Non-contact elderly-friendly handrail safety evaluation method and system

Abstract

This invention discloses a non-contact, age-friendly handrail safety evaluation method and system. The safety evaluation method includes: step S1: boundary condition identification and input; step S2: matching specific sub-databases; step S3: non-contact vibration detection; step S4: load-bearing capacity assessment and calculation; and step S5: safety level determination and result output. This method achieves low-cost, highly accurate, and easy-to-operate on-site safety evaluation of age-friendly handrails. Furthermore, database construction does not require physical testing, and the hierarchical storage and version update mechanism ensures practicality and scalability, significantly reducing promotion costs.

Description

Technical Field

[0001] This invention relates to the field of aging-friendly testing technology, and in particular to a non-contact aging-friendly handrail safety evaluation method and system. Background Technology

[0002] With the increasing aging of the population, the demand for age-friendly home renovations is surging. Handrails, as a critical safety feature, directly impact the safety of the elderly. Current testing technologies have the following prominent problems: 1. Uncertainty in boundary conditions leads to misjudgment. The vibration characteristics of a handrail structure depend not only on the damage state but are also significantly affected by boundary conditions such as material type, wall structure, and installation method. Under the same degree of damage, the natural frequencies of stainless steel handrails (E≈200GPa) and plastic handrails (E≈3GPa) may differ by 5-8 times; The effective stiffness of a solid wall support can differ from that of a lightweight partition wall support by more than 10 times. These boundary conditions lead to the risk of misjudging "similar frequencies but vastly different load-bearing capacities".

[0003] 2. The cost of building a full-combination calibration database is too high. Material type (4 types) × wall type (3 types) × installation method (3 types) × size range (3 types) × damage level (5 types) = 540 sets of basic calibration; Each group needs to undergo a static loading destructive test, with a cost of approximately 5,000 yuan per group and a total cost exceeding 2.7 million yuan. Furthermore, it is difficult to cover boundary conditions such as new materials and special walls.

[0004] 3. The contradiction between cost and reliability in purely automated identification The accuracy of recognition is affected by ambient light, surface stains, and background noise, and there is a risk of misjudgment; the algorithm is highly complex and has stringent requirements for edge computing units. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a non-contact age-friendly handrail safety evaluation method and system to overcome or at least partially solve the above problems.

[0006] According to one aspect of the present invention, a non-contact age-friendly handrail safety evaluation method is provided, the safety evaluation method comprising: Step S1: Boundary condition identification and input; Step S2: Perform specific sub-database matching; Step S3: Non-contact vibration detection; Step S4: Bearing capacity assessment and calculation; Step S5: Security level determination and result output.

[0007] Optionally, step S1: boundary condition identification and input specifically includes: Develop standardized on-site identification operating procedures: Material recognition: based on multi-sensory fusion judgment; Wall recognition: based on the sound of being struck; Installation type identification: based on structural observation; Geometric measurement: Use a measuring tape to measure the outer diameter and span, and classify them into three categories.

[0008] Optionally, step S2: performing specific sub-database matching specifically includes: Establish a hierarchical database structure with four-level indexes; Conservative strategy: When uncertainty arises, automatically select the worst-case combination; Virtual calibration construction of hierarchical databases.

[0009] Optionally, the virtual calibration construction of the hierarchical database specifically includes: Prior information extraction; Virtual vibration response calculation: Using the Euler-Bernoulli beam model combined with spring boundary conditions, the vibration characteristic parameters under different boundary conditions and damage states are calculated through analytical solutions or finite element methods. Monte Carlo simulation generates virtual samples; t-distribution fits statistical characteristics; Bayesian theory update calibration: Using experimental data as the prior, the posterior distribution is updated through Markov chain Monte Carlo sampling to optimize the mapping relationship between stiffness retention rate and bearing capacity, and finally the leaf nodes store the complete mapping.

[0010] Optionally, step S3: non-contact vibration detection specifically includes: Pneumatic micropulse excitation + laser vibration measurement technology is used: Excitation: A miniature air pump generates an airflow of 0.02-0.15MPa, a high-speed solenoid valve generates a 1-50Hz pulse, and the nozzle is 5-30mm away from the handrail; Data acquisition: Laser displacement sensor, working distance 10-30mm, resolution 0.001mm, accuracy ±0.5%; Signal processing: hardware RC filtering + software wavelet noise reduction, FFT frequency extraction, and logarithmic attenuation method for damping ratio and attenuation rate extraction.

[0011] Optionally, step S4: bearing capacity assessment and estimation specifically includes: Direct comparison: When the characteristic parameters fall within the calibration range, the calibration bearing capacity is directly used; Interpolation calculation: Based on Euler-Bernoulli beam theory, a quantitative relationship between the square of frequency and stiffness retention rate is established. k, Where f is the intact frequency and k is the stiffness retention rate, the bearing capacity is then calculated as Fest = k.Fultimate, 0; Fultimate,0 represents the ultimate bearing capacity under perfect condition, obtained through virtual calibration. Multi-parameter fusion: Combining the three parameters f, ξ, and α, a weighted voting method is used to reduce the risk of misjudgment based on a single parameter.

[0012] Optionally, step S5: security level determination and result output specifically includes: Establish a three-tiered judgment standard; The output information includes: boundary condition confirmation, measured values ​​of characteristic parameters, stiffness retention rate, estimated bearing capacity, safety level, and maintenance recommendations.

[0013] This invention also provides a non-contact age-friendly handrail safety evaluation system, which applies the aforementioned non-contact age-friendly handrail safety evaluation method. The safety evaluation system includes: The human-computer interaction module is used for boundary condition recognition and input; A hierarchical database storage module is used for matching specific sub-databases; Non-contact excitation module for non-contact vibration detection; Vibration signal acquisition and data processing module, used for load-bearing capacity assessment and calculation; The safety assessment module is used to determine the safety level and output the results.

[0014] This invention provides a non-contact safety evaluation method and system for age-friendly handrails. The safety evaluation method includes: step S1: boundary condition identification and input; step S2: matching specific sub-databases; step S3: non-contact vibration detection; step S4: load-bearing capacity assessment and calculation; and step S5: safety level determination and result output. This method achieves low-cost, highly accurate, and easy-to-operate on-site safety evaluation of age-friendly handrails. Furthermore, database construction does not require physical testing, and the hierarchical storage and version update mechanism ensures practicality and scalability, significantly reducing promotion costs.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a non-contact age-friendly handrail safety evaluation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a hierarchical database structure for establishing a four-level index, provided as an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0021] A safety evaluation method for age-friendly handrail structures based on the fusion of artificial boundary condition recognition and non-contact vibration detection includes the following steps: S1: Manual identification and input of boundary conditions Develop standardized on-site identification operating procedures to enable non-professionals to accurately determine boundary conditions: Material recognition: Based on multi-sensory fusion judgment (visual + tactile + auditory) Visual: Observe the color, luster, and texture (bright silver reflective stainless steel, matte plastic, and wood grain). Touch: Feel the surface temperature and hardness (metal is cool and hard, plastic is warm and soft). Auditory: Tap a coin lightly and listen to the pitch and duration. Wall recognition: Based on the knocking echo.

[0022] Solid wall: The sound is short and crisp, decaying completely in less than 0.1 seconds; Hollow wall: The sound is hollow and reverberates, decaying in 0.2-0.5 seconds; Lightweight walls: produce a hollow, muffled sound, and energy is quickly absorbed; Installation type identification: based on structural observation (counting the number and position of fixed ends, and feeling the strength of constraint by gently pushing). Geometric measurement: Use a measuring tape to measure the outer diameter and span, and classify them into three categories.

[0023] S2: Specific Subdatabase Matching Establish a hierarchical database structure with four-level indexes, such as... Figure 2 As shown.

[0024] Conservative strategy: When identification is uncertain, automatically select the most unfavorable combination (lowest elastic modulus material, lowest stiffness wall, most unfavorable installation method).

[0025] A method for constructing virtual calibration of hierarchical databases.

[0026] The hierarchical database of this invention does not require physical experiments and is constructed through a virtual calibration method of "public standards + literature data + statistical modeling". The specific steps are as follows: 1. Prior information extraction: Extract the statistical distribution of key parameters from publicly available technical standards (GB50009, JGJ145): Material parameters: Stainless steel E=200GPa, ρ=7850kg / m 3 ; Aluminum alloy E=70GPa, ρ=2700kg / m 3 ; Plastic E=3-5GPa, ρ=900-1200kg / m 3 Solid wood E=10-15GPa, ρ=500-700kg / m³ 3 .

[0027] Connection stiffness: For solid walls, expansion bolts k=106-107 N / m; for unreinforced hollow walls, k=10 4 -10 5 N / m; Wall stiffness: Solid concrete wall K_wall≈108N / m; Hollow partition wall K_wall≈106N / m; Masonry wall K_wall≈107N / m; Damping parameters: metal materials ξ=0.01-0.05; wood materials ξ=0.03-0.1; plastic materials ξ=0.05-0.12.

[0028] Virtual vibration response calculation: Using an Euler-Bernoulli beam model combined with spring boundary conditions, vibration characteristic parameters under different boundary conditions and damage states are calculated through analytical solutions or the finite element method. Calculation of moment of inertia of cross section Round tube handrail (outer diameter D, wall thickness t): Where I is the moment of inertia of the cross section, D is the outer diameter of the handrail, and t is the wall thickness of the square tube handrail (side length a, wall thickness t): Where 'a' is the side length of the square tube, and the other parameters are the same as those of the fixed beams at both ends of the circular tube (handrail span L): Parameter meanings: fn is the nth natural frequency, λn is the eigenvalue (λ1=4.730 when n=1, λ2=7.853 when n=2), E is the elastic modulus of the material, p is the material density, A is the cross-sectional area of ​​the handrail, and L is the single span length of the handrail.

[0029] Cantilever beam (one end fixed, one end free): Parameter meaning: The characteristic value λn is different from that of the beam with fixed ends (λ1=1.875 when n=1, λ2=4.694 when n=2), while the other parameters are the same.

[0030] Consider frequency correction for loose connections: Parameter meanings: f'n is the natural frequency in the loose state, keff is the equivalent connection stiffness after loosening, k0 is the connection stiffness in the intact state, keff=k0.(1—δ) (δ is the loosening coefficient, 0<δ<1). Relationship between damping ratio ξ and vibration amplitude attenuation: Parameter meaning: δ=ln Let Ai be the logarithmic decay rate, Ai be the i-th vibration peak, Ai+m be the (i+m)-th vibration peak, and m be the number of peak intervals. Vibration amplitude decay rate α: Parameter meanings: α is the amplitude decay rate, wn=2πf is the angular frequency, and the other parameters are the same as before.

[0031] 3. Monte Carlo simulation to generate virtual samples: Randomly sample from the parameter space (E,ρ,I,L,k,K_wall) according to the prior distribution to generate N=10 virtual samples. 4 -10 5 A set of virtual samples is used to cover all combinations of boundary conditions and damage levels. The natural frequency f, damping ratio ξ, and amplitude decay rate α of each set of samples are calculated.

[0032] (1) Parameter random sampling formula Normal distribution parameters (such as elastic modulus E, density p): X = μ + σ.Z Where X is the sampled value, μ is the mean of the parameters, σ is the standard deviation, and Z is a standard normally distributed random number (Z~N). 0 1 Uniformly distributed parameters (such as connection stiffness k, loosening coefficient δ): X = a + (b - a).U Where a is the lower limit of the parameter, b is the upper limit of the parameter, and U is a uniformly distributed random number (U~U0). 0 1 ).

[0033] (2) Sample validity screening formula Only samples that meet the actual engineering constraints are retained. Among them, F ult The minimum load-bearing capacity of the handrail is no less than 1.5kN, which is the ultimate load-bearing capacity of the handrail.

[0034] t-distribution fitting statistical characteristics: For each of the five damage levels of the boundary condition combination, the vibration parameter distribution of the virtual sample is fitted with a t-distribution to obtain the mean, standard deviation, and degrees of freedom of f, ξ, and α at each level, which are used as calibration data for the leaf nodes.

[0035] t-distribution probability density function Where x represents the vibration parameters (natural frequency f, damping ratio ξ, decay rate α), v represents the degrees of freedom (v = n - 1, where n is the number of valid samples at this level), and Γ . This is a gamma function.

[0036] (2) Estimation formula for distribution parameters Mean (location parameter): in Here, xi represents the parameter mean estimate, xi is the parameter value of the i-th sample, and n is the sample size; standard deviation (scale parameter): Degrees of freedom: =n-1 (For small samples, n=30~50; for large samples, n>100, the t-distribution approximates a normal distribution) Bayesian theory update calibration: Using a small amount of experimental data from published literature as priors, the posterior distribution is updated through Markov chain Monte Carlo (MCMC) sampling to optimize the mapping relationship between stiffness retention rate and bearing capacity, ensuring the reliability of calibration data. Finally, the leaf nodes store the complete mapping of "damage level → [f,ξ,α] distribution parameters → stiffness retention rate → reference bearing capacity".

[0037] Bayes' formula Where p θ|D p is the posterior distribution (the updated parameter distribution). D|θ p is the likelihood function (the degree to which the experimental data D supports the parameter θ). θ Given the prior distribution (parameter distribution from publicly available standards / documents), p D As evidence (normalized constant) The likelihood function (Gaussian likelihood) assumes that the error between the experimental data and the model predictions follows a normal distribution. Where m is the number of experimental data sets, di is the measurement value of the i-th experimental set, and y θ σe is the model prediction value, and σe is the standard deviation of the measurement error (taken as the repeatability standard deviation of the experimental data).

[0038] (3) Stiffness-bearing capacity mapping update formula The mapping relationship between stiffness retention rate k and bearing capacity F is optimized based on the posterior distribution: Where Fest is the updated estimated bearing capacity, Fult,0 is the ultimate bearing capacity in good condition, and is the variance of the logarithm of the stiffness retention rate (reflecting uncertainty). The final leaf node stores a complete mapping of “damage level → t-distribution parameters (mean, standard deviation, degrees of freedom) of [f,ξ,α] → stiffness retention rate → reference bearing capacity”. All data are calculated using the above formulas, without the need for physical testing, and meet the technical requirements of existing scientific literature and domestic and international standards.

[0039] S3: Non-contact vibration detection Employing mature pneumatic micropulse excitation + laser vibration measurement technology: Excitation: A miniature air pump generates an airflow of 0.02-0.15MPa, a high-speed solenoid valve generates a 1-50Hz pulse, and the nozzle is 5-30mm away from the handrail; Data acquisition: Laser displacement sensor, working distance 10-30mm, resolution 0.001mm, accuracy ±0.5%; Signal processing: hardware RC filtering + software wavelet noise reduction, FFT frequency extraction, and logarithmic attenuation method for damping ratio and attenuation rate extraction.

[0040] S4: Bearing Capacity Assessment and Calculation Direct comparison: When the characteristic parameters fall within the calibration range, the calibration bearing capacity is directly used; Interpolation calculation: Based on Euler-Bernoulli beam theory, a quantitative relationship between the square of frequency and stiffness retention rate is established. k, Where f is the frequency in good condition, k is the stiffness retention rate, and the calculated bearing capacity Fest = k.Fultimate,0 (Fultimate,0 is the ultimate bearing capacity in good condition, obtained from virtual calibration); Multi-parameter fusion: Combining the three parameters f, ξ, and α, a weighted voting method is used to reduce the risk of misjudgment based on a single parameter.

[0041] S5: Security Level Determination and Result Output Level 3 Judgment Criteria: The output information includes: boundary condition confirmation, measured values ​​of characteristic parameters, stiffness retention rate, estimated bearing capacity, safety level, and maintenance recommendations.

[0042] A modular design is used to evaluate the structural safety of age-friendly handrails, which integrates artificial boundary condition recognition and non-contact vibration detection. Human-computer interaction module: 3.5-inch touch screen, displaying material / wall / installation method selection interface, providing recognition assistance images and audio, supporting secondary confirmation and version update prompts; Tiered database storage module: It adopts a three-level storage architecture of "built-in Flash + external SPI Flash + SD card", which supports database burning and dual-mode updates; Non-contact excitation module: miniature air pump (flow rate 5L / min), high-speed solenoid valve (response <5ms), pressure regulating valve, filter, nozzle; Vibration signal acquisition module: laser displacement sensor (such as Keyence LK-G10 or equivalent domestic product), measuring range ±5mm, resolution 1μm; Data processing module: STM32F4 series MCU, 168MHz main frequency, hardware FPU supports FFT operation; Security evaluation module: Embedded evaluation algorithm, supports threshold comparison, interpolation calculation, conservatism evaluation and database integrity verification. Display and warning module: LCD digital display, three-color LED indicator, buzzer.

[0043] The database burning tool process specifically includes: 2.1 Composition of the burning tool Hardware: USB-SWD programmer (supports STM32 series), programming dock, power cord; Software: PC-based programming software (based on STM32CubeProgrammer secondary development), supporting Windows / Linux systems; Data files: index table file (index.bin), full quantum library package (subdb_package.bin), version information file (version.bin).

[0044] The burning process includes: Preparation phase: Install the PC-side burning software and import the database file package to be burned; Fix the motherboard of the device to be programmed onto the programming base, and connect the USB-SWD programmer to the PC; Open the burning software and select the burning mode (full burning / incremental burning).

[0045] 2. Full burning process (first burning): Step 1: The software automatically detects the motherboard MCU model (STM32F407) and establishes a communication connection; Step 2: Erase the database storage partition (0x08080000-0x080FFFFF) in the built-in Flash memory. Step 3: Write the index table file (index.bin) to the specified address and automatically verify the write result; Step 4: Write the commonly used sub-library files (50 sets) to the remaining space in the built-in Flash memory; Step 5: Write the full quantum library file package to the SD card (the SD card can be pre-installed during mass production). Step 6: Write the version information file and mark the burning process as complete; Step 7: Restart the motherboard and automatically verify the integrity of the database. If the burning is successful, it will return "PASS".

[0046] 3. Incremental programming process (updating some data): Step 1: The software reads the current database version information of the motherboard; Step 2: Compare the local update package with the motherboard data to identify the differences in the sub-databases; Step 3: Only erase and update the difference sub-database and index table, retaining other data; Step 4: Verify the update results and mark the update as complete.

[0047] 4. Handling programming errors: Communication failed: Check wiring and power supply, and try connecting again; Write failure: Automatically retry 3 times; if it still fails, mark it as defective. Verification failed: Erase the corresponding partition and re-flash the firmware; Burning Log: Automatically records the burning time, version, and result for each motherboard, and supports export.

[0048] Version update mechanism, supports local SD card updates: 3.1 Local SD Card Update (Offline Scenario) Update package preparation: Download the database update package (including updated sub-databases, new version index tables, and version information files) from official channels; extract the update package to the root directory of the SD card and name it "DB_UPDATE" folder.

[0049] Update Process: Step 1: Insert the SD card containing the update package into the device's SD card slot; Step 2: After powering on, the device automatically detects the update package on the SD card and reads the version information; Step 3: If the update package version is higher than the current version, the touchscreen displays an update prompt (including update content and the number of sub-databases); Step 4: After user confirmation, the device automatically disables other functions and begins the update; Step 5: Data is written in the order of "index table → frequently used sub-databases → rarely used sub-databases," and each file is verified after writing; Step 6: After the update is complete, the update package on the SD card is automatically deleted, and the device is restarted; Step 7: After restarting, the database integrity is verified. If the update is successful, the touchscreen displays "Database updated to Vx.x."

[0050] Error handling: Update interruption (e.g., power failure): Updates will automatically resume after restarting, and written data will not be lost; Data corruption: Automatic recovery using backup data, prompting the user to download the update package again; Version incompatibility: Update will be rejected, displaying "The current hardware does not support this version".

[0051] 3.2 Version Management Rules Version number format: Vx.y (x is the major version number, y is the minor version number) Major version number update: Incompatible with old version data, full update required; Sub-version update: Compatible with older versions, supports incremental updates; Backup mechanism: The current database is automatically backed up before the update, and can be rolled back to the old version if the update fails; Forced Update: When security-related data needs to be corrected, forced updates are supported to ensure detection accuracy.

[0052] Beneficial effects: Improved detection accuracy By explicitly handling boundary conditions and using a virtual calibration database, misjudgment scenarios involving "same frequency, different load-bearing capacity" are eliminated. Stainless steel cantilever handrails (f=45Hz, Fult=3kN) and plastic handrails with fixed ends (f=45Hz, Fult=1.2kN) are no longer confused; Actual measurement verification (based on publicly available data calibration): the bearing capacity estimation error decreased from ±30% to ±12%.

[0053] Costs significantly reduced 3. Extremely high operational feasibility Manual identification training time: <30 minutes; Increased operation time per test: <60 seconds; Non-professionals can complete this independently with a success rate of >95%. Supports offline detection, suitable for home scenarios without network access.

[0054] 4. Outstanding reliability and scalability Conservative assessment strategy: false negative rate <2%, significantly lower than the 8-15% of purely automated identification schemes; Comprehensive database management: Standardized programming tools ensure mass production consistency, and a dual-mode update mechanism supports continuous optimization; Highly scalable: When adding new materials / wall types, only the database needs to be updated, without any hardware modifications; Data security: Multiple verification and backup mechanisms prevent data corruption or loss.

[0055] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A non-contact, age-friendly handrail safety evaluation method, characterized in that, The safety evaluation method includes: Step S1: Boundary condition identification and input; Step S2: Perform specific sub-database matching; Step S3: Non-contact vibration detection; Step S4: Bearing capacity assessment and calculation; Step S5: Security level determination and result output.

2. The method for safety evaluation of non-contact age-friendly handrails according to claim 1, characterized in that, Step S1: Boundary condition identification and input specifically includes: Develop standardized on-site identification operating procedures: Material recognition: based on multi-sensory fusion judgment; Wall recognition: based on the sound of being struck; Installation type identification: based on structural observation; Geometric measurement: Use a measuring tape to measure the outer diameter and span, and classify them into three categories.

3. The method for safety evaluation of a non-contact age-friendly handrail according to claim 1, characterized in that, Step S2: Performing specific sub-database matching specifically includes: Establish a hierarchical database structure with four-level indexes; Conservative strategy: When uncertainty arises, automatically select the worst-case combination; Virtual calibration construction of hierarchical databases.

4. The method for safety evaluation of a non-contact age-friendly handrail according to claim 3, characterized in that, The virtual calibration construction of the hierarchical database specifically includes: Prior information extraction; Virtual vibration response calculation: Using the Euler-Bernoulli beam model combined with spring boundary conditions, the vibration characteristic parameters under different boundary conditions and damage states are calculated through analytical solutions or finite element methods. Monte Carlo simulation generates virtual samples; t-distribution fits statistical characteristics; Bayesian theory update calibration: Using experimental data as the prior, the posterior distribution is updated through Markov chain Monte Carlo sampling to optimize the mapping relationship between stiffness retention rate and bearing capacity, and finally the leaf nodes store the complete mapping.

5. The method for safety evaluation of a non-contact age-friendly handrail according to claim 1, characterized in that, Step S3: Non-contact vibration detection specifically includes: Pneumatic micropulse excitation + laser vibration measurement technology is used: Excitation: A miniature air pump generates an airflow of 0.02-0.15MPa, a high-speed solenoid valve generates a 1-50Hz pulse, and the nozzle is 5-30mm away from the handrail; Data acquisition: Laser displacement sensor, working distance 10-30mm, resolution 0.001mm, accuracy ±0.5%; Signal processing: hardware RC filtering + software wavelet noise reduction, FFT frequency extraction, and logarithmic attenuation method for damping ratio and attenuation rate extraction.

6. The method for safety evaluation of a non-contact age-friendly handrail according to claim 1, characterized in that, Step S4: Bearing capacity assessment and calculation specifically includes: Direct comparison: When the characteristic parameters fall within the calibration range, the calibration bearing capacity is directly used; Interpolation calculation: Based on Euler-Bernoulli beam theory, a quantitative relationship between the square of frequency and stiffness retention rate is established. k, where f is the optimal frequency and k is the stiffness retention rate, thus the bearing capacity is calculated as Fest = k.Fultimate, 0; Fultimate,0 represents the ultimate bearing capacity under perfect condition, obtained through virtual calibration. Multi-parameter fusion: Combining the three parameters f, ξ, and α, a weighted voting method is used to reduce the risk of misjudgment based on a single parameter.

7. The method for safety evaluation of a non-contact age-friendly handrail according to claim 1, characterized in that, Step S5: Security level determination and result output specifically includes: Establish a three-tiered judgment standard; The output information includes: boundary condition confirmation, measured values ​​of characteristic parameters, stiffness retention rate, estimated bearing capacity, safety level, and maintenance recommendations.

8. A non-contact age-friendly handrail safety evaluation system, employing the non-contact age-friendly handrail safety evaluation method described in any one of claims 1-7, characterized in that, The safety evaluation system includes: The human-computer interaction module is used for boundary condition recognition and input; A hierarchical database storage module is used for matching specific sub-databases; Non-contact excitation module for non-contact vibration detection; Vibration signal acquisition and data processing module, used for load-bearing capacity assessment and calculation; The safety assessment module is used to determine the safety level and output the results.

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