Simulated fall test method for high place work safety protection

Abstract

This application relates to the field of fall testing technology, specifically to a simulated fall testing method for safety protection during high-altitude operations. The method includes: real-time recording of various fall test data for different parts of a dummy during a simulated fall test using intelligent sensors; differential analysis of all data collected for each part under each fall test condition; mapping all differential data at the same moment to a three-dimensional space to analyze anomaly scores; using the anomaly scores to identify characteristic samples and dividing the instantaneous variable matrix into sub-matrices for different time intervals; analyzing the eigenvalues ​​of each row of data in each sub-matrix; analyzing the average impact force of each part and the comprehensive impact response value during the simulated fall test, and comparing them with preset safety standards to generate a mechanical analysis report. This application aims to accurately evaluate the dynamic performance of safety protection equipment during high-altitude operations.

Description

Technical Field

[0001] This application relates to the field of fall testing technology, and more specifically to a simulated fall testing method for safety protection in high-altitude operations. Background Technology

[0002] Working at heights is common in many fields today, such as building construction and power line installation. Because working at heights carries the risk of falls, which can cause serious injury or death, fall simulation testing technology for safety protection in high-altitude operations is crucial.

[0003] Currently, simulated fall testing technology has made some progress. In terms of hardware, simulated fall testing devices are constantly being updated and iterated. Some devices are equipped with high-precision intelligent sensors that can monitor key parameters such as speed, acceleration, and impact force in real time during the fall, providing accurate data for analyzing the performance of protective equipment. At the software algorithm level, related research focuses on optimizing the processing and analysis of sensor data, and by establishing mathematical models, more accurately evaluating the protective effect of safety equipment under different operating conditions.

[0004] However, current testing methods for evaluating the dynamic performance of protective equipment primarily focus on static performance indicators, such as the static tensile force of safety belts and the load-bearing capacity of safety nets, while neglecting the comprehensive cushioning performance assessment of protective equipment during dynamic fall events. In actual falls, the dynamic performance of protective equipment plays a crucial role in ensuring personnel safety. Existing testing technologies cannot accurately analyze the data collected from intelligent sensors during simulated tests, potentially leading to biased assessments of the actual protective effectiveness of the equipment and failing to provide sufficient safety guarantees for workers operating at heights. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a simulated fall test method for safety protection during high-altitude operations, thereby resolving the existing issues.

[0006] The simulated fall test method for safety protection in high-altitude operations proposed in this application adopts the following technical solution:

[0007] One embodiment of this application provides a simulated fall test method for safety protection in high-altitude operations, the method comprising the following steps:

[0008] S1, during the simulated fall test of the dummy, uses intelligent sensors to record various fall test data of different parts of the dummy in real time;

[0009] S2, obtain the difference sequence of all data collected for each part under each type of drop test data during the test process; use the difference sequence of all types of drop test data for each part as each row in the instantaneous variable matrix of each part;

[0010] S3, take each column of data in the instantaneous variable matrix as an instantaneous feature sample, and map all instantaneous feature samples into a three-dimensional space; obtain the abnormal score of each instantaneous feature sample in the three-dimensional space; obtain the balance coefficient of each part based on the difference value of the abnormal scores of all instantaneous feature samples between each part and all the remaining parts.

[0011] S4. Use the magnitude of the abnormal score to determine whether the instantaneous feature sample is a feature sample; divide the instantaneous variable matrix of each part into sub-matrices of different time intervals according to the feature samples in it; calculate the feature value of each row of data in each sub-matrix and form a feature vector; calculate the difference between the feature vectors of different rows in the sub-matrix, and take the average level of the difference calculated by all sub-matrices of each part as the impact response value of each part.

[0012] S5, the balance coefficient and impact response value of each part are positively integrated to form the comprehensive impact response coefficient of the corresponding part; the average impact force of each part is calculated using all the pressure data collected from each part; the average impact force is weighted and summed using the comprehensive impact response coefficient of each part to form the comprehensive impact response value in the simulated drop test process.

[0013] S6 compares the average impact force and comprehensive impact response value of each part with the preset safety standards to form a mechanical analysis report.

[0014] Preferably, the drop test data includes at least acceleration, angular velocity, and pressure data.

[0015] Preferably, the balance coefficient of the t-th part is denoted as x. t , Where m represents the number of test sites; l t and l v S(l) represents the anomaly scores of the instantaneous feature samples of the t-th and v-th parts at all acquisition times during the simulated drop test, respectively. t ,l v ) indicates l t and l v The difference between them.

[0016] Preferably, the condition for determining whether an instantaneous feature sample is a feature sample by using the magnitude of the abnormal score is: instantaneous feature samples with an abnormal score greater than 1 are taken as feature samples.

[0017] Preferably, the feature values ​​include at least the mean, variance, trend statistic, and deviation.

[0018] Preferably, the comprehensive impact response coefficient of each part is further determined by the product of the balance coefficient of the corresponding part and the impact response value.

[0019] Preferably, before weighting the average impact force using the comprehensive impact response coefficients of each part, the comprehensive impact response coefficients of each part are normalized.

[0020] Preferably, the mechanical analysis report includes, but is not limited to, the comprehensive impact response value, the average impact force at different locations, and the comparison results with preset safety standards.

[0021] Preferably, the preset safety standard is set to 6kN.

[0022] Preferably, after the mechanical analysis report is generated, the performance of the protective equipment is evaluated; if the overall impact response value and the average impact force of all parts do not exceed the preset safety standard, the protective performance is excellent; if the overall impact response value or the average impact force of a certain part exceeds the preset safety standard, the protective performance is unstable.

[0023] This application has at least the following beneficial effects:

[0024] This application addresses the current shortcomings in dynamic performance evaluation and low accuracy of data analysis for protective equipment. It analyzes the balanced response characteristics of triggering features at various locations during fall simulation tests using data collected by intelligent sensors. By comprehensively considering the overall response characteristics of fall test data, a comprehensive impact response coefficient is constructed for each location. This allows for accurate comparison of the balance differences in fall impact responses at different locations, thereby accurately evaluating the comprehensive impact response value of the fall simulation test. Combined with the average impact force obtained from pressure data, this is compared with safety standards. The beneficial effect is that it enables a more accurate evaluation of the dynamic performance of safety protective equipment during high-altitude operations, providing reliable technical support for ensuring the safety of personnel working at heights. Attached Figure Description

[0025] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of the simulated fall test method for safety protection of working at heights provided in this application;

[0027] Figure 2 A flowchart of the drop test evaluation method provided in this application. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the simulated fall test method for safety protection of high-altitude operations proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] The following description, in conjunction with the accompanying drawings, details the specific scheme of the simulated fall test method for safety protection of high-altitude operations provided in this application.

[0031] This application addresses the problem of low accuracy in simulated fall tests for safety protection during high-altitude operations by proposing a simulated fall test method for such safety protection. The method includes: setting up a test environment, preparing test equipment, simulating fall operations, and fall test evaluation steps to achieve fall simulation testing for safety protection during high-altitude operations.

[0032] Specifically, the following simulated fall test methods for safety protection in high-altitude operations are provided; please refer to [link / reference]. Figure 1 The method includes the following steps:

[0033] Step 1: Set up the test environment.

[0034] In a simulated work environment that meets the standards for working at heights, a test tower with a height no less than that commonly encountered in actual operations is erected. In this embodiment, the tower is set at a height of 15 meters. Its structure must be stable and robust enough to withstand simulated fall impacts. Safety netting is installed around the tower to prevent accidents from affecting the surrounding area. This environment simulates a real-world work at height scenario, ensuring that the fall test data reflects actual working conditions.

[0035] Step 2: Prepare the testing equipment.

[0036] Dummy Installation: A dummy equipped with high-precision smart sensors is secured to an airbag vest, which is connected to the safety harness. The sensors are distributed across the dummy's head, neck, spine, chest, and limbs to measure the impact force on each part during a fall. In this application, the smart sensors installed at each corresponding location include at least an acceleration sensor, an angular velocity sensor, and a pressure sensor, which collect acceleration, angular velocity, and pressure data for the respective location. The data collected from all locations are collectively referred to as fall test data. For example, installing an acceleration sensor on the head can accurately capture the change in acceleration at the moment of impact, providing data for evaluating the effectiveness of head protection.

[0037] Data acquisition system connection: Connect the sensors on the dummy to the data acquisition system via data cables to ensure stable data transmission. The data acquisition system needs to have high-frequency sampling capability; in this embodiment, it is set to sample 1000 times per second to accurately record instantaneous changes in impact force. Simultaneously, the system is calibrated to ensure data accuracy.

[0038] Step 3: Simulate a fall.

[0039] Initial position setting: Raise the dummy wearing the test protective equipment to the predetermined test height and adjust its posture to a normal working posture, such as standing or climbing, to ensure that it matches the actual working scenario.

[0040] Triggered fall: Using mechanical devices or simulating situations that could lead to a fall in actual operations (such as misstepping or rope breakage), the dummy is made to fall naturally. During the fall, ensure that the dummy is not subjected to any additional interference and falls freely.

[0041] Step 4: Drop test evaluation.

[0042] The flowchart of the drop test evaluation method in this application is attached. Figure 2 As shown, the specific steps include:

[0043] S1 records various drop test data of different parts of the dummy in real time during the simulated drop test.

[0044] The drop test data for each part includes at least acceleration, angular velocity, and pressure data. Simultaneously, high-speed cameras are used to capture the drop process from multiple angles, recording changes in the dummy's posture and the response of the protective equipment, providing intuitive data for subsequent analysis.

[0045] Because actual high-altitude operation scenarios are complex, data collection during the fall process using sensors is easily affected by environmental factors, resulting in low data quality. In this application, a filtering device is used to filter and reduce noise in the collected data. Specifically, the filtering device in this embodiment is a Wiener filter.

[0046] Furthermore, in order to accurately analyze the characteristics of fall simulation data during high-altitude operation safety protection, the preprocessed fall test data was analyzed. The specific calculation and analysis process is as follows:

[0047] In order to accurately analyze the differences in the triggering characteristics of airbags during a fall, as well as the instantaneous characteristics before and during triggering, this application conducts a comprehensive analysis of the instantaneous change characteristics at different times based on the simulated fall test data collected during the fall simulation process.

[0048] Specifically, a monitoring matrix for high-altitude operation fall simulation testing is constructed based on preprocessed simulated fall test data. In one embodiment of this application, the method for constructing the fall simulation test monitoring matrix is ​​as follows: each type of preprocessed fall test data is sorted according to the acquisition time order, forming a row of elements in the matrix; the monitoring matrix is ​​composed of different fall test data from top to bottom, and each column of data in the monitoring matrix corresponds to all types of fall test data collected at a given acquisition time. Based on the constructed monitoring matrix, the comprehensive instantaneous characteristics of the fall test data at different times are analyzed in depth.

[0049] S2, obtain the difference sequence of all data collected for each part under each type of drop test data during the test process; use the difference sequence of all types of drop test data for each part as each row in the instantaneous variable matrix of each part.

[0050] After constructing the monitoring matrix during the fall simulation test, it is necessary to analyze the comprehensive instantaneous characteristics of different parts during the fall process, and evaluate the performance of the fall simulation test safety protection method based on the analysis results. Specifically, since the fall test data collected at the initial test position only represents the initial state of the simulation test, the focus is on analyzing the comprehensive instantaneous characteristics at different times based on the changes in the fall test data at different times during the test after the initial test position.

[0051] Specifically, for each row of the monitoring matrix during the simulated drop test—that is, the sequence of drop test data sorted over time—the first-order difference sequence of all its elements is calculated. The first-order difference reflects the changes in drop test data at adjacent time points, thus highlighting the dynamic characteristics of the data. The matrix formed by arranging the first-order difference sequences of all row elements in their corresponding positions serves as the instantaneous variable matrix for each part. By analyzing this instantaneous variable matrix, the changing trends and magnitudes of various drop test data at different times can be clearly understood, thereby analyzing the comprehensive instantaneous characteristics during the drop process and accurately evaluating the performance of the safety protection methods used in the drop simulation test.

[0052] S3: Take each column of data in the instantaneous variable matrix as an instantaneous feature sample, and map all instantaneous feature samples into a three-dimensional space; obtain the abnormal score of each instantaneous feature sample in the three-dimensional space; and obtain the balance coefficient of each part based on the difference value of the abnormal scores of all instantaneous feature samples between each part and all the remaining parts.

[0053] Furthermore, each column of data in the instantaneous variable matrix is ​​treated as an instantaneous feature sample, and the corresponding data of all instantaneous feature samples are used as coordinate data in a three-dimensional spatial coordinate system, where the X-axis represents the instantaneous change in angular velocity, the Y-axis represents the instantaneous change in angular velocity, and the Z-axis represents the instantaneous change in pressure. By mapping all instantaneous feature samples into three-dimensional space, a precise analysis of the state-triggered feature balance is performed through the comprehensive characteristics of the instantaneous changes in drop test data at different times.

[0054] The mapping results of all instantaneous feature samples in three-dimensional space are used as input to obtain the anomaly score of each instantaneous feature sample. In this embodiment, the Local Outlier Factor (LOF) algorithm is used to obtain the LOF value of each instantaneous feature sample. The LOW algorithm is a well-known technique and will not be described in detail here. Based on the obtained LOF values ​​of the instantaneous feature samples, the triggering differences of the instantaneous comprehensive features of different parts at different times are analyzed to obtain the balance coefficient of each part during the simulated fall test. The balance coefficient x of the t-th part is used as the input. t For example, the specific calculation formula is as follows:

[0055]

[0056] Where m represents the number of test sites; l t and l v S(l) represents the anomaly scores of the instantaneous feature samples of the t-th and v-th parts at all acquisition times during the simulated drop test, respectively. t ,l v ) indicates l t and l vThe difference value between them, and the calculation method of the difference value includes, but is not limited to, Euclidean distance and DTW. It should be understood that the larger the balance coefficient obtained in the simulated drop test, the larger the deviation of the triggering characteristics of the current part, and the greater the difference in balance response during the drop test compared with the drop test data of other parts.

[0057] S4. Use the magnitude of the abnormal score to determine whether the instantaneous feature sample is a feature sample; divide the instantaneous variable matrix of each part into sub-matrices of different time intervals according to the feature samples in it; calculate the feature value of each row of data in each sub-matrix and form a feature vector; calculate the difference between the feature vectors of different rows in the sub-matrix, and take the average level of the difference calculated by all sub-matrices of each part as the impact response value of each part.

[0058] To further comprehensively evaluate the overall protective performance of different monitoring points during simulated fall tests for high-altitude operations, in addition to analyzing the response of each point to changes in impact force, it is also necessary to consider the balanced response characteristics of fall test data for each point during simulated fall monitoring; that is, the response changes of monitoring parameters at different points differ due to limb movements during the fall.

[0059] Specifically, based on the LOF values ​​of all instantaneous feature samples for each part obtained from the above analysis, the larger the LOF value, the greater the comprehensive change analysis of the fall test data for each part during the fall process, and the analysis of the change characteristics of fall test data with significant differences in trigger characteristics for the same part.

[0060] The condition for determining whether a momentary feature sample is a feature sample based on the magnitude of the anomaly score is: momentary feature samples with anomaly scores greater than 1 are considered feature samples. In other embodiments, other suitable values ​​can be selected to compare the magnitude of the anomaly scores to determine the feature samples.

[0061] Instantaneous feature samples with an LOF value greater than 1 are considered as significant trigger response feature samples during the fall. For the instantaneous variable matrix of each part, based on the column data corresponding to the significant trigger response feature samples, the instantaneous variable matrix is ​​divided into sub-matrices for different trigger response time intervals. The division is performed according to the column number of the instantaneous variable matrix from smallest to largest. Each feature sample is used as the last column of its sub-matrix. When the first column is a feature sample, all columns between the first column and the second feature sample in the instantaneous variable matrix (in ascending order of column number), as well as the first column and the second feature sample, are used as the first sub-matrix of the instantaneous variable matrix. The eigenvalues ​​of each row of data in each sub-matrix are calculated for each time interval. These eigenvalues ​​include at least the mean, variance, trend statistic, and bias. In this embodiment, the trend statistic is calculated using the Mannkendall trend verification algorithm. In other embodiments, Sen's slope estimation method can also be used to obtain the trend statistic. The Mannkendall trend verification algorithm is a well-known technique and will not be described further. In this embodiment, the deviation is set as the mean of all elements in the first-order difference sequence of each row of data arranged in chronological order. In other embodiments, the mean of the average value of each row of data can also be calculated, and the mean of the absolute values ​​of the differences between all data in each row and the mean value can be used as the deviation.

[0062] Furthermore, the vector formed by the eigenvalues ​​corresponding to each row of data is used as the eigenvector. The difference between the eigenvectors of different rows in the submatrix is ​​calculated. The calculation method for the difference includes, but is not limited to, Euclidean distance, Manhattan distance, and dot product. The larger the difference calculation result, the greater the difference in the characteristic changes of the same part in different trigger response time intervals during the fall simulation due to limb movement, and the greater the difference in the balance response during the fall.

[0063] To further reflect the impact of differences in balance response due to limb movement at the same body part on the overall impact force assessment during fall simulation testing, the differences in eigenvectors between different rows of the sub-matrix were calculated. The average level of these differences calculated across all sub-matrices for each body part was taken as the impact response value v for each body part during the fall simulation test. A larger impact response value indicates a greater difference in balance of the fall impact response due to limb movement during the simulated fall test, and the impact force on the current body part during the fall simulation may be more severe.

[0064] S5. The balance coefficient and impact response value of each part are positively integrated to form the comprehensive impact response coefficient of the corresponding part; the average impact force of each part is calculated using all the pressure data collected from each part; the average impact force is weighted and summed using the comprehensive impact response coefficient of each part to form the comprehensive impact response value in the simulated drop test process.

[0065] Based on the balance coefficient and impact response value of each part during the simulated drop test, the balance coefficient and impact response value of each part are positively integrated to form the comprehensive impact response coefficient of the corresponding part.

[0066] Taking the comprehensive impact response coefficient of the t-th part as an example, in this embodiment, it is determined by the product of the balance coefficient and the impact response value of the t-th part. The larger the calculated comprehensive impact response coefficient, the greater the difference in balance response and impact force change of the current part during the simulated drop test, and the more severe the impact force may be during the drop test.

[0067] Furthermore, the overall impact force characteristics are evaluated; based on this, this application combines mechanical analysis software to accurately evaluate the comprehensive protective performance during simulated drop testing. Specifically, in this embodiment, all pressure data collected from different locations are imported into ANSYS analysis software, and the ANSYS analysis software algorithm is used to calculate the peak impact force and duration of impact at each location; furthermore, the average impact force f at each location is calculated based on momentum change and duration of impact. The process of calculating the average impact force at each location using ANSYS analysis software is a well-known technique and will not be elaborated further. In other embodiments, ABAQUS analysis software can also be used to calculate the average impact force at each location.

[0068] Furthermore, the average impact force is weighted and summed using the comprehensive impact response coefficients of each part as the comprehensive impact response value in the simulated fall test process. The purpose is to accurately evaluate the comprehensive impact force buffering performance of the simulated fall test for safety protection of high-altitude operations by fully combining the trigger characteristic deviations of different parts with the balance response characteristics during the fall test.

[0069] Before weighting the average impact force using the comprehensive impact response coefficients of each part, the comprehensive impact response coefficients of each part are normalized.

[0070] In this embodiment, the formula for calculating the comprehensive impact response value during the simulated drop test is as follows: Where f i W represents the average impact force at the i-th location; iThis represents the normalized result of the comprehensive impact response coefficient of the i-th part. The normalization result is calculated using the normalization exponential function (Softmax function). Other suitable normalization algorithms can be selected in other embodiments.

[0071] S6 compares the average impact force and comprehensive impact response value of each part with the preset safety standards to form a mechanical analysis report.

[0072] Furthermore, based on the above calculations, the average impact force and comprehensive impact response value of each part are compared with the safety standard; wherein, the safety standard preset in this embodiment is 6kN, and other embodiments can be implemented by setting appropriate safety standard values ​​themselves.

[0073] Specifically, for example, if the average impact force on the head exceeds 6kN, the protective equipment is deemed to be inadequate for head protection, and a detailed mechanical analysis report is generated. The mechanical analysis report includes, but is not limited to, the comprehensive impact response value, the average impact force at different locations, and the comparison results with the preset safety standards.

[0074] The performance of protective equipment is evaluated using mechanical analysis. Specifically, after generating a mechanical analysis report, the performance of the protective equipment is assessed; if the overall impact response value and the average impact force of all parts do not exceed the preset safety standards, the protective performance is excellent; if the overall impact response value or the average impact force of a certain part exceeds the preset safety standards, the protective performance is unstable.

[0075] When the protective performance is unstable, it is necessary to clarify the direction of improvement, including but not limited to optimizing the deployment mechanism and adjusting the material to improve the fit, so as to provide a basis for the improvement or selection of subsequent protective equipment. The specifics are determined by the implementer.

[0076] The above technical features constitute the preferred embodiment of this application, which has strong adaptability and the best implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A simulated fall test method for safety protection in high-altitude operations, characterized in that, The method includes the following steps: S1, during the simulated fall test of the dummy, uses intelligent sensors to record various fall test data of different parts of the dummy in real time; S2, obtain the difference sequence of all data collected for each part under each type of drop test data during the test process; use the difference sequence of all types of drop test data for each part as each row in the instantaneous variable matrix of each part; S3, treat each column of data in the instantaneous variable matrix as an instantaneous feature sample, and map all instantaneous feature samples into a three-dimensional space; obtain the anomaly score of each instantaneous feature sample in the three-dimensional space; based on the difference in anomaly scores between each part and all remaining parts of all instantaneous feature samples, obtain the balance coefficient of each part; where the balance coefficient of the t-th part is denoted as... , Where m represents the number of test sites; and Let represent the anomaly scores of the instantaneous feature samples at all acquisition times during the simulated drop test for the t-th and v-th parts, respectively. express and The difference between them; S4. Use the magnitude of the abnormal score to determine whether the instantaneous feature sample is a feature sample; divide the instantaneous variable matrix of each part into sub-matrices of different time intervals according to the feature samples in it; calculate the feature value of each row of data in each sub-matrix and form a feature vector; calculate the difference between the feature vectors of different rows in the sub-matrix, and take the average level of the difference calculated by all sub-matrices of each part as the impact response value of each part. S5, the balance coefficient and impact response value of each part are positively integrated to form the comprehensive impact response coefficient of the corresponding part; the average impact force of each part is calculated using all the pressure data collected from each part; the average impact force is weighted and summed using the comprehensive impact response coefficient of each part to form the comprehensive impact response value in the simulated drop test process. S6 compares the average impact force and comprehensive impact response value of each part with the preset safety standards to generate a mechanical analysis report.

2. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The drop test data includes at least acceleration, angular velocity, and pressure data.

3. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The condition for determining whether an instantaneous feature sample is a feature sample based on the magnitude of the abnormal score is: instantaneous feature samples with an abnormal score greater than 1 are considered as feature samples.

4. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The characteristic values ​​include at least the mean, variance, trend statistics, and deviation.

5. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The comprehensive impact response coefficient of each part is further determined by the product of the balance coefficient of the corresponding part and the impact response value.

6. The simulated fall test method for safety protection of working at heights as described in claim 5, characterized in that, Before weighting the average impact force using the comprehensive impact response coefficients of each part, the comprehensive impact response coefficients of each part are normalized.

7. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The mechanical analysis report includes the overall impact response value, the average impact force at different locations, and the comparison results with preset safety standards.

8. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, The preset safety standard is set to 6kN.

9. The simulated fall test method for safety protection of high-altitude operations as described in claim 1, characterized in that, After generating the mechanical analysis report, the performance of the protective equipment is evaluated. If the overall impact response value and the average impact force of all parts do not exceed the preset safety standard, the protective performance is excellent. If the overall impact response value or the average impact force of a certain part exceeds the preset safety standard, the protective performance is unstable.

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