Method and system for testing protection performance of anti-falling waistcoat

Abstract

The invention relates to the technical field of safety performance testing, and discloses an anti-falling waistcoat protection performance testing method and system. According to the method, a bionic human body model is driven through a multi-degree-of-freedom impact simulation device, a standard test dummy wearing an anti-falling waistcoat is impacted in multiple preset postures including the combination of a pitch angle and a roll angle, and a real falling scene is simulated; a flexible piezoresistive sensor array embedded into the inner layer of the waistcoat is used for collecting dynamic pressure distribution data in real time, and an air bag unfolding time sequence image is synchronously recorded; calculating a pressure dispersion rate based on the pressure data, determining protection response delay in combination with image analysis, and generating an attitude adaptability evaluation index through a multi-attitude test result; and finally, executing a continuous inflation and deflation cycle test to evaluate the sealing performance and the sensor stability. According to the method, the dynamic protection performance, the airbag response efficiency and the long-term use reliability of the anti-falling waistcoat in a complex impact posture can be comprehensively detected, and the consistency of a test result and an actual protection effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety performance testing, and particularly relates to a method and system for testing the protection performance of a fall-proof vest. Background Art

[0002] As an important part of human protection equipment, the accurate evaluation of the protection effectiveness of a fall-proof vest is directly related to the safety of users. The current testing methods in the industry are mostly limited to static pressure detection or single-direction impact tests, which are difficult to truly simulate the complex and variable impact postures when a human body falls, resulting in a large deviation between the test results and the actual protection effect. At the same time, the existing technology has not yet improved the comprehensive evaluation system for the airbag triggering mechanism, pressure dispersion characteristics, and product durability, and cannot comprehensively reflect the protection performance of the fall-proof vest in dynamic scenarios. With the intelligent development of protection 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 upgrading and safety standard improvement of fall-proof vest products. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a method and system for testing the protection performance of a fall-proof vest.

[0004] In a first aspect, the present invention provides a method for testing the protection performance of a fall-proof vest, including the following steps: S1. Driving a bionic human model by an impact simulation device with multi-degree-of-freedom adjustment to impact the fall-proof vest worn on a standard test dummy in a preset posture, where the preset posture includes combinations of different pitch angles and roll angles; S2. Real-time collecting dynamic pressure distribution data during the impact process through a flexible piezoresistive sensor array embedded in the inner layer of the fall-proof vest, and synchronously recording the timing images of the airbag deployment process of the fall-proof vest; S3. Calculating the pressure dispersion rate based on the dynamic pressure distribution data, determining the protection response delay in combination with the timing images, and generating an attitude adaptability evaluation index according to the test results of multiple groups of the preset postures; S4. Performing continuous inflation and deflation cycles on the fall-proof vest, and monitoring the airbag sealing performance and sensor stability.

[0005] 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 model includes multiple gears, and the combinations of different pitch angles and roll angles include at least three basic postures: forward tilt, side fall, and backward tilt.

[0006] Optionally, 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 values and timestamps of each grid node at the moment of impact, and the sequential images are aligned with the pressure values in a frame-synchronous manner by a camera device.

[0007] Optionally, the wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of the anti-fall vest.

[0008] Optionally, in step S3, the calculation method of the pressure dispersion rate is: identifying the decay rate of the area of the pressure peak region over time, and the determination basis of the protection response delay is the duration from the first trigger of the sensor to the airbag volume reaching the preset volume threshold.

[0009] Optionally, the attitude adaptability evaluation index is obtained by statistically analyzing the pressure peak dispersion of multiple groups in the preset postures, and the dispersion is quantified based on the standard deviation.

[0010] Optionally, in step S4, the continuous inflation and deflation cycle includes alternately performing the operations of inflating the airbag to the rated pressure and completely deflating it. The airbag sealing performance is evaluated by the air pressure decay rate, and the sensor stability is evaluated by the sensitivity deviation rate.

[0011] Optionally, before step S1, it further includes: implanting an inertial measurement unit at the key force-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 the preset angle threshold, discard the current test data and re-perform the impact action.

[0012] Optionally, the generation of the attitude adaptability evaluation index specifically includes: Classifying the test results of multiple groups of the preset postures according to the impact direction, and extracting the spatio-temporal distribution data of the pressure peaks in each posture; Calculating the dispersion and the inter-group difference coefficient of the pressure peaks within the same type of posture group. The dispersion is quantified by the weighted value of the standard deviation and the coefficient of variation. Among them, the coefficient of variation is the ratio of the standard deviation to the mean of the pressure peaks within the same type of posture group, and is used to eliminate the dimensional difference between different posture groups; Based on the dispersion and the difference coefficient, construct an attitude adaptability scoring model, and output a multi-dimensional protection efficiency comparison map. The comparison map shows the distribution relationship between the pressure dispersion rate and the response delay of different posture groups in the form of a heat map.

[0013] In a second aspect, the present invention further provides an anti-fall vest protection performance test system for performing the anti-fall vest protection performance test method according to any one of the first aspects, including: An impact module, which is used to drive a bionic human body model through an impact simulation device with multi-degree-of-freedom adjustment to impact a fall-proof vest worn on a standard test dummy in a preset posture, and the preset posture includes combinations of different pitch angles and roll angles; An acquisition module, which 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 fall-proof vest, and synchronously record the time-sequence images of the airbag deployment process of the fall-proof vest; A generation module, which is used to calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay in combination with the time-sequence images, and generate an attitude adaptability evaluation index according to the test results of multiple groups of the preset postures; A monitoring module, which is used to perform continuous inflation and deflation cycles on the fall-proof vest to monitor the airbag tightness and sensor stability.

[0014] The present invention has the following technical effects: Through the composite posture impact test that simulates the real falling scenario, the present invention can comprehensively evaluate the dynamic protection ability of the fall-proof vest in actual use. The multi-degree-of-freedom impact device can accurately reproduce various unbalanced postures of the human body such as forward leaning, side falling, and backward leaning. Combining the cooperation of the bionic human body model and the standard test dummy solves the problem of misjudgment of the protection performance caused by a single impact angle in traditional tests. The flexible sensor array embedded in the inner layer of the vest can, without disturbing the airbag deployment, capture the spatio-temporal distribution characteristics of the impact pressure in real time, and accurately reflect the transmission path and dispersion efficiency of the impact energy in the protection structure. By synchronously recording the time-sequence images of the airbag deployment process, the trigger delay and inflation uniformity can be intuitively analyzed, avoiding the limitations of traditional sensors that only rely on pressure thresholds for determination. The comprehensive analysis of multi-posture test data further reflects the adaptability differences of the vest to different impact directions, providing an improvement direction for targeted optimization of the airbag layout and material strength. The inflation and deflation cycle test verifies the reliability and stability of the product from the perspective of long-term use, ensuring that the protection performance will not decay due to repeated use. The whole set of methods establishes a complete evaluation system from instantaneous protection to long-term effectiveness through dynamic scenario simulation, real-time data acquisition and multi-dimensional index correlation, improves the consistency between the test results and the actual protection requirements, and provides a basis for the design iteration and safety standard upgrade of the fall-proof vest. Description of the Drawings

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 Schematic diagram of the process for testing the protection performance of a fall - resistant vest provided by an embodiment of the present invention; Figure 2 Schematic diagram of an impact scenario provided by an embodiment of the present invention; Figure 3 Schematic diagram of a fall - resistant vest provided by an embodiment of the present invention; Figure 4 Schematic diagram of the structure of a system for testing the protection performance of a fall - resistant vest provided by an embodiment of the present invention. Detailed implementation manners

[0017] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0018] Figure 1 Schematic diagram of the process for testing the protection performance of a fall - resistant vest provided by an embodiment of the present invention, Figure 2 Schematic diagram of an impact scenario provided by an embodiment of the present invention, Figure 3 Schematic diagram of a fall - resistant vest provided by an embodiment of the present invention. The method includes the following steps: S1. Drive a bionic human body model through an impact simulation device with multi - degree - of - freedom adjustment to impact the fall - resistant vest worn on a standard test dummy in a preset posture, and the preset posture includes combinations of different pitch angles and roll angles; S2. Real - time collect the dynamic pressure distribution data during the impact process through a flexible piezoresistive sensor array embedded in the inner layer of the fall - resistant vest, and synchronously record the timing images of the airbag deployment process of the fall - resistant vest; S3. Calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay in combination with the timing images, and generate an attitude adaptability evaluation index according to the test results of multiple groups of preset postures; S4. Perform continuous inflation and deflation cycles on the fall - resistant vest, and monitor the airbag sealing performance and sensor stability.

[0019] 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-directional 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 rules of the real human body, which improves the authenticity of mechanical transmission during the impact. The standard test dummy A2 is fixed on a rigid base, and its body parameters are close to or approaching the same as those of the real human body. The surface is covered with a buffer layer that simulates human soft tissue, and the internal pre-embedded mechanical sensors are used 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.

[0020] Before the test begins, the operator sets the impact posture combination through the control terminal, such as the combined angle of forward tilt and side tilt. The impact simulation device B drives the bionic human model A1 to accelerate according to the instructions. During the impact, 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 sequence. 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.

[0021] During the data analysis phase, the area change trend of the pressure peak area is identified, and the contraction rate of the high-pressure area per unit time is calculated to characterize the pressure dispersion efficiency. At the same time, by analyzing high-speed images frame by frame, the time node from the issuance of the trigger signal to the full deployment of the airbag is marked, and the protection response delay is calculated in combination with the first triggering moment of the sensor. Multiple groups of test data in different postures are classified and integrated according to the impact direction, and the pressure peak distribution characteristics and response delay data under each posture are extracted. A posture adaptability scoring model is constructed through statistical methods, and a visual chart reflecting the multi-scenario adaptability of the vest is output.

[0022] In the durability test, the anti-fall vest C is installed on an automated inflation and deflation platform. The platform cyclically inflates the airbag to a full state and then completely deflates it. After each cycle, the airbag shape recovery and internal air pressure change trend are recorded. Sensor stability is evaluated by comparing the pressure data fluctuations under the same impact posture in multiple cycles. After the test, a sealing attenuation curve and sensor performance report are generated to provide a basis for product life prediction.

[0023] The entire set of test processes constructs an evaluation system covering instantaneous protection and long-term reliability through dynamic impact simulation, real-time data fusion, and long-term performance verification. The diverse settings of impact postures solve the problem of single traditional test scenarios. The embedded sensor and image synchronization technology enables refined analysis of protection effectiveness, and continuous charging and discharging tests are conducted to evaluate the durability of the product. This method provides support for the design improvement and quality control of the anti-fall vest C, promoting the development of protective equipment towards precision and scenario-based directions.

[0024] In some embodiments, in S1, the impact simulation device includes a six-axis robotic arm and a pneumatic buffer system. The impact speeds of the bionic human model include multiple gears, and the combinations of different pitch angles and roll angles include at least three basic postures: forward tilt, side fall, and backward tilt.

[0025] The impact simulation device B uses the six-axis robotic arm B1 as the core driving component to achieve free adjustment at multiple angles in space. A bionic human model fixing interface is installed at the end of the six-axis robotic arm B1. The joint movement ranges of the model's torso and limbs are close to the physiological structure of a real human body, enhancing the biomechanical rationality of the impact actions. The pneumatic buffer system B2 is integrated at the connection between the base of the six-axis robotic arm B1 and the bionic human model. Multiple damping chambers are set inside, and the buffer intensity is controlled by adjusting the air flow valve. The impact speed control module switches the speed gears through an electronic control unit. Different gears correspond to the rotational speed gradient changes of the driving motor of the six-axis robotic arm B1, simulating the natural acceleration characteristics when the human body loses balance.

[0026] Before the test, the operator selects a basic mode from the preset impact posture library on the control interface. For example, the forward tilt posture corresponds to the combination of a positive pitch angle deflection and a zero roll angle, the side fall posture corresponds to the combination of a negative roll angle deflection and a micro-adjustment of the pitch angle of the human body, and the backward tilt posture adopts the combination of a reverse pitch angle deflection and a symmetric distribution of the roll angle. The controller of the six-axis robotic arm B1 calculates the movement trajectories of each joint according to the selected posture parameters and drives the bionic human model to accelerate from the starting position to the target impact point. The pneumatic buffer system B2 is activated at the moment of impact contact and absorbs the residual kinetic energy of the six-axis robotic arm B1 through hierarchical pressure relief to avoid deformation of the dummy base structure caused by rigid collision. After the impact is completed, the six-axis robotic arm B1 automatically resets to the initial position to prepare for the next round of tests.

[0027] Tests at different speed gears are executed in sequence. The low-speed gear simulates the scenario of mild imbalance of the elderly, the medium-speed gear corresponds to the scenario of accidental slipping in daily life, and the high-speed gear reproduces situations such as sports impacts. The combined tests of the three basic postures are carried out in a cycle. After each impact, the contact surface of the bionic human model is cleaned to eliminate the interference of residual friction on the test data.

[0028] During the impact, the posture feedback system of the six-axis robot arm B1 monitors the rotation angle data of each joint in real time and dynamically calibrates it with the preset posture parameters. If the actual motion trajectory of the six-axis robot arm B1 is detected to deviate from the set value beyond 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 each gear speed is generated, which intuitively reflects the response characteristics of the anti-fall vest C to multi-scenario impacts.

[0029] This implementation achieves accurate reproduction of complex human postures and controllable energy transfer through the high degree of freedom of the six-axis robot B1 and the adaptive adjustment of the pneumatic buffer system B2. The combined test of multiple speeds and basic postures effectively covers the complete protection needs from daily minor collisions to high-intensity impacts. The coordinated control of the six-axis robot B1 and the pneumatic buffer system B2 not only ensures the biological authenticity of the impact action, but also avoids the mechanical loss of the test equipment caused by repeated impacts, providing a hardware foundation for long-term stable testing.

[0030] 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, and the time series image is aligned with the pressure value in a frame synchronization manner through a camera device.

[0031] The layout of the flexible piezoresistive sensor array D needs to be designed in advance according to the structural characteristics of the anti-fall vest C. The flexible piezoresistive sensor array D is composed of multiple (flexible piezoresistive) sensor nodes. The vest's inner lining divides the chest, back, shoulders and other key protection areas, and the sensor nodes are evenly arranged in these areas in a grid to ensure full coverage of the impact pressure distribution. Each sensor node is connected to the data acquisition module through a flexible circuit substrate. The substrate adopts a serpentine routing design to adapt to the bending deformation of the vest to avoid line breakage when the airbag is deployed. After the flexible piezoresistive sensor array D is installed, an initial calibration is performed to simulate slight pressure applied to different nodes to verify the continuity of signal transmission and the consistency of sensitivity.

[0032] The collection of dynamic pressure data is automatically executed after the impact test is started. At the moment of impact, the sensor node detects the local pressure change in real time. The data acquisition module records the pressure value of each sensor node at the preset sampling frequency, and adds a timestamp accurate to the millisecond level to each data. The pressure values of adjacent sensor nodes are used to generate a continuous pressure distribution cloud map through a spatial interpolation algorithm, dynamically showing the process of impact energy spreading from the contact point to the surrounding area. The timestamp data is bound and stored with the pressure value for subsequent analysis of the migration path and duration of the pressure peak.

[0033] The synchronous recording of the time-sequential images is achieved by a high-speed camera device installed on the side of the test area. The focus of the camera device lens is aligned with the airbag area of the anti-fall vest C, and the shooting angle is adjusted to a combination of top view and side view to ensure the three-dimensional capture of the airbag inflation process. The image recording system and the sensor data acquisition module share the same clock source and achieve frame synchronization through a hardware trigger signal. When each frame of image is generated, the corresponding global time code is automatically embedded. In the post-processing stage, the image frames and the sensor data are aligned according to the time code to establish an accurate correspondence between the pressure change and the airbag morphology.

[0034] In the data fusion stage, the pressure distribution cloud map is superimposed on the corresponding image frames in time series to generate a visualization animation containing the pressure heat map and the airbag deployment state. The animation can be analyzed frame by frame to match the high-pressure area and the airbag coverage range at a specific moment, and identify abnormal situations such as protection blind spots or response lags. The comparative analysis of the pressure peak migration path and the airbag inflation trajectory can intuitively reflect the guiding effect of the airbag on the impact energy, for example, whether the pressure peak disperses towards the edge area as the airbag unfolds.

[0035] The layout and synchronization mechanism of the entire sensor array ensure the integrity of the pressure data in the spatial dimension and the accuracy in the time dimension. The grid-like arrangement eliminates the perspective blind spots of traditional single-point detection, and the hard synchronization of the time stamp and the image frame avoids misjudgment caused by data misalignment. The fusion analysis of the pressure cloud map and the deployment animation provides intuitive and traceable visual evidence for evaluating the protection efficacy, enabling designers to quickly locate structural defects and optimize the airbag trigger strategy.

[0036] In some embodiments, the wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of the anti-fall vest.

[0037] The wiring path design of the flexible piezoresistive sensor array D needs to give priority to the airbag deployment characteristics of the anti-fall vest C. Before installing the flexible piezoresistive sensors (nodes), through the simulation analysis of the airbag folding state, mark the boundaries of the folding areas when the airbag contracts in the inner lining layer of the vest, such as the sides of the waist and the inner sides of the shoulders, which are prone to deformation. When planning the wiring path, the sensor lines bypass from the outer edge of the folding area, and adopt a serpentine wiring or a surrounding layout to ensure a safe distance between the lines and the folding area. Figure 3 In (a) is the front view of the anti-fall vest, and (b) is the side view of the anti-fall vest. In (b), it is schematically shown that the wiring avoids the folding area. The flexible circuit board is made of a highly ductile material, and its bending radius meets the natural deformation requirements when the vest is worn. At the same time, an elastic insulating layer is covered on the surface of the line to prevent the insulation layer from being damaged due to friction during the airbag deployment process.

[0038] During the installation process, the operator first arranges the sensor nodes in a grid pattern and pastes them to the specified area of the inner lining layer, and then lays the connecting wires along the pre-planned path. When the wires pass through the sandwich layer of the vest, avoid the sewing seams to prevent the stitches from cutting the wires. The key bending points are fixed in a segmented manner, and the wire path is restricted by micro-clips. At the same time, a local free movement margin is reserved to adapt to dynamic deformation. After the wiring is completed, manually simulate the airbag deployment action to observe whether the wires interfere with the folding area. If there is a risk of local extrusion, re-adjust the detour path and reinforce the fixing points.

[0039] In the functional verification stage, perform multiple inflation and deflation tests on the completed anti-fall vest C. When the airbag inflates and expands, the high-speed camera records the deformation process of the wires, and focuses on monitoring whether the wires near the folding area are stretched and twisted or scratched against the airbag surface. The sensor synchronously collects pressure data to verify the stability of signal transmission after the wires detour. For example, whether the signal is instantaneously interrupted or the noise increases due to wire displacement during the inflation process. After the test, disassemble the inner lining layer of the vest to check the wear marks on the wire surface and confirm that the integrity of the insulation layer is not damaged.

[0040] The optimized design of the wire path effectively solves the interference problem between the sensor wires and the airbag movement. The serpentine wire routing provides sufficient deformation margin to prevent the wires from being over-stretched and broken when the airbag deploys; the surrounding layout reduces the crossing of wires in the key folding area and reduces the risk of friction loss. The combination of segmented fixing and elastic insulation layer not only ensures the precise control of the wire path but also adapts to the dynamic bending requirements when the vest is worn. Through the simulation deployment and functional verification, it is ensured that the sensor maintains a stable signal acquisition ability during repeated tests, providing a hardware basis for long-term reliability evaluation.

[0041] This implementation method realizes the physical isolation between the sensor wiring and the airbag folding area through pre-planning the path, dynamic simulation verification, and structural adaptability design. The wire avoidance strategy combined with the application of flexible materials takes into account both the data acquisition accuracy and the equipment durability, overcoming the technical bottleneck of sensor failure caused by mechanical interference in traditional wiring, and providing guarantee for the accurate and long-term operation of the anti-fall vest test system.

[0042] In some embodiments, in S3, the calculation method of the pressure dispersion rate is: identify the decay rate of the area of the pressure peak region over time, and the determination basis of the protection response delay is the duration from the first trigger of the sensor to the airbag volume reaching the preset volume threshold.

[0043] 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 preprocessed 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 not good.

[0044] The determination of the protection response delay requires a combination of the sensor trigger signal and the frame analysis of the high-speed image. The first triggering moment of the (flexible piezoresistive) sensor is defined as the time point when the pressure value of a certain sensor node exceeds the preset threshold for the first time, and the system automatically records the timestamp. The high-speed image analyzes the airbag deployment process frame by frame, and calibrates the critical frame at which the airbag volume reaches the preset volume threshold through the image recognition algorithm. The judgment basis of the critical frame is that the airbag contour completely covers the preset protection area and the shape tends to be stable. The difference between the sensor first triggering timestamp and the corresponding timestamp of the critical frame is the protection response delay. For example, if the sensor is triggered at a certain moment after the impact, and the image shows that the airbag is fully deployed at a later moment, the time difference between the two directly reflects the synergistic efficiency of the triggering mechanism and the airbag inflation rate.

[0045] During the data analysis phase, the correlation between the pressure dispersion rate and the protection response delay is presented through multi-dimensional charts. The superposition analysis of the pressure dispersion rate curve and the response delay scatter plot can identify the shortcomings of the protection performance 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 trigger logic in this 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, marks abnormal data points and makes improvement suggestions, such as adjusting the response parameters of the airbag inflation valve or optimizing the sensor threshold setting.

[0046] This implementation method achieves a refined evaluation of protective effectiveness through the fusion analysis of dynamic pressure distribution and image timing. The calculation of 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 dimension of energy diffusion; the measurement of protective response delay reveals the degree of matching 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 anti-fall vests, helping designers to adjust material strength, airbag volume or sensor sensitivity in a targeted manner, thereby improving the comprehensive protection capabilities of the product in complex impact scenarios.

[0047] In some embodiments, the attitude adaptability evaluation index is obtained by statistically analyzing the pressure peak dispersion under multiple sets of preset attitudes, and the dispersion is quantified based on the standard deviation.

[0048] The calculation of the attitude adaptability evaluation index needs to be based on the pressure peak data of multiple sets of impact tests. Each set of tests corresponds to a combination of specific pitch angles and roll angles. For example, the forward tilt group includes multiple impact tests with increasing pitch angles, and the side fall group covers different roll angle parameters. In the data preprocessing stage, the system extracts the spatial distribution coordinates of the pressure peaks and the corresponding pressure values in each set of tests, and eliminates the outlier data points caused by sensor abnormalities.

[0049] The dispersion calculation uses the standard deviation to quantify the volatility of the data within the same group. Suppose a certain attitude group includes n tests, and each test records m pressure peak points. The pressure value of the jth peak point in the ith test is P ij , then the dispersion σ of this attitude group is calculated as:

[0050] where μ is the mean of all pressure peaks within the group:

[0051] P ij is the pressure value of the jth pressure peak point in the ith test; n is the number of tests in a certain attitude group; m is the number of pressure peak points recorded in each test; σ is the standard deviation, representing the data dispersion within the group.

[0052] After the standard deviation calculation is completed, the system normalizes the σ values of each attitude group and maps them to the scoring interval to generate a comparison chart.

[0053] The inter-group difference coefficient is used to measure the relative difference in dispersion between different attitude groups. Suppose the dispersions of the forward tilt group, the side fall group, and the backward tilt group are σ1, σ2, and σ3 respectively. The inter-group difference coefficient CV is calculated as:

[0054] where, is the mean of multiple standard deviations. The difference coefficient and the dispersion score are used to calculate the final attitude adaptability score through a weighted summation formula.

[0055] Quantifying the peak pressure dispersion under multiple groups of impact postures through the standard deviation can objectively reflect the differences in the protection stability of the anti-fall vest in different impact directions. The calculation of dispersion reveals the fluctuation characteristics of the pressure distribution from a statistical perspective, accurately identifies areas where the pressure is concentrated or abnormally dispersed under specific postures, and provides data basis for optimizing the airbag layout. The introduction of the inter-group difference coefficient further quantifies the deviation of the protection performance between different impact directions, helping designers to specifically strengthen the material strength or triggering logic in the weak direction. The multi-dimensional correlation analysis of the dispersion score and the difference coefficient breaks through the limitation of traditional tests that only focus on the protection efficiency of a single posture, constructs a comprehensive evaluation system for multi-scenario adaptability, and makes the optimization of the protection performance of the anti-fall vest more systematic and directional.

[0056] In some embodiments, in S4, the continuous inflation and deflation cycle includes alternately performing the operations of inflating the airbag to the rated pressure and completely discharging the pressure. The airtightness of the airbag is evaluated by the air pressure decay rate, and the stability of the sensor is evaluated by the sensitivity deviation rate.

[0057] The continuous inflation and deflation cycle test can be performed on a dedicated automated platform equipped with a pneumatic control module and a data acquisition system. At the start of the test, the anti-fall vest is fixed to a rigid bracket simulating the human torso, and the airbag is connected to the air pump pipeline through a quick connector. The air pump alternately performs the inflation and deflation operations according to a preset program. In the inflation stage, gas is injected into the airbag at a constant flow rate until the internal pressure reaches the rated pressure threshold, and in the deflation stage, the solenoid valve is opened to achieve rapid and complete exhaust. Each cycle includes a complete inflation-deflation process, and the number of cycles is set according to the test requirements. During the process, the air pressure sensor continuously monitors the change in the internal pressure of the airbag and records the decay curve.

[0058] The airtightness evaluation is achieved by analyzing the slope of the pressure decay curve. After the inflation reaches the rated pressure, the system closes the air pump and starts the pressure holding monitoring, recording the pressure drop amplitude per unit time. If the pressure decay rate continuously exceeds the baseline threshold, it indicates that there are minor leaks in the airbag or abnormal air permeability of the material. After multiple cycles, the system compares the morphological changes of the decay curves in different cycle stages to identify the decay trend of the sealing performance, such as a sudden increase in the decay rate or abnormal pressure fluctuations in the later cycles.

[0059] The sensor stability test is carried out synchronously with the inflation and deflation cycle. In each cycle, the expansion and contraction of the airbag will cause deformation of the sensor array, and the system records the output signal fluctuations of each sensor node under the same pressure conditions. By comparing the response values of the sensors at the rated pressure in the first cycle and subsequent cycles, the sensitivity deviation rate is calculated. If the deviation rate shows an upward trend with the increase in the number of cycles, it indicates that there are fatigue drift or contact impedance changes in the sensors. After the test, a sealing decay curve and a sensor deviation rate trend graph are generated to comprehensively evaluate the long-term use reliability of the anti-fall vest.

[0060] This implementation mode effectively exposes potential problems such as the aging of airbag materials, the fatigue cracking of seams, and the degradation of sensor performance by simulating the repeated charging and discharging conditions in actual use. The alternating charging and discharging operation combined with the dynamic monitoring mechanism can quantify the linear attenuation law of the sealing performance and the non-linear drift characteristics of the sensor, providing data support for predicting the product life. The efficient operation of the automated test platform and the data correlation analysis ensure that the evaluation results objectively reflect the performance stability of the anti-fall vest during its entire life cycle, realizing the evaluation of the long-term reliability of the anti-fall vest.

[0061] Optionally, before S1, it further includes: implanting an inertial measurement unit at the key force-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 the preset angle threshold, the current test data is discarded and the impact action is re-executed.

[0062] The implantation of the inertial measurement unit is planned according to the biomechanical characteristics of the standard test dummy. The key force-bearing parts of the dummy include the chest, shoulders, and hips, and these areas bear the main impact force and are prone to posture deviation during the impact. Custom cavities are opened under the surface buffer material of each selected part, and the cavity size matches the outer shape of the inertial measurement unit to ensure that the unit shell fits tightly with the internal structure of the dummy after implantation, avoiding displacement during impact. A shock-absorbing pad is laid inside the cavity to absorb the high-frequency vibration during the test and prevent the inertial measurement unit from being damaged due to instantaneous impact overload.

[0063] After the installation is completed, perform unit calibration and coordinate system alignment operations. During the calibration process, the standard test dummy is fixed on the horizontal reference platform, and its static attitude data is measured by an external high-precision attitude instrument and compared with the original signal output by the inertial measurement unit to calculate the three-axis acceleration and angular velocity deviation compensation parameters of each unit. The 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 direction of the dummy, the Y-axis corresponds to the sagittal plane direction, and the Z-axis is perpendicular upward. The calibration data is stored in the compensation database of the test system for real-time correction of the measurement results.

[0064] After the impact test is started, the inertial measurement unit continuously collects the 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 impact instant to the posture stable stage, and calculates the actual pitch angle and roll angle deviation of the standard test dummy after being impacted through the coordinate transformation algorithm. The actual angle is compared with the angle parameters of the preset impact posture. If the absolute value of the deviation exceeds the preset threshold, it is determined that the current test data is 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.

[0065] In the data verification process, the system generates an angular deviation trend chart to visually display the deviation degrees of the pitch angle and roll angle in each impact. The trend chart overlays and displays the boundary lines of the preset angle tolerance range, enabling the operator to intuitively identify the abnormal points where the deviation exceeds the threshold. For the test items that still cannot meet the angle requirements after multiple retries, the device self-check program is triggered to troubleshoot faults in the joint degrees of freedom of the bionic human body model, the fixing stability of the standard test dummy, or the signal transmission link of the inertial measurement unit.

[0066] Through the implanted inertial measurement unit and the dynamic compensation mechanism, this implementation mode realizes the real-time monitoring of the impact angle deviation and the verification of data validity. The accurate measurement of the key stress parts ensures that the angle data truly reflects the actual load-bearing situation of the fall protection vest, avoiding the distortion of test results caused by attitude deviation. The combination of the automatic retry mechanism and the fault troubleshooting program significantly improves the robustness of the test process and provides a high-confidence data basis for the evaluation of the protection performance.

[0067] In some implementation modes, the generation of the attitude adaptability evaluation index specifically includes: Classify the test results of multiple groups of preset postures according to the impact direction, and extract the spatio-temporal distribution data of the peak pressure in each posture; Calculate the dispersion degree of the peak pressure within the same type of posture group and the inter-group difference coefficient. The dispersion degree is quantified by the weighted value of the standard deviation and the coefficient of variation. Among them, the coefficient of variation is the ratio of the standard deviation to the mean of the peak pressure within the same type of posture group, which is used to eliminate the dimensional difference between different posture groups; Based on the dispersion degree and the difference coefficient, construct an attitude adaptability scoring model, and output a multi-dimensional protection efficiency comparison map. The comparison map shows the distribution relationship between the pressure dispersion rate and the response delay of different posture groups in the form of a heat map.

[0068] The generation of the attitude adaptability evaluation index is achieved through multi-step data processing and model construction. First, classify the test results of multiple groups of impact tests according to the impact direction, such as the forward tilt group, the side fall group, the backward tilt group, etc. Each group covers the test data with the same impact direction but different pitch angles or roll angles. After extracting the peak pressure and its spatio-temporal information of each group, the system automatically eliminates the abnormal data points, and then performs the following calculations: The within-group dispersion degree is quantified by the standard deviation σ, and the calculation formula is as in the above embodiment.

[0069] The within-group coefficient of variation further eliminates the dimensional difference and is calculated as:

[0070] The inter-group difference coefficient CV measures the performance balance of different impact directions, and the calculation formula is as in the above embodiment.

[0071] The scoring model generates a comprehensive score S through normalization and weighted summation 总 :

[0072] Among them, σ min and σ max are respectively the minimum and maximum values of the standard deviation of all groups; w1 and w2 are weight coefficients, and the default values of the weight coefficients are w1 = 0.6 and w2 = 0.4; the scoring result is a value between 0 and 1. A score of ≥0.8 indicates excellent protection, a score of 0.6 - 0.8 requires optimization for the dark areas of the heat map (for example, when the side fall direction score is 0.7, increase the density of the side waist airbag), and a score of <0.6 requires a redesigned (for example, replace the high-sensitivity sensor when the score is 0.5).

[0073] The result output includes a heat map and a radar chart. The horizontal axis of the heat map is the impact direction, and the color depth maps the dispersion score; the radar chart shows the dispersion and coefficient of variation of each group. Designers locate problems based on the low-score areas. For example, when the dark color is concentrated in the side fall direction of the heat map, check for airbag deployment delays or insufficient material rigidity, and verify through three iterative tests whether the optimized score has increased above 0.8.

[0074] In some embodiments, the influence 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 controllable temperature and humidity and left to stand still, and the environmental parameters simulate the real use scenario (such as high temperature and high humidity, low temperature and dryness). During the test, the environment is maintained constant, and dynamic pressure data and airbag deployment timings under different temperature and humidity combinations are collected. By comparing the data deviation between the reference environment (normal temperature and normal humidity) and the simulated environment, the environmental sensitivity coefficient K env is calculated as follows:

[0075] Among them, σ 基准 represents the standard deviation in the reference environment of normal temperature and normal humidity; σ 模拟 represents the standard deviation measured in the simulated environment (such as high temperature and high humidity); t 延迟，基准 represents the protection response delay time in the reference environment; t 延迟，模拟 represents the protection response delay time in the simulated environment.

[0076] The higher the environmental sensitivity coefficient, the greater the impact of the protection performance on the environment. The high-sensitivity coefficient areas are marked in the test report (such as when K env ≥0.3), and it is recommended to increase the environmental adaptability design (such as a hydrophobic coating on the airbag material).

[0077] In some embodiments, a virtual dummy parameter library can also be established based on user group body type data (height, weight, chest-waist ratio). Before the test, input the body type parameters of the target user, and automatically adjust the airbag fitting degree and impact contact area of the standard test dummy. During the impact process, based on the real-time pressure distribution data and the user body type matching degree, generate a personalized adaptation score S 适配 :

[0078] where P 实测 represents the pressure value distribution measured in the actual test, and P 预期 represents the ideal pressure distribution expected according to the user body type parameters.

[0079] When the score is lower than 0.7, prompt to adjust the vest size or add adjustable straps to ensure the matching of the pressure distribution and the user body type.

[0080] In some embodiments, a long-term performance decay model can also be established to quantify the combined effects of sensor drift, material fatigue, and seal degradation. The input of the model is the number of cyclic tests, and the output is the decay weights of each index:

[0081] where ΔS 总 represents the decay amount of the comprehensive score over time; Δσ, Δt 延迟 , ΔCV 组间 respectively represent the decay amounts of the standard deviation, the protection response delay time, and the coefficient of variation between groups; w3, w4, w5 are the weight coefficients of each factor, determined by historical data regression, and reflect the influence degree of different factors on the overall decay. The model predicts the score decay curve, and when the curve slope exceeds the threshold, trigger a maintenance prompt (such as replacing the sensor or strengthening the seam).

[0082] In some embodiments, a replaceable impact surface module can also be added to the existing impact test to simulate the influence of different ground materials on the protection performance. Before the test, install a standardized material panel at the contact end of the impact simulation device, including three types: hard (such as concrete), medium-hard (such as wooden floor), and soft (such as carpet). The surface texture and friction coefficient of the panel are calibrated with reference to the real ground material parameters. During the impact test, keep the impact speed and angle parameters consistent, and sequentially replace different material panels to perform the impact action in the same posture.

[0083] During the dynamic pressure data acquisition phase, the system compares the differences in peak pressure distributions under different materials. For example, when impacting a hard ground, the area of the peak pressure concentration region may shrink, but the peak pressure increases. The impact energy absorption effect of a soft ground may extend the pressure decay time. Synchronously analyze the timing images of airbag deployment to observe the influence of different materials on airbag trigger delay. For example, a hard ground may cause the airbag to trigger faster but not fully deploy. After the test, generate a material adaptability report, mark the pressure dispersion rate and the change range of response delay under each material, and put forward targeted improvement suggestions. For example, the vest used in high-incidence areas of hard ground needs to strengthen the shoulder buffer layer, and the corresponding product for soft ground can appropriately reduce the airbag inflation pressure to extend the deployment time.

[0084] By introducing a multi-material test scenario, this solution solves the limitation of existing methods that only target a single rigid impact surface, making the test results more in line with the actual usage environment of users. The standardized design and replaceable mechanism of the material panel ensure controllable test conditions and strong reproducibility, providing data support for the multi-scenario adaptation design of the anti-fall vest.

[0085] In some embodiments, a dynamic correlation model of core indicators such as pressure dispersion rate, response delay, and sealing performance can also be constructed to analyze the impact of their interactions on the overall protection performance. First, extract the numerical values of each indicator from historical test data, and calculate the correlation strength between two indicators through correlation analysis. For example, the pressure dispersion rate and the protection response delay may be negatively correlated (the higher the dispersion rate, the shorter the delay). Based on the correlation strength, construct an evaluation matrix to identify key influencing factors. For example, when the influence weight of sealing performance attenuation on the pressure dispersion rate is relatively high, it is necessary to monitor the airbag aging state preferentially.

[0086] During the test process, the system real-time monitors the change trends of each indicator. 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, synchronously increase the detection frequency of sealing performance and check whether there are leakage characteristics in the airbag pressure curve. The test report adds an "associated anomaly prompt" module to mark the deviation of high-correlation indicator groups. For example, "the pressure dispersion rate drops by 5% and the sealing performance attenuates by 3%" may indicate the collaborative failure of the sensor and the airbag. Optimize the test process according to the correlation analysis results. For example, adopt a parallel test strategy for high-correlation indicator groups to shorten the overall test cycle.

[0087] This solution improves the test efficiency and the accuracy of problem location by revealing the hidden correlations between indicators. The dynamic update mechanism of the evaluation matrix ensures that the model is continuously optimized with the accumulation of data, upgrading the protection performance analysis from isolated indicator evaluation to systematic collaborative evaluation, and providing a more comprehensive decision-making basis for product design.

[0088] Figure 4Schematic structural diagram of a fall - prevention vest protection performance testing system provided by an embodiment of the present invention, including: An impact module 401, configured to drive a bionic human body model through an impact simulation device with multi - degree - of - freedom adjustment, so as to impact the fall - prevention vest worn on a standard test dummy in a preset posture, and the preset posture is a combination of different pitch angles and roll angles; An acquisition module 402, configured 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 fall - prevention vest, and synchronously record the sequential images of the airbag deployment process of the fall - prevention vest; A generation module 403, configured to calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay in combination with the sequential images, and generate an attitude adaptability evaluation index according to the test results of multiple groups of preset postures; A monitoring module 404, configured to perform continuous inflation and deflation cycles on the fall - prevention vest, and monitor the airbag sealing performance and sensor stability.

[0089] The system provided by the embodiment of the present invention has the same technical features as the above - mentioned method, and thus may also have the same technical effects and solve the same technical problems, which will not be elaborated here.

[0090] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for testing the protective performance of a fall-proof vest, characterized in that, It includes the following steps: S1. Drive a bionic human model by an impact simulation device with multi - degree - of - freedom adjustment to impact a fall - proof vest worn on a standard test dummy in a preset posture, and the preset posture includes combinations of different pitch angles and roll angles; S2. Real - time collect dynamic pressure distribution data during the impact process through a flexible piezoresistive sensor array embedded in the inner layer of the fall - proof vest, and synchronously record the time - series images of the airbag deployment process of the fall - proof vest; S3. Calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay in combination with the time - series images, and generate an attitude adaptability evaluation index according to the test results of multiple groups of the preset postures; S4. Perform continuous inflation and deflation cycles on the fall - proof vest to monitor the airbag sealing performance and sensor stability.

2. The method for testing the protective performance of the anti-fall vest according to claim 1, characterized in that, In S1, the impact simulation device includes a six - axis robotic arm and a pneumatic buffer system. The impact speed of the bionic human model includes multiple gears, and the combination of different pitch angles and roll angles includes at least three basic postures: forward tilt, side fall, and backward tilt.

3. The anti-fall vest protection performance test method according to claim 1, characterized in that The flexible piezoresistive sensor array is arranged in a grid pattern on the inner lining layer of the fall - proof vest. The dynamic pressure distribution data includes the pressure values and timestamps of each grid node at the moment of impact, and the time - series images are aligned with the pressure values in a frame - synchronous manner through a camera device.

4. The anti-fall vest protection performance test method according to claim 3, wherein, The wiring path of the flexible piezoresistive sensor array avoids the airbag folding area of the fall - proof vest.

5. The method for testing the anti-fall vest protection performance according to claim 1, wherein, In S3, the calculation method of the pressure dispersion rate is: identify the decay rate of the area of the pressure peak region over time. The determination basis of the protection response delay is the duration from the first trigger of the sensor to the airbag volume reaching a preset volume threshold.

6. The method for testing the anti-fall performance of the anti-fall vest according to claim 5, wherein, The attitude adaptability evaluation index is obtained by statistically analyzing the pressure peak dispersion of multiple groups of the preset postures, and the dispersion is quantified based on the standard deviation.

7. The method for testing the anti-fall vest protection performance according to claim 1, wherein In S4, the continuous inflation and deflation cycle includes alternately performing the operations of inflating the airbag to the rated pressure and completely discharging the pressure. The airbag sealing performance is evaluated by the air pressure decay rate, and the sensor stability is evaluated by the sensitivity deviation rate.

8. The method for testing the anti-fall vest protection performance according to claim 1, wherein Before S1, it also includes: implanting an inertial measurement unit at the key force - bearing parts of the standard test dummy, and the inertial measurement unit is used to verify the impact angle deviation of the bionic human model; when the impact angle deviation exceeds a preset angle threshold, discard the current test data and re - execute the impact action.

9. The method for testing the protective performance of the anti-fall vest according to claim 6, characterized in that, The generation of the attitude adaptability evaluation index specifically includes: Classify the test results of multiple groups of the preset postures according to the impact direction, and extract the spatio - temporal distribution data of the pressure peaks in each posture; Calculate the dispersion of the pressure peaks within the same - type posture group and the inter - group difference coefficient. The dispersion is quantified by the weighted value of the standard deviation and the coefficient of variation. Among them, the coefficient of variation is the ratio of the standard deviation to the mean of the pressure peaks within the same - type posture group, which is used to eliminate the dimensional difference between different posture groups; Based on the dispersion and the difference coefficient, construct an attitude adaptability scoring model, and output a multi - dimensional protection efficiency comparison map. The comparison map shows the distribution relationship between the pressure dispersion rate and the response delay of different posture groups in the form of a heat map.

10. A fall-proof vest protection performance testing system for performing a fall-proof vest protection performance testing method according to any one of claims 1-9, characterized in that It includes: The impact module is used to drive a bionic human body model through an impact simulation device with multi-degree-of-freedom adjustment to impact a fall-proof vest worn on a standard test dummy in a preset posture, and the preset posture includes combinations of different pitch angles and roll angles; The acquisition module 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 fall-proof vest, and synchronously record the sequential images of the airbag deployment process of the fall-proof vest; The generation module is used to calculate the pressure dispersion rate based on the dynamic pressure distribution data, determine the protection response delay in combination with the sequential images, and generate attitude adaptability evaluation indicators according to the test results of multiple groups of the preset postures; The monitoring module is used to perform continuous inflation and deflation cycles on the fall-proof vest to monitor the airbag tightness and sensor stability.

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