A method for evaluating the load capacity of a flight simulator support connection point

Abstract

The application discloses a flight simulator support connection point bearing capacity evaluation method, comprising the following steps: determining the real-time ratio of shear stress value and normal stress value according to the fused root stress distribution data, classifying the ratio by using a support vector machine, and outputting a weld stress classification grade; evaluating the shear stress dominant type failure of the current root stress distribution data by using a dynamic load amplification factor, fusing an additional displacement amount into the dynamic load amplification factor, and outputting a root weld failure risk grade; extracting a root bending moment value according to the combination of the root weld failure risk grade and the cantilever length, processing the bending moment value by a simulation method to analyze the influence of shortening the cantilever length on the forward movement of the center of gravity, and obtaining a simulated bending moment distribution diagram; and based on the simulated bending moment distribution diagram, comparing the position offset of the ratio of the shear stress value and the normal stress value on the classification hyperplane before and after optimization, and outputting a verification grade.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for evaluating the load-bearing capacity of connection points on a flight simulator support. Background Technology

[0002] In the field of motion simulation for modern transportation, an "axis" refers to the independent degree of freedom or direction of motion that a motion platform can achieve. It represents the complexity and realism of the physical motion that the simulator can simulate. The number of axes of a motion platform directly determines how it moves the seat (or the entire cockpit), thus providing users with tactile feedback such as acceleration, tilt, and vibration. A 4-axis platform typically includes four degrees of freedom: lift, pitch, roll, and yaw. A 6-axis platform, based on the Stewart platform structure, can achieve all six degrees of freedom: three translations (lift, forward / backward, and left / right) and three rotations (pitch, roll, and yaw). When simulating dynamic scenarios such as racing cars, these platforms generate complex acceleration and attitude changes through the coordinated motion of each axis, thus applying multi-directional, time-varying dynamic loads to the seat and its adjustment mechanisms. This includes directly bearing repetitive forces and dynamic impacts from components such as control handles and gear shifters. The accuracy of its load-bearing capacity assessment has a decisive impact on the overall safety and service life of the equipment. The strength and reliability of the support connection points are crucial; failure will lead to structural fracture risks during simulation training, seriously affecting the effectiveness of training and the operator's confidence. Existing evaluation methods typically rely on simplifying assumptions, treating the support as an ideal load-bearing component and focusing only on the bending moment distribution or peak stress in a single direction under static loads. This approach ignores the coupling effects of multiple load forms in actual flight simulations, such as the simultaneous action of the torsional force of the control handle and the longitudinal impact of pushing and pulling the throttle. This leads to significant deviations between the evaluation results and real-world conditions, especially in high-frequency continuous operation scenarios, failing to accurately reflect the dynamic response characteristics of the structure. The longer the cantilever, the greater the bending moment at the root, and the more drastically the normal stress increases. For example, during a simulated high-speed dive, a sudden pull on the control handle amplifies the end-effector moment, causing the normal stress at the root weld to reach its peak. However, the situation becomes even more contradictory when considering the center of gravity offset caused by the mass of the shifter or throttle assembly itself. If the cantilever length is shortened to reduce the bending moment, the center of gravity shifts forward towards the support root. During simulated emergency shifts or rapid throttle inputs, this forward shift amplifies the inertial moment, causing a sharp increase in shear stress at the root weld, even exceeding the normal stress. For example, during simulated intense air combat maneuvers, a pilot suddenly shifts gears, causing the inertial impact to cause the shear stress to spike dramatically within milliseconds. The relative magnitudes of normal stress and shear stress change drastically, and the weld failure mode may rapidly shift from bending-dominated to shear-dominated. This transition is highly nonlinear and instantaneous, making it difficult for traditional fixed criteria to capture the critical state. For instance, in a long cantilever design, although the bending moment is larger due to the rearward-positioned center of gravity, the shear stress is relatively lower, resulting in a higher risk of failure due to normal stress dominance. Conversely, in a short cantilever design, although the normal stress is reduced, the forward-positioned center of gravity leads to a larger shear impact during the same rapid operation, causing shear stress to suddenly dominate, and the weld faces a drastically different fatigue crack propagation path.CN201711194413.8 describes an online monitoring and early warning device and method for fatigue failure of large roller circumferential welds. This method primarily monitors the cumulative fatigue damage of roller-type circumferential welds, achieving fatigue strength assessment and graded alarms through uniform strain gauge arrangement and periodic stress acquisition. However, this method is ill-suited to the complex working conditions of cantilever support root welds under varying geometry, dynamic eccentric loading, and multiaxial stress coupling. Furthermore, it cannot effectively identify the critical transition characteristics of shear stress-dominated sudden failures, thus failing to meet the real-time prediction requirements for weld safety in real-world high-risk operating scenarios for cantilever supports. Therefore, accurately identifying the critical state of the root weld transitioning from bending-dominated to shear-dominated under the combined influence of cantilever length adjustment on center of gravity offset and dynamic load amplification becomes a key issue for reliable assessment of the support's load-bearing capacity. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method for evaluating the load-bearing capacity of connection points of flight simulator support structures, thereby solving the problem of how to identify the critical state of the root weld transitioning from bending-dominated to shear-dominated in existing technologies.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for evaluating the load-bearing capacity of connection points on a flight simulator support frame, mainly including:

[0006] Real-time shear stress and normal stress values ​​were obtained from the root weld of the flight simulator support, along with the displacement of the cantilever end under operating force. These values, along with the displacement, were input into a data fusion processing system to obtain a fused root stress distribution dataset. Based on the fused root stress distribution data, the real-time ratio of shear stress to normal stress was determined. A support vector machine was used to classify this ratio, outputting the weld stress classification level. Based on the weld stress classification level indicating a high-risk level dominated by shear stress, the system identified root stress abrupt change clusters generated during rapid operation of the gear shifter or throttle assembly in simulated flight operations. By comparing the stress response peaks of different cantilever length configurations under the same operating force, the dynamic load amplification factor caused by center of gravity offset was extracted. The dynamic load amplification factor was then used... A shear stress-dominated failure assessment is performed on the current root stress distribution data, and the additional displacement is incorporated into the dynamic load amplification factor to output the root weld failure risk level. The root bending moment value is extracted based on the root weld failure risk level and cantilever length. The bending moment value is processed using simulation methods to analyze the impact of shortening the cantilever length on the forward shift of the center of gravity, resulting in a simulated bending moment distribution map. Based on the simulated bending moment distribution map, the positional shift of the ratio of shear stress to normal stress before and after optimization on the classification hyperplane is compared, and a verification level is output. For the verification level, all stress distribution data and dynamic load amplification factors are integrated to identify the critical point where the weld transitions from bending to shear. The maximum root bearing capacity at the critical point is then calculated, generating a flight simulator support root bearing capacity assessment report.

[0007] Furthermore, real-time shear stress and normal stress values ​​are obtained from the root weld of the flight simulator support, and the displacement of the operating force at the end of the cantilever is also collected. The shear stress, normal stress, and displacement values ​​are input into the data fusion processing to obtain the fused root stress distribution dataset, which includes: strain gauges are arranged on the upper and side surfaces of the root weld of the flight simulator support, respectively. The strain gauges on the upper surface are arranged along the cantilever axial direction to collect normal stress signals, and the strain gauges on the side surface are arranged along the weld circumferential direction to collect shear stress signals.

[0008] The analog signal output by the strain gauge is amplified and low-pass filtered sequentially. The filtered signal is then synchronously digitized according to a preset time sampling interval to obtain the time series values ​​of shear stress and normal stress of the root weld.

[0009] Displacement sensors are installed at the end of the cantilever, at the location where the operating handle is mounted and at the location where the gear shifter is mounted, to collect the displacement of the operating handle in the pitch direction and the displacement of the gear shifter in the push-pull direction.

[0010] Based on the cantilever length and installation angle between the end displacement sensor and the root weld, the displacement is multiplied by the cantilever lever arm length to obtain the equivalent torque component acting on the root section.

[0011] Align the shear stress time series value, the normal stress time series value, and the timestamp of the equivalent torque component, and perform weighted summation according to the inverse ratio of the distance of each sensor from the center of the root weld as the weighting coefficient. Arrange the weighted summation stress components according to the circumferential angle position of the root section to obtain the fused root stress distribution dataset.

[0012] Furthermore, the step of determining the real-time ratio of shear stress to normal stress based on the fused root stress distribution data, and using a support vector machine to classify the ratio and output the weld stress classification level includes: extracting the shear stress and normal stress values ​​corresponding to each circumferential angle position from the fused root stress distribution dataset, calculating the quotient of the shear stress value divided by the normal stress value for the stress data at the same time to obtain the stress ratio at each circumferential angle position, and constructing the feature vector of the support vector machine based on the stress ratio at all circumferential angle positions;

[0013] The feature vectors are classified using a pre-trained support vector machine. The support vector machine uses a radial basis function as its kernel function, and its classification hyperplane is pre-trained based on the stress ratio boundary of historical weld failure samples.

[0014] When the feature vector falls into the region where the stress ratio is lower than the preset lower threshold after classification, it is determined that the current weld is in a bending-dominated state; when it falls into the region where the stress ratio is higher than the preset upper threshold, it is determined that the current weld is in a shear-dominated state; when it falls into the transition region between the two thresholds, it is determined that the current weld is in a critical state of transition from bending to shear.

[0015] Based on the classification results, the weld stress classification level is output, where bending-dominant state corresponds to low risk level, critical state corresponds to medium risk level, and shear-dominant state corresponds to high risk level.

[0016] Furthermore, the feature vector includes the current value of the stress ratio, the window mean obtained by taking the arithmetic mean of the stress ratio within a preset time window, and the increase or decrease trend value of the stress ratio over time within the window.

[0017] Furthermore, based on the weld stress classification level presenting a shear-dominated high-risk level, the root stress mutation clusters generated during rapid operation of the shifter or throttle component in simulated flight operations are identified. By comparing the stress response peaks of different cantilever length configurations under the same operating force, the dynamic load amplification factor caused by the center of gravity offset is extracted. This includes: for the moment when the weld stress classification level presents a shear-dominated high-risk level, extracting the shear stress time series values ​​within a preset time range before and after that moment from the root stress distribution dataset.

[0018] Identify consecutive data points in the shear stress time series values ​​whose amplitude exceeds a preset abrupt threshold and whose rise rate exceeds a preset rate threshold, and mark the consecutive data points as root stress abrupt clusters.

[0019] Based on the end-operating force displacement corresponding to the root stress mutation cluster and the pre-stored multiple sets of cantilever length configuration data, the quasi-static theoretical shear stress borne by the root weld under the same operating force is calculated.

[0020] Divide the measured peak shear stress in the root stress mutation cluster by the quasi-static theoretical shear stress under the current cantilever length configuration to obtain the dynamic load amplification factor caused by the center of gravity offset.

[0021] Furthermore, the dynamic load amplification factor is the ratio of the measured peak shear stress to the quasi-static theoretical shear stress.

[0022] Furthermore, a shear stress-dominant failure assessment is performed on the current root stress distribution data using a dynamic load amplification factor. Additional displacement is incorporated into the dynamic load amplification factor, and the root weld failure risk level is output. This includes: correcting the shear stress values ​​in the current root stress distribution dataset based on the dynamic load amplification factor, and multiplying the shear stress values ​​at each circumferential angle position by the dynamic load amplification factor to obtain the initial corrected shear stress value.

[0023] The initial corrected shear stress value is compared with the allowable shear stress of the weld material. If it exceeds the allowable shear stress, the current weld is marked as having shear stress-dominated failure characteristics.

[0024] For welds marked with shear stress-dominated failure characteristics, the additional displacement during the current operating cycle is obtained from the displacement sensor at the end of the cantilever, and the additional displacement is divided by the normal operating displacement reference value to obtain the displacement deviation ratio.

[0025] The amplification factor after fusion is obtained by multiplying the displacement deviation ratio by one and the dynamic load amplification factor.

[0026] The secondary corrected shear stress value is obtained by multiplying the fused amplification factor by the original shear stress value. The failure risk level of the root weld is output based on the ratio of the secondary corrected shear stress value to the allowable shear stress.

[0027] Furthermore, the step of extracting the root bending moment value based on the root weld failure risk level and the cantilever length, and processing the bending moment value through simulation to analyze the impact of shortening the cantilever length on the forward shift of the center of gravity, and obtaining the simulated bending moment distribution map, includes: selecting the time points that present medium risk level and high risk level based on the root weld failure risk level, and extracting the normal stress value of the corresponding time point from the root stress distribution dataset;

[0028] Multiply the normal stress value by the current cantilever length to obtain the root bending moment value at each time point;

[0029] For the simulated scenario where the cantilever length is set to decrease step by step from the current configuration value for the root bending moment value, the distance from the center of gravity of the component to the root weld is determined based on the mass and installation position of the shifter or throttle component after each shortening of the cantilever length as the center of gravity offset distance.

[0030] The difference between the center of gravity offset distance and the center of gravity distance under the current cantilever length configuration is the center of gravity forward shift amount. The additional bending moment component is obtained by multiplying the center of gravity forward shift amount by the component self-weight.

[0031] The additional bending moment component is superimposed with the original root bending moment value to obtain the simulated bending moment value under each cantilever length configuration. The simulated bending moment distribution diagram is drawn according to the cantilever length from large to small.

[0032] Furthermore, based on the simulated bending moment distribution map, the positional shift of the ratio of shear stress value to normal stress value before and after optimization on the classification hyperplane is compared, and the verification level is output, including: selecting the cantilever length configuration point with the smallest simulated bending moment value from the simulated bending moment distribution map;

[0033] The optimized normal stress value is obtained by dividing the simulated bending moment value corresponding to the configuration point by the cantilever length, and the optimized shear stress value is obtained by multiplying the forward shift of the center of gravity corresponding to the configuration point by the component's self-weight and then dividing by the cross-sectional area of ​​the root weld.

[0034] The optimized stress ratio is obtained by calculating the quotient of the optimized shear stress value and the optimized normal stress value.

[0035] The difference between the original stress ratio and the optimized stress ratio is calculated as the position offset;

[0036] The verification level is output based on the relationship between the magnitude of the position offset and the preset verification threshold.

[0037] Furthermore, for the verification level, all stress distribution data and dynamic load amplification factors are integrated to identify the critical point where the weld transitions from bending to shear, and the maximum root bearing capacity at the critical point is calculated to generate a flight simulator support root bearing capacity assessment report, including: for the verification level, cantilever length configuration points presenting high verification level and medium verification level, and determining the critical point where the weld transitions from bending-dominated to shear-dominated based on the ratio of shear stress value to normal stress value at the corresponding configuration point;

[0038] The combined stress value is calculated based on the shear stress value and normal stress value corresponding to the critical point. The allowable stress of the weld material is divided by the combined stress value and then by the dynamic load amplification factor at the critical point to obtain the maximum allowable operating force under the critical state.

[0039] Multiply the maximum operating force by the cantilever length corresponding to the critical point to obtain the maximum bearing capacity at the root.

[0040] Extract the cantilever length corresponding to the critical point as the critical cantilever length, and extract the difference between the distance from the component's center of gravity to the root weld at the critical point and the distance from the center of gravity under the current configuration as the critical center of gravity offset, and define the range of design constraints.

[0041] Within the design constraints, select multiple cantilever length configuration points, calculate the corresponding bearing capacity value for each configuration point according to the combined stress value and dynamic load amplification factor, and plot the bearing capacity variation trend curve.

[0042] The trend curves, critical point locations, design constraint ranges, and verification levels of each configuration point are summarized and output as a flight simulator support root load-bearing capacity assessment report.

[0043] Compared to existing technologies, this invention offers the following advantages: it solves the problem of identifying the critical point where weld stress transitions from bending to shear during dynamic operation of the support structure and optimizing its load-bearing capacity. This invention collects real-time shear and normal stress values ​​from the root weld and the displacement of the operating force at the cantilever end. After fusing the data, it determines the stress ratio and uses a support vector machine for classification to accurately identify the stress critical point. Simultaneously, it assesses the risk of shear stress-dominated failure based on the dynamic load amplification factor, combines cantilever length optimization with simulated bending moment distribution, extracts the stress ratio shift before and after optimization, and verifies the design improvement effect. Finally, it back-calculates the maximum load-bearing capacity at the critical point and generates a support structure load-bearing capacity assessment report. This invention achieves closed-loop analysis of dynamic monitoring of weld failure risk and cantilever configuration optimization, significantly improving the safety and reliability of flight simulator support structure design. Attached Figure Description

[0044] Figure 1 This is a flowchart of a method for evaluating the load-bearing capacity of a flight simulator support connection point according to the present invention.

[0045] Figure 2 This is a schematic diagram of a method for evaluating the load-bearing capacity of a flight simulator support connection point according to the present invention.

[0046] Figure 3 This is another schematic diagram of a method for evaluating the load-bearing capacity of a flight simulator support connection point according to the present invention. Detailed Implementation

[0047] The present invention will now be described in detail through specific embodiments: such as Figures 1-3 This embodiment of a method for evaluating the load-bearing capacity of a flight simulator support connection point may specifically include:

[0048] S101: Real-time shear stress and normal stress values ​​are acquired from the root weld of the flight simulator support, and the displacement of the operating force at the end of the cantilever is also collected. The shear stress, normal stress, and displacement values ​​are input into the data fusion processing to obtain the fused root stress distribution dataset. In this embodiment, strain gauges are arranged on the upper and side surfaces of the root weld of the flight simulator support. The upper surface gauges are arranged along the cantilever axial direction to collect normal stress signals, and the side surface gauges are arranged along the weld circumference to collect shear stress signals. The analog signals output by the strain gauges are sequentially amplified and low-pass filtered. The filtered signals are synchronously digitized according to a preset time sampling interval to obtain the time-series values ​​of shear stress and normal stress at the root weld. When monitoring stress at the root weld of the flight simulator support, the arrangement of the strain gauges directly affects the accuracy of the collected signals. Specifically, the upper surface of the root weld primarily bears tensile or compressive deformation caused by cantilever bending. Therefore, strain gauges arranged along the cantilever axis can capture the longitudinal strain caused by normal stress. The side surface bears tangential deformation caused by torsion and shear. Strain gauges arranged circumferentially along the weld are used to collect the shear strain corresponding to the shear stress. In one possible implementation, three to five strain gauges are arranged on the upper surface according to the weld length. Three gauges are used for shorter welds to basically cover the heat-affected zone, and five gauges are used for longer welds or when high precision is required. They are arranged at equal intervals along the weld axis to cover the main area of ​​the weld heat-affected zone. Two to four strain gauges are arranged on the side surface according to the degree of shear stress concentration. Two gauges are used for low concentration, and four gauges are used for high concentration. They are distributed along the circumference of the weld in the shear stress concentration area. The analog voltage signals output by each strain gauge are amplified to improve the signal-to-noise ratio, and then filtered by a low-pass filter to remove high-frequency noise interference. The filter cutoff frequency is set to 10Hz. This cutoff frequency is set according to the typical operating frequency range of 0-5Hz for flight simulator operations, which refers to the standard operating specifications of flight simulators. The filtered signal is converted from analog to digital according to a uniform time sampling interval. The selection of the time sampling interval must satisfy the Nyquist sampling theorem to obtain the time-series values ​​of shear stress and normal stress at the root weld. Example 1: Displacement sensors are installed at the operating handle and gear shifter installation positions at the end of the cantilever to collect the displacement of the operating handle in the pitch direction and the displacement of the gear shifter in the push-pull direction. Based on the cantilever length and installation angle between the end displacement sensor and the root weld, the displacement is multiplied by the cantilever lever arm length to obtain the equivalent torque component acting on the root section. The displacement sensor at the end of the cantilever is installed near the pitch axis of the operating handle and at the end of the push-pull slide rail of the gear shifter to measure the angular displacement of the operating handle around the axis and the linear displacement of the gear shifter along the slide rail, respectively. The conversion of the displacement to the equivalent torque component is based on the mechanical relationship of the cantilever structure: for the linear displacement δ of the gear shifter, the formula is used. Calculate the equivalent torque components; for the angular displacement θ of the operating handle, use the formula... Calculate the equivalent torque components. Where M is the equivalent torque, E is the elastic modulus, I is the moment of inertia of the section, and L is the straight-line distance from the sensor mounting point to the center of the root weld. For example, multiplying the pitch displacement of the operating handle by the horizontal distance from the operating handle mounting point to the root weld yields the bending moment component in the pitch direction; multiplying the push-pull displacement of the gear shifter by the longitudinal distance from the gear shifter mounting point to the root weld yields the torque component in the push-pull direction. It should be noted that timestamp alignment during data fusion is achieved by triggering synchronous sampling of each sensor using a unified clock signal. The shear stress time series value, normal stress time series value, and equivalent torque components form a corresponding set of data at the same time. After alignment based on the timestamps of the shear stress time series value, normal stress time series value, and equivalent torque components, the three data streams are normalized respectively. The three data streams are weighted and summed using a weighting coefficient based on the inverse ratio of the distance of each sensor from the center of the root weld. Sensors closer to the weld center provide a more direct representation of the root stress state, and their weighting coefficients are larger. The weighted summed stress components are mapped to the circumferential angle positions of the root section based on the sensor positions and arranged to form a stress distribution sequence indexed by angle, thus obtaining the fused root stress distribution dataset. S102: The real-time ratio of shear stress to normal stress is determined based on the fused root stress distribution data. A support vector machine is used to classify this ratio and output the weld stress classification level. The shear stress and normal stress values ​​corresponding to each circumferential angle position are extracted from the fused root stress distribution dataset. For the stress data at the same time, the quotient of the shear stress value divided by the normal stress value is calculated to obtain the stress ratio at each circumferential angle position. Since there are multiple monitoring points along the circumference of the root weld, multiple stress ratios are generated at each time. The stress ratios of all circumferential angle positions are arranged in time sequence to form a stress ratio sequence. This sequence records the evolution of the weld stress state with the operation. A feature vector for input to a support vector machine is constructed based on the stress ratio sequence. This feature vector includes the current stress ratio value, the window mean obtained by calculating the arithmetic mean of the stress ratios within a preset time window, and the increasing / decreasing trend of the stress ratio over time within the window. This feature vector is used as the input data for the support vector machine. In one possible implementation, the construction of the feature vector comprehensively considers both the instantaneous and temporal characteristics of the stress ratio. The current stress ratio value directly reflects the stress state of the weld at the current moment. The window mean is obtained by calculating the arithmetic mean of the stress ratios within the preset time window; this mean can smooth instantaneous fluctuations and reflect the recent average stress level. The increasing / decreasing trend within the window is obtained by calculating the difference between the stress ratios at the start and end of the window and dividing by the window duration. A positive value indicates an increasing stress ratio, meaning shear stress is increasing, while a negative value indicates a decreasing stress ratio, meaning bending stress is regaining its dominant position.The feature vectors are classified using a pre-trained Support Vector Machine (SVM). The SVM uses a radial basis function as its kernel function, and its classification hyperplane is pre-trained based on the stress ratio boundaries of historical weld failure samples. As a supervised learning method, the core of SVM lies in determining a classification hyperplane through training samples, separating sample points of different categories. The training samples are derived from historical weld failure cases, including samples known to have experienced bending-dominated failures and samples known to have experienced shear-dominated failures. If the feature vector, after classification, falls into a region with a stress ratio below 0.5, the current weld is determined to be in a bending-dominated state; if it falls into a region with a stress ratio above 2.0, the current weld is determined to be in a shear-dominated state; if it falls into a transition region between 0.5 and 2.0, the current weld is determined to be in a critical state transitioning from bending to shear. Based on the classification results, a weld stress classification level is output, where bending-dominated state corresponds to a low-risk level, critical state corresponds to a medium-risk level, and shear-dominated state corresponds to a high-risk level. In the assessment of the load-bearing capacity of flight simulator supports, determining the stress state of the root weld is a crucial step in identifying structural failure risks. The ratio of shear stress to normal stress reflects the dominant load currently borne by the weld. A low ratio indicates that bending is dominant, a high ratio indicates that shear is gradually becoming dominant, and a ratio in the intermediate transition zone signifies that the weld is at a critical point of transition from bending-dominated to shear-dominated. Example 2: In practical applications of flight simulator supports, when the pilot performs smooth push-pull movements of the control levers, the root weld mainly bears bending moment, and the stress ratio remains at a low level, with the eigenvector falling within the bending-dominated region defined by the classification hyperplane. When the pilot performs simulated emergency gear shifts or rapid throttle inputs, the inertial impact of the gear shifter or throttle assembly causes the root weld to bear significant shear, the stress ratio rises rapidly, and the eigenvector may cross the classification hyperplane into the shear-dominated region. In one embodiment, the boundary threshold of the classification hyperplane is determined through statistical analysis of historical failure samples. The preset lower threshold corresponds to the upper boundary of the stress ratio in historical bending-dominated failure samples, and the preset upper threshold corresponds to the lower boundary of the stress ratio in historical shear-dominated failure samples. The area between the two thresholds is the critical transition region. When the feature vector falls into this transition region, it indicates that the stress state of the weld is at the boundary between the two dominant modes. At this point, any further load change may lead to a sudden change in the failure mode. Preferably, the risk level output adopts a real-time update mechanism. Whenever new stress data enters and the feature vector construction and classification processing are completed, the corresponding weld stress classification level is immediately output.This level of information provides flight simulator operators and maintenance personnel with a direct indication of the current load-bearing status of the weld. When the level jumps from low risk to medium or high risk, it indicates that the support is undergoing a stress transition process that may endanger structural safety. S103: Based on the weld stress classification level showing a shear-dominated high-risk level, identify root stress mutation clusters generated during rapid operation of the shifter or throttle assembly in simulated flight operations. By comparing the stress response peaks of different cantilever length configurations under the same operating force, extract the dynamic load amplification factor caused by the center of gravity offset. For the moment when the weld stress classification level shows a shear-dominated high-risk level, extract the shear stress time series values ​​within a preset time range before and after that moment from the root stress distribution dataset. Identify continuous data points in the shear stress time series values ​​whose amplitude exceeds a preset mutation threshold and whose rise rate exceeds a preset rate threshold. Mark these continuous data points as root stress mutation clusters, and record the measured shear stress peak value corresponding to each root stress mutation cluster and the end-operating force displacement when the mutation cluster is triggered. During the operation of the flight simulator support, when the weld stress classification level shows a high-risk level dominated by shear, it indicates that the root weld is experiencing significant shear load. At this time, shear stress time-series values ​​before and after the corresponding moment are extracted from the root stress distribution dataset. Root stress abrupt clusters are identified by setting amplitude and rate-of-rise thresholds. These abrupt clusters correspond to transient shear impacts generated during rapid operation of the gear shifter or throttle assembly. Specifically, the identification of root stress abrupt clusters is based on the simultaneous fulfillment of two criteria. The preset abrupt threshold is determined by the product of the weld material's yield shear strength and a preset safety factor. When the amplitude of a data point in the shear stress time-series values ​​exceeds this threshold, it indicates that the shear load on the weld has entered a high-stress range. The preset rate-of-rise threshold is determined based on the duration and stress change amplitude of a typical rapid operation. When the shear stress rise rate exceeds this threshold, it indicates that the current stress change has impact characteristics rather than slow loading characteristics. Based on the end-operating force displacement and pre-stored multiple sets of cantilever length configuration data, the quasi-static theoretical shear stress borne by the root weld under the same operating force is calculated for each cantilever length configuration by multiplying the lever arm by the applied force. The quasi-static theoretical shear stress is the shear stress value generated only by static moment without considering inertial impact. The quasi-static theoretical shear stress under different cantilever length configurations is compared with the measured peak shear stress. In one possible implementation, the cantilever length configuration data of the flight simulator bracket is pre-stored in a configuration database, containing lever arm parameters corresponding to multiple cantilever lengths. For each cantilever length configuration, the quasi-static theoretical shear stress is calculated by multiplying the lever arm by the applied force, where the lever arm is the distance from the gear shifter or throttle assembly mounting point to the center of the root weld, and the applied force is obtained by converting the end-operating force displacement according to the elastic restoring force relationship. The measured peak shear stress in the root stress mutation cluster is divided by the quasi-static theoretical shear stress under the current cantilever length configuration to obtain the dynamic load amplification factor caused by the center of gravity offset.The physical significance of dynamic load amplification lies in quantifying the amplification effect of center of gravity offset on the shear stress of the root weld. When the shifter or throttle assembly operates rapidly, the inertial force generated by the assembly's own mass will create an additional shear force at the root of the cantilever. This additional shear force, superimposed with the static shear force, forms the measured peak shear stress. Example 3: In a short cantilever configuration, the center of gravity of the assembly is closer to the root. During rapid operation, the lever arm of the inertial moment is short, but the inertial force acts directly near the root, resulting in a larger shear stress amplification factor. In a long cantilever configuration, the center of gravity of the assembly is farther from the root. Although the lever arm of the inertial moment is longer, the bending moment dominates, resulting in a relatively smaller shear stress amplification factor. By comparing the differences in amplification factors under different cantilever length configurations, the influence of center of gravity offset on the dynamic response of the root weld can be identified. S104: The dynamic load amplification factor is used to perform shear stress-dominated failure assessment on the current root stress distribution data, and the additional displacement is incorporated into the dynamic load amplification factor to output the root weld failure risk level. The shear stress values ​​in the current root stress distribution dataset are corrected based on the dynamic load amplification factor. The shear stress values ​​at each circumferential angle position are multiplied by the dynamic load amplification factor to obtain the initial corrected shear stress value. In the failure assessment of the flight simulator support, the dynamic load amplification factor reflects the amplification effect of inertial impact on the shear stress of the root weld during rapid operation. Using this amplification factor to correct the shear stress values ​​in the current root stress distribution dataset converts stress values ​​obtained under static measurement conditions into stress levels closer to actual dynamic working conditions. The initial corrected shear stress value is compared with the allowable shear stress of the weld material. If the initial corrected shear stress value exceeds the allowable shear stress, the current weld is marked as exhibiting shear stress-dominated failure characteristics. The allowable shear stress of the weld material is predetermined based on the yield shear strength of the weld material divided by a safety factor. This allowable shear stress represents the upper limit of shear stress that the weld can withstand under normal working conditions. When the initial corrected shear stress value exceeds the allowable shear stress, it indicates that the current weld has entered a dangerous zone under dynamic load, posing a potential risk of shear stress-dominated failure. For welds exhibiting shear stress-dominated failure characteristics, additional displacement within the current operating cycle is obtained from the displacement sensor at the cantilever end. This additional displacement is the difference between the peak displacement within the current operating cycle and a pre-calibrated conventional operating displacement reference value. Dividing the additional displacement by the conventional operating displacement reference value yields the displacement deviation ratio. Adding one to this ratio and multiplying it by the dynamic load amplification factor yields the fused amplification factor. Multiplying the fused amplification factor by the original shear stress value yields the secondary corrected shear stress value. In one possible implementation, the additional displacement is obtained based on the peak displacement recorded by the cantilever end displacement sensor within the current operating cycle. The conventional operating displacement reference value is pre-calibrated by repeatedly measuring typical operating actions during equipment commissioning and averaging the results; this reference value represents the displacement amplitude at the cantilever end under normal operating intensity.The purpose of adding one to the displacement deviation ratio is to ensure that when the additional displacement is zero, the amplification factor after fusion equals the original dynamic load amplification factor. When the additional displacement is positive, the amplification factor after fusion will further increase, thus more accurately reflecting the additional amplification effect of ultra-strong operation on weld stress. The ratio of the secondary corrected shear stress value to the allowable shear stress is calculated. If the ratio is lower than a preset safety threshold, a low-risk level is output; if the ratio is between the safety threshold and the failure threshold, a medium-risk level is output; if the ratio exceeds the failure threshold, a high-risk level is output. The safety threshold is less than the failure threshold. Example 4: The risk level is determined using the ratio of the secondary corrected shear stress value to the allowable shear stress. When the ratio is lower than the safety threshold, it indicates that the shear stress borne by the weld is far below the allowable value, and a low-risk level is output. When the ratio is between the safety threshold and the failure threshold, it indicates that the shear stress borne by the weld is approaching the allowable value, and a medium-risk level is output. When the ratio exceeds the failure threshold, it indicates that the shear stress borne by the weld has exceeded the allowable range, and a high-risk level is output. At this time, the support has a high probability of structural failure. S105: Root bending moment values ​​are extracted based on the root weld failure risk level and cantilever length. These bending moment values ​​are then processed using simulation methods to analyze the impact of shortening the cantilever length on the forward shift of the center of gravity, resulting in a simulated bending moment distribution map. Based on the root weld failure risk level, time points exhibiting medium and high risk levels are selected. The maximum normal stress value corresponding to these time points is extracted from the root stress distribution dataset. The cantilever length of the current flight simulator support is obtained, and the maximum normal stress value is multiplied by the cantilever length to obtain the root bending moment value corresponding to each time point. In the load-bearing capacity assessment of the flight simulator support, the extraction of root bending moment values ​​is a crucial step in analyzing the structural response characteristics under bending loads. Selecting time points exhibiting medium and high risk levels based on the root weld failure risk level corresponds to the conditions where the support bears significant loads during simulated flight operations. Extracting normal stress values ​​from these time points for bending moment calculation allows focus on the high-stress state of the structure. A simulation scenario of cantilever length shortening is established for the root bending moment value. The cantilever length is set to decrease step by step from the current configuration value to a preset lower limit value. For each shortened cantilever length, the distance from the center of gravity of the component to the root weld is re-determined based on the mass and installation position of the shifter or throttle assembly, obtaining the center of gravity offset distance corresponding to each cantilever length. The center of gravity forward shift is obtained based on the difference between the center of gravity offset distance and the distance from the center of gravity to the root weld under the current cantilever length configuration. In one possible implementation, the cantilever length shortening simulation scenario is achieved by setting a length decreasing sequence. The current cantilever length configuration value is used as the starting point of the sequence, the preset step size determines the shortening magnitude of each level, and the preset lower limit value is used as the ending point of the sequence to limit the shortening range.For example, if the current cantilever length is the baseline value, the step size is set to a certain percentage of the baseline value, and the lower limit is set to another percentage of the baseline value, a cantilever length sequence is formed that decreases progressively from the baseline value to the lower limit value. This sequence covers multiple possible cantilever configuration schemes. The additional bending moment component is obtained by multiplying the forward shift of the center of gravity by the component's self-weight. This additional bending moment component is then superimposed with the original root bending moment value to obtain the simulated bending moment value for each cantilever length configuration. The simulated bending moment values ​​corresponding to all cantilever length configurations are arranged in descending order of cantilever length. A curve is plotted with the cantilever length as the horizontal axis and the simulated bending moment value as the vertical axis. The forward shift of the center of gravity corresponding to each cantilever length is marked on the curve to obtain the simulated bending moment distribution diagram. Specifically, the additional bending moment component originates from the change in lever arm caused by the forward shift of the center of gravity. The component's self-weight is a fixed value. When the center of gravity shifts forward, the distance from the line of action of the self-weight to the center of the root weld section changes; this change is the change in the equivalent lever arm. Multiplying the forward shift of the center of gravity by the component's self-weight yields the additional bending moment component caused by the change in the center of gravity position. The superposition of this additional bending moment component with the original root bending moment value reflects the total bending moment level borne by the root weld after shortening the cantilever length. The superimposed simulated bending moment value can characterize the bending moment response characteristics under different cantilever configurations. S106: Based on the simulated bending moment distribution map, compare the positional shift of the ratio of shear stress to normal stress before and after optimization on the classification hyperplane, and output the verification level. Select the cantilever length configuration point with the smallest simulated bending moment value from the simulated bending moment distribution map. Divide the simulated bending moment value M corresponding to this configuration point by the section modulus W to obtain the optimized normal stress value σ. Calculate the forward shift of the center of gravity corresponding to this configuration point, multiply it by the component's self-weight, and then divide it by the cantilever length to obtain the shear force V. Divide V by the cross-sectional area A of the root weld to obtain the optimized shear stress value τ. Calculate the quotient of the optimized shear stress value and the optimized normal stress value to obtain the optimized stress ratio. Obtain the quotient of the shear stress value and the normal stress value in the root stress distribution data before optimization as the original stress ratio. In the verification phase of the flight simulator support load-bearing capacity assessment, extracting the stress value under the optimized configuration from the simulated bending moment distribution map is a crucial step in judging the effectiveness of the cantilever length adjustment. The cantilever length configuration point with the minimum simulated bending moment value is selected as the optimized configuration reference. This configuration point represents the cantilever length scheme that minimizes the bending load on the root weld after comprehensively considering the original bending moment and the additional bending moment. The calculation of the optimized normal stress value is based on the mechanical relationship between bending moment and section stress. The simulated bending moment value corresponding to this configuration point is divided by the cantilever length to obtain the bending moment intensity per unit length. The optimized stress ratio and the original stress ratio are compared with the boundary threshold of the support vector machine classification hyperplane to determine the positional relationship between the two stress ratios relative to the classification hyperplane.The difference between the original stress ratio and the optimized stress ratio is calculated as the position offset. If the position offset is positive and exceeds the verification threshold of 0.1, a high verification level is output; if the position offset is positive but below 0.1, a medium verification level is output; and if the position offset is negative or zero, a low verification level is output. Example 5: The verification level is based on the value and direction of the position offset. When the position offset is positive and exceeds the preset verification threshold, it indicates that the optimized adjustment of the cantilever length significantly reduces the ratio of shear stress to normal stress, and the weld stress state moves towards a safer bending-dominated region, resulting in a high verification level. When the position offset is positive but below the verification threshold, it indicates that the optimization adjustment has some effect, but the improvement is limited, resulting in a medium verification level. When the position offset is negative or zero, it indicates that the optimization adjustment has failed to improve or even worsened the stress state of the weld, resulting in a low verification level. S107: For the verification level, integrate all stress distribution data and dynamic load amplification factors to identify the critical point where the weld transitions from bending to shear, and inversely calculate the maximum root bearing capacity at the critical point to generate a flight simulator support root bearing capacity assessment report. For cantilever length configuration points presenting high and medium verification levels, integrate the normal stress value, shear stress value, and dynamic load amplification factor of the corresponding configuration points from the root stress distribution dataset. In the integrated data, locate the position where the ratio of shear stress value to normal stress value crosses the support vector machine classification hyperplane boundary threshold, and mark this position as the critical point where the weld transitions from bending-dominated to shear-dominated. The support vector machine classification hyperplane boundary threshold is defined as 0.5 based on the training stress ratio dataset. In the final stage of the flight simulator support bearing capacity assessment, data integration is performed for cantilever length configuration points presenting high and medium verification levels. These configuration points correspond to schemes where the stress state is improved after cantilever length adjustment. Extract the normal stress value, shear stress value, and dynamic load amplification factor of each configuration point from the root stress distribution dataset to form a complete stress response dataset. The critical point is located by detecting the changing trend of the ratio of shear stress to normal stress. When the stress ratio of two adjacent configuration points is lower than the boundary threshold of the support vector machine classification hyperplane and higher than the boundary threshold, it indicates that the stress state has changed from bending-dominated to shear-dominated between these two configuration points. Based on the shear stress and normal stress values ​​corresponding to the critical point, the square root of the sum of their squares is taken to obtain the combined stress value at the critical point. The allowable stress of the weld material is divided by the combined stress value to obtain the stress reserve coefficient, which is then divided by the dynamic load amplification factor at the critical point to adjust for dynamic influence, thus obtaining the maximum allowable operating force under the critical state. Multiplying the maximum operating force by the cantilever length corresponding to the critical point yields the maximum bearing capacity at the root. It should be noted that the reverse calculation process of the maximum bearing capacity at the root is based on the linear relationship between stress and load.The allowable stress of the weld material is a predetermined material parameter. Dividing the allowable stress by the combined stress at the critical point yields the stress reserve coefficient, which represents the margin between the current stress level and the material's allowable limit. Further dividing the stress reserve coefficient by the dynamic load amplification factor at the critical point eliminates the amplification effect of inertial impact on the stress, yielding the maximum allowable operating force under critical conditions. The cantilever length corresponding to the critical point is extracted as the critical cantilever length, and the difference between the distance from the component's center of gravity to the root weld at the critical point and the distance to the center of gravity under the current configuration is extracted as the critical center of gravity offset. The critical cantilever length is used as the lower limit of the cantilever length, and the critical center of gravity offset is used as the upper limit of the center of gravity offset, defining the design constraint range. Within the design constraint range, multiple cantilever length configuration points are selected. For each configuration point, the corresponding bearing capacity value is calculated based on the combined stress value and the dynamic load amplification factor, where the bearing capacity value is uniformly calculated as the maximum operating force multiplied by the cantilever length. The load-bearing capacity values ​​of all configuration points are plotted as load-bearing capacity trend curves. These trend curves, critical point locations, design constraint ranges, and verification levels for each configuration point are then summarized and output as a flight simulator support root load-bearing capacity assessment report. The assessment report also includes the maximum root load-bearing capacity value and its corresponding cantilever length configuration information, facilitating designers in selecting the optimal support configuration scheme while meeting design constraints.

[0049] If the technical solution of this application involves the acquisition of personal information, the product using this solution has clearly informed the user of the processing rules and obtained the user's consent before processing. If sensitive personal information is involved, the user's individual consent has been obtained and the "express consent" requirement has been met. For example, a clear sign is placed at the collection device to indicate the collection scope, and the user's voluntary entry is considered as consent; or authorization is obtained through pop-up windows, user uploads, etc. The processing rules include the processor, purpose, method, and type of information.

[0050] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the load-bearing capacity of a flight simulator support connection point, characterized in that, The method includes: Real-time shear stress and normal stress values ​​were obtained from the root weld of the flight simulator support, along with the displacement of the cantilever end under operating force. These values, along with the displacement, were input into a data fusion processing system to obtain a fused root stress distribution dataset. Based on the fused root stress distribution data, the real-time ratio of shear stress to normal stress was determined. A support vector machine was used to classify this ratio, outputting the weld stress classification level. Based on the weld stress classification level indicating a high-risk level dominated by shear stress, the system identified root stress abrupt change clusters generated during rapid operation of the gear shifter or throttle assembly in simulated flight operations. By comparing the stress response peaks of different cantilever length configurations under the same operating force, the dynamic load amplification factor caused by center of gravity offset was extracted. The dynamic load amplification factor was then used... A shear stress-dominated failure assessment is performed on the current root stress distribution data, and the additional displacement is incorporated into the dynamic load amplification factor to output the root weld failure risk level. The root bending moment value is extracted based on the root weld failure risk level and cantilever length. The bending moment value is processed using simulation methods to analyze the impact of shortening the cantilever length on the forward shift of the center of gravity, resulting in a simulated bending moment distribution map. Based on the simulated bending moment distribution map, the positional shift of the ratio of shear stress to normal stress before and after optimization on the classification hyperplane is compared, and a verification level is output. For the verification level, all stress distribution data and dynamic load amplification factors are integrated to identify the critical point where the weld transitions from bending to shear. The maximum root bearing capacity at the critical point is then calculated, generating a flight simulator support root bearing capacity assessment report.

2. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The process involves acquiring real-time shear stress and normal stress values ​​from the root weld of the flight simulator support, simultaneously collecting the displacement of the operating force at the end of the cantilever, and inputting the shear stress, normal stress, and displacement values ​​into a data fusion process to obtain a fused root stress distribution dataset. This includes: arranging strain gauges on the upper and side surfaces of the root weld of the flight simulator support, with the upper surface strain gauges arranged along the cantilever axial direction to collect normal stress signals, and the side surface strain gauges arranged along the weld circumferential direction to collect shear stress signals. The analog signal output by the strain gauge is amplified and low-pass filtered sequentially. The filtered signal is then synchronously digitized according to a preset time sampling interval to obtain the time series values ​​of shear stress and normal stress of the root weld. Displacement sensors are installed at the end of the cantilever, at the position of the operating handle and the position of the gear shifter, respectively, to collect the displacement of the operating handle in the pitch direction and the displacement of the gear shifter in the push-pull direction. Based on the cantilever length and installation angle between the end displacement sensor and the root weld, the displacement is multiplied by the cantilever lever arm length to obtain the equivalent torque component acting on the root section. Align the shear stress time series value, the normal stress time series value, and the timestamp of the equivalent torque component, and perform weighted summation according to the inverse ratio of the distance of each sensor from the center of the root weld as the weighting coefficient. Arrange the weighted summation stress components according to the circumferential angle position of the root section to obtain the fused root stress distribution dataset.

3. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The step of determining the real-time ratio of shear stress to normal stress based on the fused root stress distribution data, and using a support vector machine to classify the ratio and output the weld stress classification level includes: extracting the shear stress and normal stress values ​​corresponding to each circumferential angle position from the fused root stress distribution dataset, calculating the quotient of the shear stress value divided by the normal stress value for the stress data at the same time to obtain the stress ratio at each circumferential angle position, and constructing the feature vector of the support vector machine based on the stress ratio at all circumferential angle positions; The feature vectors are classified using a pre-trained support vector machine. The support vector machine uses a radial basis function as its kernel function, and its classification hyperplane is pre-trained based on the stress ratio boundary of historical weld failure samples. When the feature vector falls into the region where the stress ratio is lower than the preset lower threshold after classification, it is determined that the current weld is in a bending-dominated state; when it falls into the region where the stress ratio is higher than the preset upper threshold, it is determined that the current weld is in a shear-dominated state; when it falls into the transition region between the two thresholds, it is determined that the current weld is in a critical state of transition from bending to shear. Based on the classification results, the weld stress classification level is output, where bending-dominant state corresponds to low risk level, critical state corresponds to medium risk level, and shear-dominant state corresponds to high risk level.

4. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 3, characterized in that, The feature vector includes the current value of the stress ratio, the window mean obtained by taking the arithmetic mean of the stress ratio within a preset time window, and the increase or decrease trend value of the stress ratio over time within the window.

5. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The method is based on the weld stress classification level showing a shear-dominated high-risk level, and identifies the root stress mutation clusters generated when the shifter or throttle component is operated rapidly during simulated flight operations. By comparing the stress response peaks of different cantilever length configurations under the same operating force, the dynamic load amplification factor caused by the center of gravity offset is extracted. This includes: for the moment when the weld stress classification level shows a shear-dominated high-risk level, extracting the shear stress time series values ​​within a preset time range before and after that moment from the root stress distribution dataset. Identify consecutive data points in the shear stress time series values ​​whose amplitude exceeds a preset abrupt threshold and whose rise rate exceeds a preset rate threshold, and mark the consecutive data points as root stress abrupt clusters. Based on the end-operating force displacement corresponding to the root stress mutation cluster and the pre-stored multiple sets of cantilever length configuration data, the quasi-static theoretical shear stress borne by the root weld under the same operating force is calculated. Divide the measured peak shear stress in the root stress mutation cluster by the quasi-static theoretical shear stress under the current cantilever length configuration to obtain the dynamic load amplification factor caused by the center of gravity offset.

6. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The dynamic load amplification factor is the ratio of the measured peak shear stress to the quasi-static theoretical shear stress.

7. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The dynamic load amplification factor is used to perform shear stress-dominated failure assessment on the current root stress distribution data. The additional displacement is incorporated into the dynamic load amplification factor, and the root weld failure risk level is output. This includes: correcting the shear stress value in the current root stress distribution dataset according to the dynamic load amplification factor, and multiplying the shear stress value at each circumferential angle position by the dynamic load amplification factor to obtain the initial corrected shear stress value. The initial corrected shear stress value is compared with the allowable shear stress of the weld material. If it exceeds the allowable shear stress, the current weld is marked as having shear stress-dominated failure characteristics. For welds marked with shear stress-dominated failure characteristics, the additional displacement during the current operating cycle is obtained from the displacement sensor at the end of the cantilever, and the additional displacement is divided by the normal operating displacement reference value to obtain the displacement deviation ratio. The amplification factor after fusion is obtained by multiplying the displacement deviation ratio by one and the dynamic load amplification factor. The secondary corrected shear stress value is obtained by multiplying the fused amplification factor by the original shear stress value. The failure risk level of the root weld is output based on the ratio of the secondary corrected shear stress value to the allowable shear stress.

8. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, The method involves extracting the root bending moment value based on the root weld failure risk level and the cantilever length, processing the bending moment value through simulation to analyze the impact of shortening the cantilever length on the forward shift of the center of gravity, and obtaining a simulated bending moment distribution map. This includes: selecting time points that present medium and high risk levels based on the root weld failure risk level, and extracting the normal stress value of the corresponding time point from the root stress distribution dataset. Multiply the normal stress value by the current cantilever length to obtain the root bending moment value at each time point; For the simulated scenario where the cantilever length is set to decrease step by step from the current configuration value for the root bending moment value, the distance from the center of gravity of the component to the root weld is determined based on the mass and installation position of the shifter or throttle component after each shortening of the cantilever length as the center of gravity offset distance. The difference between the center of gravity offset distance and the center of gravity distance under the current cantilever length configuration is the center of gravity forward shift amount. The additional bending moment component is obtained by multiplying the center of gravity forward shift amount by the component self-weight. The additional bending moment component is superimposed with the original root bending moment value to obtain the simulated bending moment value under each cantilever length configuration. The simulated bending moment distribution diagram is drawn according to the cantilever length from large to small.

9. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, Based on the simulated bending moment distribution map, the positional shift of the ratio of shear stress value to normal stress value before and after optimization on the classification hyperplane is compared, and the verification level is output, including: selecting the cantilever length configuration point with the smallest simulated bending moment value from the simulated bending moment distribution map; The optimized normal stress value is obtained by dividing the simulated bending moment value corresponding to the configuration point by the cantilever length, and the optimized shear stress value is obtained by multiplying the forward shift of the center of gravity corresponding to the configuration point by the component's self-weight and then dividing by the cross-sectional area of ​​the root weld. The optimized stress ratio is obtained by calculating the quotient of the optimized shear stress value and the optimized normal stress value. The difference between the original stress ratio and the optimized stress ratio is calculated as the position offset; The verification level is output based on the relationship between the magnitude of the position offset and the preset verification threshold.

10. The method for evaluating the load-bearing capacity of a flight simulator support connection point according to claim 1, characterized in that, For each verification level, all stress distribution data and dynamic load amplification factors are integrated to identify the critical point where the weld transitions from bending to shear. The maximum root bearing capacity at the critical point is calculated, and a flight simulator support root bearing capacity assessment report is generated. This report includes: cantilever length configuration points for high and medium verification levels, and the critical point where the weld transitions from bending-dominated to shear-dominated based on the ratio of shear stress to normal stress at the corresponding configuration point. The combined stress value is calculated based on the shear stress value and normal stress value corresponding to the critical point. The allowable stress of the weld material is divided by the combined stress value and then by the dynamic load amplification factor at the critical point to obtain the maximum allowable operating force under the critical state. Multiply the maximum operating force by the cantilever length corresponding to the critical point to obtain the maximum bearing capacity at the root. Extract the cantilever length corresponding to the critical point as the critical cantilever length, and extract the difference between the distance from the component's center of gravity to the root weld at the critical point and the distance from the center of gravity under the current configuration as the critical center of gravity offset, and define the range of design constraints. Within the design constraints, select multiple cantilever length configuration points, calculate the corresponding bearing capacity value for each configuration point according to the combined stress value and dynamic load amplification factor, and plot the bearing capacity variation trend curve. The trend curves, critical point locations, design constraint ranges, and verification levels of each configuration point are summarized and output as a flight simulator support root load-bearing capacity assessment report.

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