3D animation synthesis method, device and system and storage medium

Abstract

The invention discloses a 3D animation synthesis method, device and system and a storage medium, and the method comprises the steps: obtaining a first control instruction sent by a first client, the first control instruction comprising a first equipment action execution demand; calling an action decision model corresponding to the action execution demand of the first equipment to execute a corresponding action, and sending first action execution data and the action decision model to the first client and / or at least one second client, the first client and / or the second client are / is used for carrying out animation synthesis on the basis of the first action execution data and the action decision model, and the second client is a shared client bound with the first client. According to the method, the network bandwidth and processing capacity pressure caused by a large amount of equipment data flood in the Internet of Things environment is avoided, the network congestion and delay caused by too high TPS are reduced, the equipment end and the client adopt the same state data and mathematical model, and the consistency of the presentation effect and the fluency of the 3D animation are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of the cross-integration of the Internet of Things (IoT) and computer graphics, and in particular to a 3D animation synthesis method, device, system and storage medium. Background Art

[0002] The rapid development of the Internet of Things (IoT) and real-time 3D animation technology has brought new application scenarios and development opportunities to areas such as industrial visualization monitoring, smart park management, and virtual simulation teaching. By visually presenting data collected by IoT devices as real-time 3D animation, users can experience a more immersive and interactive experience, effectively improving the efficiency of data visualization analysis.

[0003] However, in practical applications, the integration of the Internet of Things (IoT) and real-time 3D animation faces numerous challenges. Within the IoT, numerous sensors and smart devices continuously generate massive amounts of data, creating a data deluge that places enormous pressure on network bandwidth and data processing capabilities. Excessively high transaction processing rates (TPS) can easily cause network congestion, leading to increased data transmission latency and severely impacting the smoothness of real-time 3D animation playback. This can cause animation freezes and delays, degrading the user experience.

[0004] Furthermore, real-time 3D animation places extremely high demands on the real-time and accuracy of data. To ensure effective animation, a strategy of frequent data reporting and maintaining a high TPS is often adopted. While this approach can ensure smooth and accurate animation to a certain extent, it leads to excessive consumption of system resources, including network bandwidth, computing resources, and storage resources. This significantly increases hardware investment and network operation costs, limiting the widespread application of IoT and real-time 3D animation technology in a wider range of scenarios. Summary of the Invention

[0005] Based on this, it is necessary to provide a 3D animation synthesis method, device, system and storage medium to address the above technical issues, so as to solve at least one problem existing in the above-mentioned prior art.

[0006] In a first aspect, a 3D animation synthesis method is provided, comprising:

[0007] Acquire a first control instruction sent by a first client, where the first control instruction includes a first device action execution requirement;

[0008] Call the action decision model corresponding to the action execution requirement of the first device to execute the corresponding action, and send the first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

[0009] In a possible implementation, before calling a corresponding action decision model to perform a corresponding action based on the device action requirement, the method includes:

[0010] Constructing a first target action requirement, and generating model parameters based on the first target action requirement and actual test data;

[0011] Determining action state change information corresponding to executing the first target action based on the first target action requirement;

[0012] Based on the motion state change information, splitting the execution process of the first target action into multiple action stages;

[0013] Based on the model parameters, an action decision sub-model corresponding to each action stage is constructed.

[0014] In a possible implementation, after constructing the action decision sub-model corresponding to each action stage based on the model parameters, the method further includes:

[0015] Based on the action decision sub-model, generating a corresponding stage decision value;

[0016] comparing the decision value with the actual measurement value of the corresponding stage;

[0017] If the deviation between the decision value and the actual measurement value of the corresponding stage is greater than a preset threshold, determining the cause of the deviation;

[0018] Based on the cause of the deviation, the corresponding action decision sub-model is modified.

[0019] In a possible implementation, before calling a corresponding action decision model to perform a corresponding action based on the device action requirement, the method includes:

[0020] Constructing a second target action requirement, and obtaining key parameters generated when executing the second target action based on the second target action requirement;

[0021] determining a physical constraint condition corresponding to the second target action;

[0022] Based on the key parameters and the physical constraints, an action decision model corresponding to the second target action requirement is constructed.

[0023] In a possible implementation, the key parameters include multiple parameters, and after constructing the action decision model corresponding to the second target action requirement, the method further includes:

[0024] Performing data fusion on the plurality of key parameters to obtain an estimated value of a second target motion parameter;

[0025] The action decision model is modified based on the second target motion parameter estimation value.

[0026] In a possible implementation manner, after obtaining the first control instruction sent by the first client, the method further includes:

[0027] Acquire a second control instruction, where the second control instruction includes a second device action execution requirement;

[0028] determining whether the first control instruction is consistent with the second control instruction;

[0029] If they are inconsistent, determining whether the second control instruction and the first control instruction are for the same action type;

[0030] If for the same action type, based on the action execution requirements of the second device, the corresponding action decision model is called to execute the corresponding action, and the second action execution data and the action decision model are sent to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the second action execution data and the action decision model.

[0031] In a possible implementation, before calling a corresponding action decision model to execute a corresponding action based on the action execution requirement of the second device, the method further includes:

[0032] Determining a sending end of the second control instruction, where the sending end is the first client or the second client;

[0033] If the sending end is the first client, executing the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device;

[0034] If the sending end is the second client, sending instruction change confirmation information to the first client;

[0035] When the confirmation information sent by the first client is obtained, the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device is executed.

[0036] In a second aspect, a 3D animation synthesis device is provided, comprising:

[0037] A first control instruction acquiring unit, configured to acquire a first control instruction sent by a first client, wherein the first control instruction includes a first device action execution requirement;

[0038] An action execution and animation synthesis unit is used to call an action decision model corresponding to the action execution requirement of the first device to execute a corresponding action, and send first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client performs animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

[0039] In a third aspect, a 3D animation synthesis system is provided, comprising:

[0040] A client, a server, and a device, wherein the device comprises the 3D animation synthesis apparatus according to claim 7, and the client comprises a first client and at least one second client, wherein the second client is a shared client of the first client;

[0041] The first client is used to send a first control instruction to the device end, where the first control instruction includes a first device action execution requirement;

[0042] The device side is used to call the action decision model corresponding to the first device action execution requirement to perform the corresponding action, and send the first action execution data and the action decision model to the first client and / or at least one second client;

[0043] The first client and / or the second client is configured to perform animation synthesis based on the first action execution data and the action decision model.

[0044] In a fourth aspect, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-mentioned 3D animation synthesis method when executing the computer-readable instructions.

[0045] In a fifth aspect, a readable storage medium is provided, wherein the readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the 3D animation synthesis method as described above are implemented.

[0046] The above-mentioned 3D animation synthesis method, device, computer equipment and storage medium, and its method implementation include: obtaining a first control instruction sent by a first client, wherein the first control instruction includes a first device action execution requirement; calling an action decision model corresponding to the first device action execution requirement to execute a corresponding action, and sending first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client. In an embodiment of the present application, by directly calling the corresponding action decision model based on the control instruction sent by the client to execute an action and sending relevant data and models for animation synthesis, the pressure on network bandwidth and processing capacity caused by a large amount of device data in an Internet of Things environment is avoided, there is no need to frequently report data to maintain a high TPS, and network congestion and delay caused by excessive TPS are reduced. The device side and the client use the same state data and mathematical model to ensure the consistency of the presentation effect and the smoothness of the 3D animation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 This is a schematic diagram of an application environment of a 3D animation synthesis method in an embodiment of the present application;

[0049] Figure 2 This is a flow chart of a 3D animation synthesis method according to an embodiment of the present application;

[0050] Figure 3 This is a structural diagram of a 3D animation synthesis device in one embodiment of the present application;

[0051] Figure 4 This is a structural diagram of a 3D animation synthesis system in one embodiment of the present application;

[0052] Figure 5 Schematic diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

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

[0054] The 3D animation synthesis method provided in this embodiment can be applied to Figure 1 In an application environment, the client may include a first client and at least one second client, and the second client is associated with the first client. For example, in a family scenario, multiple family members can log in to the same APP account together to realize multi-terminal login. The device side is controlled through one of the clients, and the same 3D animation effect is displayed on other clients. It should be noted that the client can communicate with the server side, and the device side can also communicate with the server side. The client can send control instructions to the device side through the server side, and the device side performs corresponding actions based on the control instructions forwarded by the server side, and sends motion data and mathematical models to the client through the server side, so that the client performs actions based on the same mathematical model and motion data, and generates 3D animation through 3D animation software. Therefore, it is only necessary to send data once to realize the demonstration of 3D animation, and there is no need to frequently report data to maintain high TPS, which reduces network congestion and delay caused by excessively high TPS.

[0055] Clients include, but are not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Servers can be implemented as standalone servers or a server cluster consisting of multiple servers. Devices can include smart home devices such as smart beds, smart mattresses, smart curtains, and smart sofas.

[0056] In one embodiment, if Figure 2 As shown, a 3D animation synthesis method is provided, which is applied in Figure 1 The device side in the example is used as an example to illustrate the process, including the following steps:

[0057] In step S110, a first control instruction sent by a first client is obtained, where the first control instruction includes a first device action execution requirement;

[0058] Optionally, the data format and variables for control instructions between the client and the device can be predefined. The variables can include the device action type and the target state. For example, if the device is a smart bed, the device action type can be defined as "raise the headboard" and the target state can be "raise the headboard 20 degrees." The parameterized configuration of the device action type and its corresponding data format, such as JSON format, for communication between the device and the client can also be predefined.

[0059] Specifically, the client may be pre-installed with a 3D animation display APP to display the animation process of the action execution on the device side. The user can use the APP to send control instructions to the server side, which will be forwarded by the server side to the corresponding device side. Among them, the first control instruction may include a first device action execution requirement, and the first device action execution requirement may include the action type, such as raising the head / foot of the bed to a certain angle, closing the curtains, etc., the action execution speed, the action execution time, and the target state of the action execution, such as raising the head / foot of the bed by 20 degrees, fully closing the curtains, partially closing the curtains, etc.

[0060] In step S120, the action decision model corresponding to the action execution requirement of the first device is called to execute the corresponding action, and the first action execution data and the action decision model are sent to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

[0061] Optionally, the device side may obtain a first control instruction initiated by the first client forwarded by the server side. The first control instruction may include the first device action execution requirement information such as the target state and the action type. At this time, the device side may call the corresponding action decision model to execute the corresponding action according to the action type. At the same time, the device side may also send the first action execution data such as the target state, the current state of the device, the action type, the acceleration, the speed, the action execution time, and the action decision model to the first client and / or at least one second client through the server side. The client side may prepare corresponding moving body image materials in advance for different device sides. These moving body image materials can be obtained by simulating the device side. For example, it can be a 3D bed model, a 3D curtain model, etc. After it obtains the first action execution data and the action decision model, it can obtain the corresponding moving body image material, and can use the programming interface (such as Python script) provided by the 3D animation software to write scripts based on programming languages ​​such as Python or C++ by calling the API interface of the 3D animation software to implement customized action logic and parameter calculation, thereby driving animation generation.

[0062] Specifically, numerical calculations are first used to determine the precise position coordinates of the corresponding device at different points in time, based on the motion equations and parameters provided by the action decision model. Simultaneously, an adaptive algorithm dynamically sets the appropriate frame spacing, taking into account animation smoothness and computational resource consumption, in conjunction with the motion trajectory curve determined by the action decision model. For example, during periods of drastic device motion changes, the frame spacing is reduced to ensure detailed rendering; during periods of uniform motion, the frame spacing is appropriately increased to improve rendering efficiency.

[0063] After completing these calculations, the pre-prepared moving object image material is transformed and rendered in real time based on each point along the motion trajectory. By adjusting the model's rotation, translation, and scaling parameters, the moving object image material changes position along the trajectory. The 3D animation software's rendering engine, combined with lighting, shadows, and material effects, renders each frame into a high-quality image. Finally, these rendered images are played back sequentially at the set frame interval, creating a coherent and smooth 3D animation of the device's motion.

[0064] For example, taking the device end as an example of a bed, a 3D model of the bed corresponding to the bed can be constructed in the 3D animation software. The 3D model of the bed can include a bed frame, a mattress, and movable head and foot lifts, etc. Based on the action decision model and the first action execution data, the angle change and position information of the bed can be calculated and used as the key parameters to drive the 3D model of the bed to perform the action. Taking the head lift of the bed as an example, the rotation angle θ(t) of the head lift calculated by the action decision model is updated in real time through the animation interface of the 3D animation software. When rendering each frame, the current time t is substituted into the action decision model to obtain the corresponding angle value, and then the angle value is applied to the 3D model transformation matrix of the head lift to achieve the angle adjustment of the head lift. Similarly, the foot lift and other movable parts of the bed are also driven according to the corresponding action decision model, so that the entire bed model is dynamically displayed according to the actual motion trajectory.

[0065] It should be noted that in order to make the rendering effect of the 3D bed model more realistic and smooth, the rendering frame rate can be adjusted during the animation rendering process to ensure the smoothness of the bed movement and avoid stuttering. At the same time, combined with the lighting model and material settings, the bed movement can be displayed under different lighting conditions to enhance the three-dimensionality and realism of the scene. For example, taking the 3D bed model as an example, when the head of the bed is raised, the light reflection and shadow effects are calculated according to the lighting direction and the normal direction of the bed surface, making the bed appear more realistic in the 3D scene. In addition, animation transition effects can be added, such as using slow-in and slow-out animation effects when the bed angle starts to rise and stops, simulating the real mechanical movement process and enhancing the user's visual experience.

[0066] It is understandable that the second client and the first client can establish a binding relationship in advance, that is, the first client and the second client can use the same account to log in to the APP together, and the first client and the second client can both issue control instructions through the APP, and the first client and the second client can also perform animation display in the animation display interface. For example, taking a smart bed device as an example, it may include a left bed and a right bed. If the first client is bound to the left bed, the right bed can be shared with the second client for binding. At this time, the first client and the second client are both bound to the smart bed device. If the first client or the second client sends the first control instruction, the 3D animation can be displayed in the first client and the second client at the same time.

[0067] In an embodiment of the present application, a 3D animation synthesis method is provided, comprising: obtaining a first control instruction sent by a first client, wherein the first control instruction includes a first device action execution requirement; calling an action decision model corresponding to the first device action execution requirement to execute a corresponding action, and sending first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client. In an embodiment of the present application, by directly calling the corresponding action decision model to execute an action based on the control instruction sent by the client and sending relevant data and models for animation synthesis, the pressure on network bandwidth and processing capacity caused by a large amount of device data in an IoT environment is avoided, there is no need to frequently report data to maintain a high TPS, and network congestion and delay caused by excessive TPS are reduced. The device side and the client side use the same state data and mathematical model to ensure the consistency of the presentation effect and the smoothness of the 3D animation.

[0068] In one embodiment of the present application, before calling the corresponding action decision model to perform the corresponding action based on the device action requirement, the process includes:

[0069] Determine the first target action requirement;

[0070] Determining action state change information corresponding to executing the first target action based on the first target action requirement;

[0071] Based on the action state change information, splitting the execution process of the first target action into multiple action stages;

[0072] Based on the first target action requirement and actual test data, generating model parameters corresponding to each action stage;

[0073] Based on the model parameters corresponding to each action stage, an action decision sub-model corresponding to each action stage is constructed.

[0074] It is understandable that different types of device ends may correspond to different action decision models due to differences in their physical structure, motion characteristics and control methods. The motion trajectories, speed changes and force conditions of different action types are different, so different action types also correspond to different action decision models. When the same device end performs continuous actions, it can be divided into multiple action stages, and each action stage also corresponds to an action decision sub-model, which can refine the control logic and enable the device to perform actions according to the optimal strategy at different stages.

[0075] Specifically, when constructing an action decision model, the first target action requirement is first determined. The first target action requirement may include information such as the action type, device type, target state, and current state. Based on the above information, the entire process of the device executing the action can be split according to the state change information, thereby obtaining multiple action stages. Then, an action decision sub-model corresponding to each action stage can be constructed. Among them, the state change information may include key state nodes or motion parameters, for example, by key state divisions such as the starting state node, intermediate transition state node, and target state node, or by motion parameters such as the device motion speed and acceleration.

[0076] For example, taking a smart bed as an example, an action decision model for the angle-raising action of the end of the smart bed is constructed. It can be understood that the action of raising the angle of the end of the bed is a process of first accelerating, then uniform speed, and finally decelerating to the target angle. Therefore, it can be divided into an acceleration stage, a uniform speed stage, and a deceleration stage. Assume that the initial angle of the end of the bed is 0° and the target angle is θmax. The acceleration magnitudes of the acceleration stage and the deceleration stage are a1 and a2 respectively, and the acceleration is constant. The speed of the uniform speed stage is v. The time of each stage is divided into the acceleration stage time t1, the uniform speed stage time t2, the deceleration stage time t3, and the total time is T, then T=t1+t2+t3.

[0077] Then, the mathematical expression of the change of the bed foot angle over time in stages can be as follows:

[0078] Acceleration stage (0≤t≤t1): According to the formula of uniform acceleration rotation θ(t)=(1 / 2)a1t 2 , describing the quadratic function relationship of angle over time.

[0079] Uniform speed stage t1<t≤t1+t2: The angle increases uniformly with time, the formula is θ(t)=θ1+v(t-t1), where θ1=(1 / 2)a1t1 2 is the angle at the end of the acceleration phase, and v=a1t1 is the speed during the uniform speed phase.

[0080] Deceleration stage (t1+t2<t≤T): The uniform deceleration rotation formula is

[0081] θ(t)=θ2+v(t-t1-t2)-(1 / 2)α2(t-t1-t2) 2 , where θ2 is the angle at the beginning of the deceleration phase, (1 / 2)α1t1 2 +α1t1t2.

[0082] Among them, θmax is determined according to design requirements, and the values ​​of a1 and a2 can be obtained through experimental data.

[0083] Then, the total time T is determined according to actual needs, and the values ​​of t1, t2, and t3 are calculated through T=t1+t2+t3 and the relationship between the above stages, thereby determining the various parameters of the model.

[0084] In one embodiment of the present application, after constructing the action decision sub-model corresponding to each action stage based on the model parameters corresponding to each action stage, the method further includes:

[0085] Based on the action decision sub-model, generating a corresponding stage decision value;

[0086] comparing the decision value with the actual measurement value of the corresponding stage;

[0087] If the deviation between the decision value and the actual measurement value of the corresponding stage is greater than a preset threshold, determining the cause of the deviation;

[0088] Based on the cause of the deviation, the action decision sub-model is modified.

[0089] Optionally, after generating the action decision sub-model corresponding to each action stage, the angle θ(t) corresponding to each time point in each action stage is calculated based on the action decision sub-model, and then compared with the bed foot lifting angle corresponding to each time point obtained by actual measurement. If the angle deviation is greater than the preset angle threshold, for example, greater than 3 degrees, it means that there is an abnormality in the action decision sub-model. At this time, sensitivity analysis, error tracing algorithm and other means can be used to locate the specific factors that cause the deviation, such as the deviation between the model assumptions and the actual scene, the error in the parameter estimation method, etc., and optimize and improve the action decision sub-model. For example, corrections to factors such as friction and motor characteristic curves can be added to make the model more consistent with the actual angle lifting trajectory of the smart bed foot. After optimizing the model, simulation calculations and actual measurements need to be compared again to verify the optimization effect until the angle deviation meets the requirements.

[0090] In one embodiment of the present application, before calling the corresponding action decision model to perform the corresponding action based on the device action requirement, the method further includes:

[0091] Determine the second target action requirement;

[0092] Acquire key parameters generated when executing the second target action based on the second target action requirement;

[0093] determining a physical constraint condition corresponding to the second target action;

[0094] Based on the key parameters and the physical constraints, an action decision model corresponding to the second target action requirement is constructed.

[0095] Optionally, when constructing the action decision model, a second target action requirement is first determined. This second target action requirement may include information such as the action type, device type, target state, and current state. The corresponding device can then be controlled to execute the action specified by the second target action requirement. Simultaneously, pre-installed sensors can be used to collect key parameters generated when the device executes the second target action. For example, when executing a headboard-lifting action on a smart bed, angle sensors (such as inclination sensors) can be used to monitor the tilt angles of various bed components in real time. This data is converted into time series data, yielding discrete values ​​of the bed angle over time. With time t as the independent variable and the headboard tilt angle θ(t) as the dependent variable, a series of discrete data points (θ(t1), t1), (θ(t2), t2), etc.) can be obtained over a specific time period. Furthermore, pressure data from different areas of the bed collected by pressure sensors can be used to assist in determining the bed's load condition. This information affects parameters such as acceleration and velocity of the bed's motion. The motor encoder can provide feedback on the number of motor revolutions and velocity. Combined with the transmission ratio of the bed's mechanical transmission structure, this data can be converted into the displacement and velocity parameters of the bed's actual motion. If the motor rotates n times, the displacement of the corresponding parts of the bed can be calculated through the transmission ratio i, which is s=n×i.

[0096] The corresponding action decision model is constructed based on the key parameters collected by the above-mentioned various types of sensors. The action decision model can be a linear model, a polynomial model, a trigonometric function model, etc. Taking a smart bed as an example, if the bed angle lifting process is approximately uniform motion, a linear model can be used. Its equation form is θ(t) = θ0 + vt, where θ0 is the initial angle, v is the angle change rate, and t is time. If the bed movement involves acceleration and deceleration processes, a polynomial model is more suitable, such as the quadratic polynomial equation θ(t) = at2 + bt + c. The coefficients a, b, and c are determined based on the collected data through fitting methods such as the least squares method.

[0097] In practical applications, trigonometric models are often used to account for the physical characteristics of bed motion. For example, a modified form of the sine function is used to simulate the smooth transition of the bed's angular movement. The equation can be expressed as θ(t) = Asin(ωt + φ) + θavg, where A is the angular amplitude, ω is the angular frequency, φ is the initial phase, and θavg is the average angle. By adjusting these parameters, the equation can be made to better fit the actual bed's motion.

[0098] It should be noted that bed movement is susceptible to physical limitations, such as the angle lifting range, speed limit, structural stability, etc. Therefore, when constructing the motion decision model, these physical constraints need to be introduced into the model as constraints. For example, for angle limitations, angle thresholds θmin and θmax can be set. When the bed angle θ(t) calculated by the model exceeds θmax or is lower than θmin, the angle value is truncated to keep it within the allowable range. For speed limitations, a speed limit vmax is set in the model. If the calculated bed movement speed exceeds vmax, the model parameters are adjusted to reduce the speed to a reasonable range. By introducing these physical constraints, the model is guaranteed to conform to actual physical laws.

[0099] In one embodiment of the present application, the key parameters include multiple parameters, and after constructing the action decision model corresponding to the second target action requirement, the method further includes:

[0100] Performing data fusion on the plurality of key parameters to obtain an estimated value of a second target motion parameter;

[0101] The action decision model is modified based on the second target motion parameter estimation value.

[0102] Given that the key parameters collected by each sensor are susceptible to noise and measurement errors, data filtering, noise reduction, and fusion are required. During the data fusion process, algorithms such as weighted averaging are typically used to comprehensively process the data from the angle sensor, pressure sensor, and motor encoder. Weights are determined based on factors such as the sensor's accuracy level and the stability of historical data. Sensors with high accuracy and good stability are given higher weights, while sensors with lower accuracy are given lower weights. This results in more accurate estimates of the bed's motion parameters. These estimates are then used to optimize the parameters of the motion trajectory equation.

[0103] For example, by comparing the bed angle change speeds calculated from different sensor data, each sensor data is assigned a different weight to obtain a fused velocity value, which is then substituted into the motion trajectory equation to correct the velocity parameters. However, sensor drift and errors accumulate over time, affecting the long-term accuracy of the model. Therefore, to account for sensor drift and errors, the motion trajectory equation is regularly calibrated. Based on the actual measured bed angle and position data, the model parameters of the motion decision model are readjusted to ensure that the motion decision model accurately reflects the bed's actual motion trajectory.

[0104] In an embodiment of the present application, after obtaining the first control instruction sent by the first client, the method further includes:

[0105] Acquire a second control instruction, where the second control instruction includes a second device action execution requirement;

[0106] determining whether the first control instruction is consistent with the second control instruction;

[0107] If they are inconsistent, determining whether the second control instruction and the first control instruction are for the same action type;

[0108] If for the same action type, based on the action execution requirements of the second device, the corresponding action decision model is called to execute the corresponding action, and the second action execution data and the action decision model are sent to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the second action execution data and the action decision model.

[0109] Optionally, since there are multiple clients bound to the same account at the same time and there are multiple clients logged in at the same time, after one of the clients sends the first control instruction, the other clients may send the second control instruction. The first control instruction and the second control instruction may be the same or different. For example, the first control instruction is to lift the head of the bed, and the second control instruction is to lift the foot of the bed. Therefore, after obtaining the second control instruction, it can be compared with the first control instruction to determine whether they are consistent. If they are consistent, it is defaulted to a repeated control instruction. To avoid wasting resources caused by repeated execution, the second control instruction can be ignored. If they are inconsistent, it can be further determined whether the second control instruction and the first control instruction are for the same action type. For example, the first control instruction is to lift the head of the bed 20 degrees, and the second control instruction is to lift the head of the bed 30 degrees. Both are for the head of the bed lifting action, but the lifting angle is different. At this time, based on the action execution requirement of the second device, the corresponding action decision model can be called to execute the corresponding action, and the second action execution data and the action decision model can be sent to the first client and / or at least one second client, so that the first client and / or the second client can perform animation synthesis based on the second action execution data and the action decision model. That is, perform the action of lifting the head of the bed 30 degrees, and show the animation.

[0110] In an embodiment of the present application, after obtaining the second control instruction, the method further includes:

[0111] Determining a sending end of the second control instruction, where the sending end is the first client or the second client;

[0112] If the sending end is the first client, performing the step of determining whether the first control instruction is consistent with the second control instruction;

[0113] If the sending end is the second client, sending instruction change confirmation information to the first client;

[0114] When the confirmation information sent by the first client is obtained, the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device is executed.

[0115] Optionally, after obtaining the second control instruction, it can be determined whether the sender of the second control instruction is the first client or the second client. If the sender is the first client, it can be determined that the first control instruction is incorrect or needs to be changed, and the second control instruction is directly executed. If the sender is the second client, it is necessary to first send an instruction change confirmation message to the sender of the first control instruction, prompting the first client that the second control instruction sent by other clients has been obtained, and whether to continue to execute the first control instruction or the second control instruction. If the first client sends a confirmation message agreeing to execute the second control instruction within a preset time, the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device can be executed; otherwise, the second control instruction is ignored.

[0116] In an embodiment of the present application, by directly calling the corresponding action decision model based on the control instruction sent by the client to execute the action and sending the relevant data and model for animation synthesis, the network bandwidth and processing capacity pressure caused by the large amount of device data in the Internet of Things environment is avoided. There is no need to frequently report data to maintain high TPS, which reduces network congestion and delay caused by excessive TPS. The device side and the client side use the same state data and mathematical model to ensure the consistency of the presentation effect and the smoothness of the 3D animation.

[0117] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0118] In one embodiment, a 3D animation synthesis device is provided, which corresponds to the 3D animation synthesis method in the above embodiment. Figure 3 As shown, the 3D animation synthesis device includes a first control instruction acquisition unit 10 and an action execution and animation synthesis unit 20. The functional modules are described in detail as follows:

[0119] A first control instruction acquiring unit 10 is configured to acquire a first control instruction sent by a first client, wherein the first control instruction includes a first device action execution requirement;

[0120] The action execution and animation synthesis unit 20 is used to call the action decision model corresponding to the action execution requirement of the first device to execute the corresponding action, and send the first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client can perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

[0121] In one embodiment of the present application, the apparatus further includes: an action decision model building unit, configured to:

[0122] Determine the first target action requirement;

[0123] Determining action state change information corresponding to executing the first target action based on the first target action requirement;

[0124] Based on the action state change information, splitting the execution process of the first target action into multiple action stages;

[0125] Based on the first target action requirement and actual test data, generating model parameters corresponding to each action stage;

[0126] Based on the model parameters corresponding to each action stage, an action decision sub-model corresponding to each action stage is constructed.

[0127] In one embodiment of the present application, the action decision model building unit is further configured to:

[0128] Based on the action decision sub-model, generating a corresponding stage decision value;

[0129] comparing the decision value with the actual measurement value of the corresponding stage;

[0130] If the deviation between the decision value and the actual measurement value of the corresponding stage is greater than a preset threshold, determining the cause of the deviation;

[0131] Based on the cause of the deviation, the action decision sub-model is modified.

[0132] In one embodiment of the present application, the action decision model building unit is further configured to:

[0133] Determine the second target action requirement;

[0134] Acquire key parameters generated when executing the second target action based on the second target action requirement;

[0135] determining a physical constraint condition corresponding to the second target action;

[0136] Based on the key parameters and the physical constraints, an action decision model corresponding to the second target action requirement is constructed.

[0137] In one embodiment of the present application, the key parameters include multiple, and the action decision model construction unit is further used to:

[0138] Performing data fusion on the plurality of key parameters to obtain an estimated value of a second target motion parameter;

[0139] The action decision model is modified based on the second target motion parameter estimation value.

[0140] In one embodiment of the present application, the action execution and animation synthesis unit 20 is further configured to:

[0141] Acquire a second control instruction, where the second control instruction includes a second device action execution requirement;

[0142] determining whether the first control instruction is consistent with the second control instruction;

[0143] If they are inconsistent, determining whether the second control instruction and the first control instruction are for the same action type;

[0144] If for the same action type, based on the action execution requirements of the second device, the corresponding action decision model is called to execute the corresponding action, and the second action execution data and the action decision model are sent to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the second action execution data and the action decision model.

[0145] In one embodiment of the present application, the action execution and animation synthesis unit 20 is further configured to:

[0146] Determining a sending end of the second control instruction, where the sending end is the first client or the second client;

[0147] If the sending end is the first client, executing the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device;

[0148] If the sending end is the second client, sending instruction change confirmation information to the first client;

[0149] When the confirmation information sent by the first client is obtained, the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device is executed.

[0150] In an embodiment of the present application, by directly calling the corresponding action decision model based on the control instruction sent by the client to execute the action and sending the relevant data and model for animation synthesis, the network bandwidth and processing capacity pressure caused by the large amount of device data in the Internet of Things environment is avoided. There is no need to frequently report data to maintain high TPS, which reduces network congestion and delay caused by excessive TPS. The device side and the client side use the same state data and mathematical model to ensure the consistency of the presentation effect and the smoothness of the 3D animation.

[0151] The specific definition of the 3D animation synthesis device can be found in the definition of the 3D animation synthesis method above and will not be repeated here. The various modules in the above-mentioned 3D animation synthesis device can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0152] In one embodiment, a 3D animation synthesis system is provided, which corresponds to the 3D animation synthesis method in the above embodiment. Figure 4 As shown, the 3D animation synthesis system includes a client 1, a server 2 and a device 3. The functional modules are described in detail as follows:

[0153] The device end 3 includes the 3D animation synthesis device as described above, and the client includes a first client 11 and at least one second client 12, wherein the second client 12 is a shared client of the first client 11;

[0154] The first client 11 is used to send a first control instruction to the device end, where the first control instruction includes a first device action execution requirement;

[0155] The device end 3 is configured to call an action decision model corresponding to the first device action execution requirement to execute the corresponding action, and send first action execution data and the action decision model to the first client 11 and / or at least one second client 12;

[0156] The first client 11 and / or the second client 12 is configured to perform animation synthesis based on the first action execution data and the action decision model.

[0157] It should be noted that in many scenarios, there are multiple clients that are bound to the same device or the same device account at the same time. For example, in a family scenario, multiple family members can log in to the same APP account together, and multiple-terminal logins can be achieved. The device can be controlled through one of the clients, and the same 3D animation effects can be displayed on other clients. It should be noted that the client communicates with the server, and the device can also communicate with the server. The client can send control instructions to the device through the server, and the device performs corresponding actions based on the control instructions forwarded by the server, and sends motion data and mathematical models to the client through the server, so that the client can perform actions based on the same mathematical model and motion data after parsing, and generate 3D animations through 3D animation software. Therefore, it is only necessary to send data once to achieve the demonstration of 3D animation, and there is no need to frequently report data to maintain high TPS, which reduces network congestion and delays caused by excessive TPS.

[0158] In an embodiment of the application, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer-readable instructions are executed by the processor, a 3D animation synthesis method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0159] In an embodiment of the present application, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above-mentioned 3D animation synthesis method are implemented.

[0160] In an embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the steps of the above-mentioned 3D animation synthesis method are implemented.

[0161] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include processes in the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0163] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A 3D animation synthesis method, characterized in that: The method comprises: Acquire a first control instruction sent by a first client, where the first control instruction includes a first device action execution requirement; Call the action decision model corresponding to the action execution requirement of the first device to execute the corresponding action, and send the first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

2. The 3D animation synthesis method according to claim 1, wherein: Before calling the corresponding action decision model to perform the corresponding action based on the device action requirement, the method includes: Determine the first target action requirement; Determining action state change information corresponding to executing the first target action based on the first target action requirement; Based on the action state change information, splitting the execution process of the first target action into multiple action stages; Based on the first target action requirement and actual test data, generating model parameters corresponding to each action stage; Based on the model parameters corresponding to each action stage, an action decision sub-model corresponding to each action stage is constructed.

3. The 3D animation synthesis method according to claim 2, wherein: After constructing the action decision sub-model corresponding to each action stage based on the model parameters corresponding to each action stage, the method further includes: Based on the action decision sub-model, generating a corresponding stage decision value; comparing the decision value with the actual measurement value of the corresponding stage; If the deviation between the decision value and the actual measurement value of the corresponding stage is greater than a preset threshold, determining the cause of the deviation; Based on the cause of the deviation, the action decision sub-model is modified.

4. The 3D animation synthesis method according to claim 1, wherein: Before calling the corresponding action decision model to execute the corresponding action based on the device action requirement, the method further includes: Determine the second target action requirement; Acquire key parameters generated when executing the second target action based on the second target action requirement; determining a physical constraint condition corresponding to the second target action; Based on the key parameters and the physical constraints, an action decision model corresponding to the second target action requirement is constructed.

5. The 3D animation synthesis method according to claim 4, wherein: The key parameters include a plurality of parameters. After constructing the action decision model corresponding to the second target action requirement, the method further includes: Performing data fusion on the plurality of key parameters to obtain an estimated value of a second target motion parameter; The action decision model is modified based on the second target motion parameter estimation value.

6. The 3D animation synthesis method according to claim 1, wherein: After obtaining the first control instruction sent by the first client, the method further includes: Acquire a second control instruction, where the second control instruction includes a second device action execution requirement; determining whether the first control instruction is consistent with the second control instruction; If they are inconsistent, determining whether the second control instruction and the first control instruction are for the same action type; If for the same action type, based on the action execution requirements of the second device, the corresponding action decision model is called to execute the corresponding action, and the second action execution data and the action decision model are sent to the first client and / or at least one second client, so that the first client and / or the second client perform animation synthesis based on the second action execution data and the action decision model.

7. The 3D animation synthesis method according to claim 6, wherein: Before calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device, the method further includes: Determining a sending end of the second control instruction, where the sending end is the first client or the second client; If the sending end is the first client, executing the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device; If the sending end is the second client, sending instruction change confirmation information to the first client; When the confirmation information sent by the first client is obtained, the step of calling the corresponding action decision model to execute the corresponding action based on the action execution requirement of the second device is executed.

8. A 3D animation synthesis device, characterized in that: The device comprises: A first control instruction acquiring unit, configured to acquire a first control instruction sent by a first client, wherein the first control instruction includes a first device action execution requirement; An action execution and animation synthesis unit is used to call an action decision model corresponding to the action execution requirement of the first device to execute a corresponding action, and send first action execution data and the action decision model to the first client and / or at least one second client, so that the first client and / or the second client performs animation synthesis based on the first action execution data and the action decision model, wherein the second client is a shared client bound to the first client.

9. A 3D animation synthesis system, characterized in that: The system comprises: A client, a server, and a device, wherein the device comprises the 3D animation synthesis apparatus according to claim 7, and the client comprises a first client and at least one second client, wherein the second client is a shared client of the first client; The first client is used to send a first control instruction to the device end, where the first control instruction includes a first device action execution requirement; The device side is used to call the action decision model corresponding to the first device action execution requirement to perform the corresponding action, and send the first action execution data and the action decision model to the first client and / or at least one second client; The first client and / or the second client is configured to perform animation synthesis based on the first action execution data and the action decision model.

10. A readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the 3D animation synthesis method according to any one of claims 1 to 7 are implemented.

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