High-stress trajectory extraction method of articulated mechanical device

Abstract

The invention provides a high-stress track extraction method of a joint type mechanical device. The method comprises the following steps: acquiring an n-dimensional stress vector; performing dimension reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector; generating a stress level index according to the n-dimensional stress vector, wherein the stress level index is used for measuring the overall stress of the articulated mechanical device; obtaining a target stress section of the articulated mechanical device in the operation process according to the stress level index; constructing a stress distribution model, wherein the stress distribution model comprises a stress amplitude distribution model and a stress mode distribution model; and determining and generating a high stress track sequence for the acceleration test of the articulated mechanical device according to the stress distribution model.

Description

Technical Field

[0001] The present application mainly relates to the field of testing of articulated mechanical devices, and in particular to a method for extracting high-stress trajectories of articulated mechanical devices. Background Art

[0002] Accelerated life testing is a crucial tool for verifying product reliability. Traditional accelerated life testing primarily exposes potential product failures by applying environmental stresses (such as high and low temperatures, humidity, and vibration) to accelerate the exposure. However, for complex electromechanical systems like surgical robots, relying solely on environmental factors cannot fully reflect the complex variations in mechanical stress experienced during actual operation. When a surgical robot performs a surgical operation, its joint motors, connecting rods, and surgical instruments are subjected to multi-dimensional dynamic loads, resulting in dramatic changes in mechanical stress. Current accelerated life testing of surgical robots typically commands the robot to perform a specific pattern of motion, such as repeated reciprocating motion along a straight line of fixed length. However, this pattern of motion deviates significantly from the robot's actual operating trajectory and, more importantly, it fails to capture the diverse range of extreme operating conditions encountered during surgical robot operation. Therefore, a testing method based on actual robot trajectory data and focused on mechanical stress characteristics is urgently needed to improve the coverage of accelerated life testing for real-world operating conditions and avoid missing potential extreme failure modes. Summary of the Invention

[0003] In response to the above technical problems, the present application provides a high-stress trajectory extraction method that can automatically extract high-stress trajectory sequences for accelerated testing of articulated mechanical devices.

[0004] In order to solve the above technical problems, the present application provides a high-stress trajectory extraction method for an articulated mechanical device, wherein the articulated mechanical device includes at least one movable joint, including: obtaining an n-dimensional stress vector, wherein the n-dimensional stress vector corresponds to n-dimensional stress data of the articulated mechanical device during operation, and the n-dimensional stress data is related to the stress to which the articulated mechanical device is subjected during operation; performing dimensionality reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector, wherein n and k are both positive integers and n>k; generating a stress level index according to the n-dimensional stress vector, wherein the stress level index is used to measure the overall force magnitude of the articulated mechanical device; obtaining a target stress section of the articulated mechanical device during operation according to the stress level index, wherein the stress level index of the articulated mechanical device in the target stress section is greater than a stress threshold; constructing a stress distribution model, wherein the stress distribution model It includes a stress amplitude distribution model and a stress pattern distribution model, wherein the stress amplitude distribution model is constructed according to the probability distribution of the stress level index during the operation of the articulated mechanical device, and the stress pattern distribution model is constructed according to the distribution of the k-dimensional eigenvector in the feature space, wherein the distribution of the k-dimensional eigenvector in the feature space is used to reflect the load conditions of multiple stress change patterns; and a strategy for the accelerated test of the articulated mechanical device is determined according to the stress distribution model, including: determining the time compression ratio of the accelerated test of the articulated mechanical device according to the stress amplitude distribution model; selecting representative stress fragments from the target stress segment according to the stress pattern distribution model; adjusting the target proportion of the representative stress fragments in the accelerated test sequence of the articulated mechanical device according to the time compression ratio; and splicing the representative stress fragments according to the target proportion to generate a high stress trajectory sequence for the accelerated test of the articulated mechanical device.

[0005] In one embodiment of the present application, the dimensionality reduction processing of the n-dimensional stress vector includes: performing principal component analysis on the n-dimensional stress vector, the k-dimensional eigenvector is a k-dimensional principal component eigenvector, and the feature space is a principal component eigenspace.

[0006] In one embodiment of the present application, generating a stress level index according to the n-dimensional stress vector includes: using the Euclidean norm of the n-dimensional stress vector as the stress level index, or using the Euclidean norm of the k-dimensional eigenvector as the stress level index.

[0007] In one embodiment of the present application, obtaining the target stress segment of the articulated mechanical device during the operation process based on the stress level indicator includes: on the time axis, in response to the stress level indicator being greater than the stress threshold for a duration greater than or equal to a first duration, determining the current moment as the starting moment of the target stress segment; in response to the stress level indicator being less than or equal to the stress threshold for a duration greater than or equal to a second duration, determining the current moment as the ending moment of the target stress segment.

[0008] In one embodiment of the present application, after obtaining the target stress segment of the articulated mechanical device during the operation according to the stress level index, the method further includes: merging two adjacent target stress segments with a time interval less than an interval threshold into one target stress segment.

[0009] In one embodiment of the present application, determining the time compression ratio of the accelerated test of the articulated mechanical device based on the stress amplitude distribution model includes: determining the time proportion of the target stress based on the stress amplitude distribution model, wherein the amplitude of the target stress is greater than the amplitude threshold; obtaining the target time proportion of the target stress expected in the accelerated test of the articulated mechanical device; and taking the ratio of the target time proportion to the time proportion as the time compression ratio.

[0010] In one embodiment of the present application, the stress pattern distribution model is constructed by the following method: a clustering algorithm is applied to divide the k-dimensional principal component feature vector into several clusters in the principal component feature space, wherein each cluster corresponds to a stress change pattern; and the stress pattern distribution model is constructed using the cluster center and cluster frequency of each cluster, wherein the cluster center represents the average load of the corresponding stress change pattern, and the cluster frequency represents the frequency of occurrence of the corresponding stress change pattern during the operation of the articulated mechanical device.

[0011] In one embodiment of the present application, selecting a representative stress segment from the target stress segment according to the stress pattern distribution model includes: selecting the target stress segment that appears at the cluster center and near the cluster center as the representative stress segment.

[0012] In one embodiment of the present application, adjusting the target proportion of the representative stress segment in the accelerated test sequence of the articulated mechanical device according to the time compression ratio includes: obtaining a cluster probability of the representative stress segment; and using the product of the cluster probability and the time compression ratio as the target proportion of the representative stress segment in the accelerated test sequence of the articulated mechanical device, wherein the adjusted target proportion of each stress change pattern satisfies: ,in, Represents the target proportion of the jth stress variation mode.

[0013] In one embodiment of the present application, splicing the representative stress fragments according to the target proportion includes: splicing multiple representative stress fragments in a cyclic or random order so that the proportion of each representative stress fragment in the high stress trajectory sequence is equal to the target proportion.

[0014] The high-stress trajectory extraction method for an articulated mechanical device of the present application can construct a stress distribution model based on the actual operation data of the articulated mechanical device, and then determine the strategy for the accelerated test of the articulated mechanical device according to the stress distribution model, and extract a high-stress trajectory sequence for the accelerated test of the articulated mechanical device. The high-stress trajectory sequence can realistically reflect the mechanical load characteristics of the articulated mechanical device during actual operation, and the experimental stress spectrum is consistent with the real stress spectrum, thereby greatly improving the coverage and acceleration efficiency of the accelerated life test of the articulated mechanical device, and providing a powerful tool for reliability verification of the articulated mechanical device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are included to provide a further understanding of the present application. They are incorporated into and constitute a part of this application. The accompanying drawings illustrate embodiments of the present application and, together with this specification, serve to explain the principles of the present application. In the accompanying drawings:

[0016] Figure 1 is an exemplary flow chart of a method for extracting high stress trajectories of an articulated mechanical device according to an embodiment of the present application;

[0017] Figure 2 is a schematic diagram of a histogram of stress amplitude distribution of stress level indicators obtained according to a high stress trajectory extraction method according to an embodiment of the present application;

[0018] Figure 3 is a schematic diagram of a target stress section obtained according to a high stress trajectory extraction method according to an embodiment of the present application;

[0019] Figure 4 is an exemplary flow chart for constructing a stress pattern distribution model according to a high stress trajectory extraction method according to an embodiment of the present application;

[0020] Figure 5 is a schematic diagram of stress pattern distribution results of a stress pattern distribution model constructed according to a high stress trajectory extraction method according to an embodiment of the present application;

[0021] Figure 6 is an exemplary flow chart for determining the time compression ratio of an accelerated test of an articulated mechanical device according to a high stress trajectory extraction method according to an embodiment of the present application;

[0022] Figure 7This is an exemplary flow chart of a method for extracting high stress trajectories according to an embodiment of the present application for adjusting a target proportion of representative stress segments in an accelerated test sequence of an articulated mechanical device according to a time compression ratio;

[0023] Figure 8 is a schematic diagram comparing the overall stress spectrum of the high stress trajectory sequence generated by the high stress trajectory extraction method of the present application with the true spectrum;

[0024] Figure 9 4 is a system block diagram of a high-stress trajectory extraction device for an articulated mechanical device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0026] As used herein, unless the context clearly indicates otherwise, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0027] Unless otherwise specified, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. Technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0028] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is solely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. Furthermore, while the terms used in this application are selected from commonly known and commonly used terms, some terms mentioned in this specification may have been selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant sections of this description. Furthermore, this application should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0029] Flowcharts are used in this application to illustrate the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0030] The inventors of the present application have discovered that in real surgical robot logs, the forces acting on the robot's end effector may fluctuate significantly in a very short period of time. For example, at a certain moment, the resultant force on the tool tip may instantly soar to about 68.6 N, and then quickly drop to about 15.3 N. This drastic change in mechanical load cannot be accurately reproduced by environmental stress tests such as constant high temperature and random vibration. Therefore, the present application proposes a method and device for extracting high-stress trajectories of articulated mechanical devices, which can scientifically extract high-stress segments based on real data of articulated mechanical devices including robots during actual operation, so as to form a high-stress trajectory sequence that can be used for accelerated testing of articulated mechanical devices. Life testing based on this high-stress trajectory sequence can effectively reproduce the wear and failure modes of articulated mechanical devices in actual use.

[0031] It should be noted that the articulated mechanical device described in this application refers to a mechanical device comprising at least one movable joint, such as a robotic arm, a robot, etc. This application specification uses a surgical robot as an example for description, but is not limited thereto.

[0032] Figure 1 FIG is an exemplary flow chart of a method for extracting high stress trajectories of an articulated mechanical device according to an embodiment of the present application. Figure 1 As shown, the high stress trajectory extraction method 100 of this embodiment includes the following steps:

[0033] Step S110: Obtaining an n-dimensional stress vector, where the n-dimensional stress vector corresponds to n-dimensional stress data of the articulated mechanical device during operation, and the n-dimensional stress data is related to the stress to which the articulated mechanical device is subjected during operation;

[0034] Step S120: performing dimensionality reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector, wherein n and k are both positive integers and n>k;

[0035] Step S130: generating a stress level index according to the n-dimensional stress vector, wherein the stress level index is used to measure the overall force magnitude of the articulated mechanical device;

[0036] Step S140: obtaining a target stress section of the articulated mechanical device during operation according to the stress level index, wherein the stress level index of the articulated mechanical device in the target stress section is greater than a stress threshold;

[0037] Step S150: constructing a stress distribution model, which includes a stress amplitude distribution model and a stress pattern distribution model. The stress amplitude distribution model is constructed based on the probability distribution of stress level indicators during the operation of the articulated mechanical device, and the stress pattern distribution model is constructed based on the distribution of k-dimensional eigenvectors in the feature space. The distribution of the k-dimensional eigenvectors in the feature space is used to reflect the load conditions of various stress change patterns.

[0038] Step S160: Determine a strategy for the accelerated test of the articulated mechanical device based on the stress distribution model, including: determining a time compression ratio for the accelerated test of the articulated mechanical device based on the stress amplitude distribution model; selecting representative stress segments from the target stress segment based on the stress pattern distribution model; adjusting a target proportion of the representative stress segments in the accelerated test sequence of the articulated mechanical device based on the time compression ratio; and splicing the representative stress segments according to the target proportion to generate a high-stress trajectory sequence for the accelerated test of the articulated mechanical device.

[0039] According to the above-mentioned high-stress trajectory extraction method 100, a stress distribution model can be constructed based on the actual operation data of the articulated mechanical device, and then the strategy of the accelerated test of the articulated mechanical device can be determined according to the stress distribution model, and a high-stress trajectory sequence for the accelerated test of the articulated mechanical device can be extracted. The high-stress trajectory sequence can realistically reflect the mechanical load characteristics of the articulated mechanical device during the actual operation process, and the experimental stress spectrum is consistent with the real stress spectrum, thereby greatly improving the coverage and acceleration efficiency of the accelerated life test of the articulated mechanical device, and providing a powerful tool for the reliability verification of the articulated mechanical device.

[0040] The above steps S110 - S160 will be described in detail below with reference to the accompanying drawings.

[0041] In step S110, n is a positive integer greater than or equal to 1. It should be noted that the n-dimensional stress data is actual data related to the stress to which the articulated mechanical device is subjected during operation. The required n-dimensional stress data can be extracted from the operation trajectory log of the surgical robot in a real operation or simulated operation. The log usually includes the motion and force information of each joint of the robot, such as: time series joint angle, joint driving force / current, end effector position posture and force, etc. In step S110, the original stress-related data can be first extracted from the log as the n-dimensional stress data. For example, the n-dimensional stress data includes at least the joint motor torque of the articulated mechanical device. Taking a six-axis surgical robot as an example, it includes 6 joints, and each joint has a corresponding joint motor torque, so the dimension of the n-dimensional stress data is equal to 6, including 6 joint motor torques.

[0042] In some embodiments, the n-dimensional stress data includes the joint motor torque and the end force. The end force can be the force condition of the end effector. The end force can specifically be the three-dimensional force sensor reading of the end tool of the robot recorded in the log. For a robot system without a direct force sensor, the joint motor torque and end force can also be obtained by dynamic model calculation. The n-dimensional stress data collected at each moment is organized into a vector form to obtain an n-dimensional stress vector. For example, at a certain moment, the torque of each joint motor of the six-axis robot is measured as: (N•m), the end force component is F x , F y , F z (Newton). For example, a 9-dimensional stress vector can be constructed :

[0043] ,

[0044] in, They are the joint motor torques of the 6 joints respectively. are the components of the force on the end in the X, Y, and Z axes of space, respectively. t represents time. As you can see, the n-dimensional stress vector contains time information and is a function of time.

[0045] In practical applications, the composition of the stress vector can be selected according to the structure of the specific articulated mechanical device and the sensor configuration.

[0046] In some embodiments, the n-dimensional stress data also includes any item in the joint motor current, drive power and joint speed. For example, in the case of a robot without a force sensor, alternative indicators related to stress such as joint motor current, drive power, joint speed can be selected as n-dimensional stress data. Although these alternative indicators are not direct stress data, they can also reflect the stress situation of the robot. For example, if the joint motor current has a positive correlation with stress, then by establishing the relationship between the joint motor current and the stress magnitude, the joint motor current can be used as stress data to reflect the magnitude of the stress suffered by the robot.

[0047] In some embodiments, after obtaining the n-dimensional stress vector in step S110, the process further includes: pre-processing the stress components of each dimension, such as filtering and denoising, removing the gravity static load component, and normalizing the dimension, so as to obtain a stable and reliable stress indicator signal. After this step, the stress vector in the form of a time series is obtained. , laying the foundation for subsequent analysis. Where i = 1~T, T is the total number of time sampling points.

[0048] In step S120, this application does not limit the specific dimensionality reduction method. Any conventional dimensionality reduction method in the art may be used. In some embodiments, dimensionality reduction is performed on the n-dimensional stress vector, including performing principal component analysis (PCA) on the n-dimensional stress vector, where the k-dimensional eigenvector is a k-dimensional principal component eigenvector, and the feature space is a principal component eigenspace.

[0049] In these embodiments, the n-dimensional stress vector sequence obtained in step S110 can be input into a principal component analysis module to extract the main stress variation pattern characteristics. The principal component analysis module here can be a software module for implementing principal component analysis. For example, the principal component analysis can be performed using the following steps:

[0050] (1) The n-dimensional stress vector is centered according to its dimension, that is, the mean of each dimensional component is subtracted to eliminate the offset effect of different dimensions.

[0051] (2) Constructing the data matrix , where T is the total number of time sampling points and n is the stress vector dimension. For example, n = 9 dimensions. The stress vector sequence length T may reach millions (depending on the log duration and sampling frequency). Because directly performing eigendecomposition on large, high-dimensional matrices is computationally intensive, in some embodiments, singular value decomposition (SVD) techniques are used to efficiently implement PCA. This involves performing SVD on the matrix X using existing linear algebra libraries:

[0052] ,

[0053] in 、 、 . The column vector of matrix V That is The eigenvectors of represent the principal component directions of the original stress data. Singular values ​​on The square of is proportional to the variance of the corresponding principal component direction. Select the principal components corresponding to the first k singular values ​​(so that If the predetermined variance contribution rate threshold is reached, such as 95%, the original stress data can be approximately reconstructed using the linear combination of these k principal component directions.

[0054] (3) Project the original stress vector to the selected principal component subspace:

[0055] ,

[0056] Get the k-dimensional principal component eigenvector . These principal component features are used to reveal the main change patterns in the robot stress data. The stress change pattern of the robot may include: changes in the "overall force amplitude", "a pattern in which two specific joints are subjected to opposite forces", "an independent mode in which the end exerts force in a certain direction", and so on. The stress change pattern of the robot is related to the n-dimensional stress vector. However, due to the huge amount of data of the n-dimensional stress vector, in step S120, after dimensionality reduction processing, k-dimensional eigenvectors are obtained, and these eigenvectors also have a high correlation with the stress change pattern. In other words, each stress change pattern is related to the multiple principal component features, so that different stress change patterns can be reflected in the subsequent k-dimensional principal component eigenvectors.

[0057] Step S120 significantly reduces the data dimension and noise interference in subsequent analysis through dimensionality reduction processing, making it easier for us to identify abnormal stress changes.

[0058] In step S130, the stress level index may be defined in different ways. In some embodiments, generating the stress level index according to the n-dimensional stress vector includes: The Euclidean norm of Continuing with the previous example, it can be specifically expressed as:

[0059] ,

[0060] That is, the Euclidean norm of each stress component, which serves as a measure of the overall force and the degree of stress at each moment.

[0061] In some other embodiments, generating a stress level index according to an n-dimensional stress vector includes: converting a k-dimensional principal component eigenvector of the n-dimensional stress vector into According to these embodiments, only the information in the principal component space is considered, and the stress vector norm reconstructed using the first k principal components is used. As an approximation, it is as follows:

[0062] ,

[0063] Moments with high stress usually have larger projections in the principal component space, so they can also be used as a measure of the overall force and the degree of stress at each moment.

[0064] In other embodiments, for a specific failure mode, the stress level indicator may also include: the percentage of the joint motor torque approaching the rated upper limit, or the force in a specific direction of the end exceeding a certain threshold, etc.

[0065] In step S140, the target stress section is the high stress period during the operation of the joint mechanical device. To determine the high stress period, it is necessary to determine the high stress moment according to a preset stress threshold. In some embodiments, the stress level index time series can be traversed to locate the high stress moment: Exceeding stress threshold When the stress is high, mark the moment as a "high stress moment".

[0066] In some embodiments, the stress threshold is determined based on the statistical distribution of n-dimensional stress data. .

[0067] Figure 2 This is a histogram diagram of the stress amplitude distribution (Stress Amplitude Distribution) of the stress level indicator obtained according to the high stress trajectory extraction method of an embodiment of the present application. The horizontal axis is the stress amplitude (Stress Magnitude) of the stress level indicator, and the gear is Newton (N) or normalized vector (norm). The vertical axis is frequency or frequency (Frequency), which has no unit. Figure 2 As shown, the stress amplitudes with higher frequencies are all in the middle. Relatively high stress amplitudes occur less frequently. For example, the value corresponding to the upper tail 5% of the stress data distribution is selected as the stress threshold (HighStress Threshold). , represented by a vertical dashed line. A stress amplitude higher than the stress threshold corresponds to a high stress. In other embodiments, an absolute value can be set as the stress threshold according to the mechanical tolerance of the material / component. .

[0068] Considering that the failure of joints or components in actual applications is often not caused by instantaneous peaks, but by the accumulation of continuous high stress, after obtaining the high stress moment, it is necessary to further identify the "high stress section".

[0069] In some embodiments, the duration of the target stress segment is greater than or equal to a preset duration. According to these embodiments, it is possible to avoid classifying some unexpected high stress moments as target stress segments, such as noise data. Therefore, a preset duration is set, and only when the stress data exceeds the stress threshold Only when the duration reaches the preset duration will it be regarded as the target stress segment. If it is less than the preset duration, it will not be regarded as the target stress segment.

[0070] In some embodiments, step S140 of obtaining a target stress segment during operation of the articulated mechanical device based on the stress level indicator includes: on the time axis, in response to the stress level indicator being greater than the stress threshold for a duration greater than or equal to a first duration, determining the current moment as the start moment of the target stress segment; in response to the stress level indicator being less than or equal to the stress threshold for a duration greater than or equal to a second duration, determining the current moment as the end moment of the target stress segment. According to these embodiments, in order to avoid statistical noise interference, the stress threshold described above is equivalent to Add hysteresis or require duration based on the time. For example, when the first duration is exceeded continuously All maintained It is determined to enter the high stress section when When s(t) is continuously lower than the threshold The duration exceeds the second duration Leave the high stress section at this moment and record this moment as the end point of the high stress section This gives us a series of time periods. Within each segment, the robot experiences high mechanical stress. These segments correspond to intense or strenuous movements during surgical procedures, which are likely to have a significant impact on the robot's lifespan. This step outputs a set of identified high-stress trajectory segments, along with each segment's start and end timestamps and corresponding stress signature descriptions.

[0071] In some embodiments, the first duration and the second duration It can be the same or different.

[0072] In some embodiments, after obtaining the target stress segments in step S140, the method further includes merging two adjacent target stress segments whose time interval is less than a threshold into a single target stress segment. In some cases, there may be brief pauses during the robot's continuous operation, even though these operations actually involve the same type of motion or stress change pattern. In these embodiments, merging adjacent target stress segments can further reduce the number of target stress segments.

[0073] Figure 3 Schematic diagram of target stress section obtained by the high stress trajectory extraction method according to an embodiment of the present application. Figure 3 As shown, the horizontal axis is time (in seconds) and the vertical axis is stress amplitude (Stress Magnitude). The horizontal dotted line shows the stress threshold ,and Figure 3 Stress threshold shown The gray areas 310 and 320 represent several target stress segments (smaller in area) located within [60, 80].

[0074] In step S150, the stress amplitude distribution model may be the probability distribution of s(t) during the operation of the articulated mechanical device. For example, it may be specifically expressed as Figure 2 The following figure shows a histogram of the stress level indicator s(t). Alternatively, a probability function p(s) of the stress level indicator s(t) can be used. This allows the time proportions of different stress levels (low, medium, and high) to be determined. For example, a robot might experience a combined stress below 50% of the rated load 80% of the time, between 50% and 90% for 19% of the time, and above 90% of the ultimate load for less than 1% of the time. This distribution helps define the stress spectrum for accelerated testing, ensuring that the overall distribution matches actual usage. It also allows the proportion of high stress levels to be artificially exaggerated to shorten test time.

[0075] Figure 4 This is an exemplary flow chart of constructing a stress pattern distribution model according to a high stress trajectory extraction method according to an embodiment of the present application. Figure 4 As shown, in some embodiments, the stress pattern distribution model in step S150 can be constructed using the following method:

[0076] Step S410: applying a clustering algorithm to divide the n-dimensional stress vector into a number of clusters in the principal component space, wherein each cluster corresponds to a stress variation pattern;

[0077] Step S420: constructing a stress pattern distribution model using the cluster center and frequency of each cluster, wherein the cluster center represents the average load of the corresponding stress change pattern, and the cluster frequency represents the occurrence frequency of the corresponding stress change pattern during the operation of the articulated mechanical device.

[0078] According to step S410 and step S420, a clustering algorithm (such as k-means or Gaussian mixture model) is applied to divide all stress vectors into several clusters in the principal component space, each cluster corresponding to a common stress variation pattern.

[0079] Figure 5FIG is a schematic diagram of stress pattern distribution results of a stress pattern distribution model constructed according to a high stress trajectory extraction method according to an embodiment of the present application. Figure 5 As shown, there are three clusters obtained based on the first principal component and the second principal component, namely Cluster 1 (Cluster 1), Cluster 2 (Cluster 2), and Cluster 3 (Cluster 3), where "×" represents the cluster center of each cluster. The horizontal axis is the direction of the first principal component, which represents the mode with the largest variance in the stress data. The vertical axis is the direction of the second principal component. The direction of the second principal component is orthogonal to the direction of the first principal component. The two coordinate axes themselves are characteristic quantities after mathematical projection and have no physical units. Among them, Cluster 1 may correspond to "idle / small movement mode with low load on most joints", Cluster 2 corresponds to "mode in which a certain combination of joints bears high load", and Cluster 3 corresponds to "mode in which a specific direction of the end encounters high resistance", etc. After clustering is completed, the cluster center can be obtained. and the frequency of the cluster (Ratio of total data points). The cluster center represents the average load condition of this stress pattern, and the frequency reflects its probability of occurrence in actual operation. In some embodiments, the degree of dispersion (variance or covariance matrix) of the data within each cluster can also be calculated to understand the range of stress variations within this pattern.

[0080] The aforementioned stress change patterns are merely examples and can be defined based on the specific motion characteristics of the articulated mechanical device. In some embodiments, the stress change patterns include any of the following: an idle / small motion mode where most joints experience low loads; a mode where two joints experience opposing forces; a mode where specific combinations of joints experience high loads; and a mode where the end effector encounters high resistance in specific directions.

[0081] also, Figure 5 The illustration is merely an example. In other embodiments, multiple clusters can be obtained based on at least three principal components, each cluster corresponding to a stress variation pattern. According to these embodiments, each stress variation pattern is associated with at least three principal component eigenvectors.

[0082] In some embodiments, a stress pattern distribution model may be constructed by fitting a multivariate probability distribution.

[0083] Step S150 simultaneously generates two stress distribution models: a stress amplitude distribution model and a stress pattern distribution model. These two models, combined, fully describe the temporal distribution characteristics of the robot's stresses: the cumulative probability of amplitude and the proportion of pattern components. This provides a data foundation for developing accelerated test load spectra.

[0084] Step S160 utilizes the stress distribution model established in step S150 to develop a motion trajectory sampling and combination strategy for the accelerated life test, outputting a sequence of typical high-stress trajectory segments for use in the test. This process increases the frequency of rare high-stress events in the test while maintaining the overall stress spectrum similar to the true spectrum.

[0085] Figure 6 This is an exemplary flow chart for determining the time compression ratio of an articulated mechanical device acceleration test according to a high-stress trajectory extraction method according to an embodiment of the present application. In some embodiments, determining the time compression ratio of an articulated mechanical device acceleration test based on a stress amplitude distribution model in step S160 includes:

[0086] Step S610: determining a time proportion of a target stress according to a stress amplitude distribution model, wherein the amplitude of the target stress is greater than an amplitude threshold;

[0087] Step S620: Obtaining a target time ratio of a desired target stress in an accelerated test of the articulated mechanical device;

[0088] Step S630: taking the ratio of the target time proportion to the time proportion as the time compression ratio.

[0089] The following is an example to illustrate the above steps S610-S630.

[0090] Assume that based on actual data, the stress with amplitude or intensity > 90% accounts for 0.5% of the total time T (e.g. Figure 2 The stress threshold The stress section on the right side of the test is shown in the figure. According to the accelerated test design, if we aim to achieve 5% of this high stress, this is equivalent to a 10-fold acceleration, or a time compression ratio of R = 10. The desired target time percentage can be determined based on project time requirements and equipment tolerances.

[0091] In some embodiments, selecting representative stress segments from the target stress segment based on the stress pattern distribution model in step S160 includes selecting target stress segments that appear at or near the cluster center as representative stress segments. According to these embodiments, within each major stress pattern cluster, representative trajectory segments are selected as test load units. These segments should cover typical stress variations within the cluster. The vicinity of the cluster center can be defined as a circle or an area of ​​any shape with the cluster center as the center or center and a distance from the cluster center within a preset radius. In some embodiments, for particularly important or extreme stress patterns (e.g., clusters with stress levels approaching the upper limit of the robot's capabilities), multiple different segments can be selected to prevent accidental deviations.

[0092] Figure 7This is an exemplary flow chart of a method for extracting high-stress trajectories according to an embodiment of the present application, for adjusting the target proportion of representative stress segments in an accelerated test sequence of an articulated mechanical device based on a time compression ratio. In some embodiments, adjusting the target proportion of representative stress segments in an accelerated test sequence of an articulated mechanical device based on a time compression ratio in step S160 includes:

[0093] Step S710: obtaining cluster probabilities representing stress segments;

[0094] Step S720: The product of the cluster probability and the time compression ratio is used as the target proportion of the representative stress segment in the accelerated test sequence of the articulated mechanical device, wherein the adjusted target proportion of each stress change pattern satisfies: ,in, Represents the target proportion of the jth stress variation mode.

[0095] In order to achieve the purpose of acceleration in the accelerated test, it is necessary to appropriately increase the proportion of high-stress, low-probability clusters. Therefore, according to steps S710 and S720, the target proportion of stress-representing segments can be adjusted according to cluster probability.

[0096] Example: Assume that the probability of a cluster representing a stress fragment =0.5%. Time compression ratio R=10. Then the target proportion of the representative stress segment is =5%. The sum of the target proportions of each stress change mode after adjustment is equal to 1, and , corresponding to high stress clusters.

[0097] In some embodiments, the step S160 of splicing representative stress segments according to the target proportion includes: splicing multiple representative stress segments in a cyclic or random order so that the proportion of each representative stress segment in the high stress trajectory sequence is equal to the target proportion. After splicing, a long test trajectory sequence can be generated. The target proportion also corresponds to the number of repetitions of each type of stress segment in the experimental load sequence. The higher the target proportion, the greater the number of repetitions. This application does not limit the specific splicing order of each representative stress segment.

[0098] In some embodiments, after splicing representative stress segments according to target proportions, the method further includes inserting transition segments between adjacent representative stress segments, where the transition segments correspond to smooth, low-stress motion. These embodiments prevent the robot from abruptly switching from one extreme condition to another, ensuring the rationality and safety of test load changes.

[0099] The high-stress trajectory sequence generated by the high-stress trajectory extraction method of this application is approximately R times shorter than the actual usage time. This compressed time captures various high-stress events that would only occur once every several times longer in real life. Furthermore, the overall stress spectrum of the high-stress trajectory sequence is similar to the actual spectrum.

[0100] Figure 8 This is a schematic diagram comparing the overall stress spectrum of the high stress trajectory sequence generated by the high stress trajectory extraction method of this application and the real spectrum. Among them, the test stress spectrum 820 (Test Stress Spectrum) represents the overall stress spectrum of the high stress trajectory sequence, and the real stress spectrum 810 (Real Stress Spectrum) represents the overall stress spectrum of the real data. Figure 8 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents stress magnitude in Newtons (N) or normalized values ​​(norm). Clearly, the trends of the actual stress spectrum 810 and the measured stress spectrum 820 are similar. This demonstrates that the high-stress trajectory sequence generated using the method of this application can represent the stress distribution of real data.

[0101] During accelerated life testing, the aforementioned high-stress trajectory sequence can be loaded into the surgical robot's control system and repeatedly executed, combined with traditional environmental stresses (such as normal room temperature or slightly elevated ambient vibration). During the test, the response of key robot components to these repetitive high-stress loads (e.g., temperature rise, wear, clearance changes, etc.) is monitored, and the time of any anomalies or failures is recorded. Compared to traditional methods, this test significantly condenses the high-stress events, enabling potential failure modes to be elicited in a shorter timeframe.

[0102] The beneficial effects of this application are as follows:

[0103] (1) Realistic reproduction of real working conditions. The stress distribution model is constructed based on the actual operation data of the surgical robot. The extracted test trajectory can realistically reflect the mechanical load characteristics of the robot during the operation, rather than simply the constant environmental stress effect. This ensures that the test is closer to the real working conditions, the stress spectrum is more reasonable and comprehensive, and the relevance of the life test results to actual usage is improved.

[0104] (2) Automatically extract high-stress segments to cover extreme scenarios. This application uses an algorithm to automatically scan massive operating logs and accurately identify key segments with the highest mechanical stress, avoiding omissions that may occur through manual experience screening. The resulting typical trajectory library covers various high-stress scenarios, including extreme operating conditions that are rare in normal operation but may cause severe wear or failure. This greatly improves the coverage of potential failure modes in accelerated life testing and avoids the risk of missing extreme failure modes.

[0105] (3) High test efficiency and significant acceleration effect. By increasing the proportion of high-stress trajectories in the test sequence, the robot's life consumption can be accelerated. That is, high stress loads far exceeding the normal frequency are repeatedly applied in a short period of time, causing key components to accumulate fatigue and wear faster. Compared with traditional random vibration or uniform task sequences, the test sequence generated by this application can more effectively "accelerate" the equivalent use time, shorten the cycle required for life testing, and significantly improve test efficiency.

[0106] (4) Quantitative guidance for test design. The use of stress principal component analysis and statistical modeling provides a quantitative basis for the design of the test load spectrum. Test planners can intuitively understand the importance and frequency of various stress modes based on the stress distribution model, and then adjust the acceleration factor and sampling strategy in a targeted manner. For example, based on the probability of occurrence of a certain extreme torque event of one in ten thousand in actual use, its frequency of occurrence can be appropriately increased to one in a thousand in the accelerated test, thereby ensuring the rigor of the test while taking into account a reasonable test time. This method makes the design of accelerated life tests more scientific and transparent.

[0107] (5) The method of the present application can adaptively adjust the selection of stress indicators and threshold settings according to different models of robots and different types of surgical operations, and is universal.

[0108] Practical applications have shown that life tests using the test trajectories generated by this application can more effectively reproduce the wear and failure modes of robots in clinical use. On the one hand, the test data verified that the high-stress trajectories extracted by this method did play a dominant role in causing failures; on the other hand, it also proved that the accelerated test would not introduce unrealistic invalid stresses, thereby ensuring the effectiveness and scientific nature of the test. Therefore, this application provides an efficient and intelligent loading trajectory generation technology for accelerated life tests of articulated mechanical devices, which can significantly improve the coverage depth and acceleration ratio of reliability tests, and provide guarantees for the safe and stable operation of products.

[0109] The present application also includes a high-stress trajectory extraction device for an articulated mechanical device, comprising a memory and a processor. The memory is configured to store instructions executable by the processor, and the processor is configured to execute the instructions to implement the high-stress trajectory extraction method for an articulated mechanical device described above.

[0110] Figure 9 This is a system block diagram of a high stress trajectory extraction device for an articulated mechanical device according to an embodiment of the present application. Figure 9As shown, the high-stress trajectory extraction device 900 may include an internal communication bus 901, a processor 902, a read-only memory (ROM) 903, a random access memory (RAM) 904, and a communication port 905. When used on a personal computer, the high-stress trajectory extraction device 900 may also include a hard disk 906. The internal communication bus 901 enables data communication between components of the high-stress trajectory extraction device 900. The processor 902 can make decisions and issue prompts. In some embodiments, the processor 902 may be composed of one or more processors. The communication port 905 enables data communication between the high-stress trajectory extraction device 900 and the external environment. In some embodiments, the high-stress trajectory extraction device 900 can send and receive information and data from a network via the communication port 905. The high-stress trajectory extraction device 900 may also include various types of program storage units and data storage units, such as a hard disk 906, a read-only memory (ROM) 903, and a random access memory (RAM) 904, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 902. The processor executes these instructions to implement the main part of the method. The results processed by the processor are transmitted to the user device through the communication port and displayed on the user interface.

[0111] The high stress trajectory extraction method described above may be implemented as a computer program, stored in the hard disk 906 , and loaded into the processor 902 for execution to implement the high stress trajectory extraction method of the present application.

[0112] The present application also includes a computer-readable medium storing computer program code, which, when executed by a processor, implements the high-stress trajectory extraction method described above.

[0113] When the high stress trajectory extraction method is implemented as a computer program, it can also be stored in a computer-readable storage medium as an article of manufacture. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memories (EPROMs), cards, sticks, key drives). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0114] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or a combination thereof.

[0115] Some aspects of this application may be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. These hardware and software components may be referred to as "data blocks," "modules," "engines," "units," "components," or "systems." A processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, various aspects of this application may be embodied as computer products embodied in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes), optical disks (e.g., compact disks, digital versatile disks, DVDs), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0116] A computer-readable medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination thereof. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit the program for use. The program code on the computer-readable medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above.

[0117] The basic concepts have been described above. It will be apparent to those skilled in the art that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0118] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0119] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which may vary according to the characteristics required by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

Claims

1. A method for extracting high stress trajectories of an articulated mechanical device, wherein the articulated mechanical device comprises at least one movable joint, characterized in that: include: Obtaining an n-dimensional stress vector, wherein the n-dimensional stress vector corresponds to n-dimensional stress data of the articulated mechanical device during operation, and the n-dimensional stress data is related to the stress to which the articulated mechanical device is subjected during operation; Performing dimensionality reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector, wherein n and k are both positive integers and n>k; generating a stress level index according to the n-dimensional stress vector, wherein the stress level index is used to measure the overall force applied to the articulated mechanical device; obtaining a target stress section of the articulated mechanical device during the operation according to the stress level index, wherein the stress level index of the articulated mechanical device in the target stress section is greater than a stress threshold; Constructing a stress distribution model, the stress distribution model including a stress amplitude distribution model and a stress pattern distribution model, wherein the stress amplitude distribution model is constructed based on the probability distribution of the stress level indicator during the operation of the articulated mechanical device, and the stress pattern distribution model is constructed based on the distribution of the k-dimensional eigenvector in a feature space, wherein the distribution of the k-dimensional eigenvector in the feature space is used to reflect load conditions of multiple stress change patterns; and A strategy for an accelerated test of an articulated mechanical device is determined based on the stress distribution model, including: determining a time compression ratio for the accelerated test of the articulated mechanical device based on the stress amplitude distribution model; selecting representative stress segments from the target stress segment based on the stress pattern distribution model; adjusting a target proportion of the representative stress segments in the accelerated test sequence of the articulated mechanical device based on the time compression ratio; and splicing the representative stress segments according to the target proportion to generate a high-stress trajectory sequence for the accelerated test of the articulated mechanical device.

2. The high stress trajectory extraction method according to claim 1, characterized in that: The performing dimensionality reduction processing on the n-dimensional stress vector includes: A principal component analysis is performed on the n-dimensional stress vector, the k-dimensional eigenvector is a k-dimensional principal component eigenvector, and the eigenspace is a principal component eigenspace.

3. The high stress trajectory extraction method according to claim 1, characterized in that: Generating a stress level index according to the n-dimensional stress vector includes: using the Euclidean norm of the n-dimensional stress vector as the stress level index, or using the Euclidean norm of the k-dimensional eigenvector as the stress level index.

4. The high stress trajectory extraction method according to claim 1, characterized in that: The method of obtaining a target stress segment of the articulated mechanical device during operation according to the stress level indicator includes: on a time axis, in response to the stress level indicator being greater than a stress threshold for a duration greater than or equal to a first duration, determining the current moment as the start moment of the target stress segment; and in response to the stress level indicator being less than or equal to the stress threshold for a duration greater than or equal to a second duration, determining the current moment as the end moment of the target stress segment.

5. The high stress trajectory extraction method according to claim 1, characterized in that: After obtaining the target stress segment of the articulated mechanical device during operation according to the stress level indicator, the method further includes: merging two adjacent target stress segments with a time interval less than an interval threshold into one target stress segment.

6. The high stress trajectory extraction method according to claim 1, characterized in that: Determining the time compression ratio of the accelerated test of the articulated mechanical device according to the stress amplitude distribution model includes: determining a time proportion of a target stress according to the stress amplitude distribution model, wherein the amplitude of the target stress is greater than an amplitude threshold; Obtaining a target time ratio of the target stress expected in the accelerated test of the articulated mechanical device; and The ratio of the target time proportion to the time proportion is used as the time compression ratio.

7. The high stress trajectory extraction method according to claim 2, characterized in that: The stress pattern distribution model is constructed using the following method: Applying a clustering algorithm to divide the k-dimensional principal component feature vector into a plurality of clusters in the principal component feature space, wherein each cluster corresponds to a stress variation pattern; and The stress pattern distribution model is constructed using the cluster center and cluster frequency of each cluster, wherein the cluster center represents the average load of the corresponding stress change pattern, and the cluster frequency represents the frequency of occurrence of the corresponding stress change pattern during the operation of the articulated mechanical device.

8. The high stress trajectory extraction method according to claim 7, characterized in that: The selecting a representative stress segment from the target stress segment according to the stress pattern distribution model includes: selecting the target stress segment that appears at the cluster center and near the cluster center as the representative stress segment.

9. The high stress trajectory extraction method according to claim 7, characterized in that: The step of adjusting the target proportion of the representative stress segment in the accelerated test sequence of the articulated mechanical device according to the time compression ratio includes: Obtaining cluster probabilities of the representative stress fragments; and The product of the cluster probability and the time compression ratio is used as the target proportion of the representative stress segment in the accelerated test sequence of the articulated mechanical device, wherein the adjusted target proportion of each stress change mode satisfies: ,in, Represents the target proportion of the jth stress variation mode.

10. The high stress trajectory extraction method according to claim 1, characterized in that: The splicing of the representative stress segments according to the target proportion includes: splicing a plurality of the representative stress segments in a cyclic or random order so that the proportion of each representative stress segment in the high stress trajectory sequence is equal to the target proportion.

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