Method, device, medium and product for visualizing spatial distribution of marine sediment classification

Abstract

The application discloses a marine sediment classification space distribution visualization method, equipment, medium and product, and the method comprises the following steps: collecting sediment samples of each space sampling point in a target sea area, and obtaining sediment grain composition data; calculating the sediment energy intensity of each point, combining a standard sediment classification threshold set, and determining the corresponding optimized sediment classification threshold set of each point; using the optimized threshold set and the grain composition data to determine the sediment bottom type of each sampling point; constructing a space triangulation network based on all the sampling points, combining the grain data, the bottom type and the standard threshold set, and interpolating the classification boundary points on the triangulation network; dividing the sea area into polygonal regions corresponding to the bottom types according to the boundary points, performing automatic color filling and type labeling, and finally generating a marine sediment space distribution map. The technical scheme of the embodiment of the application can realize comprehensive and accurate classification of marine sediments and complete the visualization mapping of the space distribution of the marine sediments.

Description

Technical Field

[0001] This invention relates to the field of marine mapping technology, and in particular to a method, device, medium, and product for visualizing the spatial distribution of marine sediment classification. Background Technology

[0002] With the deepening of marine resource development, engineering construction, and environmental protection, accurate and efficient visualization of seabed sediment types and their spatial distribution has become a fundamental requirement for marine geological surveys, navigation safety assurance, and marine scientific research. However, the seabed sedimentary environment is complex and variable, and traditional static classification and manual mapping methods are insufficient to meet current requirements for accuracy, efficiency, and adaptability.

[0003] In existing technologies, seabed sediment classification and mapping mainly rely on two types of methods: one is a static classification system based on fixed empirical thresholds and a single triangular model. This method cannot uniformly handle gravelly sediments, and the fixed classification thresholds have poor applicability under different hydrodynamic environments, resulting in classification results that do not match reality. The other is a traditional mapping process that uses ordinary spatial interpolation combined with manual sketching and coloring. This method is prone to distortion in sparse areas of sample points, and it is highly dependent on the experience of professionals, resulting in low efficiency and difficulty in ensuring consistency. Summary of the Invention

[0004] This invention provides a method, device, medium, and product for visualizing the spatial distribution of marine sediment classification, so as to achieve dynamic adaptive classification and efficient automated mapping of seabed sediments.

[0005] According to one aspect of the present invention, a method for visualizing the spatial distribution of marine sediment classification is provided, the method comprising:

[0006] Sediment samples were collected from multiple spatial sampling points in the target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point.

[0007] Based on the sediment grain size composition data corresponding to each spatial sampling point, the sediment energy intensity corresponding to each spatial sampling point is calculated, and based on each sediment energy intensity and the standard sediment classification threshold set, the optimized sediment classification threshold set corresponding to each spatial sampling point is determined.

[0008] Based on the optimized sediment classification threshold set corresponding to each spatial sampling point and the sediment grain size composition data, the sediment substrate type corresponding to each spatial sampling point is determined.

[0009] A spatial triangulation network was constructed based on each spatial sampling point. Based on the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point, interpolation was performed on the spatial triangulation network to obtain classification boundary points used to distinguish different sediment substrate types.

[0010] Based on the classification boundary points corresponding to each sediment substrate type, the target sea area is divided into polygonal regions corresponding to each sediment substrate type. Then, each polygonal region is automatically filled with color and labeled with sediment substrate type to generate a spatial distribution map of marine sediments.

[0011] According to another aspect of the present invention, a visualization device for the spatial distribution of marine sediment classification is provided, the device comprising:

[0012] The data acquisition module is used to collect sediment samples at multiple spatial sampling points in the target sea area and obtain sediment grain size composition data corresponding to each spatial sampling point.

[0013] The optimized threshold determination module is used to calculate the sedimentation energy intensity corresponding to each spatial sampling point based on the sediment grain size composition data corresponding to each spatial sampling point, and to determine the optimized sediment classification threshold set corresponding to each spatial sampling point based on each sedimentation energy intensity and the standard sediment classification threshold set.

[0014] The sediment type determination module is used to determine the sediment type corresponding to each spatial sampling point based on the optimized sediment classification threshold set and sediment grain size composition data corresponding to each spatial sampling point.

[0015] The classification boundary point generation module is used to construct a spatial triangulation based on each spatial sampling point, and to perform interpolation processing on the spatial triangulation based on the sediment grain size composition data, sediment substrate type and standard sediment classification threshold set of each spatial sampling point to obtain classification boundary points used to distinguish different sediment substrate types.

[0016] The visual distribution map generation module is used to divide the target sea area into polygonal regions corresponding to each sediment substrate type based on the classification boundary points corresponding to each sediment substrate type, and automatically fill each polygonal region with color and label the sediment substrate type to generate a spatial distribution map of marine sediments.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a method for visualizing the spatial distribution of marine sediment classification according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement a method for visualizing the spatial distribution of marine sediment classification according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in any embodiment of the present invention.

[0023] The technical solution of this invention involves collecting sediment samples at multiple spatial sampling points in a target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point; calculating the sediment energy intensity corresponding to each spatial sampling point based on the sediment grain size composition data; determining an optimized sediment classification threshold set corresponding to each spatial sampling point based on this sediment energy intensity and a standard sediment classification threshold set; using this optimized sediment classification threshold set and the sediment grain size composition data, determining the sediment substrate type corresponding to each spatial sampling point; subsequently, constructing a spatial triangular network based on all spatial sampling points, and performing interpolation processing on the spatial triangular network in conjunction with its sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set to obtain classification boundary points used to distinguish different sediment substrate types; finally, based on the classification boundary points corresponding to each sediment substrate type, dividing the target sea area into corresponding polygonal regions, and automatically color-filling and sediment substrate type labeling are performed on each region to generate a marine sediment spatial distribution map. This approach addresses the shortcomings of traditional methods in seabed sediment classification, such as poor adaptability due to fixed thresholds, and the reliance on manual labor, low efficiency, and insufficient accuracy in spatial distribution mapping. It achieves the beneficial results of enabling adaptive and accurate sediment type classification and efficient, automated, and high-quality visualization of spatial distribution.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for visualizing the spatial distribution of marine sediment classification according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of another method for visualizing the spatial distribution of marine sediment classification according to Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram of a gravel-sand-mud triangle model provided in Embodiment 2 of the present invention;

[0029] Figure 4 This is a schematic diagram of a sand-silt-clay triangular model provided in Embodiment 2 of the present invention;

[0030] Figure 5 This is a flowchart illustrating the classification of marine sediments in a specific scenario applicable to an embodiment of the present invention.

[0031] Figure 6 This is a color mapping representation of a sediment substrate type in a specific scenario applicable to the embodiments of the present invention.

[0032] Figure 7 This is a spatial distribution map of marine sediments in a specific scenario to which this embodiment of the invention applies;

[0033] Figure 8 This is a schematic diagram of the structure of a visualization device for the spatial distribution of marine sediment classification according to Embodiment 3 of the present invention;

[0034] Figure 9 This is a schematic diagram of the structure of an electronic device for implementing a method for visualizing the spatial distribution of marine sediment classification according to an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Example 1

[0038] Figure 1 This is a flowchart of a method for visualizing the spatial distribution of marine sediment classification according to Embodiment 1 of the present invention. This embodiment is applicable to the analysis and visualization of marine sediment types. The method can be executed by a marine sediment classification spatial distribution visualization device, which can be implemented in hardware and / or software and is generally configured in an electronic device.

[0039] Correspondingly, such as Figure 1 As shown, the method includes:

[0040] S110. Collect sediment samples at multiple spatial sampling points in the target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point.

[0041] Sediments can be understood as loose material that naturally accumulates on the ocean floor, mainly composed of rocks, mineral particles, and biological debris formed through weathering, transportation, and deposition. Sediment grain size composition data can be understood as quantitative data about the percentage of particles of different sizes (such as gravel, sand, silt, clay, etc.) in a sediment sample.

[0042] In this embodiment, within the target sea area under study, multiple spatially representative locations were selected for in-situ sampling to collect seabed sediment samples. Experimental analysis of these samples yielded the percentage of particles of different sizes in the sediment at each sampling point, i.e., sediment grain size composition data.

[0043] S120. Based on the sediment grain size composition data corresponding to each spatial sampling point, calculate the sediment energy intensity corresponding to each spatial sampling point, and based on each sediment energy intensity and the standard sediment classification threshold set, determine the optimized sediment classification threshold set corresponding to each spatial sampling point.

[0044] Sedimentary energy intensity can be understood as a quantitative indicator calculated using a mathematical formula, directly reflecting the strength of the hydrodynamic environment (such as currents and waves) at a certain point on the seabed. This intensity value is calculated based on the grain size composition data of sediments at the sampling points; the stronger the energy, the coarser the sediments are usually. Standard sediment classification thresholds can be understood as a set of predefined, universal numerical standards that specify the proportion of particles of different sizes in the sediment and determine which sediment substrate type it should be classified into.

[0045] In this embodiment, based on the obtained particle size composition data, the depositional energy intensity of the environment at each sampling point is calculated using a specific formula. This depositional energy intensity reflects the strength of dynamic conditions such as water flow and waves. Subsequently, the calculated depositional energy intensity value is combined and compared with a set of preset standard sediment classification thresholds. For the specific hydrodynamic conditions of each sampling point, an optimized set of sediment classification thresholds that better fits the actual situation at that point is dynamically adjusted and determined, thereby making the classification standard more spatially targeted.

[0046] Specifically, based on the standard sediment classification threshold, the spatial sampling points are classified into sediments. This set clearly defines the detailed rules for determining the sediment substrate type based on grain size composition data as follows: For gravelly (grain size greater than 2 mm) sediments, the classification includes: (1) marking all points with a gravel content greater than 80% as G; (2) marking all points with a gravel content greater than 30% and a mud-to-sand ratio greater than 1 as mG; (3) marking all points with a gravel content greater than 30% and a mud-to-sand ratio less than 0.1 as sG; (4) marking the remaining points with a gravel content greater than 30% as msG; (5) marking all points with a gravel content greater than 5% and a mud-to-sand ratio greater than 1 as gM; (6) marking all points with a gravel content greater than 5% and a mud-to-sand ratio less than 0.1 as g S; (7) Mark the remaining gravel content greater than 5% as gmS; (8) Mark all the gravel content greater than 0.01% and mud-to-sand ratio greater than 1 as (g)M; (9) Mark all the gravel content greater than 0.01% and mud-to-sand ratio less than 0.1 as (g)S; (10) Mark the remaining gravel content greater than 0.01% as (g)mS; (11) Mark all the gravel content less than 0.01% and mud-to-sand ratio greater than 9 as M; (12) Mark all the gravel content less than 0.01% and mud-to-sand ratio greater than 1 as sM; (13) Mark all the gravel content less than 0.01% and mud-to-sand ratio less than 0.1 as mS; (14) Mark the remaining gravel content less than 0.01% as S. The classification of gravel-free sediments includes: (1) marking all points with a sand content greater than 90% as S; (2) marking all points with a sand content greater than 50% and a clay content greater than 2 times the silt content as cS; (3) marking all points with a sand content greater than 50% and a clay content less than 0.5 times the silt content as zS; (4) marking the remaining points with a sand content greater than 50% as mS; (5) marking all points with a sand content greater than 10% and a clay content greater than 2 times the silt content as sC; (6) marking all points with a sand content greater than 10% and a clay content less than 0.5 times the silt content as sZ; (7) marking the remaining points with a sand content greater than 10% as sM; (8) marking all points with a sand content less than 10% and a clay content greater than 2 times the silt content as C; (9) marking all points with a sand content less than 10% and a clay content less than 0.5 times the silt content as Z; and (10) marking the remaining points with a sand content less than 10% as M.

[0047] Optionally, based on the above embodiments, calculating the depositional energy intensity corresponding to each spatial sampling point according to the sediment grain size composition data corresponding to each spatial sampling point may include:

[0048] Extract the median grain size from the current sediment grain size composition data corresponding to the current spatial sampling point. ;

[0049] According to the formula Calculate the current deposition energy intensity corresponding to the current spatial sampling point. .

[0050] Generally, in marine geological surveys, after obtaining sediment grain size composition data from a sampling point, the first step is to extract a characteristic value that represents the overall grain size level. This characteristic value is usually called the median grain size. The median grain size refers to the critical grain size value where half of the grains are larger than it and the other half are smaller. It is a core parameter that effectively and comprehensively reflects the average grain size of the sediments at that point, and its magnitude directly indicates whether the sediments are coarse or fine. Extracting this parameter lays the foundation for subsequent quantification of the energy level of the sedimentary environment at that point.

[0051] Generally speaking, through formula The method is used to calculate sedimentary energy intensity, where the exponent 1.5 and the coefficient 100 are empirical relationships summarized from sedimentological theory and observational data. The exponent 1.5 indicates that sedimentary energy intensity increases rapidly with the increase of the median grain size (non-linear growth), while the coefficient 100 is mainly used to adjust the numerical value of the calculation result to a suitable and easy-to-use order of magnitude range.

[0052] S130. Based on the optimized sediment classification threshold set corresponding to each spatial sampling point and the sediment grain size composition data, determine the sediment substrate type corresponding to each spatial sampling point.

[0053] Among them, sediment substrate type can be understood as a specific category divided according to the grain size composition of sediments and established classification rules.

[0054] In this embodiment, after obtaining the optimized classification threshold set specific to each sampling point, it is applied to the original grain size composition data of that point. By comparing and classifying the grain size data with the optimized thresholds, it is determined which type of sediment substrate the seabed sediments below each spatial sampling point belong to, such as sandy, silty, or clayey sediments.

[0055] S140. Construct a spatial triangulation network based on each spatial sampling point, and perform interpolation on the spatial triangulation network based on the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point to obtain classification boundary points used to distinguish different sediment substrate types.

[0056] In this context, a spatial triangulation can be understood as a grid structure consisting of countless interconnected triangles, where all discrete sampling points within the sea area are used as vertices and connected in pairs according to certain rules, forming a grid covering the entire study area. Classification boundary points can be understood as the points on the constructed spatial triangulation network, calculated through interpolation, that indicate the boundary between regions of two different sediment substrate types. These points are obtained through spatial analysis, integrating grain size data from surrounding sampling points, sediment substrate types, and classification thresholds.

[0057] In this embodiment, to obtain continuous classification results for the entire target sea area, rather than just discrete sampling points, it is necessary to connect the spatial locations of all sampling points to construct a spatial triangular network covering the entire target sea area. Based on the grain size composition data of each sampling point, the determined sediment substrate type, and the standard classification threshold, spatial interpolation calculations are performed on the grid of this triangular network. This process aims to infer and identify the boundary locations that can distinguish regions with different sediment substrate types, thereby obtaining a series of clear classification boundary points.

[0058] S150. Based on the classification boundary points corresponding to each sediment substrate type, the target sea area is divided into polygonal regions corresponding to each sediment substrate type. The polygonal regions are automatically filled with color and labeled with sediment substrate types to generate a spatial distribution map of marine sediments.

[0059] In this embodiment, based on the classification boundary points associated with various sediment substrate types, these points are connected on a map of the target sea area, thereby dividing the entire sea area into multiple polygonal regions with uniform internal substrate. Each polygonal region represents a sediment type. Subsequently, different polygonal regions are automatically filled with different colors for visual distinction, and the corresponding sediment substrate type names are labeled on the map, ultimately generating a complete and visualized spatial distribution map of marine sediments.

[0060] The technical solution of this invention involves collecting sediment samples at multiple spatial sampling points in a target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point; calculating the sediment energy intensity corresponding to each spatial sampling point based on the sediment grain size composition data; determining an optimized sediment classification threshold set corresponding to each spatial sampling point based on this sediment energy intensity and a standard sediment classification threshold set; using this optimized sediment classification threshold set and the sediment grain size composition data, determining the sediment substrate type corresponding to each spatial sampling point; subsequently, constructing a spatial triangular network based on all spatial sampling points, and performing interpolation processing on the spatial triangular network in conjunction with its sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set to obtain classification boundary points used to distinguish different sediment substrate types; finally, based on the classification boundary points corresponding to each sediment substrate type, dividing the target sea area into corresponding polygonal regions, and automatically color-filling and sediment substrate type labeling are performed on each region to generate a marine sediment spatial distribution map. This approach addresses the shortcomings of traditional methods in seabed sediment classification, such as poor adaptability due to fixed thresholds, and the reliance on manual labor, low efficiency, and insufficient accuracy in spatial distribution mapping. It achieves the beneficial results of enabling adaptive and accurate sediment type classification and efficient, automated, and high-quality visualization of spatial distribution.

[0061] Example 2

[0062] Figure 2 This is a flowchart of a method for visualizing the spatial distribution of marine sediment classification according to Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiments. Specifically, the step of "determining the optimized sediment classification threshold set corresponding to each spatial sampling point based on each sediment energy intensity and the standard sediment classification threshold set" has been refined.

[0063] Correspondingly, such as Figure 2 As shown, the method includes:

[0064] S210. Collect sediment samples at multiple spatial sampling points in the target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point.

[0065] S220. Based on the sediment grain size composition data corresponding to each spatial sampling point, calculate the sediment energy intensity corresponding to each spatial sampling point.

[0066] S230. Obtain the current deposition energy intensity corresponding to the current spatial sampling point, and determine the current energy range that matches the current deposition energy intensity based on the pre-constructed energy intensity ranges corresponding to different energy ranges.

[0067] In this embodiment, the calculated depositional energy intensity value, representing the hydrodynamic conditions at the current sampling point, is compared with several predefined energy level ranges. This comparison process determines whether the depositional environment at the sampling point belongs to a low-energy state (weak energy), a medium-energy state (moderate energy), or a high-energy state (strong energy). By completing this determination, a specific energy intensity value is categorized into an energy range with clear physical meaning, laying the foundation for subsequent differentiated parameter adjustments.

[0068] Optionally, based on the above embodiments, the energy range may include: a low-energy region, a medium-energy region, and a high-energy region, wherein the energy intensity of the low-energy region is less than that of the medium-energy region, and the energy intensity of the medium-energy region is less than that of the high-energy region; the correction coefficient of the low-energy region is less than that of the medium-energy region, and the correction coefficient of the medium-energy region is less than that of the high-energy region.

[0069] Generally speaking, the low-energy zone corresponds to the environment with the weakest energy intensity, such as deep-sea basins with gentle currents or sheltered harbors, where sediments are mainly composed of fine-grained materials such as silt and clay; the medium-energy zone represents a transitional environment with moderate energy intensity, such as an open continental shelf, where sediments are usually a mixture of sand and silt; and the high-energy zone corresponds to the environment with the strongest energy intensity, such as nearshore or strait areas affected by strong tides and waves, where sediments are mainly composed of coarse-grained materials such as sand and gravel.

[0070] Specifically, based on the energy range of the actual sedimentary environment, Divided into three intervals:

[0071] in, It represents the unit of energy intensity of deposition, and its physical meaning is how many ergs of energy are in an area of ​​one square centimeter. In physics, it can be understood as energy flux or energy density.

[0072] S240. Based on the pre-constructed correction coefficients corresponding to different energy ranges, obtain the current correction coefficient that matches the current energy range.

[0073] In this embodiment, after determining the energy range to which the current sampling point belongs, a specific coefficient value is selected based on a pre-configured floating correction coefficient for each different energy range. This correction coefficient is a multiplier factor used to fine-tune the classification criteria, and its value is directly related to the energy range. For example, the coefficient in a low-energy environment is usually less than one, while the coefficient in a high-energy environment is usually greater than one. This coefficient can either directly use the default empirical value of the corresponding range, or it can be simply calibrated and fine-tuned using a small number of measured samples from the target sea area to obtain a value that better reflects the local conditions.

[0074] Specifically, deposition energy intensity The division can also be adjusted based on historical sediment data and the characteristics of different sea areas, for each energy range. Set correction coefficient The positive coefficient is set with a floating reference range based on the energy range. A more accurate value can be obtained through calibration with a small number of samples. The value is:

[0075]

[0076] S250. Based on the current correction coefficient, the standard sediment classification thresholds in the standard sediment classification threshold set are corrected to obtain the optimized sediment classification threshold set corresponding to the current spatial sampling point.

[0077] In this embodiment, a correction coefficient matching the current environment is used as a unified adjustment factor to multiply each threshold in a preset, universal set of standard sediment classification thresholds. This multiplication operation scales the original classification criteria proportionally, thereby generating a completely new set of optimized sediment classification thresholds specific to the energy environment of the current sampling point.

[0078] Specifically, the standard sediment classification threshold correction formula is as follows:

[0079]

[0080] in, For each standard sediment classification threshold, This is a correction factor.

[0081] Furthermore, based on the above embodiments, before determining the optimized sediment classification threshold set corresponding to each spatial sampling point based on each sediment energy intensity and the standard sediment classification threshold set, the following may also be included:

[0082] Based on the sediment grain size composition data corresponding to each spatial sampling point, determine whether the gravel content in the target sea area is greater than or equal to the preset gravel judgment threshold.

[0083] If the value is greater than or equal to the preset gravel determination threshold, then the first sediment classification threshold set matching the gravel-sand-mud triangle model is obtained as the standard sediment classification threshold set; otherwise, the second sediment classification threshold set matching the sand-silt-clay triangle model is obtained as the standard sediment classification threshold set.

[0084] Generally, before beginning a detailed classification of seabed sediments, a basic categorization based on their fundamental material composition is necessary. This is because gravelly and gravelly-free sediments differ fundamentally in grain composition and classification logic, requiring two completely different classification standards. Based on sediment grain size data obtained from each spatial sampling point, the "gravel content," representing the coarse grain portion, is extracted and compared to a predefined threshold distinguishing between "gravelly" and "gravelless" states. If the gravel content at a sampling point reaches or exceeds this threshold, that point and the local area it represents are classified as "gravelly"; otherwise, they are classified as "gravelless."

[0085] Generally, once the sediments in a target sea area are determined to be "gravelly" type, subsequent classification work will be based on the "gravel-sand-mud triangle model," invoking a complete set of classification rules and threshold standards (i.e., the first set of sediment classification thresholds) that match this model. The thresholds in this set, such as the critical values ​​used to distinguish different gravel content levels and the critical values ​​used to define the mud-to-sand ratio, are specifically designed and optimized for handling sediments composed of gravel, sand, and mud, aiming to provide a detailed classification of gravelly sediments.

[0086] Specifically, Figure 3 A schematic diagram of the gravel-sand-mud triangle model, as shown below. Figure 3 As shown, the gravel-sand-mud triangle model is represented by an equilateral triangle, with its three sides corresponding to the percentage content of the three components: gravel, sand, and mud. The interior of the triangle is divided into multiple polygonal regions by a series of boundaries defined according to the component proportions. Each region is clearly labeled with its corresponding sediment type code, such as G, sG, mG, etc. On the left side of the triangle, along the "gravel" component axis, a continuous scale is marked indicating its content from less than 0.01% to greater than 80%. At the bottom of the triangle, the sand-mud ratio, used to define the relative proportion of mud and sand, is marked, ranging from less than 0.0625 mm to several key critical values, including 9:1. Furthermore, the lower right corner of the figure provides detailed explanations of the various sediment type codes appearing in the figure.

[0087] Generally, once the sediments in the target sea area are determined to be "gravelless," the "sand-silt-clay triangle model" is adopted. Correspondingly, the set of second-category sediment classification thresholds corresponding to this model is invoked. These thresholds, such as the critical values ​​for distinguishing different sand content levels and defining the ratio of clay to silt, are specifically designed for handling sediments composed of finer-grained components like sand, silt, and clay, aiming to accurately classify gravelless fine-grained sediments. This preliminary discrimination and model selection step establishes a correct and targeted framework of rules for the entire classification process.

[0088] Specifically, Figure 4 This is a schematic diagram of a sand-silt-clay triangular model, as shown below. Figure 4 As shown, this sand-silt-clay triangular model is also represented by an equilateral triangle, with its three vertices corresponding to 100% sand, silt, and clay components, respectively. The triangle is divided by a series of key content boundaries: horizontal parallel lines clearly indicate the critical positions of 90%, 50%, and 10% sand content; two vertical boundary lines correspond to the important dividing criteria of clay-silt content ratios of 2:1 and 1:2, respectively. Each polygonal partition in the diagram clearly labels the corresponding sediment type code and name, such as pure sand, silty sand, clayey sand, sandy silt, sandy clay, silt, and mud. This schematic diagram systematically defines all the quantitative rules and standard thresholds for type classification based on the relative contents of the three fine-grained components: sand, silt, and clay.

[0089] S260. Based on the optimized sediment classification threshold set corresponding to each spatial sampling point and the sediment grain size composition data, determine the sediment substrate type corresponding to each spatial sampling point.

[0090] S270. Construct a spatial triangulation network based on each spatial sampling point, and perform interpolation on the spatial triangulation network based on the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point to obtain classification boundary points used to distinguish different sediment substrate types.

[0091] Optionally, based on the above embodiments, constructing a spatial triangulation network according to each spatial sampling point may include:

[0092] Traverse the spatial coordinates of each spatial sampling point to determine the global maximum and minimum values ​​of the x-coordinate and the global maximum and minimum values ​​of the y-coordinate of all spatial sampling points.

[0093] The horizontal span is calculated based on the global maximum and global minimum values ​​of the horizontal coordinate;

[0094] The vertical span is calculated based on the global maximum and global minimum values ​​of the vertical coordinate;

[0095] The larger of the horizontal span and the vertical span is selected and defined as the baseline span;

[0096] Calculate the average of the global maximum and global minimum values ​​of the x-coordinate to obtain the central x-coordinate; calculate the average of the global maximum and global minimum values ​​of the y-coordinate to obtain the central y-coordinate;

[0097] Based on the baseline span, the central x-coordinate, and the central y-coordinate, the coordinates of the three vertices of an initial hypertriangle are generated, and the initial hypertriangle completely encloses all spatial sampling points;

[0098] Each spatial sampling point is used as a new vertex and is sequentially inserted into the initial triangular mesh formed by the initial hypertriangles;

[0099] After each insertion of a new vertex, Delaunay optimization is performed on the affected local triangulation structure, empty circle detection is performed on the relevant triangles, and edge flipping operation is performed on triangles that do not meet the empty circle criterion.

[0100] After all spatial sampling points are inserted and optimized, all triangles in the current triangulation that contain any vertex of the initial hypertriangle are removed, resulting in a restricted Delaunay triangulation composed of all spatial sampling points as vertices, which is the spatial triangulation.

[0101] Generally, when constructing a continuous spatial analysis model using discrete spatial sampling points, the overall spatial distribution range must first be defined. By systematically traversing the planar coordinates of all sampling points, their global maximum and minimum values ​​on the x and y coordinates can be determined, thus precisely defining the smallest rectangular region enclosing all points. Next, the lateral and longitudinal spans of this region are calculated, both describing the distribution pattern of the sampling point cluster. To construct an initial geometric framework that reliably encloses all points, the larger of the lateral and longitudinal spans is selected as the baseline span to ensure sufficient margin even in the shortest direction. Simultaneously, the average values ​​of the east-west and north-south boundary coordinates are calculated to obtain the central x and y coordinates, respectively. This center point, together with the baseline span, constitutes the geometric reference for subsequent operations. Based on this, a huge initial triangle, or hypertriangle, that completely encloses all sampling points can be calculated and generated, providing a containing initial space for subsequent work.

[0102] Generally, after obtaining an inclusive initial triangulation (i.e., composed of hypertriangles), real sampled points are inserted one by one as new vertices. The process is sequential; the insertion of each sampled point alters the triangulation structure of its local region, splitting the original triangles. To maintain high quality throughout the dynamic insertion process and ensure that the triangulation meets specific geometric optimization criteria (i.e., the Delaunay criterion), the affected local triangulation structure must be optimized immediately after each new vertex insertion. The core of this optimization is "empty circle detection," which checks whether the circumcircle of the relevant triangles contains other vertices that should not exist. Once a triangle pair that does not meet the criterion is detected, its common edge is swapped through an edge flipping operation. This iterative optimization process ensures that the triangles in the triangulation are as close to equiangular as possible, effectively avoiding extremely elongated triangles, thus laying a stable and accurate geometric foundation for subsequent spatial interpolation analysis.

[0103] Generally, once all real sampling points have been inserted into the triangulation as vertices and local optimization has been completed after each insertion, the role of the initially introduced hypertriangles (whose vertices are artificially added virtual points) has been fulfilled. To obtain a triangulation purely composed of actual sampling points that reflects the real spatial distribution, all triangles containing vertices of any of the aforementioned virtual hypertriangles must be removed from the current triangulation. After removal, the remaining triangular mesh has vertices composed entirely of actual spatial sampling points, and the connection relationships between vertices still satisfy the Delaunay criterion, ultimately generating a high-quality restricted Delaunay triangulation.

[0104] Optionally, based on the above embodiments, interpolation processing is performed on a spatial triangulation network according to the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point to obtain classification boundary points for distinguishing different sediment substrate types. This may include:

[0105] Based on the set of standard sediment classification thresholds, determine the critical values ​​of multiple sets of grain size components used to distinguish different sediment substrate types;

[0106] For each set of critical values ​​for particle size composition, traverse each edge of the spatial triangular network, obtain the sediment particle size composition data of the two endpoints of the edge, and extract the particle size composition content value corresponding to the current set of critical values ​​for particle size composition.

[0107] Determine whether the critical value of the current group's particle size component content is between the content values ​​of the two extracted endpoint particle size components;

[0108] If so, the spatial location point corresponding to the critical value of the current group's granular component content on the edge is calculated by linear interpolation, and it is used as a classification boundary point. The critical value of the current group's granular component content corresponding to the classification boundary point is recorded.

[0109] After completing the traversal and interpolation calculation of all edges of the spatial triangulation, all classification boundary points corresponding to the critical values ​​of the same set of particle size components are traced and connected to the next classification boundary point that is closest to the classification boundary point and belongs to the same contour line, based on the adjacency relationship of triangles in the triangulation. This process is repeated until a closed broken line is formed or the region boundary is reached, thus generating a contour line that represents the set of critical values.

[0110] The contour lines generated from the critical values ​​of different particle size components are used together as classification boundaries to distinguish different sediment substrate types.

[0111] Generally, after determining the sediment substrate type and constructing a spatial triangulation network for discrete sampling points, it is necessary to determine the specific mapping basis for subsequent spatial distribution mapping based on preset classification standards. This process first analyzes and extracts the values ​​that play a key demarcation role from the set of standard sediment classification thresholds on which the entire classification rule system is based. These values ​​are multiple sets of critical values ​​for grain size composition, such as "sand content is 50%" or "mud-sand ratio is 1:1". These critical values ​​essentially define the specific numerical standards for each theoretical boundary line that needs to be drawn on the map to distinguish different sediment substrate types.

[0112] Generally, after determining the theoretical boundary line values ​​to be drawn, the actual trajectory of each such boundary line in real space needs to be accurately found on the established spatial triangulation network. This requires independent checking of each edge of the triangulation network. For a specific set of critical values ​​being processed, the program obtains the sediment grain size composition data of the sampling points at the two endpoints of the edge and extracts the specific content of the component corresponding to the current critical value. Then, it determines whether the current critical value lies exactly between the component contents corresponding to the two endpoints of the edge. If this condition is met, it means that there must exist a point on the line segment connecting the two sampling points whose component content is exactly equal to the current critical value. Thus, by assuming that the component content varies uniformly between the two known endpoints, the precise spatial location of this point can be calculated. This point is recorded as a "classification boundary point" and marked with its corresponding critical value information.

[0113] Generally, after traversing and calculating all edges of the entire triangulation network as described above, a large number of discretely distributed boundary points belonging to different critical values ​​are obtained. At this point, it is necessary to correctly connect all points belonging to the same set of critical values ​​to form a continuous boundary line. The connection process strictly follows the inherent triangular adjacency relationship of the triangulation network itself. Starting from a point, in the triangle containing that point, find the next adjacent point belonging to the same critical value and connect them. This process moves from one triangle to an adjacent triangle, tracing and connecting them sequentially, until the line closes to form a polygon or extends to the boundary of the study area. Ultimately, this continuous broken line, composed of a series of connected straight line segments and representing a fixed critical value, is a "contour line".

[0114] Generally, a single isoline can only express the spatial distribution of one attribute condition (such as "sand content equals a certain value"). To completely delineate all different sediment substrate types, multiple isolines generated based on different critical values ​​need to be superimposed. These isolines intersect and connect in space, weaving together a complex network. This network of multiple isolines ultimately divides the entire study area into several continuous polygonal regions, each with consistent substrate properties. Therefore, this complete set of isolines collectively constitutes the final classification boundary with clear spatial location and geometric shape, used to distinguish and express different sediment substrate types.

[0115] S280. Based on the classification boundary points corresponding to each sediment substrate type, the target sea area is divided into polygonal regions corresponding to each sediment substrate type. The polygonal regions are automatically filled with color and labeled with sediment substrate types to generate a spatial distribution map of marine sediments.

[0116] Optionally, based on the above embodiments, automatically color-filling and sediment type labeling are performed on each polygonal region to generate a marine sediment spatial distribution map, which may include:

[0117] Based on the sediment substrate type-color mapping table, determine the standard fill color associated with the sediment substrate type for each polygonal region;

[0118] Automated color filling of the corresponding polygonal regions is performed using a defined standard fill color.

[0119] Within each polygonal region that has been filled with color, the name of the corresponding sediment substrate type is automatically labeled at the geometric centroid of that polygonal region;

[0120] Output the final image with completed color filling and annotation, as a spatial distribution map of the marine sediments.

[0121] Generally, after dividing the target sea area into polygonal regions, a specific visual style needs to be assigned to each region representing different sediment substrate types in order to generate an intuitive and easy-to-understand visualization map. This process begins by querying a predefined rule table, namely the "Sediment Substrate Type-Color Mapping Table." This table clearly specifies the unique standard fill color corresponding to each possible sediment substrate type (such as sand, silty sand, gravelly mud, etc.). The program compares the sediment substrate type represented by each polygonal region with this mapping table to determine the standard color that should be used for each region.

[0122] Generally, after obtaining the standard fill color information for each polygonal region, the next step is to perform an automated color fill operation. The drawing program will automatically and in batches color the internal space of all polygonal regions on the map based on the color value assigned to each polygonal region in the previous step.

[0123] Generally, after filling the background color of all polygonal regions, the map's visualization information still needs further enrichment to directly deduce the specific type of each region. Therefore, text labels need to be added to each colored polygonal region. The label's location is usually chosen at the "geometric centroid" of the polygonal region, that is, the center of mass of its planar shape. This location ensures the label text is relatively centered within the region, resulting in a more balanced and aesthetically pleasing appearance. The label content is the standard name of the sediment substrate type represented by that region. This standard name is associated with the previously determined sediment attributes of the region; the program automatically extracts and converts it into a text label, placing it at the pre-calculated centroid coordinates.

[0124] Generally, once all the above steps of color filling and text labeling are completed, a complete thematic map with a professional style is generated. This map integrates all core visualization elements such as spatial boundaries, color partitions, and type labels, intuitively showing the spatial distribution pattern of different sediment types in the target sea area.

[0125] The technical solution of this invention involves collecting sediment samples at multiple spatial sampling points in a target sea area to obtain sediment grain size composition data for each sampling point; calculating the sediment energy intensity at each point and determining its energy range based on this intensity value, thereby obtaining a dynamic correction coefficient corresponding to the energy range; using this correction coefficient to adjust the standard sediment classification threshold set, thereby obtaining an optimized sediment classification threshold set that matches the environment of each sampling point; subsequently, based on this optimized threshold set and the grain size composition data of each point, determining the sediment substrate type of each sampling point; on this basis, constructing a spatial triangular network based on all sampling points, and combining its grain size data, sediment substrate type, and standard threshold set, performing interpolation processing on the triangular network to obtain classification boundary points; finally, dividing the target sea area into polygonal regions corresponding to each sediment substrate type based on these boundary points, and performing automated color filling and type labeling to generate a marine sediment spatial distribution map. This solution addresses the problems of traditional seabed sediment classification methods, which cannot adapt to the differences in energy environments of different sea areas due to the use of fixed thresholds, and the problems of spatial mapping processes relying on manual labor, resulting in low efficiency and insufficient accuracy. It achieves the beneficial effect of realizing dynamic adaptive optimization of classification standards and significantly improving the level of automation and accuracy of classification mapping.

[0126] To facilitate understanding, the specific application scenarios applicable to each embodiment of the present invention are described below. In this specific embodiment, for sea areas with complex seabed sediment types and varied spatial distribution patterns, the present invention designs a complete scheme for accurate identification and distribution visualization of marine sediment types based on dynamic adaptive classification and automated spatial mapping.

[0127] Specifically, Figure 5 A flowchart for classifying marine sediments, such as Figure 5 As shown, the process begins by reading sediment sample data from various spatial sampling points in the target sea area. This data includes the proportions of components such as gravel, sand, mud, silt, and clay. To ensure the numerical stability of subsequent calculations, zero values ​​in the data are smoothed and replaced with a predetermined minimum value. The process then proceeds to the core decision point: determining whether a sample belongs to "gravelly-bearing sediments" or "gravelless sediments" based on its gravel content, and selecting the appropriate classification model (gravel-sand-mud triangle model or sand-silt-clay triangle model). Regardless of the chosen branch, a "dynamic threshold optimization" operation is ultimately performed. This optimizes the preset classification threshold based on the sedimentary energy intensity of each sampling point, obtaining an optimized classification threshold that adapts to the local sedimentary environment, and ultimately determining the sediment substrate type for each sampling point.

[0128] After classifying all spatial sampling points, a restricted Delaunay triangulation is constructed based on their spatial location data. Interpolation is then performed on the triangulation based on optimized classification thresholds to generate classification boundaries (contour lines) that distinguish different sediment substrate types, thereby dividing the target sea area into polygonal regions corresponding to each sediment substrate type. To achieve standardized and automated output of distribution maps, this scheme predefines, as follows: Figure 6 The diagram illustrates a sediment substrate type-color mapping representation. This table systematically defines the standard fill colors for various substrate types and their subtypes, from bedrock and sand to mud and mixed sediments, providing a basis for automated color filling. Finally, the scheme automatically fills and labels the types of each polygonal region with color, generating a representation such as... Figure 7 The map shown illustrates the spatial distribution of marine sediments. Through differentiated colors and detailed numerical labels, this map visually demonstrates the spatial distribution characteristics of sediments in different complex marine areas. Figure 7 As shown in the upper-middle figure, for an area dominated by a mixture of sandy and clayey sediments, the resulting map clearly distinguishes the spatial boundaries of various sediment types using light blue (muddy sand), purple (sandy mud), pink (clayey sand), and green (sandy clay) color blocks. Specific material composition ratios are precisely marked within each polygon and in adjacent blank areas (e.g., "10%" at the edge, "50%" and "0.5" inside), presenting the complex interweaving of sand, mud, and clay in this area. Figure 7 As shown in the lower figure, for more complex mixed sedimentary areas containing gravel, the resulting map introduces specific color schemes such as yellow (gravel), cyan (muddy-sandy gravel), and purplish-red (gravelly-muddy sand) to characterize the distribution of gravelly sediments. A dark outer border further clarifies the boundaries of various mixed sediment types. Furthermore, the map not only labels the core type names within each region but also provides key grain size percentages (such as "50%", "30%", "5%", and "0.01%)", even including indicators for very small proportions of "gravel" composition. Overall, both maps, by automatically labeling sediment type names and detailed distribution proportions at the geometric center of the region, vividly and accurately reflect the spatial distribution patterns and subtle differences of sediments in different types of complex marine areas.

[0129] Example 3

[0130] Figure 8 This invention provides a visualization device for the spatial distribution of marine sediment classification in Embodiment 3, such as... Figure 8 As shown, the device includes:

[0131] The data acquisition module 810 is used to collect sediment samples at multiple spatial sampling points in the target sea area and obtain sediment grain size composition data corresponding to each spatial sampling point.

[0132] The optimized threshold determination module 820 is used to calculate the sedimentation energy intensity corresponding to each spatial sampling point based on the sediment grain size composition data corresponding to each spatial sampling point, and to determine the optimized sediment classification threshold set corresponding to each spatial sampling point based on each sedimentation energy intensity and the standard sediment classification threshold set.

[0133] The sediment type determination module 830 is used to determine the sediment sediment type corresponding to each spatial sampling point based on the optimized sediment classification threshold set and sediment grain size composition data corresponding to each spatial sampling point.

[0134] The classification boundary point generation module 840 is used to construct a spatial triangular network based on each spatial sampling point, and to perform interpolation processing on the spatial triangular network based on the sediment grain size composition data, sediment substrate type and standard sediment classification threshold set of each spatial sampling point to obtain classification boundary points used to distinguish different sediment substrate types.

[0135] The visual distribution map generation module 850 is used to divide the target sea area into polygonal regions corresponding to each sediment substrate type based on the classification boundary points corresponding to each sediment substrate type, and automatically fill each polygonal region with color and label the sediment substrate type to generate a spatial distribution map of marine sediments.

[0136] The technical solution of this invention involves collecting sediment samples at multiple spatial sampling points in a target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point; calculating the sediment energy intensity corresponding to each spatial sampling point based on the sediment grain size composition data; determining an optimized sediment classification threshold set corresponding to each spatial sampling point based on this sediment energy intensity and a standard sediment classification threshold set; using this optimized sediment classification threshold set and the sediment grain size composition data, determining the sediment substrate type corresponding to each spatial sampling point; subsequently, constructing a spatial triangular network based on all spatial sampling points, and performing interpolation processing on the spatial triangular network in conjunction with its sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set to obtain classification boundary points used to distinguish different sediment substrate types; finally, based on the classification boundary points corresponding to each sediment substrate type, dividing the target sea area into corresponding polygonal regions, and automatically color-filling and sediment substrate type labeling are performed on each region to generate a marine sediment spatial distribution map. This approach addresses the shortcomings of traditional methods in seabed sediment classification, such as poor adaptability due to fixed thresholds, and the reliance on manual labor, low efficiency, and insufficient accuracy in spatial distribution mapping. It achieves the beneficial results of enabling adaptive and accurate sediment type classification and efficient, automated, and high-quality visualization of spatial distribution.

[0137] Based on the above embodiments, the threshold determination module 820 is optimized and is specifically used for:

[0138] Extract the median grain size from the current sediment grain size composition data corresponding to the current spatial sampling point. ;

[0139] According to the formula Calculate the current deposition energy intensity corresponding to the current spatial sampling point. .

[0140] Based on the above embodiments, the threshold determination module 820 is optimized and is specifically used for:

[0141] Obtain the current deposition energy intensity corresponding to the current spatial sampling point, and determine the current energy range that matches the current deposition energy intensity based on the pre-constructed energy intensity ranges corresponding to different energy ranges;

[0142] Based on the pre-constructed correction coefficients corresponding to different energy ranges, obtain the current correction coefficient that matches the current energy range;

[0143] Based on the current correction coefficient, the standard sediment classification thresholds in the standard sediment classification threshold set are corrected to obtain the optimized sediment classification threshold set corresponding to the current spatial sampling point.

[0144] Based on the above embodiments, the energy range includes: a low-energy region, a medium-energy region, and a high-energy region, wherein the energy intensity of the low-energy region is less than that of the medium-energy region, and the energy intensity of the medium-energy region is less than that of the high-energy region; the correction coefficient of the low-energy region is less than that of the medium-energy region, and the correction coefficient of the medium-energy region is less than that of the high-energy region.

[0145] Furthermore, based on the above embodiments, a visualization device for the spatial distribution of marine sediment classification may further include:

[0146] The gravel content determination module is used to determine whether the gravel content in the target sea area is greater than or equal to the preset gravel determination threshold based on the sediment grain size composition data corresponding to each spatial sampling point, before determining the optimized sediment classification threshold set corresponding to each spatial sampling point based on the sediment energy intensity and the standard sediment classification threshold set.

[0147] The triangular model matching module is used to obtain the first set of sediment classification thresholds that match the gravel-sand-mud triangular model if the threshold is greater than or equal to the preset gravel determination threshold, and use it as the standard sediment classification threshold set; otherwise, it obtains the second set of sediment classification thresholds that match the sand-silt-clay triangular model, and uses it as the standard sediment classification threshold set.

[0148] Based on the above embodiments, the classification boundary point generation module 840 is specifically used for:

[0149] Traverse the spatial coordinates of each spatial sampling point to determine the global maximum and minimum values ​​of the x-coordinate and the global maximum and minimum values ​​of the y-coordinate of all spatial sampling points.

[0150] The horizontal span is calculated based on the global maximum and global minimum values ​​of the horizontal coordinate;

[0151] The vertical span is calculated based on the global maximum and global minimum values ​​of the vertical coordinate;

[0152] The larger of the horizontal span and the vertical span is selected and defined as the baseline span;

[0153] Calculate the average of the global maximum and global minimum values ​​of the x-coordinate to obtain the central x-coordinate; calculate the average of the global maximum and global minimum values ​​of the y-coordinate to obtain the central y-coordinate;

[0154] Based on the baseline span, the central x-coordinate, and the central y-coordinate, the coordinates of the three vertices of an initial hypertriangle are generated, and the initial hypertriangle completely encloses all spatial sampling points;

[0155] Each spatial sampling point is used as a new vertex and is sequentially inserted into the initial triangular mesh formed by the initial hypertriangles;

[0156] After each insertion of a new vertex, Delaunay optimization is performed on the affected local triangulation structure, empty circle detection is performed on the relevant triangles, and edge flipping operation is performed on triangles that do not meet the empty circle criterion.

[0157] After all spatial sampling points are inserted and optimized, all triangles in the current triangulation that contain any vertex of the initial hypertriangle are removed, resulting in a restricted Delaunay triangulation composed of all spatial sampling points as vertices, which is the spatial triangulation.

[0158] Based on the above embodiments, the classification boundary point generation module 840 is specifically used for:

[0159] Based on the set of standard sediment classification thresholds, determine the critical values ​​of multiple sets of grain size components used to distinguish different sediment substrate types;

[0160] For each set of critical values ​​for particle size composition, traverse each edge of the spatial triangular network, obtain the sediment particle size composition data of the two endpoints of the edge, and extract the particle size composition content value corresponding to the current set of critical values ​​for particle size composition.

[0161] Determine whether the critical value of the current group's particle size component content is between the content values ​​of the two extracted endpoint particle size components;

[0162] If so, the spatial location point corresponding to the critical value of the current group's granular component content on the edge is calculated by linear interpolation, and it is used as a classification boundary point. The critical value of the current group's granular component content corresponding to the classification boundary point is recorded.

[0163] After completing the traversal and interpolation calculation of all edges of the spatial triangulation, all classification boundary points corresponding to the critical values ​​of the same set of particle size components are traced and connected to the next classification boundary point that is closest to the classification boundary point and belongs to the same contour line, based on the adjacency relationship of triangles in the triangulation. This process is repeated until a closed broken line is formed or the region boundary is reached, thus generating a contour line that represents the set of critical values.

[0164] The contour lines generated from the critical values ​​of different particle size components are used together as classification boundaries to distinguish different sediment substrate types.

[0165] Based on the above embodiments, the visual distribution map generation module 850 is specifically used for:

[0166] Based on the sediment substrate type-color mapping table, determine the standard fill color associated with the sediment substrate type for each polygonal region;

[0167] Automated color filling of the corresponding polygonal regions is performed using a defined standard fill color.

[0168] Within each polygonal region that has been filled with color, the name of the corresponding sediment substrate type is automatically labeled at the geometric centroid of that polygonal region;

[0169] Output the final image with completed color filling and annotation, as a spatial distribution map of the marine sediments.

[0170] The marine sediment classification spatial distribution visualization device provided in this embodiment of the invention can execute the marine sediment classification spatial distribution visualization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0171] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0172] Example 4

[0173] Figure 9A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0174] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0175] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0176] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing a method for visualizing the spatial distribution of marine sediment classification as described in any embodiment of the present invention, i.e.:

[0177] Sediment samples were collected from multiple spatial sampling points in the target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point.

[0178] Based on the sediment grain size composition data corresponding to each spatial sampling point, the sediment energy intensity corresponding to each spatial sampling point is calculated, and based on each sediment energy intensity and the standard sediment classification threshold set, the optimized sediment classification threshold set corresponding to each spatial sampling point is determined.

[0179] Based on the optimized sediment classification threshold set corresponding to each spatial sampling point and the sediment grain size composition data, the sediment substrate type corresponding to each spatial sampling point is determined.

[0180] A spatial triangulation network was constructed based on each spatial sampling point. Based on the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point, interpolation was performed on the spatial triangulation network to obtain classification boundary points used to distinguish different sediment substrate types.

[0181] Based on the classification boundary points corresponding to each sediment substrate type, the target sea area is divided into polygonal regions corresponding to each sediment substrate type. Then, each polygonal region is automatically filled with color and labeled with sediment substrate type to generate a spatial distribution map of marine sediments.

[0182] In some embodiments, a method for visualizing the spatial distribution of marine sediment classification as described in any one of the embodiments of the present invention can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for visualizing the spatial distribution of marine sediment classification as described above as described in any one of the embodiments of the present invention can be performed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to perform the method for visualizing the spatial distribution of marine sediment classification as described in any one of the embodiments of the present invention.

[0183] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0185] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0186] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0187] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0188] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0189] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0190] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for visualizing the spatial distribution of marine sediment classification, characterized in that, The method includes: Sediment samples were collected from multiple spatial sampling points in the target sea area to obtain sediment grain size composition data corresponding to each spatial sampling point. Based on the sediment grain size composition data corresponding to each spatial sampling point, the sediment energy intensity corresponding to each spatial sampling point is calculated, and based on each sediment energy intensity and the standard sediment classification threshold set, the optimized sediment classification threshold set corresponding to each spatial sampling point is determined. Based on the optimized sediment classification threshold set corresponding to each spatial sampling point and the sediment grain size composition data, the sediment substrate type corresponding to each spatial sampling point is determined. A spatial triangulation network was constructed based on each spatial sampling point. Based on the sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set of each spatial sampling point, interpolation was performed on the spatial triangulation network to obtain classification boundary points used to distinguish different sediment substrate types. Based on the classification boundary points corresponding to each sediment substrate type, the target sea area is divided into polygonal regions corresponding to each sediment substrate type. Then, each polygonal region is automatically filled with color and labeled with sediment substrate type to generate a spatial distribution map of marine sediments.

2. The method according to claim 1, characterized in that, Based on the sediment grain size composition data corresponding to each spatial sampling point, the sedimentary energy intensity corresponding to each spatial sampling point is calculated, including: Extract the median grain size from the current sediment grain size composition data corresponding to the current spatial sampling point. ; According to the formula Calculate the current deposition energy intensity corresponding to the current spatial sampling point. .

3. The method according to claim 1, characterized in that, Based on the sedimentary energy intensities and the standard sediment classification threshold set, an optimized sediment classification threshold set corresponding to each spatial sampling point is determined, including: Obtain the current deposition energy intensity corresponding to the current spatial sampling point, and determine the current energy range that matches the current deposition energy intensity based on the pre-constructed energy intensity ranges corresponding to different energy ranges; Based on the pre-constructed correction coefficients corresponding to different energy ranges, obtain the current correction coefficient that matches the current energy range; Based on the current correction coefficient, the standard sediment classification thresholds in the standard sediment classification threshold set are corrected to obtain the optimized sediment classification threshold set corresponding to the current spatial sampling point.

4. The method according to claim 3, characterized in that, The energy range includes: low energy region, medium energy region and high energy region, wherein the energy intensity of the low energy region is less than that of the medium energy region, and the energy intensity of the medium energy region is less than that of the high energy region; the correction coefficient of the low energy region is less than that of the medium energy region, and the correction coefficient of the medium energy region is less than that of the high energy region. Before determining the optimized sediment classification threshold set corresponding to each spatial sampling point based on each sediment energy intensity and the standard sediment classification threshold set, the following steps are also included: Based on the sediment grain size composition data corresponding to each spatial sampling point, determine whether the gravel content in the target sea area is greater than or equal to the preset gravel judgment threshold. If so, obtain the first set of sediment classification thresholds that matches the gravel-sand-mud triangle model as the standard sediment classification threshold set; otherwise, obtain the second set of sediment classification thresholds that matches the sand-silt-clay triangle model as the standard sediment classification threshold set.

5. The method according to any one of claims 1-4, characterized in that, A spatial triangulation network is constructed based on each spatial sampling point, including: Traverse the spatial coordinates of each spatial sampling point to determine the global maximum and minimum values ​​of the x-coordinate and the global maximum and minimum values ​​of the y-coordinate of all spatial sampling points. The horizontal span is calculated based on the global maximum and global minimum values ​​of the horizontal coordinate; The vertical span is calculated based on the global maximum and global minimum values ​​of the vertical coordinate; The larger of the horizontal span and the vertical span is selected and defined as the baseline span; Calculate the average of the global maximum and global minimum values ​​of the x-coordinate to obtain the central x-coordinate; calculate the average of the global maximum and global minimum values ​​of the y-coordinate to obtain the central y-coordinate; Based on the baseline span, the central x-coordinate, and the central y-coordinate, the coordinates of the three vertices of an initial hypertriangle are generated, and the initial hypertriangle completely encloses all spatial sampling points; Each spatial sampling point is used as a new vertex and is sequentially inserted into the initial triangular mesh formed by the initial hypertriangles; After each insertion of a new vertex, Delaunay optimization is performed on the affected local triangulation structure, empty circle detection is performed on the relevant triangles, and edge flipping operation is performed on triangles that do not meet the empty circle criterion. After all spatial sampling points are inserted and optimized, all triangles in the current triangulation that contain any vertex of the initial hypertriangle are removed, resulting in a restricted Delaunay triangulation composed of all spatial sampling points as vertices, which is the spatial triangulation.

6. The method according to any one of claims 1-4, characterized in that, Based on sediment grain size composition data, sediment substrate type, and standard sediment classification threshold set from each spatial sampling point, interpolation is performed on a spatial triangulation network to obtain classification boundary points for distinguishing different sediment substrate types, including: Based on the set of standard sediment classification thresholds, determine the critical values ​​of multiple sets of grain size components used to distinguish different sediment substrate types; For each set of critical values ​​for particle size composition, traverse each edge of the spatial triangular network, obtain the sediment particle size composition data of the two endpoints of the edge, and extract the particle size composition content value corresponding to the current set of critical values ​​for particle size composition. Determine whether the critical value of the current group's particle size component content is between the content values ​​of the two extracted endpoint particle size components; If so, the spatial location point corresponding to the critical value of the current group's granular component content on the edge is calculated by linear interpolation, and it is used as a classification boundary point. The critical value of the current group's granular component content corresponding to the classification boundary point is recorded. After completing the traversal and interpolation calculation of all edges of the spatial triangulation, all classification boundary points corresponding to the critical values ​​of the same set of particle size components are traced and connected to the next classification boundary point that is closest to the classification boundary point and belongs to the same contour line, based on the adjacency relationship of triangles in the triangulation. This process is repeated until a closed broken line is formed or the region boundary is reached, thus generating a contour line that represents the set of critical values. The contour lines generated from the critical values ​​of different particle size components are used together as classification boundaries to distinguish different sediment substrate types.

7. The method according to claim 1, characterized in that, Automated color filling and sediment type labeling are performed on each polygonal region to generate a spatial distribution map of marine sediments, including: Based on the sediment substrate type-color mapping table, determine the standard fill color associated with the sediment substrate type for each polygonal region; Automated color filling of the corresponding polygonal regions is performed using a defined standard fill color. Within each polygonal region that has been filled with color, the name of the corresponding sediment substrate type is automatically labeled at the geometric centroid of that polygonal region; Output the final image with complete color filling and annotation, as a spatial distribution map of the marine sediments.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for visualizing the spatial distribution of marine sediment classification as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for visualizing the spatial distribution of marine sediment classification as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for visualizing the spatial distribution of marine sediment classification according to any one of claims 1-7.

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