A method and system for testing the protective performance of anti-fall vest

Abstract

The present invention relates to the technical field of safety performance testing, and discloses a method and system for testing the protective performance of an anti-fall vest. The method drives a bionic human body model through a multi-degree-of-freedom impact simulation device, and impacts a standard test dummy wearing an anti-fall vest in a variety of preset postures including a combination of pitch angle and roll angle, simulating a real fall scenario; utilizes a flexible piezoresistive sensor array embedded in the inner layer of the vest to collect dynamic pressure distribution data in real time, and synchronously records the airbag deployment time series image; calculates the pressure dispersion rate based on the pressure data, determines the protection response delay in combination with image analysis, and generates a posture adaptability evaluation index through multi-posture test results; finally, performs a continuous inflation and deflation cycle test to evaluate the sealing and sensor stability. This method can comprehensively detect the dynamic protection performance, airbag response efficiency and long-term use reliability of the anti-fall vest under complex impact postures, and improve the consistency between the test results and the actual protection effect.

Description

Technical Field

[0001] The present invention relates to the technical field of safety performance testing, and in particular to a method and system for testing the protective performance of a fall-resistant vest. Background Art

[0002] As an important part of human protective equipment, the accurate evaluation of the protective effectiveness of anti-fall vests is directly related to the safety of users. The current testing methods in the industry are mostly limited to static pressure testing or single-direction impact tests, which make it difficult to truly simulate the complex and changeable impact postures of the human body when falling, resulting in a large deviation between the test results and the actual protective effect. At the same time, the existing technology has not yet perfected the comprehensive evaluation system of airbag triggering mechanism, pressure dispersion characteristics and product durability, and cannot fully reflect the protective performance of anti-fall vests in dynamic scenarios. With the development of intelligent protective equipment, there is an urgent need for a testing method that can simulate real impact conditions in multiple dimensions, quantify key protection indicators and adapt to long-term stability verification, so as to promote the technical upgrade of anti-fall vest products and improve safety standards. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a method and system for testing the protective performance of an anti-fall vest.

[0004] In a first aspect, the present invention provides a method for testing the protective performance of a fall-resistant vest, comprising the following steps:

[0005] S1. Using a multi-degree-of-freedom impact simulation device, the bionic human model is driven to impact a fall-resistant vest worn by a standard test dummy in a preset posture, wherein the preset posture includes a combination of different pitch angles and roll angles;

[0006] S2, collecting dynamic pressure distribution data during the impact process in real time through a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, and synchronously recording a time-series image of the airbag deployment process of the anti-fall vest;

[0007] S3. Calculating a pressure dispersion rate based on the dynamic pressure distribution data, determining a protection response delay in combination with the time-series images, and generating a posture adaptability evaluation index based on the test results of the plurality of sets of preset postures;

[0008] S4. Continuously inflate and deflate the anti-fall vest to monitor the airbag sealing and sensor stability.

[0009] Optionally, in S1, the impact simulation device includes a six-axis robotic arm and a pneumatic buffer system, the impact speed of the bionic human body model includes multiple gears, and the combination of different pitch angles and roll angles includes at least three basic postures: leaning forward, falling sideways, and leaning back.

[0010] Optionally, the flexible piezoresistive sensor array is arranged in a grid pattern on the inner lining layer of the anti-fall vest, and the dynamic pressure distribution data includes the pressure value and timestamp of each grid node at the moment of impact, and the time series image is aligned with the pressure value in a frame synchronization manner through a camera device.

[0011] Optionally, the wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of ​​the anti-fall vest.

[0012] Optionally, in S3, the pressure dispersion rate is calculated by identifying the decay rate of the pressure peak area over time, and the basis for determining the protection response delay is the time from the first triggering of the sensor to the airbag volume reaching a preset volume threshold.

[0013] Optionally, the posture adaptability evaluation index is obtained by statistically calculating the pressure peak dispersion under multiple groups of the preset postures, and the dispersion is quantified based on the standard deviation.

[0014] Optionally, in S4, the continuous inflation and deflation cycle includes alternately performing airbag inflation to a rated pressure and complete pressure relief operations, the airbag sealing is evaluated by a pressure decay rate, and the sensor stability is evaluated by a sensitivity deviation rate.

[0015] Optionally, before S1, it also includes: implanting an inertial measurement unit in a key stress-bearing part of the standard test dummy, wherein the inertial measurement unit is used to verify the impact angle deviation of the bionic human body model; when the impact angle deviation exceeds a preset angle threshold, abandoning the current test data and re-executing the impact action.

[0016] Optionally, the generation of the posture adaptability evaluation index specifically includes:

[0017] Classifying the test results of the plurality of preset postures according to the impact direction, and extracting the temporal and spatial distribution data of the pressure peak under each posture;

[0018] Calculating the dispersion of the peak pressure values ​​within the same type of posture group and the coefficient of difference between groups, wherein the dispersion is quantified by the weighted value of the standard deviation and the coefficient of variation, wherein the coefficient of variation is the ratio of the standard deviation of the peak pressure values ​​within the same type of posture group to the mean, and is used to eliminate the dimensional differences between different posture groups;

[0019] A posture adaptability scoring model is constructed based on the discreteness and difference coefficient, and a multi-dimensional protection effectiveness comparison map is output. The comparison map displays the distribution relationship between the pressure dispersion rate and the response delay of different posture groups in the form of a heat map.

[0020] In a second aspect, the present invention further provides a system for testing the protective performance of a fall-resistant vest, which is used to perform a method for testing the protective performance of a fall-resistant vest as described in any one of the first aspects, comprising:

[0021] An impact module is used to drive a bionic human model through a multi-degree-of-freedom adjustable impact simulation device to impact an anti-fall vest worn by a standard test dummy in a preset posture, wherein the preset posture includes a combination of different pitch angles and roll angles;

[0022] An acquisition module, configured to acquire dynamic pressure distribution data during an impact in real time through a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, and to simultaneously record time-series images of the airbag deployment process of the anti-fall vest;

[0023] a generation module, configured to calculate a pressure dispersion rate based on the dynamic pressure distribution data, determine a protection response delay in combination with the time-series images, and generate a posture adaptability evaluation index based on test results of multiple sets of preset postures;

[0024] The monitoring module is used to continuously inflate and deflate the anti-fall vest and monitor the airbag sealing and sensor stability.

[0025] The present invention has the following technical effects:

[0026] The present invention can comprehensively evaluate the dynamic protective capabilities of anti-fall vests in actual use through composite posture impact tests that simulate real fall scenarios. The multi-degree-of-freedom impact device can accurately reproduce various unbalanced postures of the human body, such as leaning forward, falling sideways, and leaning back. Combined with the collaboration of the bionic human body model and the standard test dummy, it solves the problem of misjudgment of protective performance caused by a single impact angle in traditional tests. The flexible sensor array embedded in the inner layer of the vest can capture the spatiotemporal distribution characteristics of the impact pressure in real time without interfering with the deployment of the airbag, accurately reflecting the transmission path and dispersion efficiency of the impact energy in the protective structure. By synchronously recording the time-series images of the airbag deployment process, the trigger delay and expansion uniformity can be intuitively analyzed, avoiding the limitations of traditional sensors that only rely on pressure threshold judgment. The comprehensive analysis of multi-posture test data further reflects the differences in the vest's adaptability to different impact directions, providing improvement directions for the targeted optimization of the airbag layout and material strength. The inflation and deflation cycle test verifies the reliability and stability of the product from a long-term use perspective, ensuring that the protective performance will not decay due to repeated use. The entire method establishes a complete evaluation system from instantaneous protection to long-term effectiveness through dynamic scene simulation, real-time data collection and multi-dimensional indicator correlation, improves the consistency between test results and actual protection needs, and provides a basis for the design iteration and safety standard upgrade of anti-fall vests. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A schematic flow chart of a method for testing the protective performance of a fall-resistant vest provided in an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of an impact scenario provided by an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of a fall-resistant vest provided by an embodiment of the present invention;

[0031] Figure 4 A schematic structural diagram of a system for testing the protective performance of a fall-resistant vest provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0033] Figure 1 A flow chart of a method for testing the protective performance of a fall-resistant vest provided by an embodiment of the present invention is provided. Figure 2 A schematic diagram of a collision scenario provided by an embodiment of the present invention is shown. Figure 3 A schematic diagram of a fall-resistant vest provided by an embodiment of the present invention, wherein the method comprises the following steps:

[0034] S1. Using a multi-degree-of-freedom impact simulation device, the bionic human model is driven to impact the anti-fall vest worn by a standard test dummy in a preset posture. The preset posture includes a combination of different pitch angles and roll angles.

[0035] S2. Using a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, dynamic pressure distribution data during the impact process is collected in real time, and time-series images of the airbag deployment process of the anti-fall vest are simultaneously recorded;

[0036] S3. Calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay based on the time-series images, and generate posture adaptability evaluation indicators based on the test results of multiple sets of preset postures;

[0037] S4. Perform continuous inflation and deflation cycles on the anti-fall vest to monitor the airbag sealing and sensor stability.

[0038] The implementation of the protective performance test of the anti-fall vest C first requires the construction of an impact simulation test platform. The platform contains a multi-directionally adjustable mechanical structure that can drive the bionic human model A1 to complete impact actions at different angles. The bionic human model A1 adopts a humanoid joint design, and the movement of the torso and limbs is close to the movement laws of the real human body, which improves the authenticity of the mechanical transmission during the impact. The standard test dummy A2 is fixed on a rigid base. Its body parameters are close to or approximately the same as those of a real human body. The surface is covered with a buffer layer that simulates human soft tissue, and mechanical sensors are embedded inside to calibrate the impact energy. The anti-fall vest C is worn on the torso of the standard test dummy A2, and the tightness is adjusted to fit the dummy surface. The airbag trigger device and sensor circuit are integrated in the vest interlayer to avoid external interference.

[0039] Before the test begins, the operator sets the collision posture combination through the control terminal, such as the combined angle of forward and side tilt. The collision simulation device B drives the bionic human model A1 to accelerate according to the instructions. During the collision, the flexible piezoresistive sensor array distributed on the lining of the anti-fall vest C captures the pressure changes in each area in real time. The sensor nodes are arranged in a grid to cover the key protection areas of the vest. The data acquisition module records the pressure peak and its diffusion path in a synchronized time series. The high-speed camera device is set up on the side of the test area, with the lens aimed at the airbag deployment area. The image recording frame rate is synchronized with the sensor sampling frequency to ensure the temporal correlation between the pressure data and the airbag morphology changes.

[0040] During the data analysis phase, the area change trend of the peak pressure region is identified, and the contraction rate of the high-pressure region per unit time is calculated to characterize the pressure dispersion efficiency. Furthermore, by analyzing high-speed images frame by frame, the time from the airbag trigger signal to full deployment is marked, and the protection response delay is calculated based on the first sensor trigger moment. Multiple sets of test data from different postures are categorized and integrated by impact direction, extracting the peak pressure distribution characteristics and response delay data for each posture. A posture adaptability scoring model is constructed using statistical methods, and a visual chart is output reflecting the vest's multi-scenario adaptability.

[0041] During the durability test, the impact-resistant vest C was installed on an automated inflation and deflation platform. The platform cyclically inflated the airbag to full capacity and then completely deflated it. After each cycle, the airbag's shape recovery and internal pressure trends were recorded. Sensor stability was assessed by comparing pressure data fluctuations under the same impact posture over multiple cycles. After the test, a seal degradation curve and sensor performance report were generated to provide a basis for product life prediction.

[0042] The entire testing process, through dynamic impact simulation, real-time data fusion, and long-term performance verification, establishes an evaluation system covering both instantaneous protection and long-term reliability. The diverse impact posture settings address the single-scenario nature of traditional testing. Embedded sensors and image synchronization technology enable refined analysis of protective effectiveness, and continuous inflation and deflation testing evaluates product durability. This approach supports the design improvements and quality control of the Anti-Fall Vest C, driving the development of protective equipment towards precision and scenario-based design.

[0043] In some embodiments, in S1, the impact simulation device includes a six-axis robotic arm and a pneumatic buffer system, the impact speed of the bionic human body model includes multiple gears, and the combination of different pitch angles and roll angles includes at least three basic postures: forward leaning, sideways, and backward leaning.

[0044] Impact Simulator B utilizes a six-axis robotic arm B1 as its core drive component, enabling free adjustment at multiple angles within space. A bionic manikin mounting interface is installed at the end of the six-axis robotic arm B1. The range of motion of the manikin's torso and limbs closely mimics the human anatomy, enhancing the biomechanical rationality of the impact motion. The pneumatic cushioning system B2, integrated at the junction of the six-axis robotic arm B1's base and the bionic manikin, houses a multi-stage damping chamber whose cushioning intensity is controlled by adjusting airflow valves. The impact velocity control module switches speed levels via an electronic control unit. Different speed levels correspond to gradient changes in the speed of the six-axis robotic arm B1's drive motor, simulating the natural acceleration characteristics of a person experiencing imbalance.

[0045] Before testing, the operator selects a basic model from a library of preset impact postures on the control interface. For example, a forward-leaning posture corresponds to a combination of positive pitch deflection and zero roll angle, a sideways posture corresponds to a combination of negative roll angle deflection and slight pitch adjustment, and a backward posture uses a combination of negative pitch angle deflection and symmetrically distributed roll angles. The six-axis robotic arm B1 (its controller) calculates the motion trajectory of each joint based on the selected posture parameters, driving the bionic manikin to accelerate from its starting position to the target impact point. The pneumatic cushioning system B2 activates at the moment of impact, absorbing residual kinetic energy from the six-axis robotic arm B1 through graded pressure relief, preventing deformation of the dummy's base structure caused by a rigid impact. After the impact, the six-axis robotic arm B1 automatically returns to its initial position, ready for the next round of testing.

[0046] The tests were performed sequentially at different speeds: low speed simulates mild imbalance in the elderly, medium speed corresponds to everyday accidental slips, and high speed replicates sports shocks. A combination of the three basic postures was tested in a cycle. After each impact, the contact surface of the bionic mannequin was cleaned to eliminate residual friction that could interfere with the test data.

[0047] During the collision, the posture feedback system of the six-axis robot arm B1 monitors the angle data of each joint in real time and dynamically calibrates it with the preset posture parameters. If it is detected that the actual motion trajectory of the six-axis robot arm B1 deviates from the set value and exceeds the tolerance range, the test will be automatically interrupted and an alarm will be triggered. The pressure sensor of the pneumatic buffer system B2 continuously collects the air pressure changes in the damping chamber, and the data is uploaded to the central processor to generate a buffering efficiency curve, which is used to evaluate the energy absorption stability of the system under different impact intensities. After the test, an impact mechanics data set containing three basic postures at various gear speeds is generated, which intuitively reflects the response characteristics of the anti-fall vest C to multi-scenario impacts.

[0048] This implementation leverages the high degrees of freedom of the six-axis robotic arm (B1) and the adaptive adjustment of the pneumatic cushioning system (B2) to achieve precise reproduction of complex human postures and controlled energy transfer. The combined testing of multiple speeds and basic postures effectively covers the full range of protection requirements, from daily minor collisions to high-intensity impacts. The coordinated control of the six-axis robotic arm (B1) and the pneumatic cushioning system (B2) ensures the biological authenticity of the impact motion while preventing mechanical wear and tear on the test equipment caused by repeated impacts, providing the hardware foundation for long-term, stable testing.

[0049] In some embodiments, the flexible piezoresistive sensor array is arranged in a grid pattern on the inner lining layer of the anti-fall vest, and the dynamic pressure distribution data includes the pressure value and timestamp of each grid node at the moment of impact. The time series image is aligned with the pressure value in a frame-synchronous manner through a camera device.

[0050] The layout of the flexible piezoresistive sensor array D is pre-designed based on the structural characteristics of the anti-fall vest C. The flexible piezoresistive sensor array D consists of multiple (flexible piezoresistive) sensor nodes. The vest's inner lining demarcates key protection zones, such as the chest, back, and shoulders, and the sensor nodes are evenly distributed in a grid across these areas to ensure comprehensive coverage of impact pressure distribution. Each sensor node is connected to the data acquisition module via a flexible circuit substrate. The substrate utilizes a serpentine routing design to adapt to the vest's bending deformation and prevent circuit breakage during airbag deployment. After installation, the flexible piezoresistive sensor array D undergoes initial calibration, simulating slight pressure applied to different nodes to verify signal transmission continuity and sensitivity consistency.

[0051] Dynamic pressure data collection is automatically performed after the impact test is initiated. At the moment of impact, sensor nodes detect local pressure changes in real time. The data acquisition module records the pressure values ​​of each sensor node at a preset sampling frequency, and a timestamp is attached to each data point with millisecond accuracy. The pressure values ​​of adjacent sensor nodes are spatially interpolated to generate a continuous pressure distribution cloud map, dynamically displaying the diffusion of impact energy from the contact point to the surrounding area. The timestamp data is stored in conjunction with the pressure value for subsequent analysis of the migration path and duration of the pressure peak.

[0052] Synchronous recording of time-series images is achieved using a high-speed camera mounted to the side of the test area. The camera lens focuses on the airbag area of ​​​​Anti-Fall Vest C, with shooting angles adjusted to a combination of top-down and side views to ensure three-dimensional capture of the airbag inflation process. The image recording system and the sensor data acquisition module share the same clock source, achieving frame synchronization through hardware trigger signals. Each image frame is automatically embedded with the corresponding global time code when it is generated. In the post-processing stage, the image frame is aligned with the sensor data according to the time code to establish a precise correspondence between pressure changes and airbag morphology.

[0053] During the data fusion phase, pressure distribution cloud maps are superimposed on corresponding image frames in a time series, generating a visual animation that includes a pressure thermogram and the airbag deployment status. This animation analyzes the degree of alignment between high-pressure areas and the airbag coverage at specific moments, identifying blind spots or abnormal response delays. Comparative analysis of the pressure peak migration path and the airbag inflation trajectory provides a visual indicator of the airbag's effectiveness in channeling impact energy, such as whether pressure peaks disperse toward the edges as the airbag deploys.

[0054] The layout and synchronization of the entire sensor array ensures the spatial integrity and temporal accuracy of pressure data. The grid-like arrangement eliminates the blind spots of traditional single-point detection, and the hard synchronization of timestamps and image frames prevents misjudgments caused by data misalignment. The fusion analysis of pressure cloud images and deployment animations provides intuitive and traceable visual evidence for evaluating protective effectiveness, enabling designers to quickly locate structural defects and optimize airbag deployment strategies.

[0055] In some embodiments, the wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of ​​the anti-fall vest.

[0056] The routing design for the flexible piezoresistive sensor array D prioritizes the airbag deployment characteristics of the anti-fall vest C. Before installing the flexible piezoresistive sensors (nodes), a simulation of the airbag's folded state is performed to identify the boundaries of the vest's inner lining where the airbag collapses. These areas, such as the sides of the waist and inner shoulders, are prone to deformation. When planning the routing, the sensor lines are routed around the outer edges of the folding area, using a serpentine or wraparound layout to ensure a safe distance between the lines and the folding area. Figure 3 (a) shows a front view of the vest, and (b) shows a side view. (b) schematically illustrates how the wiring avoids the folding area. The flexible circuit substrate is made of a highly ductile material with a bending radius that meets the natural deformation requirements of the vest when worn. The circuit surface is covered with an elastic insulation layer to prevent damage to the insulation layer due to friction during airbag deployment.

[0057] During installation, operators first adhered the sensor nodes to designated areas of the inner lining in a grid pattern, then laid the connecting wiring along a pre-planned path. When passing through the vest interlayer, the wiring avoided sewing seams to prevent the stitching from cutting the wiring. Key bending points were fixed in sections, with micro-clip buckles constraining the wiring path while retaining local free movement to accommodate dynamic deformation. After wiring was completed, the airbag deployment was manually simulated to observe whether the wiring interfered with the folding area. If there was a risk of localized compression, the detour was readjusted and the fixing points reinforced.

[0058] During the functional verification phase, the installed Anti-Fall Vest C was subjected to multiple inflation and deflation tests. As the airbag inflated, a high-speed camera recorded the deformation of the wiring, focusing on monitoring the wiring near the folding area for any stretching, twisting, or scraping against the airbag surface. Sensors simultaneously collected pressure data to verify the stability of signal transmission after the wiring was detoured. For example, they examined whether wiring displacement during inflation caused momentary signal interruptions or increased noise. After the test, the vest's inner lining was removed to inspect the wiring surface for signs of wear and tear, confirming that the insulation layer was intact.

[0059] The optimized wiring path design effectively resolves interference between the sensor wiring and the airbag's movement. The serpentine routing provides ample deformation margin, preventing excessive stretching and breakage during airbag deployment. The wraparound layout reduces cross-wiring at critical folding areas, minimizing the risk of frictional losses. The combination of segmented fixation and an elastic insulation layer ensures precise control of wiring routing while adapting to the dynamic flexing requirements of the vest when worn. Simulated deployment and functional verification ensure that the sensor maintains stable signal acquisition capabilities during repeated testing, providing the hardware foundation for long-term reliability assessment.

[0060] This implementation achieves physical isolation between sensor wiring and the airbag folding area through pre-planned routing, dynamic simulation verification, and structural adaptability design. This route avoidance strategy, combined with the use of flexible materials, balances data acquisition accuracy with device durability, overcoming the technical bottleneck of sensor failure caused by mechanical interference in traditional wiring. This ensures the precise and long-term operation of the anti-fall vest testing system.

[0061] In some embodiments, in S3, the pressure dispersion rate is calculated by identifying the decay rate of the area of ​​the pressure peak region over time, and the basis for determining the protection response delay is the time from the first triggering of the sensor to the time when the airbag volume reaches a preset volume threshold.

[0062] The calculation of pressure dispersion rate and protection response delay needs to be based on the precise correlation between dynamic pressure data and image timing. During the test, the pressure data of each sensor node collected in real time by the flexible piezoresistive sensor array is pre-processed and a continuous pressure distribution cloud map is generated through a spatial interpolation algorithm. The algorithm automatically identifies the area in the cloud map that exceeds the preset pressure threshold, marks it as the pressure peak area, and tracks the trend of its area change over time. The area change curve of the peak area is used to calculate the area reduction ratio per unit time, reflecting the efficiency of the airbag in dispersing impact energy. For example, if the area of ​​the peak area shrinks rapidly after the impact, it indicates that the airbag quickly disperses the concentrated impact force to a larger contact surface, and the pressure dispersion rate is high; if the area shrinks slowly or there is no obvious change, it indicates that the energy dispersion effect is poor.

[0063] Determining the protection response delay requires combining sensor trigger signals with frame analysis of high-speed imagery. The first trigger moment of a (flexible piezoresistive) sensor is defined as the time point at which the pressure value at a particular sensor node first exceeds a preset threshold, and the system automatically records this timestamp. High-speed imagery analyzes the airbag deployment process frame by frame, and an image recognition algorithm is used to calibrate the critical frame at which the airbag volume reaches the preset volume threshold. The critical frame is determined when the airbag contour completely covers the preset protection area and its shape stabilizes. The difference between the sensor's first trigger timestamp and the corresponding critical frame timestamp represents the protection response delay. For example, if the sensor triggers at a certain moment after an impact, but the image shows the airbag fully deployed at a later time, the time difference directly reflects the synergistic efficiency of the triggering mechanism and the airbag's inflation rate.

[0064] During the data analysis phase, the correlation between pressure dispersion rate and protection response delay is presented through multi-dimensional charts. Overlay analysis of the pressure dispersion rate curve and the response delay scatter plot can identify shortcomings in protection effectiveness under specific impact postures. For example, when the pressure dispersion rate of a certain posture is low and the response delay is long, it indicates that the airbag layout or triggering logic in that direction needs to be optimized; if the dispersion rate is high but the delay fluctuates greatly, it reflects that the airbag deployment speed is unstable. The system automatically generates trend reports for key indicators, annotates abnormal data points, and provides improvement suggestions, such as adjusting the response parameters of the airbag inflation valve or optimizing the sensor threshold setting.

[0065] This implementation achieves a refined assessment of protective effectiveness through the fusion analysis of dynamic pressure distribution and image timing. The calculation of the pressure dispersion rate breaks through the limitations of traditional single-peak pressure detection and quantifies the dynamic response characteristics of the protective structure from the perspective of energy diffusion. The measurement of the protective response delay reveals the degree of match between the trigger mechanism and the mechanical properties of the airbag. The collaborative analysis of the two provides multi-dimensional data support for the performance optimization of the anti-fall vest, helping designers to specifically adjust material strength, airbag volume, or sensor sensitivity, thereby improving the product's comprehensive protection capabilities in complex impact scenarios.

[0066] In some embodiments, the posture adaptability evaluation index is obtained by statistically calculating the pressure peak dispersion under multiple sets of preset postures, and the dispersion is quantified based on the standard deviation.

[0067] The calculation of posture adaptability evaluation metrics is based on peak pressure data from multiple sets of impact tests. Each set of tests corresponds to a specific combination of pitch and roll angles. For example, the forward tilt test includes multiple impact tests with increasing pitch angles, while the sideways test covers different roll angle parameters. During the data preprocessing stage, the system extracts the spatial distribution coordinates and corresponding pressure values ​​of the peak pressures in each set of tests, removing outlier data points caused by sensor anomalies.

[0068] The standard deviation is used to quantify the volatility of the same group of data. Assume that a posture group contains n trials, each trial records m pressure peak points, and the pressure value of the jth peak point of the i-th trial is P ij , then the discreteness σ of the posture group is calculated as:

[0069]

[0070] Where μ is the mean of all peak pressures in the group:

[0071]

[0072] P ij is the pressure value of the jth pressure peak point in the i-th trial; n is the number of trials in a certain posture group; m is the number of pressure peak points recorded in the trial; σ is the standard deviation, which represents the dispersion of data within the group.

[0073] After the standard deviation is calculated, the system normalizes the σ value of each posture group and maps it to the scoring range to generate a comparison chart.

[0074] The inter-group difference coefficient is used to measure the relative difference in the dispersion of different posture groups. The dispersion of the forward leaning group, sideways leaning group, and backward leaning group is set to σ1, σ2, and σ3 respectively. The inter-group difference coefficient CV is calculated as:

[0075]

[0076] in, is the mean of multiple standard deviations. The coefficient of difference and the dispersion score are used to calculate the final posture adaptability score through the weighted summation formula.

[0077] By quantifying the pressure peak dispersion under multiple groups of impact postures through standard deviation, it is possible to objectively reflect the differences in the protective stability of anti-fall vests in different impact directions. The dispersion calculation reveals the fluctuating characteristics of the pressure distribution from a statistical perspective, accurately identifies areas with abnormal pressure concentration or dispersion under specific postures, and provides a data basis for optimizing the airbag layout. The introduction of the inter-group difference coefficient further quantifies the deviation in protective performance between different impact directions, helping designers to strengthen the material strength or trigger logic in weak directions in a targeted manner. The multi-dimensional correlation analysis of the dispersion score and the difference coefficient breaks through the limitation of traditional testing that only focuses on the protective effectiveness of a single posture, and constructs a comprehensive evaluation system with multi-scenario adaptability, making the optimization of the protective performance of anti-fall vests more systematic and directional.

[0078] In some embodiments, in S4, the continuous inflation and deflation cycle includes alternately inflating the airbag to the rated pressure and completely defusing the pressure, the airbag sealing is evaluated by the pressure decay rate, and the sensor stability is evaluated by the sensitivity deviation rate.

[0079] The continuous inflation and deflation cycle test can be performed on a dedicated automated platform equipped with an air pressure control module and a data acquisition system. At the beginning of the test, the anti-fall vest is fixed to a rigid bracket that simulates the human torso, and the airbag is connected to the air pump pipeline through a quick interface. The air pump performs inflation and depressurization operations alternately according to a preset program. During the inflation phase, gas is injected into the airbag at a constant flow rate until the internal pressure reaches the rated pressure threshold. During the depressurization phase, the solenoid valve is opened to achieve rapid and complete exhaust. Each cycle includes a complete inflation-depressurization process. The number of cycles is set according to the test requirements. During the process, the air pressure sensor monitors the changes in the internal pressure of the airbag in real time and records the attenuation curve.

[0080] Sealing performance is assessed by analyzing the slope of the pressure decay curve. After inflation reaches the rated pressure, the system shuts off the air pump and initiates pressure monitoring, recording the pressure drop per unit time. If the pressure decay rate remains above the baseline threshold, it indicates a minor leak in the airbag or abnormal material permeability. After multiple cycles, the system compares the changes in the decay curve morphology at different stages of the cycle to identify trends in sealing performance degradation, such as a sharp increase in the decay rate or abnormal pressure fluctuations in the later cycles.

[0081] Sensor stability testing is performed simultaneously with the inflation and deflation cycles. During each cycle, the expansion and contraction of the airbag causes deformation of the sensor array. The system records the output signal fluctuations of each sensor node under the same pressure conditions. The sensitivity deviation rate is calculated by comparing the sensor response values ​​at rated pressure during the first cycle with those in subsequent cycles. If the deviation rate increases with the number of cycles, it indicates sensor fatigue drift or changes in contact impedance. After the test, a sealing attenuation curve and a sensor deviation rate trend chart are generated to comprehensively evaluate the long-term reliability of the anti-fall vest.

[0082] This implementation effectively exposes potential issues such as airbag material aging, seam fatigue cracking, and sensor performance degradation by simulating the repeated inflation and deflation conditions experienced in actual use. Alternating inflation and deflation operations, combined with a dynamic monitoring mechanism, quantify the linear attenuation of sealing performance and the nonlinear drift characteristics of the sensor, providing data support for predicting product lifespan. The efficient operation of the automated testing platform and data correlation analysis ensure that the evaluation results objectively reflect the performance stability of the anti-fall vest throughout its lifecycle, enabling an assessment of its long-term reliability.

[0083] Optionally, S1 also includes: implanting an inertial measurement unit in the key stress-bearing parts of the standard test dummy, and the inertial measurement unit is used to verify the impact angle deviation of the bionic human body model; when the impact angle deviation exceeds a preset angle threshold, abandoning the current test data and re-executing the impact action.

[0084] The implantation of the IMU is planned based on the biomechanical characteristics of a standard test dummy. Key stress-bearing areas of the dummy include the chest, shoulders, and hips, which bear the primary impact forces during a collision and are prone to postural deviation. Custom cavities are created beneath the surface cushioning material at each selected location. The cavity dimensions match the IMU's shape, ensuring a tight fit between the unit's outer shell and the dummy's internal structure after implantation, preventing displacement during impact. Shock-absorbing pads are placed within these cavities to absorb high-frequency vibrations during testing and protect the IMU from damage due to transient impact overloads.

[0085] After installation, unit calibration and coordinate system alignment are performed. During calibration, a standard test dummy is fixed to a horizontal reference platform. Its static attitude data is measured using an external high-precision attitude sensor. This data is compared with the raw signals output by the inertial measurement unit (IMU) to calculate the three-axis acceleration and angular velocity deviation compensation parameters for each unit. Coordinate system alignment ensures that the spatial reference system of all units is consistent with the anatomical coordinate system of the dummy. For example, the X-axis of the chest unit corresponds to the coronal plane of the dummy, the Y-axis corresponds to the sagittal plane, and the Z-axis is vertically upward. The calibration data is stored in the test system's compensation database and used to correct measurement results in real time.

[0086] After the impact test is initiated, the inertial measurement unit continuously collects three-axis acceleration and angular velocity data of the standard test dummy. When the bionic human body model completes the impact action, the system extracts the dynamic data stream from the moment of impact to the posture stabilization stage, and calculates the actual pitch and roll angle deviations of the standard test dummy after the impact through a coordinate transformation algorithm. The actual angle is compared with the angular parameters of the preset impact posture. If the absolute value of the deviation exceeds the preset threshold, the current test data is deemed invalid. The system automatically marks the abnormal test batch, clears the associated sensor and image data, and triggers the six-axis robotic arm reset command to re-execute the impact action.

[0087] During the data verification phase, the system generates an angular deviation trend graph, visualizing the degree of deviation between the pitch and roll angles during each impact. The trend graph overlays the boundaries of the preset angular tolerance range, allowing operators to intuitively identify abnormal points where deviations exceed the threshold. If a test item fails to meet the angular requirements after multiple retries, the equipment triggers a self-check to troubleshoot faults in the bionic mannequin's joint degrees of freedom, the standard test dummy's fixation stability, or the inertial measurement unit's signal transmission link.

[0088] This implementation utilizes an implantable inertial measurement unit (IMU) and a dynamic compensation mechanism to enable real-time monitoring of impact angle deviation and data validation. Accurate measurement of key stress points ensures that the angle data truly reflects the actual load on the vest, preventing distortion of test results due to posture deviation. The combination of an automatic retry mechanism and troubleshooting procedures significantly improves the robustness of the testing process, providing a high-confidence data foundation for protective performance evaluation.

[0089] In some embodiments, the generation of the posture adaptability evaluation index specifically includes:

[0090] The test results of multiple sets of preset postures are classified according to the impact direction, and the temporal and spatial distribution data of the pressure peak under each posture are extracted;

[0091] The dispersion of peak pressure values ​​within the same posture group and the coefficient of variation between groups were calculated. The dispersion was quantified by the weighted value of the standard deviation and the coefficient of variation. The coefficient of variation is the ratio of the standard deviation to the mean of the peak pressure values ​​within the same posture group, which is used to eliminate the dimensional differences between different posture groups.

[0092] A posture adaptability scoring model is constructed based on the dispersion and difference coefficient, and a multi-dimensional protection effectiveness comparison map is output. The comparison map shows the distribution relationship between the pressure dispersion rate and response delay of different posture groups in the form of a heat map.

[0093] The generation of posture adaptability evaluation indicators is achieved through multi-step data processing and model building. First, multiple groups of impact test results are classified by impact direction, such as forward tilt group, side tilt group, and rear tilt group. Each group includes test data from the same impact direction but different pitch or roll angles. After extracting the pressure peaks and their spatiotemporal information for each group, the system automatically removes abnormal data points and then performs the following calculations:

[0094] The dispersion within the group is quantified by the standard deviation σ, and the calculation formula is the same as the above embodiment.

[0095] The intra-group coefficient of variation further eliminates dimensional differences and is calculated as:

[0096]

[0097] The inter-group difference coefficient CV measures the performance balance of different impact directions, and the calculation formula is the same as the above embodiment.

[0098] The scoring model generates a comprehensive score S through normalization and weighted summation 总 :

[0099]

[0100] Among them, σ min , σ max are the minimum and maximum standard deviations of all groups, respectively; w1 and w2 are weight coefficients, with the default values ​​of w1=0.6 and w2=0.4; the score is a value from 0 to 1, ≥0.8 indicates excellent protection, 0.6-0.8 requires optimization of the dark areas of the heat map (e.g., when the side-fall direction score is 0.7, increase the density of the side waist airbags), and <0.6 requires reconstruction of the design (e.g., when the score is 0.5, replace the high-sensitivity sensor).

[0101] The resulting output includes a heat map and radar chart. The heat map's horizontal axis represents the impact direction, with light and dark colors mapping the dispersion score. The radar chart displays the dispersion and coefficient of variation for each group. Designers identify issues based on low-scoring areas. For example, if the heat map shows a concentration of dark colors in the side-fall direction, they check for airbag deployment delays or insufficient material rigidity. Three iterative tests verify that the optimized score has risen above 0.8.

[0102] In some embodiments, the impact of environmental factors on the test can also be considered. Therefore, before the impact test, the anti-fall vest can be placed in a simulated environment with controlled temperature and humidity, and the environmental parameters simulate the actual use scenario (such as high temperature and high humidity, low temperature and dryness). During the test, the environment is maintained constant, and the dynamic pressure data and airbag deployment timing under different temperature and humidity combinations are collected. By comparing the data deviation of the reference environment (normal temperature and humidity) and the simulated environment, the environmental sensitivity coefficient K is calculated. env :

[0103]

[0104] Among them, σ 基准 Represents the standard deviation under the reference environment of normal temperature and humidity; σ 模拟 Indicates the standard deviation measured under simulated environment (such as high temperature and high humidity); t 延迟，基准 represents the protection response delay time under the benchmark environment; t 延迟，模拟 Indicates the protection response delay in a simulation environment.

[0105] The higher the environmental sensitivity coefficient, the greater the impact of the environment on the protective performance. The test report will mark the high sensitivity coefficient area (such as K env ≥0.3), it is recommended to increase the environmental adaptability design (such as hydrophobic coating of airbag material).

[0106] In some embodiments, a virtual dummy parameter library can also be established based on the user group's body shape data (height, weight, chest-to-waist ratio). Before the test, the target user's body shape parameters are input to automatically adjust the airbag fit and impact contact area of ​​the standard test dummy. During the impact, a personalized adaptation score S is generated based on the real-time pressure distribution data and the user's body shape matching degree. 适配 :

[0107]

[0108] Among them, P 实测 Indicates the pressure value distribution measured in the actual test, P 预期 Represents the ideal pressure distribution expected based on the user's body parameters.

[0109] If the score is lower than 0.7, it is suggested to adjust the size of the vest or add adjustable straps to ensure that the pressure distribution matches the user's body shape.

[0110] In some embodiments, a long-term performance degradation model can also be established to quantify the coupled effects of sensor drift, material fatigue, and seal degradation. The model input is the number of cycle tests and outputs the degradation weight of each indicator:

[0111]

[0112] Where, ΔS 总 Indicates the attenuation of the comprehensive score over time; Δσ, Δt 延迟 , ΔCV 组间 w3, w4, and w5 represent the attenuation of the standard deviation, protection response delay time, and inter-group difference coefficient, respectively. w3, w4, and w5 are the weight coefficients for each factor, determined through historical data regression, reflecting the impact of different factors on overall attenuation. The model predicts a score attenuation curve. When the slope of the curve exceeds a threshold, a maintenance prompt (such as replacing a sensor or reinforcing a joint) is triggered.

[0113] In some implementations, a replaceable impact surface module can be added to existing impact tests to simulate the impact of different floor materials on protective performance. Before testing, standardized panels of three materials, hard (e.g., concrete), medium-hard (e.g., wood flooring), and soft (e.g., carpet), are installed at the contact end of the impact simulator. The panel's surface texture and coefficient of friction are calibrated using actual floor material parameters. During the impact test, the impact velocity and angle parameters are maintained consistent, and the same impact action is performed using panels of different materials, one after another.

[0114] During the dynamic pressure data collection phase, the system compares the differences in pressure peak distribution under different materials. For example, when colliding with hard ground, the area of ​​the pressure peak concentration region may shrink but the peak pressure increases, while the impact energy absorption effect of the soft ground may prolong the pressure decay time. The airbag deployment timing images are analyzed simultaneously to observe the impact of different materials on the airbag triggering delay. For example, hard ground may cause the airbag to trigger faster but not fully deploy. After the test is completed, a material adaptability report is generated, marking the pressure dispersion rate and response delay change amplitude under each material, and making targeted improvement suggestions. For example, vests used in areas with high incidence of hard ground need to strengthen the shoulder cushioning layer, and products corresponding to soft ground can appropriately reduce the airbag inflation pressure to extend the deployment time.

[0115] By introducing multi-material testing scenarios, this solution overcomes the limitations of existing methods that only target a single rigid impact surface, ensuring test results more closely reflect actual user environments. The standardized design and interchangeable material panels ensure controllable and reproducible testing conditions, providing data support for the multi-scenario adaptation of anti-fall vests.

[0116] In some implementations, a dynamic correlation model can be constructed for core indicators such as pressure dispersion rate, response delay, and sealing performance to analyze the impact of their interactions on overall protective performance. First, the numerical values ​​of each indicator are extracted from historical test data, and the correlation strength between each indicator is calculated through correlation analysis. For example, the pressure dispersion rate and protective response delay may be negatively correlated (the higher the dispersion rate, the shorter the delay). An evaluation matrix is ​​constructed based on the correlation strength to identify key influencing factors. For example, if the influence of sealing attenuation on the pressure dispersion rate is high, it is necessary to prioritize monitoring the aging status of the airbag.

[0117] During the test, the system monitors the changing trends of various indicators in real time. When a certain indicator deviates from the expected range, it automatically triggers key detection of related indicators. For example, if the response delay of a certain test increases abnormally, the frequency of sealing detection will be increased simultaneously, and the airbag pressure curve will be checked for leakage characteristics. A new "Correlation Abnormal Prompt" module has been added to the test report to mark the deviation of highly correlated indicator groups. For example, "pressure dispersion rate drops by 5% and sealing attenuates by 3%" may indicate a coordinated failure of the sensor and airbag. The test process is optimized based on the results of the correlation analysis. For example, a parallel testing strategy is adopted for highly correlated indicator groups to shorten the overall test cycle.

[0118] This solution improves testing efficiency and problem location accuracy by revealing implicit correlations between indicators. The dynamic update mechanism of the evaluation matrix ensures continuous model optimization as data accumulates, upgrading protection performance analysis from isolated indicator evaluation to a systematic and collaborative assessment, providing a more comprehensive decision-making basis for product design.

[0119] Figure 4A schematic diagram of a system for testing the protective performance of a fall-resistant vest provided in an embodiment of the present invention includes:

[0120] Impact module 401 is used to drive the bionic human model through a multi-degree-of-freedom adjustable impact simulation device to impact the anti-fall vest worn by the standard test dummy in a preset posture, with different combinations of pitch angles and roll angles of the preset posture;

[0121] The acquisition module 402 is used to collect dynamic pressure distribution data during the impact process in real time through a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, and simultaneously record time-series images of the airbag deployment process of the anti-fall vest;

[0122] A generation module 403 is used to calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay based on the time-series images, and generate a posture adaptability evaluation index based on the test results of multiple sets of preset postures;

[0123] The monitoring module 404 is used to continuously inflate and deflate the anti-fall vest and monitor the airbag sealing and sensor stability.

[0124] The system provided by the embodiment of the present invention has the same technical features as the above method, and therefore can also have the same technical effects and solve the same technical problems, which will not be described in detail here.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for testing the protective performance of a fall-resistant vest, characterized in that: The following steps are involved: S1. Using a multi-degree-of-freedom impact simulation device, the bionic human model is driven to impact a fall-resistant vest worn by a standard test dummy in a preset posture, wherein the preset posture includes a combination of different pitch angles and roll angles; S2, collecting dynamic pressure distribution data during the impact process in real time through a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, and synchronously recording a time-series image of the airbag deployment process of the anti-fall vest; S3. Calculating a pressure dispersion rate based on the dynamic pressure distribution data, determining a protection response delay in combination with the time-series images, and generating a posture adaptability evaluation index based on the test results of multiple sets of preset postures; S4, continuously inflating and deflating the anti-fall vest to monitor the airbag sealing and sensor stability; In S3, the pressure dispersion rate is calculated by identifying the decay rate of the pressure peak area over time, the protection response delay is determined based on whether the time from the first triggering of the sensor to the airbag volume reaching a preset volume threshold is exceeded, and the posture adaptability evaluation index is obtained by statistically analyzing the pressure peak dispersion under multiple groups of preset postures, and the dispersion is quantified based on the standard deviation; The generation of the posture adaptability evaluation index specifically includes: Classifying the test results of the plurality of preset postures according to the impact direction, and extracting the temporal and spatial distribution data of the pressure peak under each posture; Calculating the dispersion of the peak pressure values ​​within the same type of posture group and the coefficient of difference between groups, wherein the dispersion is quantified by the weighted value of the standard deviation and the coefficient of variation, wherein the coefficient of variation is the ratio of the standard deviation of the peak pressure values ​​within the same type of posture group to the mean, and is used to eliminate the dimensional differences between different posture groups; A posture adaptability scoring model is constructed based on the discreteness and the inter-group difference coefficient, and a multi-dimensional protection effectiveness comparison map is output. The comparison map displays the distribution relationship between the pressure dispersion rate and the protection response delay of different posture groups in the form of a heat map.

2. The method for testing the protective performance of a fall-resistant vest according to claim 1, wherein: In S1, the collision simulation device includes a six-axis robotic arm and a pneumatic buffer system, the collision speed of the bionic human body model includes multiple gears, and the combination of different pitch angles and roll angles includes at least three basic postures: forward leaning, sideways leaning and backward leaning.

3. The method for testing the protective performance of a fall-resistant vest according to claim 1, wherein: The flexible piezoresistive sensor array is arranged in a grid pattern on the inner lining layer of the anti-fall vest. The dynamic pressure distribution data includes the pressure value and timestamp of each grid node at the moment of impact. The time series image is aligned with the pressure value in a frame synchronization manner through a camera device.

4. The method for testing the protective performance of a fall-resistant vest according to claim 3, wherein: The wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of ​​the anti-fall vest.

5. The method for testing the protective performance of a fall-resistant vest according to claim 1, wherein: In S4, the continuous inflation and deflation cycle includes alternately performing airbag inflation to a rated pressure and complete pressure relief operations, the airbag sealing performance is evaluated by a pressure decay rate, and the sensor stability is evaluated by a sensitivity deviation rate.

6. The method for testing the protective performance of a fall-resistant vest according to claim 1, wherein: Before S1, the method also includes: implanting an inertial measurement unit in a key stress-bearing part of the standard test dummy, wherein the inertial measurement unit is used to verify the impact angle deviation of the bionic human body model; when the impact angle deviation exceeds a preset angle threshold, abandoning the current test data and re-executing the impact action.

7. A system for testing the protective performance of a fall-resistant vest, for executing the method for testing the protective performance of a fall-resistant vest according to any one of claims 1 to 6, characterized in that: include: An impact module is used to drive a bionic human model through a multi-degree-of-freedom adjustable impact simulation device to impact an anti-fall vest worn by a standard test dummy in a preset posture, wherein the preset posture includes a combination of different pitch angles and roll angles; An acquisition module, configured to acquire dynamic pressure distribution data during an impact in real time through a flexible piezoresistive sensor array embedded in the inner layer of the anti-fall vest, and to simultaneously record time-series images of the airbag deployment process of the anti-fall vest; a generation module, configured to calculate a pressure dispersion rate based on the dynamic pressure distribution data, determine a protection response delay in combination with the time-series images, and generate a posture adaptability evaluation index based on test results of multiple sets of preset postures; A monitoring module, used to continuously inflate and deflate the anti-fall vest, and monitor the airbag sealing and sensor stability; The pressure dispersion rate is calculated by identifying the decay rate of the pressure peak area over time. The protection response delay is determined based on whether the time from the first triggering of the sensor to the airbag volume reaching the preset volume threshold is exceeded. The posture adaptability evaluation index is obtained by statistically analyzing the pressure peak dispersion under multiple sets of preset postures, and the dispersion is quantified based on the standard deviation. The generation of the posture adaptability evaluation index specifically includes: Classifying the test results of the plurality of preset postures according to the impact direction, and extracting the temporal and spatial distribution data of the pressure peak under each posture; Calculating the dispersion of the peak pressure values ​​within the same type of posture group and the coefficient of difference between groups, wherein the dispersion is quantified by the weighted value of the standard deviation and the coefficient of variation, wherein the coefficient of variation is the ratio of the standard deviation of the peak pressure values ​​within the same type of posture group to the mean, and is used to eliminate the dimensional differences between different posture groups; A posture adaptability scoring model is constructed based on the discreteness and the inter-group difference coefficient, and a multi-dimensional protection effectiveness comparison map is output. The comparison map displays the distribution relationship between the pressure dispersion rate and the protection response delay of different posture groups in the form of a heat map.

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