Simulated falling test method for high-place operation safety protection

Abstract

The invention relates to the technical field of falling test, in particular to a falling simulation test method for high-place operation safety protection, and the method comprises the steps: recording various falling test data of each part of a dummy in a dummy falling simulation test process in real time through an intelligent sensor; carrying out difference on all data collected by each part under each kind of falling test data; all differential data at the same moment are mapped into a three-dimensional space to analyze abnormal scores; judging a feature sample by using the abnormal score, and dividing the instantaneous variable matrix into sub-matrixes of different time intervals; analyzing the characteristic value of each row of data in each sub-matrix; and analyzing the average impact force of each part and the comprehensive impact response value in the simulated falling test process, and comparing the average impact force and the comprehensive impact response value with preset safety standards to form a mechanical analysis report. The invention aims to accurately evaluate the dynamic performance of the safety protection equipment during high-place operation.

Description

Technical Field

[0001] This application relates to the technical field of fall testing, and particularly to a simulated fall testing method for safety protection in high-altitude operations. Background Art

[0002] In many fields such as current construction and power line laying, high-altitude operations are very common. Due to the risk of personnel falling in high-altitude operations, which may cause serious casualties, the simulated fall testing technology for high-altitude operation safety protection is crucial.

[0003] At present, certain progress has been made in the simulated fall testing technology. In terms of hardware devices, the simulated fall testing devices are continuously updated. Some devices are equipped with high-precision intelligent sensors that can real-time monitor key parameters such as speed, acceleration, and impact force during the fall process, providing accurate data for analyzing the performance of protective equipment. At the software algorithm level, related research is committed to optimizing the processing and analysis of sensor data. By establishing mathematical models, the protective effects of safety protection equipment under different working conditions can be more accurately evaluated.

[0004] However, in the evaluation of the dynamic performance of protective equipment, current testing methods mostly focus on static performance indicators, such as the static tensile force of safety belts and the load-bearing capacity of safety nets, while the evaluation of the comprehensive buffering performance of protective equipment during the dynamic fall process is insufficient. In actual falls, the dynamic performance of protective equipment plays a key role in ensuring personnel safety. The existing testing technology cannot accurately analyze the intelligent sensor data collected during the simulated testing process, which may lead to deviations in the evaluation of the actual protective effect of protective equipment and cannot provide sufficient safety protection for high-altitude operators. Summary of the Invention

[0005] To solve the above technical problems, this application provides a simulated fall testing method for safety protection in high-altitude operations to solve the existing problems.

[0006] The simulated fall testing method for safety protection in high-altitude operations of this application adopts the following technical solutions:

[0007] An embodiment of this application provides a simulated fall testing method for safety protection in high-altitude operations, and this method includes the following steps:

[0008] S1, during the simulated fall testing of a dummy, various fall testing data of each part of the dummy are recorded in real time through intelligent sensors;

[0009] S2, obtain the difference sequence for all the data collected for each part under each type of fall testing data during the testing process; use the difference sequences obtained for all types of fall testing data of 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 the 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 value of the anomaly scores of all the instantaneous feature samples between each part and all the remaining parts, obtain the balance coefficient of each part.

[0011] S4. Use the numerical value of the anomaly score to judge 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 therein; calculate the eigenvalues of each row of data in each sub-matrix respectively to form eigenvectors; calculate the difference between the eigenvectors of different rows in the sub-matrix, and take the average level of the calculated differences of all the sub-matrices of each part as the impact response value of each part.

[0012] S5. Take the result of the positive fusion of the balance coefficient and the impact response value of each part as the comprehensive impact response coefficient of the corresponding part; calculate the average impact force of each part by using all the pressure data collected by each part; take the result of the weighted sum of the average impact force of each part by using the comprehensive impact response coefficient of each part as the comprehensive impact response value during the simulated fall test.

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

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

[0015] Preferably, denote the balance coefficient of the t-th part as x t , where m represents the number of parts tested; l t and l v respectively represent the anomaly scores of the instantaneous feature samples at all the acquisition moments during the simulated fall test of the t-th and v-th parts, and S(l t , l v ) represents the difference value between l t and l v .

[0016] Preferably, the condition for using the numerical value of the anomaly score to judge whether the instantaneous feature sample is a feature sample is: take the instantaneous feature sample with the anomaly score greater than the value 1 as the feature sample.

[0017] Preferably, the eigenvalues include at least mean value, 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 and the impact response value of the corresponding part.

[0019] Preferably, before weighting the average impact force by using the comprehensive impact response coefficient of each part, the comprehensive impact response coefficient of each part is normalized.

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

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

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

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

[0024] Aiming at the problems of insufficient dynamic performance evaluation and low data analysis accuracy of current protective equipment, the present application analyzes the balance response characteristics of trigger characteristics in each part during the falling simulation test through the data collected by intelligent sensors, comprehensively considers the comprehensive response characteristics of the falling test data, constructs the comprehensive impact response coefficient of each part, and then accurately compares the balance differences of the impact responses of different parts to the falling impact, and further accurately evaluates the comprehensive impact response value of the falling simulation test. Combining with the average impact force obtained from the pressure data and comparing it with the safety standard together, the beneficial effect is that it can more accurately evaluate the dynamic performance of the safety protection equipment during high-altitude operations, providing reliable technical support for ensuring the life safety of high-altitude operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a flowchart of the simulated falling test method for high-altitude operation safety protection provided by the present application;

[0027] Figure 2 It is a flowchart of the falling test evaluation method provided by the present application. Specific Embodiment

[0028] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on the specific embodiment, structure, features, and effects of the simulated fall test method for high-altitude operation safety protection proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the 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 those skilled in the technical field to which this application belongs.

[0030] The following will specifically describe the specific solution of the simulated fall test method for high-altitude operation safety protection provided by this application in conjunction with the accompanying drawings.

[0031] In response to the problem of low accuracy in the simulated fall test for high-altitude operation safety protection, this application proposes a simulated fall test method for high-altitude operation safety protection, which specifically includes: building a test environment, preparing test equipment, performing a simulated fall operation, and evaluating the fall test to achieve a fall simulation test for high-altitude operation safety protection.

[0032] Specifically, the following simulated fall test method for high-altitude operation safety protection is provided. Please refer to Figure 1 , and this method includes the following steps:

[0033] Step 1: Build a test environment.

[0034] In a simulated site that meets the high-altitude operation standards, build a test tower with a height not lower than the common height in actual operations. In this embodiment, the built height is set to 15 meters, and its structure needs to be stable and firm to withstand the simulated fall impact. Set up a safety protection net around the tower to prevent accidents from affecting the surrounding area. This environment simulates a real high-altitude operation scenario to ensure that the fall test data can reflect the actual working conditions.

[0035] Step 2: Prepare test equipment.

[0036] Dummy installation: Fix a dummy equipped with high-precision intelligent sensors in an airbag vest, where the airbag vest is connected to a seat belt; specifically, the sensors should be distributed at key parts of the dummy's head, neck, spine, chest, and limbs to measure the impact force received by each part during the fall. In this application, the types of intelligent sensors installed at each corresponding part at least include acceleration sensors, angular velocity sensors, and pressure sensors, which respectively collect acceleration data, angular velocity data, and pressure data of the corresponding parts. The data collected from each part is collectively referred to as fall test data. For example, installing an acceleration sensor on the head can accurately capture the acceleration changes at the moment of impact and provide data for evaluating the head protection effect.

[0037] Data acquisition system connection: Connect the sensors on the dummy to the data acquisition system through data lines to ensure stable data transmission. The data acquisition system needs to have the ability of high-frequency sampling. In this embodiment, it is set to sample 1000 times per second to accurately record the instantaneous impact force changes. At the same time, calibrate the system to ensure data accuracy.

[0038] Step three: Simulate the fall operation.

[0039] Initial position setting: Lift the dummy wearing the test protection equipment to the predetermined test height and adjust its posture to a normal working posture, such as a standing or climbing posture, to ensure it is consistent with the actual working scenario.

[0040] Trigger the fall: Through a mechanical device or simulate the situations that may cause a fall in actual operations (such as simulating stepping on air and rope breakage), make the dummy fall in a free-fall manner. During the fall, ensure that the dummy is not subject to additional interference and falls freely.

[0041] Step four: Fall test evaluation.

[0042] In this application, the flow chart of the fall test evaluation method is as shown in the appendix Figure 2 and specifically includes the following steps:

[0043] S1, during the dummy's simulated fall test process, record various fall test data of each part of the dummy in real time.

[0044] The fall test data of each part at least includes acceleration, angular velocity, and pressure data. At the same time, use a high-speed camera to shoot the fall process from multiple angles to record the dummy's posture changes and the response of the protection equipment, providing intuitive materials for subsequent analysis.

[0045] Since the actual high-altitude operation scenario is relatively complex, when collecting data during the falling process through the set sensors, it is easily interfered by environmental factors, resulting in low-quality collected data. In this application, the collected data is filtered and denoised through a filtering device. Specifically, the filtering device set in this embodiment is a Wiener filter.

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

[0047] To accurately analyze the triggering characteristic differences of the airbag during the falling process, as well as the instantaneous characteristics before and during triggering, this application conducts a comprehensive analysis of the instantaneous change characteristics at different times for the simulated falling test data collected during the falling simulation process.

[0048] Specifically, based on the preprocessed simulated falling test data, a monitoring matrix for the high-altitude operation falling simulation test is constructed. In an implementation manner of this application, the method for constructing the falling simulation test monitoring matrix is as follows: Sort each type of preprocessed falling test data according to the collection time sequence so that it forms a row element in the matrix; the monitoring matrix is composed of different falling test data from top to bottom in sequence, and each column of data in the monitoring matrix corresponds to all types of falling test data collected at a collection moment. Based on the constructed monitoring matrix, an in-depth analysis of the comprehensive instantaneous characteristics of the falling test data at different times is carried out.

[0049] S2. Obtain the difference sequence for all the data collected at each part under each type of falling test data during the test; use the difference sequences obtained from all types of falling test data for each part as each row in the instantaneous variable matrix of each part.

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

[0051] Specifically, for each row element of the monitoring matrix during the simulated fall test, that is, the sequence of each type of fall test data sorted by time, calculate the first-order difference sequence of all its elements. The first-order difference can reflect the changes in the fall test data at adjacent time points, thereby highlighting the dynamic change characteristics of the data. The matrix formed by arranging the first-order difference sequences of all row elements in corresponding positions is used as the instantaneous variable matrix for each part. By analyzing this instantaneous variable matrix, the change trends and amplitudes of various fall test data at different times can be clearly understood, and then the comprehensive instantaneous characteristics during the fall process can be analyzed to accurately evaluate the performance of the safety protection method in the fall 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 anomaly score of each instantaneous feature sample in the three-dimensional space; based on the difference values of the anomaly scores of all instantaneous feature samples between each part and the remaining all parts, obtain the balance coefficient of each part.

[0053] Furthermore, take each column of data in the instantaneous variable matrix as an instantaneous feature sample, and use the corresponding data of all instantaneous feature samples as the coordinate data in the three-dimensional space coordinate system, where the X coordinate is the instantaneous change amount of the angular velocity, the Y-axis coordinate is the instantaneous change amount of the angular velocity, and the Z-axis coordinate is the instantaneous change amount of the pressure. Map all instantaneous feature samples into a three-dimensional space, and conduct a precise analysis of the balance of the state trigger features through the comprehensive instantaneous characteristics of the fall test data at different times.

[0054] Take the mapping results of all instantaneous feature samples in the three-dimensional space as the input, and obtain the anomaly score of each instantaneous feature sample. In this embodiment, the local outlier factor algorithm is used to obtain the LOF value of each instantaneous feature sample. The local outlier factor algorithm is a well-known technology and will not be elaborated here. Based on the obtained LOF values of the instantaneous feature samples, analyze the triggering differences of the instantaneous comprehensive characteristics of different parts at different times, and obtain the balance coefficient of each part during the simulated fall test. Taking the balance coefficient x of the t-th part as an example, the specific calculation formula is as follows: t For example, the specific calculation relationship is as follows:

[0055]

[0056] Among them, m represents the number of parts tested; l t and l v respectively represent the anomaly scores of the instantaneous feature samples of the t-th and v-th parts at all acquisition times during the simulated fall test, and S(l t , l v ) represents l t and l vThe difference value between them, and the calculation methods of the difference value include, but are not limited to, Euclidean distance and DTW. It should be understood that the greater the balance coefficient calculated during the simulated fall test, the greater the deviation of the triggering characteristics of the current part, indicating a significant difference in the balance response during the fall test data of other parts compared to the fall test data of this part.

[0057] S4. Use the numerical value of the anomaly 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 therein; calculate the eigenvalues of each row of data in each sub-matrix respectively to form eigenvectors; calculate the differences between the eigenvectors of different rows in the sub-matrix respectively, and use the average level of the differences calculated for all sub-matrices of each part as the impact response value of each part.

[0058] To further comprehensively evaluate the comprehensive protection performance of different monitoring parts during the simulated fall test of high-altitude operation safety protection, in addition to analyzing the response of the impact force change of each part, it is also necessary to combine the balance response characteristics of the fall test data of each part during the simulated fall monitoring process; that is, there are differences in the response changes of the monitoring parameters of different parts due to limb activities during the fall process.

[0059] Specifically, based on the LOF values of all instantaneous feature samples of each part obtained from the above analysis, if the LOF value is larger, it indicates an analysis of the change characteristics of the fall test data with significant triggering feature differences for the same part by comprehensively analyzing the comprehensive changes of the fall test data of each part during the comprehensive fall process.

[0060] Among them, the condition for using the numerical value of the anomaly score to determine whether the instantaneous feature sample is a feature sample is: taking the instantaneous feature sample with an anomaly score greater than the value 1 as the feature sample. In other embodiments, other appropriate values can also be selected to compare the size of the anomaly score to determine the feature sample.

[0061] The instantaneous feature samples with LOF values greater than 1 are used as the feature samples with significant trigger responses during the falling process. For the instantaneous variable matrix of each part, according to the column data corresponding to the feature samples with significant trigger responses, the instantaneous variable matrix is divided into sub-matrices of different trigger response time intervals. Among them, the division is carried out in ascending order of the column numbers of the instantaneous variable matrix, and each feature sample is used as the last column data of the sub-matrix after its division. When the first column is a feature sample, all the columns included between the first column and the second feature sample in the ascending order of the column numbers in the instantaneous variable matrix, as well as the first column and the second feature sample, are used as the first sub-matrix divided from the instantaneous variable matrix. Calculate the eigenvalues of each row of data in each sub-matrix in each time interval respectively. The eigenvalues at least include the mean value, variance, trend statistic, and deviation. Among them, the trend statistic is calculated by the Mannkendall trend test algorithm in this embodiment. In other embodiments, the Sen's slope estimation method can also be used to obtain the trend statistic. The Mannkendall trend test algorithm is a well-known technology and will not be elaborated here. The deviation is set as the mean value of all elements of the first-order difference sequence formed by each row of data in chronological order in this embodiment. In other embodiments, the average value of each row of data can also be calculated, and the mean value of the absolute values of the differences between all the data in each row and the average value is used as the deviation.

[0062] Further, the vectors formed by the eigenvalues corresponding to each row of data are used as feature vectors, and the differences between the feature vectors of different rows in the sub-matrix are calculated respectively. The calculation methods of the differences include but are not limited to Euclidean distance, Manhattan distance, and dot product. The larger the calculation result of the difference is, the greater the difference in the feature changes of different falling test data caused by limb activities in different trigger response time intervals during the falling simulation process, and the greater the difference in the balance response during the falling process.

[0063] To further reflect the influence of the difference in the balance response caused by limb activities in the same part during the falling simulation test process on the comprehensive impact force assessment, the differences between the feature vectors of different rows in the sub-matrix are calculated respectively, and the average level of the differences calculated for all sub-matrices of each part is used as the impact response value v during the falling simulation test process of each part. The larger the impact response value is, the greater the balance difference in the change of the falling impact response caused by limb activities during the simulation test falling process, and the more serious the influence of the impact force on the current part during the falling simulation process may be.

[0064] S5. The result of positively fusing the balance coefficients and impact response values of each part is used as the comprehensive impact response coefficient of the corresponding part. Calculate the average impact force of each part using all the pressure data collected from each part. The result of weighted summation of the average impact force using the comprehensive impact response coefficients of each part is used as the comprehensive impact response value during the simulated fall test.

[0065] Based on the balance coefficients and impact response values of each part during the simulated fall test, the result of positively fusing the balance coefficients and impact response values of each part is used as the comprehensive impact response coefficient of the corresponding part.

[0066] Among them, 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 balance response and the difference in impact force change of the current part during the simulated fall test, and the more serious the impact force influence during the fall test may be.

[0067] Furthermore, evaluate the overall impact force action characteristics. Accordingly, this application combines a mechanical analysis software to accurately evaluate the comprehensive protection performance during the simulated fall test. Specifically, in this embodiment, all the pressure data collected from different parts are imported into the ANSYS analysis software, and the peak impact force and action time of each part are calculated using the ANSYS analysis software algorithm. Further, the average impact force f of each part is calculated according to the momentum change and action time. The process of calculating the average impact force of each part using the ANSYS analysis software is a well-known technology and will not be elaborated here. In other embodiments, the ABAQUS analysis software can also be used to calculate the average impact force of each part.

[0068] Furthermore, the result of weighted summation of the average impact force using the comprehensive impact response coefficients of each part is used as the comprehensive impact response value during the simulated fall test. Its purpose is to fully combine the trigger characteristic deviations of different parts for the balance response characteristics during the fall test and accurately evaluate the comprehensive impact force buffering performance of the simulated fall test for high-altitude operation safety protection.

[0069] Among them, before weighting the average impact force using the comprehensive impact response coefficients of each part, normalize the comprehensive impact response coefficients of each part.

[0070] In this embodiment, the specific calculation formula for the comprehensive impact response value during the simulated fall test is: Where f i represents the average impact force of the i-th part; W iIt represents the normalization result of the comprehensive impact response coefficient of the i-th part. The calculation method of the normalization result uses the normalization exponential function (Softmax function) for normalization processing. In other embodiments, other suitable normalization algorithms can be selected.

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

[0072] Furthermore, after the above calculations, compare the calculation results of the average impact force of each part and the comprehensive impact response value with the safety standards. Among them, the preset safety standard in this embodiment is set to 6 kN. In other embodiments, the implementer can set appropriate safety standard values by themselves.

[0073] Specifically, for example, if the average impact force on the head exceeds 6 kN, it is determined that the protection of the protective equipment for the head does not meet the standard, and a detailed mechanical analysis report is formed. Among them, the mechanical analysis report includes, but is not limited to, the comprehensive impact response value, the average impact force of different parts, and the comparison results with the preset safety standards respectively.

[0074] Combined with the mechanical analysis, evaluate the performance of the protective equipment. Specifically, after the mechanical analysis report is formed, evaluate the performance of the protective equipment. If both the comprehensive impact response value and the average impact force of all parts do not exceed the preset safety standards, the protection performance is excellent. If there is a comprehensive impact response value or the average impact force of a certain part exceeds the preset safety standards, the protection performance is unstable.

[0075] When the protection performance is unstable, it is necessary to clarify the improvement direction, including but not limited to optimizing the deployment mechanism and adjusting the material to improve the fitting degree, providing a basis for the improvement or selection of subsequent protective equipment, which is specifically set by the implementer himself.

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

Claims

1. A simulated fall test method for safety protection of high-altitude operations, characterized in that: The method comprises the following steps: S1, during the simulated fall test of the dummy, various fall test data of various parts of the dummy are recorded in real time through intelligent sensors; S2, obtaining a differential sequence for all data collected from each part under each drop test data during the test process; using the differential sequence obtained from all types of drop test data for each part as each row in the instantaneous variable matrix of each part; S3, taking each column of data in the instantaneous variable matrix as an instantaneous feature sample, mapping all instantaneous feature samples into the three-dimensional space; obtaining the abnormality score of each instantaneous feature sample in the three-dimensional space; obtaining the balance coefficient of each part based on the difference value of the abnormality score of all instantaneous feature samples between each part and all remaining parts; S4, using the numerical value of the abnormal score to determine whether the instantaneous feature sample is a feature sample; dividing the instantaneous variable matrix of each part into sub-matrices of different time intervals according to the feature samples therein; respectively calculating the eigenvalue of each row of data in each sub-matrix and forming a eigenvector; respectively calculating the difference of the eigenvectors between different rows in the sub-matrix, and taking the average level of the difference calculated by all sub-matrices of each part as the impact response value of each part; S5, forward fusion of the balance coefficient and impact response value of each part as the comprehensive impact response coefficient of the corresponding part; using all the pressure data collected from each part to calculate the average impact force of each part; using the comprehensive impact response coefficient of each part to perform weighted summation of the average impact force as the comprehensive impact response value in the simulated fall test process; S6, compare the average impact force and comprehensive impact response value of each part with the preset safety standards to form a mechanical analysis report.

2. The simulated fall test method for safety protection of high-altitude operations according to 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 according to claim 1, characterized in that: The balance coefficient of the tth part is recorded as x t , Where m represents the number of tested parts; l t and l v They represent the abnormal scores of the instantaneous feature samples of the t-th and v-th parts at all acquisition moments during the simulated fall test, S(l t ,l v ) indicates l t and l v The difference between .

4. The simulated fall test method for safety protection of high-altitude operations according to claim 1, characterized in that: The condition for using the numerical value of the abnormality score to determine whether the instantaneous feature sample is a feature sample is: taking the instantaneous feature sample whose abnormality score is greater than the numerical value 1 as the feature sample.

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

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

7. The simulated fall test method for safety protection of high-altitude operations according to claim 6, characterized in that: Before weighting the average impact force using the comprehensive impact response coefficient of each part, the comprehensive impact response coefficient of each part is normalized.

8. The simulated fall test method for safety protection of working at heights according to claim 1, characterized in that: The mechanical analysis report includes but is not limited to the comprehensive impact response value, the average impact force of different parts and the comparison results with the preset safety standards.

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

10. The simulated fall test method for safety protection of high-altitude operations according to claim 1, characterized in that: After the mechanical analysis report is formed, the performance of the protective equipment is evaluated; if the comprehensive 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 comprehensive 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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