A method, device, electronic device and storage medium for monitoring the distribution and evolution characteristics of seabed sediments

Abstract

The present invention discloses a method, device, electronic device and storage medium for monitoring the evolution characteristics of seabed sediment distribution. The method comprises: obtaining initial surface pattern data based on the boundary data of the sediment distribution area of ​​the target sea area and the sampling and analysis of the seabed surface; performing vector change identification on the initial surface pattern data of the two periods before and after, and obtaining second surface pattern data according to the result of the vector change identification; and obtaining the sediment distribution evolution characteristics of the target sea area through transfer matrix quantification based on the pattern areas corresponding to the initial surface pattern data and the second surface pattern data. The embodiment of the present invention significantly improves the efficiency, accuracy and scientific nature of seabed sediment monitoring through automation, quantification and comprehensive coverage, has important application value and promotion prospects, and can be widely used in the field of data evolution analysis technology.

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 the sediments on the seabed, including mud, sand, rocks, and reefs. Comprehensive surveys of various seabed features, including topography, landforms, and sediments, as well as sediment analysis and tracking and monitoring of the seabed's geographic environment and sediment characteristics, have been a hot topic in seabed surveying and marine environmental monitoring. Advanced monitoring and analysis methods can provide a better understanding of the seabed's geographic environment and sediment characteristics, providing essential information for marine resource exploration, marine scientific research, marine engineering construction, and marine military applications.

[0003] In the existing technology, in order to identify the types and changes of unknown seabed sediments, common methods of exploring the seabed include obtaining seabed samples through point operations such as drilling, grabbing, dragging boxes, and trawl nets. 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. The current conventional methods mainly extract change areas through manual visual interpretation or human-computer interaction, which results in a large workload for interpretation, low work efficiency, and low accuracy. Summary of the Invention

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

[0005] In one aspect, an embodiment of the present invention provides a method for monitoring the distribution and evolution characteristics of seabed sediments, comprising the following steps:

[0006] 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;

[0007] Performing vector change recognition on the initial planar pattern data of the two phases before and after, and obtaining the second planar pattern data according to the result of the vector change recognition;

[0008] 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 obtained by transfer matrix quantification.

[0009] Optionally, obtaining initial surface patch data based on bottom sediment distribution area boundary data of the target sea area and seabed surface sampling analysis includes the following steps:

[0010] The backscatter intensity data and seabed sonar images of the target sea area are obtained through the multi-beam bathymetry system, and then the quantitative bottom distribution area boundary data of the target sea area based on the acoustic reflection intensity is obtained through joint delineation.

[0011] Obtain seabed sediment sample data based on seabed surface sampling in the target sea area;

[0012] The initial surface patch data is obtained by interpreting and judging the boundary data of the bottom distribution area in combination with the seabed sediment sample data.

[0013] Optionally, obtaining backscatter intensity data of the target sea area includes the following steps:

[0014] Multi-dimensional error correction and gross error elimination are performed 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;

[0015] The pre-processed measurement data is converted from the depth datum to the elevation datum, and then the grid interpolation is performed using the interpolation method;

[0016] The grid interpolation results are resampled to obtain backscattering intensity data.

[0017] Optionally, 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:

[0018] The grayscale images corresponding to the initial surface pattern data of the two periods are converted into grayscale images by weighted average method;

[0019] Performing algebraic division operation on the image pixel values ​​of the grayscale images of the previous and next periods to generate a difference image;

[0020] A preset threshold is used to extract the change area from the difference map to obtain the vectorized change pattern range; and the second planar pattern data is determined according to the vectorized change pattern range.

[0021] Optionally, extracting the changed area from the difference map using a preset threshold to obtain the vectorized changed area includes the following steps:

[0022] 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 value according to the overall variance;

[0023] Based on a preset threshold, the changed areas and unchanged areas of the difference map are distinguished and assigned values, and converted into a binary map; the range of the vectorized change map is determined based on the binary map.

[0024] Optionally, according to the patch areas corresponding to the initial planar patch data and the second planar patch data, obtaining the sediment distribution evolution characteristics of the target sea area by transfer matrix quantification includes the following steps:

[0025] The patch area corresponding to the patch data is obtained by using a seabed sediment surface area calculation model; the patch data includes initial planar patch data and second planar patch data;

[0026] Based on the transfer matrix, the dynamic process information of the mutual transformation between the two patch areas is quantitatively counted to obtain the evolution characteristics of the bottom sediment distribution in the target sea area.

[0027] Optionally, obtaining the patch area corresponding to the patch data using a seabed sediment surface area calculation model comprises the following steps:

[0028] Based on the 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 true surface area of ​​the patch;

[0029] Obtain mathematical elevation model data of the target sea area and then determine the slope of the target sea area;

[0030] Based on the real surface area and slope of the patch, the grid surface area is processed using a grid calculation model; and the patch area is determined based on the grid surface area.

[0031] In another aspect, an embodiment of the present invention provides a device for monitoring the distribution and evolution characteristics of seabed sediments, comprising:

[0032] The first module is used to obtain initial surface patch data based on the bottom distribution area boundary data of the target sea area and the seabed surface sampling analysis;

[0033] 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;

[0034] The third module is used to quantify the sediment distribution evolution characteristics of the target sea area through the transfer matrix based on the patch areas corresponding to the initial surface patch data and the second surface patch data.

[0035] On the other hand, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned method of monitoring the evolution characteristics of seabed sediment distribution.

[0036] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned method for monitoring the evolution characteristics of the seabed sediment distribution.

[0037] The present invention uses an analysis method based on the boundary data of the target seabed distribution area and seafloor surface sampling to obtain initial surface pattern data. Vector change recognition is performed on the initial surface pattern data from two periods, and second surface pattern data is obtained based on the results of the vector change recognition. The evolution characteristics of the target seabed distribution are quantified using a transfer matrix based on the corresponding pattern areas of the initial and second surface pattern data. This embodiment of the present invention has the following beneficial effects:

[0038] 1. Improved Work Efficiency: Traditional methods rely on manual visual interpretation or human-computer interaction to extract change regions, resulting in heavy interpretation workload and low efficiency. However, the present invention significantly reduces manual intervention and improves work efficiency through automated vector change identification and transfer matrix quantification.

[0039] 2. Improved accuracy: Manual interpretation is easily affected by subjective factors, resulting in low accuracy. The embodiments of the present invention reduce human errors and improve the accuracy of monitoring sediment distribution changes through automated data analysis.

[0040] 3. Comprehensive coverage: The embodiments of the present invention utilize the initial planar pattern data and the high coverage data of the multi-beam acoustic system to comprehensively and continuously monitor the distribution and changes of the seabed sediments.

[0041] 4. Quantitative analysis: Quantifying the evolution of sediment distribution through the transfer matrix can provide more accurate and scientific analysis results, helping researchers better understand the changing patterns of seabed sediments.

[0042] In summary, the embodiments of the present invention significantly improve the efficiency, accuracy and scientificity of seabed sediment monitoring through automation, quantification and comprehensive coverage, and have important application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0044] Figure 1 This is a schematic diagram of an implementation environment for a method for monitoring the distribution and evolution characteristics of seabed sediments provided by an embodiment of the present invention;

[0045] Figure 2 1 is a flow chart of a method for monitoring the distribution and evolution characteristics of seabed sediments provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the expanded flow of step S200 provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram illustrating an overall process of a method for monitoring the distribution and evolution characteristics of seabed sediments provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of delineating initial bottom sediment classification surface pattern data provided by an embodiment of the present invention;

[0049] Figure 6 A schematic diagram of an example of an initial sediment classification map (M1) provided in an embodiment of the present invention;

[0050] Figure 7 A schematic diagram of an example of updating a bottom sediment classification map (M2) provided in an embodiment of the present invention;

[0051] Figure 8 A schematic diagram of an example of a transfer matrix operation result provided by an embodiment of the present invention;

[0052] Figure 9 A schematic structural diagram of a device for monitoring the distribution and evolution characteristics of seabed sediments provided by an embodiment of the present invention;

[0053] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0055] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0056] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] It is understandable that the method for monitoring the evolution characteristics of the 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 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 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. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0058] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features that may appear in the embodiments of the present invention:

[0059] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 , the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0060] 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), as well as big data and artificial intelligence platforms.

[0061] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0062] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0063] Based on the example Figure 1In the implementation environment shown, an embodiment of the present invention provides a method for monitoring the evolution characteristics of the seabed sediment distribution. The following is explained using the example of the method for monitoring the evolution characteristics of the seabed sediment distribution in the server 101. It can be understood that the method for monitoring the evolution characteristics of the seabed sediment distribution can also be applied to the terminal 102.

[0064] Reference Figure 2 , Figure 2 The flowchart of the method for monitoring the evolution characteristics of the seabed sediment distribution applied to the server provided in the embodiment of the present invention can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0065] S100, obtaining initial surface patch data based on the bottom distribution area boundary data of the target sea area and the seabed surface sampling analysis;

[0066] It should be noted that, in some embodiments, step S100 may include the following steps: obtaining backscatter intensity data and seabed sonar images of the target sea area through a multi-beam sounding system, and then jointly delineating to obtain quantitative bottom distribution area boundary data of the target sea area based on acoustic reflection intensity; obtaining seabed sediment sample data based on seabed surface sampling of the target sea area; interpreting and judging the bottom distribution area boundary data in combination with the seabed sediment sample data to obtain initial surface pattern data.

[0067] For example, in some specific implementations, the backscatter intensity data and seabed sonar images obtained by the multi-beam bathymetry system can be used to jointly delineate and obtain quantitative bottom distribution area boundary data based on acoustic reflection intensity, supplemented by interpretation and judgment of seabed sediment sample data obtained by seabed surface sampling (2m), to obtain initial bottom classification surface patch data (M1) of the target sea area.

[0068] Among them, in some embodiments, obtaining the backscatter intensity data of the target sea area may include 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 preprocessed measurement data; the error correction dimensions include sound speed, tide level and draft; converting the preprocessed measurement data from a depth reference to an elevation reference, and then using an interpolation method to perform grid interpolation; resampling the results of the grid interpolation to obtain backscatter intensity data.

[0069] For example, in some specific implementations, the backscatter intensity data used to delineate the initial bottom classification surface patch data are mainly generated using comprehensive geological and geophysical survey results data such as multi-beam bathymetry and side-scan sonar conducted in marine regional geological surveys at scales of 1:250,000 and 1:50,000. The specific key generation methods include: first, performing error corrections such as sound velocity, tide level, and draft on the shipborne beam bathymetry data, as well as gross error elimination, and then using the conversion relationship between land and sea elevation and depth benchmarks to complete the conversion from depth benchmark to elevation benchmark, using the tension spline function interpolation method to perform 10″×10″ grid interpolation, and then resampling the interpolation result to 5″×5″, and using this result as the reference grid DEM data. In order to make the generated DEM surface smoother, the weight value tends to be larger when using the regular spline function method, and the values ​​are 0.001, 0.01, 0.1, and 0.5. It should be noted that the parameter values ​​used in the above implementation scheme can be adjusted accordingly according to actual application requirements, and all the above parameter values ​​are only for example description.

[0070] S200, performing vector change recognition on the initial planar pattern data of the two previous and subsequent phases, and obtaining second planar pattern data according to the result of the vector change recognition;

[0071] It should be noted that, in some embodiments, Figure 3 As shown, step S200 may include the following steps: S201, performing grayscale image conversion on the initial surface pattern data of the previous and next periods by weighted averaging method to obtain grayscale images corresponding to the initial surface pattern data of the previous and next periods; S202, performing algebraic division operation on the image pixel values ​​of the grayscale images of the previous and next periods to generate a difference map; S203, using a preset threshold to extract the change area from the difference map to obtain the vectorized change pattern range; and determining the second surface pattern data according to the vectorized change pattern range.

[0072] For example, in some specific implementations, the weighted averaging method can be used to first convert the two images into grayscale images, and then a difference image can be generated by performing an algebraic division operation on the pixel values ​​of the two images, and then a threshold is used to extract the changed area from the difference image to obtain the vectorized change map range.

[0073] In some specific application scenarios, step S200 can be implemented as follows: using a clipping tool to clip the two full-coverage high-precision seabed raster topographic maps (determined based on the initial surface pattern data) to obtain the seabed raster image of the target area, using automatic change detection technology to identify the range of the changed area, generating a change raster in .dat format, and after raster-to-surface vectorization and format conversion, obtaining the vector range of the newly added changes and the unchanged ones respectively, combining surface sampling to classify, verify and graphic edit the change information, and obtain the updated surface pattern data (M2, i.e., the second surface pattern data) with clear edges and contours of various bottom materials in the target sea area.

[0074] Among them, in some embodiments, using a preset threshold to extract the changed area from the difference map to obtain the vectorized change map range can include the following steps: calculating the overall variance based on the grayscale value of each pixel in the difference map and the average value of all pixels, and determining the preset threshold according to the overall variance; based on the preset threshold, distinguishing and assigning values ​​to the changed area and the unchanged area of ​​the difference map, and converting it into a binary map; determining the vectorized change map range based on the binary map.

[0075] For example, in some specific implementations, a threshold is used to generate a binary image to determine whether a location has changed, and a value of 1 is assigned to the changed area and a value of 0 is assigned to the unchanged area, as shown in the formula:

[0076] BI(x,y) =

[0077] Where BI (x, y) represents the image value of each pixel in the binary image; Represents the grayscale value of each pixel in the difference map; T represents the threshold set for extracting the change area, which is determined by the overall variance of the average value. The calculation formula is as follows:

[0078] T=

[0079] Where, Represents the grayscale value of the jth pixel in the difference image (i.e., difference map), represents the number of pixels, Represents the average grayscale value of all pixels in the difference image.

[0080] S300, obtaining the sediment distribution evolution characteristics of the target sea area through transfer matrix quantification based on the patch areas corresponding to the initial planar patch data and the second planar patch data.

[0081] It should be noted that, in some embodiments, step S300 may include the following steps: using a seabed sediment surface area calculation model to obtain the patch area corresponding to the patch data; the patch data includes initial planar patch data and second planar patch data; based on the transfer matrix, quantitatively and statistically analyze the dynamic process information of the mutual transformation between the two patch areas to obtain the sediment distribution evolution characteristics of the target sea area.

[0082] For example, in some specific embodiments, the initial sediment classification map M1 (i.e., initial surface map data) and the updated sediment classification map M2 (i.e., second surface map data) of the target sea area are calculated using a seabed sediment surface area calculation model to obtain the map area, and based on the transfer matrix model, the dynamic process information of the mutual transformation between the surface areas of each sediment type in the previous and subsequent periods is quantitatively counted to quantitatively reflect the structural characteristics of the changes in the distribution of the seabed sediment area in a large area and the direction and scale of the changes in each sediment type, revealing the sediment evolution characteristics at the beginning and end of a certain period in a certain area.

[0083] Among them, in some embodiments, using the seabed sediment surface area calculation model to obtain the patch area corresponding to the patch data can include the following steps: based on the patch data, using the Albers equal-area conic projection method of the national geodetic coordinate system reference ellipsoid to calculate the projection area to obtain the true surface area of ​​the patch; obtaining the mathematical elevation model data of the target sea area, and then determining the slope of the target sea area; based on the true surface area and slope of the patch, using the grid calculation model to process the grid surface area; determining the patch area according to the grid surface area.

[0084] For example, in some specific embodiments, the seafloor surface area calculation model typically uses the Albers equal-area conic projection of the CGCS2000 (China Geodetic Coordinate System 2000) reference ellipsoid to calculate the projected area, resulting in a local approximation of the Earth's surface area. To obtain the true surface area of ​​the two-phase bottom classification patch, this method utilizes a raster calculation model to calculate the seafloor surface area. In some specific application scenarios, the zonal statistics tool within GIS (Geographic Information System) software can be used to calculate the sum of the surface areas of all pixels within a specific elevation or slope zone.

[0085] The above grid calculation model calculates the surface area of ​​each pixel in the grid based on the slope and the grid area (determined based on the actual surface area). The specific formula is as follows:

[0086]

[0087] Where, represents the grid area of ​​pixels, represents the corresponding grid surface area, It represents the slope calculated based on DEM (Digital Elevation Model) data.

[0088] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0089] First of all, it should be noted that the present invention addresses the defects of the existing technology and provides a method and device for monitoring the evolution characteristics of seabed sediment distribution, which mainly solves the problems of detection and update of sediment distribution change information in large seabed areas and monitoring statistical efficiency.

[0090] To achieve the above object, the present invention adopts the following technical solutions: a method for monitoring the distribution and evolution characteristics of the seabed sediments, such as Figure 4 As shown, the following steps may be included:

[0091] S1, firstly, based on the backscatter intensity data obtained by the multi-beam bathymetry system and the seabed sonar image, quantitative bottom distribution area boundary data based on acoustic reflection intensity are obtained (such as Figure 5 As shown in the figure), the seabed sediment sample data obtained by seabed surface sampling (2m) is interpreted and judged to obtain the initial bottom classification surface pattern data (M1) of the target sea area, as shown in the figure. Figure 6 As shown;

[0092] S2, using the cropping tool to crop the two phases of high-precision seafloor raster topographic maps with full coverage, obtain the seafloor raster image of the target area, use automatic change detection technology to identify the range of the changed area, generate a .dat format change raster, and after raster-to-surface vectorization and format conversion, obtain the vector range of the newly changed and unchanged areas respectively. Combined with surface sampling, the change information is classified, verified and graphically edited to obtain the updated surface patch data (M2) of the target sea area with clear edges and contours of various types of bottom sediments. Figure 7 As shown;

[0093] S3, for the above-mentioned initial bottom classification map M1 and updated bottom classification map M2 of the target sea area, the bottom surface area calculation model is used to obtain the map area, and based on the transfer matrix model, the dynamic process information of the mutual transformation between the surface areas of each bottom type in the two periods is quantitatively counted, quantitatively reflecting the structural characteristics of the changes in the distribution of bottom area in a large area and the direction and scale of the changes in each bottom type, revealing the bottom evolution characteristics at the beginning and end of a certain period in a certain area.

[0094] In some specific application scenarios, the embodiment of the present invention proposes a method for monitoring the evolution characteristics of the distribution of seabed sediments. First, based on the seabed sediment surface coverage classification surface element dataset obtained by multi-period dynamic tracking, two or more periods of seabed raster images of the target sea area are used to obtain the updated sediment classification map through change monitoring technology, and then the actual area of ​​the map is obtained by using the seabed sediment surface area calculation model, and based on the transfer matrix model calculation, the dynamic process information of the mutual conversion between the surface areas of each sediment type in the two periods is quantitatively counted, and the quantitative change direction of each seabed sediment type is analyzed to evaluate the change rate and trend of the spatial pattern of seabed sediments. It can effectively reveal the evolution characteristics of the sediments in a specific sea area affected by external factors. Specifically, the above technical solution can be implemented as follows:

[0095] In S1, the backscatter intensity data used to delineate the initial bottom classification patch data were generated primarily from comprehensive geological and geophysical surveys, primarily multibeam bathymetry and side-scan sonar, conducted in marine regional geological surveys at scales of 1:250,000 and 1:50,000. Key methods for generating these data include: first, correcting the shipborne beam bathymetry data for errors such as sound velocity, tide level, and draft, as well as removing gross errors. Then, utilizing the conversion relationship between land and sea elevation / depth datums, the depth datum was converted to an elevation datum. Tension spline interpolation was used to generate a 10×10 grid, and the interpolated result was resampled to a 5×5 grid, which served as the baseline gridded DEM data. To smooth the generated DEM surface, the weights used in the regularized spline method were increased, with values ​​ranging from 0.001, 0.01, 0.1, and 0.5.

[0096] In S2, the automatic change detection model generates a difference image by performing an algebraic division operation on the pixel values ​​of the two previous and subsequent images, and then extracts the change area from the difference image using a threshold value to obtain the vectorized change patch range. The calculation method includes:

[0097] DIF(x,y)=M1(x,y) / (M2(x,y) + C)

[0098] Where M1(x, y) represents the grayscale value of any position when the previous image is converted to a grayscale image, M2(x, y) represents the grayscale value of any position when the later image is converted to a grayscale image, and C is a constant to avoid instability caused by the denominator being close to 0.

[0099] The weighted average method is used to convert the two-phase images into grayscale images, where the conversion formula for obtaining the grayscale image value is:

[0100]

[0101] 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.

[0102] To determine whether a location has changed, a threshold is used to generate a binary image to express it. The changed area is assigned a value of 1 and the unchanged area is assigned a value of 0, as shown in the formula:

[0103] BI(x,y) =

[0104] Where T represents the threshold set for extracting the change area, which is determined by the overall variance of the mean value. The calculation formula is as follows:

[0105] T=

[0106] Where, Represents the gray value of the j-th pixel in the difference image, It represents the average grayscale value of all pixels in the difference image, and N represents the number of pixels.

[0107] In S3, the seafloor surface area calculation model typically uses the Albers equal-area conic projection of the CGCS2000 reference ellipsoid to calculate the projected area, making it locally close to the Earth's surface area. To obtain the true surface area of ​​the two-period bottom classification patch, this method uses a raster calculation model to calculate the seafloor surface area. Then, using the zonal statistics tool in GIS software, the sum of the surface areas of all pixels within a specific elevation or slope zone is calculated.

[0108] The above grid calculation model calculates the surface area of ​​each pixel in the grid based on the slope and grid area. The specific formula is as follows:

[0109]

[0110] Where, represents the grid area of ​​pixels, represents the corresponding grid surface area, Represents the slope obtained based on DEM data.

[0111] In S3, the transfer matrix calculation model is the application of the Markov model in marine geology. The transfer matrix reflects the dynamic process information of the mutual transformation between the areas of various types of sediments at the beginning and end of a certain period in a certain area. It not only includes the static area data of various types of sediments at a certain point in time in a certain area, but also contains more abundant information on the outflow of various types of sediments at the beginning and the inflow of various types of sediments at the end. Each element of the transfer matrix Indicates before transfer Substrate converted into transferred The surface area of ​​the substrate is expressed mathematically as:

[0112]

[0113] in, represents surface area; represents the number of substrate types before and after transfer; 、 ( , =1,2,…, ) represent the substrate types before and after transfer, respectively; Indicates before transfer Substrate converted into transferred The surface area of ​​the substrate.

[0114] The transfer surface area matrix formula is obtained as follows:

[0115]

[0116] in, represents surface area; represents the number of substrate types before and after transfer; 、 ( , =1,2,…, ) represent the substrate types before and after transfer, respectively; Indicates before transfer Substrate converted into transferred The surface area of ​​the bottom. The sum of the row data in the transfer matrix represents the total surface area of ​​a certain bottom type at the beginning of the study, and each row value represents the direction and scale of the transfer of the bottom type; the sum of the column data represents the total surface area of ​​a certain bottom type at the end of the study, and each column value represents the direction and scale of the transfer of the bottom type. The calculation results of the transfer matrix are as follows: Figure 8 shown.

[0117] In summary, the present invention uses the transfer matrix model to monitor and analyze the distribution, change and flow direction of different types of seabed sediments, which has good applicability and superiority. First, based on the seabed sediment surface cover classification surface element dataset obtained by multi-period dynamic tracking, the seabed raster images of the target sea area in two or more periods are used to obtain the updated seabed classification map through change monitoring technology, and then the seabed sediment surface area calculation model is used to obtain the actual area of ​​the map. Based on the transfer matrix model, the dynamic process information of the mutual transformation between the surface areas of each sediment type in the two periods is quantitatively counted, which assists in analyzing the quantitative change direction of each seabed sediment type, and evaluates the change rate and trend of the spatial pattern of seabed sediments, which can effectively reveal the evolution characteristics of the sediment in a specific sea area affected by external factors.

[0118] On the other hand, Figure 9 As shown, an embodiment of the present invention provides a device 900 for monitoring the distribution and evolution characteristics of seabed sediments, which may include:

[0119] The first module 901 is used to obtain initial surface patch data based on the bottom distribution area boundary data of the target sea area and the seabed surface sampling analysis;

[0120] The second module 902 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;

[0121] The third module 903 is used to obtain the sediment distribution evolution characteristics of the target sea area through transfer matrix quantification based on the patch areas corresponding to the initial planar patch data and the second planar patch data.

[0122] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for monitoring the distribution and evolution characteristics of seafloor sediments. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0124] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] like Figure 10 As shown, Figure 10The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes:

[0126] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention.

[0127] The memory 1002 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). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0128] Input / output interface 1003, used to implement information input and output;

[0129] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0130] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0131] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0132] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0133] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0134] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0135] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0136] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0137] The present invention also discloses a computer program product or computer program, which includes computer instructions 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 execute the computer instructions, causing the computer device to perform the above method.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains 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 box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] It should be noted that although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0140] Through the above description of the embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes instructions for causing a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present invention.

[0141] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0142] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into 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 will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0143] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0144] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus (e.g., a computer-based apparatus, a processor-included apparatus, or other apparatus that can fetch and execute instructions from, an instruction execution apparatus, device, or apparatus). For purposes of 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 conjunction with, an instruction execution apparatus, device, or apparatus.

[0145] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0146] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0147] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0148] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0149] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for monitoring the distribution and evolution characteristics of seabed sediments, 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 two preceding and subsequent phases, and obtaining second planar pattern data according to the result of the vector change recognition; The step of performing vector change recognition on the initial planar pattern data of the two preceding 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 two periods before and after using a weighted average method to obtain grayscale images corresponding to the initial planar pattern data of the two periods before and after; Performing a division algebraic operation on the image pixel values ​​of the grayscale images of the previous and next periods to generate a difference image; The expression of the difference map is: DIF(x,y)=M1(x,y) / (M2(x,y) + C) Where M1(x, y) represents the grayscale value of any position in the grayscale image converted from the previous image, M2(x, y) represents the grayscale value of any position in the grayscale image converted from the later image, and C is a constant. Extracting a change area from the difference map using a preset threshold to obtain a vectorized change pattern range; determining the second planar pattern data based on the vectorized change pattern range; According to the patch areas corresponding to the initial planar patch data and the second planar patch data, the 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 analysis, including the following steps: Acquiring backscatter intensity data and seabed sonar images of the target sea area through a multi-beam bathymetry system, and then jointly delineating and obtaining quantitative bottom distribution area boundary data of the target sea area based on 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 bottom sediment distribution area boundary data in combination with the seabed sediment sample data.

3. The method for monitoring the distribution and evolution characteristics of the seabed sediment according to claim 2, characterized in that: The step of obtaining 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; Converting the pre-processed measurement data from a depth reference to an elevation reference, and then performing grid interpolation using an interpolation method; The grid interpolation result is resampled to obtain the backscattering intensity data.

4. The method for monitoring the distribution and evolution characteristics of the seabed sediment according to claim 1, characterized in that: The method of extracting the changed area from the difference map using a preset threshold to obtain a vectorized changed area includes the following steps: Calculating a population variance based on the grayscale value of each pixel in the difference map and the average value of all pixels, and determining the preset threshold value according to the population variance; Based on the preset threshold, the changed area and the unchanged area of ​​the difference map are distinguished and assigned values ​​to obtain a binary map; and the range of the vectorized change pattern is determined according to the binary map.

5. The method for monitoring the distribution and evolution characteristics of the seabed sediment according to claim 1, characterized in that: The method of obtaining the sediment distribution evolution characteristics of the target sea area by quantifying the patch areas corresponding to the initial planar patch data and the second planar patch data through a transfer matrix comprises the following steps: Calculating the patch area corresponding to the patch data using a seabed sediment surface area calculation model; the patch data includes the initial planar patch data and the second planar patch 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 sediment distribution evolution characteristics of the target sea area.

6. The method for monitoring the distribution and evolution characteristics of seabed sediments according to claim 5, characterized in that: The method of using the seabed sediment surface area calculation model to obtain the patch area corresponding to the patch data includes the following steps: Based on the 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 true surface area of ​​the patch; Acquiring mathematical elevation model data of the target sea area, and then determining the slope of the target sea area; Based on the real surface area of ​​the patch and the slope, a grid surface area is processed using a grid calculation model; and the patch area is determined according to the grid surface area.

7. A device for monitoring the distribution and evolution of seabed sediments, characterized in that: include: The first module is used to obtain initial surface patch data based on the bottom distribution area boundary data 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 two previous and subsequent periods, and obtain second planar pattern data according to the result of the vector change recognition; The step of performing vector change recognition on the initial planar pattern data of the two preceding 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 two periods before and after using a weighted average method to obtain grayscale images corresponding to the initial planar pattern data of the two periods before and after; Performing a division algebraic operation on the image pixel values ​​of the grayscale images of the previous and next periods to generate a difference image; The expression of the difference map is: DIF(x,y)=M1(x,y) / (M2(x,y) + C) Where M1(x, y) represents the grayscale value of any position in the grayscale image converted from the previous image, M2(x, y) represents the grayscale value of any position in the grayscale image converted from the later image, and C is a constant. Extracting a change area from the difference map using a preset threshold to obtain a vectorized change pattern range; determining the second planar pattern data based on the vectorized change pattern range; The third module is used to obtain the bottom sediment distribution evolution characteristics of the target sea area through transfer matrix quantification based on the patch areas corresponding to the initial planar patch data and the second planar patch data.

8. 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 6.

9. 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 6 when executed by the processor.

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