A first-person perspective respiration simulation method, system, device and storage medium

Abstract

This invention proposes a first-person perspective breathing simulation method, system, device, and storage medium, relating to the field of game graphics technology. The method includes: initializing a parent node and child nodes of a first-person character; generating corresponding fractal noise random forces for the parent node and child nodes; configuring mechanical parameters for the parent node and child nodes based on the character's motion state; calculating the rotation and displacement of the parent node based on the fractal noise random forces, stiffness, and damping corresponding to the parent node; calculating the rotation and displacement of the child node based on the fractal noise random forces, stiffness, and damping corresponding to the child node and the rotation and displacement of the parent node; and generating a first-person perspective breathing scene based on the rotation and displacement of the child node. By performing mechanical calculations on the child nodes based on the rotation and displacement of the parent node, the animation of the chest cavity and the perspective is superimposed, eliminating abrupt changes and jitter, making the screen smooth and continuous, and improving immersion and visual experience.

Description

Technical Field

[0001] This invention relates to the field of game graphics technology, and more specifically, to a first-person perspective breathing simulation method, system, device, and storage medium. Background Technology

[0002] With the continuous evolution of 3D game engine technology, first-person shooter (FPS) has been established as the core presentation paradigm for action shooting and adventure exploration games. To meet players' deep demand for immersive experiences, character breathing animation, as a key visual element to maintain game immersion, is typically implemented using pre-rendered skeletal animation sequences to drive the camera to perform periodic movements.

[0003] Traditional animation solutions use pre-generated character breathing animation sequences to drive camera movement, achieving breathing simulation in the game's first-person perspective. For example, pre-made and stored animations are played in chronological order during game runtime to simulate the character breathing.

[0004] However, since the breathing animation and the camera shake animation are played separately, misalignment or sudden changes can easily occur, causing screen shake and affecting immersion and visual experience. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a first-person perspective breathing simulation method, system, device and storage medium. The method uses the rotation and displacement of the parent node as the target position of the child node, and calculates the rotation and displacement of the child node by combining the stiffness, damping and fractal noise random force of the child node itself. This allows the child node to perform its own mechanical simulation calculation based on the target position of the parent node, thereby achieving the superposition of breathing animation and perspective animation effects, eliminating random changes and jitter of noise force, ensuring smooth and continuous breathing screen, and thus improving immersion and visual experience.

[0006] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for determining the accuracy of battery cell installation. The method includes: initializing a parent node and a child node of a first-person character, wherein the parent node represents the character's chest cavity and the child node represents the character's viewpoint; generating corresponding fractal noise random forces for the parent node and the child node, wherein the fractal noise random forces are used to drive simulated breathing movements of the parent node and the child node; configuring mechanical parameters for the parent node and the child node based on the character's movement state, wherein the mechanical parameters include stiffness and damping; calculating the rotation and displacement of the parent node based on the fractal noise random forces, stiffness, and damping corresponding to the parent node; using the rotation and displacement of the parent node as the target position of the child node's movement, and calculating the rotation and displacement of the child node based on the fractal noise random forces, stiffness, damping, and the target position; and generating a first-person view breathing scene based on the rotation and displacement of the child node.

[0007] In this embodiment, continuous fractal noise random forces are generated for the parent node (character's chest cavity) and child node (character's viewpoint) to drive the parent and child nodes to simulate the character's breathing motion. Then, stiffness and damping are assigned to the parent and child nodes according to the character's motion state, so that the rotation and displacement of the parent node can be calculated first from the stiffness and damping of the parent node and the fractal noise random forces. Then, the rotation and displacement of the parent node are used as the target position of the child node, so that the rotation and displacement of the child node can be calculated based on the child node's own stiffness, damping and fractal noise random forces at the target position. This allows the child node to perform its own mechanical simulation calculation based on the rotation and displacement of the parent node, realizing the superposition of chest cavity breathing animation and viewpoint animation, eliminating random abrupt changes and jitter, ensuring that the entire breathing scene is smooth and continuous, thereby improving immersion and visual experience.

[0008] In some embodiments, generating corresponding fractal noise random forces for the parent node and the child node includes: generating original fractal noise signals for the parent node and the child node based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node; and modulating the original fractal noise signals with noise force intensity parameters to generate fractal noise random forces.

[0009] This setup ensures that the breathing disturbances of parent and child nodes are independent by assigning independent random initial values ​​to different nodes, thus avoiding the mechanical feeling caused by motion synchronization. Furthermore, by combining the noise force intensity parameter to adjust the amplitude of the original fractal noise signal, precise control over breathing irregularities can be achieved, thereby improving the realism and performance flexibility of the simulation.

[0010] In some embodiments, generating original fractal noise signals for the parent node and the child node based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node includes: generating a basic fractal noise signal corresponding to the parent node based on the game timestamp and the random starting value corresponding to the parent node, and smoothing the basic fractal noise signal corresponding to the parent node to obtain the original fractal noise signal corresponding to the parent node; generating a basic fractal noise signal corresponding to the child node based on the game timestamp and the random starting value corresponding to the child node, and smoothing the basic fractal noise signal corresponding to the child node to obtain the original fractal noise signal corresponding to the child node.

[0011] This setup, by performing time-domain filtering on the basic fractal noise signal, eliminates potential minute abrupt changes between frames, fundamentally preventing mechanical response distortion caused by discontinuous input force, ensuring the stability and continuity of the movement of parent and child nodes, and significantly improving the visual comfort and realism of the first-person perspective breathing scene.

[0012] In some embodiments, calculating the rotation and displacement of the parent node based on the fractal noise random force, stiffness, and damping corresponding to the parent node includes: calculating the damping ratio of the parent node based on the stiffness and damping corresponding to the parent node; comparing the damping ratio with a preset value to determine the damping state of the character's chest cavity; determining a corresponding first preset mathematical model based on the damping state of the character's chest cavity; and inputting the fractal noise random force, stiffness, and damping corresponding to the parent node into the first preset mathematical model to calculate the rotation and displacement of the parent node.

[0013] This setup, through the damping ratio discrimination mechanism, selects a suitable mathematical model, ensuring that the parent node can generate a breathing motion trajectory that conforms to physical laws under any combination of stiffness and damping parameters. This effectively eliminates motion abrupt changes or oscillation distortions caused by model mismatch, and significantly improves simulation accuracy, stability, and visual coherence.

[0014] In some embodiments, calculating the rotation and displacement of the sub-node based on the fractal noise random force, stiffness, damping, and target position corresponding to the sub-node includes: calculating the damping ratio of the sub-node according to the stiffness and damping corresponding to the sub-node; comparing the damping ratio of the sub-node with a preset value to determine the damping state of the character's perspective; determining a corresponding second preset mathematical model according to the damping state of the character's perspective; and inputting the fractal noise random force, stiffness, damping, and target position corresponding to the sub-node into the second preset mathematical model to calculate the rotation and displacement of the sub-node.

[0015] This setup, through the damping ratio discrimination mechanism, selects the appropriate mathematical model to ensure that the child nodes can still maintain physically accurate response behavior under the guidance of dynamic targets; combined with fractal noise random force to achieve the natural performance of the viewpoint tremor, it not only inherits the macroscopic breathing rhythm of the parent node, but also superimposes independent high-frequency detail motion, significantly improving the realism and immersion of the first-person view.

[0016] In some embodiments, determining the corresponding second preset mathematical model based on the damping state of the character's perspective, and inputting the fractal noise random force, stiffness, damping, and target position of the child node into the second preset mathematical model to calculate the rotation and displacement of the child node includes: calculating the offset and rotation of the child node relative to the parent node based on the target position; determining the corresponding second preset mathematical model based on the damping state of the character's perspective, and inputting the fractal noise random force, stiffness, damping, offset, and rotation of the child node into the second preset mathematical model to calculate the rotation and displacement of the child node.

[0017] This setup, by introducing the offset and rotation of child nodes relative to parent nodes as modeling inputs, ensures that the dynamic response of the first-person perspective is consistent with the position and orientation of the character's chest cavity. This effectively avoids abnormal shaking or visual distortion caused by ignoring the initial assembly relationship, significantly improving the spatial rationality and visual realism of camera movement.

[0018] In some embodiments, configuring mechanical parameters for the parent node and the child node based on the character's motion state includes: obtaining the character's current motion state; determining the corresponding target breathing intensity level based on the motion state, and generating a target parameter adjustment signal corresponding to the target breathing intensity level based on a preset mapping relationship between breathing intensity levels and parameter adjustment signals; and configuring mechanical parameters for the parent node and the child node based on the target parameter adjustment signal.

[0019] This setup, by establishing a hierarchical mapping mechanism from the character's motion state to mechanical parameters, enables real-time adaptive adjustment of breathing frequency, rhythm, and irregularity. This allows the breathing performance from the first-person perspective to accurately reflect the character's real-time physiological condition, significantly enhancing the realism and immersion of the interaction, while avoiding the abrupt transitions that occur during traditional animation switching.

[0020] Secondly, embodiments of the present invention provide a first-person perspective breathing simulation system, the system comprising: a configuration module, configured to initialize a parent node and a child node of a first-person character, wherein the parent node represents the character's chest cavity and the child node represents the character's perspective; generate corresponding fractal noise random forces for the parent node and the child node, the fractal noise random forces being used to drive the simulated breathing movements of the parent node and the child node; configure mechanical parameters for the parent node and the child node based on the character's movement state, the mechanical parameters including stiffness and damping; a processing module, configured to calculate the rotation and displacement of the parent node based on the fractal noise random forces, stiffness, and damping corresponding to the parent node; use the rotation and displacement of the parent node as the target position of the child node's movement, and calculate the rotation and displacement of the child node based on the fractal noise random forces, stiffness, damping corresponding to the child node and the target position; and a screen generation module, configured to generate a first-person perspective breathing screen based on the rotation and displacement of the child node.

[0021] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the first-person perspective breathing simulation method as described in the first aspect.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the first-person perspective breathing simulation method as described in the first aspect.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a first-person perspective breathing simulation method provided in an embodiment of the present invention; Figure 2 for Figure 1 Flowchart of sub-steps S201~S202 of step S200; Figure 3 for Figure 1 Flowchart of sub-steps S301~S303 in step S300; Figure 4 for Figure 1 Flowcharts of sub-steps S401~S403 in step S400; Figure 5 for Figure 1 Flowchart of sub-steps S501~S503 of step S500; Figure 6 A schematic diagram of the functional modules of a first-person perspective breathing simulation system provided in an embodiment of the present invention; Figure 7 A block diagram of an electronic device provided in an embodiment of the present invention.

[0026] Icons: 1000 - First-person perspective breathing simulation system; 1100 - Configuration module; 1200 - Processing module; 1300 - Screen generation module; 2000 - Electronic device; 2100 - Processor; 2200 - Memory; 2300 - Bus; 2400 - Communication interface. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0030] The following is a brief introduction to some concepts that may be involved in the embodiments of the present invention.

[0031] Spring stiffness: Controls the strength or stiffness of a spring system. The higher the value, the faster the system response and the higher the simulated breathing frequency.

[0032] Spring damping: controls the rate at which energy dissipates in a spring system, affecting the ratio of exhalation to inhalation time.

[0033] Noise force: A parameter that controls the degree of irregularity or urgency of breathing. The higher the value, the faster and more unstable the breathing; the lower the value, the smoother and milder the breathing.

[0034] As described in the background section, traditional animation solutions simulate breathing in a game's first-person perspective by pre-generating a sequence of character breathing animations to drive camera movement. For example, pre-made and stored animations are played sequentially during gameplay to simulate a character breathing. However, because the breathing animation and camera movement animation are played separately, misalignment or abrupt changes can easily occur, resulting in screen shake and affecting immersion and visual experience.

[0035] To address this, this invention provides a first-person perspective breathing simulation method. It uses the rotation and displacement of the parent node as the target position of the child node, and calculates the child node's rotation and displacement by combining the child node's own stiffness, damping, and fractal noise random forces. This allows the child node to perform its own mechanical simulation calculations based on the parent node's target position, achieving a superposition of breathing animation and perspective animation (camera animation). This eliminates random abrupt changes and jitter in noise forces, ensuring a smooth and continuous breathing scene, thereby improving immersion and visual experience. (See also...) Figure 1 , Figure 1 A flowchart of a first-person perspective breathing simulation method is provided in this embodiment of the invention. The first-person perspective breathing simulation method includes steps S100~S600: S100. Initialize the parent and child nodes of the first-person character. The parent node represents the character's chest cavity, and the child node represents the character's perspective.

[0036] In this embodiment, the parent and child nodes of the first-person character are initialized to construct the hierarchical structural foundation for the respiratory dynamics simulation. The parent node corresponds to the chest cavity of the character model, serving as the source of the main respiratory motion; the child nodes correspond to the first-person view camera of the character, used to present the final visual effect. Each node is defined as an independent mechanical model structure containing state parameters such as position, velocity, target position, stiffness, and damping. Specifically, during initialization, initial position and velocity values ​​are assigned to each node, typically set to zero or the resting equilibrium point, and independent stiffness and damping parameters are set to reflect the differences in motion characteristics of different physiological parts. For example, the stiffness of the character node is configured to 12.0, and the damping to 4.5, corresponding to a lower frequency and a slower recovery rhythm; the stiffness of the camera node is configured to 35.0, and the damping to 8.0, making its response faster and its oscillations more subtle. In addition, a random starting value (i.e., seed) required for fractal noise generation is configured for each node to ensure that its noise sequence is independent and reproducible.

[0037] S200 generates corresponding fractal noise random forces for the parent and child nodes. The fractal noise random forces are used to drive the simulated breathing motion of the parent and child nodes.

[0038] In this embodiment, the generation of fractal noise random force is based on a global game timestamp and a unique random starting value for each node. Specifically, a gradient noise algorithm is first used to construct a composite noise signal by superimposing multiple noise layers with different frequencies and amplitudes. This process uses 4 to 8 layers, with the frequency of each layer increasing by a gap factor (usually 2.0) and the amplitude decreasing stepwise by a persistence factor (usually 0.5), forming a continuous random scalar signal F_noise with natural texture characteristics. It should be noted that the fractal noise described here is a spatially smooth but non-periodic random function, suitable for simulating the irregular rhythms of organisms.

[0039] S300: Configure mechanical parameters for parent and child nodes based on the character's motion state. The mechanical parameters include stiffness and damping.

[0040] In this embodiment, the mechanical parameters are not fixed but dynamically adjusted according to the character's current behavioral state. Specifically, the character's motion state information is acquired in real time, such as whether they are stationary, walking, running, or injured, and the corresponding breathing intensity level is determined accordingly. It should be noted that the breathing frequency is proportional to the square root of the stiffness, so increasing the stiffness directly accelerates the breathing rhythm; at the same time, adjusting the damping coefficient can affect the time ratio of exhalation to inhalation, while increasing the noise intensity enhances the irregularity of breathing. For example, when the character transitions from a stationary state to a running state, the stiffness of the parent node is gradually increased from 12.0 to 18.0 according to the character's velocity coefficient using linear or curvilinear interpolation, while the noise intensity is increased from 1.0 to 1.5, thereby simulating a rapid and drastically fluctuating physiological response to breathing. These parameter adjustments can be adaptively completed by the program or manually modified through the debugging interface, providing highly flexible control capabilities. It can be understood that this mechanism achieves the technical advantage of dynamically changing breathing performance without recreating animation resources.

[0041] S400. Calculate the rotation and displacement of the parent node based on the random force, stiffness and damping of the fractal noise corresponding to the parent node.

[0042] In this embodiment, the displacement and rotation calculations of the parent node rely on a three-mode damping mathematical solution model. Specifically, the damping ratio ζ of the parent node is first calculated using the formula ζ = damping / (2 × √stiffness) to determine its physical state. Then, based on the damping state of the parent node, the mathematical model corresponding to that state is used for further calculation. For example, fractal noise random force is introduced as an external force term into the dynamic equation of the above mathematical model, affecting the dynamic evolution of the target position (goal), thus causing the motion trajectory to exhibit natural wave characteristics. Therefore, this three-mode solution mechanism can automatically select the optimal mathematical model based on different parameter combinations, ensuring motion continuity and physical rationality.

[0043] S500: The rotation and displacement of the parent node are used as the target position of the child node's motion. The rotation and displacement of the child node are calculated based on the fractal noise random force, stiffness, damping and target position corresponding to the child node.

[0044] In this embodiment, child nodes and parent nodes are fused hierarchically. Specifically, the rotation and displacement results output by the parent node after completing the calculation for this frame no longer only apply to the character model itself, but are passed to the child node as its new target position. It should be noted that the target position mentioned here is a reference benchmark in the child node's mechanical model, representing the spatial posture it is expected to approach. Within its own mechanical framework, the child node, with its independent stiffness, damping parameters, and noise force input, combined with this dynamic target position, performs the three-mode damping solution process again. Furthermore, when calculating the motion response of the child node, it is also necessary to first calculate its own damping ratio ζ = damping / (2 × √stiffness), and select the corresponding mathematical model for solution based on the ζ value, the process being consistent with that of the parent node. For example, the camera node, due to its high stiffness (e.g., 35.0) and moderate damping (e.g., 8.0), has a damping ratio of approximately 0.676, which is underdamped, thus generating a high-frequency, small-amplitude oscillation response, superimposed with fine-grained jitter on the low-frequency fluctuations provided by the parent node. This demonstrates that the recursive overlay mechanism achieves a seamless integration of multiple breathing effects, maintaining overall harmony while enriching the expressive details.

[0045] S600 generates a first-person perspective breathing scene based on the rotation and displacement of child nodes.

[0046] In this embodiment, the final rotation and displacement data calculated by the child nodes are fed into the graphics rendering to update the transformation matrix of the first-person camera. Specifically, the displacement data is used to adjust the camera position offset, while the rotation data is used to fine-tune the gaze direction, thereby presenting a perspective shake effect with a natural breathing rhythm on the screen. Furthermore, all the aforementioned calculations are completed in real time in each frame update loop, without relying on any pre-baked animation sequences, significantly reducing memory usage and asset production costs. It can be understood that, because the entire system is built based on physical simulation and signal processing technology, its output has high continuity and dynamic adaptability, automatically presenting matching breathing behavior under different character states (such as resting, running, and injured), significantly improving immersion and development efficiency. Based on the above description, this method achieves a lightweight, adjustable, and jitter-free first-person breathing simulation technology solution.

[0047] In some embodiments, fractal noise random force is used to drive the simulated breathing movements of parent and child nodes, with the aim of simulating the irregularity and rapidity of breathing; therefore, see [reference needed]. Figure 2 , Figure 2 for Figure 1 The flowchart of sub-steps S201~S202 of step S200, wherein steps S201~S202 include: S201. Based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node, the original fractal noise signal is generated for the parent node and the child node respectively.

[0048] In this embodiment, the main focus is on generating independent and continuous basic random signals for both parent and child nodes to drive their respective "breathing" movements. During the execution of these steps, the global game timestamp is used as one of the input variables, combined with each node's unique random starting value (i.e., seed) to jointly affect the fractal noise algorithm, ensuring that the noise sequences between different nodes do not interfere with each other and are reproducible. Specifically, for the parent node, based on the current game timestamp and the corresponding random starting value, a set of multi-layered gradient noise signals is generated. The frequency of each layer of noise increases according to the gap factor, while the amplitude decreases according to the persistence factor. Typically, the gap factor is 2.0, the persistence factor is 0.5, and the number of layers is set to 4 to 8, ultimately forming a smooth, complex, and naturally characteristic basic fractal noise signal. Similarly, for the child nodes, based on the same game timestamp but using the child node's unique random starting value, another set of structurally similar but content-different basic fractal noise signals is independently generated. It should be noted that the original fractal noise signal mentioned here is the preliminary output without filtering and may still contain slight inter-frame jumps. Therefore, further smoothing processing is required to meet the continuity requirements of the physical simulation.

[0049] For example, a basic fractal noise signal corresponding to the parent node is generated based on the game timestamp and the random starting value corresponding to the parent node, and the basic fractal noise signal corresponding to the parent node is smoothed to obtain the original fractal noise signal corresponding to the parent node.

[0050] In this embodiment, a driving signal foundation with temporal continuity and spatial naturalness is constructed for the parent node. During the execution of the above steps, the current global game timestamp is used as a dynamic input variable, combined with the parent node's unique random starting value (i.e., seed) as input parameters for the fractal noise algorithm to generate the initial stage basic fractal noise signal. It should be noted that although the basic noise has good spatial continuity, there may still be slight numerical jumps between frames, which may cause motion discontinuity if used directly. Therefore, after generating the basic signal, it is further subjected to temporal smoothing processing. Specifically, an exponential smoothing moving average algorithm is used, and its calculation formula is F_smoothing current = a × F_noise current + (1 - a) × F_smoothing previous value, where a is a smoothing factor, with a value between 0 and 1, used to adjust the response speed and smoothness; when a is small, the historical value has a higher weight, and the output change is slower and more stable. The signal output after this filtering process is the original fractal noise signal corresponding to the parent node, which has high-order temporal continuity and is suitable as the input driving force source for the subsequent mechanical model.

[0051] For example, a basic fractal noise signal corresponding to a child node is generated based on the game timestamp and the random starting value corresponding to the child node, and the basic fractal noise signal corresponding to the child node is smoothed to obtain the original fractal noise signal corresponding to the child node.

[0052] In this embodiment, a unique original fractal noise signal is generated independently for each child node to ensure that it is decoupled from the noise output of the parent node. Specifically, the input parameters also include a global game timestamp and a unique random initial value for each child node. The latter is used to initialize the state of the noise generator, ensuring that even at the same time point, the noise sequence generated by the child node is different from that of the parent node. Based on this, the basic fractal noise signal for the child node is generated using the same fractal noise construction method, namely, by superimposing 4 to 8 layers of gradient noise with increasing frequency and decreasing amplitude. This signal retains the typical characteristics of fractal noise: long-term correlation, no obvious periodicity, and local randomness, making it suitable for simulating irregular physiological fluctuations. Furthermore, to eliminate potential inter-frame abrupt changes and ensure smooth motion, the basic signal undergoes the same time-domain smoothing process as the parent node. Specifically, a first-order exponential smoothing filter algorithm is applied, and the basic noise value of the current frame is weighted and fused with the smoothing result of the previous frame to obtain the final smoothed output. It should be noted that the original fractal noise signal mentioned here is an intermediate result before noise force intensity modulation. Its purpose is to provide a stable, continuous, and personalized random source for subsequent force signal generation. Thus, the parent node and child nodes each possess independently generated and smoothed noise channels, ensuring both the differentiated characteristics of their respective motions and jointly meeting the overall requirement for high continuity.

[0053] S202. Modulate the original fractal noise signal with noise force intensity parameters to generate fractal noise random force.

[0054] In this embodiment, the original fractal noise signal generated in the previous step is first subjected to time-domain smoothing filtering to eliminate potential inter-frame abrupt changes. Then, the smoothed noise signal is multiplied by a preset noise force intensity parameter to complete the final force signal modulation. It should be noted that the noise force intensity parameter mentioned here is used to control the "irregularity" or "rapidity" of the breathing motion; the larger the value, the more rapid and unstable the generated breathing; conversely, the more stable and mild the breathing. For example, in the character's resting state, the noise force intensity of the character's viewpoint node can be set to 0.5, while during vigorous movement, the noise force intensity of the character's chest cavity node can be increased to 1.5, thereby achieving dynamic response. Thus, this modulation process transforms the basic noise signal into an actual external force input that can be used to drive the mechanical model, preserving natural randomness while ensuring the stability and controllability of the motion.

[0055] In some embodiments, to facilitate real-time adjustment of parameters and dynamic changes in breathing effects under different character states, see [reference needed]. Figure 3 , Figure 3 for Figure 1 The flowchart of sub-steps S301~S303 of step S300, wherein steps S301~S303 include: S301, Get the character's current movement state.

[0056] In this embodiment, the game engine's state monitoring module collects the character's behavioral data in real time, including but not limited to movement speed, whether the character is running, jumping, injured, or stationary. Specifically, the character's current motion state is continuously updated as an input signal to determine its physiological load level. For example, when the character is moving at high speed, it is identified as running; when the character's health drops below a threshold, it is identified as injured; and when there is no input, it is identified as resting. It should be noted that the motion state described here includes not only discrete behavioral categories but can also be represented as a continuous variable, such as using a normalized speed coefficient as a measurement indicator. Thus, this state information forms the basis for subsequent adaptive parameter adjustment, ensuring that the breathing simulation can realistically reflect the character's real-time physiological changes.

[0057] S302. Determine the corresponding target breathing intensity level based on the motion state, and generate the target parameter adjustment signal corresponding to the target breathing intensity level based on the preset mapping relationship between breathing intensity level and parameter adjustment signal.

[0058] In this embodiment, the character's motion state obtained in the previous step is mapped to a quantified target breathing intensity level. Specifically, the breathing intensity level characterizes the rapidity and fluctuation of breathing, and is divided into multiple levels such as resting, light activity, and vigorous exercise. After determining the target level, a pre-configured mapping table is invoked. This table defines the association rules between different breathing intensity levels and parameters such as stiffness, damping, and noise force intensity. Based on this mapping relationship, a set of corresponding parameter adjustment signals, i.e., target parameter adjustment signals, are generated to indicate the direction and magnitude of adjustment for each mechanical parameter. It should be noted that the mapping relationship described here can be pre-set by the developer, supporting linear interpolation or nonlinear curve fitting methods to achieve a more natural transition response. For example, when the character transitions from a static state to a running state, the target breathing intensity level is determined to increase, and a corresponding adjustment signal is generated, indicating that the stiffness parameter needs to gradually increase from 12.0 to 18.0, and the noise force intensity from 1.0 to 1.5. Thus, this mechanism realizes an automated conversion process from semantic state to physical parameters.

[0059] S303. Configure mechanical parameters for parent and child nodes according to the target parameter adjustment signal.

[0060] In this embodiment, the target parameter adjustment signals generated in the previous step are applied to the mechanical models of the parent and child nodes respectively, completing the dynamic update of parameters. Specifically, the parent node (character's chest cavity) first receives the stiffness and damping update values ​​in the adjustment signal and writes them into its own state parameter structure, thereby changing its breathing frequency and rhythm characteristics. Since the breathing frequency is proportional to the square root of the stiffness, increasing the stiffness value will directly accelerate the breathing cycle; while adjusting the damping affects the time ratio of exhalation and inhalation, achieving fine control over the breathing pattern. Similarly, the child node (character's perspective) also updates its own stiffness, damping, and noise force intensity parameters according to the same adjustment signal or independently configured child node-specific mapping rules. Furthermore, the parameter update process can use a smooth interpolation method to avoid visual jitter caused by abrupt changes. It should be noted that this configuration process supports differentiated settings, such as enhancing only the breathing amplitude of the parent node in the injured state without affecting the camera node, thereby achieving local enhancement of performance. As can be seen, through this step, the technical capability of responding to changes in character state in real time without redesigning animation resources is achieved, significantly improving the realism and flexibility of interaction.

[0061] In some embodiments, when calculating the rotation and displacement of the parent node, it is necessary to combine the calculation with a preset mathematical model corresponding to the damped state. (See [reference needed]). Figure 4 , Figure 4 for Figure 1 The flowchart of sub-steps S401~S403 of step S400, wherein steps S401~S403 include: S401. Calculate the damping ratio of the parent node based on the stiffness and damping of the parent node.

[0062] In this embodiment, based on the stiffness and damping parameters configured on the parent node, its damping ratio ζ is calculated according to the physical formula ζ = damping / (2 × √stiffness). Specifically, this damping ratio is a dimensionless quantity that reflects the balance between energy dissipation and recovery capacity, and is a core indicator for determining the type of motion mode. It should be noted that the stiffness and damping mentioned here are adjustable parameters set during the initialization or dynamic adjustment phase, and their values ​​directly affect the frequency, rhythm, and stability of the breathing motion. For example, when the stiffness of the parent node is set to 12.0 and the damping is set to 4.5, the calculated damping ratio is approximately 1.3, indicating an overdamped state; while if the stiffness is 35.0 and the damping is 8.0, the damping ratio is approximately 0.676, which falls into the underdamped category. Thus, this calculation process can accurately quantify the physical behavior tendency.

[0063] S402. Compare the damping ratio with the preset value to determine the damping state of the character's chest cavity.

[0064] In this embodiment, the damping ratio ζ calculated in the previous step is compared with the critical damping threshold of 1.0 to determine the specific damping state of the parent node. Specifically, if |ζ - 1.0| < ε (where ε is the allowable error, exemplarily taken as 0.001), it is considered to be in a critical damping state, i.e., the response is fastest and there is no oscillation when returning to the target position; if ζ < 1.0, it is determined to be in an underdamped state, which will generate periodic oscillations near the target position, suitable for simulating the fluctuation rhythm of normal breathing; if ζ > 1.0, it is determined to be in an overdamped state, which will approach the target slowly and without oscillations, simulating the motion characteristics of suppressed or heavy breathing. It should be noted that the preset value mentioned here refers to the theoretical critical point of 1.0, which comes from the analysis conclusion of classical second-order dynamics. Furthermore, this judgment logic ensures the accurate classification of behavior under different parameter combinations, providing a reliable basis for the selection of subsequent mathematical models.

[0065] S403. Determine the corresponding first preset mathematical model based on the damping state of the character's chest cavity, and input the fractal noise random force, stiffness and damping corresponding to the parent node into the first preset mathematical model to calculate the rotation and displacement of the parent node.

[0066] In this embodiment, based on the damping state determined in S402, one of the three preset mathematical models is selected to solve the motion response of the parent node. The first preset mathematical model includes an exponential decay model, a trigonometric function oscillation model, and a double exponential decay model. Specifically, if the state is critically damped, the exponential decay model x(t) = goal + (C1 + C2 * t) * exp(-ω * t) is used, where ω = stiffness, and C1 and C2 are determined by the initial position and initial velocity, achieving fast and stable convergence. If the state is underdamped, the trigonometric function oscillation model x(t) = goal + exp(-ζ * ω * t) * [C1 * cos(ω_d * t) + C2 * sin(ω_d * t)] is used, where ω_d = ω * sqrt(1 – ζ * t) * exp(- ... 2 This is used to generate breathing waveforms with natural fluctuations; if in an overdamped state, a double exponential decay model is used: x(t) = goal + C1 * exp(-λ1 * t) + C2 * exp(-λ2 * t), where λ1 = ω * (ζ - sqrt(ζ)). 2 - 1)), λ2 = ω * (ζ + sqrt(ζ 2- 1)), C1 and C2 are constants determined by the initial conditions, describing the slow recovery process. Furthermore, the fractal noise random force is introduced as an external force term into the dynamic equations of the above model, affecting the dynamic evolution of the target position (goal), thus giving the displacement output irregular fluctuation characteristics. This can be understood as the trimodal solution mechanism adaptively matching respiratory performance under different physiological conditions, ensuring that the motion trajectory conforms to both physical laws and biological naturalness. Finally, based on this, the precise position and velocity of the parent node in the current frame are calculated, and its rotation state is updated synchronously, completing the complete pose calculation of the node.

[0067] In some embodiments, the rotation and displacement of child nodes are based on the rotation and displacement of parent nodes; that is, the rotation and displacement of the parent node are used as the target position of the child nodes. (See [reference]) Figure 5 , Figure 5 for Figure 1 The flowchart of sub-steps S501~S503 of step S500, wherein steps S501~S503 include: S501. Calculate the damping ratio of the sub-node based on the stiffness and damping of the sub-node.

[0068] In this embodiment, this step is used to determine the dynamic response characteristics of the sub-node's mechanics, providing a basis for accurately calculating its motion behavior. During the execution of the above step, the currently configured stiffness and damping values ​​of the sub-node are called, and the damping ratio ζ of the sub-node is calculated according to the physical formula ζ = damping / (2 × √stiffness). Specifically, this damping ratio reflects the ability and method by which the sub-node recovers its equilibrium state after being disturbed by external forces, and is a key indicator for judging whether it will oscillate and how quickly it decays. It should be noted that the stiffness and damping parameters mentioned here are adjustable values ​​set during the initialization phase or dynamically adjusted according to the character's state, directly affecting the frequency and amplitude of camera viewpoint jitter. For example, when the stiffness of the sub-node is set to 35.0 and the damping is set to 8.0, the calculated damping ratio is approximately 0.676, which is less than 1.0, indicating that it is in an underdamped state, suitable for generating high-frequency, small-amplitude natural swaying. Therefore, this calculation process achieves a quantitative characterization of the sub-node's motion characteristics.

[0069] S502. Compare the damping ratio of the child node with the preset value to determine the damping state of the character's perspective.

[0070] In this embodiment, the damping ratio of the sub-node calculated in S501 is compared with the critical damping threshold of 1.0 to accurately identify its physical motion mode. Specifically, if |ζ - 1.0| < ε (where ε is the allowable error, exemplarily set to 0.001), it is determined to be in a critical damping state, indicating that it will approach the target position at the fastest speed without oscillation; if ζ < 1.0, it is determined to be in an underdamped state, which will generate periodic oscillations near the target position, suitable for simulating slight head tremors caused by breathing in a first-person perspective; if ζ > 1.0, it is determined to be in an overdamped state, with a slow response and no oscillation, suitable for special situations requiring suppression of lens shake. It should be noted that the preset value mentioned here refers to the theoretical critical point of 1.0, derived from the standard analysis model of second-order linearity. Furthermore, this judgment mechanism ensures the correct classification of sub-node behavior under different parameter configurations, providing a reliable basis for subsequent selection of an appropriate mathematical model.

[0071] S503. Determine the corresponding second preset mathematical model based on the damping state of the character's perspective, and input the fractal noise random force, stiffness, damping and target position of the sub-node into the second preset mathematical model to calculate the rotation and displacement of the sub-node.

[0072] In this embodiment, based on the damping state determined in S502, a suitable mathematical model is selected from three preset mathematical models to solve the motion response of the sub-node. The second preset mathematical model includes an exponential decay model, a trigonometric function oscillation model, and a double exponential decay model. Specifically, if the state is critically damped, the exponential decay model x(t) = goal + (C1 + C2×t) × exp(-ω×t) is used, where ω = √stiffness, and C1 and C2 are determined by initial conditions, achieving rapid and stable convergence. If the state is underdamped, the oscillation model containing trigonometric functions x(t) = goal + exp(-ζ×ω×t) × [C1×cos(ω_d×t) + C2×sin(ω_d×t)] is used, where ω_d = ω×√(1 – ζ). 2 This can generate small displacements with natural wave characteristics; if it is in an overdamped state, the double exponential decay model x(t) = goal + C1×exp(-λ1×t) + C2×exp(-λ2×t) is adopted, where λ1 = ω×(ζ - √(ζ)). 2 - 1)), λ2 =ω×(ζ + √(ζ) 2- 1)) describes a slow, non-oscillating regression process. Further, fractal noise random force is introduced as an external driving force term into the dynamic equations of the above model, acting on the dynamic evolution of the target position (goal), giving the output displacement irregularity and physiological realism. Simultaneously, the rotation and displacement output by the parent node are set as the target position in the current frame, forming the reference coordinate system for the child node's motion. This can be understood as the trimodal solution mechanism, combining dynamic target input and noise excitation, adaptively generating physically consistent visual motion under different breathing states, ensuring visual smoothness and enhancing the immersive experience. Finally, based on this, the position, velocity, and rotation state of the child node in the current frame are updated, outputting the final pose data for rendering.

[0073] For example, the offset and rotation of the child node relative to the parent node are calculated based on the target position. A corresponding second preset mathematical model is determined according to the damping state from the character's perspective. The fractal noise random force, stiffness, damping, offset, and rotation of the child node are input into the second preset mathematical model to calculate the rotation and displacement of the child node.

[0074] In this embodiment, this step further refines the input relationship for child node motion calculation, emphasizing the explicit processing of relative motion quantities during hierarchical fusion. During the execution of the above steps, the rotation and displacement results output by the parent node after calculation in the current frame are first used as the target position of the child node. Using this as a reference coordinate system, the initial offset and rotation of the child node relative to this target position are calculated. Specifically, the offset refers to the position difference of the child node relative to the static configuration of the parent node before being subjected to breathing disturbances, while the rotation represents the angular difference between its default orientation and the parent node's pose. It should be noted that the offset and rotation mentioned here are not dynamically changing driving signals, but rather are used to establish the basic geometric relationship of the child node's local motion space, ensuring its correct attachment to the parent node's motion trajectory.

[0075] Furthermore, based on the calculation and judgment processes in S501 and S502, the current damping state of the child node is determined, and the corresponding second preset mathematical model is selected for motion solution. If the child node is in a critically damped state, the exponential decay model x(t) = goal + (C1 + C2×t) × exp(-ω×t) is used; if it is in an underdamped state, the oscillatory model x(t) = goal + exp(-ζ×ω×t) × [C1×cos(ω_d×t) + C2×sin(ω_d×t)] is used; if it is in an overdamped state, the double exponential decay model x(t) = goal + C1×exp(-λ1×t) + C2×exp(-λ2×t) is used. In the model solution process, the fractal noise random force is the instantaneous acceleration affected by the external force input, and the response characteristics are determined by the stiffness and damping parameters. The offset and rotation obtained from the aforementioned calculations together constitute the initial conditions or reference benchmarks of the child node in the local coordinate system. This can be understood as these parameters working together on the second preset mathematical model, causing child nodes to inherit the overall movement trend of their parent nodes while incorporating subtle jitters determined by their own mechanical properties, thus achieving a natural fusion of multi-layered breathing effects. Therefore, this method not only ensures the coordination and consistency between the camera's perspective and the character's chest movement but also enhances the realism and detail of the image through local dynamics simulation. The final output of child node rotation and displacement data will be directly used to update the first-person camera's pose, completing the visual presentation loop.

[0076] Based on the above method, embodiments of the present invention also provide a system corresponding to the above method, such as... Figure 6 As shown, Figure 6 This is a functional module diagram of a first-person perspective breathing simulation system 1000 provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the first-person perspective breathing simulation system 1000 provided in this embodiment are the same as those in the above method embodiments. For the sake of brevity, parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiments.

[0077] In this embodiment, the first-person perspective breathing simulation system 1000 includes a configuration module 1100, a processing module 1200, and a scene generation module 1300. The configuration module 1100 initializes the parent and child nodes of the first-person character, where the parent node represents the character's chest cavity and the child node represents the character's perspective; it generates corresponding fractal noise random forces for the parent and child nodes, which drive the simulated breathing movements of the parent and child nodes; and it configures mechanical parameters for the parent and child nodes based on the character's motion state, including stiffness and damping. It can be understood that the configuration module 1100 performs the above steps S100~S300.

[0078] The processing module 1200 is used to calculate the rotation and displacement of the parent node based on the fractal noise random force, stiffness, and damping corresponding to the parent node; using the rotation and displacement of the parent node as the target position for the child node's movement, the rotation and displacement of the child node are calculated based on the fractal noise random force, stiffness, damping, and target position corresponding to the child node. It can be understood that the processing module 1200 is used to execute the above steps S400~S500.

[0079] The scene generation module 1300 is used to generate a first-person perspective breathing scene based on the rotation and displacement of child nodes. It can be understood that the scene generation module 1300 is used to perform the above step S600.

[0080] In some embodiments, the configuration module 1100 is used to generate original fractal noise signals for the parent node and child node respectively based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node; and to modulate the original fractal noise signals using noise force intensity parameters to generate fractal noise random force. It can be understood that the configuration module 1100 is used to perform the above steps S201~S202.

[0081] For example, the configuration module 1100 is used to generate a basic fractal noise signal corresponding to the parent node based on the game timestamp and the random starting value corresponding to the parent node, and to smooth the basic fractal noise signal corresponding to the parent node to obtain the original fractal noise signal corresponding to the parent node; and to generate a basic fractal noise signal corresponding to the child node based on the game timestamp and the random starting value corresponding to the child node, and to smooth the basic fractal noise signal corresponding to the child node to obtain the original fractal noise signal corresponding to the child node.

[0082] In some embodiments, the configuration module 1100 is used to acquire the current motion state of the character; determine the corresponding target breathing intensity level based on the motion state, and generate a target parameter adjustment signal corresponding to the target breathing intensity level based on a preset mapping relationship between breathing intensity levels and parameter adjustment signals; and configure mechanical parameters for the parent node and child nodes according to the target parameter adjustment signal. It can be understood that the configuration module 1100 is used to perform the above steps S301~S303.

[0083] In some embodiments, the processing module 1200 is used to calculate the damping ratio of the parent node based on the stiffness and damping corresponding to the parent node; compare the damping ratio with a preset value to determine the damping state of the character's chest cavity; determine the corresponding first preset mathematical model based on the damping state of the character's chest cavity; and input the fractal noise random force, stiffness, and damping corresponding to the parent node into the first preset mathematical model to calculate the rotation and displacement of the parent node. It can be understood that the processing module 1200 is used to perform the above steps S401~S403.

[0084] In some embodiments, the processing module 1200 is used to calculate the damping ratio of the sub-node based on the stiffness and damping corresponding to the sub-node; compare the damping ratio of the sub-node with a preset value to determine the damping state at the character's perspective; determine the corresponding second preset mathematical model based on the damping state at the character's perspective; and input the fractal noise random force, stiffness, damping, and target position corresponding to the sub-node into the second preset mathematical model to calculate the rotation and displacement of the sub-node. It can be understood that the processing module 1200 is used to perform the above steps S501~S503.

[0085] For example, the processing module 1200 is specifically used to calculate the offset and rotation of the child node relative to the parent node based on the target position; determine the corresponding second preset mathematical model according to the damping state of the character's perspective; and input the fractal noise random force, stiffness, damping, offset and rotation of the child node into the second preset mathematical model to calculate the rotation and displacement of the child node.

[0086] Based on the same inventive concept disclosed above, the present invention also provides a block diagram of an electronic device 2000 performing the above method. Please refer to... Figure 7 , Figure 7 This is a block diagram of an electronic device 2000 provided in an embodiment of the present invention. The electronic device 2000 includes a processor 2100, a memory 2200, a bus 2300, and a communication interface 2400. The processor 2100 and the memory 2200 are connected via the bus 2300, and the processor 2100 communicates with external devices via the communication interface 2400.

[0087] Processor 2100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 2100 or through software instructions. The processor 2100 may be a general-purpose processor 2100, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0088] The memory 2200 is used to store computer programs. For example, the first-person perspective breathing simulation system 1000 in this embodiment of the invention includes at least one software function module that can be stored in the memory 2200 in the form of software or firmware. After receiving the execution instruction, the processor 2100 executes the program to implement the first-person perspective breathing simulation method in this embodiment of the invention.

[0089] The memory 2200 may include high-speed random access memory (RAM) or non-volatile memory. Optionally, the memory 2200 may be a storage device built into the processor 2100 or a storage device independent of the processor 2100.

[0090] Bus 2300 can be ISA bus 2300, PCI bus 2300 or EISA bus 2300, etc. Figure 7 It is indicated by only one double-headed arrow, but does not mean that there is only one bus 2300 or one type of bus 2300.

[0091] Electronic devices 2000 can be mobile phones, tablets, laptops, desktop computers, and other computer devices.

[0092] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor 2100, implements the first-person perspective breathing simulation method described above. This computer-readable storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A first-person perspective breathing simulation method, characterized in that, The method includes: Initialize the parent and child nodes of the first-person character, where the parent node represents the character's chest cavity and the child node represents the character's viewpoint; A corresponding fractal noise random force is generated for the parent node and the child node, and the fractal noise random force is used to drive the simulated breathing movement of the parent node and the child node; Based on the character's motion state, configure mechanical parameters for the parent node and the child node, the mechanical parameters including stiffness and damping; The rotation and displacement of the parent node are calculated based on the fractal noise random force, stiffness, and damping corresponding to the parent node. The rotation and displacement of the parent node are used as the target position for the movement of the child node. The rotation and displacement of the child node are calculated based on the fractal noise random force, stiffness, damping and the target position corresponding to the child node. A first-person perspective breathing scene is generated based on the rotation and displacement of the child nodes.

2. The method according to claim 1, characterized in that, The step of generating corresponding fractal noise random forces for the parent node and child node includes: Based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node, original fractal noise signals are generated for the parent node and the child node, respectively. The original fractal noise signal is modulated with noise force intensity parameters to generate fractal noise random force.

3. The method according to claim 2, characterized in that, The step of generating original fractal noise signals for the parent node and child node based on the game timestamp, the random starting value corresponding to the parent node, and the random starting value corresponding to the child node includes: Based on the game timestamp and the random starting value corresponding to the parent node, a basic fractal noise signal corresponding to the parent node is generated, and the basic fractal noise signal corresponding to the parent node is smoothed to obtain the original fractal noise signal corresponding to the parent node. Based on the game timestamp and the random starting value corresponding to the child node, a basic fractal noise signal corresponding to the child node is generated, and the basic fractal noise signal corresponding to the child node is smoothed to obtain the original fractal noise signal corresponding to the child node.

4. The method according to claim 1, characterized in that, The calculation of the rotation and displacement of the parent node based on the fractal noise random force, stiffness, and damping corresponding to the parent node includes: The damping ratio of the parent node is calculated based on the stiffness and damping of the parent node. The damping ratio is compared with a preset value to determine the damping state of the character's chest cavity; Based on the damping state of the character's chest cavity, a corresponding first preset mathematical model is determined. The fractal noise random force, stiffness, and damping corresponding to the parent node are input into the first preset mathematical model to calculate the rotation and displacement of the parent node.

5. The method according to claim 1, characterized in that, The calculation of the rotation and displacement of the sub-node based on the fractal noise random force, stiffness, damping, and target position corresponding to the sub-node includes: The damping ratio of the sub-node is calculated based on the stiffness and damping of the sub-node. The damping ratio of the sub-node is compared with a preset value to determine the damping state of the character's perspective. Based on the damping state of the character's perspective, a corresponding second preset mathematical model is determined. The fractal noise random force, stiffness, damping, and target position of the sub-node are input into the second preset mathematical model to calculate the rotation and displacement of the sub-node.

6. The method according to claim 5, characterized in that, The step of determining the corresponding second preset mathematical model based on the damping state of the character's perspective, and inputting the fractal noise random force, stiffness, damping, and target position of the sub-node into the second preset mathematical model to calculate the rotation and displacement of the sub-node includes: Calculate the offset and rotation of the child node relative to the parent node based on the target position; Based on the damping state of the character's perspective, a corresponding second preset mathematical model is determined. The fractal noise random force, stiffness, damping, offset, and rotation of the child node are input into the second preset mathematical model to calculate the rotation and displacement of the child node.

7. The method according to claim 1, characterized in that, The configuration of mechanical parameters for the parent node and child node based on the character's motion state includes: Get the character's current movement state; The target breathing intensity level is determined based on the motion state, and a target parameter adjustment signal corresponding to the target breathing intensity level is generated based on the preset mapping relationship between breathing intensity level and parameter adjustment signal. Configure mechanical parameters for the parent node and the child node according to the target parameter adjustment signal.

8. A first-person perspective breathing simulation system, characterized in that, The system includes: A configuration module is used to initialize the parent and child nodes of a first-person character, where the parent node represents the character's chest cavity and the child node represents the character's viewpoint; generate corresponding fractal noise random forces for the parent and child nodes, whereby the fractal noise random forces are used to drive the simulated breathing movements of the parent and child nodes; and configure mechanical parameters for the parent and child nodes based on the character's motion state, whereby the mechanical parameters include stiffness and damping. The processing module is used to calculate the rotation and displacement of the parent node based on the fractal noise random force, stiffness and damping corresponding to the parent node; and to use the rotation and displacement of the parent node as the target position of the child node's movement, and to calculate the rotation and displacement of the child node based on the fractal noise random force, stiffness and damping corresponding to the child node and the target position. The image generation module is used to generate a first-person perspective breathing image based on the rotation and displacement of the child nodes.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that can be executed by the processor to implement the first-person perspective breathing simulation method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the first-person perspective breathing simulation method as described in any one of claims 1-7.

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