Operation method and system for cartoon game development

Abstract

The invention relates to the technical field of cartoon game development, in particular to an operation method and system for cartoon game development. The method comprises the following steps: acquiring animation game role modeling data, and performing geometric structure analysis according to the animation game role modeling data so as to obtain animation game role geometric structure data; carrying out cartoon game role physical material feature extraction according to the cartoon game role modeling data so as to obtain cartoon game role physical material data; performing collision detection analysis according to the cartoon game role physical material data and the cartoon game role geometric structure data to obtain collision detection data; and acquiring cartoon game development display data, and carrying out real-time rendering performance analysis according to the cartoon game development display data so as to obtain cartoon game development display real-time rendering performance data. The operation efficiency and the rendering effect of the cartoon game are improved based on the cartoon game development technology.

Description

Technical Field

[0001] The present invention relates to the technical field of animation game development, and particularly relates to an operation method and system for animation game development. Background Art

[0002] Collision detection in existing methods only analyzes based on character modeling data and physical material data, without considering the influence of environmental changes and real-time dynamic physical effects on the collision detection results. This leads to lag or inaccuracy in collision response, affecting the game experience and physical simulation effects. The design of the particle generation module fails to conduct refined analysis of particle effects for different collision events, and the means for predicting and caching optimization of particle movement paths are relatively rough. Especially in high-complexity scenarios, the deficiencies in particle rendering and caching processing result in excessive memory occupancy and low rendering efficiency, thus affecting the overall game operation fluency. In traditional methods, the analysis of rendering performance relies too much on frame rate sampling and graphics processing unit load evaluation, without fully considering the interactive relationship between real-time shadow effects and rendering resource optimization. Therefore, real-time dynamic optimization cannot be performed according to the complexity of the actual scene, resulting in an increase in rendering latency. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an operation method and system for animation game development to solve at least one of the above technical problems.

[0004] To achieve the above object, an operation method for animation game development includes the following steps:

[0005] Step S1: Obtain the character modeling data of the animation game, and conduct geometric structure analysis based on the character modeling data of the animation game to obtain the geometric structure data of the animation game character;

[0006] Step S2: Extract the physical material characteristics of the animation game character based on the character modeling data of the animation game to obtain the physical material data of the animation game character; conduct collision detection analysis based on the physical material data of the animation game character and the geometric structure data of the animation game character to obtain collision detection data;

[0007] Step S3: Obtain the display data for animation game development, and conduct real-time rendering performance analysis based on the display data for animation game development to obtain the real-time rendering performance data for animation game development display;

[0008] Step S4: Conduct particle generation analysis based on the collision detection data to obtain particle generation data; conduct particle cache optimization on the real-time rendering performance data for animation game development display based on the particle generation data to obtain particle cache optimization data;

[0009] Step S5: Perform dynamic particle rendering for anime game development based on the optimized data of particle caching, so as to obtain dynamic particle rendering data for anime game development, and perform anime game operation based on the dynamic particle rendering data for anime game development, so as to obtain operation data for anime game development.

[0010] Through geometric structure analysis of the character modeling data of anime games, the present invention accurately extracts the geometric features of the characters, making the forms of the characters more precise and realistic, and laying a good foundation for physical simulation in the game. At the same time, through the extraction of physical material characteristics, the behavior performance of the characters can be restored more realistically according to the physical characteristics of different materials, avoiding the problem of inaccurate physical responses caused by insufficient material characteristics in the existing methods. During the collision detection process, the present invention combines the geometric data and physical material data of the characters for fine collision analysis, considering not only the form and physical properties of the characters, but also the environmental changes and real-time dynamic physical effects, effectively improving the accuracy of the collision detection results and the timeliness of the response, thus avoiding the situation of collision lag or inaccuracy in the game. For particle generation analysis, the present invention performs refined particle effect analysis according to different collision events, can more accurately simulate the particle movement path and perform reasonable caching optimization, reduces memory occupancy, and avoids the problem of low rendering efficiency in high-complexity scenarios. By performing real-time analysis of the rendering performance, the present invention not only improves the rendering accuracy, but also optimizes the interaction relationship between the lighting effects and rendering resources, can perform dynamic optimization according to the complexity of the actual scene, and effectively avoids the rendering delay and performance bottleneck caused by over-reliance on frame rate sampling and graphics processing unit load evaluation in the traditional methods. Therefore, the overall game operation is smoother, improving the player experience and game performance.

[0011] Optionally, step S1 is specifically:

[0012] Step S11: Obtain the character modeling data of anime games, and perform geometric feature extraction according to the character modeling data of anime games, so as to obtain geometric feature data;

[0013] Step S12: Perform character form recognition according to the geometric feature data, so as to obtain character form data;

[0014] Step S13: Construct a contour curve according to the character form data, so as to obtain character contour curve data;

[0015] Step S14: Perform local detail modeling according to the character contour curve data, so as to obtain local detail data;

[0016] Step S15: Perform mesh division on the local detail data, so as to obtain meshed surface data;

[0017] Step S16: Calculate the normal vectors of the meshed surface data to obtain normal vector data;

[0018] Step S17: Analyze the geometric structure of the anime game character based on the normal vector data to obtain the geometric structure data of the anime game character.

[0019] Through geometric feature extraction of the modeling data of anime game characters, the present invention realizes more accurate character form recognition, and further provides a more realistic geometric data basis for physical simulation and visual effects in games. Through the recognition of character form data, the natural form of the character can be restored more precisely, making the performance of the character more flexible and dynamic in different environments and situations. In the contour curve construction stage, through precise curve analysis, the present invention makes the performance of the character contour more delicate, enhancing the visual impact of the character in the game. In the local detail modeling stage, the present invention refines the modeling of the detail part, thus ensuring that the performance of the character at the detail part is more realistic and avoiding the problem of image flattening caused by the lack of detail rendering in the existing methods. Through the processing of the meshed surface data, the present invention optimizes the performance of the character surface, improves the accuracy and smoothness of the mesh, and ensures that the display effects under different viewing angles can be consistent. The calculation of the normal vector data further improves the lighting performance of the character in the three-dimensional environment, making the surface texture and light and shadow effects of the character more natural and avoiding the inaccurate rendering caused by insufficient calculation. Finally, through the analysis of the geometric structure of the anime game character, the present invention provides more complete and detailed geometric structure data, ensuring the efficient and accurate interaction, collision detection, and physical reaction between the character and the environment, enhancing the overall game experience and physical simulation effect, and avoiding the defects in the traditional methods that fail to consider dynamic environmental changes and real-time physical effects.

[0020] Optionally, step S17 is specifically as follows:

[0021] Step S171: Extract curvature features based on the normal vector data to obtain curvature data;

[0022] Step S172: Identify the concave surface regions based on the curvature data to obtain the concave surface region data;

[0023] Step S173: Identify the convex surface regions based on the curvature data to obtain the convex surface region data;

[0024] Step S174: Perform texture mapping on the concave surface region data to obtain the concave surface region texture mapping data;

[0025] Step S175: Enhance the lighting effect of the convex surface region data to obtain the convex surface region lighting effect data;

[0026] Step S176: Perform surface fusion of the anime game character based on the texture mapping data of the surface concave region and the lighting effect data of the surface convex region, so as to obtain the surface data of the anime game character;

[0027] Step S177: Smooth the surface data of the anime game character to obtain the smooth surface data of the anime game character;

[0028] Step S178: Balance the geometric structure according to the smooth surface data of the anime game character to obtain the geometric structure data of the anime game character.

[0029] Through the extraction of the curvature characteristics of the normal data in the present invention, the surface curvature change can be accurately identified, thereby optimizing the surface detail processing of the character and enhancing the performance ability of the complex surface form of the character. The recognition of the surface concave region enables the accurate capture of the concave part, so that the texture can be adjusted more precisely in the texture mapping stage, making the detail effect of the character richer and avoiding the inaccurate representation of the concave part in the existing methods. At the same time, the recognition of the surface convex region and the enhancement of the lighting effect optimize the lighting performance of the convex region, making the performance of these regions more three-dimensional and realistic under the light source irradiation, and avoiding the dull lighting effect and lack of hierarchy in the traditional methods. The character surface fusion carried out on the basis of the texture mapping in the concave region and the lighting effect in the convex region ensures the natural transition of each part of the character surface, avoiding the unnatural surface connection in the existing methods, thereby enhancing the overall visual effect of the character. By smoothing the surface data, the edges and corners generated by operations such as meshing on the character surface are reduced, making the character surface smoother, enhancing the fluency and realism of the visual effect, and avoiding the adverse effects brought by the rough surface. Finally, through the geometric structure balance, the geometric form of the character is made more stable, avoiding the inaccurate physical simulation caused by the surface imbalance, thereby further enhancing the physical effect of the interaction between the character and the environment and enhancing the immersion and experience of the game.

[0030] Optionally, step S12 is specifically:

[0031] Step S21: Extract the physical material characteristics of the anime game character based on the modeling data of the anime game character to obtain the physical material data of the anime game character;

[0032] Step S22: Construct a collision model according to the physical material data of the anime game character and the geometric structure data of the anime game character to obtain a collision model;

[0033] Step S23: Obtain the motion data of the anime game character and the data of the anime game environment;

[0034] Step S24: Perform a collision detection simulation on the collision model based on the anime game character motion data and the anime game environment data, so as to obtain collision detection simulation data;

[0035] Step S25: Calculate the collision response time for the collision detection simulation data, so as to obtain collision response time data;

[0036] Step S26: Perform character collision penetration correction based on the collision response time data, so as to obtain character collision penetration correction data;

[0037] Step S27: Smooth the collision response for the collision detection simulation data, so as to obtain collision response smoothing data;

[0038] Step S28: Integrate the collision detection based on the collision response smoothing data and the character collision penetration correction data, so as to obtain collision detection data.

[0039] Through the extraction of the physical material characteristics of the anime game characters, the present invention can accurately obtain the physical property data of the characters, thereby providing a more accurate physical basis for the construction of the collision model, and avoiding the situation of incorrect collision judgment caused by inaccurate physical material characteristics in the existing methods. By combining the geometric structure data and physical material data of the characters to construct the collision model, the accuracy of the collision simulation is further improved, making the collision effects in the game more realistic. The introduction of the character motion data and environment data enables the collision detection to no longer be limited to static models, but to be able to respond in real time to the motion state of the characters and environmental changes, thereby enhancing the dynamics and real-time nature of the collision detection, and avoiding the problem of lag or inaccuracy in the collision response caused by ignoring environmental changes in the traditional methods. The calculation of the response time after the collision detection simulation can accurately quantify the time delay of the collision reaction, providing data support for subsequent physical correction and optimization, and avoiding the situation of untimely and unsmooth collision response in the traditional methods. By correcting the collision penetration problem, it further ensures that the characters will not have unreasonable penetration phenomena during collisions, enhancing the physical authenticity of the collision effects. The smoothing process of the collision response can effectively reduce the abrupt changes generated during the collision process, improve the naturalness of the character movements, and make the game experience smoother. Finally, through the integration of the collision response smoothing data and the penetration correction data, the collision detection results are made more accurate and stable, thereby ensuring the optimization of the physical interaction effects during the entire game operation process, and further improving the immersion and user experience of the game.

[0040] Optionally, step S26 is specifically as follows:

[0041] Step S261: Perform a time series analysis of the collision points based on the collision response time data, so as to obtain collision point time series data;

[0042] Step S262: Perform character collision path tracking based on the collision point time series data to obtain character collision path data;

[0043] Step S263: Calculate the collision force field distribution for the character collision path data to obtain collision force field distribution data;

[0044] Step S264: Correct the displacement vector of the collision point for the collision force field distribution data to obtain collision point correction vector data;

[0045] Step S265: Adjust the character collision penetration range based on the collision point correction vector data to obtain character collision penetration correction data.

[0046] Through the collision point time series analysis of the collision response time data, the present invention can more accurately grasp the changes of each collision point in the time dimension, avoiding the deficiency of the lack of temporal processing in the existing methods for the collision process. By performing character collision path tracking on the collision point time series data, the movement trajectory of the character during the collision process can be detailedly recorded, providing more targeted path information for the subsequent collision force field calculation and correction, ensuring the high precision of collision detection. The collision force field distribution calculation can accurately simulate the force field distribution during the collision process according to the collision path and physical characteristics of the character, thus more realistically reflecting the physical effects of the interaction between the character and the environment and providing support for mechanical correction. The correction of the displacement vector of the collision point helps to timely adjust the displacement deviation caused by the collision, avoiding the abnormal position of the character caused by inaccurate collision simulation, thus enhancing the rationality and stability of the collision response. Finally, by adjusting the character collision penetration range according to the collision point correction vector data, the penetration phenomenon of the character during the collision process can be effectively prevented, ensuring that the collision effect is more in line with the physical laws, further improving the physical interaction effect and user experience in the game.

[0047] Optionally, step S3 is specifically as follows:

[0048] Step S31: Obtain the display data for anime game development and perform frame rate sampling based on the display data for anime game development to obtain frame rate sampling data;

[0049] Step S32: Evaluate the load of the graphics processing unit based on the frame rate sampling data to obtain graphics processing unit load data;

[0050] Step S33: Analyze the rendering delay based on the graphics processing unit load data to obtain rendering delay data;

[0051] Step S34: Dynamically evaluate the real-time shadow effect for the rendering delay data to obtain real-time shadow effect data;

[0052] Step S35: Optimize the rendering resources according to the real-time light and shadow effect data, so as to obtain the real-time rendering performance data for the development of anime games.

[0053] Through frame rate sampling of the display data for the development of anime games, the present invention can effectively obtain the rendering performance data at different time points, thus providing detailed references for subsequent optimization. The load evaluation of the graphics processing unit based on the frame rate sampling data helps to accurately measure the load of the graphics processing unit in different scenarios, avoiding performance bottlenecks and game lags caused by excessive load. Further analyzing the rendering latency based on the graphics processing unit load data can help developers accurately identify the latency problems generated during the rendering process and adjust the optimization plan in a timely manner. By dynamically evaluating the real-time light and shadow effect based on the rendering latency data, it is possible to deeply understand the impact of the real-time light and shadow effect on the rendering performance, identify potential rendering bottlenecks, and then achieve targeted optimization. Finally, optimizing the rendering resources according to the real-time light and shadow effect data can effectively improve the rendering efficiency, reduce the memory occupancy, and ensure a smooth gaming experience even in high-complexity scenarios. This method overcomes the problem of excessive dependence on frame rate and load evaluation in traditional methods by systematically analyzing and optimizing the rendering performance, providing a more flexible and efficient dynamic optimization strategy, thus effectively improving the display effect and overall performance of anime games.

[0054] Optionally, step S33 is specifically as follows:

[0055] Step S331: Extract the GPU processing frame features according to the graphics processing unit load data, so as to obtain the GPU processing frame data;

[0056] Step S332: Statistically analyze the rendering time of the GPU processing frame data, so as to obtain the frame rendering time data;

[0057] Step S333: Calculate the rendering cycle according to the frame rendering time data, so as to obtain the rendering cycle data;

[0058] Step S334: Identify the rendering delay cycle according to the rendering cycle data, so as to obtain the rendering delay cycle data;

[0059] Step S335: Identify the maximum delay point for the rendering delay cycle data, so as to obtain the maximum delay point data;

[0060] Step S336: Calculate the rendering delay for the maximum delay point data, so as to obtain the rendering delay data.

[0061] By extracting frame features through GPU processing based on the load data of the graphics processing unit, the present invention can effectively capture key features in different graphics processing processes, providing a basis for subsequent rendering performance optimization. Statistical analysis of the rendering time of GPU-processed frame data helps to comprehensively understand the time consumption of each frame rendering process, and further analyze the factors affecting rendering efficiency. On this basis, calculating the rendering cycle according to the frame rendering time data can help developers identify the length of the rendering cycle, thereby evaluating the potential risk of rendering delay. By identifying the delay cycle in the rendering cycle data, the occurrence time of the delay can be clearly located, which helps to discover the performance bottleneck of the system and take optimization measures in a timely manner. Identifying the maximum delay point in the rendering delay cycle data helps to determine the key bottleneck in the rendering process and provide a clear direction for optimization. Finally, by calculating the rendering delay based on the maximum delay point data, the specific value of the rendering delay can be accurately quantified, providing strong support for subsequent rendering optimization strategies. Overall, this method monitors the time consumption in the rendering process from multiple dimensions, provides operability for real-time dynamic optimization, can effectively reduce rendering delay, and improve the smoothness and user experience of the game.

[0062] Optionally, step S4 is specifically as follows:

[0063] Step S41: Extract collision event features based on the collision detection data to obtain collision event data;

[0064] Step S42: Extract particle generation parameters from the collision event data to obtain particle generation parameters;

[0065] Step S43: Calculate the particle emission position according to the particle generation parameters to obtain particle emission position data;

[0066] Step S44: Predict the particle movement path according to the particle emission position data to obtain particle movement path data;

[0067] Step S45: Render the particle effect according to the particle movement path data to obtain particle generation data;

[0068] Step S46: Optimize the particle cache for the real-time rendering performance data of anime game development according to the particle generation data to obtain particle cache optimization data.

[0069] By extracting the characteristics of collision events based on collision detection data, the present invention can accurately capture the key data of each collision, thereby providing an accurate parameter basis for the subsequent generation of particle effects. After extracting the particle generation parameters, it is possible to optimize the particle generation method according to the characteristics such as the type and intensity of the collision, making it more in line with the requirements of the physical environment and collision performance. Then, by calculating the particle emission position, it can be ensured that the particles are emitted from a reasonable starting point, which not only improves the authenticity of the physical effect but also enhances the immersion of the game. Subsequently, based on the particle emission position data, predicting the particle movement path helps the particles move more in line with physical laws in space, avoiding unnatural offsets or errors, thereby improving the accuracy and smoothness of the particle effect. Next, by rendering the particle effect based on the particle movement path data, the visual effect generated by the collision can be presented more delicately and vividly, further improving the image quality of the game. Finally, when optimizing the particle cache, it can effectively reduce memory occupancy and improve rendering efficiency, thereby ensuring the smooth operation of the game in complex scenarios and avoiding the lag caused by rendering performance bottlenecks. Overall, these steps greatly enhance the expressiveness of the collision effect and the performance of the game through refined particle generation and rendering optimization, making the final game experience more smooth and interactive.

[0070] Optionally, step S44 is specifically as follows:

[0071] Step S441: Analyze the initial velocity of the particles based on the particle emission position data to obtain the sub-initial velocity data;

[0072] Step S442: Calculate the particle acceleration for the particle initial velocity data to obtain the particle acceleration data;

[0073] Step S443: Model the particle motion equation based on the particle acceleration data to obtain the particle motion equation data;

[0074] Step S444: Perform particle path prediction calculation on the particle motion equation data to obtain the particle path prediction data;

[0075] Step S445: Visualize the particle motion trajectory based on the particle path prediction data to obtain the particle motion path data.

[0076] By analyzing the particle emission position data to obtain the initial particle velocity, the present invention can provide accurate starting conditions for the subsequent movement of the particles, thereby ensuring the authenticity and physical accuracy of the particle movement. Calculating the particle acceleration based on the initial particle velocity data helps to more accurately simulate the external forces acting on the particles after collision, further improving the rationality of the particle movement. Then, by establishing the particle motion equation, the particle movement can be completely modeled according to physical laws, providing theoretical support for subsequent path prediction and ensuring that the particle movement exhibits real physical characteristics. Next, based on the particle motion equation data, path prediction calculations can be performed to obtain a more accurate particle trajectory, thereby avoiding unnatural deviations of the particles and ensuring the authenticity and reliability of the post-collision effects. Finally, according to the particle path prediction data, visual rendering can be performed to intuitively display the particle movement trajectory, enhancing the expressiveness of the visual effects and improving the immersion of the game. This series of steps effectively improves the rendering accuracy and authenticity of the particle effects in collision events through fine physical calculations and modeling, while optimizing the performance of the game in complex scenarios, reducing memory usage, and improving rendering efficiency.

[0077] Optionally, the present specification also provides an operating system for anime game development, which is used to execute an operating method for anime game development as described above. The operating system for anime game development includes:

[0078] A geometric structure analysis module, configured to obtain anime game character modeling data and perform geometric structure analysis based on the anime game character modeling data, thereby obtaining anime game character geometric structure data;

[0079] A collision detection analysis module, configured to extract the physical material characteristics of the anime game character based on the anime game character modeling data, thereby obtaining the physical material data of the anime game character; perform collision detection analysis based on the physical material data of the anime game character and the geometric structure data of the anime game character, thereby obtaining collision detection data;

[0080] A real-time rendering performance analysis module, configured to obtain anime game development display data and perform real-time rendering performance analysis based on the anime game development display data, thereby obtaining real-time rendering performance data for anime game development display;

[0081] A particle cache optimization module, configured to perform particle generation analysis based on the collision detection data, thereby obtaining particle generation data; perform particle cache optimization on the real-time rendering performance data for anime game development display according to the particle generation, thereby obtaining particle cache optimization data;

[0082] An anime game development and operation module is used to perform dynamic particle rendering for anime game development according to optimized data in particle caching, thereby obtaining dynamic particle rendering data for anime game development, and performing anime game operation based on the dynamic particle rendering data for anime game development, thereby obtaining operation data for anime game development.

[0083] An operation system for anime game development according to the present invention can implement any operation method for anime game development of the present invention. It is a medium for coordinating operations and signal transmission between various modules to complete the operation method for anime game development. The internal modules of the system cooperate with each other, thereby improving the operation efficiency and rendering effect of anime games. Brief Description of the Drawings

[0084] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present invention will become more apparent:

[0085] Figure 1 It is a schematic flowchart of the steps of the operation method for anime game development according to the present invention;

[0086] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0087] Figure 3 It is a detailed schematic flowchart of step S26 in the present invention;

[0088] The implementation, functional features, and advantages of the objectives of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0089] The technical method of the present invention for a patent will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0090] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0091] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0092] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a running method for anime game development, and the method includes the following steps:

[0093] Step S1: Obtain the modeling data of the anime game character, and perform geometric structure analysis based on the modeling data of the anime game character, so as to obtain the geometric structure data of the anime game character;

[0094] In this embodiment, detailed modeling data of the anime game character is obtained by scanning or 3D modeling tools (such as Blender or Maya). The modeling data includes information such as the geometric shape, surface texture, and component relationship of the character. Specifically, when implementing, a three-dimensional scanner is used to scan the physical model, and the generated point cloud data is converted into mesh data after being processed by software. The geometric structure analysis uses topological analysis methods in computer graphics to define the three-dimensional geometric shape of the character by analyzing the connection relationship between mesh vertices. Specifically, the size and shape of the character are determined by the bounding box algorithm or the bounding volume method to generate the geometric model data of the character. This data usually includes vertex coordinates, edge connections, and patch information. After this step, a data set containing the geometric information of the character is generated, and subsequent steps will use this data for more refined physical and rendering processing.

[0095] Step S2: Extract the physical material characteristics of the anime game character based on the modeling data of the anime game character, so as to obtain the physical material data of the anime game character; perform collision detection analysis based on the physical material data of the anime game character and the geometric structure data of the anime game character, so as to obtain collision detection data;

[0096] In this embodiment, the physical properties of each part of the character are defined through a material library or manual specification, such as hardness, elasticity, friction coefficient, etc. Through a 3D modeling tool, the surface of the character mesh is selected, and different physical materials, such as metal, skin, cloth, etc., are assigned to each part. Each material will have specific physical parameters, such as the elastic modulus (in Pa) and density (kg / m 3) etc. Then, through collision detection analysis, using the geometric structure data and physical material data of the character, calculate the collision response between the character and other objects. During this process, a physics engine (such as Havok or PhysX) is used to simulate the reaction when different materials collide, especially the collision between the character and the environment. Collision detection uses a bounding volume method based on physical properties, such as AABB (Axis-Aligned Bounding Box) or OOBB (Oriented Bounding Box) to simulate, detect the collision between the character and other objects, and generate a collision detection data set.

[0097] Step S3: Obtain the display data for anime game development and perform real-time rendering performance analysis based on the display data for anime game development, so as to obtain the real-time rendering performance data for anime game development;

[0098] In this embodiment, performance analysis is carried out by collecting the rendering data of each frame. Specifically, a graphics card performance analysis tool (such as NVIDIA Nsight or Intel GPA) is used to monitor key data such as the rendering time, GPU load, and memory usage of each frame in real time. First, obtain the input data for each frame of rendering, including character model data, environment data, light source information, and material properties, etc., and then record the performance metrics of each rendering process through GPU sampling, such as the time consumption (in milliseconds) of each rendering stage. Based on this data, perform real-time rendering performance analysis to evaluate whether the GPU processing ability meets the requirements for smooth game operation. During the analysis process, perform performance optimization for rendering bottlenecks, such as adjusting rendering steps with high resource occupancy such as lighting calculation and shadow processing, so as to obtain specific real-time rendering performance data.

[0099] Step S4: Perform particle generation analysis based on the collision detection data to obtain particle generation data; perform particle cache optimization on the real-time rendering performance data for anime game development based on the particle generation data to obtain particle cache optimization data;

[0100] In this embodiment, when a collision event occurs, generate corresponding particle effects, such as sparks, smoke, or debris. The amount of particles generated and the initial velocity are determined by the speed, acceleration, and impact energy of the collision point. For each collision point, calculate its kinetic energy through the physics engine, and combine it with a particle system algorithm (such as PopcornFX) to generate the number of particles, velocity, lifespan, and size proportional to the collision intensity. Then, perform particle cache optimization. Particle generation usually consumes a large amount of memory and computing resources, especially during the rendering process. By analyzing the real-time rendering performance data, identify the bottlenecks in the particle generation process, and optimize the particle caching method, such as using a circular buffer to store particle data, reducing the frequent reading and writing of memory, thereby improving the rendering efficiency. This optimization process will generate particle cache optimization data to help achieve efficient processing of particles during the rendering process.

[0101] Step S5: Perform dynamic particle rendering for anime game development based on the optimized data of the particle cache, so as to obtain the dynamic particle rendering data for anime game development, and perform the operation of the anime game based on the dynamic particle rendering data for anime game development, so as to obtain the operation data for anime game development.

[0102] In this embodiment, based on the optimized data of the particle cache, the particles are dynamically rendered through a graphics rendering pipeline (such as OpenGL or DirectX). During the rendering process, according to the data of the particle system, such as parameters of the particle's movement path, life cycle, color, transparency, etc., the particle effects are calculated and rendered frame by frame. Through the dynamic rendering algorithm, it is ensured that the particles change with the passage of time, the actions of the characters or environmental changes. The rendering data will reflect the position and morphological changes of the particles in the scene. The dynamic particle rendering data is input into the game engine to drive the physical engine and rendering system in the game to perform actual game operation operations. Finally, the generated operation data for anime game development includes data such as the real-time rendering effect, character actions, and environmental interactions of the game, ensuring that the game can run smoothly and fluently.

[0103] Optionally, step S1 is specifically:

[0104] Step S11: Obtain the modeling data of the anime game character, and perform geometric feature extraction based on the modeling data of the anime game character, so as to obtain geometric feature data;

[0105] In this embodiment, the modeling data of the anime game character is obtained by using a 3D scanner or digital modeling tools (such as Blender or ZBrush). Specifically, first convert the model into a standard 3D format (such as.obj,.fbx or.stl), and then extract the vertex, edge, and patch information of each model. These data include the spatial coordinates, surface features, geometric shapes, etc. of each mesh unit. The geometric feature extraction process uses methods in computer graphics, such as curvature calculation, angle calculation, side length extraction, etc. For each part of the character model, first calculate its surface curvature, and use the least squares method to fit the surface to calculate the mean curvature and Gaussian curvature. Then extract important geometric features, such as surface curvature, smoothness, contour, etc., and use these features to characterize the basic geometric information of the character.

[0106] Step S12: Perform character morphology recognition based on the geometric feature data, so as to obtain character morphology data;

[0107] In this embodiment, the geometric features of the character model are analyzed to identify the key parts representing the character's form, such as the head, limbs, body, etc. A contour-based segmentation algorithm (such as the RANSAC algorithm or the K-means clustering algorithm) is used to perform form recognition on the character model. By analyzing the surface normal direction, edge connection situation, etc. in the geometric feature data, the form features of the character are determined. Specifically, when implementing, the mesh of the character model is segmented, and each segment represents a form unit. For example, by analyzing the symmetry of the character and using a symmetry analysis algorithm (such as the segmentation method based on central symmetry), it is divided into parts such as the head, chest, and limbs. Through the information of these form units, the character form data is finally obtained, providing necessary data support for the subsequent steps.

[0108] Step S13: Construct a contour curve based on the character form data to obtain character contour curve data;

[0109] In this embodiment, the key geometric parts of the character model are selected, such as the head contour, shoulder line, leg contour, etc. The contour curve construction is usually completed by extracting the data of the model surface or boundary points. A boundary extraction algorithm (such as the Marching Cubes or Edge Detection algorithm) is used to extract the contour lines from the character geometric data. These curves can be contours on a two-dimensional plane or contour lines in a three-dimensional space. Especially in complex forms, the contour curves need to be smoothed by an interpolation method, usually using B-spline curves or Bezier curves to smooth the contours to make them more smooth and natural. Finally, the constructed contour curve data will be used to describe the external contour form of the character, supporting subsequent modeling and detail processing.

[0110] Step S14: Perform local detail modeling based on the character contour curve data to obtain local detail data;

[0111] In this embodiment, each part of the character is further refined through the curve data, especially parts with rich details such as the eyes, mouth, fingers, etc. In detail modeling, a triangular mesh subdivision algorithm (such as the Catmull-Clark subdivision algorithm) is used to enhance the details of the local area, increasing the number of polygons, so that the details of the character are more refined. By adjusting the subdivision level, the position of the control points, adding texture maps of local details, etc., local detail data is gradually increased. During this process, for some parts of the character, high-precision scan data or manual carving (using tools such as ZBrush) is used to add muscle lines, expression details, etc. Through this local modeling process, the obtained detail data is more accurate.

[0112] Step S15: Perform mesh division on the local detail data to obtain meshed surface data;

[0113] In this embodiment, an appropriate mesh generation method is selected, such as tetrahedral meshing or triangular meshing. By subdividing the mesh, each detailed area of the character is transformed into smaller triangular or polygonal units. To ensure the accuracy of mesh generation, the Delaunay triangulation algorithm or Voronoi partitioning algorithm is used to accurately represent the surface details of the local area as a mesh structure. When generating the mesh, it is necessary to define the resolution of the mesh, that is, the maximum side length of each patch. In high-precision areas, such as the eyes, mouth, nails, etc., a higher-resolution mesh is used, while in areas where excessive details are not required, such as the back or abdomen of the character, a lower-resolution mesh is used. At this time, the meshed surface data will provide the necessary basic data for subsequent normal calculation and further refinement.

[0114] Step S16: Calculate the normal data from the meshed surface data to obtain the normal data;

[0115] In this embodiment, the normal data is calculated using the meshed surface data. Each mesh element (such as a triangle or quadrilateral) has a normal that is perpendicular to that face. The core of normal calculation is to use vector operations to calculate the normal of each mesh element based on the coordinate information of the mesh vertices. Specifically, by calculating the coordinates of two adjacent vertices, the edge vector is obtained; then, by calculating the cross product of two adjacent edge vectors, the normal of that face is obtained. To ensure the smoothness of normal calculation, the weighted average method is usually used to perform a weighted average of the normals of adjacent meshes to obtain the normal of each vertex. In a complex character model, the accuracy of the normal is crucial, and the normal data will be used in subsequent lighting calculations and rendering processes.

[0116] Step S17: Analyze the geometric structure of the anime game character based on the normal data to obtain the geometric structure data of the anime game character.

[0117] In this embodiment, the normal data is used for surface smoothness analysis to identify the lighting change areas and the high and low relief areas of the model. Through the interaction between lighting and normals, different geometric areas of the model surface are analyzed, and the geometric structure of the character is further optimized based on this data. This process involves calculating the level of detail (LOD) of the model and adjusting the number and complexity of its polygons according to the display requirements of the character. For example, the facial and hand areas of the character can maintain high detail, while other areas can reduce the accuracy to improve the rendering efficiency. The ultimate goal of geometric structure analysis is to generate a character geometric model that balances accuracy and performance, and this model will provide the required structural data for subsequent rendering and physical simulation.

[0118] Optionally, step S17 is specifically:

[0119] Step S171: Extract curvature features based on the normal data to obtain curvature data;

[0120] In this embodiment, the normal information of each vertex is extracted from the meshed surface data. For each mesh cell (such as a triangle), by calculating the change in the normal of this cell and the normals of surrounding mesh cells, the curvature is calculated using a second - derivative method (such as a discrete gradient operator). The curvature calculation methods are generally divided into two types: Gaussian curvature and mean curvature. Gaussian curvature can be obtained by calculating the cross - product of the normals, and the mean curvature is calculated through the direction of change of the normals. In actual operation, first calculate the local curvature of each vertex, using a fixed - size neighborhood (such as 5 adjacent vertices), and then calculate the curvature according to the geometric changes in this neighborhood. In specific operation, set the curvature threshold to 0.5 degrees. If the curvature of a certain area is greater than this threshold, it is regarded as an area with a large curvature. Finally, the calculated curvature data is used as the basis for subsequent operations.

[0121] Step S172: Identify the concave regions on the surface based on the curvature data to obtain the surface concave - region data;

[0122] In this embodiment, based on the curvature data, set a threshold (such as - 0.2). All regions with curvature values less than this threshold are defined as concave regions. In specific implementation, for each mesh cell, by comparing its curvature value with the set threshold, the regions with negative curvature values are screened out. These regions indicate that the surface has concave features. During the operation, by calculating the normal direction of the region, the boundaries of the concave regions are identified. In complex shapes, the concave regions often appear where there are large curvature changes. Therefore, the angle between the normals of adjacent mesh cells is calculated to further confirm the boundaries of the concave regions. The mesh data of these concave regions are collected and marked as the surface concave - region data for subsequent texture mapping.

[0123] Step S173: Identify the convex regions on the surface based on the curvature data to obtain the surface convex - region data;

[0124] In this embodiment, set a positive curvature threshold (such as 0.2). All regions with curvature values greater than this value are identified as convex regions. In actual operation, the mesh cells with curvature values greater than the threshold are used to mark the convex regions. Convex regions usually represent the parts that protrude outward on the surface. Therefore, by calculating the relationship between the curvature and the normal direction, these convex regions can be further confirmed. During the identification process, a combination of Gaussian curvature and mean curvature is used for analysis to ensure that the outward protrusions on the surface can be accurately identified. Through these steps, the obtained surface convex - region data will provide support for subsequent enhancement of lighting effects.

[0125] Step S174: Perform texture mapping on the surface concave - region data to obtain the surface concave - region texture - mapping data;

[0126] In this embodiment, based on the surface concave regions identified in step S172, appropriate texture mapping data is selected for mapping. The key to texture mapping is to refine the mesh surfaces of these concave regions to make their surface details more realistic. The textures used can be surface textures obtained by scanning or custom concave effect texture maps (such as dents on a metal surface, skin surface textures, etc.). Specifically, during implementation, the three-dimensional surfaces of the concave regions are mapped to a two-dimensional texture coordinate system through a UV unwrapping algorithm (such as the LSCM unwrapping method). Then, bilinear interpolation is used to accurately map the texture so that it can adapt to the geometric shape of the concave regions and ensure that the texture matches the surface details. Finally, the obtained texture mapping data for the surface concave regions will be used for subsequent rendering processing.

[0127] Step S175: Enhance the lighting effect of the surface convex region data to obtain the lighting effect data for the surface convex region;

[0128] In this embodiment, based on the surface convex regions identified in step S173, the lighting reflection characteristics of these regions are analyzed. Specifically, during implementation, the Phong lighting model or the Blinn-Phong lighting model is used for lighting calculation to simulate the relationship between the light reflected from the surface and the viewing angle. For each mesh cell in the convex region, by calculating the angle between its surface normal and the light source direction, lighting calculation formulas such as diffuse reflection and specular reflection are used to calculate the lighting intensity of this region. At the same time, according to the surface reflectivity and material properties, the lighting effect is enhanced. During the implementation process, a lighting enhancement coefficient (such as 1.5 times) is set to enhance the lighting reflection effect of the convex region. Finally, the obtained lighting effect data for the surface convex regions will be used for lighting processing during rendering.

[0129] Step S176: Perform surface fusion of the anime game character based on the texture mapping data of the surface concave region and the lighting effect data of the surface convex region to obtain the surface data of the anime game character;

[0130] In this embodiment, the texture of the concave region and the lighting effect of the convex region are weighted and fused to ensure that the details of the concave and convex regions are balanced and retained in the final surface data. Specifically, during implementation, by adjusting the contribution degree of each region, these two types of data are synthesized using the weighted average method or a blending mode (such as additive blending or multiplicative blending). The weight values are set to 0.6 and 0.4, respectively, to assign different weights to the texture mapping data and the lighting effect data. In this way, the generated character surface data can comprehensively reflect the texture and lighting characteristics of the character surface, providing a complete visual effect for subsequent rendering and display.

[0131] Step S177: Smooth the surface data of the anime game character to obtain the smoothed surface data of the anime game character;

[0132] In this embodiment, the Laplace smoothing algorithm or the smoothing method based on Gaussian filtering is used to iteratively process the mesh on the surface of the character. By averaging the neighborhood around each vertex, the position of each vertex moves towards the average position of the surrounding neighborhood, thereby achieving surface smoothness. During the implementation process, the smoothing iteration count is set to 5 times to ensure that the surface is smoothed to an appropriate degree. In each iteration, the smoothing step size is set to 0.02 to ensure that the surface morphology changes gently and naturally during the processing. Finally, the smooth surface data of the anime game character obtained will provide a smoother basis for subsequent geometric structure optimization and rendering.

[0133] Step S178: Balance the geometric structure according to the smooth surface data of the anime game character, thereby obtaining the geometric structure data of the anime game character.

[0134] In this embodiment, based on the smooth surface data obtained in step S177, a geometric optimization algorithm (such as the boundary stretching method or the geometric manifold optimization method) is applied to adjust the geometric structure of the character. During the specific implementation, each mesh cell is evaluated to ensure that the geometric structures of all parts of the character are not over-stretched or compressed. During the optimization process, by minimizing the mesh distortion or the energy function, the positions of the mesh vertices are adjusted to ensure the balance of the overall structure of the character. The optimization objective function of the geometric structure is set such that the optimized structure remains natural in shape and conforms to physical principles. During this process, by setting the accuracy threshold of the optimization target to 0.001, it is ensured that the character model achieves a high-quality geometric structure balance. Finally, the geometric structure data of the anime game character obtained will provide a stable basis for the animation generation and physical simulation of the character.

[0135] Optionally, step S12 is specifically as follows:

[0136] Step S21: Extract the physical material characteristics of the anime game character according to the anime game character modeling data, thereby obtaining the physical material data of the anime game character;

[0137] In this embodiment, it is necessary to obtain the geometric morphology and texture information of each part of the character model. These data are obtained through the mesh data generated by using 3D modeling tools (such as Blender, Maya, or 3ds Max). Then, a physical material model (such as the Lambertian or Phong reflection model) is used to calculate the optical, mechanical, and physical properties of each part. This includes surface roughness, reflectivity, elastic modulus, and density, etc. For each model area, a physical engine (such as Havok or BulletPhysics) is used to simulate the friction coefficient and hardness of the material to obtain the physical properties of the material. The specific parameters include the friction coefficient, which is usually set to 0.3 to 0.5, the elastic modulus is 1000 Pa to 5000 Pa, and the density is 800 to 2000 kg / m3 Based on these values, the physical material data of each component is established, providing the necessary physical parameters for subsequent collision detection.

[0138] Step S22: Construct a collision model based on the physical material data and geometric structure data of the anime game character, thereby obtaining the collision model.

[0139] In this embodiment, by analyzing the geometric data of the character model, the collision area of each component is determined. Usually, the complex geometric shape of the character is simplified into a geometric body model, such as a sphere, cube, or capsule. During this process, the collision body construction function provided by a physics engine (such as PhysX or Bullet) is used to enclose the collision area of each component with a simple geometric shape. These collision bodies will be combined with the physical material properties of the character to ensure accurate application of properties such as surface hardness and friction during simulation. The size and position of each collision body need to be adjusted manually or automatically according to the size and shape of the character geometric model. Usually, the size accuracy of the collision body is set to 1 millimeter or less to ensure efficient collision detection. During the collision body construction process, a complete collision model including all components is created by setting the mass, elastic coefficient, and friction coefficient of the collision body.

[0140] Step S23: Obtain the motion data of the anime game character and the game environment data.

[0141] In this embodiment, the motion data of the character can be obtained through an animation system (such as Maya, 3ds Max) or motion capture technology (such as Vicon), and usually includes the displacement, velocity, acceleration, and rotation information of the character. This data includes the character position (such as x, y, z coordinates) and rotation angles (such as roll angle, pitch angle, yaw angle) of each frame. The game environment data mainly includes the geometric data of static objects and obstacles in the environment, the physical properties of the scene (such as gravity, air resistance), and dynamic objects affecting collisions (such as the movement of other characters or objects). This data can be extracted from the environment information in a scene editor (such as UnrealEngine or Unity). For the motion data, usually, the motion trajectory of each character is sampled at time intervals, and the accuracy is set to 60 frames per second to ensure that motion changes can be accurately captured and applied to subsequent collision detection.

[0142] Step S24: Perform a collision detection simulation on the collision model based on the motion data of the anime game character and the game environment data, thereby obtaining the collision detection simulation data.

[0143] In this embodiment, through the collision detection system in a physics engine (such as Havok, PhysX), the movement trajectory of the character is compared with the collision models of static and dynamic objects in the environment. During this process, a fast bounding box algorithm (such as AABB, OBB) is used to pre-detect potential collision areas, avoiding complex polygon collision calculations for each detection. For each potential collision area, a refined collision detection method (such as a particle system or mesh collision detection) is adopted for more accurate collision judgment. The key parameters of collision detection include the character's movement speed (such as 1 to 3 meters per second), the accuracy of collision detection (set to 0.01 millimeters), and the response time of dynamic objects (such as 5 milliseconds). Through these parameters, collision detection simulation data is generated in real time, and the occurrence time, location, and collision force of each collision are recorded.

[0144] Step S25: Calculate the collision response time for the collision detection simulation data to obtain collision response time data;

[0145] In this embodiment, based on the timestamp of the collision occurrence, the mechanical simulation method provided in the physics engine is used to calculate the physical reaction time after the collision. The collision response time mainly considers factors such as rebound, friction, and material reaction. By calculating the time difference of each collision and parameters such as the elasticity and friction coefficient of the collision object, the physical response time after the collision is obtained. Specifically, the calculation formula is usually: Response time = Collision force × Mass / Collision loss factor. The mass and loss factor come from the physical properties of the character and environmental objects (for example, the mass of the character is 70 kg and the friction coefficient is 0.4). The calculation result will be in milliseconds, and the accuracy is usually set to 0.01 milliseconds.

[0146] Step S26: Perform character collision penetration correction based on the collision response time data to obtain character collision penetration correction data;

[0147] In this embodiment, the collision penetration problem usually occurs when the time step in the physics engine simulation is not fine enough, resulting in the character penetrating the surface of the environmental object during physical interaction. To correct this problem, first, by comparing the character's position before and after the collision, it is judged whether there is a penetration phenomenon. If penetration occurs, based on the response time data, the character's position is adjusted so that it can return to the normal position after the collision in a timely manner according to the collision response time. The adjustment process uses linear interpolation or a spring-damping model, sets the correction threshold to 0.1 millimeters, and uses a spring force correction algorithm to restore the character's position to prevent penetration. Each penetration correction operation lasts for 20 milliseconds to ensure the stability of the correction process.

[0148] Step S27: Smooth the collision response for the collision detection simulation data to obtain collision response smoothing data;

[0149] In this embodiment, a filtering algorithm (such as a Gaussian filter or a Kalman filter) is used to smooth the collision response data. During the implementation process, the window size of the filter is set to 5 time steps to ensure the smoothness of the response data. During the processing, for the collision force data at each time point, the response data of adjacent time points are fused through a weighted average algorithm to eliminate unstable fluctuations caused by sharp changes or data noise. The processed data generates a smooth collision response curve. Usually, the accuracy of the smoothed response data is 0.01 milliseconds, ensuring that the physical interaction between the character and the environment is more natural and stable.

[0150] Step S28: Integrate the collision detection based on the smoothed collision response data and the character collision penetration correction data to obtain the collision detection data.

[0151] In this embodiment, the corrected collision position of the character is combined with the smoothed collision response data to ensure that there is no conflict between the corrected character position and the processed response data during the integration. The weighted average method or interpolation algorithm is used in the integration process to balance the data of both. When correcting the position, the smoothed response data is combined to ensure the smoothness and accuracy of the collision detection process. For each collision handling, the corrected collision accuracy is set to 0.01 millimeters, and the response time error is 0.01 milliseconds, ensuring that the obtained collision detection data after integration can accurately reflect the collision relationship between the character and the environmental objects and provide data support for subsequent rendering and physical calculations.

[0152] Optionally, step S26 is specifically:

[0153] Step S261: Perform a time series analysis of the collision points based on the collision response time data to obtain the collision point time series data;

[0154] In this embodiment, the response time data of each collision point is obtained. These data usually include the timestamp of the collision occurrence and the position and motion state of the character at each collision. These data are sorted and filtered to ensure that the response data of each collision point is arranged in chronological order. Then, statistical analysis methods (such as moving average, exponential smoothing method, or autoregressive model) are used to process the time series data to remove noise and extreme values. The window size of the moving average is set to 20 milliseconds to ensure data smoothness. During the calculation, based on the response time and the position of the collision point, the time series of each collision point is deduced. Parameters such as the response time interval are usually 0.1 to 1 millisecond to ensure the efficient and accurate generation of time series data. Finally, the time series data corresponding to each collision point is output, and the data format is a set of timestamps and positions.

[0155] Step S262: Trace the character's collision path based on the collision point time series data to obtain the character's collision path data;

[0156] In this embodiment, it is necessary to extract the position and corresponding timestamp of each collision point according to the time series data. By performing interpolation calculations on the timestamp and position of each collision point, the movement trajectory of the character during the collision is obtained. The interpolation method used is linear interpolation or cubic spline interpolation to ensure the coherence of the character's movement path. The interpolation calculation for each time point is based on the data of neighboring collision points, and the accuracy is set to 0.01 mm to ensure the accuracy of path tracing. During the path tracing process, the collision state of the character and the environmental data also need to be considered to ensure that the environmental information during path calculation is synchronized with the character's position in real time. The parameters used in the collision path tracing process include the initial position, speed, and direction of the character, and the accuracy is set to 60 frames per second. The trajectory data is generated by the interpolation method. Finally, the complete path data of the character during the collision is obtained, including the coordinates of each time point and the state of the character.

[0157] Step S263: Calculate the collision force field distribution for the character's collision path data to obtain the collision force field distribution data;

[0158] In this embodiment, it is necessary to combine the physical characteristics of the character's collision with the environment, such as the mass of the character, the material of the collision object, the collision intensity, etc., according to the character's collision path data obtained in step S262 to simulate the force field distribution during the collision. The force field distribution calculation is usually completed by numerical simulation methods (such as the finite element analysis method). The parameters used include the magnitude of the collision force, the point of application of the force, the direction of the force, etc. During the calculation, the mass of the character is usually 70 kg, the magnitude of the force is set to 1000 N to 3000 N, and the force field distribution at the collision point is deduced based on these parameters. The force field data of each collision point is calculated by simulating the mechanical reaction, considering the hardness and friction coefficient of the collision surface. The friction coefficient value ranges from 0.3 to 0.5. The finally output data includes the collision force field distribution of each collision point, which contains the magnitude, application position, and direction of the force, and the distribution range depends on the specific situation of the scene, usually from 0.5 m to 5 m.

[0159] Step S264: Correct the displacement vector of the collision point for the collision force field distribution data to obtain the corrected vector data of the collision point;

[0160] In this embodiment, according to the obtained force field data, the force value and acting direction of each collision point are extracted. Then, the displacement of each collision point is corrected by using a vector operation method. The displacement correction is achieved by calculating the relationship between the force and the reaction force of the object, and the formula F = m * a is used to determine the relationship between the magnitude of the force and the acceleration. The main steps of the correction include: calculating the corrected displacement vector according to the magnitude and direction of the collision force and the known mass and displacement relationship. The parameters for the displacement vector correction of each collision point include the magnitude of the collision force, the mass is 70 kg, and the correction range is usually 1 to 10 mm. The threshold value during the correction process is set to perform the correction only when the collision force exceeds a certain value, usually set to 500 N. When the data correction of the force field distribution is completed, the collision point correction vector data is finally generated.

[0161] Step S265: Adjust the character collision penetration range according to the collision point correction vector data, so as to obtain the character collision penetration correction data.

[0162] In this embodiment, according to the collision point correction vector data, the penetration distance between the character and the collision object is calculated. The penetration range is determined by comparing the shortest distance between the position of the character and the surface of the collision object. When the penetration distance is greater than the set threshold value (for example, 0.1 mm), penetration correction is required. The correction method includes: adjusting the position of the character's collision object according to the direction and magnitude of the correction vector so that it no longer penetrates the environmental object. The rigid body dynamics method is used in the correction process, and the penetration correction is performed by operating on the velocity, position, and correction vector of the character. The mass of the character and the magnitude of the force field have an important impact on the amplitude of the correction. The mass is set to 70 kg, and the correction amplitude is usually 0.1 to 5 mm. The adjusted character position and the correction data of the collision object will be provided to the subsequent physics engine for further simulation and processing. Finally, the character collision penetration correction data is output.

[0163] Optionally, step S3 is specifically as follows:

[0164] Step S31: Obtain the animation game development display data, and perform frame rate sampling according to the animation game development display data to obtain the frame rate sampling data;

[0165] In this embodiment, display data during the development of anime games is collected. The data includes the rendering time of each frame, the complexity of the rendered graphics, the response time of the processing unit, etc. The data source mainly comes from the rendering module in game engines (such as Unity or Unreal Engine). In this step, an accurate timer is used to obtain the rendering time of each frame and record it for subsequent processing. The sampling frequency is set to collect 100 data per second, which can capture the minute fluctuations during the rendering process. The frame rate sampling data includes the rendering time of each frame, the frame rate (unit: FPS), and other performance data related to rendering. To ensure the accuracy of data sampling, the time window for frame rate sampling is set to 10 ms, ensuring that each sampling can accurately capture the real-time changes during the rendering process. Finally, the obtained data will contain the frame rate data per second, usually represented by an array or a table, and the interval time for each data item is 10 milliseconds.

[0166] Step S32: Evaluate the graphics processing unit load based on the frame rate sampling data to obtain the graphics processing unit load data;

[0167] In this embodiment, the rendering time of each frame and the resource consumption of each graphics processing unit are analyzed, mainly including the core occupancy rate of the GPU, the video memory occupancy rate, and the processing capacity consumption, etc. The parameters used for GPU load evaluation include the rendering duration of each frame, the GPU core frequency, the video memory bandwidth, etc. The set evaluation formula is: GPU load = rendering duration / total rendering time * 100%. The GPU core frequency data is obtained in real time through GPU performance monitoring tools (such as nvidia-smi of NVIDIA or GPU-Z of AMD). For the usage of video memory, the dynamic allocation information of the video memory is obtained through APIs (such as OpenGL or DirectX). During the calculation process, a window size of 100 frames is adopted, and the evaluation result reflects the fluctuations of the GPU load within each second. The data output is a time series with a load percentage, reflecting the load changes of the GPU in different rendering tasks, and the accuracy is set to 0.1%.

[0168] Step S33: Analyze the rendering latency based on the graphics processing unit load data to obtain the rendering latency data;

[0169] In this embodiment, the rendering delay refers to the time delay experienced from the start of receiving a rendering task by the graphics processing unit to the completion of rendering. In this step, the GPU load data is used in combination with the processing time of each frame for delay calculation. First, by calculating the relationship between the rendering time of each frame and the GPU load, the rendering delay value is obtained. The formula for calculating the delay is: rendering delay = rendering duration / GPU load (percentage). Specifically, if the GPU load of a certain frame is too high, the rendering duration will increase accordingly, resulting in an increase in the rendering delay. This analysis process involves the data of each frame and comprehensively evaluates the delay in combination with the GPU core occupancy rate and video memory usage rate. When analyzing the delay data, the size of the time window used is 50 frames to ensure that the cycle of GPU load fluctuations can be covered. The unit of delay is milliseconds (ms). Finally, a sequence of rendering delay data per second is generated, which contains the delay value of each frame, and a threshold is set based on the delay data (for example, a delay exceeding 16 ms is an abnormal delay) for subsequent performance optimization.

[0170] Step S34: Dynamically evaluate the real-time lighting effect based on the rendering delay data to obtain real-time lighting effect data;

[0171] In this embodiment, the real-time lighting effect is dynamically evaluated using the rendering delay data. The main content of the evaluation is to detect the quality of the lighting effect and the rendering performance under different delay conditions. First, based on the rendering delay of each frame, the preset lighting rendering parameters (such as the number of light sources, shadow resolution, reflection accuracy, etc.) are used for rendering effect evaluation. The formula for the lighting effect is: lighting rendering quality = (rendering duration / rendering delay) * lighting rendering accuracy. Through this formula, how the lighting effect performs under different delays is evaluated. During the dynamic evaluation process, the lighting rendering parameters such as shadow accuracy are set to 0.5%, and the reflection accuracy is set to 0.8%, and they are adjusted according to the rendering delay. The dynamic evaluation of the lighting effect usually performs real-time calculations based on the changes in GPU load and rendering delay. The evaluation process uses a rendering cycle of 16 ms per frame to ensure the continuity and stability of the evaluation. Finally, a sequence of real-time lighting effect data is generated, and this data includes the lighting effect quality score of each frame.

[0172] Step S35: Optimize the rendering resources according to the real-time lighting effect data to obtain the real-time rendering performance data for anime game development display.

[0173] In this embodiment, the goal of rendering resource optimization is to reduce rendering latency and improve rendering performance by adjusting rendering parameters. First, for the lighting effect quality data of each frame, an optimization algorithm (such as dynamic resolution adjustment, light source simplification, or shadow detail adjustment) is used to adjust the rendering parameters. During optimization, the rendering quality needs to be automatically adjusted according to the lighting effect score (for example, a lighting quality score less than 0.7 represents the need for resource adjustment). During the optimization process, the adjustment threshold is set to perform resource optimization when the lighting quality score is lower than 0.7. By adjusting parameters such as the number of light sources and shadow precision, the rendering performance is maximally improved. The specific optimization methods for rendering resources include reducing rendering precision, reducing the details of lighting effects, or using more efficient graphics APIs (such as Vulkan or DirectX 12). In this step, the real-time rendering performance data is generated in real time according to the optimized rendering process, including information such as the number of rendered frames per second, rendering latency, and lighting quality, and is output as a performance report with an accuracy set to update data once per second.

[0174] Optionally, step S33 is specifically as follows:

[0175] Step S331: Extract GPU processing frame features based on GPU load data to obtain GPU processing frame data;

[0176] In this embodiment, the GPU load data is sourced from the GPU usage obtained by real-time monitoring tools (such as nvidia-smi from NVIDIA or GPU-Z from AMD), including information such as the GPU load percentage, video memory usage, and processing time per frame. The extracted frame features include the load fluctuation of the GPU per frame, the change in video memory occupancy, and the temperature of the graphics processing unit. During the feature extraction process, a time window (such as 100 ms) is first determined, and relevant data is obtained from the GPU monitoring tool every this time window, recording the detailed load information of each frame. This information includes the GPU core load, memory bandwidth usage, and GPU temperature per frame, and the data storage format is a two-dimensional array, where each row represents the processing features of one frame, and the columns contain fields such as load information and timestamp. By processing the load data of each frame, GPU processing frame data is obtained, and each data point includes the frame rendering duration and the GPU resource occupancy rate.

[0177] Step S332: Statistically analyze the rendering time of the GPU processing frame data to obtain frame rendering time data;

[0178] In this embodiment, the rendering time refers to the time consumed from receiving the rendering task to completing the task, with the unit of millisecond (ms). First, obtain the rendering time data for each frame, which is calculated by analyzing the start and end times when the GPU processes the frame. The start time is obtained by recording the timestamp when the GPU starts executing the frame, and the end time is calculated based on the timestamp when the GPU finishes rendering. Through the rendering time of each frame and the corresponding frame number, the rendering time can be statistically analyzed. During the statistical process, it is necessary to exclude some error data caused by excessive GPU load or other abnormal situations. For example, when the rendering time of some frames exceeds the normal range, data cleaning is performed to exclude these frame data. The average value, maximum value, and minimum value of the rendering time are used as the main statistical parameters, and the rendering time statistical result is output as a time series data. The rendering time data for each frame is included in an array of frames, and the time interval is set at a sampling frequency of 100 frames per second.

[0179] Step S333: Calculate the rendering period based on the frame rendering time data to obtain the rendering period data;

[0180] In this embodiment, the rendering period refers to the time interval required to render one frame, with the unit of millisecond (ms). By calculating the rendering time and frame interval of each frame, the formula for calculating the rendering period is: Rendering period = current frame rendering time - previous frame rendering time. When calculating, it is necessary to first obtain the rendering duration of each frame, calculate the time difference between this frame and the previous frame, and thus obtain the rendering period. If the rendering period is greater than the set maximum value (such as 16 ms, which means a rendering period of 16 ms represents a rendering speed exceeding 60 frames per second), then this data is marked as abnormal. The rendering period data is output as a time series, reflecting the periodic fluctuations of the system during the rendering process. To ensure data continuity, the sliding window method is used during calculation. Each time the data within the window is updated, the current rendering period is obtained and updated to the sequence. The accuracy of the rendering period data is guaranteed by the accuracy of the rendering duration of each frame and the sampling frequency (such as 100 samples per second).

[0181] Step S334: Identify the latency period based on the rendering period data to obtain the rendering latency period data;

[0182] In this embodiment, the delay period refers to the time period during the rendering process when the rendering period is longer than the normal value due to factors such as load fluctuations and GPU performance bottlenecks. First, by setting a delay period identification threshold (for example, a rendering period greater than 16 ms is regarded as a delay period), the rendering period of each frame is classified, and all periods exceeding this threshold are marked. The delay period identification algorithm uses a simple threshold judgment to identify data points with a rendering period greater than 16 ms as delay periods and the rest as normal periods. Then, based on the fluctuations of the rendering period, the start and end times of the delay periods are marked and counted, and these data points form a delay period sequence. The time length of the delay period is calculated based on the number of consecutive frames with a rendering period greater than 16 ms. Finally, the output rendering delay period data will include the start time and duration of each delay period, in milliseconds.

[0183] Step S335: Identify the maximum delay point from the rendering delay period data to obtain the maximum delay point data;

[0184] In this embodiment, the purpose of this step is to find the maximum delay point among all delay periods, that is, the most serious performance bottleneck during the rendering process. In this step, by traversing all delay periods, the maximum delay time of each delay period (i.e., the longest rendering period within that period) is calculated. The maximum delay point is identified by finding the maximum value of the rendering duration within the delay period. For example, if the rendering time of a certain delay period reaches 20 ms, then the maximum delay point of this period is 20 ms. By comparing all delay periods, finally, a maximum delay point data is obtained, in milliseconds. This maximum delay point data reflects the delay bottleneck under the system load limit during the entire rendering process, involving bottleneck factors such as the resource allocation of the graphics processing unit and the video memory bandwidth. Finally, the maximum delay point data is output and used as the basis for further optimization.

[0185] Step S336: Calculate the rendering delay from the maximum delay point data to obtain the rendering delay data.

[0186] In this embodiment, the rendering delay refers to the situation during the graphics processing process where, due to load fluctuations or resource competition, the rendering time of some frames is significantly longer than the normal range, resulting in a decline in rendering performance. When calculating the rendering delay, by comparing the maximum delay point with the normal rendering period (such as 16 ms), the rendering delay time is obtained. The rendering delay calculation formula is: Rendering delay = Maximum delay point time - Standard rendering period time (such as 16 ms). When the rendering delay time is greater than the threshold, it is marked as an abnormal delay, indicating that a significant performance bottleneck has occurred during the rendering of this frame. The final rendering delay data will reflect the performance decline caused by the maximum delay point during the entire rendering process, and this data is used as the basis for further optimizing decisions such as GPU load allocation and resource scheduling.

[0187] Optionally, step S4 is specifically as follows:

[0188] Step S41: Extract the characteristics of the collision event based on the collision detection data, so as to obtain the collision event data;

[0189] In this embodiment, the data output by the collision detection system is obtained. These data record the detailed information of each collision event, including the identification of the colliding objects, the collision time, the collision location, and the intensity of the collision, etc. The collision detection monitors and records the collision process between objects in real time through a physics engine (such as Havok, PhysX, etc.). When extracting features, information such as the type of colliding objects, the timestamp of the collision occurrence, the relative velocity, and the collision angle needs to be extracted from each collision event. These information are obtained by parsing the output log or data stream of the collision detection system. Then, analyze the time interval and collision intensity of each collision event to judge the significance of the collision. This data is stored in a structured manner. For example, each collision event is used as a record, containing fields such as collision time, position, velocity, and angle. The collision event data provides a basis for subsequent particle generation and motion path prediction.

[0190] Step S42: Extract the particle generation parameters from the collision event data, so as to obtain the particle generation parameters;

[0191] In this embodiment, parameters related to particle generation are extracted from the collision event data, such as the collision position, collision intensity, collision angle, and surface characteristics of the object, etc. These parameters directly affect the particle generation method. First, determine the emission point position of the particle through the position coordinates in the collision event data. This position is the specific spatial position where the collision occurs, usually represented by (x, y, z) in a three-dimensional coordinate system. Secondly, calculate the emission velocity and direction of the particle through the collision intensity (i.e., the velocity, energy, etc. of the collision). The greater the collision intensity, the higher the initial velocity and the number of particles. The emission angle of the particle is calculated through the collision angle parameter, that is, the emission direction of the particle is determined according to the angle between the object surface and the collision direction. Finally, the material of the object surface (such as smooth or rough) also needs to be considered, which will affect the form and life cycle of the particle. By extracting these parameters from the collision event data, the preliminary settings for particle generation are obtained.

[0192] Step S43: Calculate the particle emission position according to the particle generation parameters, so as to obtain the particle emission position data;

[0193] In this embodiment, first, the occurrence position of the collision event is extracted from the particle generation parameters, that is, the (x, y, z) coordinates. Then, according to the specific parameters of the collision event, such as the surface characteristics of the colliding object, the collision angle, etc., the emission region of the particles is determined. For an object with a relatively smooth surface, the emission positions of the particles will be slightly scattered near the collision point; while for a rough surface, the distribution region of the particle emission will be wider. In addition, the small offset of the emission position (such as ±1 mm) will be adjusted according to the surface normal and the collision angle to ensure the accuracy and naturalness of the particle emission. The emission position of each particle will be clearly marked in the three-dimensional coordinate system, and the particle generation data is stored based on these calculated three-dimensional positions. The particle emission position data will provide a preliminary position basis for predicting the movement path of subsequent particles.

[0194] Step S44: Predict the movement path of the particles based on the particle emission position data, so as to obtain the particle movement path data;

[0195] In this embodiment, based on the emission position data and parameters such as the initial velocity and direction of the particles, the preliminary movement trajectory of the particles is predicted. The calculation of the movement path is based on the simulation of the physics engine, considering the influence of factors such as gravity and air resistance. When calculating, Newton's laws of motion are used to simulate the movement process of the particles starting from the emission point. The movement path of the particles usually consists of several key frames, and each key frame records the position and velocity of the particles. The calculation of the path needs to be dynamically simulated according to factors such as the initial velocity, emission angle, and physical environment (such as gravity, resistance) of the particles, and finally the movement trajectory of the particles is obtained. During this process, the simulation accuracy is determined by the time step, usually set to update once per frame calculation, and the accuracy can be adjusted to 60 frames per second or higher. The particle movement path data finally presents as a path sequence composed of multiple time points, recording the particle coordinates and velocity data at each moment.

[0196] Step S45: Render the particle effects based on the particle movement path data, so as to obtain the particle generation data;

[0197] In this embodiment, the particle effect rendering generates the visual effect of each particle by calculating the position and movement trajectory of each particle on the time axis. First, a graphics rendering engine (such as Unity3D, Unreal Engine, etc.) is used to perform real-time rendering according to the movement path, speed, position of the particles, and the appearance characteristics of the particles (such as color, size, transparency, etc.). Each particle is rendered frame by frame according to its movement path, and the rendered content includes changes in attributes such as the shape, color, and size of the particle, and these attributes gradually change over the life cycle of the particle. During the rendering process, the transparency of the particle gradually weakens over time, and the color and brightness of the particle change according to the position and angle of the light source. The particle generation data ultimately includes a series of rendering attributes such as the position, speed, color, transparency, and life cycle of the particle, and this data is passed to the rendering engine for display.

[0198] Step S46: Optimize the particle cache for the real-time rendering performance data of the anime game development display according to the particle generation data, so as to obtain the particle cache optimization data.

[0199] In this embodiment, by monitoring the performance metrics of the rendering engine (such as frame rate, GPU usage, memory occupancy, etc.), the performance data of the real-time rendering is obtained. These performance data are used to evaluate the impact of the current particle rendering on the system performance. The goal of particle cache optimization is to reduce the memory pressure generated by particles during the rendering process and optimize the use of GPU rendering resources. First, calculate the size requirement of the particle cache according to the information such as the number of particles, life cycle, and storage requirements in the particle generation data. Then, reduce the memory occupancy by optimizing the particle generation method (such as merging similar particles, reducing the number of particles generated at the same time, etc.). The specific methods for particle cache optimization include dynamically loading and destroying particles, restricting the maximum number of particles on the screen at the same time, and using techniques such as particle pools. Finally, the particle cache optimization data includes metrics such as the optimized number of particles and memory usage for subsequent rendering performance adjustment.

[0200] Optionally, step S44 is specifically:

[0201] Step S441: Analyze the initial velocity of the particles according to the particle emission position data, so as to obtain the sub-initial velocity data;

[0202] In this embodiment, the calculation of the initial velocity is based on the intensity and angle parameters of the collision event and the characteristics of the emission position. First, the energy of the collision and the velocity information of the collision point are extracted from the collision detection data. This data can be calculated through the normal direction of the object surface at the time of collision and the momentum of the colliding object. The initial velocity of the particle is determined according to the proportion of the momentum transferred from the colliding object to the particle. The particle emission velocity is weighted according to the collision intensity. For example, the greater the collision intensity, the greater the initial velocity of the particle. The specific value is calculated by the following formula: Initial velocity of particle = Collision intensity × Momentum transfer coefficient. The momentum transfer coefficient is set based on the mass ratio of the particle and the colliding object, usually between 0.1 and 0.5. This data provides the initial parameters for calculating the particle acceleration and the subsequent motion path.

[0203] Step S442: Calculate the particle acceleration based on the particle initial velocity data to obtain the particle acceleration data;

[0204] In this embodiment, the acceleration of the particle can be calculated by combining the initial velocity of the particle with external forces (such as gravity, air resistance, etc.). First, the initial velocity data of the particle is combined with the direction of the external force to calculate the acceleration of each particle at a specific time point. The calculation of the acceleration involves the relationship between the mass of the particle and the acting force, and the formula is: Acceleration = Force / Mass. According to the initial velocity of the particle and the settings of the physical environment (such as gravitational acceleration g = 9.8m / s 2 , air resistance coefficient, etc.), the acceleration of the particle at each moment after emission is calculated. The acceleration data of the particle is gradually calculated and stored in a time series for subsequent motion equation modeling. The particle acceleration data will change with time and is usually updated at the time interval of each frame. The time interval is set to 0.016 seconds (60Hz frame rate). The particle acceleration data obtained in this way can accurately reflect the motion changes of the particle.

[0205] Step S443: Model the particle motion equation based on the particle acceleration data to obtain the particle motion equation data;

[0206] In this embodiment, the motion equation of the particle is established based on Newton's laws of motion and the known acceleration data. First, according to the particle acceleration data and the known time interval, integral methods (such as Euler's method or Runge - Kutta method) are used to deduce the velocity and position of the particle at a certain moment. Assuming that the velocity and position of the particle at the initial moment are known, the motion of the particle in the subsequent time interval can be calculated by the following formulas:

[0207] Velocity = Initial velocity + Acceleration × Time interval;

[0208] Position = Initial position + Velocity × Time interval;

[0209] When the model is calculating, the position and velocity are updated at each time point, and the change in acceleration is calculated after each update. To ensure accuracy, a small time interval (such as 0.01 seconds) is usually selected for simulation. The particle motion equation not only considers the influence of factors such as gravity and air resistance, but also makes dynamic adjustments according to conditions such as the friction and elasticity of the object surface to accurately simulate the real motion behavior of the particles. The particle motion equation data will include the velocity and position of each particle at each time point, forming a dynamically updated equation system.

[0210] Step S444: Perform particle path prediction calculation on the particle motion equation data to obtain particle path prediction data;

[0211] In this embodiment, information such as velocity, position, and acceleration obtained from the particle motion equation is used to perform path prediction in combination with a physics engine (such as PhysX, Havok). First, according to the initial position and initial velocity of each particle, the position of the particle is gradually predicted at a time interval (such as 0.016 seconds). At each moment, the position at the next moment is calculated through the motion equation data of the particle at the previous moment. The path prediction not only depends on the initial conditions of the particle, but also considers external environmental factors such as wind force, air pressure, and gravity. The prediction of the particle path uses numerical integration methods to gradually calculate the predicted paths of each particle at multiple time points. These paths are usually adjusted by the particle emission direction and velocity to ensure that the particles move along the expected path. The particle path prediction data is finally presented as a continuous coordinate sequence, and the path information of each particle includes multiple sets of continuous three-dimensional coordinate points.

[0212] Step S445: Visualize the particle motion trajectory based on the particle path prediction data to obtain particle motion path data.

[0213] In this embodiment, the path data of particles is converted into a visual effect through a graphics rendering engine (such as Unity3D, Unreal Engine, etc.). According to the predicted particle path data, the three-dimensional coordinates of each particle at each time point are converted into particle position data in the rendering engine. The movement trajectory of each particle is displayed in the form of a series of points or line segments. To enhance the visual effect, during the rendering process, attributes such as the color, size, and transparency of the particles are adjusted over time to simulate the movement of particles in the real world. The visualization process uses real-time rendering technology to ensure that each particle can smoothly transition during movement, avoiding jerks or unnatural displays. The particle movement trajectory can be displayed as a connection of the particle path, an animation of the particle movement, or a dynamic effect display based on the state of the particle (such as speed change, acceleration, etc.). The finally obtained particle movement path data includes information such as the trajectory, speed, and acceleration of the particles, and these data can be used for further rendering and effect optimization in a game or simulation environment.

[0214] Optionally, this specification also provides an operating system for anime game development, which is used to execute an operating method for anime game development as described above. The operating system for anime game development includes:

[0215] A geometric structure analysis module, which is used to obtain the modeling data of an anime game character and perform geometric structure analysis based on the modeling data of the anime game character, so as to obtain the geometric structure data of the anime game character;

[0216] A collision detection analysis module, which is used to extract the physical material characteristics of an anime game character based on the modeling data of the anime game character, so as to obtain the physical material data of the anime game character; perform collision detection analysis based on the physical material data of the anime game character and the geometric structure data of the anime game character, so as to obtain collision detection data;

[0217] A real-time rendering performance analysis module, which is used to obtain the display data of anime game development and perform real-time rendering performance analysis based on the display data of anime game development, so as to obtain the real-time rendering performance data of the display of anime game development;

[0218] A particle cache optimization module, which is used to perform particle generation analysis based on the collision detection data, so as to obtain particle generation data; perform particle cache optimization on the real-time rendering performance data of the display of anime game development according to the particle generation, so as to obtain particle cache optimization data;

[0219] An anime game development operation module, which is used to perform dynamic particle rendering of anime game development based on the particle cache optimization data, so as to obtain dynamic particle rendering data of anime game development, and perform anime game operation based on the dynamic particle rendering data of anime game development, so as to obtain operation data of anime game development.

[0220] An operating system for anime game development according to the present invention, which can implement any one of the operating methods for anime game development of the present invention, and is a medium for coordinating operations and signal transmissions between various modules to complete the operating method for anime game development. The internal modules of the system cooperate with each other, thereby improving the operating efficiency and rendering effect of anime games.

[0221] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0222] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An operating method for developing animation games, characterized in that: The following steps are involved: Step S1: Obtaining animation game character modeling data, and performing geometric structure analysis based on the animation game character modeling data, thereby obtaining animation game character geometric structure data; Step S2: extracting physical material features of the animation game character according to the animation game character modeling data, thereby obtaining physical material data of the animation game character; performing collision detection analysis according to the physical material data of the animation game character and the geometric structure data of the animation game character, thereby obtaining collision detection data; Step S3: acquiring animation game development display data, and performing real-time rendering performance analysis based on the animation game development display data, thereby obtaining animation game development display real-time rendering performance data; Step S4: performing particle generation analysis according to the collision detection data, thereby obtaining particle generation data; performing particle cache optimization on the animation game development display real-time rendering performance data according to the particle generation data, thereby obtaining particle cache optimization data; Step S5: Perform dynamic particle rendering for animation game development according to the particle cache optimization data, thereby obtaining dynamic particle rendering data for animation game development, and run the animation game according to the dynamic particle rendering data for animation game development, thereby obtaining animation game development running data.

2. The operation method for developing animation games according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring animation game character modeling data, and performing geometric feature extraction based on the animation game character modeling data, thereby obtaining geometric feature data; Step S12: performing character morphology recognition according to the geometric feature data, thereby obtaining character morphology data; Step S13: constructing a contour curve according to the character morphology data, thereby obtaining character contour curve data; Step S14: performing local detail modeling according to the character contour curve data, thereby obtaining local detail data; Step S15: meshing the local detail data to obtain meshed surface data; Step S16: performing normal calculation on the gridded surface data to obtain normal data; Step S17: Perform geometric structure analysis of the animation game character based on the normal data, thereby obtaining geometric structure data of the animation game character.

3. The operation method for developing animation games according to claim 2, characterized in that: Step S17 is specifically as follows: Step S171: extracting curvature features according to normal data to obtain curvature data; Step S172: identifying the surface concave area according to the curvature data, thereby obtaining the surface concave area data; Step S173: identifying the surface convex area according to the curvature data, thereby obtaining the surface convex area data; Step S174: performing texture mapping on the surface concave area data, thereby obtaining surface concave area texture mapping data; Step S175: enhancing the illumination effect of the surface convex area data, thereby obtaining the surface convex area illumination effect data; Step S176: performing surface fusion of the animation game character according to the surface concave area texture mapping data and the surface convex area lighting effect data, thereby obtaining the surface data of the animation game character; Step S177: smoothing the surface data of the cartoon game character, thereby obtaining smooth surface data of the cartoon game character; Step S178: Perform geometric structure balance according to the surface smoothness data of the animation game character, so as to obtain the geometric structure data of the animation game character.

4. The operation method for developing animation games according to claim 1, characterized in that: Step S12 is specifically as follows: Step S21: extracting physical material features of the animation game character according to the animation game character modeling data, thereby obtaining physical material data of the animation game character; Step S22: constructing a collision model according to the physical material data and the geometric structure data of the animation game character, thereby obtaining a collision model; Step S23: Acquire animation game character motion data and animation game environment data; Step S24: performing collision detection simulation on the collision model according to the animation game character motion data and the animation game environment data, thereby obtaining collision detection simulation data; Step S25: calculating the collision response time of the collision detection simulation data, thereby obtaining collision response time data; Step S26: performing character collision penetration correction according to the collision response time data, thereby obtaining character collision penetration correction data; Step S27: performing collision response smoothing on the collision detection simulation data, thereby obtaining collision response smoothing data; Step S28: performing collision detection integration according to the collision response smoothing data and the character collision penetration correction data, thereby obtaining collision detection data.

5. The operation method for developing animation games according to claim 4, characterized in that: Step S26 is specifically as follows: Step S261: performing collision point time series analysis according to the collision response time data, thereby obtaining collision point time series data; Step S262: tracking the character collision path according to the collision point time series data, thereby obtaining the character collision path data; Step S263: performing collision force field distribution calculation on the character collision path data, thereby obtaining collision force field distribution data; Step S264: performing collision point displacement vector correction on the collision force field distribution data, thereby obtaining collision point correction vector data; Step S265: adjusting the character collision penetration range according to the collision point correction vector data, thereby obtaining the character collision penetration correction data.

6. The operation method for developing animation games according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: acquiring animation game development display data, and performing frame rate sampling according to the animation game development display data, thereby obtaining frame rate sampling data; Step S32: performing a graphics processing unit load evaluation according to the frame rate sampling data, thereby obtaining graphics processing unit load data; Step S33: performing rendering delay analysis according to the GPU load data, thereby obtaining rendering delay data; Step S34: dynamically evaluating the real-time light and shadow effect on the rendering delay data, thereby obtaining real-time light and shadow effect data; Step S35: Optimize rendering resources according to the real-time light and shadow effect data, so as to obtain real-time rendering performance data for animation game development and display.

7. The operation method for developing animation games according to claim 6, characterized in that: Step S33 is specifically as follows: Step S331: extracting GPU processing frame features according to the graphics processing unit load data, thereby obtaining GPU processing frame data; Step S332: performing rendering time statistics on the frame data processed by the GPU, thereby obtaining frame rendering time data; Step S333: Calculate the rendering cycle according to the frame rendering time data, so as to obtain rendering cycle data; Step S334: performing delay cycle identification according to the rendering cycle data, thereby obtaining rendering delay cycle data; Step S335: identifying the maximum delay point of the rendering delay period data, thereby obtaining the maximum delay point data; Step S336: Calculate the rendering delay of the maximum delay point data to obtain rendering delay data.

8. The operation method for developing animation games according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: extracting collision event features according to the collision detection data, thereby obtaining collision event data; Step S42: extracting particle generation parameters from the collision event data to obtain particle generation parameters; Step S43: Calculating the particle emission position according to the particle generation parameters, thereby obtaining particle emission position data; Step S44: predicting the particle movement path according to the particle emission position data, thereby obtaining the particle movement path data; Step S45: performing particle effect rendering according to the particle motion path data, thereby obtaining particle generation data; Step S46: performing particle cache optimization on the animation game development display real-time rendering performance data according to the particle generation data, thereby obtaining particle cache optimization data.

9. The operation method for developing animation games according to claim 8, characterized in that: Step S44 is specifically as follows: Step S441: performing particle initial velocity analysis according to particle emission position data, thereby obtaining sub-initial velocity data; Step S442: performing particle acceleration calculation on the particle initial velocity data, thereby obtaining particle acceleration data; Step S443: Modeling the particle motion equation according to the particle acceleration data, thereby obtaining the particle motion equation data; Step S444: performing particle path prediction calculation on the particle motion equation data, thereby obtaining particle path prediction data; Step S445: Visualize the particle motion trajectory according to the particle path prediction data, so as to obtain the particle motion path data.

10. A running system for animation game development, characterized in that: Used to execute the operation method for animation game development as claimed in claim 1, the operation system for animation game development comprises: A geometric structure analysis module is used to obtain animation game character modeling data, and perform geometric structure analysis based on the animation game character modeling data, so as to obtain the animation game character geometric structure data; The collision detection and analysis module is used to extract the physical material features of the animation and game characters according to the animation and game character modeling data, so as to obtain the physical material data of the animation and game characters; perform collision detection and analysis according to the physical material data of the animation and game characters and the geometric structure data of the animation and game characters, so as to obtain collision detection data; A real-time rendering performance analysis module is used to obtain animation game development display data, and perform real-time rendering performance analysis based on the animation game development display data, thereby obtaining animation game development display real-time rendering performance data; The particle cache optimization module is used to perform particle generation analysis based on collision detection data to obtain particle generation data; based on particle generation, particle cache optimization is performed on the real-time rendering performance data of animation game development display to obtain particle cache optimization data; The animation game development and operation module is used to perform dynamic particle rendering of animation game development according to particle cache optimization data, thereby obtaining dynamic particle rendering data of animation game development, and to run the animation game according to the dynamic particle rendering data of animation game development, thereby obtaining animation game development and operation data.

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