error correction and gross error rejection processing on the ship-carried beam water depth measurement data of the target sea area to obtain preprocessed measurement data; the dimensions of error correction include sound velocity, tide level, and draft; converting the preprocessed measurement data from the depth datum to the elevation datum, and then using the interpolation method for grid interpolation; and resampling the result of the grid interpolation to obtain the backscattering intensity data.

[0034] Exemplarily, in some specific embodiments, the backscattering intensity data used to delineate the initial seabed sediment classification planar patch data is mainly generated from the comprehensive geological and geophysical survey result data such as multibeam water depth measurement and sidescan sonar carried out in marine regional geological surveys at scales of 1:250,000 and 1:50,000. The key methods for specific generation include: first, performing error corrections such as sound velocity, tide level, and draft, and gross error rejection processing on the ship-carried beam water depth measurement data, then using the conversion relationship between the land-sea elevation and depth datums to complete the conversion from the depth datum to the elevation datum, using the tension spline function interpolation method for 10″×10″ grid interpolation, and then resampling the interpolation result to 5″×5″, and taking this result as the reference grid DEM data. To make the generated DEM surface smoother, when using the regular spline function method, the weight value tends to be larger, and the values are 0.001, 0.01, 0.1, and 0.5. It should be noted that the parameter values applied in the above implementation schemes can be adjusted correspondingly according to actual application requirements, and all the above parameter values are only for illustrative purposes.

[0035] S200. Perform vector change recognition on the initial planar patch data of the previous and current periods, and obtain the second planar patch data according to the result of the vector change recognition; It should be noted that in some embodiments, such as Figure 3As shown in the figure, step S200 may include the following steps: S201, perform grayscale image conversion on the initial planar patch data of the previous and next periods through the weighted average method to obtain the grayscale images corresponding to the initial planar patch data of the previous and next periods; S202, perform division algebraic operation on the image pixel values of the grayscale images of the previous and next periods to generate a difference map; S203, use a preset threshold to extract the changed area from the difference map to obtain the vectorized changed patch range; determine the second planar patch data according to the vectorized changed patch range.

[0036] Exemplarily, in some specific embodiments, first, the weighted average method can be used to convert the two-period images into grayscale images, and then a difference map is generated by performing division algebraic operation on the pixel values of the previous and next period images, and then the changed area is extracted from the difference image using a threshold to obtain the vectorized changed patch range.

[0037] In some specific application scenarios, step S200 can be implemented as follows: use a cropping tool to crop the high-precision seabed raster topographic maps (determined based on the initial planar patch data) of the previous and next periods to obtain the seabed raster images of the target area, adopt an automatic change detection technology to identify the changed area range, generate a changed raster in.dat format, and through raster-to-polygon vectorization and format conversion, respectively obtain the vector ranges of the newly added changes and the unchanged areas, and combine surface sampling to classify and verify the change information and perform graphic editing to obtain the updated various bottom sediment edge and clear contour planar patch data (M2, that is, the second planar patch data) of the target sea area.

[0038] Among them, in some embodiments, using a preset threshold to extract the changed area from the difference map to obtain the vectorized changed patch range may include the following steps: calculate the overall variance based on the grayscale value of each pixel in the difference map and the average value of all pixels, and determine the preset threshold according to the overall variance; based on the preset threshold, distinguish and assign values to the changed area and the unchanged area of the difference map to convert it into a binary map; determine the vectorized changed patch range according to the binary map.

[0039] Exemplarily, in some specific embodiments, a threshold is used to generate a binary map to represent whether a change occurs at a certain position, the changed area is assigned a value of 1, and the unchanged area is assigned a value of 0, as shown in the formula: BI(x, y) =

[0040] In the formula, BI(x, y) represents the image value of each pixel in the binary map; represents the grayscale value of each pixel in the difference map; T represents the threshold set for extracting the changed area, which is determined by the overall variance of the average value, and the calculation formula is as follows: T =

[0041] In the formula, represents the gray value of the j-th pixel in the difference image (i.e., the difference map), represents the number of pixels, represents the average value of the gray values of all pixels in the difference image.

[0042] S300. According to the patch areas corresponding to the initial areal patch data and the second areal patch data, the evolution characteristics of the seabed sediment distribution in the target sea area are quantified through a transfer matrix.

[0043] It should be noted that in some embodiments, step S300 may include the following steps: obtaining the patch areas corresponding to the patch data by using a seabed sediment surface area calculation model; the patch data includes initial areal patch data and second areal patch data; based on the transfer matrix, quantifying and statistically analyzing the dynamic process information of the mutual conversion between the two patch areas to obtain the evolution characteristics of the seabed sediment distribution in the target sea area.

[0044] Exemplarily, in some specific embodiments, for the initial seabed sediment classification patch M1 (i.e., the initial areal patch data) and the updated seabed sediment classification patch M2 (i.e., the second areal patch data) of the above-mentioned target sea area, the patch areas are obtained by using a seabed sediment surface area calculation model, and based on the transfer matrix model, the dynamic process information of the mutual conversion between the surface areas of each sediment type in the two periods before and after is quantified and statistically analyzed, quantitatively reflecting the structural characteristics of the change in the seabed sediment area distribution in a large area and the direction and scale of the change of each sediment type, and revealing the sediment evolution characteristics at the beginning and end of a certain period in a certain area.

[0045] Among them, in some embodiments, obtaining the patch areas corresponding to the patch data by using a seabed sediment surface area calculation model may include the following steps: based on the patch data, calculating the projected area by using the Albers equal-area conic projection method on the reference ellipsoid of the national geodetic coordinate system to obtain the true surface area of the patch; obtaining the digital elevation model data of the target sea area, and then determining the slope of the target sea area; based on the true surface area of the patch and the slope, processing the grid surface area by using a grid calculation model; determining the patch area according to the grid surface area.

[0046] Exemplarily, in some specific embodiments, for the calculation model of the seabed sediment surface area, the Albers equal-area conic projection method of the reference ellipsoid surface of CGCS2000 (China Geodetic Coordinate System 2000) is usually adopted to calculate the projected area, making it locally close to the earth's surface area. In order to obtain the true surface area of the sediment classification polygon patches in the two consecutive periods, this method uses a raster calculation model to obtain the seabed sediment surface area; among them, in some specific application scenarios, the surface area sum of all pixels within a specific elevation band or slope band can be obtained by using the zonal statistics tool in GIS (Geographic Information System) software.

[0047] The above raster calculation model obtains the surface area of each pixel in the raster according to the slope and the raster area (determined according to the true surface area), specifically as shown in the formula:

[0048] In the formula, represents the raster area of the pixel, represents the corresponding raster surface area, represents the slope obtained for the DEM (Digital Elevation Model) data.

[0049] To explain the principle of the technical solution of the present invention in detail, the overall process of the present invention will be described below in conjunction with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0050] First of all, it should be noted that the present invention provides a method and device for monitoring the evolution characteristics of seabed sediment distribution in view of the defects of the prior art, mainly solving the problems of detecting and updating the information of seabed sediment distribution changes in large areas and the monitoring and statistical efficiency.

[0051] To achieve the above object, the present invention adopts the following technical solution: A method for monitoring the evolution characteristics of seabed sediment distribution, as Figure 4 shown, may include the following steps: S1, first, based on the backscatter intensity data and seabed sonar images obtained by the multibeam echosounding system, jointly demarcate to obtain the quantitative sediment distribution area boundary data based on acoustic reflection intensity (as Figure 5 shown), supplemented by the seabed sediment sample data obtained by seabed surface sampling (2m) for interpretation and judgment, to obtain the initial sediment classification polygon patch data (M1) of the target sea area, as Figure 6 shown; S2. Use a cropping tool to crop the high-precision seabed grid topographic maps of two consecutive periods to obtain the seabed grid image of the target area. Adopt the automatic change detection technology to identify the range of the changed area, generate the changed grid in.dat format, and through grid-to-polygon vectorization and format conversion, obtain the vector ranges of the newly added changes and the unchanged areas respectively. Combine surface sampling to classify and verify the change information and perform graphic editing to obtain the updated data of various seabed sediment edges and clear planar patch data (M2) in the target sea area, as Figure 7 shown; S3. For the initial seabed sediment classification patch M1 and the updated seabed sediment classification patch M2 of the above target sea area, use the seabed sediment surface area calculation model to obtain the patch area, and calculate based on the transition matrix model to quantitatively count the dynamic process information of the mutual conversion between the surface areas of each seabed sediment type in the two periods, quantitatively reflect the structural characteristics of the change in the seabed sediment area distribution in a large area and the direction and scale of the change of each seabed sediment type, and reveal the seabed sediment evolution characteristics at the beginning and end of a certain period in a certain area.

[0052] In some specific application scenarios, the embodiment of the present invention proposes a method for monitoring the evolution characteristics of seabed sediment distribution. First, according to the multi-period dynamic tracking and obtained dataset of the surface coverage classification planar elements of seabed sediment, use the change monitoring technology for two or more periods of seabed grid images in the target sea area to obtain the updated seabed sediment classification patches. Then, use the seabed sediment surface area calculation model to obtain the actual area of the patches, and calculate based on the transition matrix model to quantitatively count the dynamic process information of the mutual conversion between the surface areas of each seabed sediment type in the two periods, assist in analyzing the quantity change flow direction of each seabed sediment type, and evaluate the change rate, trend, etc. of the spatial pattern of seabed sediments, which can effectively reveal the evolution characteristics contained in the seabed sediment of a specific sea area affected by external factors. Specifically, the above technical solution can be specifically implemented as follows: In S1, the backscatter intensity data used to delineate the initial seabed sediment classification planar patch data is mainly generated from the comprehensive geological and geophysical survey result data such as multi-beam bathymetry and side-scan sonar carried out in marine regional geological surveys of 1:250,000, 1:50,000, etc. The key methods for specific generation include: First, perform error corrections such as sound velocity, tide level, and draft on the shipborne beam bathymetry data, as well as gross error rejection and other processing. Then, use the conversion relationship between the land-sea elevation / depth datum to complete the conversion from the depth datum to the elevation datum. Adopt the tension spline function interpolation method for 10″×10″ grid interpolation, and then resample the interpolation result to 5″×5″, and use this result as the reference grid DEM data. To make the generated DEM surface smoother, when using the regular spline function method, the weight value tends to be larger, and the values are 0.001, 0.01, 0.1, 0.5.

[0053] In S2, the automatic change detection model generates a difference map through algebraic operations of dividing the pixel values of two consecutive images, and then uses a threshold to extract the changed areas from the difference image to obtain the vectorized changed patch range. Its calculation method includes: DIF(x, y) = M 1 (x, y) / (M 2 (x, y) + C) In the formula, M 1 (x, y) represents the gray value at any position of the previous image converted to a grayscale image, and M 2 (x, y) represents the gray value at any position of the later image converted to a grayscale image. C is a constant to avoid instability caused by the denominator approaching 0.

[0054] The weighted average method is used to convert two consecutive images into grayscale images. Among them, the conversion formula for obtaining the grayscale image value is:

[0055] represents the weight value of the i-th band channel, represents the pixel value of the i-th band channel, and n is the number of bands.

[0056] To determine whether a change has occurred at a certain position, a threshold is used to generate a binary map for expression. The changed area is assigned a value of 1, and the unchanged area is assigned a value of 0, as shown in the formula: BI(x, y) =

[0057] In the formula, T represents the threshold set for extracting the changed area, which is determined by the overall variance of the average value. The calculation formula is as follows: T =

[0058] In the formula, represents the gray value of the j-th pixel in the difference image, represents the average value of all pixel gray values in the difference image, and N represents the number of pixels.

[0059] In S3, the seabed sediment surface area calculation model usually calculates the projected area using the Albers equal-area conic projection method of the CGCS2000 reference ellipsoid surface to make it locally close to the earth's surface area. In order to obtain the true surface area of the sediment classification planar patches in two consecutive periods, this method uses a raster calculation model to obtain the seabed sediment surface area, and then uses the zonal statistics tool in GIS software to obtain the sum of the surface areas of all pixels within a specific elevation band or slope band.

[0060] The above raster calculation model obtains the surface area of each pixel in the raster according to the slope and raster area. See the formula specifically:

[0061] In the formula, represents the grid area of the pixel, represents the corresponding grid surface area, represents the slope obtained for the DEM data.

[0062] In S3, the transfer matrix calculation model is an application of the Markov model in marine geology. The transfer matrix reflects the dynamic process information of the mutual conversion of the areas of various sediment types at the beginning and end of a certain period in a certain area. It not only includes the area data of various sediment types at a certain time point in a static certain area, but also contains richer information on the transfer out of the areas of various sediment types at the beginning and the transfer in of the areas of various sediment types at the end. Each element of the transfer matrix represents the sediment before transfer being converted into the surface area of the sediment after transfer. The mathematical representation formula is:

[0063] where, represents the surface area; represents the number of sediment types before and after transfer; , ( , = 1, 2, …, ) represent the sediment types before and after transfer respectively; represents the sediment before transfer being converted into the surface area of the sediment after transfer.

[0064] Thus, the formula for the transfer surface area matrix is obtained as:

[0065] where, represents the surface area; represents the number of sediment types before and after transfer; , ( , = 1, 2, …, ) represent the sediment types before and after transfer respectively; represents the sediment before transfer being converted into the The surface area of the substrate. The sum of the row data in the transfer matrix represents the total surface area of a certain type of seabed substrate at the initial stage of the study. Each value in a row represents the transfer direction and scale of that seabed substrate type; the sum of the column data represents the total surface area of a certain type of seabed substrate at the end of the study. Each value in a column represents the transfer direction and scale of that seabed substrate type. The calculation results of the transfer matrix are as Figure 8 shown.

[0066] In summary, the present invention has good applicability and superiority in using the transfer matrix model to monitor and analyze the distribution change flow direction of different types of seabed substrates. First, based on the dataset of the surface coverage classification planar elements of the seabed substrate obtained by multi-period dynamic tracking exploration, the updated substrate classification patches are obtained by using the change monitoring technology on the seabed grid images of the target sea area in two periods or multiple periods. Then, the actual area of the patches is obtained by using the seabed substrate surface area calculation model, and based on the transfer matrix model calculation, the dynamic process information of the mutual transformation between the surface areas of each substrate type in two periods is quantitatively statistically analyzed, assisting in analyzing the quantity change flow direction of each seabed sediment type, and evaluating the change rate, trend, etc. of the spatial pattern of the seabed sediment, which can effectively reveal the evolution characteristics contained in the seabed substrate affected by external factors in a specific sea area.

[0067] On the other hand, as Figure 9 shown, an embodiment of the present invention provides a device 900 for monitoring the evolution characteristics of seabed substrate distribution, which may include: A first module 901, configured to obtain initial planar patch data based on the boundary data of the seabed substrate distribution area in the target sea area and seabed surface sampling analysis; A second module 902, configured to perform vector change recognition on the initial planar patch data of two consecutive periods, and obtain second planar patch data according to the result of the vector change recognition; A third module 903, configured to quantify and obtain the evolution characteristics of the seabed substrate distribution in the target sea area through a transfer matrix according to the patch areas corresponding to the initial planar patch data and the second planar patch data.

[0068] The content of the method embodiment of the present invention is applicable to the device embodiment of the present invention. The functions specifically implemented by the device embodiment are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method.

[0069] On the other hand, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method for monitoring the evolution characteristics of seabed substrate distribution as described above is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0070] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present invention. The functions specifically implemented by the device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0071] As Figure 10 shown, Figure 10 FIG. schematically shows the hardware structure of an electronic device 1000 according to another embodiment. The electronic device 1000 includes: A processor 1001, which can be implemented by using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention; A memory 1002, which can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of the present specification through software or firmware, the relevant program codes are stored in the memory 1002 and are called by the processor 1001 to execute the network node population optimization method of the embodiments of the present invention; An input / output interface 1003, which is used to implement information input and output; A communication interface 1004, which is used to implement communication interaction between the present device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.); A bus 1005, which transmits information between various components of the device (such as the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004); Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are communicatively connected to each other inside the device through the bus 1005.

[0072] The above-described embodiments of the electronic device are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] The content of the method embodiments of the present invention is applicable to the embodiments of this electronic device. The functions specifically implemented by the embodiments of this electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0074] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium. The storage medium stores a program, and the program is executed by a processor to implement the foregoing method.

[0075] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0076] The content of the method embodiments of the present invention is applicable to the embodiments of this computer-readable storage medium. The functions specifically implemented by the embodiments of this computer-readable storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0077] An embodiment of the present invention also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the foregoing method.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0079] It should be noted that although several modules of a device for performing actions are mentioned in the foregoing detailed description, such a division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by a plurality of modules or units.

[0080] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0081] In some alternative embodiments, the functions / operations recited in the block diagrams may not occur in the order presented in the operational illustrations. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. Further, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and in which sub-operations described as part of a larger operation are performed independently.

[0082] Moreover, although the present invention has been described in the context of functional modules, it should be understood that one or more of the functions and / or features, unless otherwise stated to the contrary, may be integrated in a single physical device and / or software module or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the module will be understood within the ordinary skill of an engineer, given the attributes, functions and internal relationships of the various functional modules in the devices disclosed herein. Accordingly, those of ordinary skill in the art will be able to implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the particular concepts disclosed are illustrative only and not intended to limit the scope of the present invention, the scope of which is determined by the full scope of the appended claims and their equivalents.

[0083] If the functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art or part of the technical solution, may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as a USB flash drive, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0084] Logic and / or steps represented in a flowchart or otherwise described herein can, for example, be considered as a definitional sequence of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution apparatus, device, or equipment, such as a computer-based device, a device including a processor, or other devices that can fetch and execute instructions from the instruction execution apparatus, device, or equipment. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution apparatus, device, or equipment or in connection with these instruction execution apparatus, devices, or equipment.

[0085] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other suitable processing, and then stored in a computer memory.

[0086] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution apparatus. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0087] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0088] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

[0089] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. A method for monitoring the evolution characteristics of seabed sediment distribution, characterized in that: The following steps are involved: The initial surface patch data is obtained based on the boundary data of the bottom distribution area of ​​the target sea area and the seabed surface sampling analysis; Performing vector change recognition on the initial planar pattern data of the previous and next two periods, and obtaining second planar pattern data according to the result of the vector change recognition; According to the patch areas corresponding to the initial planar patch data and the second planar patch data, the bottom sediment distribution evolution characteristics of the target sea area are obtained by transfer matrix quantification.

2. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 1, characterized in that: The initial surface pattern data is obtained based on the bottom distribution area boundary data of the target sea area and the seabed surface sampling and analysis, including the following steps: Acquiring the backscatter intensity data and seabed sonar images of the target sea area through a multi-beam bathymetric system, and then jointly delineating and obtaining the quantitative boundary data of the bottom distribution area of ​​the target sea area based on the acoustic reflection intensity; Acquire seabed sediment sample data based on the seabed surface sampling of the target sea area; The initial surface pattern data is obtained by interpreting and judging the boundary data of the bottom distribution area in combination with the seabed sediment sample data.

3. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 2, characterized in that: The step of obtaining the backscatter intensity data of the target sea area comprises the following steps: Performing multi-dimensional error correction and gross error elimination processing on the shipborne beam depth measurement data of the target sea area to obtain pre-processed measurement data; the error correction dimensions include sound speed, tide level and draft; The pre-processed measurement data is converted from a depth reference to an elevation reference, and then grid interpolation is performed using an interpolation method; The grid interpolation result is resampled to obtain the backscattering intensity data.

4. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 1, characterized in that: The step of performing vector change recognition on the initial planar pattern data of the two previous and subsequent phases and obtaining the second planar pattern data according to the result of the vector change recognition comprises the following steps: Performing grayscale image conversion on the initial planar pattern data of the previous and next two periods by weighted average method to obtain grayscale images corresponding to the initial planar pattern data of the previous and next two periods; Performing algebraic division operation on the image pixel values ​​of the grayscale images of the previous and next periods to generate a difference map; A preset threshold is used to extract a change area from the difference map to obtain a vectorized change pattern range; and the second planar pattern data is determined according to the vectorized change pattern range.

5. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 4, characterized in that: The method of extracting the change area from the difference map by using a preset threshold to obtain a vectorized change map range includes the following steps: Calculate the overall variance based on the gray value of each pixel in the difference map and the average value of all pixels, and determine the preset threshold value according to the overall variance; Based on the preset threshold, the changed area and the unchanged area of ​​the difference map are distinguished and assigned values, and converted into a binary map; and the vectorized change pattern range is determined according to the binary map.

6. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 1, characterized in that: The method of obtaining the bottom sediment distribution evolution characteristics of the target sea area by quantifying the area of ​​the patches corresponding to the initial planar patch data and the second planar patch data through a transfer matrix comprises the following steps: The spot area corresponding to the spot data is obtained by using a seabed bottom surface area calculation model; the spot data includes the initial planar spot data and the second planar spot data; Based on the transfer matrix, the dynamic process information of the mutual transformation between the two image patch areas is quantitatively counted to obtain the evolution characteristics of the bottom sediment distribution in the target sea area.

7. The method for monitoring the evolution characteristics of seabed sediment distribution according to claim 6, characterized in that: The method of using the seabed sediment surface area calculation model to obtain the spot area corresponding to the spot data comprises the following steps: Based on the image patch data, the projection area is calculated using the Albers equal-area conic projection method of the national geodetic coordinate system reference ellipsoid to obtain the real surface area of ​​the image patch; Acquire mathematical elevation model data of the target sea area, and then determine the slope of the target sea area; Based on the real surface area of ​​the image patch and the slope, a grid surface area is processed using a grid calculation model; and the image patch area is determined according to the grid surface area.

8. A device for monitoring the evolution of seabed sediment distribution, characterized in that: include: The first module is used to obtain the initial surface pattern data based on the boundary data of the bottom distribution area of ​​the target sea area and the seabed surface sampling analysis; The second module is used to perform vector change recognition on the initial planar pattern data of the previous and next two periods, and obtain the second planar pattern data according to the result of the vector change recognition; The third module is used to quantify the evolution characteristics of the bottom sediment distribution of the target sea area through transfer matrix according to the patch areas corresponding to the initial surface patch data and the second surface patch data.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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