Ship navigation marine environment modeling method and system based on grid network

Abstract

The invention relates to the technical field of marine environment modeling, and provides a ship navigation marine environment modeling method and system based on a grid network in order to solve the effective modeling problem of a ship navigation marine environment, and the modeling method comprises the steps: 1, obtaining a ship navigation marine environment image pixel value; 2, converting the pixel value of the marine environment image of ship navigation to obtain a grayscale image; 3, determining the grid granularity of the grayscale image; and step 4, rasterizing the marine environment of ship navigation based on the grid granularity, and generating a binary nautical chart by adopting a rectangular coordinate method. An actual scene can be restored by utilizing the constructed marine environment model and is quantitatively represented on a sea chart, and a foundation is laid for carrying out marine ship path planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine environment modeling, and in particular relates to a method and system for modeling a marine environment for ship navigation based on a grid network. Background Art

[0002] Marine environment modeling is a way to represent the real marine environment in mathematical language. The environmental model can be restored to the actual scene and quantitatively represented on the nautical chart. Environmental modeling is an important part of the marine path planning process. Accurately extracting environmental information and describing the navigation environment of the route in an appropriate way are the premise and basis for carrying out marine ship path planning.

[0003] For ships, the environmental modeling and analysis of their navigation areas first requires clarifying the ship's starting point, destination, exact location of obstacles, and radar coordinate information, so as to determine a feasible route. There are many methods for environmental modeling, among which free space method, link graph method, topology method, grid method, and visible graph method are more commonly used.

[0004] Quattrochi et al. used the free space method to conduct multi-scale analysis of multi-scale remote sensing data for the design and implementation of geographic information system software modules, and established a variety of different spatial analysis techniques; Li et al. used free space to establish an underwater environment model for the three-dimensional path planning problem of autonomous underwater vehicles in underwater environments; Hao et al. used the free space method to establish a 30-by-20 grid matrix for environmental modeling and simplified calculations for the marine environment in which unmanned surface vehicles work; Liu et al. took ship maritime transportation as the research background and developed an environmental modeling optimization technology based on links and nonlinear graphs; Lv et al. conducted an innovative review of the application status of digital twin technology (DTT) in various fields of marine industries such as shipbuilding industry (SBI), offshore oil and gas industry, marine fisheries and marine energy industry, among which they innovatively mentioned the actual application status of the link graph method in marine environment modeling; Liu et al. focused on the problem of time-limited adaptive formation control of unmanned surface vehicle (USV) systems under random noise and switch topology, and adopted topological developed an environmental model under a robust adaptive specified time adaptive formation control strategy. The established control algorithm can prove that all signals of the USV system maintain mean square boundaries within the specified time and ensure that the leading vehicle can be effectively tracked. Liu et al. simulated the marine environment and divided the environment into particles for route planning using grids to model the pirate area environment based on the law of maritime pirate activities. The modeled environment was represented by a series of discrete grids. Similarly, Liu et al. also tried to use gridded networks for network search to predict water quality indicators and water quality certification processes. Sun et al. used the available information of electronic navigation charts (ENCs), automatic identification systems (AISs) and marine radars to construct grid grid maps in platform-based marine radar target detection of unmanned surface vehicles (USVs), effectively solving the problems of identifying obstructions and maintaining the stability of sound tracking performance in the marine environment with detection obstructions. Yang et al. used the visualization method to establish a marine monitoring model, create a feature-based marine measurement and map network, and a three-dimensional (3D) model in the marine environment.

[0005] Among these methods, grid network has attracted much attention due to its universality and wide application range. It was originally designed to solve the problem of robot path planning, but with the continuous development of technology, the grid method has gradually been applied by many experts and scholars to ship path planning, and has achieved quite ideal results.

[0006] There are three main ways to express the grid method: serial number method, coordinate method and discretization processing. Phetprayoon et al. The serious erosion problem in the PhraPhloeng watershed was solved by using a grid-based curve number (CN) method to estimate the surface runoff depth of each grid cell in the watershed through GIS drainage analysis on DEM, which can be applied to surface runoff estimation; Tarboton et al. proposed a new method to represent flow direction and calculate upslope area using a rectangular grid digital elevation model. The process is based on representing the flow direction as a single angle and can be applied to the calculation of digital elevation dataset results; Lei et al. introduced a new singularity-free spherical (global) coordinate system, which decomposes the earth into several identical triangular regions and constructs a local coordinate combination for each region to form a new global grid system with a consistent hierarchical structure and a simple relationship with traditional coordinates, which is suitable for organizing, representing and analyzing spatial data; Karimi-Fard et al. established fine grid discretization, upscaling and coarse grid discretization of maps based on grids, which can be shared with each other without any additional processing; Asouti et al. proposed a Navier-Stokes solver for steady and unsteady turbulent flows on unstructured / hybrid grids with triangular and quadrilateral elements, which can be applied in graphics processing.

[0007] Since computers prefer binary when processing data, that is, they are better at processing discrete data, discretization of environmental data becomes a better choice. For nautical charts used for ship navigation, discretization means dividing the charts into navigable waters and non-navigable waters. In the algorithm processing, the charts are gridded and represented in the form of matrices of 0 and 1 respectively, simplifying the image processing process and binarizing the charts. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a modeling method and system for the marine environment of ship navigation based on a grid network, uses the imread function to obtain an RGB image, uses the rgb2gray function to convert the RGB image into a grayscale image, dynamically determines the grid size of the grayscale image according to the complexity of the marine environment, uses the unit grid to discretize the marine environment, and binarizes the nautical chart according to actual needs, presenting two situations in black and white. It can provide a theoretical basis for ship marine route planning.

[0009] In order to realize the binarization processing of the nautical chart, the present invention provides the following technical solutions:

[0010] The present invention provides a first solution: a ship navigation ocean environment modeling method based on a grid network, comprising:

[0011] Step 1: Obtain pixel values ​​of the ocean environment image where the ship is sailing;

[0012] Step 2: Convert the pixel values ​​of the ocean environment image where the ship is sailing to obtain a grayscale image;

[0013] Step 3: Determine the grid granularity of the grayscale image;

[0014] Step 4: Based on the grid granularity, rasterize the ocean environment where the ship is sailing, and use the rectangular coordinate method to generate a binary nautical chart.

[0015] Preferably, in step 1, the imread function and the whos command are used to extract the read-in ocean environment image of the ship sailing, and the imshow function is used to display the image.

[0016] Preferably, in step 2, the RGB value of each pixel of the ocean environment image is read using the imread function in MATLAB to obtain an RGB image, and the RGB image is converted into a grayscale image using the rgb2gray function.

[0017] Preferably, in step 3, the grid granularity needs to be dynamically determined according to the proportion of obstacles in the ocean environment map, and the specific process is as follows:

[0018] Step 301: Calculate the sum of the areas of all obstacles in the ocean environment map, as shown in the following formula:

[0019]

[0020] Among them, S ob is the area of ​​all obstacles, S i is the area of ​​the i-th obstacle;

[0021] Step 302: Determine the grid size according to the proportion of the area of ​​all obstacles in the entire ocean environment image, calculate the obstacle area estimate by filling the irregular obstacles into standard rectangles, and find the X coordinates of all the vertices of the obstacles in the rectangular coordinate system. max ,X min ,Y max ,Y min , then (X max ,X min ) and (Y max ,Y min ) as the diagonal vertices to draw a rectangle, then the area of ​​the rectangle S = (X max -X min )×(Y max -Y min ), calculate the granularity of the grid as shown below:

[0022]

[0023] Among them, k1 is the grid adjustment coefficient, L max is the maximum side length of the defined grid, which is closely related to obstacles. min It is the minimum side length of the grid, which is generally determined by the moving step length of the ship.

[0024] Preferably, the specific steps of step 4 are:

[0025] Step 401: Divide the two-dimensional navigation environment into a series of unit grids according to the grid granularity;

[0026] Step 402: using rectangular coordinate method to mark the unit grid;

[0027] Step 403: Discretize the two-dimensional navigation environment identified by the rectangular coordinate method, and use a matrix to represent the two-dimensional navigation environment map. The matrix only contains 0 and 1, which represent idle and occupied respectively, and represent navigable waters and non-navigable waters in the two-dimensional navigation environment, and generate a binary raster nautical chart.

[0028] Preferably, the total number of unit grids in step 401 is expressed as follows:

[0029]

[0030] Where n x is the number of unit grid columns, n y is the number of unit grid rows, ceil is the rounding operation; then assign a value to each unit grid, if the i-th unit grid is an obstacle grid, then assign a value to the obstacle grid n i Assign a value of 1; if the jth unit grid is a free grid, then for the free grid n j The value is assigned as 0. When the path algorithm is implemented, 1 represents non-navigable waters and 0 represents navigable waters. Therefore, the global navigation environment can be decomposed into many unit grids with binary information using the rectangular coordinate method.

[0031] Preferably, the specific process of step 402 is as follows:

[0032] (1) Create a rectangular coordinate system on the two-dimensional grid environment, with the lower left corner of the two-dimensional grid environment as the coordinate origin, the horizontal direction of the two-dimensional grid environment as the x-axis, and the vertical direction of the two-dimensional grid environment as the y-axis, and make the unit lengths of the two coordinate axes be integer multiples of the side length of the unit grid;

[0033] (2) In the created rectangular coordinate system, the position m of any m-th unit grid x The corresponding coordinates (m x ,m y ) to determine, using M(x m ,y m) is used to represent the two-dimensional grid environment model in the rectangular coordinate system. The expression of the two-dimensional grid environment model is as follows:

[0034] M(x m ,y m )={X m X m =0,X m =1,m∈N} (4)

[0035] Among them, X m =0 means the current grid is a free grid, X m =1 means the current grid is an obstacle grid;

[0036] (3) The obstacle grid is represented by black, and the free grid is not colored.

[0037] The present invention provides a second solution: a grid network-based ship navigation ocean environment modeling system, which is used to implement the ship navigation ocean environment modeling method according to any one of claims 1 to 6, comprising:

[0038] An image acquisition module is used to obtain pixel values ​​of the marine environment image where the ship is sailing;

[0039] Grayscale processing module, converting the pixel values ​​of the marine environment image of the ship to obtain a grayscale image;

[0040] A grid granularity calculation module calculates the grid granularity of the grayscale image;

[0041] The binary chart generation module rasterizes the ocean environment where the ship is sailing based on the grid granularity, and uses the rectangular coordinate method to generate the binary chart.

[0042] The present invention provides a third solution: an electronic device, a memory storing computer instructions, and a processor for executing the computer instructions stored in the memory to implement the steps of a method for modeling the marine environment of a ship's navigation based on a grid network.

[0043] The present invention provides a fourth solution: a computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to execute the steps of a method for modeling the marine environment of ship navigation based on a grid network.

[0044] Beneficial Effects

[0045] Compared with the prior art, the present invention is based on a grid network model and adopts a discretization processing method to rasterize the nautical chart and represent it in the form of a matrix of 0 and 1 respectively, thereby simplifying the image processing flow and binarizing the nautical chart, which can provide a theoretical basis for ship maritime route planning.

[0046] The present invention adopts a grid network combined with discretization processing to simplify the image processing flow, and directly divides the ocean environment map into navigable waters and non-navigable waters, so that the chart processing is direct and simple. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a rectangular coordinate grid model;

[0048] Figure 2 Get the rendering for the pixel;

[0049] Figure 3 This is the pixel value conversion effect diagram;

[0050] Figure 4 Modify the rendering for the number of pixels;

[0051] Figure 5 is the pixel value statistics result;

[0052] Figure 6 Grayscale Figure 2 The resulting graph generated by the value transformation. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example

[0055] This embodiment specifically provides a method for modeling a ship's navigation ocean environment based on a grid network, including:

[0056] Step 1: Obtain pixel values ​​of the ocean environment image where the ship is sailing;

[0057] Step 2: Convert the pixel values ​​of the ocean environment image where the ship is sailing to obtain a grayscale image;

[0058] Step 3: Determine the grid granularity of the grayscale image;

[0059] Step 4: Based on the grid granularity, rasterize the ocean environment where the ship is sailing, and use the rectangular coordinate method to generate a binary nautical chart.

[0060] The above steps involve the following:

[0061] 1. Model assumptions and model initialization

[0062] 1.1 Basic assumptions of the model

[0063] When a ship plans its navigation path based on the marine navigation environment, it needs to convert the global navigation environment into a two-dimensional environment model that can be recognized by the computer system, that is, a grid environment model. Since the marine environment is very complex, the following assumptions are made for the grid environment model:

[0064] (1) The sailing area is relatively calm and the impact of wind, waves and currents can be ignored.

[0065] (2) The navigation environment is two-dimensional. The number, position, shape and size of obstacles are fixed and will not change relatively during the navigation of the ship.

[0066] (3) Common obstacles in navigable waters include: lighthouses, reefs (including exposed reefs, dry reefs, submerged reefs and hidden reefs), dangerous shipwrecks, navigation marks, islands and shallow waters.

[0067] 1.2 Grid granularity determination

[0068] The idea of ​​the grid method is to abstract the three-dimensional environment of the ship's navigation into a two-dimensional environment, and divide the navigation environment into small grids on the electronic chart. The granularity of the grid division should be determined according to the actual situation: if the grid division is too small, the constructed environmental chart is more accurate, but it will lead to lower execution efficiency of the program and occupy more memory; if the grid division is too large, although the execution efficiency of the program will be improved, it will lead to insufficient accuracy of the generated environmental chart. Therefore, the determination of grid granularity is an important part of path planning. The size of the grid needs to be dynamically determined according to the complexity of the environment, that is, the proportion of obstacles in the chart. First, the sum of the areas of all obstacles in the chart is calculated, as shown in the following formula (1).

[0069]

[0070] Among them, S ob is the area of ​​all obstacles, S i is the area of ​​the ith obstacle.

[0071] The size of the grid is determined according to the proportion of the area of ​​the obstacle in the entire map environment. The obstacle area estimate is calculated by filling the irregular obstacle into a standard rectangle and finding the X coordinates of all the vertices of the obstacle in the rectangular coordinate system. max ,X min ,Y max ,Y min , then (X max ,X min ) and (Y max ,Y min ) as the diagonal vertices to draw a rectangle, then the area of ​​the rectangle S = (X max -X min )×(Ymax -Y min ), calculate the granularity of the grid, as shown in the following formula (2).

[0072]

[0073] Where k1 is the grid adjustment coefficient, L max is the maximum side length of the defined grid, which is closely related to obstacles. min It is the minimum side length of the grid, which is generally determined according to the moving step length of the ship. It can be seen that when the proportion of obstacles in the environment is low, the complexity of the planned path is low, and the calculation amount of selecting a smaller grid spacing is still within the acceptable range; when the proportion of obstacles is high and the environment is relatively complex, a relatively large grid spacing is selected to avoid excessive path planning calculations, excessive occupation of system resources, and reduced ship response capabilities.

[0074] 2. Grid identification and discretization

[0075] The global navigation environment is rasterized using the raster method. The specific process is as follows:

[0076] Assume that the length of the global two-dimensional driving environment in the horizontal direction is x, the length in the vertical direction is y, and the side length of each unit grid is a. If the length is less than one unit grid, it is processed as one unit grid. Thus, the two-dimensional driving environment can be divided into a series of unit grids. The total number of unit grids is shown in the following formula (3), which can be expressed as:

[0077]

[0078] Where n x is the number of unit grid columns, n y is the number of unit grid rows, ceil is the rounding operation; then assign a value to each unit grid, if the i-th unit grid is an obstacle grid, then assign a value to the obstacle grid n i Assign a value of 1; if the jth unit grid is a free grid, then for the free grid n j The value is 0. When the path algorithm is implemented, 1 represents non-navigable waters and 0 represents navigable waters. In this way, the global navigation environment can be decomposed into many unit grids with binary information, and these binary unit grids are identified.

[0079] Grid Identification: Cartesian Coordinate Method

[0080] A rectangular coordinate system is created on the two-dimensional grid model, with the lower left corner of the two-dimensional grid model as the coordinate origin, the horizontal direction of the grid model as the x-axis, and the vertical direction of the grid model as the y-axis, and the unit lengths of the two coordinate axes are integer multiples of the side lengths of the unit grid. In the created rectangular coordinate system, the position m of any m-th unit grid xThe corresponding coordinates (m x ,m y ) to determine, using M(x m ,y m ) is used to represent the grid environment model in the rectangular coordinate system. The mathematical model of the grid environment is shown in the following formula (4), which can be expressed as:

[0081] M(x m ,y m )={X m X m =0,X m =1,m∈N} (4)

[0082] Where, X m =0 means the current grid is a free grid, X m =1 means the current grid is an obstacle grid.

[0083] The rectangular coordinate grid model is as follows Figure 1 As shown, Figure 1 The coordinates in are the coordinates of the unit grid. In order to facilitate the observation and analysis of the grid model, the unit grid with obstacles is represented by black, and the free grid is not colored.

[0084] Discretization

[0085] The processing of grid charts uses a discretization method to represent them in a matrix, and this matrix contains only two numbers, 0 (Free) and 1 (Occupied), which represent free and occupied areas respectively, and represent navigable and non-navigable areas in the chart. The chart generated at this time is the so-called binary grid chart. Figure 1 The nautical chart marked by the rectangular coordinate method is discretized. It should be noted that the position (x, y) corresponding to the coordinate method is the row coordinate and column coordinate corresponding to the discretized matrix; since the matrix coordinate system starts from the upper left corner, and the rectangular coordinate system takes the lower left corner as the origin, it is equivalent to turning the chart upside down, so we get

[0086] The result after discretization is:

[0087]

[0088] Example Analysis

[0089] A rasterized marine environment example analysis was conducted on some nautical charts captured in the Bashi Channel.

[0090] (1) Pixel value acquisition

[0091] Use the imread function and the whos command to extract the basic information of the read image, and use the imshow function to display the image. The effect is as follows: Figure 2 shown.

[0092] (2) Pixel value conversion

[0093] In MATLAB, the imread function can be used to read the RGB value of each pixel in the image. The obtained image is an RGB image. The pixel value of each pixel in the RGB image is generally composed of three variables r, g, and b (0-255). In order to simplify the calculation, we need to convert the RGB image into a grayscale image in which the pixel value of each pixel has only one value (0-255). The rgb2gray function can be used to convert an RGB image into a grayscale image. The effect is as follows: Figure 3 shown.

[0094] (3) Determination of grid granularity

[0095] The imresize function can be used to change the number of pixels, such as changing 1000×1000 to 500×500, and finally selecting a spatial resolution of 200×200 based on the determination of the grid granularity. Figure 4 shown.

[0096] (4) Cartesian coordinate method: Binarization chart generation

[0097] There are many methods for generating binary nautical charts. Here, the navigable sea area information is extracted through grayscale images and generated through directional conversion.

[0098] Since the imbinarize function replaces all values ​​above the global threshold with 1 and sets all other values ​​to 0, it is impossible to achieve the specified pixel value of 0 and the rest of the values ​​of 1. Therefore, the zeros function is used to generate a matrix of all zeros of the same size as the pixel matrix, and all pixel values ​​in the chart are counted, and the result is as follows: Figure 5 As shown in the figure. The pixel values ​​of the navigable area are assigned to 1, and the rest remain unchanged. The generated results are as follows Figure 6 shown.

[0099] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for modeling the marine environment of a ship's navigation based on a grid network, characterized in that: The modeling approach includes the following steps: Step 1: Obtain pixel values ​​of the ocean environment image where the ship is sailing; Step 2: Convert the pixel values ​​of the ocean environment image where the ship is sailing to obtain a grayscale image; Step 3: Determine the grid granularity of the grayscale image; Step 4: Based on the grid granularity, rasterize the ocean environment where the ship is sailing, and use the rectangular coordinate method to generate a binary nautical chart.

2. According to the grid network-based ship navigation ocean environment modeling method of claim 1, it is characterized in that: In step 1, the imread function and the whos command are used to extract the image of the ocean environment where the ship is sailing, and the imshow function is used to display the image.

3. The method for modeling a ship's navigation ocean environment based on a grid network according to claim 1, characterized in that: In step 2, the imread function is used in MATLAB to read the RGB value of each pixel of the ocean environment image, and the obtained image is an RGB image, and the rgb2gray function is used to convert the RGB image into a grayscale image.

4. The method for modeling a ship's navigation ocean environment based on a grid network according to claim 1, characterized in that: In step 3, the grid granularity needs to be dynamically determined according to the proportion of obstacles in the ocean environment map. The specific process is as follows: Step 301: Calculate the sum of the areas of all obstacles in the ocean environment map, as shown in the following formula: Among them, S ob is the area of ​​all obstacles, S i is the area of ​​the i-th obstacle; Step 302: Determine the grid size according to the proportion of the area of ​​all obstacles in the entire ocean environment image, calculate the obstacle area estimate by filling the irregular obstacles into standard rectangles, and find the X coordinates of all the vertices of the obstacles in the rectangular coordinate system. max , X min , Y max , Y min , then (X max , X min ) and (Y max , Y min ) as the diagonal vertices to draw a rectangle, then the area of ​​the rectangle S = (X max -X min )×(Y max -Y min ), calculate the granularity of the grid as shown below: Among them, k1 is the grid adjustment coefficient, L max is the maximum side length of the defined grid, which is closely related to obstacles. min It is the minimum side length of the grid, which is generally determined by the moving step length of the ship.

5. The method for modeling a ship's navigation ocean environment based on a grid network according to claim 4 is characterized in that: Specific steps for step 4: Step 401: Divide the two-dimensional navigation environment into a series of unit grids according to the grid granularity; Step 402: using rectangular coordinate method to mark the unit grid; Step 403: Discretize the two-dimensional navigation environment identified by the rectangular coordinate method, and use a matrix to represent the two-dimensional navigation environment map. The matrix only contains 0 and 1, which represent idle and occupied respectively, and represent navigable waters and non-navigable waters in the two-dimensional navigation environment, and generate a binary raster nautical chart.

6. The method for modeling the marine environment of a ship's navigation based on a grid network according to claim 5 is characterized in that: The total number of unit grids in step 401 is expressed as follows: Where n x is the number of unit grid columns, n y is the number of unit grid rows, ceil is the rounding operation; then assign a value to each unit grid, if the i-th unit grid is an obstacle grid, then assign a value to the obstacle grid n i Assign a value of 1; If the jth unit grid is a free grid, then for the free grid n j The value is assigned as 0. When the path algorithm is implemented, 1 represents non-navigable waters and 0 represents navigable waters. Therefore, the global navigation environment can be decomposed into many unit grids with binary information using the rectangular coordinate method.

7. The method for modeling a ship's navigation ocean environment based on a grid network according to claim 5, characterized in that: The specific process of step 402 is as follows: (1) Create a rectangular coordinate system on the two-dimensional grid environment, with the lower left corner of the two-dimensional grid environment as the coordinate origin, the horizontal direction of the two-dimensional grid environment as the x-axis, and the vertical direction of the two-dimensional grid environment as the y-axis, and make the unit lengths of the two coordinate axes be integer multiples of the side length of the unit grid; (2) In the created rectangular coordinate system, the position m of any m-th unit grid x The corresponding coordinates (m x , m y ) to determine, using M(x m ,y m ) is used to represent the two-dimensional grid environment model in the rectangular coordinate system. The expression of the two-dimensional grid environment model is as follows: M(x m ,y m )={X m |X m =0,X m =1,m∈N} (4) Among them, X m =0 means the current grid is a free grid, X m =1 means the current grid is an obstacle grid; (3) The obstacle grid is represented by black, and the free grid is not colored.

8. A ship navigation ocean environment modeling system based on a grid network, characterized in that: The system is used to implement the ship navigation ocean environment modeling method according to any one of claims 1 to 6, comprising: An image acquisition module is used to obtain pixel values ​​of the marine environment image where the ship is sailing; Grayscale processing module, converting the pixel values ​​of the marine environment image of the ship to obtain a grayscale image; A grid granularity calculation module calculates the grid granularity of the grayscale image; The binary chart generation module rasterizes the ocean environment where the ship is sailing based on the grid granularity, and uses the rectangular coordinate method to generate the binary chart.

9. An electronic device, comprising a processor and a memory, characterized in that: The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory to implement the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to execute the steps of the method as claimed in any one of claims 1 to 7.

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