Dynamic water polo chart real-time generation method based on Berlin noise

Abstract

The invention relates to the technical field of data visualization, in particular to a dynamic water ball chart real-time generation method based on Berlin noise, which comprises the following steps of: acquiring a UV matrix, extracting a Y value in the matrix and mapping the Y value to a range of 0-1.875, generating an offset vector offset UV matrix by combining the current frame time, generating a Berlin noise matrix in a range of 0-0.875 on the offset matrix, and superposing data values, according to the dynamic water ball graph real-time generation method based on the Berlin noise, a color matrix is obtained through binaryzation and color mapping, then the alpha value of the color matrix is adjusted based on the Euclidean distance of the center coordinate of the matrix, and finally the color is assigned to the corresponding vertex and the fragment element, water ripples can have C2 continuity and non-repeatability, the edge is free of sawteeth, and the dynamic water ball graph real-time generation method based on the Berlin noise can be used for generating the dynamic water ball graph. The dynamic effect and the real-time data are accurately associated, visual distortion is avoided, execution is efficient, and the visualization quality of the water polo chart is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data visualization, and particularly relates to a dynamic water ball graph real-time generation method based on Berlin noise. BACKGROUND

[0002] As a commonly used component in the field of data visualization, the core technical challenge of the water ball graph lies in how to convert discrete data values into fluid dynamic effects with continuous sense to more intuitively present data changes. At present, the existing technologies for realizing the dynamic effect of the water ball graph mainly include two categories: one is a linear interpolation method, which vertically displaces the UV coordinates through a vertex shader and generates color transition by using HSV color space interpolation to simulate the dynamic changes of the water ball graph; the other is a sinusoidal wave superposition method, which generates ripple effects by superimposing 3-5 sinusoidal waves with different frequencies, and simulates water surface reflection by means of a normal map to enhance the visual realism. These two methods are the mainstream schemes for realizing the dynamic effect of the water ball graph, but there are still many problems to be improved in actual application.

[0003] In the application process of the existing water ball graph related technology, the following problems mainly exist: the transition mode of the water level line of the traditional water ball graph is relatively harsh, and the liquid surface tension effect cannot be effectively simulated, resulting in a lack of natural sense of the dynamic effect of the real fluid; the existing dynamic effect lacks accurate association with real-time data, and it is difficult to accurately adjust the dynamic presentation effect according to the real-time data changes, which affects the accuracy of data visualization; the edge area of the water ball graph lacks natural transition, and the sawtooth phenomenon easily occurs, which reduces the aesthetic degree of visual presentation; at the same time, the existing technology lacks a multi-parameter collaborative control mechanism, and visual distortion problems easily occur under the action of multiple parameters, which further affects the visualization quality of the water ball graph. In addition, the wave shape generated by the linear interpolation method has the problem of mechanical repetition, and lacks natural disturbance effect, while the sinusoidal wave superposition method not only has high energy consumption, but also cannot respond to data mutations in real time, and it is difficult to meet the efficient and real-time water ball graph visualization demand, therefore, in view of the above status, it is urgent to develop a dynamic water ball graph real-time generation method based on Berlin noise to overcome the deficiencies in current actual application. SUMMARY

[0004] The present application aims to provide a dynamic water ball graph real-time generation method based on Berlin noise to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: A dynamic water ball graph real-time generation method based on Berlin noise, comprising the following steps: Step 1: obtaining a UV matrix for generating a dynamic water ball graph; Step 2: extract Y values of each element position information in the UV matrix to form a Y value matrix; Step 3: range mapping processing is performed on the Y value matrix to obtain a mapping matrix; Step 4: obtain the execution time of the current frame, generate an offset vector based on the execution time, and offset the UV matrix according to the offset vector to obtain an offset matrix; Step 5: generate a Berlin noise matrix on the offset matrix; Step 6: superimpose the Berlin noise matrix and the data value to be visualized to obtain a superimposed matrix; Step 7: compare the mapping matrix and the superimposed matrix element by element, and perform binaryzation processing according to the comparison result to obtain a binary matrix; Step 8: color mapping processing is performed on the binary matrix to obtain a color matrix containing background color or foreground color information; Step 9: determine the center coordinates of the color matrix, calculate the Euclidean distance of each element coordinate in the color matrix to the center coordinates, and adjust the alpha value of each element color according to the Euclidean distance; Step 10: assign the color of each element in the color matrix with adjusted alpha value to the vertex and fragment in the corresponding position of the UV matrix, and real-time generate a dynamic water ball graph.

[0006] As a further scheme of the application: in step 3, the range mapping processing is to map the value range of 0-1 in the Y value matrix to a preset target range consistent with the superimposed range of the noise value range of the Berlin noise matrix and the data value range to be visualized.

[0007] As a further scheme of the application: the noise value range of the Berlin noise matrix is 0-0.875, the range of the data value to be visualized is 0-1, and the preset target range is 0-1.875.

[0008] As a further scheme of the application: in step 4, the expression of the offset vector is V=(Tmodk, 0), wherein T is the execution time of the current frame, k is a period parameter, and 0 is the y direction component.

[0009] As a further scheme of the application: in step 4, the operation relationship of the UV matrix offset according to the offset vector is D=A⊕V, wherein D is the offset matrix, A is the UV matrix, and represents the matrix offset operation.

[0010] As a further scheme of the application: in step 7, the specific process of the binaryzation processing is: When the value of an element in the superposition matrix is greater than or equal to the value of the element in the same position in the mapping matrix, the element in the corresponding position of the binary matrix is set to 1; When the value of an element in the superposition matrix is less than the value of the element in the same position in the mapping matrix, the element in the corresponding position of the binary matrix is set to 0.

[0011] As a further scheme of the present application: in step 8, the specific process of the color mapping processing is: When the value of an element in the binary matrix is 0, the corresponding position in the color matrix is mapped to a background color; When the value of an element in the binary matrix is 1, the corresponding position in the color matrix is mapped to a foreground color.

[0012] As a further scheme of the present application: in step 9, the number of rows and columns of the color matrix is n, and the center coordinate is (n / 2, n / 2); The specific process of the alpha value adjustment is: when the Euclidean distance of the coordinates of an element in the color matrix to the center coordinate is greater than n / 2, the alpha value of the color of the element is set to 0; when the Euclidean distance is less than or equal to n / 2, the alpha value of the color of the element is set to 1.

[0013] As a further scheme of the present application: in step 5, the Berlin noise superposition algorithm is used to generate the Berlin noise matrix.

[0014] As a further scheme of the present application: the binarization processing of step 7 and the alpha value adjustment of step 9 constitute a double criterion, that is, the condition for the value of an element in the binary matrix to be 1 is: The value of the corresponding element in the superposition matrix is greater than or equal to the value of the corresponding element in the mapping matrix, and the Euclidean distance of the element coordinates to the center coordinate is less than or equal to n / 2, wherein n is the number of rows and columns of the color matrix.

[0015] Compared with the prior art, the present application has the following advantages: 1. The present application uses the Berlin noise superposition algorithm to generate a noise matrix, and the Berlin noise has a smoothing property, so that the generated water ripples have C 2 continuity, and at the same time avoids the defect of mechanical repetition of the wave shape of the traditional linear interpolation method, so that the wave shape has non-repetitive nature and is closer to the actual fluid wave effect; 2. By establishing a nonlinear coupling mechanism between the Berlin noise and the data value, and designing a time-space synchronous algorithm of the dynamic offset matrix, the dynamic effect of the water ball graph can be accurately associated with the real-time data, effectively solving the problem of inaccurate association between the traditional dynamic effect and the real-time data, and improving the accuracy of data visualization; 3、The application creates an adaptive transparent mask based on the Euclidean distance, adjusts the alpha value of the edge area, realizes the natural transition of the edge area, eliminates the sawtooth phenomenon of the traditional water ball graph edge, ensures the sharpness of the water ball graph boundary and the smoothness of the transition, and balances the visual aesthetics and clarity; 4、The application realizes real-time fusion of multi-dimensional parameters, solves the visual distortion problem caused by the lack of multi-parameter collaborative control in the prior art, and the overall algorithm execution process is simple and efficient, avoids the high energy consumption defect of the sine wave superposition method, can meet the needs of water ball graph real-time generation and display, and improves the overall performance and user experience of water ball graph dynamic visualization. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The core step (one) flow chart of the dynamic water ball graph generation in the embodiment of the application.

[0017] Figure 2 The core step (two) flow chart of the dynamic water ball graph generation in the embodiment of the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be described in detail below with reference to the drawings of the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0019] The specific implementation of the application will be described in detail below in combination with specific embodiments.

[0020] Please refer to Figure 1 and Figure 2 The dynamic water ball graph real-time generation method based on Berlin noise provided in the embodiment of the application can solve the pain points of the traditional water level line transition being stiff, the dynamic effect being not accurately associated with real-time data, the edge area having sawtooth phenomenon, and the visual distortion caused by the lack of multi-parameter collaborative control. The Value involved in the embodiment is the real-time data value to be visualized, the BackColor is the background color of the water ball graph, the FrontColor is the foreground color of the water ball graph, and each step is executed in a single frame to ensure real-time generation and display of the dynamic water ball graph.

[0021] I. Basic definition and parameter explanation In this embodiment, the matrices involved are all constructed based on UV coordinates, which contain vertex position and color information, and the number of rows and columns of the UV coordinates is the same, denoted as n (n is the number of rows and columns of UV, and the number of rows and columns of UV is the same), that is, the number of rows and columns of each matrix derived from the UV coordinates (such as UV matrix A, Y value matrix B, etc.) is n. In addition, the noise value range of the Berlin noise matrix is 0 to 0.875, the value range of the Value value is 0 to 1, and the value range after superimposing the two is 0 to 1.875, and the above numerical range provides a basis for subsequent matrix operation and range mapping.

[0022] II. Specific implementation steps Step one: obtain UV matrix A; Intra-frame execution, directly obtain the UV matrix currently used to generate the water ball graph, denoted as matrix A. The UV matrix is the basis for all subsequent matrix operations and water ball graph generation, and the vertex position and color information it contains provide original data support for subsequent extraction of position parameters and implementation of color mapping, ensuring that subsequent steps can be based on a unified coordinate system.

[0023] Step two: extract Y value matrix B; Intra-frame execution, process the UV matrix A obtained in step one, extract the y value of each element position information in matrix A, and form a matrix containing only y values, denoted as matrix B. The value range of each element in matrix B is 0 to 1, and this matrix directly reflects the position distribution of each vertex in the vertical direction (y axis) in the UV coordinates, providing vertical position parameters for subsequent dynamic simulation and range mapping of water level lines.

[0024] Step three: range mapping to obtain matrix C; Intra-frame execution, range mapping processing is performed on each element in matrix B, mapping the value range of 0 to 1 in matrix B to the value range of 0 to 1.875, to obtain the mapped matrix, denoted as matrix C. Since the noise value range of the subsequent Berlin noise matrix is 0 to 0.875, the Value value range is 0 to 1, and the value range after superimposing the two is 0 to 1.875, this step realizes the alignment of the numerical range of matrix C and the subsequent superimposed result (matrix F) through range mapping, providing a unified judgment basis for subsequent binary processing based on numerical comparison, avoiding water level line judgment errors caused by mismatched numerical ranges.

[0025] Step four: obtain the current frame execution time T; Intra-frame execution, real-time acquisition of the execution time of the current frame, denoted as time T. Time T is a key time parameter for realizing the dynamic effect of the water ball graph, providing time dimension input for subsequent construction of time-varying offset vectors and driving water ripples to dynamically fluctuate with time, ensuring that the dynamic effect is synchronized with time.

[0026] Step 5: Define the offset vector V; Executed within a frame, the time T obtained in step four is used as the x-axis component, and 0 is used as the y-axis component. An offset vector V is defined, satisfying V=(Tmodk,0), where k is the periodic parameter. This step introduces the time parameter T into the x-axis component of the offset vector, giving it the characteristic of periodic change over time; simultaneously, the y-axis component is set to 0, ensuring that the offset occurs only in the horizontal direction, laying the foundation for subsequent horizontal matrix offset and the formation of a horizontal water ripple dynamic effect.

[0027] Step 6: Obtain matrix D by matrix offset; Intra-frame execution involves offsetting the UV matrix A obtained in step one according to the offset vector V defined in step five, resulting in the offset matrix, denoted as matrix D. The offset operation satisfies D = A ⊕ V (where ⊕ represents matrix offset operation). Through the operation between matrix A and the time-varying offset vector V, the UV coordinates of matrix D generate a horizontal phase difference over time, thereby realizing the dynamic horizontal offset of water ripples. This solves the problem of traditional water sphere images having a single dynamic effect and lacking dynamic changes in the time dimension, providing a dynamic coordinate basis for subsequent superposition of Burmester noise to generate naturally undulating water ripples.

[0028] Step 7: Generate the Berlin noise matrix E; Intra-frame execution: Using the Berlin noise superposition algorithm, Berlin noise is generated on the matrix D obtained in step six, forming a matrix containing noise values, denoted as noise matrix E. The noise value of each element in noise matrix E ranges from 0 to 0.875. Noise is essentially a set of random numbers (taking a two-dimensional matrix as an example, the x and y axes represent the rows and columns of the matrix, and the z-axis represents the noise value). Traditional random noise is chaotic and discontinuous, while Berlin noise generates smooth random numbers, where each element maintains smoothness relative to its surrounding elements. The noise matrix E generated by the Berlin noise superposition algorithm in this step avoids the defects of mechanical repetition and lack of natural perturbation in traditional linear interpolation methods, as well as the high energy consumption and inability to respond to data mutations in real time in sine wave superposition methods, thus enabling the subsequently generated water ripples to possess C... 2 The continuity and non-repetitive wave pattern more closely resemble the wave effect of real fluids.

[0029] Step 8: Superimpose Values ​​to obtain matrix F; Intra-frame execution involves superimposing the noise value of each element in the noise matrix E obtained in step seven with the real-time data value Value to be visualized, resulting in a superimposed matrix, denoted as matrix F. Each element in matrix F ranges from 0 to 1.875 (obtained by superimposing noise values ​​of 0-0.875 with Value values ​​of 0-1). This step establishes a non-linear coupling mechanism between the real-time data value Value and the Berlin noise value by directly superimposing them, enabling a precise correlation between the dynamic effect of the water ripples and the real-time data value. This solves the problem of inaccurate correlation between existing dynamic effects and real-time data, ensuring that the dynamic display of the water sphere map can reflect data changes in real time.

[0030] Step 9: Binarize the data to obtain matrix G; Within the frame, an element-wise comparison operation is performed on matrix C obtained in step three and matrix F obtained in step eight: when the value of an element in matrix F is greater than or equal to the value of the element at the same position in matrix C, the result for that position is returned as 1; when the value of an element in matrix F is less than the value of the element at the same position in matrix C, the result for that position is returned as 0. Through the above binarization operation, a matrix containing only 0 and 1 is obtained, denoted as matrix G. This step, through binarization processing, transforms the numerical comparison result between matrix C (mapped vertical position parameters) and matrix F (noise + real-time data) into discrete 0 / 1 signals, providing a clear judgment basis for subsequent color mapping, ensuring that the boundary between foreground and background colors can be accurately defined based on real-time data and position parameters, and avoiding visual distortion caused by blurred color transitions.

[0031] Step 10: Obtain the color matrix H through color mapping; Executed within the frame, the matrix G obtained in step nine undergoes color mapping: when an element in matrix G has a value of 0, that position is mapped to the background color (BackColor); when an element in matrix G has a value of 1, that position is mapped to the foreground color (FrontColor). Through this color mapping operation, a matrix containing color information is obtained, denoted as color matrix H, and the color of each element in color matrix H is associated one-to-one with the UV coordinates of the corresponding position in UV matrix A. This step achieves precise color mapping of the water globe image by explicitly corresponding 0 / 1 signals to background and foreground colors, making the visual presentation of the water globe image clearly distinguish between the foreground (data visualization area) and background. Simultaneously, combined with the matrix F associated with real-time data in the previous steps, it ensures that the color mapping results can reflect data changes in real time, improving the intuitiveness of data visualization.

[0032] Step 11: Determine the coordinates of the matrix center; Execution occurs within the frame. Since the number of rows and columns in matrix H is the same as that in UV matrix A, both being n, the center coordinates of matrix H are calculated and determined based on the number of rows and columns n. These center coordinates are (n / 2, n / 2). The center coordinates of the matrix serve as the benchmark for subsequent edge region identification and achieving natural edge transitions. By clearly defining the center coordinates, a positional reference is provided for subsequent edge processing based on Euclidean distance, thus resolving the issue of jagged edges in traditional water globe images.

[0033] Step 12: Adjust the alpha value to optimize edge transition; Executed within a frame, the alpha value (i.e., the 'a' value in RGBA color mode, used to represent color transparency) of the color of each element in matrix H is adjusted: the Euclidean distance between the coordinates of each element in matrix H and the center coordinates (n / 2, n / 2) determined in step eleven is calculated. When the Euclidean distance is greater than n / 2, the alpha value of the element's color is set to 0 (completely transparent); when the Euclidean distance is less than or equal to n / 2, the alpha value of the element's color is set to 1 (completely opaque), resulting in a new matrix H with adjusted alpha values. This step creates an adaptive transparent mask based on Euclidean distance. Through dynamic adjustment of the alpha value, elements in matrix H that extend beyond the central region (Euclidean distance > n / 2) become completely transparent, eliminating the jagged edges of traditional water globe images and achieving a natural transition in the edge region. At the same time, the central region (Euclidean distance ≤ n / 2) remains completely opaque, ensuring the visual clarity of the main body of the water globe image, balancing sharp boundaries and smooth transitions.

[0034] Step 13: Assign color values ​​to generate the water sphere image; Execution occurs within a single frame. The color of each element in the new matrix H obtained in step 12 is assigned to the corresponding vertex and fragment in the UV matrix A, completing the generation of a single-frame dynamic water sphere image. By directly associating the color information of the color matrix H with vertices and fragments, the dynamic effects and color information obtained in the preceding steps based on Burmester noise, matrix dynamic transformation, and real-time data are ultimately transformed into a visualized water sphere image. Furthermore, since each step is executed within a single frame, combined with the time-varying offset vectors from steps four to six, continuous-frame dynamic water sphere image generation can be achieved, ensuring that the dynamic effects of the water sphere image are real-time and smooth.

[0035] This implementation method further ensures the achievement of the technical effect by using the following core algorithm in conjunction with the above steps: Dynamic offset matrix generation algorithm (corresponding to steps five and six): The offset vector V is defined as V=(Tmodk,0), where k is the period parameter (1 second / frame). The horizontal offset of the UV matrix A is achieved through the matrix offset operation D=A⊕V. This algorithm ensures that the matrix D generated in each frame produces a horizontal phase difference, giving the water ripples a dynamic fluctuation effect that changes over time, avoiding a monotonous dynamic effect. At the same time, the period parameter k can be flexibly adjusted to adjust the fluctuation period, adapting to the visualization needs of different scenarios.

[0036] Berlin noise superposition algorithm (corresponding to step seven): The Berlin noise superposition algorithm is used to generate a noise matrix E. Compared with the chaotic and discontinuous nature of traditional random noise, the smoothness of Berlin noise gives the water ripples a C-like quality. 2 The continuity and non-repetitiveness more closely resemble the wave pattern of real fluids, while avoiding the high energy consumption of the sinusoidal superposition method, thus improving the generation efficiency of dynamic water sphere diagrams.

[0037] Dual criterion mechanism (corresponding to steps nine and twelve): The criterion for this mechanism is: G(x,y)=1 if and only if F(x,y)≥C(x,y)∧||(x,y)-(c,c)||2≤r; Where (x, y) are the coordinates of the matrix elements, (c, c) are the center coordinates (n / 2, n / 2), and r is n / 2. This represents the Euclidean distance.

[0038] By using the dual criteria of "numerical comparison (F≥C) + position judgment (Euclidean distance≤r)," we can ensure that the water ripples are accurately correlated with real-time data and achieve natural edge transitions. This solves the visual distortion problem caused by the lack of multi-parameter collaborative control, while also ensuring the execution efficiency of the algorithm and meeting the requirements for real-time generation.

[0039] In summary, this implementation method achieves real-time fusion of multi-dimensional parameters through steps such as UV matrix processing, range mapping, time-driven matrix offset, Berlin noise superposition, real-time data fusion, binarization, color mapping, edge alpha value optimization, and color assignment. Combined with the core algorithms of dynamic offset matrix, Berlin noise superposition, and dual criteria, the generated dynamic water sphere map has the sense of real fluid fluctuation, solves the various technical pain points of traditional water sphere maps, and has high execution efficiency.

[0040] It should be noted that, in this invention, although the specification describes the embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A dynamic water ball map real-time generation method based on Berlin noise, characterized by, The method comprises the following steps: Step 1: obtaining a UV matrix for generating a dynamic water ball graph; Step 2: extracting Y values of position information of each element in the UV matrix to form a Y value matrix; Step 3: performing range mapping processing on the Y value matrix to obtain a mapping matrix; Step 4: obtaining an execution time of a current frame, generating an offset vector based on the execution time, and offsetting the UV matrix according to the offset vector to obtain an offset matrix; Step 5: generating a Berlin noise matrix on the offset matrix; Step 6: performing superposition operation on the Berlin noise matrix and a data value to be visualized to obtain a superposition matrix; Step 7: performing element-by-element comparison on the mapping matrix and the superposition matrix, and performing binarization processing according to a comparison result to obtain a binary matrix; Step 8: performing color mapping processing on the binary matrix to obtain a color matrix containing background color or foreground color information; Step 9: determining a center coordinate of the color matrix, calculating Euclidean distances from coordinates of each element in the color matrix to the center coordinate, and adjusting alpha values of colors of each element according to the Euclidean distances; Step 10: assigning colors of each element in the color matrix with adjusted alpha values to vertices and fragments at corresponding positions in the UV matrix to generate a dynamic water ball graph in real time.

2. The Berlin noise-based dynamic water polo graph real-time generation method according to claim 1, wherein, In step 3, the range mapping processing is to map a value range of 0-1 in the Y value matrix to a preset target range consistent with a superposition range of a noise value range of the Berlin noise matrix and a range of the data value to be visualized.

3. The Berlin noise-based dynamic water polo graph real-time generation method according to claim 2, wherein, The noise value range of the Berlin noise matrix is 0-0.875, the range of the data value to be visualized is 0-1, and the preset target range is 0-1.

875.

4. The real-time generation method of Berlin noise-based dynamic water polo graph according to claim 1, wherein, In step 4, an expression of the offset vector is V=(Tmodk, 0), where T is an execution time of a current frame, k is a period parameter, and 0 is a y direction component.

5. The Berlin noise-based dynamic water polo graph real-time generation method according to claim 4, characterized by, In step 4, an operation relationship of offsetting the UV matrix according to the offset vector is D=A⊕V, where D is the offset matrix, A is the UV matrix, and represents a matrix offset operation.

6. The real-time Berlin noise-based dynamic water polo graph generation method according to claim 1, wherein, In step 7, a specific process of the binarization processing is as follows: When a value of an element in the superposition matrix is greater than or equal to a value of an element at a same position in the mapping matrix, an element at a corresponding position in the binary matrix is set to 1; When the value of the element in the superposition matrix is less than the value of the element at the same position in the mapping matrix, the element at the corresponding position in the binary matrix is set to 0.

7. The Berlin noise-based dynamic water polo graph real-time generation method according to claim 1, wherein, In step 8, a specific process of the color mapping processing is as follows: When a value of an element in the binary matrix is 0, a corresponding position in the color matrix is mapped to a background color; When the value of the element in the binary matrix is 1, the corresponding position in the color matrix is mapped to a foreground color.

8. The real-time Berlin noise-based dynamic water polo graph generation method according to claim 1, wherein, In step 9, a row number and a column number of the color matrix are both n, and the center coordinate is (n / 2, n / 2). The specific process of the alpha value adjustment is: when the Euclidean distance of the element coordinate in the color matrix to the center coordinate is greater than n / 2, the alpha value of the element color is set to 0; when the Euclidean distance is less than or equal to n / 2, the alpha value of the element color is set to 1.

9. The real-time Berlin noise-based dynamic water polo graph generation method according to claim 1, wherein, In step 5, the Berlin noise superposition algorithm is used to generate the Berlin noise matrix.

10. The real-time Berlin noise-based dynamic water polo graph generation method according to claim 1, wherein, The binarization processing of step 7 and the alpha value adjustment of step 9 constitute a double criterion, that is, the condition for the element value in the binarization matrix being 1 is: The corresponding element value of the superposition matrix is greater than the corresponding element value of the mapping matrix, and the Euclidean distance of the element coordinate to the center coordinate is less than or equal to n / 2, wherein n is the number of rows and columns of the color matrix.

Images