Dynamic performance testing method for self-locking artificial vertebral body with adjustable mechanical load

Abstract

The invention relates to the technical field of mechanical testing of medical instruments, in particular to a dynamic performance testing method for a self-locking artificial vertebral body with an adjustable mechanical load, which comprises the following steps: establishing a reference dynamic testing environment comprising a mechanical loading device, a space positioning module and a structural state monitoring module, periodic loads are applied to the artificial vertebral body, real-time deformation field data are collected, and the deformation field data and load boundary conditions are subjected to synchronization processing by combining position and attitude information, so that a dynamic performance data set is formed. And generating a structure behavior deviation matrix by calculating structure behavior consistency measurement and comparing the structure behavior consistency measurement with the reference model, adjusting the initial loading protocol according to the deviation matrix, and executing the updated dynamic loading instruction. According to the method, the synchronous matching of the test data and the loading parameters can be realized, the structure behavior difference is quantitatively represented, the closed-loop regulation and control of the test and the loading are formed, and the accuracy of the dynamic performance test and the data integrity are improved.

Description

Technical Field

[0001] This invention relates to the field of mechanical testing technology for medical devices, and in particular to a method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load. Background Technology

[0002] Conventional adjustable mechanical load self-locking artificial vertebral bodies dynamic performance tests often use mechanical loading devices with fixed parameters to apply periodic loads. They only collect local deformation data of the artificial vertebral body through conventional monitoring components, without using a spatial positioning module to obtain the vertebral body's position and orientation information. The deformation field data and load boundary conditions are collected independently, which can only form basic test data.

[0003] Existing testing methods cannot achieve spatiotemporal synchronization between deformation data and load conditions, making it difficult to fully reflect the true structural state of the artificial vertebra under dynamic loads. The completeness and synchronicity of the test data are significantly insufficient. Conventional tests can only complete data acquisition and simple recording, and cannot quantitatively calculate the structural behavior of the artificial vertebra during the testing process. Nor can they set up a benchmark structural behavior model for comparative analysis, and therefore cannot obtain quantitative results on structural behavior deviations.

[0004] The system cannot rely on quantified structural behavior deviations to adjust the preset initial loading protocol in real time. The mechanical loading device always operates according to fixed initial parameters, which cannot adapt to real-time structural changes during the artificial vertebral body testing process. The dynamic loading process lacks closed-loop control and cannot accurately complete the dynamic performance test of the adjustable mechanical load self-locking artificial vertebral body. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load, comprising: A benchmark dynamic test environment is established for the self-locking artificial vertebrae with adjustable mechanical load. The benchmark dynamic test environment includes a mechanical loading device, a spatial positioning module, and a structural state monitoring module. The mechanical loading device applies a periodic load to the self-locking artificial vertebra with adjustable mechanical load according to a preset initial loading protocol, while the structural state monitoring module collects and generates real-time deformation field data of the self-locking artificial vertebra with adjustable mechanical load. Combining the position and attitude information obtained by the spatial positioning module, the real-time deformation field data and the preset load boundary conditions are synchronized to form a dynamic performance dataset of the self-locking artificial vertebra with adjustable mechanical load. Based on the dynamic performance dataset, the structural behavior consistency measure of the self-locking artificial vertebra with adjustable mechanical load during the test process is calculated, and the structural behavior consistency measure is compared with the preset benchmark structural behavior model to generate a structural behavior deviation matrix. Based on the structural behavior deviation matrix, the mechanical loading device is driven to adjust the preset initial loading protocol, and generate and execute the updated dynamic loading instructions.

[0007] As a further aspect of the present invention, a benchmark dynamic testing environment is established for the self-locking artificial vertebrae with adjustable mechanical load, specifically including: The mechanical loading device is configured to have the ability to independently apply and adjust forces and torques along multiple main directions; The spatial positioning module is configured, which consists of at least one optical motion capture system and an inertial measurement unit array, and is used to calculate in real time the three-dimensional coordinates, Euler angles and angular velocities of the self-locking artificial vertebra with adjustable mechanical load in the test space. The structural state monitoring module is deployed. The structural state monitoring module consists of a set of high-resolution digital image correlation systems and an embedded micro-strain sensor network. It is used to synchronously capture the full-field displacement and strain distribution of the surface of the self-locking artificial vertebra with adjustable mechanical load under the periodic load. Before the test begins, the self-calibration process of the benchmark dynamic test environment is executed. The self-calibration process includes calibrating the output force value of the mechanical loading device, registering the coordinate system of the spatial positioning module, and synchronizing the sampling frequency of the structural state monitoring module to ensure that the data of the mechanical loading device, the spatial positioning module, and the structural state monitoring module are aligned in time and space.

[0008] As a further aspect of the present invention, the real-time deformation field data is synchronized with the preset load boundary conditions, specifically including: From the position and attitude information obtained by the spatial positioning module, the pose transformation matrix of the self-locking artificial vertebra with adjustable mechanical load at each acquisition moment is extracted. Using the pose transformation matrix, the real-time deformation field data acquired by the structural state monitoring module at the same acquisition time is transformed into spatial coordinates, and the real-time deformation field data is mapped from the sensor local coordinate system to a global reference coordinate system that is the same as the preset load boundary conditions. In the global reference coordinate system, the mapped real-time deformation field data is aligned with the preset load boundary conditions in the time dimension. The preset load boundary conditions are a predefined set of force and displacement constraints that vary with time. After alignment, the aligned full-field displacement and strain information from the real-time deformation field data at each time point is associated and bound with the corresponding force and displacement constraint information from the preset load boundary conditions to form a data entry in the dynamic performance dataset. The dynamic performance dataset is time-series and contains a multi-dimensional data set of force, displacement, strain, position, and attitude.

[0009] As a further aspect of the present invention, calculating the structural behavior consistency metric of the self-locking artificial vertebra with adjustable mechanical load during the testing process specifically includes: From the dynamic performance dataset, the input load sequence under the preset initial loading protocol and the response data sequence of the self-locking artificial vertebra with adjustable mechanical load to the input load sequence are separated, the response data sequence including displacement response, strain response and attitude change response; The preset benchmark structural behavior model is retrieved from the test database. The preset benchmark structural behavior model is a set of mathematical relationships that describes the expected response under standard load input, established through theoretical calculations or preliminary calibration experiments, with the idealized self-locking artificial vertebrae with adjustable mechanical load as the object. Using the input load sequence as input to the preset benchmark structural behavior model, the expected response sequence of the self-locking artificial vertebra with adjustable mechanical load under ideal conditions is calculated. Within the same time-domain window, the expected response sequence is compared point by point with the response data sequence extracted from the dynamic performance dataset, and the response deviation value at each comparison point is calculated. The response deviation value includes displacement deviation, strain deviation and attitude angle deviation. The response deviation values ​​of all comparison points are normalized, and a dimensionless scalar between zero and one is calculated based on the normalization result. The dimensionless scalar is defined as the structural behavior consistency measure. The closer its value is to one, the higher the degree of agreement between the actual response of the self-locking artificial vertebra with adjustable mechanical load and the preset benchmark structural behavior model during the test period.

[0010] As a further aspect of the present invention, generating a structural behavior deviation matrix specifically includes: Construct a two-dimensional matrix, where the row index of the two-dimensional matrix corresponds to different load condition identifiers and the column index corresponds to different performance evaluation dimensions. The performance evaluation dimensions include at least stiffness, damping characteristics, energy dissipation rate and self-locking stability margin. For each load condition identifier, the actual performance evaluation index value of the self-locking artificial vertebra with adjustable mechanical load is calculated based on the data segment corresponding to the load condition in the dynamic performance dataset. Simultaneously, using the preset benchmark structural behavior model, the benchmark performance evaluation index value of the self-locking artificial vertebra under the same load condition is calculated. The difference between the actual performance evaluation index value and the corresponding benchmark performance evaluation index value is used to obtain the performance deviation value of the load condition in the performance evaluation dimension. The performance deviation values ​​of all load conditions across all performance evaluation dimensions are filled in according to the row and column index rules of the two-dimensional matrix to fully construct the structural behavior deviation matrix. The dimensions of the structural behavior deviation matrix are determined by the total number of load conditions and the total number of performance evaluation dimensions.

[0011] As a further aspect of the present invention, based on the structural behavior deviation matrix, the mechanical loading device is driven to adjust the preset initial loading protocol, generating and executing updated dynamic loading instructions, specifically including: A dynamic loading strategy mapping table is set up, which defines the correspondence between different types of performance deviation values ​​and loading parameter adjustment actions; Iterate through each non-zero element in the structural behavior deviation matrix, i.e., each performance deviation value; For the performance deviation value currently traversed, query the dynamic loading strategy mapping table to find one or more loading parameter adjustment suggestions that match its type. The loading parameters include loading force amplitude, loading frequency, phase, load application point and load direction. Summarize all loading parameter adjustment suggestions obtained in the current traversal cycle, and merge all loading parameter adjustment suggestions according to the preset priority rules and conflict resolution rules to form a set of conflict-free and specific loading parameter adjustment amounts; Based on the parameters of the current preset initial loading protocol, the loading parameter adjustment amount is applied to generate the updated dynamic loading instruction. The updated dynamic loading instruction clarifies the time history, spatial distribution and variation law of the load to be applied by the mechanical loading device in subsequent loading cycles. The updated dynamic loading command is sent to the control unit of the mechanical loading device, which drives the mechanical loading device to interrupt the currently executing preset initial loading protocol and immediately start running according to the updated dynamic loading command.

[0012] As a further aspect of the present invention, after generating and executing the updated dynamic loading instruction, the method further includes: recording a complete test trajectory log containing the real-time deformation field data, the structural behavior consistency measure, the structural behavior deviation matrix, and the updated dynamic loading instruction; The process of recording a complete test trajectory log also includes: The process of the mechanical loading device executing the updated dynamic loading command is continuously monitored, and new loading data and new response data generated in this process are collected in real time. These data are then merged with the dynamic performance dataset from the previous stage to form an extended dynamic performance dataset. Based on the extended dynamic performance dataset, the steps of calculating the new structural behavior consistency metric and generating the new structural behavior deviation matrix are repeated. The extended dynamic performance dataset, the new structural behavior consistency metric, the new structural behavior deviation matrix, and the previously recorded real-time deformation field data, the structural behavior consistency metric, the structural behavior deviation matrix, and the updated dynamic loading instructions are integrated and associated in chronological order. All integrated data, including all load conditions experienced by the self-locking artificial vertebrae with adjustable mechanical load throughout the test, all responses generated, and all records of dynamic adjustments, are structured and stored in the test database in the form of timestamped data packets, forming the complete test trajectory log.

[0013] As a further aspect of the present invention, the structural behavior deviation matrix is ​​also used to perform offline analysis, specifically: After a single complete test is completed, extract all the structural behavior deviation matrices for the entire test cycle from the complete test trajectory log; All extracted structural behavior deviation matrices are categorized and arranged in time series according to load condition identifiers to form a behavior deviation sequence that evolves over time for each load condition. Statistical analysis was performed on the behavioral deviation sequence for each load condition to calculate its mean, variance, maximum value, minimum value and trend of change, in order to evaluate the long-term performance degradation mode and stability characteristics of the self-locking artificial vertebra with adjustable mechanical load under different types of loads. The results of the offline analysis are stored independently to provide data support for the design improvement, life prediction, or clinical application risk assessment of the self-locking artificial vertebra with adjustable mechanical load.

[0014] As a further aspect of the present invention, in the synchronization processing step, if there is a time deviation between the sampling times of the spatial positioning module and the structural state monitoring module that is not an integer multiple, an interpolation compensation method is adopted, specifically as follows: On the global reference timeline, the timestamps of the devices at the high-frequency sampling end are used as the reference. For data collected by low-frequency sampling devices, linear interpolation or spline interpolation is used to estimate the data value at each reference timestamp on the global reference time axis by utilizing the data values ​​of adjacent timestamps. The interpolation compensation method ensures that the real-time deformation field data and the preset load boundary conditions have synchronized data pairs at every discrete point in time on the global reference time axis.

[0015] As a further aspect of the present invention, the test database has a hierarchical structure for efficient storage and retrieval of the complete test trajectory log: The top layer of the test database is the test task record, which records the unique identifier of each test task, the test target, and the model and number of the self-locking artificial vertebra with adjustable mechanical load used. The intermediate layer is a time-series data block, which stores the real-time deformation field data, the structural behavior consistency measure, the structural behavior deviation matrix, and the sequence of updated dynamic loading instructions in the order of the test process. The bottom layer is a raw data and metadata storage area, used to store the raw data streams from the mechanical loading device, the spatial positioning module and the structural state monitoring module that have not been synchronized, as well as metadata describing the data format, units and coordinate system; The hierarchical structure of the test database supports the retrospective, comparative analysis, and mining of all test data throughout the entire life cycle of the self-locking artificial vertebra with adjustable mechanical load.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: Based on dynamic performance datasets, a structural behavior consistency metric for self-locking artificial vertebrae under adjustable mechanical loads is calculated during testing. This consistency metric is compared with a pre-defined baseline structural behavior model to generate a corresponding structural behavior deviation matrix. This allows for the quantitative characterization of the artificial vertebrae's structural behavior under periodic dynamic loads, accurately acquiring the differences between the vertebrae's test state and the baseline model. It clearly presents the specific distribution and real-time changes of structural behavior deviations, fully reflecting the structural state changes of the artificial vertebrae during dynamic loading. This refines the quantitative representation of the vertebrae's structural response under dynamic loads, enhances the analytical dimensions of test data, and transforms the dynamic performance testing and analysis of the vertebrae from basic data recording to quantitative feature comparison. This strengthens the accuracy of structural state monitoring, fully presents the structural behavior changes of the vertebrae throughout the entire dynamic testing process, and ensures the objectivity and relevance of deviation data.

[0017] Based on the structural behavior deviation matrix, the mechanical loading device adjusts the preset initial loading protocol, generates and executes updated dynamic loading instructions, and ensures that the operating parameters of the mechanical loading device are adapted to the real-time structural state of the artificial vertebra. This enables synchronous adjustment of loading parameters as structural behavior changes during testing, constructs a closed-loop execution process for dynamic test data acquisition and mechanical loading control, eliminates the mismatch between the fixed initial loading protocol and the real-time structural state of the vertebra, and ensures that the application of mechanical loading closely matches the actual stress and deformation state of the artificial vertebra. It synchronously matches the position and attitude information obtained by the spatial positioning module and the deformation field data collected by the structural state monitoring module, optimizes the execution logic of dynamic loading, and ensures that the update of loading instructions corresponds to changes in structural behavior deviation in real time. This improves the adaptability and accuracy of the dynamic loading process, restores the true mechanical response state of the artificial vertebra under dynamic load, and perfects the control process for dynamic performance testing of self-locking artificial vertebrae with adjustable mechanical load. Attached Figure Description

[0018] Figure 1 This is a flowchart of the dynamic performance testing method for the self-locking artificial vertebra with adjustable mechanical load according to the present invention. Figure 2 A flowchart for synchronizing data processing; Figure 3 A graph showing the trend of structural behavior consistency measurement as a function of load cycle number; Figure 4 A heatmap of the structural behavior deviation matrix of an adjustable mechanical load self-locking artificial vertebra; Figure 5 This is a time-series variation analysis diagram for the structural behavior consistency measure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1 A benchmark dynamic testing environment for the self-locking artificial vertebra with adjustable mechanical load is established. This benchmark dynamic testing environment includes a mechanical loading device, a spatial positioning module, and a structural state monitoring module. The mechanical loading device applies periodic loads to the self-locking artificial vertebra with adjustable mechanical load according to a preset initial loading protocol. Simultaneously, the structural state monitoring module collects and generates real-time deformation field data of the self-locking artificial vertebra with adjustable mechanical load. Combining the position and attitude information obtained by the spatial positioning module, the real-time deformation field data is synchronized with preset load boundary conditions to form a dynamic performance dataset of the self-locking artificial vertebra with adjustable mechanical load. Based on this dynamic performance dataset, a structural behavior consistency metric of the self-locking artificial vertebra with adjustable mechanical load is calculated during the testing process. This structural behavior consistency metric is compared with a preset benchmark structural behavior model to generate a structural behavior deviation matrix. Based on the structural behavior deviation matrix, the mechanical loading device is driven to adjust the preset initial loading protocol, generating and executing updated dynamic loading instructions.

[0022] In one embodiment of the present invention, establishing the benchmark dynamic test environment involves the specific configuration and integration of the mechanical loading device, the spatial positioning module, and the structural state monitoring module. The mechanical loading device is configured to independently apply and adjust axial force and bending moment along the three principal axes of the Cartesian coordinate system. The core of the mechanical loading device is a multi-axis servo hydraulic actuation system, which includes multiple independent actuating cylinders and high-precision force sensors, allowing the reproduction of complex multi-dimensional dynamic load spectra under the command of the control system. The spatial positioning module relies on an optical motion capture system containing eight high-speed infrared cameras symmetrically distributed around the test space. Simultaneously, an inertial measurement unit array consisting of a three-axis gyroscope and a three-axis accelerometer is securely mounted at a specific location on the self-locking artificial cone with adjustable mechanical loads. The optical motion capture system captures the spatial positions of reflective markers attached to the surface of the self-locking artificial vertebra with adjustable mechanical loads. An inertial measurement unit array measures its own angular velocity and linear acceleration. The data from both systems are fused using a calculation algorithm to output in real-time the three-dimensional coordinates of the centroid of the self-locking artificial vertebra with adjustable mechanical loads, the Euler angles around the three axes, and the instantaneous angular velocity. The structural state monitoring module consists of two components. The first is a high-resolution digital image correlation system using two rigorously calibrated high-speed cameras to synchronously capture pre-prepared speckle patterns on the surface of the self-locking artificial vertebra with adjustable mechanical loads at a fixed frequency. The second is an embedded micro-strain sensor network. This network consists of multiple micro-strain gauges connected in a Wheatstone bridge configuration and integrated into the key stress areas of the self-locking artificial vertebra with adjustable mechanical loads. The digital image correlation system calculates the full-field displacement and strain through cross-correlation analysis of continuous images, while the micro-strain sensor network directly outputs the strain electrical signals at local points. Together, they synchronously capture the full-field displacement and strain distribution on the surface of the self-locking artificial vertebra with adjustable mechanical loads under periodic loads.

[0023] In some embodiments, coordinate system registration of the spatial positioning module is achieved through the following process: A standard calibration frame with known geometric dimensions is fixed in the test space, and a series of control points with precisely known positions are arranged on the standard calibration frame. The optical motion capture system identifies and calculates the coordinates of these control points in its own coordinate system. By solving a coordinate transformation problem, the rotation matrix and translation vector from the optical motion capture system coordinate system to the global world coordinate system are obtained. The mounting matrix of the inertial measurement unit array is determined by a static multi-position calibration method. The self-locking artificial cone with adjustable mechanical load and inertial measurement unit array mounted is placed in multiple known postures, and the output of the inertial measurement unit array is recorded, thereby calculating the direction cosine matrix between the sensitive axis of the inertial measurement unit array and the coordinate system of the self-locking artificial cone with adjustable mechanical load. The mathematical expression of the coordinate transformation is: in: This represents the coordinate vector of a point in the global world coordinate system. This represents the rotation matrix from the local sensor coordinate system to the global world coordinate system. This represents the coordinate vector of a point in the local sensor coordinate system. This represents the translation vector from the origin of the local sensor coordinate system to the origin of the global world coordinate system. It can be understood that this registration process ensures that the position and attitude information output by the spatial positioning module is unified within a global reference frame.

[0024] Optionally, the sampling frequency synchronization between the high-resolution digital image correlation system and the embedded micro-strain sensor network in the structural condition monitoring module is achieved through a unified central timing controller. The central timing controller generates a highly stable master clock signal, which is sent as trigger pulses to both the high-speed camera and the data acquisition card of the micro-strain sensor network. The high-speed camera performs exposure and image acquisition upon receiving the trigger pulse, while the data acquisition card of the micro-strain sensor network samples the strain electrical signal at the same trigger pulse edge, ensuring strict time alignment of the acquired data. The output force calibration of the mechanical loading device is performed before loading begins. The actuator of the mechanical loading device is directly connected to a standard force sensor, which is then connected to a metrologically certified static force analyzer. A series of stepped standard force commands are input into the control system. The readings of the force sensor built into the mechanical loading device and the actual force values ​​measured by the static force analyzer are recorded. The force calibration curve and correction coefficients are obtained through least squares fitting, and the correction coefficients are written into the control software of the mechanical loading device.

[0025] Understandably, the final step in the self-calibration process is to verify the temporal and spatial alignment of the entire benchmark dynamic testing environment. A standard calibration rod of known size and stiffness is mounted on the fixture of the mechanical loading device. The surface of the standard calibration rod is decorated with reflective markers and speckle patterns, and strain gauges are attached. The mechanical loading device applies a simple uniaxial sinusoidal load, simultaneously recording data streams from the mechanical loading device, the spatial positioning module, and the structural condition monitoring module. Offline analysis is performed on the data streams to check whether the data points describing the same physical event (such as the load peak point) from different modules are consistent in timestamp, and to check whether the motion trajectory of the standard calibration rod calculated by the spatial positioning module matches the displacement field calculated by the digital image correlation system in space. When the time deviation is less than one sampling interval and the spatial position deviation is less than the preset tolerance, the data from the mechanical loading device, the spatial positioning module, and the structural condition monitoring module are considered to be aligned in time and space, and the benchmark dynamic testing environment is considered to be established successfully.

[0026] In one embodiment of the present invention, see [reference] Figure 2 A core step is synchronizing real-time deformation field data with preset load boundary conditions. This process begins by extracting the pose transformation matrix of the self-locking artificial cone with adjustable mechanical load at each acquisition moment from the timestamped streaming data acquired by the spatial positioning module. The pose transformation matrix is ​​a 4x4 homogeneous transformation matrix that comprehensively expresses the rotation and translation relationship of the self-locking artificial cone with adjustable mechanical load from its own carrier coordinate system to the global reference coordinate system. The real-time deformation field data acquired by the structural state monitoring module at the same acquisition moment, such as the displacement vector calculated by the digital image correlation system based on the image pixel coordinates or the strain value output by the embedded micro-strain sensor network based on the sensor's local coordinates, are all associated with their respective local coordinate systems. Using the extracted pose transformation matrix, spatial coordinate transformation is performed on these real-time deformation field data, mapping the real-time deformation field data from the sensor's local coordinate system to the same global reference coordinate system as the preset load boundary conditions. The preset load boundary conditions are a set of time-varying force and displacement constraints defined in advance in the global reference coordinate system. They specify the direction and magnitude change curve of the force vector applied by the actuator of the mechanical loading device, as well as the displacement constraints of the fixture.

[0027] In some embodiments, the pose transformation matrix is ​​extracted based on the quaternion and translation vector output by the spatial positioning module. For each acquisition moment, the spatial positioning module provides the position vector of the center of mass of the self-locking artificial vertebra with adjustable mechanical load in the global world coordinate system, along with a unit quaternion representing its attitude. This unit quaternion is converted into a 3x3 rotation matrix, and combined with the position vector, the complete pose transformation matrix can be constructed. In the global reference coordinate system, the mapped real-time deformation field data and the preset load boundary conditions need to be strictly aligned in the time dimension. The alignment mechanism relies on high-precision synchronous timestamps assigned to all data streams. The timestamps are assigned by the same central timing controller at the moment of data acquisition. The alignment process involves finding the corresponding real-time deformation field data points with timestamp differences within the allowable error range for each preset load boundary condition data point on the global reference time axis and pairing them.

[0028] Optionally, after time alignment is complete, the synchronization process enters the data binding stage. Each successfully paired time point constitutes a data entry in the dynamic performance dataset. This data entry integrates force and displacement constraint information from preset load boundary conditions, as well as full-field displacement and strain information from real-time deformation field data after coordinate transformation. The dynamic performance dataset is thus presented as a time-series multidimensional data table, with each row corresponding to a synchronization time point and each column representing a specific physical quantity, such as X-axis force, Y-axis displacement, Mises strain at key points, and yaw angle around the Z-axis in the global coordinate system. The dynamic performance dataset is the foundation for all subsequent calculations and analyses.

[0029] In some embodiments, due to limitations in the physical characteristics of the equipment, the sampling times of the spatial positioning module and the structural status monitoring module may have a non-integer multiple relationship, meaning the sampling rates of the two systems are asynchronous and not integer multiples of each other. In this case, an interpolation compensation method is used. On the global reference time axis, the timestamp sequence of the device with the higher sampling frequency is used as the base time axis. For data collected by the device with the lower sampling frequency, the data value at each timestamp on the base time axis is estimated using linear interpolation, utilizing the data values ​​of its adjacent timestamps. The calculation of linear interpolation can be expressed as: in: This represents the target timestamp on the baseline timeline. and It is in low sampling rate data streams and Two adjacent timestamps and satisfy , and Is and The raw data values ​​collected at any given time. Is The estimated data value obtained by interpolation at any given time.

[0030] It is understandable that, through the aforementioned coordinate transformation, time alignment, and necessary interpolation compensation, real-time deformation field data originally from different sensors, in different coordinate systems, and at different time series, along with preset load boundary conditions, are fused into a set of spatiotemporally strictly aligned dynamic performance datasets. This dynamic performance dataset ensures that, at any specified global time point, the load input to the self-locking artificial cone with adjustable mechanical loads and its resulting structural response are directly comparable both spatially and numerically, creating the necessary conditions for subsequent calculations of structural behavior consistency metrics.

[0031] In one embodiment of the invention, the process of calculating a structural behavior consistency metric and generating a structural behavior deviation matrix from a dynamic performance dataset begins with data separation. This separation involves separating the input load sequence under a preset initial loading protocol and the response data sequence of the self-locking artificial vertebra with adjustable mechanical loads to the input load sequence from the dynamic performance dataset. The input load sequence is a sequence of force and moment vectors arranged in chronological order, extracted from the "Load" related column of the dynamic performance dataset. The response data sequence is a data sequence corresponding to the time points of the input load sequence, extracted from the "Response" related column of the dynamic performance dataset. The response data sequence includes the displacement response at a specified observation point obtained from digital image correlation system data, the strain response obtained from embedded micro-strain sensor network data, and the attitude change response obtained from spatial positioning module data. A preset benchmark structural behavior model is retrieved from the test database. This preset benchmark structural behavior model is established using an idealized, defect-free, and material-uniform self-locking artificial vertebra with adjustable mechanical loads, through finite element simulation calculations and prior calibration experiments in a standard environment. The pre-defined baseline structural behavior model contains a set of mathematical relationships, such as the stiffness matrix describing the linear force-displacement relationship, the damping coefficient matrix describing the damping force-velocity relationship, and the threshold function describing the critical slip force of the self-locking mechanism. These mathematical relationships together constitute a set of mathematical relationships describing the expected response under standard load input.

[0032] In some embodiments, using the input load sequence as input to a preset benchmark structural behavior model means substituting each force and moment vector in the input load sequence into the corresponding mathematical model for forward calculation. For example, for a given input force vector, multiplying it with the stiffness matrix in the preset benchmark structural behavior model yields the expected displacement vector under the action of that force vector; multiplying the damping coefficient matrix with the velocity vector obtained by differencing the input load sequence yields the expected damping force component. By traversing the entire input load sequence and performing a series of calculations, the expected response sequence of the self-locking artificial vertebra with adjustable mechanical loads under ideal conditions, synchronized with the input, is finally obtained. The expected response sequence includes data on the expected displacement, expected strain, and expected attitude angle changes over time. Within the same time window, the expected response sequence is compared point by point with the response data sequence extracted from the dynamic performance dataset. This means that at each same timestamp, the calculated expected displacement response value is subtracted from the actual collected displacement response value, the expected strain response value is subtracted from the actual strain response value, and the expected attitude angle response value is subtracted from the actual attitude angle response value, thereby calculating the displacement deviation value, strain deviation value, and attitude angle deviation value at each comparison point.

[0033] Optionally, the displacement deviation, strain deviation, and attitude angle deviation values ​​of all comparison points are normalized. The denominators used in the normalization process are the maximum theoretical deviation ranges allowed for displacement, strain, and attitude angle as defined in the preset benchmark structural behavior model. Based on the normalization results, a dimensionless scalar between zero and one is calculated. This dimensionless scalar is defined as a structural behavior consistency measure, and the formula for calculating the structural behavior consistency measure is: in: This represents a measure of structural behavior consistency. This indicates the total number of comparison points involved in the calculation. Indicates the first Displacement deviation values ​​of each comparison point Indicates the first The strain deviation value of each comparison point Indicates the first The attitude angle deviation values ​​of each comparison point This indicates the maximum theoretical allowable range of displacement. This indicates the maximum theoretical allowable deviation range of strain. This represents the maximum theoretical deviation range allowed for the attitude angle. The closer the structural behavior consistency metric is to one, the better the actual response of the self-locking artificial vertebra under adjustable mechanical loads matches the pre-set baseline structural behavior model within the test period. Generating the structural behavior deviation matrix requires constructing a two-dimensional matrix. The row indices of the two-dimensional matrix correspond to different load condition identifiers. The column indices of the two-dimensional matrix correspond to different performance evaluation dimensions, which at least include stiffness, damping characteristics, energy dissipation rate, and self-locking stability margin.

[0034] In some embodiments, for each load condition identifier, the actual performance evaluation index value of the self-locking artificial vertebra with adjustable mechanical load is calculated based on the data segment corresponding to the load condition in the dynamic performance dataset. The actual value of stiffness is obtained by least-squares linear fitting of the slope of the linear segment of the load-displacement relationship curve within the data segment; the actual value of damping characteristics is calculated by logarithmic decay method of the peak value of the free vibration decay curve within the data segment; the actual value of energy dissipation rate is obtained by numerically integrating the area of ​​the force-displacement hysteresis curve formed by a complete load cycle within the data segment; and the actual value of self-locking stability margin is obtained by analyzing the ratio of the residual displacement of the self-locking mechanism to the critical disturbance force after applying a small disturbance under the load condition. Simultaneously, using a preset benchmark structural behavior model, the standard load conditions corresponding to the load condition identifier are taken as input, and the corresponding standard parameters within the model are called for calculation to obtain the benchmark stiffness value, benchmark damping characteristic value, benchmark energy dissipation rate value, and benchmark self-locking stability margin value of the self-locking artificial vertebra with adjustable mechanical load under the same load condition identifier.

[0035] It can be understood that the difference between the actual performance evaluation index value and the corresponding benchmark performance evaluation index value yields the performance deviation value of the load condition in each performance evaluation dimension. The performance deviation value is a numerical value with physical dimensions; for example, the unit of stiffness deviation is Newton-meter (N / m), and the unit of energy dissipation rate deviation is Joule (J). The performance deviation values ​​of all load conditions in all performance evaluation dimensions are filled into the corresponding positions of the matrix according to the row and column indexing rules of a two-dimensional matrix, thus completely constructing the structural behavior deviation matrix. The dimensions of the structural behavior deviation matrix are determined by the total number of load conditions and the total number of performance evaluation dimensions. For example, if the test includes 8 different load conditions and evaluates performance in 4 dimensions, the generated structural behavior deviation matrix is ​​an 8x4 matrix. The element value in the i-th row and j-th column of the matrix represents the deviation between the self-locking artificial vertebra with adjustable mechanical load and the theoretical expected value of the benchmark model in the j-th performance evaluation dimension under the i-th load condition.

[0036] See Figure 3This is a graph showing the trend of structural behavior consistency metric as a function of load cycles, reflecting the performance stability of an adjustable mechanical load self-locking artificial vertebra under cyclic loading. The consistency metric decreases linearly with the number of load cycles, gradually decreasing from an initial 0.90 to 0.71, a decrease of approximately 21.1%. For every additional 100 cycles, the consistency metric decreases by an average of approximately 0.03, indicating that the fit between the artificial vertebra and the baseline model continuously deteriorates with increasing cycle count. A value closer to 1 indicates a better match between the actual structural response and the baseline model, and more stable performance; a decreasing value indicates performance degradation of the artificial vertebra under cyclic loading. To improve stability, targeted optimization of material fatigue resistance, self-locking mechanism reliability, or structural stiffness design can be implemented. This curve is one of the core results of dynamic performance testing and can be combined with the structural behavior deviation matrix to comprehensively evaluate the long-term mechanical performance of the artificial vertebra.

[0037] In one embodiment of the present invention, the process of adjusting a preset initial loading protocol by a mechanical loading device based on a structural behavior deviation matrix begins with setting a dynamic loading strategy mapping table. The dynamic loading strategy mapping table defines the correspondence between different types of performance deviation values ​​and loading parameter adjustment actions, and is stored in the control system in the form of a data structure table. Each non-zero element in the structural behavior deviation matrix is ​​traversed, i.e., each performance deviation value is traversed. For the currently traversed performance deviation value, the dynamic loading strategy mapping table is queried to find one or more loading parameter adjustment suggestions that match its type. The type of the performance deviation value is determined by its column in the structural behavior deviation matrix. For example, if the performance deviation value is located in the "stiffness" column, its type is "negative stiffness deviation" or "positive stiffness deviation." Loading parameters include loading force amplitude, loading frequency, phase, load application point, and load direction. See Table 1.

[0038] Table 1: Example Fragment Table of Dynamic Loading Strategy Mapping In some embodiments, after traversing the structural behavior deviation matrix and querying the dynamic loading strategy mapping table, all loading parameter adjustment suggestions obtained within the current traversal cycle are summarized. Since a single performance deviation value may correspond to multiple adjustment suggestions, and queries of deviation values ​​from different rows and columns may generate adjustment suggestions for the same loading parameter with different values ​​or even opposite directions, it is necessary to fuse these suggestions according to preset priority rules and conflict resolution rules. Priority rules stipulate that adjustment suggestions triggered by performance deviations involving key safety parameters have the highest priority, followed by adjustment suggestions involving core mechanical performance. Conflict resolution rules handle different adjustment amounts for the same loading parameter, fusing them using a weighted average method. The weight is determined by the absolute value of the performance deviation value that triggered the adjustment suggestion and the priority of its corresponding performance evaluation dimension. The final adjustment amount for a single loading parameter is then calculated through fusion. The formula is: in: Indicates loading parameters The final fusion adjustment amount (such as force amplitude), It refers to the loading parameters. The number of all proposed adjustments It is the first The adjustment suggestions provide the parameter adjustment amounts. It is the first The weight of each adjustment suggestion, weight The absolute value of the performance deviation that triggered the suggestion and its underlying priority coefficient for the performance dimension. The decision is made jointly, and the calculation method is as follows: It is understandable that by merging all the suggested adjustments to the loading parameters using the above rules, a set of conflict-free and specific adjustments to the loading parameters is ultimately formed.

[0039] Optionally, based on the parameters of the current preset initial loading protocol, an updated dynamic loading command is generated by applying loading parameter adjustments. For example, if the preset initial loading protocol specifies a sinusoidal axial compressive load with an amplitude of 100N and a frequency of 1Hz, and the fused adjustment requires "increasing the axial compressive load amplitude by 10%" and "decreasing the load frequency by 15%", then the updated dynamic loading command will specify a new load time history as a sinusoidal axial compressive load with an amplitude of 110N and a frequency of 0.85Hz. The updated dynamic loading command exists in the form of a structured data file, which clearly defines the time history, spatial distribution, and variation law of the loads to be applied by each actuator of the mechanical loading device in subsequent loading cycles. The updated dynamic loading command is sent to the control unit of the mechanical loading device through the fieldbus network, driving the mechanical loading device to interrupt the currently executing preset initial loading protocol and immediately begin running according to the updated dynamic loading command, realizing dynamic and adaptive adjustment of the test load.

[0040] In some embodiments, the structural behavior deviation matrix is ​​also used to perform offline analysis after a single complete test. The offline analysis process extracts all structural behavior deviation matrices from the complete test trajectory log for the entire test cycle, arranged in chronological order of generation. All extracted structural behavior deviation matrices are categorized and arranged chronologically according to load case identifiers. For example, deviation values ​​corresponding to the "stiffness" performance dimension are extracted from all structural behavior deviation matrices generated at different time points under the "load case_axial compression_1Hz" condition and sorted chronologically to form a stiffness behavior deviation sequence for the "load case_axial compression_1Hz" load case. Statistical analysis is performed on the behavior deviation sequence for each performance evaluation dimension under each load case, calculating its mean, variance, maximum value, minimum value, and trend. The trend can be calculated by linearly fitting the behavior deviation sequence to obtain its slope. The results of the offline analysis are stored independently as analysis report files, which are used to record the long-term performance degradation patterns of the self-locking artificial vertebrae under different types of loads with adjustable mechanical loads, such as the stiffness decay curve with the number of cycles, and stability characteristics, such as the fluctuation range of the self-locking stability margin deviation.

[0041] See Figure 4This is a heatmap of the structural behavior deviation matrix of an adjustable mechanical load self-locking artificial vertebra, used to visually display the deviations of the actual performance of the artificial vertebra from the benchmark model in four dimensions under different load conditions. The deviations are: negative deviation in extension stiffness and positive deviation in energy dissipation rate; negative deviation in torsional self-locking stability margin and negative deviation in energy dissipation rate; negative deviation in flexion damping characteristics; positive deviation in lateral bending stiffness; and positive deviation in axial compression energy dissipation rate. The overall deviation is smallest under axial compression, representing the most stable mechanical performance of the artificial vertebra. Significant deviations exist in multiple dimensions under extension and torsional conditions, representing key areas for performance optimization. For extension conditions, the structural stiffness design is optimized to balance energy dissipation capacity and avoid excessive energy consumption leading to insufficient stiffness. For torsional conditions, the stability design of the self-locking mechanism is strengthened, and damping characteristics are improved to enhance energy dissipation.

[0042] In one embodiment of the present invention, recording a complete test trajectory log, including real-time deformation field data, structural behavior consistency metrics, structural behavior deviation matrices, and updated dynamic loading commands, is a systematic data management process. The process of recording the complete test trajectory log includes continuously monitoring the mechanical loading device's execution of the updated dynamic loading commands, and real-time acquisition of new loading data and new response data generated during this process. The new loading data includes the force and torque values ​​actually output by the mechanical loading device according to the updated dynamic loading commands. The new response data includes new full-field displacement and strain data acquired by the structural state monitoring module under the new loading conditions, as well as new position and attitude information acquired by the spatial positioning module. The new loading data, new response data, and the dynamic performance dataset from the previous stage are merged to form an extended dynamic performance dataset. This extended dynamic performance dataset continues the timeline of the previous dynamic performance dataset and includes all synchronized load and response information from the start of the test to the current moment.

[0043] In some embodiments, based on an extended dynamic performance dataset, the steps of calculating a new structural behavior consistency metric and generating a new structural behavior deviation matrix are repeatedly performed. The calculation of the new structural behavior consistency metric uses the same pre-defined baseline structural behavior model and normalization method as before, but the input is a data segment from the extended dynamic performance dataset corresponding to the latest time window. The generation of the new structural behavior deviation matrix is ​​also based on the latest portion of the extended dynamic performance dataset, and may dynamically expand the set of load cases corresponding to the rows of the structural behavior deviation matrix as the load conditions change due to dynamic adjustments. The extended dynamic performance dataset, the new structural behavior consistency metric, and the new structural behavior deviation matrix are integrated and correlated with previously recorded real-time deformation field data, structural behavior consistency metric, structural behavior deviation matrix, and updated dynamic loading instructions in chronological order. Integration and correlation are achieved by attaching a uniform time interval marker and version number to each data block or measurement result, ensuring that every decision point, every load adjustment, and its corresponding system response during the testing process can be clearly traced.

[0044] Optionally, all integrated data, including all load conditions experienced by the self-locking artificial vertebrae with adjustable mechanical loads throughout the entire test, all responses generated, and all records of dynamic adjustments, will be structured and stored in the test database in the form of timestamped data packets, forming a complete test trajectory log. Each data packet's filename includes a unique test identifier, a timestamp of packet generation, and a data packet type code, and its naming follows these rules: <testid> _ <timestamp> _ <datatype>.pkg, where <testid>It is a unique identifier string for the test task. <timestamp>It is the precise timestamp when the data packet was generated. <datatype>This is an encoding that identifies the data packet type; for example, "DPD" represents Dynamic Performance Dataset, and "SBCM" represents Structural Behavior Consistency Metric. Structured storage means that the data packets are organized internally according to a predefined binary or structured text format, ensuring that the data can be read, written, and parsed efficiently. The test database has a hierarchical structure for efficiently storing and retrieving complete test trajectory logs. The top layer of the test database contains test task records, which record the unique identifier of each test task, the test objective, the model and number of the self-locking artificial cone used for adjustable mechanical loads, and the start and end times of the test. The middle layer of the test database contains time-series data blocks, which store real-time deformation field data, structural behavior consistency metrics, structural behavior deviation matrices, and the updated sequence of dynamic loading instructions in chronological order of the test process. The data in the middle layer is associated with the test task records in the top layer through foreign keys.

[0045] In some embodiments, the bottom layer of the test database is a raw data and metadata storage area, which stores unsynchronized raw data streams from the mechanical loading device, spatial positioning module, and structural condition monitoring module. The raw data streams include raw voltage signals from the mechanical loading device controller, raw two-dimensional image coordinate data captured by each infrared camera in the spatial positioning module, raw voltage outputs from the inertial measurement unit array, and raw speckle images captured by the high-speed camera in the structural condition monitoring module, as well as raw voltage readings from the embedded micro-strain sensor network. The metadata includes configuration files describing the formats of these raw data, the physical units of the data, the definitions of the coordinate systems of each sensor, and their transformation relationships with the global coordinate system. It can be understood that the hierarchical structure of the test database supports the retrospective, comparative analysis, and mining of all test data throughout the entire lifecycle of the self-locking artificial vertebra with adjustable mechanical loads. Users can quickly locate a specific test by querying the top-level test task records, and then drill down to view the synchronized dynamic performance data or unprocessed raw data streams at any point in time within that test, achieving full-chain data access from macroscopic test information to microscopic raw signals.

[0046] See Figure 5 This is a time-series analysis chart of structural behavior consistency metrics, visually reflecting the performance response patterns before and after load adjustment. From 0 to 5 seconds, the consistency metric rapidly decreases from 0.95 to 0.90; from 5 to 10 seconds, it recovers from 0.90 to 0.95; at the 10-second load adjustment point, the consistency metric jumps to 0.97; from 10 to 15 seconds, it decreases from 0.97 to 0.94; and from 15 to 20 seconds, it recovers from 0.94 and stabilizes at 0.97. The 10-second load adjustment significantly improved the baseline level of the consistency metric with a smaller fluctuation range, demonstrating that the dynamic loading strategy effectively improves the stability of structural behavior. After adjustment, the consistency metric at all times is above the 0.90 threshold, eliminating the risk of substandard performance and verifying the reliability of the closed-loop control logic. Before adjustment, the fluctuation amplitude was 0.05; after adjustment, the fluctuation amplitude decreased to 0.03, resulting in a smoother structural response.

[0047] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.< / datatype> < / timestamp> < / testid> < / datatype> < / timestamp> < / testid>

Claims

1. A method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load, characterized in that, include: A benchmark dynamic test environment is established for the self-locking artificial vertebrae with adjustable mechanical load. The benchmark dynamic test environment includes a mechanical loading device, a spatial positioning module, and a structural state monitoring module. The mechanical loading device applies a periodic load to the self-locking artificial vertebra with adjustable mechanical load according to a preset initial loading protocol, while the structural state monitoring module collects and generates real-time deformation field data of the self-locking artificial vertebra with adjustable mechanical load. Combining the position and attitude information obtained by the spatial positioning module, the real-time deformation field data and the preset load boundary conditions are synchronized to form a dynamic performance dataset of the self-locking artificial vertebra with adjustable mechanical load. Based on the dynamic performance dataset, the structural behavior consistency measure of the self-locking artificial vertebra with adjustable mechanical load during the test process is calculated, and the structural behavior consistency measure is compared with the preset benchmark structural behavior model to generate a structural behavior deviation matrix. Based on the structural behavior deviation matrix, the mechanical loading device is driven to adjust the preset initial loading protocol, and generate and execute the updated dynamic loading instructions.

2. The method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load according to claim 1, characterized in that, Establishing a benchmark dynamic testing environment for the self-locking artificial vertebrae with adjustable mechanical loads, specifically including: The mechanical loading device is configured to have the ability to independently apply and adjust forces and torques along multiple main directions; The spatial positioning module is configured, which consists of at least one optical motion capture system and an inertial measurement unit array, and is used to calculate in real time the three-dimensional coordinates, Euler angles and angular velocities of the self-locking artificial vertebra with adjustable mechanical load in the test space. The structural state monitoring module is deployed. The structural state monitoring module consists of a set of high-resolution digital image correlation systems and an embedded micro-strain sensor network. It is used to synchronously capture the full-field displacement and strain distribution of the surface of the self-locking artificial vertebra with adjustable mechanical load under the periodic load. Before the test begins, the self-calibration process of the benchmark dynamic test environment is executed. The self-calibration process includes calibrating the output force value of the mechanical loading device, registering the coordinate system of the spatial positioning module, and synchronizing the sampling frequency of the structural state monitoring module to ensure that the data of the mechanical loading device, the spatial positioning module, and the structural state monitoring module are aligned in time and space.

3. The method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load according to claim 2, characterized in that, The real-time deformation field data is synchronized with the preset load boundary conditions, specifically including: From the position and attitude information obtained by the spatial positioning module, the pose transformation matrix of the self-locking artificial vertebra with adjustable mechanical load at each acquisition moment is extracted. Using the pose transformation matrix, the real-time deformation field data acquired by the structural state monitoring module at the same acquisition time is transformed into spatial coordinates, and the real-time deformation field data is mapped from the sensor local coordinate system to a global reference coordinate system that is the same as the preset load boundary conditions. In the global reference coordinate system, the mapped real-time deformation field data is aligned with the preset load boundary conditions in the time dimension. The preset load boundary conditions are a predefined set of force and displacement constraints that vary with time. After alignment, the aligned full-field displacement and strain information from the real-time deformation field data at each time point is associated and bound with the corresponding force and displacement constraint information from the preset load boundary conditions to form a data entry in the dynamic performance dataset. The dynamic performance dataset is time-series and contains a multi-dimensional data set of force, displacement, strain, position, and attitude.

4. The method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load according to claim 3, characterized in that, The calculation of the structural behavior consistency metric of the self-locking artificial vertebra with adjustable mechanical load during the testing process specifically includes: From the dynamic performance dataset, the input load sequence under the preset initial loading protocol and the response data sequence of the self-locking artificial vertebra with adjustable mechanical load to the input load sequence are separated, the response data sequence including displacement response, strain response and attitude change response; The preset benchmark structural behavior model is retrieved from the test database. The preset benchmark structural behavior model is a set of mathematical relationships that describes the expected response under standard load input, established through theoretical calculations or preliminary calibration experiments, with the idealized self-locking artificial vertebrae with adjustable mechanical load as the object. Using the input load sequence as input to the preset benchmark structural behavior model, the expected response sequence of the self-locking artificial vertebra with adjustable mechanical load under ideal conditions is calculated. Within the same time-domain window, the expected response sequence is compared point by point with the response data sequence extracted from the dynamic performance dataset, and the response deviation value at each comparison point is calculated. The response deviation value includes displacement deviation, strain deviation and attitude angle deviation. The response deviation values ​​of all comparison points are normalized, and a dimensionless scalar between zero and one is calculated based on the normalization result. The dimensionless scalar is defined as the structural behavior consistency measure. The closer its value is to one, the higher the degree of agreement between the actual response of the self-locking artificial vertebra with adjustable mechanical load and the preset benchmark structural behavior model during the test period.

5. The method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load according to claim 4, characterized in that, Generate the structural behavior deviation matrix, specifically including: Construct a two-dimensional matrix, where the row index of the two-dimensional matrix corresponds to different load condition identifiers and the column index corresponds to different performance evaluation dimensions. The performance evaluation dimensions include at least stiffness, damping characteristics, energy dissipation rate and self-locking stability margin. For each load condition identifier, the actual performance evaluation index value of the self-locking artificial vertebra with adjustable mechanical load is calculated based on the data segment corresponding to the load condition in the dynamic performance dataset. Simultaneously, using the preset benchmark structural behavior model, the benchmark performance evaluation index value of the self-locking artificial vertebra under the same load condition is calculated. The difference between the actual performance evaluation index value and the corresponding benchmark performance evaluation index value is used to obtain the performance deviation value of the load condition in the performance evaluation dimension. The performance deviation values ​​of all load conditions across all performance evaluation dimensions are filled in according to the row and column index rules of the two-dimensional matrix to fully construct the structural behavior deviation matrix. The dimensions of the structural behavior deviation matrix are determined by the total number of load conditions and the total number of performance evaluation dimensions.

6. The method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load according to claim 5, characterized in that, Based on the structural behavior deviation matrix, the mechanical loading device is driven to adjust the preset initial loading protocol, generate and execute updated dynamic loading instructions, specifically including: A dynamic loading strategy mapping table is set up, which defines the correspondence between different types of performance deviation values ​​and loading parameter adjustment actions; Iterate through each non-zero element in the structural behavior deviation matrix, i.e., each performance deviation value; For the performance deviation value currently traversed, query the dynamic loading strategy mapping table to find one or more loading parameter adjustment suggestions that match its type. The loading parameters include loading force amplitude, loading frequency, phase, load application point and load direction. Summarize all loading parameter adjustment suggestions obtained in the current traversal cycle, and merge all loading parameter adjustment suggestions according to the preset priority rules and conflict resolution rules to form a set of conflict-free and specific loading parameter adjustment amounts; Based on the parameters of the current preset initial loading protocol, the loading parameter adjustment amount is applied to generate the updated dynamic loading instruction. The updated dynamic loading instruction clarifies the time history, spatial distribution and variation law of the load to be applied by the mechanical loading device in subsequent loading cycles. The updated dynamic loading command is sent to the control unit of the mechanical loading device, which drives the mechanical loading device to interrupt the currently executing preset initial loading protocol and immediately start running according to the updated dynamic loading command.

7. The method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load according to claim 6, characterized in that, After generating and executing the updated dynamic loading instruction, the method further includes: recording a complete test trajectory log containing the real-time deformation field data, the structural behavior consistency metric, the structural behavior deviation matrix, and the updated dynamic loading instruction. The process of recording a complete test trajectory log also includes: The process of the mechanical loading device executing the updated dynamic loading command is continuously monitored, and new loading data and new response data generated in this process are collected in real time. These data are then merged with the dynamic performance dataset from the previous stage to form an extended dynamic performance dataset. Based on the extended dynamic performance dataset, the steps of calculating the new structural behavior consistency metric and generating the new structural behavior deviation matrix are repeated. The extended dynamic performance dataset, the new structural behavior consistency metric, the new structural behavior deviation matrix, and the previously recorded real-time deformation field data, the structural behavior consistency metric, the structural behavior deviation matrix, and the updated dynamic loading instructions are integrated and associated in chronological order. All integrated data, including all load conditions experienced by the self-locking artificial vertebrae with adjustable mechanical load throughout the test, all responses generated, and all records of dynamic adjustments, are structured and stored in the test database in the form of timestamped data packets, forming the complete test trajectory log.

8. The method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load according to claim 7, characterized in that, The structural behavior deviation matrix is ​​also used to perform offline analysis, specifically: After a single complete test is completed, extract all the structural behavior deviation matrices for the entire test cycle from the complete test trajectory log; All extracted structural behavior deviation matrices are categorized and arranged in time series according to load condition identifiers to form a behavior deviation sequence that evolves over time for each load condition. Statistical analysis was performed on the behavioral deviation sequence for each load condition to calculate its mean, variance, maximum value, minimum value and trend of change, in order to evaluate the long-term performance degradation mode and stability characteristics of the self-locking artificial vertebra with adjustable mechanical load under different types of loads. The results of the offline analysis are stored independently to provide data support for the design improvement, life prediction, or clinical application risk assessment of the self-locking artificial vertebra with adjustable mechanical load.

9. The method for testing the dynamic performance of a self-locking artificial vertebral body with adjustable mechanical load according to claim 8, characterized in that, In the synchronization process, if there is a time deviation between the sampling times of the spatial positioning module and the structural state monitoring module that is not an integer multiple, an interpolation compensation method is adopted, specifically: On the global reference timeline, the timestamps of the devices at the high-frequency sampling end are used as the reference. For data collected by low-frequency sampling devices, linear interpolation or spline interpolation is used to estimate the data value at each reference timestamp on the global reference time axis by utilizing the data values ​​of adjacent timestamps. The interpolation compensation method ensures that the real-time deformation field data and the preset load boundary conditions have synchronized data pairs at every discrete point in time on the global reference time axis.

10. The method for testing the dynamic performance of a self-locking artificial vertebra with adjustable mechanical load according to claim 9, characterized in that, The test database has a hierarchical structure for efficient storage and retrieval of the complete test trajectory log. The top layer of the test database is the test task record, which records the unique identifier of each test task, the test target, and the model and number of the self-locking artificial vertebra with adjustable mechanical load used. The intermediate layer is a time-series data block, which stores the real-time deformation field data, the structural behavior consistency measure, the structural behavior deviation matrix, and the sequence of updated dynamic loading instructions in the order of the test process. The bottom layer is a raw data and metadata storage area, used to store the raw data streams from the mechanical loading device, the spatial positioning module and the structural state monitoring module that have not been synchronized, as well as metadata describing the data format, units and coordinate system; The hierarchical structure of the test database supports the retrospective, comparative analysis, and mining of all test data throughout the entire life cycle of the self-locking artificial vertebra with adjustable mechanical load.

Images