A Method and Device for Time Statistical Analysis of Motion Mechanisms Based on One-Way Digital Twin

Abstract

This application provides a method and apparatus for time statistical analysis of motion mechanisms based on unidirectional digital twins. The method includes: receiving and identifying motion mechanism operation data according to a preset time interval to determine the corresponding time statistical results; collecting historical motion mechanism data, performing feature extraction on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, performing interactive feature construction on the motion efficiency physical characteristics to determine the corresponding physical feature dataset, training a preset initial model based on the physical feature dataset to determine the corresponding robot time adjustment model; inputting the time statistical results into the robot time adjustment model to determine the corresponding optimal time parameters, and performing robot control based on the optimal time parameters. This application can improve the control efficiency and accuracy of motion mechanisms based on time statistics and adjustment.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for time statistical analysis of motion mechanisms based on unidirectional digital twins. Background Technology

[0002] In the development of industrial robots, the controller, as the "brain" of the robot, directly determines its overall performance. However, traditional controller development methods face numerous challenges. First, the development and testing process based on actual controller prototypes is cumbersome and time-consuming, especially when there are limited prototypes available to a limited number of developers, which significantly restricts development efficiency. Second, testing motors and robotic arms directly before the controller is fully developed increases development costs and may lead to equipment damage or safety accidents due to unstable controller performance.

[0003] To overcome these challenges, the industry has begun exploring digital twin technology for the development of motion mechanism controllers. This involves fully utilizing data from physical models, sensor updates, and operational history to integrate multi-disciplinary, multi-physical-quantity, multi-scale, and multi-probabilistic simulation processes, mapping these data in a virtual space to reflect the entire lifecycle of the corresponding physical equipment. Simulation technology can simulate the motion, control, and interaction of robots in a virtual environment, helping developers to verify algorithms, evaluate performance, and optimize them without relying on physical machines. However, existing simulation technologies still have some shortcomings. For example, many simulation software programs only support specific brands of robots, limiting their versatility and flexibility; furthermore, these software programs also have deficiencies in data processing, trajectory analysis, and time statistics, making it difficult to meet the needs of complex application scenarios.

[0004] Against this backdrop, a time statistical analysis method for motion mechanisms based on unidirectional digital twins is proposed. This method aims to analyze the corresponding trajectory point information by parsing the controller entity data to perform controller testing, enabling functions such as robot pose tracing and time statistics. This provides an efficient, flexible, and accurate solution for the development of motion mechanism controllers. This method not only significantly improves development efficiency and reduces development costs but also enhances the performance stability and safety of robots, providing strong support for the widespread application of motion mechanisms. Summary of the Invention

[0005] To address the problems in the prior art, this application provides a method and apparatus for time statistical analysis of motion mechanisms based on unidirectional digital twins, which can improve the control efficiency and accuracy of motion mechanisms based on time statistics and adjustments.

[0006] To solve at least one of the above problems, this application provides the following technical solution:

[0007] Firstly, this application provides a method for time statistical analysis of motion mechanisms based on unidirectional digital twins, including:

[0008] The motion mechanism operation data is received and identified according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0009] The pose of the corresponding motion mechanism is determined based on the joint angle data and the preset robot forward kinematics algorithm. The running trajectory of the corresponding motion mechanism is determined based on the expandable additional parameters. Time statistics are performed based on the pose of the motion mechanism and the running trajectory of the motion mechanism to determine the corresponding time statistics results.

[0010] Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model.

[0011] The time statistics are input into the robot time adjustment model to determine the corresponding optimal time parameters. The trajectory point data is updated according to the optimal time parameters, and the robot is controlled according to the updated trajectory point data.

[0012] Further, determining the corresponding motion mechanism trajectory based on the expandable additional parameters includes:

[0013] The expandable additional parameters are identified to determine the corresponding trajectory start point, trajectory end point, and trajectory distance between each trajectory point.

[0014] The trajectory distances between each trajectory point are accumulated to determine the corresponding total trajectory distance. The running trajectory of the corresponding motion mechanism is determined based on the trajectory start point, the trajectory end point, and the total trajectory distance.

[0015] Further, the step of performing time statistics based on the pose of the motion mechanism and the trajectory of the motion mechanism to determine the corresponding time statistics results includes:

[0016] The trajectory running time is determined based on the number of trajectory points contained in the motion mechanism's running trajectory and the preset time interval;

[0017] A pose mapping operation is performed on the trajectory running time and the total trajectory distance based on the pose of the motion mechanism. A time statistics operation is then performed on the trajectory running time and the total trajectory distance after the mapping operation to determine the corresponding time statistics result.

[0018] Furthermore, before performing feature extraction operations on the historical motion mechanism data based on a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, the process includes:

[0019] The corresponding robot kinematic model is determined based on the preset robot joint angles, velocities, and accelerations. The robot kinematic model is used to represent the robot joint motion relationships.

[0020] The corresponding robot dynamics model is determined based on the preset robot torque, load, and energy consumption. The robot dynamics model is used to represent the physical characteristics of the robot's motion process.

[0021] Further, the step of performing interactive feature construction operations on the physical features of motion efficiency to determine the corresponding physical feature dataset includes:

[0022] Different motion efficiency physical characteristics are interactively combined to determine the corresponding composite characteristics;

[0023] The corresponding physical feature dataset is determined based on the composite features.

[0024] Further, updating the parameters of the initial model based on the performance evaluation results to determine the corresponding robot time adjustment model includes:

[0025] The parameters of the initial model are analyzed for sensitivity using a preset sensitivity analysis algorithm to determine the corresponding high-sensitivity parameters.

[0026] The high-sensitivity parameters are iteratively updated based on the performance evaluation results to determine the corresponding robot time adjustment model.

[0027] Furthermore, after performing robot control based on the updated trajectory point data, the method further includes:

[0028] The updated trajectory point data is saved, and the corresponding saved trajectory points are determined.

[0029] A backtracking operation is performed on the saved trajectory points to determine the corresponding robot backtracking data. The backtracking operation includes simulated dragging or double-clicking of trajectory points, and the backtracking data includes at least one of robot pose and robot running state.

[0030] Secondly, this application provides a motion mechanism time statistical analysis device based on unidirectional digital twins, comprising:

[0031] The communication and drive module is used to receive and identify the motion mechanism operation data according to a preset time interval, and determine the corresponding trajectory point data. The trajectory point data includes joint angle data and expandable additional parameters. The historical form usage data includes at least one of historical form performance data and historical form structure change data.

[0032] The time statistics module is used to determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm, determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters, perform time statistics operation based on the motion mechanism pose and the running trajectory of the motion mechanism, and determine the corresponding time statistics result.

[0033] The time adjustment model training module is used to collect historical motion mechanism data, perform feature extraction operations on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction operations on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model.

[0034] The time adjustment and data storage module is used to input the time statistics results into the robot time adjustment model, determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control based on the updated trajectory point data.

[0035] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the motion mechanism time statistical analysis method based on unidirectional digital twin.

[0036] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the described method for time statistical analysis of motion mechanisms based on unidirectional digital twins.

[0037] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the aforementioned motion mechanism time statistical analysis method based on unidirectional digital twins.

[0038] As can be seen from the above technical solution, this application provides a method and apparatus for time statistical analysis of motion mechanisms based on unidirectional digital twins. It receives and identifies motion mechanism operation data according to preset time intervals to determine the corresponding time statistical results; collects historical motion mechanism data; extracts features from the historical motion mechanism data based on preset robot kinematics and dynamics models to determine the corresponding motion efficiency physical features; performs interactive feature construction on the motion efficiency physical features to determine the corresponding physical feature dataset; trains a preset initial model based on the physical feature dataset to determine the corresponding robot time adjustment model; inputs the time statistical results into the robot time adjustment model to determine the corresponding optimal time parameters; and controls the robot based on the optimal time parameters. This allows for improved control efficiency and accuracy of the motion mechanism based on time statistics and adjustments. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is one of the flowcharts illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0041] Figure 2 This is the second flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in this application embodiment;

[0042] Figure 3 This is the third flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0043] Figure 4 This is the fourth flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0044] Figure 5 This is the fifth flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0045] Figure 6 This is the sixth flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0046] Figure 7This is the seventh flowchart illustrating the motion mechanism time statistical analysis method based on unidirectional digital twin in the embodiments of this application;

[0047] Figure 8 This is a structural diagram of the motion mechanism time statistical analysis device based on unidirectional digital twin in the embodiments of this application;

[0048] Figure 9 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0049] Figure label:

[0050] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0053] Considering that existing motion mechanism simulation technologies can only approximate the average motion time in time statistics, failing to demonstrate the acceleration and deceleration processes of motion mechanisms, leading to low simulation control efficiency, this application provides a motion mechanism time statistical analysis method and apparatus based on unidirectional digital twins. This method involves receiving and identifying motion mechanism operation data at preset time intervals to determine corresponding time statistical results; collecting historical motion mechanism data; extracting features from the historical data based on preset robot kinematics and dynamics models to determine corresponding motion efficiency physical characteristics; constructing interactive features from these physical characteristics to determine the corresponding physical feature dataset; training a preset initial model based on the physical feature dataset to determine the corresponding robot time adjustment model; inputting the time statistical results into the robot time adjustment model to determine the optimal time parameters; and controlling the robot based on these optimal time parameters. This approach improves the control efficiency and accuracy of motion mechanisms based on time statistics and adjustments.

[0054] To improve the control efficiency and accuracy of motion mechanisms based on time statistics and adjustments, this application provides an embodiment of a motion mechanism time statistical analysis method based on unidirectional digital twins, see [link to embodiment]. Figure 1 The motion mechanism time statistical analysis method based on unidirectional digital twin specifically includes the following:

[0055] Step S101: Receive and identify motion mechanism operation data according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0056] Optionally, in this embodiment, the motion mechanism operation data is sent by the controller entity and received and identified by the simulation software, which is used to simulate the operation of the physical robot in virtual space.

[0057] Optionally, in this embodiment, the software, through built-in support for multiple communication protocols such as Socket, MQTT, and serial communication, defines the communication data format between the software and the controller entity; the JSON string contains the robot name, joint angle data, and expandable additional parameters (pathstart, pathend, distance), and the software supports simulation of multiple controller entities. The controller entity acts as the communication server.

[0058] Optionally, in this embodiment, the preset time interval indicates that the communication time interval with the controller entity is fixed and known, preferably in the millisecond range, 20ms or less. Data is received at the preset time interval, and each received data is recorded as a trajectory point data. The trajectory point data includes the robot's joint angle data and expandable additional parameters.

[0059] For example, here is an example of trajectory point data:

[0060] "Robots":[

[0061] {

[0062] Name":"STEP-SA1800",

[0063] "JointCount":6,

[0064] "A0":"0.0",

[0065] "A1":"0.0",

[0066] "A2":"0.0",

[0067] "A3":"0.0",

[0068] "A4":"1.622",

[0069] "A5":"0.0",

[0070] "PathStart":true,

[0071] "PathEnd":false,

[0072] "Distance":"200"

[0073] },

[0074] Among the trajectory points mentioned above, there are six joint angle data, representing the angles of each joint of the motion mechanism; the expandable additional parameters are "PathStart", "PathEnd" and "Distance". When the parameter "PathStart" is detected as true, the point is considered to be the starting point of a trajectory segment, and when the parameter "PathEnd" is detected as true, the point is considered to be the ending point of a trajectory segment. "Distance" represents the robot's movement distance from the previous data point to this data point, in mm.

[0075] Understandably, joint angle data can be used to represent the robot's pose, while receiving and parsing scalable additional parameters at fixed time intervals, when the time interval is in the millisecond range, can realistically simulate the acceleration and deceleration process on the robot's running trajectory and the real simulation trajectory approximation process. This increases the accuracy of the simulation and lays a solid foundation for subsequent time statistical analysis.

[0076] Step S102: Determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm; determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters; perform time statistics operation based on the pose of the motion mechanism and the running trajectory of the motion mechanism; and determine the corresponding time statistics result.

[0077] Optionally, in step S101, trajectory point data is obtained by receiving and identifying controller entity signals. In this step, time statistics are performed by parsing the trajectory point data.

[0078] Optionally, in this embodiment, forward kinematics (FK) is used to calculate the position and orientation of the end effector (such as the wrist of a robotic hand or a tool) in space based on the robot's joint angles. In simulation software, the forward kinematics algorithm is used to convert joint angles into the robot's specific position in three-dimensional space, thereby simulating the robot's motion in a virtual environment.

[0079] Optionally, in this embodiment, the running trajectory of the corresponding motion mechanism is determined according to the expandable additional parameters.

[0080] Specifically, the system identifies expandable additional parameters. When the parameter "PathStart" is detected as true, the point is considered to be the starting point of a trajectory segment; when the parameter "PathEnd" is detected as true, the point is considered to be the ending point of a trajectory segment. After parsing and storing the data sent by the controller, the number of trajectory points under each trajectory segment is known and denoted as PtCount. "Distance" represents the distance the robot travels from the previous trajectory point to this trajectory point.

[0081] Understandably, the simulation software may identify multiple trajectory points, but only the two trajectory points with the expandable additional parameters "PathStart" and "PathEnd" set to true, along with the trajectory points contained between these two points, are used for trajectory time statistics.

[0082] More specifically, since the distance between each trajectory point is known, the sum of these distances is the total running distance (length) of the trajectory. The robot's running trajectory can be determined by the trajectory's starting point, ending point, and total distance.

[0083] Optionally, in this embodiment, a time statistics operation is performed based on the pose of the motion mechanism and the trajectory of the motion mechanism to determine the corresponding time statistics result.

[0084] Optionally, in this embodiment, time statistics refer to the statistics of which trajectories the robot has run, the distance of these trajectories, and the time required for each trajectories to run.

[0085] Specifically, the robot's trajectory and the corresponding trajectory distance are known. The actual trajectory running time can be calculated using the number of trajectory points PtCount and a fixed time interval, i.e., the actual trajectory running time = (PtCount-1)*T.

[0086] More specifically, after obtaining the motion mechanism's trajectory data, the pose of the motion mechanism running on the trajectory is mapped. Time statistics are then performed on the mapped motion mechanism to obtain the time statistics results, namely the robot's specific acceleration and deceleration process, and the robot's pose state during acceleration and deceleration. By combining the robot's pose state with time statistics, the accuracy of the time statistics can be improved. Combining the pose state allows for a more accurate simulation of the robot's acceleration and deceleration process, ensuring that the time statistics consider not only linear motion but also dynamic changes during the motion process.

[0087] In terms of effectiveness, by defining fixed time intervals and expandable additional parameters, the time statistics of the running trajectory can be realized to realistically simulate the robot's acceleration and deceleration process and the trajectory approximation process. By combining the running trajectory with robot joint angle data, the running time and distance of the robot in different pose states can be analyzed, bottlenecks in the production process can be identified, and then optimization measures can be taken to improve overall production efficiency. Finally, by combining the time statistics of the motion mechanism's pose states, it is possible to achieve time balance on the production line, ensure coordinated operation between various processes, and reduce waiting time and resource waste. This combination enables the simulation system to not only perform simple motion simulations, but also lay the foundation for subsequent complex time analysis and optimization, and to expand the functionality and application scope of the simulation system.

[0088] Step S103: Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model;

[0089] Optionally, in this embodiment, the purpose is to use a machine learning model to predict the impact of different motion parameters on efficiency and dynamically adjust the robot's motion time to optimize the workflow.

[0090] Optionally, in this embodiment, the robot kinematic model mainly describes the motion relationships of the robot's joints, without involving factors such as force and mass. The following physical characteristics can be obtained through the kinematic model:

[0091] Joint angles: These describe the rotation angle of each joint and directly affect the position and orientation of the robot's end effector.

[0092] Velocity: The angular and linear velocities of each joint reflect the robot's dynamic performance during movement.

[0093] Acceleration: Joint angular acceleration can be used to analyze the rate of change during motion, which helps to optimize motion trajectory.

[0094] Specifically, during the kinematic model construction process, a basic coordinate system and a local coordinate system are established. The basic coordinate system is used to establish a global coordinate system (usually a ground coordinate system) for the robot; the local coordinate system is used to establish a local coordinate system for each joint and end effector, and is defined according to the DH (Denavit-Hartenberg) parameter method.

[0095] More specifically, DH parameters: Define the DH parameters for each joint (including joint angle θ, link length a, link offset d, and torsion angle α). These parameters are used to describe the relative positional relationships between the joints.

[0096] Understandably, kinematic models constructed using forward kinematics and DH parameters focus on the relationship between position and attitude.

[0097] Optionally, in this embodiment, the robot dynamics model considers the forces and torques acting on the robot during its movement, providing more in-depth physical characteristics, such as:

[0098] Torque: The torque required for each joint is crucial for assessing the energy consumption and stability of robot motion.

[0099] Load: The load on each joint of the robot reflects the force state of the robot when performing a task, and helps to determine whether it is within a safe range.

[0100] Energy consumption: By calculating the power required during exercise, energy efficiency can be evaluated under different exercise modes.

[0101] Specifically, in the process of constructing the dynamic model, the mass, center of gravity position, and moment of inertia of each part of the robot (such as links and joints) are first determined. These parameters are crucial for dynamic analysis. Secondly, the previously established kinematic model is used as the basis for the dynamic model to describe the robot's motion in specific states. Then, the Newton-Euler method is applied to establish dynamic equations by analyzing the forces and moments at each joint. This method is suitable for scenarios requiring real-time control and rapid response. The dynamic model constructed through the above steps focuses on the relationship between forces, accelerations, and motion states.

[0102] Understandably, the construction of kinematic and dynamic models complement each other. Kinematic models simplify the analysis of dynamic models, while dynamic models provide a deeper understanding of kinematics. Through proper modeling and verification, the performance and accuracy of robot control systems can be effectively improved.

[0103] Optionally, in this embodiment, feature extraction is performed on the historical motion mechanism data based on a preset robot kinematics model and dynamics model to determine the corresponding physical characteristics of motion efficiency.

[0104] Specifically, based on the robot's kinematic and dynamic models, physical characteristics related to motion efficiency are calculated. For example, physical quantities such as joint loads and torques can be calculated as input features to the model. These features are not only directly related to motion parameters but also provide more physical background information, helping the model better understand the factors influencing motion efficiency.

[0105] Understandably, the collected motion mechanism data should include multiple different types of tasks and combinations of motion parameters to obtain effective training and testing samples. With such a large amount of data, key feature extraction using kinematic and dynamic models is crucial. By extracting features relevant to improving motion efficiency, the model can better learn patterns in the data, thereby improving prediction accuracy.

[0106] Ultimately, the key features extracted include joint angles, velocity, acceleration, load, torque, motion path, and running time. By selecting features highly correlated with motion efficiency, the trained model can predict the impact of different motion parameters on efficiency.

[0107] Optionally, in this embodiment, an interactive feature construction operation is performed on the motion efficiency physical features to determine the corresponding physical feature dataset.

[0108] Specifically, interactive feature construction operations include generating new composite features by combining different features. For example, using the product of joint angle and velocity as a new feature can capture the comprehensive impact of joint motion. Such interactive features not only provide richer information but may also reveal potential relationships between features, further improving the model's performance.

[0109] After obtaining composite features through feature extraction, feature selection is performed on these composite features. The goal of this process is to identify the features that contribute most to the model's predictive ability while removing irrelevant or redundant features. The subset of composite features obtained through feature selection is then used to construct a physical feature dataset for training the initial model.

[0110] Optionally, in this embodiment, the parameters of the initial model are updated based on the performance evaluation results to determine the corresponding robot time adjustment model.

[0111] Specifically, before the performance evaluation results are available, the initial model is trained using the feature dataset. The initial model chosen is the random forest model, which is suitable for handling complex nonlinear relationships and has strong interpretability.

[0112] The dataset was split into a training set (70%) and a test set (30%) to ensure the model's generalization ability. The random forest model was trained using the training set, and hyperparameters were tuned to optimize model performance.

[0113] Specifically, performance evaluation uses the coefficient of determination (R²) on the test set. 2 The performance of the model is evaluated, which helps to determine the effectiveness of the model and update the model parameters accordingly.

[0114] Before updating the model parameters, a sensitivity analysis algorithm is used to perform sensitivity analysis on the parameters of the initial model. Based on the sensitivity analysis results, the highly sensitive parameters that have the greatest impact on the model output are identified.

[0115] Based on the model's performance evaluation results, a parameter adjustment strategy is formulated. In each iteration, the values ​​of one or more highly sensitive parameters are updated according to the parameter adjustment strategy, or a better parameter value is searched using the gradient descent algorithm. When the model performance reaches the preset standard or can no longer be significantly improved through parameter adjustment, the iteration process stops. The model at this point is the optimized robot time adjustment model.

[0116] Step S104: Input the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control according to the updated trajectory point data.

[0117] Optionally, in this embodiment, the time statistics obtained in step S102 are input into the robot time adjustment model trained in step S103, and the robot's motion time is optimized based on the model prediction.

[0118] For example, when developing a new motion mechanism for complex assembly tasks, the model predicts the operating efficiency of different motion parameters and finds that a specific combination of joint angle and velocity can shorten the assembly time by 20%. In this case, the original time parameters and their corresponding multiple trajectory point data are updated according to the parameters predicted by the model.

[0119] Optionally, in this embodiment, after obtaining the updated trajectory point data, the simulation software will save these updated optimal trajectory point data.

[0120] In terms of technical effectiveness, the saved data can be used for simulation, with fast-forwarding and rewinding playback, which greatly solves the problem of real-machine testing of the controller. The received joint angle data is stored as pose trajectory points, facilitating retrospective viewing of the robot's pose at that point. When a user wants to retrospectively view or pay particular attention to the robot's state during a certain motion phase, they only need to use the software's double-click to point and trajectory simulation functions to move the robot to the specified point and view its state by rotating and zooming the view. Furthermore, because the updated trajectory point data incorporates expandable additional parameters, the simulated robot trajectory will realistically reflect the robot's acceleration and deceleration, as well as the trajectory approximation process. Compared to current time-based statistical techniques that can only obtain an approximate average of the robot's motion speed for a given trajectory, this application has the following advantages:

[0121] 1. Realistic simulation can display the true state of a trajectory motion, including acceleration and deceleration, as well as the trajectory approximation process, thus improving the accuracy of robot controller testing.

[0122] 2. Pose backtracking: Using the software's backtracking function, quickly locate the trajectory points of interest during the simulation, and view the robot's status by rotating and zooming the view.

[0123] 3. Simulation Adjustment: Using simulation adjustment models, the pose, running time, and distance of each segment of the robot's trajectory are statistically analyzed more accurately. The impact of different motion parameters on efficiency is predicted. Based on the motion efficiency prediction results, the cycle time of the robot's motion time is intelligently modified, and its impact on the simulation results is observed. Optimal time statistics parameters are generated to optimize the workflow and improve control efficiency.

[0124] This example demonstrates how this embodiment performs time statistics and intelligently adjusts the time statistics results based on the time adjustment model to realistically and accurately simulate the operation process of the motion mechanism, thereby improving the efficiency of simulation testing.

[0125] As described above, the motion mechanism time statistical analysis method based on unidirectional digital twins provided in this application can determine the corresponding time statistical results by receiving and identifying motion mechanism operation data according to a preset time interval; collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, perform interactive feature construction on the motion efficiency physical characteristics to determine the corresponding physical feature dataset, train a preset initial model according to the physical feature dataset to determine the corresponding robot time adjustment model; input the time statistical results into the robot time adjustment model to determine the corresponding optimal time parameters, and perform robot control according to the optimal time parameters. Thus, the control efficiency and accuracy of the motion mechanism can be improved based on time statistics and adjustment.

[0126] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 2 It can also specifically include the following:

[0127] Step S201: Perform an identification operation on the expandable additional parameters to determine the corresponding trajectory start point, trajectory end point, and trajectory distance between each trajectory point;

[0128] Step S202: Accumulate the trajectory distances between each trajectory point to determine the corresponding total trajectory distance, and determine the corresponding motion mechanism running trajectory based on the trajectory start point, the trajectory end point, and the total trajectory distance.

[0129] Optionally, in this embodiment, the expandable additional parameters are identified. When the parameter "PathStart" is detected as true, the point is considered to be the starting point of a trajectory segment; when the parameter "PathEnd" is detected as true, the point is considered to be the ending point of a trajectory segment. After parsing and storing the data sent by the controller, the number of trajectory points under each trajectory segment is known and denoted as PtCount; "Distance" represents the robot's movement distance from the previous trajectory point to this trajectory point.

[0130] Understandably, the simulation software may identify multiple trajectory points, but only the two trajectory points with the expandable additional parameters "PathStart" and "PathEnd" set to true, along with the trajectory points contained between these two points, are used for trajectory time statistics.

[0131] More specifically, since the distance between each trajectory point is known, the sum of these distances is the total running distance (length) of the trajectory. The robot's running trajectory can be determined by the trajectory's starting point, ending point, and total distance.

[0132] Through step S202, this embodiment calculates the running trajectory by identifying expandable additional parameters, laying the foundation for subsequent time statistics of the trajectory.

[0133] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 3 It can also specifically include the following:

[0134] Step S301: Determine the trajectory running time based on the number of trajectory points contained in the motion mechanism's running trajectory and the preset time interval;

[0135] Step S302: Perform a pose mapping operation on the trajectory running time and the total trajectory distance according to the pose of the motion mechanism, and perform a time statistics operation on the trajectory running time and the total trajectory distance after the mapping operation to determine the corresponding time statistics result.

[0136] Optionally, in this embodiment, the robot's running trajectory and the corresponding trajectory distance are known. The actual running time of the trajectory can be obtained by using the number of trajectory points PtCount and a fixed time interval, that is, the actual running time of the trajectory = (PtCount-1)*T.

[0137] Optionally, in this embodiment, the pose of the motion mechanism running on the trajectory is mapped, and time statistics are performed on the mapped motion mechanism to obtain the time statistics results, namely the specific acceleration and deceleration process of the robot, and the robot's pose state during acceleration and deceleration. By combining the robot's pose state with time statistics, the accuracy of time statistics can be improved. Combining the pose state allows for a more accurate simulation of the robot's acceleration and deceleration process, so that the time statistics not only consider linear motion but also the dynamic changes during the motion process.

[0138] Through step S302, this embodiment realizes the mapping of robot pose with trajectory distance and time, and performs time statistics based on the mapped data, laying the foundation for subsequent adjustment of time statistics parameters by combining the pose state of the motion mechanism.

[0139] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 4 It can also specifically include the following:

[0140] Step S401: Determine the corresponding robot kinematic model based on the preset robot joint angles, velocities, and accelerations. The robot kinematic model is used to represent the robot joint motion relationships.

[0141] Step S402: Determine the corresponding robot dynamics model based on the preset robot torque, load, and energy consumption. The robot dynamics model is used to represent the physical characteristics of the robot's motion process.

[0142] Optionally, in this embodiment, two mathematical models are constructed for subsequent feature extraction tasks. By analyzing the impact of motion parameters on efficiency, the most critical features are obtained, thereby improving the model's prediction performance.

[0143] Optionally, in this embodiment, the kinematic model mainly describes the motion relationships of the robot's joints, without involving factors such as force and mass. The following physical characteristics can be obtained through the kinematic model:

[0144] Joint angles: These describe the rotation angle of each joint and directly affect the position and orientation of the robot's end effector.

[0145] Velocity: The angular and linear velocities of each joint reflect the robot's dynamic performance during movement.

[0146] Acceleration: Joint angular acceleration can be used to analyze the rate of change during motion, which helps to optimize motion trajectory.

[0147] Specifically, during the kinematic model construction process, a basic coordinate system and a local coordinate system are established. The basic coordinate system is used to establish a global coordinate system (usually a ground coordinate system) for the robot; the local coordinate system is used to establish a local coordinate system for each joint and end effector, and is defined according to the DH (Denavit-Hartenberg) parameter method.

[0148] More specifically, DH parameters: Define the DH parameters for each joint (including joint angle θ, link length a, link offset d, and torsion angle α). These parameters are used to describe the relative positional relationships between the joints.

[0149] Understandably, kinematic models constructed using forward kinematics and DH parameters focus on the relationship between position and attitude.

[0150] Optionally, in this embodiment, the robot dynamics model considers the forces and torques acting on the robot during its movement, providing more in-depth physical characteristics, such as:

[0151] Torque: The torque required for each joint is crucial for assessing the energy consumption and stability of robot motion.

[0152] Load: The load on each joint of the robot reflects the force state of the robot when performing a task, and helps to determine whether it is within a safe range.

[0153] Energy consumption: By calculating the power required during exercise, energy efficiency can be evaluated under different exercise modes.

[0154] Specifically, in the process of constructing the dynamic model, the mass, center of gravity position, and moment of inertia of each part of the robot (such as links and joints) are first determined. These parameters are crucial for dynamic analysis. Secondly, the previously established kinematic model is used as the basis for the dynamic model to describe the robot's motion in specific states. Then, the Newton-Euler method is applied to establish dynamic equations by analyzing the forces and moments at each joint. This method is suitable for scenarios requiring real-time control and rapid response. The dynamic model constructed through the above steps focuses on the relationship between forces, accelerations, and motion states.

[0155] Understandably, the construction of kinematic and dynamic models complement each other. Kinematic models simplify the analysis of dynamic models, while dynamic models provide a deeper understanding of kinematics. Through proper modeling and verification, the performance and accuracy of robot control systems can be effectively improved.

[0156] Through step S403, this embodiment successfully constructed two mathematical models and generated the most critical features for improving robot motion efficiency, laying a solid foundation for subsequent feature extraction.

[0157] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 5 It can also specifically include the following:

[0158] Step S501: Perform interactive combination operations on different motion efficiency physical characteristics to determine the corresponding composite characteristics;

[0159] Step S502: Determine the corresponding physical feature dataset based on the composite features.

[0160] Optionally, in this embodiment, the interactive feature construction operation includes generating new composite features by combining different features. For example, using the product of joint angle and velocity as a new feature can capture the comprehensive impact of joint motion. Such interactive features not only provide richer information but may also reveal potential relationships between features, further improving the model's performance.

[0161] After obtaining composite features through feature extraction, feature selection is performed on these composite features. The goal of this process is to identify the features that contribute most to the model's predictive ability while removing irrelevant or redundant features. The subset of composite features obtained through feature selection is then used to construct a physical feature dataset for training the initial model.

[0162] Through step S502, this embodiment successfully constructed a physical feature dataset, laying a solid foundation for subsequent model training.

[0163] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 6 It can also specifically include the following:

[0164] Step S601: Perform sensitivity analysis on the parameters of the initial model according to the preset sensitivity analysis algorithm to determine the corresponding high-sensitivity parameters;

[0165] Step S602: Iteratively update the high-sensitivity parameters based on the performance evaluation results to determine the corresponding robot time adjustment model.

[0166] Optionally, in this embodiment, a sensitivity analysis algorithm is used to perform sensitivity analysis on the parameters of the initial model, and based on the sensitivity analysis results, the highly sensitive parameters that have the greatest impact on the model output are identified.

[0167] Specifically, before the performance evaluation results are available, the initial model is trained using the feature dataset. The initial model chosen is the random forest model, which is suitable for handling complex nonlinear relationships and has strong interpretability.

[0168] Specifically, performance evaluation uses the coefficient of determination (R²) on the test set. 2 The performance of the model is evaluated, which helps to determine the effectiveness of the model and update the model parameters accordingly.

[0169] Based on the model's performance evaluation results, a parameter adjustment strategy is formulated. In each iteration, the values ​​of one or more highly sensitive parameters are updated according to the parameter adjustment strategy, or a better parameter value is searched using the gradient descent algorithm. When the model performance reaches the preset standard or can no longer be significantly improved through parameter adjustment, the iteration process stops. The model at this point is the optimized robot time adjustment model.

[0170] Through step S602, this embodiment successfully trained and optimized the initial model, obtaining the robot time adjustment model, laying a solid foundation for subsequent time optimization.

[0171] In one embodiment of the motion mechanism time statistical analysis method based on unidirectional digital twins in this application, see [link to relevant documentation]. Figure 7 It can also specifically include the following:

[0172] Step S701: Save the updated trajectory point data and determine the corresponding saved trajectory points;

[0173] Step S702: Perform a backtracking operation on the saved trajectory points to determine the corresponding robot backtracking data. The backtracking operation includes simulated dragging or double-clicking of trajectory points, and the backtracking data includes at least one of robot pose and robot running state.

[0174] Optionally, the simulation software will save these updated optimal trajectory point data.

[0175] In terms of technical effectiveness, the saved data can be used for simulation, with fast-forwarding and rewinding playback, which greatly solves the problem of real-machine testing of the controller. The received joint angle data is stored as pose trajectory points, facilitating retrospective viewing of the robot's pose at that point. When users want to retrospectively view or focus specifically on the robot's state during a particular motion phase, they only need to use the software's double-click to point and trajectory simulation functions to move the robot to the specified point and view its state by rotating and zooming the view. Furthermore, because the updated trajectory point data incorporates expandable additional parameters, the simulated robot's running trajectory will realistically reflect the robot's acceleration, deceleration, and trajectory approximation process.

[0176] Through step S702, this embodiment successfully enables the direct backtracking of the robot's pose at a specific point by double-clicking or dragging, without having to wait for the specified program to finish running and reach that point for observation, thus increasing simulation control efficiency.

[0177] To improve the control efficiency and accuracy of motion mechanisms based on time statistics and adjustments, this application provides an embodiment of a motion mechanism time statistical analysis device based on unidirectional digital twins for implementing all or part of the aforementioned motion mechanism time statistical analysis method. See [link to embodiment]. Figure 8 The motion mechanism time statistical analysis device based on unidirectional digital twin specifically includes the following components:

[0178] The communication and drive module 10 is used to receive and identify the motion mechanism operation data according to a preset time interval, and determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0179] The time statistics module 20 is used to determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm, determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters, perform time statistics operation based on the pose of the motion mechanism and the running trajectory of the motion mechanism, and determine the corresponding time statistics result.

[0180] The time adjustment model training module 30 is used to collect historical motion mechanism data, perform feature extraction operations on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction operations on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model.

[0181] The time adjustment and data storage module 40 is used to input the time statistics results into the robot time adjustment model, determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control according to the updated trajectory point data.

[0182] As described above, the motion mechanism time statistics analysis device based on unidirectional digital twin provided in this application can determine the corresponding time statistics results by receiving and identifying motion mechanism operation data according to a preset time interval; collect historical motion mechanism data, perform feature extraction operations on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, perform interactive feature construction operations on the motion efficiency physical characteristics to determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset to determine the corresponding robot time adjustment model; input the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, and perform robot control according to the optimal time parameters. Thus, the control efficiency and accuracy of the motion mechanism can be improved based on time statistics and adjustment.

[0183] From a hardware perspective, in order to improve the control efficiency and accuracy of motion mechanisms based on time statistics and adjustments, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned motion mechanism time statistical analysis method based on unidirectional digital twins. The electronic device specifically includes the following components:

[0184] The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the motion mechanism time statistical analysis method based on unidirectional digital twins and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the motion mechanism time statistical analysis method based on unidirectional digital twins in the present embodiment, and the contents of the embodiments are incorporated herein, and repeated parts will not be described again.

[0185] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0186] In practical applications, some parts of the motion mechanism time statistical analysis method based on unidirectional digital twins can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0187] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0188] Figure 9 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 9 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0189] In one embodiment, the motion mechanism time statistical analysis method based on unidirectional digital twins can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0190] Step S101: Receive and identify motion mechanism operation data according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0191] Step S102: Determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm; determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters; perform time statistics operation based on the pose of the motion mechanism and the running trajectory of the motion mechanism; and determine the corresponding time statistics result.

[0192] Step S103: Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model;

[0193] Step S104: Input the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control according to the updated trajectory point data.

[0194] As described above, the electronic device provided in this application embodiment receives and identifies motion mechanism operation data according to a preset time interval to determine the corresponding time statistics; collects historical motion mechanism data, performs feature extraction on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, performs interactive feature construction on the motion efficiency physical characteristics to determine the corresponding physical feature dataset, trains a preset initial model based on the physical feature dataset to determine the corresponding robot time adjustment model; inputs the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, and performs robot control based on the optimal time parameters. This enables the improvement of motion mechanism control efficiency and accuracy based on time statistics and adjustments.

[0195] In another embodiment, the motion mechanism time statistical analysis method based on unidirectional digital twin can be configured separately from the central processing unit 9100. For example, the motion mechanism time statistical analysis method based on unidirectional digital twin can be configured as a chip connected to the central processing unit 9100, and the function of the motion mechanism time statistical analysis method based on unidirectional digital twin can be realized through the control of the central processing unit.

[0196] like Figure 9 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 9 All components shown; in addition, the electronic device 9600 may also include Figure 9 For components not shown, please refer to existing technologies.

[0197] like Figure 9 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0198] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0199] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0200] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0201] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0202] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0203] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0204] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the motion mechanism time statistical analysis method based on unidirectional digital twins, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the motion mechanism time statistical analysis method based on unidirectional digital twins, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0205] Step S101: Receive and identify motion mechanism operation data according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0206] Step S102: Determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm; determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters; perform time statistics operation based on the pose of the motion mechanism and the running trajectory of the motion mechanism; and determine the corresponding time statistics result.

[0207] Step S103: Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model;

[0208] Step S104: Input the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control according to the updated trajectory point data.

[0209] As described above, the computer-readable storage medium provided in this application embodiment receives and identifies motion mechanism operation data according to a preset time interval to determine the corresponding time statistics; collects historical motion mechanism data, performs feature extraction on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical features, performs interactive feature construction on the motion efficiency physical features to determine the corresponding physical feature dataset, trains a preset initial model according to the physical feature dataset to determine the corresponding robot time adjustment model; inputs the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, and performs robot control according to the optimal time parameters. This enables the improvement of motion mechanism control efficiency and accuracy based on time statistics and adjustments.

[0210] Embodiments of this application also provide a computer program product capable of implementing all steps in the motion mechanism time statistical analysis method based on unidirectional digital twins, where the execution subject is a server or client, as described in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the motion mechanism time statistical analysis method based on unidirectional digital twins. For example, the computer program / instruction implements the following steps:

[0211] Step S101: Receive and identify motion mechanism operation data according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters;

[0212] Step S102: Determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm; determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters; perform time statistics operation based on the pose of the motion mechanism and the running trajectory of the motion mechanism; and determine the corresponding time statistics result.

[0213] Step S103: Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive feature construction on the motion efficiency physical features, determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model;

[0214] Step S104: Input the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control according to the updated trajectory point data.

[0215] As described above, the computer program product provided in this application embodiment receives and identifies motion mechanism operation data according to a preset time interval to determine the corresponding time statistics; collects historical motion mechanism data, performs feature extraction on the historical motion mechanism data according to a preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, performs interactive feature construction on the motion efficiency physical characteristics to determine the corresponding physical feature dataset, trains a preset initial model based on the physical feature dataset to determine the corresponding robot time adjustment model; inputs the time statistics results into the robot time adjustment model to determine the corresponding optimal time parameters, and performs robot control based on the optimal time parameters. This enables the improvement of motion mechanism control efficiency and accuracy based on time statistics and adjustments.

[0216] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for time statistical analysis of motion mechanisms based on unidirectional digital twins, characterized in that, The method includes: The motion mechanism operation data is received and identified according to a preset time interval to determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters; The pose of the corresponding motion mechanism is determined based on the joint angle data and the preset robot forward kinematics algorithm. The running trajectory of the corresponding motion mechanism is determined based on the expandable additional parameters, including identifying the expandable additional parameters to determine the corresponding trajectory start point, trajectory end point, and trajectory distance between each trajectory point. Time statistics are performed based on the pose of the motion mechanism and the running trajectory of the motion mechanism to determine the corresponding time statistics results. Collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive combination operations on different motion efficiency physical features to determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model. The time statistics are input into the robot time adjustment model to determine the corresponding optimal time parameters. The trajectory point data is updated according to the optimal time parameters, and the robot is controlled according to the updated trajectory point data.

2. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 1, characterized in that, The step of determining the corresponding motion mechanism trajectory based on the expandable additional parameters further includes: The trajectory distances between each trajectory point are accumulated to determine the corresponding total trajectory distance. The running trajectory of the corresponding motion mechanism is determined based on the trajectory start point, the trajectory end point, and the total trajectory distance.

3. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 2, characterized in that, The step of performing time statistics based on the pose and trajectory of the motion mechanism to determine the corresponding time statistics results includes: The trajectory running time is determined based on the number of trajectory points contained in the motion mechanism's running trajectory and the preset time interval; A pose mapping operation is performed on the trajectory running time and the total trajectory distance based on the pose of the motion mechanism. A time statistics operation is then performed on the trajectory running time and the total trajectory distance after the mapping operation to determine the corresponding time statistics result.

4. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 1, characterized in that, Before performing feature extraction operations on the historical motion mechanism data based on the preset robot kinematics model and dynamics model to determine the corresponding motion efficiency physical characteristics, the process includes: The corresponding robot kinematic model is determined based on the preset robot joint angles, velocities, and accelerations. The robot kinematic model is used to represent the robot joint motion relationships. The corresponding robot dynamics model is determined based on the preset robot torque, load, and energy consumption. The robot dynamics model is used to represent the physical characteristics of the robot's motion process.

5. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 1, characterized in that, The step of interactively combining different physical features of motion efficiency to determine the corresponding physical feature dataset includes: Different motion efficiency physical characteristics are interactively combined to determine the corresponding composite characteristics; The corresponding physical feature dataset is determined based on the composite features.

6. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 1, characterized in that, The step of updating the parameters of the initial model based on the performance evaluation results to determine the corresponding robot time adjustment model includes: The parameters of the initial model are analyzed for sensitivity using a preset sensitivity analysis algorithm to determine the corresponding high-sensitivity parameters. The high-sensitivity parameters are iteratively updated based on the performance evaluation results to determine the corresponding robot time adjustment model.

7. The method for time statistical analysis of motion mechanisms based on unidirectional digital twins according to claim 1, characterized in that, After performing robot control based on the updated trajectory point data, the process also includes: The updated trajectory point data is saved, and the corresponding saved trajectory points are determined. A backtracking operation is performed on the saved trajectory points to determine the corresponding robot backtracking data. The backtracking operation includes simulated dragging or double-clicking of trajectory points, and the backtracking data includes at least one of robot pose and robot running state.

8. A motion mechanism time statistical analysis device based on unidirectional digital twin, characterized in that, The device includes: The communication and drive module is used to receive and identify the motion mechanism operation data according to a preset time interval, and determine the corresponding trajectory point data, wherein the trajectory point data includes joint angle data and expandable additional parameters; The time statistics module is used to determine the pose of the corresponding motion mechanism based on the joint angle data and the preset robot forward kinematics algorithm, and to determine the running trajectory of the corresponding motion mechanism based on the expandable additional parameters. This includes identifying the expandable additional parameters to determine the corresponding trajectory start point, trajectory end point, and trajectory distance between each trajectory point; and performing time statistics based on the motion mechanism pose and the motion mechanism running trajectory to determine the corresponding time statistics result. The time adjustment model training module is used to collect historical motion mechanism data, perform feature extraction on the historical motion mechanism data according to the preset robot kinematics model and dynamics model, determine the corresponding motion efficiency physical features, perform interactive combination operations on different motion efficiency physical features to determine the corresponding physical feature dataset, train the preset initial model according to the physical feature dataset, evaluate the performance of the trained model according to the preset determination coefficient, determine the corresponding performance evaluation result, update the parameters of the initial model according to the performance evaluation result, and determine the corresponding robot time adjustment model. The time adjustment and data storage module is used to input the time statistics results into the robot time adjustment model, determine the corresponding optimal time parameters, update the trajectory point data according to the optimal time parameters, and perform robot control based on the updated trajectory point data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the motion mechanism time statistical analysis method based on unidirectional digital twin as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the motion mechanism time statistical analysis method based on unidirectional digital twins as described in any one of claims 1 to 7.

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