Digital human motion data processing system

Abstract

The invention provides a digital human motion data processing system. According to the system, acquired human body posture data are sent to a server through a communication link by terminal equipment, a digital human model is generated in the server according to the human body posture data, a target motion posture control model is determined from a preset motion posture control model set according to the link state of the communication link, and then the target motion posture control model is sent to the server. And the server outputs the motion control parameters according to the target motion attitude control model, and sends the digital human motion data generated according to the motion control parameters to the terminal equipment for display, so that a model selection mechanism adapting to the link state of the communication link is realized, the adaptability and robustness of the system are improved, and the user experience is improved. And a stable digital human motion effect can be provided in different network environments.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a digital human motion data processing system. Background Art

[0002] With the rapid development of digital human technology, digital humans are increasingly used in entertainment, education, medical care, industry and other fields. As an important part of digital human technology, the fidelity and smoothness of digital human movement directly affect the user experience and application effect. However, in the process of realizing digital human movement, there are many technical challenges, among which the most critical one is how to ensure the stability and smoothness of digital human movement in different network environments.

[0003] In traditional digital human motion data processing systems, fixed motion posture control models are usually used to process human posture data and generate digital human motion. However, this fixed mode approach has obvious limitations. On the one hand, due to the variability and uncertainty of the network environment, fixed control models are often difficult to adapt to data transmission requirements under different network conditions. Especially in low-bandwidth, high-latency network environments, data transmission delays and packet loss can cause digital human motion to freeze, delay, or even be interrupted, seriously affecting the user experience.

[0004] On the other hand, different application scenarios have different requirements for digital human motion. For example, in some application scenarios that have extremely high requirements for real-time and smoothness (such as online games, virtual reality, etc.), digital human motion needs to achieve extremely high update frequency and fine motion control; while in some application scenarios with relatively low real-time requirements (such as distance education, online meetings, etc.), more attention is paid to the stability and basic continuity of digital human motion. Traditional fixed control models are difficult to meet these diverse requirements at the same time. Summary of the invention

[0005] The present application provides a digital human motion data processing system for implementing a model selection mechanism that adapts to the link state of a communication link, so as to ensure the stability of digital human motion control under different network environments.

[0006] In a first aspect, the present application provides a digital human motion data processing system, including: a terminal device and a server; The terminal device sends the acquired human body posture data to the server via a communication link; The server generates a digital human model according to the human posture data, and determines a target motion posture control model from a preset motion posture control model set according to a link state of the communication link; The server outputs motion control parameters according to the target motion posture control model, and sends digital human motion data generated according to the motion control parameters to the terminal device for display.

[0007] In the above scheme, the acquired human posture data is sent to the server through the communication link by the terminal device, and a digital human model is generated in the server according to the human posture data, and a target motion posture control model is determined from a preset motion posture control model set according to the link state of the communication link. Then, the server outputs motion control parameters according to the target motion posture control model, and sends the digital human motion data generated according to the motion control parameters to the terminal device for display, so that the motion posture control model can be dynamically adjusted according to the link state of the communication link. When the link state is good, the system can select a more complex model with higher control accuracy (such as a control model established based on a proportional integral differential controller) to generate a smoother and more natural digital human motion. When the link state is not good, the system will automatically switch to a simpler model with lower data transmission requirements (such as a control model established based on a proportional differential controller) to ensure that the basic continuity and stability of the digital human motion can be maintained in a low-bandwidth, high-latency environment. This model selection mechanism that adapts to the link state of the communication link not only improves the adaptability and robustness of the system, but also ensures that a stable digital human motion effect can be provided in different network environments.

[0008] Optionally, the preset motion posture control model set includes at least a first motion posture control model and a second motion posture control model, wherein the first motion posture control model is a control model established based on a proportional differential controller, and the second motion posture control model is a control model established based on a proportional integral differential controller.

[0009] In the above scheme, the first motion posture control model is established based on the proportional differential controller, while the second motion posture control model is established based on the proportional integral differential controller. These two models can meet the motion control requirements in different scenarios. Among them, the first motion posture control model realizes rapid response and stable control of motion posture through the two links of proportional and differential. When the communication link is in poor condition, such as when there is a delay in data transmission, the first motion posture control model can reduce the amount of data processing, thereby achieving faster control response, so as to ensure the smoothness of the movement of the digital human model under poor network conditions. The second motion posture control model realizes rapid response and stable control of motion posture through the three links of proportional, integral and differential. When the communication link is in good condition, that is, when the data transmission delay is low and the bandwidth is sufficient, the integral link can make the motion posture control accuracy higher, so as to achieve the motion accuracy of the digital human model under good network conditions.

[0010] Optionally, if the link state of the communication link is the first link state, the target motion posture control model is the first motion posture control model; If the link state of the communication link is a second link state, the target motion posture control model is the second motion posture control model, wherein the second link state is superior to the first link state.

[0011] In the above scheme, when the link state of the communication link is the first link state, the system selects the first motion posture control model as the target motion posture control model. The first link state usually means that the network conditions are relatively poor and there may be delays in data transmission. In this case, the first motion posture control model (based on the proportional differential controller) is selected for its simplicity and fast response characteristics to ensure the basic continuity and stability of the digital human movement. When the link state of the communication link is the second link state, the system selects the second motion posture control model as the target motion posture control model. The second link state is better than the first link state, which usually means that the network conditions are good, the data transmission delay is low, and the bandwidth is sufficient. In this case, the second motion posture control model (based on the proportional integral differential controller) is selected for its high precision and strong anti-interference ability to generate a smoother and more natural digital human movement effect.

[0012] Optionally, the default configuration of the target motion posture control model is the second motion posture control model.

[0013] In the above scheme, the default configuration of the target motion posture control model is set to the second motion posture control model, which enhances the stability of the system in the initial state. Due to its inherent high-precision adjustment capability, the second motion posture control model can quickly respond and compensate for system deviations through the synergy of the three links of proportion, integration and differentiation, and can maintain the smoothness and accuracy of the digital human motion trajectory.

[0014] In addition, it is worth mentioning that when switching from a poor network state to a good network state, the error in the previous control of the first motion posture control model can also be adjusted through the integral link in the second motion posture control model, and the motion posture can be gradually adjusted to achieve error supplementary control of the digital human model and reduce the discontinuity or jitter of the digital human movement caused by network fluctuations.

[0015] Optionally, the preset motion posture control model set only includes the target motion posture control model, the target motion posture control model is a control model established based on a proportional differential controller, and the gain parameter configuration in the target motion posture control model is associated with the link state.

[0016] In the above scheme, changes in link status (such as delay, bandwidth fluctuation, etc.) will directly affect the real-time and accuracy of digital human motion data transmission, and thus affect the effect of motion posture control. By dynamically associating the gain parameter configuration in the target motion posture control model with the link status of the communication link, the system can adjust the sensitivity and response speed of the controller in real time to adapt to the motion control requirements in different network environments and avoid motion posture distortion or interruption caused by network fluctuations.

[0017] Optionally, before the server outputs the motion control parameters according to the target motion posture control model, the method further includes: If the link state of the communication link is the third link state, configuring a first gain parameter for the target motion posture control model, wherein the first gain parameter includes a first proportional gain parameter and a first differential gain parameter; If the link state of the communication link is the fourth link state, a second gain parameter is configured for the target motion posture control model, wherein the second gain parameter includes a second proportional gain parameter and a second differential gain parameter.

[0018] In the above scheme, when the communication link is in the third link state, the system configures the first gain parameter (including the first proportional gain parameter and the first differential gain parameter) for the target motion posture control model, which can ensure the accuracy of the controller and achieve a smoother and more accurate digital human motion effect. When the communication link is in the fourth link state, the system configures the second gain parameter (including the second proportional gain parameter and the second differential gain parameter). This configuration is usually aimed at relatively poor network conditions. By adjusting the proportional gain and differential gain, the response speed and stability of the controller are optimized to ensure that the digital human motion posture can still maintain a certain degree of consistency and accuracy when the network fluctuates greatly.

[0019] Optionally, a configuration parameter mapping table is configured in the server, and the third link state corresponds to the first gain parameter in the configuration parameter mapping table, and the fourth link state corresponds to the second gain parameter in the configuration parameter mapping table.

[0020] In the above solution, a configuration parameter mapping table is configured in the server, which directly associates the link state of the communication link (the third link state and the fourth link state) with the gain parameters of the target motion posture control model (the first gain parameter and the second gain parameter), thereby significantly improving the convenience and efficiency of system configuration. Through the mapping table, the system can quickly determine the gain parameters that should be used in the current link state without the need for complex calculations and reasoning processes, thereby speeding up the configuration of control parameters.

[0021] It is worth noting that when the link status is poor, the system can appropriately reduce the proportional gain parameter to reduce the control overshoot caused by network delay and improve the stability of the system; conversely, when the link status is good, increasing the proportional gain parameter can speed up the system response and improve the control accuracy. The adjustment of the differential gain parameter helps the system predict and compensate for future errors, especially when the link status fluctuates greatly. By dynamically adjusting the differential gain parameter, oscillation can be effectively suppressed and the robustness of the system can be improved.

[0022] In a second aspect, the present application provides a method for processing digital human motion data, which is applied to a digital human motion data processing system, the system comprising: a terminal device and a server; The terminal device sends the acquired human body posture data to the server via a communication link; The server generates a digital human model according to the human posture data, and determines a target motion posture control model from a preset motion posture control model set according to a link state of the communication link; The server outputs motion control parameters according to the target motion posture control model, and sends digital human motion data generated according to the motion control parameters to the terminal device for display.

[0023] In a third aspect, the present application provides an electronic device, including: processor; and, A memory, configured to store executable instructions of the processor; The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0024] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0025] The digital human motion data processing system provided by the present application sends the acquired human posture data to the server through the communication link through the terminal device, generates a digital human model in the server according to the human posture data, and determines the target motion posture control model from the preset motion posture control model set according to the link state of the communication link. Then, the server outputs the motion control parameters according to the target motion posture control model, and sends the digital human motion data generated according to the motion control parameters to the terminal device for display, so that the motion posture control model can be dynamically adjusted according to the link state of the communication link. When the link state is good, the system can select a more complex model with higher control accuracy to generate a smoother and more natural digital human motion. When the link state is not good, the system will automatically switch to a simpler model with lower data transmission requirements to ensure that the basic continuity and stability of the digital human motion can be maintained in a low-bandwidth, high-latency environment. This model selection mechanism that adapts to the link state of the communication link not only improves the adaptability and robustness of the system, but also ensures that a stable digital human motion effect can be provided in different network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0027] Figure 1 is a structural schematic diagram of a digital human motion data processing system according to an exemplary embodiment of the present application; Figure 2 is a flowchart of a method for processing digital human motion data according to an exemplary embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0028] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0029] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0030] Figure 1 FIG. 1 is a schematic diagram of a digital human motion data processing system according to an exemplary embodiment of the present application. Figure 1 As shown, the digital human motion data processing system 100 provided in this embodiment includes: a terminal device 110 and a server 120.

[0031] The terminal device 110 is responsible for collecting human posture data and sending the data to the server through a communication link. The terminal device 110 may include electronic devices of various sensors, such as a camera, a depth sensor, an accelerometer, etc., for capturing human motion information. The terminal device 110 may be a host device or a user device.

[0032] The server 120 is used to receive the human body posture data sent by the terminal device 110, generate a digital human model based on the data, and determine the target motion posture control model from the preset motion posture control model set according to the link state of the communication link. The server 120 is also responsible for outputting motion control parameters according to the target motion posture control model, and sending the generated digital human motion data back to the terminal device 110 for display.

[0033] In one possible implementation, the preset motion posture control model set configured in the server includes at least a first motion posture control model and a second motion posture control model, wherein the first motion posture control model is a control model established based on a proportional differential controller, and the second motion posture control model is a control model established based on a proportional integral differential controller.

[0034] The first motion posture control model is a controller based on proportional and differential links, which can achieve fast response and stable control of motion posture. The proportional link produces a control effect according to the size of the error, and the differential link produces a control effect according to the rate of change of the error. The two work together to reduce the error and improve the stability of the system.

[0035] When the communication link status is not good (such as high latency, insufficient bandwidth), the first motion posture control model is selected as the target motion posture control model. This is because the first motion posture control model is relatively simple, has low requirements for data transmission, and can maintain the basic continuity and stability of digital human motion in a poor network environment.

[0036] Specifically, the server may pre-build the first motion posture control model and set corresponding proportional gain and differential gain parameters. When the link state meets the conditions for selecting the first motion posture control model, the server will start the model and output motion control parameters according to the input human body posture data.

[0037] The second motion posture control model adds an integral link, which can achieve higher precision control of the motion posture. The integral link produces a control effect based on the accumulation of errors, which helps to eliminate steady-state errors and improve the control accuracy of the system.

[0038] When the communication link is in good condition (such as low latency and sufficient bandwidth), the PID controller model is selected as the target motion posture control model. This is because the PID controller model has higher control accuracy and anti-interference ability, and can generate smoother and more natural digital human motion effects in a good network environment.

[0039] Specifically, the second motion posture control model is pre-built in the server, and corresponding proportional gain, integral gain and differential gain parameters are set. When the link state meets the conditions for selecting the second motion posture control model, the server will start the model and output motion control parameters according to the input human body posture data.

[0040] The server evaluates the link status by monitoring the delay, bandwidth and other parameters of the communication link. These parameters can be obtained through the interface provided by the network protocol stack, or measured through a dedicated link status monitoring tool. According to the monitored link status, the server selects a suitable target motion posture control model from the preset motion posture control model set. If the link status is good, the second motion posture control model is selected; if the link status is not good, the first motion posture control model is selected.

[0041] During the motion process, the server may adjust the target motion posture control model based on real-time feedback and monitoring results. For example, if the motion trajectory of the digital human model deviates from expectations or the link status changes, the server may reselect the control model or adjust the control parameters to improve the stability and accuracy of motion control.

[0042] Furthermore, the default configuration of the target motion posture control model is the second motion posture control model. Specifically, when the system starts, the model management module loads the second motion posture control model into the memory and sets the default parameters. Then, the link status monitoring module continuously collects the link RTT and packet loss rate.

[0043] If RTT < 50ms and packet loss rate < 1%, the second motion posture control model is kept as the active model. For example, in a local area network environment, the link RTT is stable at 10ms and the packet loss rate is 0%. The system uses the second motion posture control model by default to generate a high-precision, low-jitter digital human motion effect.

[0044] When the link quality decreases, when RTT ≥ 50ms or packet loss rate ≥ 1%, the model switching is triggered. The model management module preloads the first motion posture control model parameters, and only the control logic pointer needs to be updated during switching, and the switching delay is < 5ms.

[0045] When the link quality is restored (RTT < 50ms and packet loss rate < 1%), the system automatically switches back to the second motion posture control model.

[0046] It is worth noting that the first link state corresponds to a first transmission frame rate, and the second link state corresponds to a second transmission frame rate, and the second transmission frame rate is higher than the first transmission frame rate.

[0047] In the digital human motion data processing system, the transmission frame rate is an important indicator to measure the efficiency and quality of data transmission. By dynamically adjusting the transmission frame rate according to the link status, the system can use network resources more effectively. When the link status is the first link status (i.e., a relatively poor status), the system selects a lower transmission frame rate (the first transmission frame rate) to reduce the amount of data transmission, reduce the network burden, and avoid data loss or delay caused by network congestion. On the contrary, when the link status is upgraded to the second link status (i.e., a better status), the system selects a higher transmission frame rate (the second transmission frame rate) to make full use of the network bandwidth and improve the real-time and accuracy of data transmission.

[0048] The increase in transmission frame rate not only means faster data transmission speed, but also directly affects the smoothness and realism of digital human movement. In the first link state, although the lower transmission frame rate may cause slight lag or delay in the movement of the digital human, the system can make up for this deficiency to a certain extent by selecting the first motion posture control model based on the proportional differential controller, ensuring the basic continuity and stability of the digital human movement. In the second link state, the higher transmission frame rate makes the digital human movement smoother and more natural, and the details are more abundant, thus greatly improving the user's visual experience.

[0049] Associating the transmission frame rate with the link status also enhances the adaptability of the digital human motion data processing system to different network environments. Whether facing an unstable environment with large network fluctuations or a stable environment with good network conditions, the system can ensure the continuity and stability of the digital human motion by dynamically adjusting the transmission frame rate and selecting the corresponding motion posture control model. This adaptive mechanism not only improves the robustness of the system, but also enables the system to provide stable digital human motion effects in various network environments.

[0050] In another possible implementation, the above-mentioned preset motion posture control model set only includes a target motion posture control model, which is a control model established based on a proportional differential controller, and the gain parameter configuration in the target motion posture control model is associated with the link state.

[0051] Specifically, after the system is started, the initialization operation is first performed, including loading a preset motion posture control model set (in this embodiment, only the target motion posture control model is included), configuring the initial gain parameters of the PD controller, etc. The terminal device obtains human posture data through sensors and other devices, and sends it to the server through a communication link. The server monitors the link status of the communication link in real time, including key indicators such as latency and bandwidth. According to the monitored link status, the server dynamically adjusts the gain parameters of the PD controller. The server generates motion control parameters using the target motion posture control model based on the adjusted gain parameters and the received human posture data. The server generates digital human motion data based on the motion control parameters, and sends it to the terminal device for display.

[0052] Among them, the PD controller realizes the control of motion posture through proportional link and differential link. The proportional link adjusts the control output according to the current error, while the differential link predicts and compensates the future error according to the rate of change of the error. This control strategy enables the system to maintain a stable motion effect under different link states. In the PD controller, the configuration of gain parameters (including proportional gain and differential gain) is crucial to the control effect. In this embodiment, these gain parameters are set to be associated with the link state of the communication link. This means that as the link state changes, the gain parameters will also be adjusted accordingly to adapt to different network environments.

[0053] Further, if the link state of the communication link is the third link state, a first gain parameter is configured for the target motion posture control model, wherein the first gain parameter includes a first proportional gain parameter and a first differential gain parameter. If the link state of the communication link is the fourth link state, a second gain parameter is configured for the target motion posture control model, wherein the second gain parameter includes a second proportional gain parameter and a second differential gain parameter.

[0054] Specifically, if it is determined to be the third link state, the server configures the first gain parameter for the target motion posture control model, including setting a higher first proportional gain parameter and a first differential gain parameter. If it is determined to be the fourth link state, the server configures the second gain parameter for the target motion posture control model, including setting a lower second proportional gain parameter and a second differential gain parameter. Among them, the first gain parameter and the second gain parameter can also be a dynamic function, thereby achieving dynamic adjustment. After configuring the appropriate gain parameters, the server generates motion control parameters according to the target motion posture control model and the received human body posture data.

[0055] In addition, a configuration parameter mapping table is configured in the server, the third link state corresponds to the first gain parameter in the configuration parameter mapping table, and the fourth link state corresponds to the second gain parameter in the configuration parameter mapping table.

[0056] Optionally, the third link state may be superior to the fourth link state, the second proportional gain parameter is an adjustment parameter based on the first proportional gain parameter, and the second differential gain parameter is an adjustment parameter based on the first differential gain parameter.

[0057] As an adjustment parameter based on the first proportional gain parameter, its value is dynamically adjusted according to the difference between the third link state and the fourth link state. In the third link state, due to the superior network conditions, the system tends to increase the proportional gain to speed up the response speed; while in the fourth link state, in order to reduce the overshoot caused by network fluctuations, the system will appropriately reduce the proportional gain.

[0058] As an adjustment parameter based on the first differential gain parameter, its adjustment logic is similar to that of the proportional gain. In the third link state, the differential gain is increased to improve the system's ability to predict and compensate for error changes; in the fourth link state, the differential gain is reduced to reduce the system's sensitivity to noise and improve stability.

[0059] By subdividing the link state into the third link state and the fourth link state, and configuring corresponding gain parameters for different states, the system can adapt to different network environments more finely. In the third link state (superior network conditions), the system can respond to network changes more quickly by increasing the proportional gain and differential gain, thereby improving the smoothness and accuracy of the digital human's movement; while in the fourth link state (poor network conditions), the system reduces the control overshoot and jitter caused by network fluctuations by reducing the gain parameters, thereby ensuring the basic continuity and stability of the digital human's movement.

[0060] The adjustment strategy of the gain parameters directly affects the accuracy and stability of the digital human motion control model. Under the design that the second proportional gain parameter and the second differential gain parameter are adjusted based on the first gain parameter, the system can dynamically balance the control accuracy and stability according to the changes in network conditions. Under excellent network conditions, increasing the gain parameters can enhance the system's precise control of the digital human's motion posture; while under poor network conditions, reducing the gain parameters can avoid excessive adjustments of the system due to network delays or packet loss, and maintain the stability of the digital human's motion.

[0061] By adjusting based on the first gain parameter, the setting of the second gain parameter realizes a smooth transition mechanism. When the network conditions gradually improve from the fourth link state to the third link state, the gradual increase of the gain parameter can ensure the smooth transition of the digital human motion control and avoid discontinuous motion or jitter caused by sudden gain changes. Similarly, when the network conditions deteriorate, the gradual reduction of the gain parameter can also effectively slow down the change of the control effect and maintain the stability of the digital human motion.

[0062] Ultimately, the above technical effects work together to improve user experience. In different network environments, users can watch smooth and natural digital human motion effects without worrying about motion freezes, delays or distortions caused by network fluctuations. This stable digital human motion experience not only enhances users' trust in the system, but also improves users' acceptance and satisfaction with digital human interaction technology.

[0063] Optionally, the third link state corresponds to a third transmission frame rate, the fourth link state corresponds to a fourth transmission frame rate, and the third transmission frame rate is higher than the fourth transmission frame rate.

[0064] Furthermore, the first proportional gain parameter and the first differential gain parameter in the first gain parameter are preset default configuration parameters in the motion posture control model.

[0065] Specifically, the first proportional gain parameter and the first differential gain parameter are set as the preset default configuration parameters of the motion posture control model, which simplifies the system initialization process. When the system starts, the motion posture control model can be quickly put into operation without a complex parameter tuning process. This not only improves the startup efficiency of the system, but also reduces the risk of system abnormality or performance degradation caused by improper parameter configuration.

[0066] The preset default configuration parameters are usually fully tested and optimized, and can maintain stable operation of the system in a variety of network environments. By using these parameters as the first gain parameters, the system can maintain a certain control accuracy and stability under different link states, ensuring accurate transmission and display of digital human motion data. This stability is crucial to providing a high-quality digital human motion interactive experience.

[0067] Setting the first gain parameter as the preset default configuration parameter also facilitates subsequent parameter adjustment and optimization. During system operation, algorithm experts or system administrators can adjust the first gain parameter according to the actual network environment and user needs to further optimize the digital human motion control effect. At the same time, this adjustment method based on the preset default configuration parameter also makes the parameter optimization process more intuitive and controllable.

[0068] In the process of dynamically adjusting the gain parameter according to the link status, setting the first gain parameter as the preset default configuration parameter can achieve fast switching of the gain parameter. When the link status changes, the system can quickly switch from the preset default configuration parameter to the gain parameter suitable for the current link status, thereby ensuring the continuity and stability of the digital human motion control. This fast switching mechanism is of great significance for coping with network fluctuations and improving the system response speed.

[0069] In addition, by setting the first gain parameter as a preset default configuration parameter, the system can maintain consistent performance and stability in different application scenarios and hardware platforms. This versatility and scalability makes it easier for the system to be integrated into a wider range of digital human motion interaction systems, thereby promoting the popularization and application of digital human technology.

[0070] Figure 2 FIG. 1 is a flow chart of a method for processing digital human motion data according to an exemplary embodiment of the present application. Figure 2 As shown, the method for processing digital human motion data provided by this embodiment includes: S201. The terminal device sends the acquired human body posture data to the server via a communication link.

[0071] Specifically, the terminal device collects human posture data in real time through built-in sensors. This data may include joint angles, position information, speed information, etc., which are used to describe the movement state of the human body. After preprocessing, the collected human posture data is sent to the server through a communication link (such as a wireless network, a wired network, etc.). The status of the communication link (such as latency, bandwidth, etc.) will directly affect the real-time and accuracy of data transmission.

[0072] S202: The server generates a digital human model according to the human body posture data, and determines a target motion posture control model from a preset motion posture control model set according to the link state of the communication link.

[0073] Specifically, after receiving the human body posture data sent by the terminal device, the server first generates a digital human model based on the data. The digital human model is a virtual human representation whose movement state is consistent with the real human body.

[0074] The server first receives the human posture data sent by the terminal device through the communication link. These data usually include joint angles, position information, speed information, etc., which are used to fully describe the movement state of the human body. The received data will go through a preprocessing stage to eliminate noise, fill in missing values, etc., to ensure the accuracy and completeness of the data. The preprocessed data will serve as the basis for generating a digital human model.

[0075] The server pre-stores the framework of the digital human model, which defines the basic structure of the digital human, including the skeletal structure, joint connection, muscle distribution, etc. This framework is the basic template for generating the digital human model. The server maps the pre-processed human posture data to the framework of the digital human model. This usually involves converting the joint angles, positions and other information of the real human body into corresponding parameters in the digital human model. After the mapping is completed, the server will adjust the digital human model according to these parameters to keep its motion state consistent with that of the real human body.

[0076] Then, the server determines the target motion posture control model from the preset motion posture control model set according to the link state of the communication link. The preset motion posture control model set includes a variety of motion posture control models, such as a control model based on a proportional derivative (PD) controller, a control model based on a proportional integral derivative (PID) controller, etc. These models have different complexities and control accuracy and are suitable for different network environments.

[0077] When the communication link is in good condition (such as low latency and sufficient bandwidth), the server selects a model with higher control accuracy (such as a PID controller model) to generate smoother and more natural digital human movements.

[0078] When the communication link is in poor condition (such as high latency or insufficient bandwidth), the server selects a simpler model with lower data transmission requirements (such as the PD controller model) to ensure the basic continuity and stability of the digital human's movement in a low-bandwidth, high-latency environment.

[0079] S203: The server outputs motion control parameters according to the target motion posture control model, and sends the digital human motion data generated according to the motion control parameters to the terminal device for display.

[0080] Specifically, the server outputs motion control parameters according to the determined target motion posture control model. These parameters may include target joint angles, target positions, target speeds, etc., which describe the motion state that the digital human model should achieve, that is, these parameters are used to describe the motion trajectory, speed, acceleration and other information of the digital human model. According to the motion control parameters, the server generates digital human motion data. These data include the position, posture and other information of the digital human model at various time points, which are used for real-time display on the terminal device.

[0081] After receiving the digital human motion data sent by the server, the terminal device performs real-time rendering and display. Users can observe the digital human model that is consistent with the real human motion state through the terminal device.

[0082] In this embodiment, the acquired human posture data is sent to the server through the communication link by the terminal device, and a digital human model is generated in the server according to the human posture data, and a target motion posture control model is determined from a preset motion posture control model set according to the link state of the communication link. Then, the server outputs motion control parameters according to the target motion posture control model, and sends the digital human motion data generated according to the motion control parameters to the terminal device for display, so that the motion posture control model can be dynamically adjusted according to the link state of the communication link. When the link state is good, the system can select a more complex model with higher control accuracy to generate a smoother and more natural digital human motion. When the link state is not good, the system will automatically switch to a simpler model with lower data transmission requirements to ensure that the basic continuity and stability of the digital human motion can be maintained in a low-bandwidth, high-latency environment. This model selection mechanism that adapts to the link state of the communication link not only improves the adaptability and robustness of the system, but also ensures that a stable digital human motion effect can be provided in different network environments.

[0083] Figure 3 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 3 As shown, an electronic device 300 provided in this embodiment includes: a processor 301 and a memory 302; wherein: The memory 302 is used to store computer programs, and the memory may also be a flash memory.

[0084] The processor 301 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0085] Optionally, the memory 302 may be independent or integrated with the processor 301 .

[0086] When the memory 302 is a device independent of the processor 301, the electronic device 300 may further include: The bus 303 is used to connect the memory 302 and the processor 301 .

[0087] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0088] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0089] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0090] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A digital human motion data processing system, characterized in that: include: Terminal equipment and servers; The terminal device sends the acquired human body posture data to the server via a communication link; The server generates a digital human model according to the human posture data, and determines a target motion posture control model from a preset motion posture control model set according to a link state of the communication link, wherein the preset motion posture control model set includes at least a first motion posture control model established based on a proportional differential controller and a second motion posture control model established based on a proportional integral differential controller; The server outputs motion control parameters according to the target motion posture control model, and sends digital human motion data generated according to the motion control parameters to the terminal device for display.

2. The digital human motion data processing system according to claim 1, characterized in that: If the link state of the communication link is the first link state, the target motion posture control model is the first motion posture control model; If the link state of the communication link is a second link state, the target motion posture control model is the second motion posture control model, wherein the second link state is superior to the first link state.

3. The digital human motion data processing system according to claim 1 or 2, characterized in that: The default configuration of the target motion posture control model is the second motion posture control model.

4. The digital human motion data processing system according to claim 1, characterized in that: The preset motion posture control model set only includes the target motion posture control model, which is a control model established based on a proportional differential controller, and the gain parameter configuration in the target motion posture control model is associated with the link state.

5. The digital human motion data processing system according to claim 4, characterized in that: Before the server outputs the motion control parameters according to the target motion posture control model, the method further includes: If the link state of the communication link is the third link state, configuring a first gain parameter for the target motion posture control model, wherein the first gain parameter includes a first proportional gain parameter and a first differential gain parameter; If the link state of the communication link is the fourth link state, a second gain parameter is configured for the target motion posture control model, wherein the second gain parameter includes a second proportional gain parameter and a second differential gain parameter.

6. The digital human motion data processing system according to claim 5, characterized in that: The server is configured with a configuration parameter mapping table, the third link state corresponds to the first gain parameter in the configuration parameter mapping table, and the fourth link state corresponds to the second gain parameter in the configuration parameter mapping table.

7. A method for processing digital human motion data, characterized in that: Applied to a digital human motion data processing system, the system comprising: a terminal device and a server; The terminal device sends the acquired human body posture data to the server via a communication link; The server generates a digital human model according to the human posture data, and determines a target motion posture control model from a preset motion posture control model set according to a link state of the communication link, wherein the preset motion posture control model set includes at least a first motion posture control model established based on a proportional differential controller and a second motion posture control model established based on a proportional integral differential controller; The server outputs motion control parameters according to the target motion posture control model, and sends digital human motion data generated according to the motion control parameters to the terminal device for display.

8. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to perform the method of claim 7 by executing the executable instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to claim 7 when executed by a processor.

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