A method and system for detecting aerial work platforms based on actual pressure values

Abstract

The present invention provides a method and system for detecting an aerial work platform based on actual pressure values, and relates to the technical field of aerial work platforms. The method for detecting an aerial work platform based on actual pressure values ​​comprises the following steps: obtaining the actual pressure value by using a pressure sensor pre-installed on the supporting structure of the aerial work platform; collecting the three-dimensional point cloud data of the aerial work platform and constructing a three-dimensional model of the aerial work platform; mapping the obtained actual pressure value to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by using a singular spectrum analysis method; evaluating the fatigue life of the aerial work platform according to the stress analysis results; and formulating maintenance plans and improvement measures for the aerial work platform based on the evaluation results. The present invention can help managers better manage and maintain the aerial work platform, extend its service life, and improve work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of aerial work platforms, and in particular to a method and system for detecting aerial work platforms based on actual pressure values. Background Art

[0002] Aerial work platforms are a type of engineering machinery designed to safely and efficiently transport workers, work tools and materials to specific aerial locations for various operations. The structure of this type of equipment usually includes a chassis, a slewing mechanism, an extension mechanism and a working basket, and its design is to ensure the safety and work efficiency of workers at high altitudes. Aerial work platforms are used in a wide range of fields, whether it is highway maintenance, fire rescue, or transportation construction and building construction. Especially in scenarios where manned operations are required, aerial work platforms play an irreplaceable role. Due to its manned characteristics, the safety performance of the platform is extremely high. Therefore, during the design and manufacturing process, relevant safety standards must be strictly followed to ensure the structural strength, stability and ease of operation of the platform, thereby ensuring the life safety of the workers.

[0003] Aerial work platforms are equipment designed to provide elevated working positions in various construction, maintenance and repair operations. The safe operation of these platforms is essential to prevent accidents and protect the lives of operators. Therefore, the inspection of AWPs is a complex and important process, which aims to ensure that all mechanical, electrical and hydraulic systems operate properly in accordance with prescribed safety standards. Traditional inspection methods rely on manual vision and manual operation, which is not only inefficient but also susceptible to subjective factors, resulting in inconsistent inspection results, as well as difficulty in accurately identifying and predicting potential mechanical failures or structural defects, especially those with internal damage or tiny cracks, making it difficult to ensure the comprehensiveness and accuracy of the inspection.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of this, and in response to the problems in the related art, the present invention provides a method and system for detecting aerial work platforms based on actual pressure values ​​to solve the above-mentioned problem of difficulty in ensuring the comprehensiveness and accuracy of detection.

[0006] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0007] According to one aspect of the present invention, a method for detecting an aerial work platform based on actual pressure values ​​is provided, and the method for detecting an aerial work platform based on actual pressure values ​​comprises the following steps:

[0008] S1. Apply different pressures to the aerial work platform and obtain the actual pressure value using a pressure sensor pre-installed on the supporting structure of the aerial work platform;

[0009] S2. According to the structural parameters of the aerial work platform, collect the three-dimensional point cloud data of the aerial work platform and construct a three-dimensional model of the aerial work platform;

[0010] S3, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis;

[0011] S4. Evaluate the fatigue life of the aerial work platform based on the stress analysis results;

[0012] S5. Based on the evaluation results, formulate maintenance plans and improvement measures for aerial work platforms.

[0013] Preferably, according to the structural parameters of the aerial work platform, collecting the three-dimensional point cloud data of the aerial work platform and constructing the three-dimensional model of the aerial work platform includes the following steps:

[0014] S21. Determine the range of point cloud data to be collected according to the structural parameters of the aerial work platform;

[0015] S22, using a high-resolution 3D scanner to perform a 3D scan on the aerial work platform to obtain point cloud data of the aerial work platform;

[0016] S23, preprocessing the acquired point cloud data, the preprocessing including noise removal and smoothing;

[0017] S24, using the preprocessed point cloud data as input, and performing interpolation processing on the missing parts of the point cloud data using a discretized thin plate spline function method;

[0018] S25. Using the point cloud data after interpolation processing, a three-dimensional modeling software is used to perform three-dimensional reconstruction of the aerial work platform to obtain a three-dimensional model of the aerial work platform.

[0019] Preferably, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis method comprises the following steps:

[0020] S31, obtaining the installation position of the pressure sensor, and matching the position of each pressure sensor with a corresponding point in the three-dimensional model;

[0021] S32, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and simulating the stress response of the three-dimensional model of the aerial work platform under the applied pressure by finite element analysis method, to determine the stress data of the three-dimensional model of the aerial work platform under different applied pressures;

[0022] S33, using a singular spectrum analysis method to perform singular decomposition analysis on stress data of the three-dimensional model of the aerial work platform under different applied pressures, and extract stress parameters, where the stress parameters include trend components, periodic components, and noise components;

[0023] S34. Based on the extracted stress parameters, a preset ARIMA model is used to predict the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future.

[0024] Preferably, singular spectrum analysis is used to perform singular decomposition analysis on stress data of the three-dimensional model of the aerial work platform under different applied pressures, and extracting stress parameters comprises the steps of:

[0025] S331, sorting stress data of the three-dimensional model of the aerial work platform under different applied pressures, and sorting them according to the magnitude of the applied pressures to obtain a stress sequence;

[0026] S332, setting the embedding window length based on the stress sequence length, and converting the stress sequence into a trajectory matrix;

[0027] S333, performing singular value decomposition on the trajectory matrix to decompose it into a number of singular values ​​and corresponding left and right singular vectors;

[0028] S334, constructing a feature triplet according to the obtained singular value and the corresponding left and right singular vectors, and dividing the feature triplet into a plurality of groups according to the component characteristics of the singular value;

[0029] S335. Perform a reconstruction operation on each set of feature triples and extract stress parameters of the stress data.

[0030] Preferably, according to the extracted stress parameters, using a preset ARIMA model to predict the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future includes the following steps:

[0031] S341, constructing an ARIMA model based on the trend component, periodic component and noise component in the extracted stress parameters;

[0032] S342, training the constructed ARIMA model according to the stress data of the three-dimensional model of the aerial work platform, and evaluating and optimizing the trained ARIMA model through a cross-validation method;

[0033] S343. Using the optimized ARIMA model, the stress response of the three-dimensional model of the aerial work platform under different applied pressures within a future preset time period is predicted to generate a prediction result of the stress response.

[0034] Preferably, constructing an ARIMA model based on the trend component, periodic component and noise component in the extracted stress parameters comprises the following steps:

[0035] S3411. Analyze the trend component in the stress parameters and determine the number of differences in the ARIMA model;

[0036] S3412. Analyze the periodic components in the stress parameters and determine the seasonal parameters and period length of the ARIMA model;

[0037] S3413. Analyze the noise components in the stress parameters and determine the order of the moving average term in the ARIMA model;

[0038] S3414. Based on the analysis results of the trend component, the cycle component and the noise component, determine the order of the autoregressive term of the ARIMA model and establish the ARIMA model.

[0039] Preferably, based on the stress analysis results, evaluating the fatigue life of the aerial work platform includes the following steps:

[0040] S41, determining a stress variation curve of stress variation over time according to the prediction results of the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a future preset time period;

[0041] S42, analyzing the stress variation curve, and determining the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform;

[0042] S43. Determine fatigue life assessment parameters of the aerial work platform by correlation analysis method according to stress concentration areas of the aerial work platform under different applied pressures;

[0043] S44. The fatigue life assessment parameters determined by the correlation analysis method are used, and a pre-set fatigue life prediction model is used to predict the fatigue life of the aerial work platform under various applied pressures within a preset time period in the future.

[0044] Preferably, analyzing the stress variation curve and determining the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform comprises the following steps:

[0045] S421. Analyze the trend and peak-valley values ​​of stress in different time periods using the stress variation curve;

[0046] S422. Determine the stress distribution of the aerial work platform under different applied pressures within a preset time period in the future based on the three-dimensional model of the aerial work platform through finite element analysis and calculation;

[0047] S423. Based on the analysis results of the stress variation curve and the finite element analysis results, determine the stress concentration area of ​​the aerial work platform under different applied pressures.

[0048] Preferably, determining the fatigue life assessment parameters of the aerial work platform by correlation analysis method according to the stress concentration areas of the aerial work platform under different applied pressures comprises the following steps:

[0049] S431, extracting stress concentration region characteristics of the stress concentration region according to the stress concentration region, where the stress concentration region characteristics include stress magnitude, direction, and distribution;

[0050] S432. Collect historical usage data of aerial work platforms and performance data of materials used in aerial work platforms;

[0051] S433. Based on the characteristics of stress concentration areas, historical usage data of aerial work platforms, and performance data of materials used in aerial work platforms, a correlation model between the characteristics of stress concentration areas and fatigue life is established using the correlation analysis method;

[0052] S434. Determine the fatigue life assessment parameters that affect the aerial work platform through the correlation model.

[0053] According to another aspect of the present invention, there is provided a high-altitude work platform detection system based on actual pressure values, the high-altitude work platform detection system based on actual pressure values ​​comprising: a pressure value acquisition module, a model construction module, a stress analysis module, a fatigue life assessment module and a maintenance measure formulation module, and the pressure value acquisition module, the model construction module, the stress analysis module, the fatigue life assessment module and the maintenance measure formulation module are sequentially connected;

[0054] A pressure value acquisition module is used to apply different pressures to the aerial work platform and obtain the actual pressure value by using a pressure sensor pre-installed on the supporting structure of the aerial work platform;

[0055] A model building module, used to collect three-dimensional point cloud data of the aerial work platform and build a three-dimensional model of the aerial work platform according to the structural parameters of the aerial work platform;

[0056] A stress analysis module is used to map the actual pressure value obtained to the three-dimensional model of the aerial work platform, and perform stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by using a singular spectrum analysis method;

[0057] Fatigue life assessment module, used to assess the fatigue life of aerial work platforms based on stress analysis results;

[0058] The maintenance measures formulation module is used to formulate maintenance plans and improvement measures for aerial work platforms based on the evaluation results.

[0059] Compared with the prior art, the present invention provides a method and system for detecting an aerial work platform based on actual pressure values, which has the following beneficial effects:

[0060] (1) The data obtained by the actual pressure sensor of the present invention can accurately reflect the situation of the aerial work platform under different pressures, thereby improving the accurate assessment of the platform status. Combined with the three-dimensional point cloud data and singular spectrum analysis method, the structure and stress distribution of the aerial work platform can be fully understood, which is helpful for a comprehensive analysis of the working status of the platform. Through stress analysis and fatigue life assessment, potential problems and risks can be identified in advance, and preventive measures can be taken to avoid safety accidents and equipment damage. The maintenance plan and improvement measures formulated according to the assessment results can help managers better manage and maintain the aerial work platform, extend its service life, and improve work efficiency.

[0061] (2) The present invention predicts the stress response of the aerial work platform under different applied pressures within a preset time period in the future by establishing an ARIMA model, which helps to discover potential problems in advance and reduce the possibility of accidents. Through stress analysis and fatigue life assessment, the structural fatigue and damage of the aerial work platform can be discovered in time, which helps to ensure the safety of the workers. Through reasonable maintenance and predictive management, the service life of the aerial work platform can be extended, the utilization rate of the equipment can be improved, and maintenance costs and resources can be saved.

[0062] (3) The present invention can comprehensively evaluate the fatigue condition and life of the aerial work platform through a comprehensive analysis of factors such as stress change curves, stress concentration areas, and material properties. By using the stress response prediction under different applied pressures within a preset time period, the fatigue life of the aerial work platform under different working conditions can be evaluated in advance, which is helpful for formulating reasonable maintenance plans and arrangements. By establishing a correlation model between the characteristics of the stress concentration area and the fatigue life through the correlation analysis method, the fatigue life of the aerial work platform can be more accurately evaluated to avoid accidents caused by fatigue damage. By predicting and evaluating the fatigue life, the safe operation of the aerial work platform can be guaranteed and the risks and losses caused by structural fatigue can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0064] Figure 1 is a flow chart of a method for detecting an aerial work platform based on actual pressure values ​​according to an embodiment of the present invention;

[0065] Figure 2 It is a principle block diagram of an aerial work platform detection system based on actual pressure values ​​according to an embodiment of the present invention.

[0066] In the figure:

[0067] 1. Pressure value acquisition module; 2. Model building module; 3. Stress analysis module; 4. Fatigue life assessment module; 5. Maintenance measures formulation module. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0069] According to an embodiment of the present invention, a method and system for detecting an aerial work platform based on actual pressure values ​​are provided.

[0070] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to another embodiment of the present invention, a method and system for detecting an aerial work platform based on actual pressure values ​​are provided. The method for detecting an aerial work platform based on actual pressure values ​​comprises the following steps:

[0071] S1. Apply different pressures to the aerial work platform and obtain the actual pressure value using a pressure sensor pre-installed on the supporting structure of the aerial work platform;

[0072] Specifically, by pre-installing pressure sensors at key supporting points or structural nodes of the aerial work platform, different pressures are applied to the aerial work platform, and then the pre-installed pressure sensors are used to collect pressure data of various parts of the platform in real time.

[0073] S2. According to the structural parameters of the aerial work platform, collect the three-dimensional point cloud data of the aerial work platform and construct a three-dimensional model of the aerial work platform;

[0074] As a preferred implementation, according to the structural parameters of the aerial work platform, collecting the three-dimensional point cloud data of the aerial work platform and constructing the three-dimensional model of the aerial work platform includes the following steps:

[0075] S21. Determine the range of point cloud data to be collected according to the structural parameters of the aerial work platform;

[0076] It should be noted that, according to the structural parameters of the aerial work platform, determining the range of point cloud data to be collected includes the following steps:

[0077] Analyze the structural parameters of the aerial work platform, including the size, shape, location and size of key structural components of the platform;

[0078] According to the structural parameter analysis, determine the key structural parts on the platform, such as support columns, beams, connectors, etc.;

[0079] According to the above analysis results, set appropriate point cloud data collection accuracy and range.

[0080] S22, using a high-resolution 3D scanner to perform a 3D scan on the aerial work platform to obtain point cloud data of the aerial work platform;

[0081] S23, preprocessing the acquired point cloud data, the preprocessing including noise removal and smoothing;

[0082] It should be noted that point cloud data is often affected by various factors during the collection process, resulting in noise in the data. The noise is removed by calculating the standard deviation of the distance between each point and its neighboring points and removing those points that deviate too much from the average distance standard deviation.

[0083] The smoothing of point cloud data is to reduce the sharp edges and irregularities in the data, making the point cloud smoother and facilitating the subsequent model construction.

[0084] S24, using the preprocessed point cloud data as input, and performing interpolation processing on the missing parts of the point cloud data using a discretized thin plate spline function method;

[0085] It should be noted that the thin plate spline function is an interpolation method that can smoothly pass through all given data points. Thin plate spline interpolation is based on the principle of deformation of thin plates under stress in physics, and can produce a smooth surface in a minimum energy state, which is suitable for filling the missing areas of point cloud data.

[0086] Specifically, the preprocessed point cloud data is used as input, and the discretized thin plate spline function method is used to interpolate the missing parts of the point cloud data, including the following steps:

[0087] Before interpolation, the missing areas in the point cloud data are identified by analyzing the distribution of the point cloud data, and for each identified missing area, its boundary is determined;

[0088] A series of points are selected on the boundary of the missing area as interpolation points. These points will serve as control points of the thin plate spline interpolation function. The thin plate spline interpolation function is constructed using these control points. The interpolation function can minimize the bending energy of the surface, thereby generating a smooth interpolation surface.

[0089] According to the selected interpolation points and their corresponding point cloud data positions, a linear equation group is established, which relates the coefficients of the thin plate spline function and the interpolation points, and the linear equation group is solved to obtain the parameters of the thin plate spline function;

[0090] Using the interpolation function parameters obtained, interpolation calculation is performed on each point in the missing area to generate new point cloud data.

[0091] S25. Using the point cloud data after interpolation processing, a three-dimensional modeling software is used to perform three-dimensional reconstruction of the aerial work platform to obtain a three-dimensional model of the aerial work platform.

[0092] S3, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis;

[0093] As a preferred implementation, mapping the actual pressure value obtained to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis method includes the following steps:

[0094] S31, obtaining the installation position of the pressure sensor, and matching the position of each pressure sensor with a corresponding point in the three-dimensional model;

[0095] It should be noted that during the installation of the pressure sensor, the specific installation location information of each pressure sensor is collected, and then in the 3D modeling software, the corresponding point of each sensor is marked on the 3D model of the aerial work platform according to the collected pressure sensor location information.

[0096] S32, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and simulating the stress response of the three-dimensional model of the aerial work platform under the applied pressure by finite element analysis method, to determine the stress data of the three-dimensional model of the aerial work platform under different applied pressures;

[0097] Specifically, by assigning correct material properties and defining boundary adjustments to each structure in the three-dimensional model of the aerial work platform based on the previous correspondence between the pressure sensor position and the three-dimensional model points of the aerial work platform, the three-dimensional model of the aerial work platform is meshed through three-dimensional modeling software or finite element analysis software, and appropriate element types (such as tetrahedral or hexahedral elements) and mesh density are selected to balance calculation accuracy and calculation time; finite element software is then used to perform static or dynamic stress analysis, and the stress response of the three-dimensional model of the aerial work platform under applied pressure is calculated based on the set boundary conditions and material properties.

[0098] S33, using a singular spectrum analysis method to perform singular decomposition analysis on stress data of the three-dimensional model of the aerial work platform under different applied pressures, and extract stress parameters, where the stress parameters include trend components, periodic components, and noise components;

[0099] As a preferred implementation, singular spectrum analysis is used to perform singular decomposition analysis on stress data of the three-dimensional model of the aerial work platform under different applied pressures, and extracting stress parameters includes the following steps:

[0100] S331, sorting stress data of the three-dimensional model of the aerial work platform under different applied pressures, and sorting them according to the magnitude of the applied pressures to obtain a stress sequence;

[0101] S332, setting the embedding window length based on the stress sequence length, and converting the stress sequence into a trajectory matrix;

[0102] It should be noted that the embedding window length L is a key parameter, which determines the number of data points contained in each vector when the stress sequence is converted into a trajectory matrix. The choice of L depends on the length of the stress sequence. The commonly used rule is to choose L as half of the sequence length N. The trajectory matrix is ​​formed by sliding the original stress sequence in sequence according to the window length L.

[0103] S333, performing singular value decomposition on the trajectory matrix to decompose it into a number of singular values ​​and corresponding left and right singular vectors;

[0104] It should be noted that for the trajectory matrix, the singular value decomposition can decompose it into three main matrices, the matrix composed of the left singular vectors and the right singular vectors, and the diagonal matrix. The elements on the diagonal of the diagonal matrix are singular values, which represent the importance or energy of different components in the data;

[0105] S334, constructing a feature triplet according to the obtained singular value and the corresponding left and right singular vectors, and dividing the feature triplet into a plurality of groups according to the component characteristics of the singular value;

[0106] It should be noted that each feature triplet consists of a singular value and the corresponding left and right singular vectors. The feature triplet is divided into several groups according to the component characteristics of the singular values. Usually, larger singular values ​​correspond to the main components in the data, such as trends or main cycles, while smaller singular values ​​may represent noise or minor components. By grouping, different structural features of the data can be distinguished, for example, trend, periodicity and noise components can be extracted separately.

[0107] S335. Perform a reconstruction operation on each set of feature triples and extract stress parameters of the stress data.

[0108] It should be noted that each set of feature triplets represents a main component or pattern of the data. By selecting one or several sets of feature triplets, the contribution of these components in the original data sequence can be reconstructed; the reconstruction operation usually uses each set of selected singular values ​​and corresponding singular vectors to reconstruct the data components according to the inverse operation of singular value decomposition.

[0109] In addition, the trend component can be extracted separately from the data series, which is usually done by selecting the feature triplet corresponding to the largest singular value; the trend component reflects the long-term trend of stress data over time.

[0110] Periodic components can be reconstructed by selecting feature triplets that reflect the characteristics of periodic variations; these components show repetitive patterns or periodic fluctuations in the stress data.

[0111] The noise components can be reconstructed from the remaining feature triplets, which usually correspond to smaller singular values.

[0112] S34. Based on the extracted stress parameters, a preset ARIMA model is used to predict the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future.

[0113] As a preferred embodiment, according to the extracted stress parameters, using a preset ARIMA model to predict the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future includes the following steps:

[0114] S341, constructing an ARIMA model based on the trend component, periodic component and noise component in the extracted stress parameters;

[0115] As a preferred embodiment, based on the trend component, periodic component and noise component in the extracted stress parameters, constructing an ARIMA model includes the following steps:

[0116] S3411. Analyze the trend component in the stress parameters and determine the number of differences in the ARIMA model;

[0117] It should be noted that by observing the trend of the stress series, if the series shows obvious non-stationarity, it is necessary to perform differentiation to eliminate the trend, and then a unit root test (such as an ADF test) is performed to determine the number of differences to ensure that the series becomes stable.

[0118] S3412. Analyze the periodic components in the stress parameters and determine the seasonal parameters and period length of the ARIMA model;

[0119] It should be noted that by observing whether there is an obvious seasonal change pattern in the stress series, the seasonal pattern may manifest as periodic fluctuations that recur within a specific time range. Then, the original stress series is decomposed into three parts: trend, seasonality, and residual using seasonal decomposition technology. The autocorrelation function and partial autocorrelation function analysis of the seasonal component are performed. Based on the results of seasonal decomposition and autocorrelation function analysis, the seasonal parameters and the length of the seasonal cycle are determined. The seasonal parameters are the number of seasonal differences.

[0120] S3413. Analyze the noise components in the stress parameters and determine the order of the moving average term in the ARIMA model;

[0121] It should be noted that the noise component represents the random fluctuations in the time series that cannot be explained by the trend or seasonal components. The residual analysis is performed on the stress series from which the trend and seasonal components have been removed to obtain the residual series. The residual, that is, the difference between the stress series and its fitted value, can be regarded as the noise component of the stress series. The stationarity test is performed on the residual series. Once the residual series is stationary, its autocorrelation function graph and partial autocorrelation function graph can be plotted. If the autocorrelation function graph shows significant truncation after a certain order of delay (that is, the autocorrelation coefficients after a certain order are all within the confidence interval), and the partial autocorrelation function graph gradually decays, this indicates that the residual series contains a moving average component of order q, that is, the moving average term of the ARIMA model should be of order q.

[0122] S3414. Based on the analysis results of the trend component, the cycle component and the noise component, determine the order of the autoregressive term of the ARIMA model and establish the ARIMA model.

[0123] It should be noted that the most appropriate order of autoregressive terms is determined based on the analysis results of trends, cycles and noise components, combined with empirical judgment and statistical criteria. Usually, the partial autocorrelation function graph can be used to determine the order of autoregressive terms, that is, the partial autocorrelation function is truncated to zero after the order, which indicates that the order is an appropriate order of autoregressive terms. After determining the order of the autoregressive terms, the ARIMA model can be constructed according to the selected order.

[0124] S342, training the constructed ARIMA model according to the stress data of the three-dimensional model of the aerial work platform, and evaluating and optimizing the trained ARIMA model through a cross-validation method;

[0125] It should be noted that, first, the existing aerial work platform stress data set is divided into a training set and a test set. The training set data is used to train the model using the parameters of the determined ARIMA model (including the order of autoregressive terms, the order of differences, the moving average terms, etc.). The ARIMA model is applied to the training set data, and the model is fitted. The trained model is evaluated and optimized using the cross-validation method. Cross-validation divides the data set into multiple subsets, and then repeatedly trains and tests the model to obtain more reliable performance indicators; according to the results of cross-validation, the model is adjusted and optimized.

[0126] S343. Using the optimized ARIMA model, the stress response of the three-dimensional model of the aerial work platform under different applied pressures within a future preset time period is predicted to generate a prediction result of the stress response.

[0127] S4. Evaluate the fatigue life of the aerial work platform based on the stress analysis results;

[0128] As a preferred embodiment, based on the stress analysis results, the fatigue life of the aerial work platform is evaluated, including the following steps:

[0129] S41, determining a stress variation curve of stress variation over time according to the prediction results of the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a future preset time period;

[0130] Specifically, the predicted stress response data are sorted in chronological order, and a stress variation curve showing the stress changing with time is plotted based on the prediction results under different applied pressures; the stress variation curve may show different trends and amplitudes on the time axis.

[0131] S42, analyzing the stress variation curve, and determining the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform;

[0132] As a preferred implementation, analyzing the stress variation curve and determining the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform includes the following steps:

[0133] S421. Analyze the trend and peak-valley values ​​of stress in different time periods using the stress variation curve;

[0134] It should be noted that by observing and analyzing whether the stress change curve has an obvious trend, including rising, falling or remaining stable, the overall change direction of stress and the possible change rate can be determined; then the peak and valley values ​​are marked on the stress change curve. The peak value indicates the highest point reached by the stress, which may have an important impact on the aerial work platform. The valley value indicates the lowest point reached by the stress, representing a period of lighter load.

[0135] S422. Determine the stress distribution of the aerial work platform under different applied pressures within a preset time period in the future based on the three-dimensional model of the aerial work platform through finite element analysis and calculation;

[0136] S423. Based on the analysis results of the stress variation curve and the finite element analysis results, determine the stress concentration area of ​​the aerial work platform under different applied pressures.

[0137] It should be noted that the analysis results of the stress change curve are combined with the stress distribution results of the finite element analysis to comprehensively consider the information of the two dimensions of time and space; identify which areas have particularly significant stress concentration under different applied pressure conditions; and based on the comprehensive analysis results, determine the key stress concentration areas of the aerial work platform under the predicted different applied pressures.

[0138] S43. Determine fatigue life assessment parameters of the aerial work platform by correlation analysis method according to stress concentration areas of the aerial work platform under different applied pressures;

[0139] As a preferred embodiment, according to the stress concentration area of ​​the aerial work platform under different applied pressures, determining the fatigue life assessment parameters of the aerial work platform by correlation analysis method includes the following steps:

[0140] S431, extracting stress concentration region characteristics of the stress concentration region according to the stress concentration region, where the stress concentration region characteristics include stress magnitude, direction, and distribution;

[0141] S432. Collect historical usage data of aerial work platforms and performance data of materials used in aerial work platforms;

[0142] It should be noted that the historical usage data of aerial work platforms includes stress information, frequency and duration of use, load conditions, maintenance and failure records, etc.; material performance data includes basic physical and chemical properties, fatigue performance parameters, etc.

[0143] S433. Based on the characteristics of stress concentration areas, historical usage data of aerial work platforms, and performance data of materials used in aerial work platforms, a correlation model between the characteristics of stress concentration areas and fatigue life is established using the correlation analysis method;

[0144] It should be noted that the stress monitoring data and usage time corresponding to the stress concentration area are screened out from the historical usage data; the relationship between stress and usage time is fitted by the least square method using the screened data to establish a stress-life curve;

[0145] Analyze the fatigue characteristics of materials, such as fatigue limit, fatigue crack growth rate, etc., based on the performance data of materials; determine the fatigue life of materials at different stress levels by combining stress-life curves and material fatigue characteristic data;

[0146] Based on the above analysis, select the features that have a significant impact on fatigue life, such as the maximum stress in the stress concentration area, the average stress of historical usage data, etc.

[0147] Using correlation analysis, a correlation model between stress concentration area characteristics and fatigue life is established.

[0148] S434. Determine the fatigue life assessment parameters that affect the aerial work platform through the correlation model.

[0149] It should be noted that by observing the size and sign of the coefficients of each feature in the correlation model; in the correlation model, the larger the absolute value of the coefficient, the more significant the impact of the feature on fatigue life; the positive and negative signs of the coefficient indicate the nature of the relationship between the feature and fatigue life (positive correlation or negative correlation). Then check the statistical significance of each feature in the correlation model, usually evaluated by t-test and corresponding P value; features with P values ​​less than the pre-set significance level (such as 0.05) are considered to have a significant impact on fatigue life; finally, the features that have a significant impact on fatigue life are screened out as key evaluation parameters.

[0150] S44. The fatigue life assessment parameters determined by the correlation analysis method are used, and a pre-set fatigue life prediction model is used to predict the fatigue life of the aerial work platform under various applied pressures within a preset time period in the future.

[0151] Specifically, based on the key evaluation parameters that affect fatigue life and the pre-set stress application scenarios within a preset time period in the future, the fatigue life in each scenario is predicted using a pre-set fatigue life prediction model.

[0152] S5. Based on the evaluation results, formulate maintenance plans and improvement measures for aerial work platforms.

[0153] Specifically, based on the assessment results, the maintenance plan and improvement measures for aerial work platforms include the following steps:

[0154] Analyze the assessment results to identify areas with short fatigue life or severe stress concentration;

[0155] Evaluate the risk level of each identified area based on the predicted value of fatigue life and actual working conditions, and prioritize those with higher risk;

[0156] Develop targeted regular inspection plans based on fatigue life prediction and risk assessment; increase the frequency and detail of inspections for higher risk areas to detect cracks or fatigue signs at an early stage; in addition to regular inspections, preventive maintenance measures should also be included, such as replacing parts that are close to the limit of fatigue life and applying surface treatment technology to reduce stress concentration;

[0157] Organize training to improve operators' awareness of fatigue damage, guide them on how to use the platform correctly, and reduce the impact of improper operation on the fatigue life of the platform.

[0158] like Figure 2 As shown, according to another embodiment of the present invention, there is provided a high-altitude work platform detection system based on actual pressure values, the high-altitude work platform detection system based on actual pressure values ​​comprising: a pressure value acquisition module 1, a model construction module 2, a stress analysis module 3, a fatigue life assessment module 4 and a maintenance measure formulation module 5, and the pressure value acquisition module 1, the model construction module 2, the stress analysis module 3, the fatigue life assessment module 4 and the maintenance measure formulation module 5 are sequentially connected;

[0159] The pressure value acquisition module 1 is used to apply different pressures to the aerial work platform and obtain the actual pressure value by using a pressure sensor pre-installed on the supporting structure of the aerial work platform;

[0160] Model building module 2, used to collect three-dimensional point cloud data of the aerial work platform and build a three-dimensional model of the aerial work platform according to the structural parameters of the aerial work platform;

[0161] The stress analysis module 3 is used to map the actual pressure value obtained to the three-dimensional model of the aerial work platform, and perform stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by using a singular spectrum analysis method;

[0162] Fatigue life assessment module 4, used to assess the fatigue life of the aerial work platform based on the stress analysis results;

[0163] The maintenance measures formulation module 5 is used to formulate maintenance plans and improvement measures for aerial work platforms based on the evaluation results.

[0164] In summary, with the help of the above-mentioned technical scheme of the present invention, the data obtained by the actual pressure sensor of the present invention can accurately reflect the situation of the aerial work platform under different pressures, improve the accurate evaluation of the platform status, and combine the three-dimensional point cloud data and singular spectrum analysis method to fully understand the structure and stress distribution of the aerial work platform, which is helpful to comprehensively analyze the working status of the platform. Through stress analysis and fatigue life assessment, potential problems and risks can be identified in advance, and preventive measures can be taken to avoid safety accidents and equipment damage. The maintenance plan and improvement measures formulated according to the evaluation results can help managers better manage and maintain the aerial work platform, extend its service life, and improve work efficiency. The present invention predicts the stress response of the aerial work platform under different applied pressures in a preset time period in the future by establishing an ARIMA model, which is helpful to discover potential problems in advance and reduce the possibility of accidents. Through stress analysis and fatigue life assessment, it can Timely discovery of structural fatigue and damage of the aerial work platform is helpful to ensure the safety of the staff. Through reasonable maintenance and predictive management, the service life of the aerial work platform can be extended, the utilization rate of the equipment can be improved, and maintenance costs and resources can be saved. The present invention can comprehensively evaluate the fatigue condition and life of the aerial work platform through a comprehensive analysis of factors such as stress change curves, stress concentration areas, and material properties. By using the stress response prediction under different applied pressures within a preset time period, the fatigue life of the aerial work platform under different working conditions can be evaluated in advance, which is helpful to formulate reasonable maintenance plans and arrangements. By establishing a correlation model between stress concentration area characteristics and fatigue life through correlation analysis, the fatigue life of the aerial work platform can be more accurately evaluated to avoid accidents caused by fatigue damage. Through the prediction and evaluation of fatigue life, the safe operation of the aerial work platform can be guaranteed and the risks and losses caused by structural fatigue can be reduced.

[0165] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0166] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A method for detecting an aerial work platform based on actual pressure value, characterized in that: The aerial work platform detection method based on actual pressure value comprises the following steps: S1. Apply different pressures to the aerial work platform and obtain the actual pressure value using a pressure sensor pre-installed on the supporting structure of the aerial work platform; S2. According to the structural parameters of the aerial work platform, collect the three-dimensional point cloud data of the aerial work platform and construct a three-dimensional model of the aerial work platform; S3, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis; S4. Evaluate the fatigue life of the aerial work platform based on the stress analysis results; S5. Based on the evaluation results, formulate maintenance plans and improvement measures for aerial work platforms.

2. The aerial work platform detection method based on actual pressure value according to claim 1 is characterized in that: The method of collecting the three-dimensional point cloud data of the aerial work platform and constructing the three-dimensional model of the aerial work platform according to the structural parameters of the aerial work platform comprises the following steps: S21. Determine the range of point cloud data to be collected according to the structural parameters of the aerial work platform; S22, using a high-resolution 3D scanner to perform a 3D scan on the aerial work platform to obtain point cloud data of the aerial work platform; S23, preprocessing the acquired point cloud data, wherein the preprocessing includes noise removal and smoothing; S24, using the preprocessed point cloud data as input, and performing interpolation processing on the missing parts of the point cloud data using a discretized thin plate spline function method; S25. Using the point cloud data after interpolation processing, a three-dimensional modeling software is used to perform three-dimensional reconstruction of the aerial work platform to obtain a three-dimensional model of the aerial work platform.

3. The aerial work platform detection method based on actual pressure value according to claim 1 is characterized in that: The method of mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform and performing stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by using a singular spectrum analysis method comprises the following steps: S31, obtaining the installation position of the pressure sensor, and matching the position of each pressure sensor with a corresponding point in the three-dimensional model; S32, mapping the acquired actual pressure value to the three-dimensional model of the aerial work platform, and simulating the stress response of the three-dimensional model of the aerial work platform under the applied pressure by finite element analysis method, to determine the stress data of the three-dimensional model of the aerial work platform under different applied pressures; S33, using a singular spectrum analysis method to perform singular decomposition analysis on stress data of the three-dimensional model of the aerial work platform under different applied pressures, and extract stress parameters, wherein the stress parameters include a trend component, a periodic component, and a noise component; S34. Based on the extracted stress parameters, a preset ARIMA model is used to predict the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future.

4. The aerial work platform detection method based on actual pressure value according to claim 3 is characterized in that: The singular spectrum analysis method is used to perform singular decomposition analysis on the stress data of the three-dimensional model of the aerial work platform under different applied pressures to extract stress parameters, including the steps of: S331, sorting stress data of the three-dimensional model of the aerial work platform under different applied pressures, and sorting them according to the magnitude of the applied pressures to obtain a stress sequence; S332, setting the embedding window length based on the stress sequence length, and converting the stress sequence into a trajectory matrix; S333, performing singular value decomposition on the trajectory matrix to decompose it into a number of singular values ​​and corresponding left and right singular vectors; S334, constructing a feature triplet according to the obtained singular value and the corresponding left and right singular vectors, and dividing the feature triplet into a plurality of groups according to the component characteristics of the singular value; S335. Perform a reconstruction operation on each set of feature triples and extract stress parameters of the stress data.

5. The aerial work platform detection method based on actual pressure value according to claim 3 is characterized in that: The method of predicting the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a preset time period in the future by using a preset ARIMA model according to the extracted stress parameters includes the following steps: S341, constructing an ARIMA model based on the trend component, periodic component and noise component in the extracted stress parameters; S342, training the constructed ARIMA model according to the stress data of the three-dimensional model of the aerial work platform, and evaluating and optimizing the trained ARIMA model through a cross-validation method; S343. Using the optimized ARIMA model, the stress response of the three-dimensional model of the aerial work platform under different applied pressures within a future preset time period is predicted to generate a prediction result of the stress response.

6. The aerial work platform detection method based on actual pressure value according to claim 5 is characterized in that: The ARIMA model is constructed based on the trend component, periodic component and noise component extracted from the stress parameters, and includes the following steps: S3411. Analyze the trend component in the stress parameters and determine the number of differences in the ARIMA model; S3412. Analyze the periodic components in the stress parameters and determine the seasonal parameters and period length of the ARIMA model; S3413. Analyze the noise components in the stress parameters and determine the order of the moving average term in the ARIMA model; S3414. Based on the analysis results of the trend component, the cycle component and the noise component, determine the order of the autoregressive term of the ARIMA model and establish the ARIMA model.

7. The aerial work platform detection method based on actual pressure value according to claim 5, characterized in that: The fatigue life assessment of the aerial work platform according to the stress analysis results includes the following steps: S41, determining a stress variation curve of stress variation over time according to the prediction results of the stress response of the three-dimensional model of the aerial work platform under different applied pressures in a future preset time period; S42, analyzing the stress variation curve, and determining the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform; S43. Determine fatigue life assessment parameters of the aerial work platform by correlation analysis method according to stress concentration areas of the aerial work platform under different applied pressures; S44. The fatigue life assessment parameters determined by the correlation analysis method are used, and a pre-set fatigue life prediction model is used to predict the fatigue life of the aerial work platform under various applied pressures within a preset time period in the future.

8. The aerial work platform detection method based on actual pressure value according to claim 7, characterized in that: The analysis of the stress variation curve and determination of the stress concentration area of ​​the aerial work platform under different applied pressures according to the three-dimensional model of the aerial work platform include the following steps: S421. Analyze the trend and peak-valley values ​​of stress in different time periods using the stress variation curve; S422. Determine the stress distribution of the aerial work platform under different applied pressures within a preset time period in the future based on the three-dimensional model of the aerial work platform through finite element analysis and calculation; S423. Based on the analysis results of the stress variation curve and the finite element analysis results, determine the stress concentration area of ​​the aerial work platform under different applied pressures.

9. The aerial work platform detection method based on actual pressure value according to claim 7, characterized in that: Determining fatigue life assessment parameters of the aerial work platform by correlation analysis according to stress concentration areas of the aerial work platform under different applied pressures comprises the following steps: S431, extracting stress concentration region characteristics of the stress concentration region according to the stress concentration region, wherein the stress concentration region characteristics include stress magnitude, direction, and distribution; S432. Collect historical usage data of aerial work platforms and performance data of materials used in aerial work platforms; S433. Based on the characteristics of stress concentration areas, historical usage data of aerial work platforms, and performance data of materials used in aerial work platforms, a correlation model between the characteristics of stress concentration areas and fatigue life is established using the correlation analysis method; S434. Determine the fatigue life assessment parameters that affect the aerial work platform through the correlation model.

10. A high-altitude work platform detection system based on actual pressure value, used to implement the high-altitude work platform detection method based on actual pressure value according to any one of claims 1 to 9, characterized in that: The aerial work platform detection system based on actual pressure value includes: a pressure value acquisition module, a model construction module, a stress analysis module, a fatigue life assessment module and a maintenance measure formulation module, and the pressure value acquisition module, the model construction module, the stress analysis module, the fatigue life assessment module and the maintenance measure formulation module are connected in sequence; The pressure value acquisition module is used to apply different pressures to the aerial work platform and obtain the actual pressure value by using a pressure sensor pre-installed on the supporting structure of the aerial work platform; The model building module is used to collect the three-dimensional point cloud data of the aerial work platform and build a three-dimensional model of the aerial work platform according to the structural parameters of the aerial work platform; The stress analysis module is used to map the acquired actual pressure value to the three-dimensional model of the aerial work platform, and perform stress analysis on the three-dimensional model of the aerial work platform under different applied pressures by singular spectrum analysis; The fatigue life assessment module is used to assess the fatigue life of the aerial work platform according to the stress analysis results; The maintenance measures formulation module is used to formulate a maintenance plan and improvement measures for the aerial work platform based on the evaluation results.

Images