Aerial building factory structure health monitoring method and system

Abstract

The invention relates to the technical field of structural health monitoring, in particular to a structural health monitoring method and system for an air building factory, and the method comprises the steps: building a node stress difference and displacement gradient comparison based on a load change rate and a vibration offset, and enabling a structural stress state to form a time sequence stress response matrix; the method comprises the following steps: calculating and introducing a dynamic Bayesian network to construct a node time sequence relationship based on a stress dependency rate, correcting a periodic load difference and a counter-force difference value to enable a confidence level updating process to have gradual iteration and smooth characteristics, screening a descending over-threshold node for a confidence distribution change rate, and combining curve difference and form similarity comparison to obtain a confidence level updating result. And the curve trend is predicted through a long-short-term memory network, so that the degradation trend can be revealed in advance and unified into a clustering trajectory, a dispersion result generated by abnormal judgment is avoided, and the degradation trajectory is subjected to curve slope, inflection point and span extraction, combined with rate comparison and periodic attenuation length fitting, so that degradation boundary recognition has high sensitivity and interval judgment capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural health monitoring, in particular to a structural health monitoring method and system for an air building factory. BACKGROUND

[0002] The technical field of structural health monitoring aims to realize comprehensive evaluation and dynamic management of safety, stability and service life of engineering structures and mechanical systems through real-time detection, data analysis and state evaluation means, identify structural performance degradation, detect potential damage, predict failure risk, and provide quantitative basis for operation and maintenance and safety decision-making, serving bearing systems such as building structures, bridges, tunnels, large mechanical equipment and aerospace structures, and focusing on the mechanical response and health change law of structures under the influence of stress, vibration, temperature and environment.

[0003] The structural health monitoring method for an air building factory aims to realize quantitative evaluation and abnormal early warning of the overall structural health state of the air building factory by establishing a systematic structural state perception and data analysis mechanism, real-time acquisition of stress state, vibration response and displacement change of the building factory during construction, judgment of whether the structure is in a safe range, prevention of structural damage and safety accidents caused by local component fatigue, connection failure and uneven stress, continuous tracking of the mechanical stability of the building factory structure, accurate identification of potential fatigue risk, optimization of maintenance cycle and improvement of the overall operation safety level of the construction equipment.

[0004] The prior art mainly relies on real-time detection and static data analysis of a single index in structural health monitoring, lacks modeling of the dynamic coupling relationship between stress state, vibration response and displacement change, resulting in local judgment of state evaluation, which is difficult to reflect the global evolution trend of the structure in time sequence, and the prior art uses a fixed threshold triggering method, ignores the dynamic differences of periodic load change and reaction force distribution, causes fatigue accumulation and potential degradation to be unable to be captured in the early stage, causes early warning lag, when the node displacement and vibration do not reach the preset threshold, the system determines that the state is safe, but the node stress distribution has been uneven, forming potential hidden dangers, the life prediction adopts experience regression and linear extrapolation, lacks extraction of curve change form and modeling of periodic attenuation law, which easily leads to large deviation between the life calculation result and the actual value, and weakens the scientificity of the maintenance strategy. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and a structural health monitoring method and system for an air building factory are provided.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a structural health monitoring method for an air building factory, comprising the following steps: S1: Based on the load change rate and vibration offset of the main load-bearing arm, lifting guide rail, and cantilever frame, calculate the nodal stress difference and displacement gradient, compare the load increase direction and reaction force distribution offset, form a time-series stress response matrix, and generate a set of dynamic stress sequence of the structure. S2: Based on the dynamic sequence set of structural stress, calculate the stress dependence rate of guide rail nodes and hydraulic support arm, establish node temporal dependence relationship using dynamic Bayesian network, compare the difference of periodic load and the difference of reaction force change, correct the node confidence level, update the state matrix and smooth the confidence sequence, and generate structural health confidence distribution set. S3: Based on the aforementioned structural health confidence distribution set, compare the confidence change rates of the main bearing arm and the slewing support axis, screen out nodes that exceed the threshold for decline, calculate the curve difference and compare the morphological similarity, use a long short-term memory network to predict the trend of confidence curve changes, divide the degradation clusters to generate a unified curve, and generate a set of structural degradation trajectories. S4: Based on the set of structural degradation trajectories, extract the slope, inflection point and span of the guide rail segment and support node curves, compare the interval rate to determine the degradation boundary, fit the linear rate of change and the periodic decay length to form a set of degradation stage distributions; S5: Based on the degradation stage distribution set, calculate the confidence decline rate and period length ratio of the bearing arm and the slewing frame stages, screen out the overthreshold stage to extract the termination time point, substitute it into the confidence expression to calculate the decay time, and obtain the structural remaining life index.

[0007] As a further aspect of the present invention, the structural stress dynamic sequence set includes a load change sequence, a stress distribution sequence, and a displacement gradient sequence; the structural health confidence distribution set includes a node confidence value sequence, a state transition matrix, and a smooth confidence curve; the structural degradation trajectory set includes a degradation curve sequence, a degradation node set, and a confidence rate of change curve; the degradation stage distribution set includes a stage boundary location set, a degradation rate set, and a periodic decay interval set; and the structural remaining life index includes a remaining life value, life decay equation parameters, and a time distribution range.

[0008] As a further aspect of the present invention, the specific steps for generating the dynamic force sequence set of the structure are as follows: Based on the load change rate and vibration offset of the main load-bearing arm, lifting guide rail, and cantilever frame, the force value and displacement difference of the nodes are calculated. The force direction is determined by the moment balance calculation between nodes. The cumulative displacement change over a continuous period is used to form a time-series distribution table and generate a node force difference matrix. Based on the node stress difference matrix, the load change and reaction force distribution values ​​of each node are proportionally converted, and linearly superimposed and grouped according to the monitoring time axis to form a continuous mapping matrix of node stress difference in the time dimension, which is defined as the time-series stress response matrix. Based on the time sequence stress response matrix, the stress change rate and displacement gradient distribution of each node in the continuous monitoring period are calculated, and a stress-displacement response corresponding table is established with time sequence as index, forming the overall stress response sequence relationship, and generating the structure stress dynamic sequence set.

[0009] As a further scheme of the present application, the specific steps for generating the structure health confidence distribution set are: Based on the structure stress dynamic sequence set, the stress and load change values of the guide rail nodes and the hydraulic support arms in the continuous monitoring period are collected, the force difference between nodes is calculated and compared with the average value in the period to determine the dependent direction, and the difference and ratio conversion are performed in time intervals, the time sequence conditional dependence relationship and state transmission structure between nodes are established by using dynamic Bayesian network, and the node stress dependence rate set is generated; Based on the node stress dependence rate set, the numerical comparison of the period load difference and the reaction force change difference value is performed, the state change amplitude is determined by synchronous calculation of the node force value difference and the reaction force deviation, and the linear correction and proportional reduction of the node confidence value deviating from the limit value are performed, and the node confidence correction matrix is generated; Based on the node confidence correction matrix, the node confidence values are arranged in time sequence and interval smoothing processing is performed, the confidence curve balance adjustment is completed by confidence value averaging and extreme value removal in sliding section, and the continuous mapping relationship between periods in the sequence is established, and the structure health confidence distribution set is generated; The node confidence level, state matrix and confidence sequence, in the structure stress dynamic sequence set generated in S1, each node corresponds to a group of time sequences containing load change value, stress difference value and displacement gradient, the force stability degree index of the node in the current monitoring period is obtained by standardizing the ratio calculation of the force difference and reaction force difference at each time point in the sequence, the index is defined as the node confidence level, the confidence level values of all nodes are arranged in time index and a two-dimensional matrix is constructed, the rows of the matrix correspond to the node numbers, the columns correspond to the monitoring time periods, and the units in the matrix are the confidence level values, the matrix is defined as the state matrix, in the time continuous monitoring process, the confidence level change sequence of each node in the adjacent monitoring period is arranged in time sequence to form the confidence sequence.

[0010] As a further scheme of the present application, the dynamic Bayesian network first takes the monitoring data of the guide rail node, the hydraulic support arm and the main bearing arm as the input source, constructs a node set according to the stress, load and vibration change values in a continuous monitoring period, establishes a time sequence dependent connection between the node states by taking the stress difference and load ratio of each node between the previous period and the current period as the conditional variables, forms a state transition chain structure through the calculation and time index sorting of the conditional probability of adjacent nodes, and corrects the conditional probability distribution according to the updated observation value in each monitoring period, so that the nodes remain dynamically associated in the time sequence, obtains a time sequence probability structure containing the node dependent relationship and state transmission direction, and generates a node stress dependent rate set.

[0011] As a further scheme of the present application, the specific steps for generating the structure degradation trajectory set are: Based on the structure health confidence distribution set, a confidence value sequence of the main bearing arm and the rotary support shaft is extracted, a change rate is determined by calculating the confidence difference between adjacent time points, and a direction judgment and amplitude normalization are performed according to the period index to form a time sequence confidence difference record and generate a confidence change difference value set; Based on the confidence change difference value set, a confidence drop rate threshold node is screened, a degradation trend is determined by rearranging the confidence sequence and calculating the average change rate of adjacent sections, and the fluctuation interval range and stable period length are measured in each sequence to generate a degradation node sequence set; Based on the degradation node sequence set, a difference operation is performed on the node confidence curve and a shape offset value is calculated, similar node sets are aggregated by comparing the distance between curves, a long short-term memory network is used for time-dependent prediction and sequence fitting of the confidence curve, and time synchronization balance calculation is performed within the group to form a continuous degradation trajectory sequence, and a structure degradation trajectory set is generated.

[0012] As a further scheme of the present application, the long short-term memory network takes the confidence value time sequence of each node in the degradation node sequence set as input, constructs a sequence vector according to the time sequence of the confidence change rate, fluctuation amplitude and period index value of each node, and takes the confidence difference between adjacent time periods as a time step input unit, accumulates the influence of historical confidence changes on the current node state through the state transmission of the network between the previous and subsequent time steps, and performs state memory and output mapping operations between time steps, then performs continuous time expansion and numerical regression on the output sequence to obtain the degradation change sequence of the corresponding node, and finally synchronously arranges the degradation sequences of each node according to the time index and integrates the trajectories to generate a structure degradation trajectory set.

[0013] As a further scheme of the present application, the specific steps for generating the degradation phase distribution set are: Based on the structure degradation trajectory set, the slope change rate of the guide rail segment and the support node curve is calculated, the degradation rate distribution is judged by comparing the curve shape difference in adjacent periods, and the curve turning points and trend paragraphs are located in the time interval to generate the degradation boundary interval set; Based on the degradation boundary interval set, the rate change amount of each interval and the period attenuation value are segmented and counted, the average attenuation period length is obtained by linear segment difference value operation, and the interval attenuation ratio is sequentially arranged to form a stage division table, and a degradation stage distribution set is generated.

[0014] As a further scheme of the present application, the specific steps for generating the structure residual life index are: Based on the degradation stage distribution set, the confidence decline rate and the period length ratio of the bearing arm and the rotary frame stage are calculated, the degradation stage boundary is identified by rate threshold judgment, and the termination time of the period node is sequentially arranged to generate a life critical stage set; Based on the life critical stage set, the termination time and the confidence decline rate are time product calculated to form a life attenuation amount table, and the cumulative attenuation amount and the period distribution parameter are multiplied and added to generate a structure residual life index.

[0015] An aerial building factory structure health monitoring system for executing the above-mentioned aerial building factory structure health monitoring method, the system comprising: A load response collection module: based on the main bearing arm, the lifting guide rail, the cantilever frame load change rate and the vibration offset, the node stress difference and the displacement gradient are calculated, the direction is judged by comparing the reaction force distribution and the load increment to form a node stress matrix, and a structure stress dynamic sequence set is generated; A time sequence dependent calculation module: based on the structure stress dynamic sequence set, the guide rail node and the hydraulic support arm dependence rate are calculated, the period stress difference and the reaction force difference ratio are compared, the node time sequence dependence relationship is established by using a dynamic Bayesian network, the state matrix is updated and the confidence sequence is smoothed, and a structure health confidence distribution set is generated; A confidence evolution inference module: based on the structure health confidence distribution set, the confidence change rate is calculated and the declining nodes are screened, the confidence sequence is rearranged to calculate the change interval, the long short-term memory network is used to predict the confidence curve change trend, a continuous degradation sequence is formed, and a structure degradation trajectory set is generated; A degradation stage identification module: based on the structure degradation trajectory set, the curve slope and the inflection point are extracted, the degradation boundary is judged by comparing the interval rate, and the attenuation period is fitted to generate a degradation stage distribution set; A life quantitative evaluation module: based on the degradation stage distribution set, the confidence decline rate and the period length ratio are calculated, the termination time point is screened and substituted into the expression to calculate the attenuation time, and a structure residual life index is obtained.

[0016] Compared with the prior art, the application has the advantages and positive effects that: In the application, the node stress difference and displacement gradient comparison are established based on the load change rate and vibration offset, the time sequence stress response matrix of the structure stress state is formed, the continuity and dynamics of the stress information on the time axis are ensured, the node time sequence relationship is calculated and introduced into the dynamic Bayesian network relying on the stress dependence rate, the confidence level updating process has the gradual iteration and smoothing characteristics by correcting the cycle load difference and the reaction force difference, and a stable and reliable confidence distribution set is formed; In the application, the descending super-threshold node is screened through the confidence distribution change rate, the curve difference and the shape similarity are compared, and the long short-term memory network is used to predict the curve trend, so that the degradation trend can be revealed in advance and unified as a clustering trajectory, the dispersion result caused by abnormal judgment is avoided, and the degradation boundary recognition has high sensitivity and interval determination capability through the extraction of the curve slope, the inflection point and the span, the rate comparison and the cycle attenuation length fitting. In the application, the termination time point is extracted through the calculation of the stage confidence descending rate and the cycle length ratio, and the attenuation time is calculated by substituting the termination time point into the confidence expression, so that the quantitative prediction of the remaining life is realized, the foresight of abnormal identification, the aggregation of degradation trajectory and the accuracy of life prediction are improved, and dynamic, continuous and quantifiable results are provided for structure safety evaluation under complex operating conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The figure is a schematic diagram of the working process of the application; Figure 2 The figure is a system flowchart of the application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0019] Example 1 Please refer to Figure 1 The application provides a technical scheme: an air building factory structure health monitoring method, comprising the following steps: S1: based on the load change rate and vibration offset of the main bearing arm, lifting guide rail and cantilever frame, calculate the node stress difference and displacement gradient, compare the load increase direction and reaction force distribution offset, form a time sequence stress response matrix, and generate a structure stress dynamic sequence set; S2: Based on the structure stress dynamic sequence set, the stress dependence rate of the guide rail node and the hydraulic support arm is calculated, the node time sequence dependence relationship is established by using dynamic Bayesian network, the period load difference and the change difference of the reaction force are compared, the node confidence level is corrected, the state matrix is updated and the confidence sequence is smoothed, and the structure health confidence distribution set is generated; S3: Based on the structure health confidence distribution set, the confidence change rate of the main bearing arm and the rotary support shaft is compared, the descending super-threshold node is screened, the curve difference is calculated and the shape similarity is compared, the long short-term memory network is used to predict the confidence curve trend, and the unified curve is divided into degradation clusters to generate the structure degradation trajectory set; S4: Based on the structure degradation trajectory set, the curve slope, inflection point and span of the guide rail segment and support node are extracted, the degradation boundary is judged by comparing the interval rate, the linear change rate and the period decay length are fitted to form the degradation stage distribution set; S5: Based on the degradation stage distribution set, the stage confidence drop rate and period length ratio of the bearing arm and rotary frame are calculated, the super-threshold stage is screened to extract the end time point, the confidence expression is substituted to obtain the decay time, and the structure remaining life index is obtained.

[0020] The structure stress dynamic sequence set includes load change sequence, stress distribution sequence and displacement gradient sequence, the structure health confidence distribution set includes node confidence value sequence, state transition matrix and smoothed confidence curve, the structure degradation trajectory set includes degradation curve sequence, degradation node set and confidence change rate curve, the degradation stage distribution set includes stage boundary position set, degradation rate set and period decay interval set, and the structure remaining life index includes remaining life value, life decay equation parameter and time distribution range.

[0021] The specific steps for generating the structure stress dynamic sequence set are as follows: Based on the load change rate and vibration offset of the main bearing arm, lifting guide rail and cantilever frame, the node stress value and displacement difference are calculated, the force direction is judged by the moment balance calculation between nodes, and the displacement change in continuous time period is accumulated to form the time sequence distribution table, and the node stress difference matrix is generated; Based on the node stress difference matrix, the proportional conversion of the load change and reaction force distribution value of each node is performed, and linear superposition and interval grouping are performed according to the monitoring time axis to form the continuous mapping matrix of the node stress difference in the time dimension, and it is defined as the time sequence stress response matrix; Based on the time sequence stress response matrix, the stress change rate and displacement gradient distribution of each node in the continuous monitoring period are calculated, and the stress-displacement response correspondence table is established with time sequence as index to form the overall stress response sequence relationship, and the structure stress dynamic sequence set is generated; Based on the load change rate and vibration offset of the main bearing arm, lifting guide rail and cantilever frame, the numerical solution of the node stress is carried out by using the finite element analysis method. Specifically, a three-dimensional model of the overall structure is established, the main bearing arm, lifting guide rail and cantilever frame are divided into equidistant eight-node cubic elements, the element size is set to 5mm, the material property parameters are set, including elastic modulus The elastic modulus is 200GPa, the Poisson's ratio is 0.3, the density is 7850kg / m3, the three translation degrees of freedom of the fixed end node are constrained, the load change rate input value is 500N / s, the vibration offset of the loaded node is recorded in millimeters, the node stress value is calculated in the time step of sampling once every 0.05 seconds, the force difference between nodes is calculated, the force direction is determined according to the moment balance formula between nodes, the node time sequence distribution table is formed by accumulating the displacement change of each time step, the matrix structure is constructed according to the node number, the force difference between nodes is arranged in rows and columns, and the force difference matrix between nodes is generated; Based on the node force difference matrix, the load change value and the reaction force distribution value of each node are executed proportional conversion, linear superposition and interval grouping calculation are carried out according to the monitoring time axis, the node force difference data is input into the calculation module in time sequence, the time step is set to 0.1 seconds, the force change of each node in adjacent time steps is linearly accumulated, the load change is in units of Newton per second, the reaction force distribution value is in units of Newton, and the node force change ratio is recorded in the form of ratio. Then, according to the time interval length of 5 seconds, the section is divided, the ratio value in the same section is averaged, the interval force sequence is formed, and the force difference between adjacent intervals is superimposed again to obtain the continuous distribution matrix of the node force difference with time. The matrix reflects the dynamic mapping relationship of the node force response in the time dimension, which is defined as the time sequence stress response matrix; Based on the time sequence stress response matrix, the stress change rate and displacement gradient of each node in the continuous monitoring period are calculated, a stress-displacement response correspondence table is established with time sequence as index, an overall force response sequence relationship is formed, the sequence relationship is used to describe the force evolution characteristics of the structure under multiple nodes and multiple periods, and finally a structure force dynamic sequence set is generated, which is used as the input basis for subsequent health confidence distribution construction.

[0022] The specific steps of generating the structure health confidence distribution set are as follows: Based on the structure force dynamic sequence set, the stress and load change values of the guide rail node and the hydraulic support arm in the continuous monitoring period are collected, the force difference between nodes is calculated and compared with the average value to determine the dependent direction, and the difference and ratio conversion are executed in time intervals. The time sequence conditional dependence relationship and state transmission structure between nodes are established by using dynamic Bayesian network to generate the node stress dependence rate set; Based on the node stress dependency set, the difference value of the periodic load difference and the reaction force difference is compared, the state change amplitude is determined by synchronous calculation of the node force value difference and the reaction force deviation, and the linear correction and proportional reduction of the node confidence value deviating from the limit value are performed to generate the node confidence correction matrix; Based on the node confidence correction matrix, the node confidence values are arranged in time sequence and interval smoothing processing is performed, the confidence curve balance adjustment is completed through the confidence value averaging and extreme value removal in the sliding section, and the continuous mapping relationship between periods in the sequence is established to generate the structure health confidence distribution set; The node confidence level, state matrix and confidence sequence, in the S1 generated structure stress dynamic sequence set, each node corresponds to a group of time sequence containing load change value, stress difference and displacement gradient, the stress difference and reaction force difference at each time point in the sequence are calculated by standardization ratio, to get the stress stability index of the node in the current monitoring period, the index is defined as the node confidence level, the confidence level values of all nodes are arranged according to time index and a two-dimensional matrix is constructed, the rows of the matrix correspond to the node number, the columns correspond to the monitoring time period, and the units in the matrix are the confidence level values. The matrix is defined as the state matrix. In the time continuous monitoring process, the confidence level change sequence of each node in the adjacent monitoring period is taken, and the confidence sequence is arranged in time sequence; Based on the structure stress dynamic sequence set, the stress and load change values of the guide rail nodes and the hydraulic support arm in the continuous monitoring period are collected, the dynamic Bayesian network is used to establish the time sequence conditional dependence relationship and state transfer structure, the preset period length is 600 seconds and the sampling frequency is 100 Hz, the preset time interval length is 2 seconds and the step length is 1 second, the stress difference between nodes is calculated and compared with the periodic average value to determine the dependence direction, the difference is calculated by subtracting the adjacent time step sequence and using the mirror image extension method, the proportional conversion uses the current difference value divided by the interval mean value and records the sign bit, the discretization uses the three state division scheme, the stress low interval is 0 to 80 megapascal, the medium interval is 80 to 160 megapascal, and the high interval is 160 to 240 megapascal, the load low interval is 0 to 2000 newtons, the medium interval is 2000 to 4000 newtons, and the high interval is 4000 to 6000 newtons. The network structure sets two state nodes of guide rail node stress and load and two state nodes of hydraulic support arm stress and load in each time slice, and sets the cross-time slice self-loop and the edge from the dependent direction to the dependent node. The conditional probability table is initialized with Dirichlet prior parameters of 1, 1 and 1, the state transition matrix is initialized with self-maintaining probability of 0.7, forward transition probability of 0.2 and reverse transition probability of 0.1, the training uses the expectation maximization algorithm with iteration number of 50 and convergence threshold of 1e-4, the missing segment processing uses the combination strategy of forward filling and interval mean interpolation with threshold of 10 percent, and the output of each node in each time slice is the dependent direction and the conditional probability value, which are integrated into the dependence rate sequence to generate the node stress dependence rate set; Based on the node stress dependence rate set, the numerical comparison of the period load difference and the force change difference value is carried out, the synchronous time stamp alignment mode and the linear interpolation mode are adopted to fill the gap, the state change amplitude calculation adopts the weighted summation scheme, the weight load difference is 0.6, the weight force difference is 0.4, the deviation limit value setting adopts the median absolute deviation multiplied by three rules and is calculated independently according to the node, after time alignment, the node force value difference and the force deviation of each node are calculated synchronously in each time slice and the sign bit is recorded, the confidence value range is limited to 0 to 1 and the rounding precision is taken to four decimal places, the confidence value of the deviation limit value node is executed linear correction, the correction coefficient k is 0.2, the deviation amount is multiplied by the coefficient to execute the deduction processing and the sign is kept consistent, the confidence value after correction is executed again Proportional reduction, the reduction coefficient is 0.85, which is applied independently according to the node and the boundary clipping is completed in each time slice, the matrix layout adopts time as row and node as column and confidence as unit value, the index adopts time stamp ascending order and node number ascending order, and the node confidence correction matrix is generated; Based on the node confidence correction matrix, the node confidence value is arranged according to the time sequence and the interval smoothing processing is executed, the smoothing adopts the sliding average window length of 15 samples, the step length of 5 samples and the equal weight scheme, the extreme value processing adopts the Hampel filter window length of 15 samples, the threshold coefficient of 3 and the replacement strategy of the window median, the smoothing order adopts the first extreme value elimination and then the sliding average, which is executed independently according to the node, the cross-period mapping adopts the segmented linear interpolation mode, inserts 20 connection samples at the adjacent period boundary and the sampling rate is 10 Hz, and the boundary two end values are used as the end points, the sequence alignment adopts the uniform time axis length equal to the total number of samples of each period and executes linear filling for the missing section, the discrete distribution construction adopts the box number of 100 and the box width of 0.01 and the count normalization to 0 to 1 range, and the output generates the time alignment sequence and the distribution direct dimension vector and the period mark three tuple set according to the node, and generates the structure health confidence distribution set.

[0023] The dynamic Bayesian network first takes the monitoring data of the guide rail node, the hydraulic support arm and the main bearing arm as the input source, constructs the node set according to the stress, load and vibration change value in the continuous monitoring period, and takes the stress difference and load ratio of each node between the previous period and the current period as the conditional variable to establish the time sequence dependence connection between the node states, calculates and time index sorts the conditional probability of adjacent nodes to form a state transition chain structure, and modifies the conditional probability distribution according to the updated observation value in each monitoring period, so that the nodes are dynamically associated in time sequence, and a time sequence probability structure containing node dependence relationship and state transmission direction is obtained, and a node stress dependence rate set is generated; The dynamic Bayesian network is according to the formula:

[0024] Among them: indicates the stress value of the air building factory guide rail node at time t, This represents the stress value of the hydraulic support arm in the aerial building construction factory at time t. This represents the average stress difference between the guide rail node and the hydraulic support arm during the monitoring period. This represents the change in the hydraulic pressure difference between the guide rail node and the hydraulic support arm at time t. This represents the temperature gradient difference between the guide rail node and the hydraulic support arm at time t. This represents the correction term for the friction coefficient between the guide rail node and the hydraulic support arm. The weighting coefficient representing the stress difference. This represents the weighting coefficient for the hydraulic pressure difference. The weighting coefficients represent the temperature gradient difference. This represents the weighting coefficient of the friction correction term. This represents the improved stress dependence rate calculation value of the aerial building factory at time t. Execution process: First, collect stress data sequences of the guide rail nodes and hydraulic support arms during continuous monitoring cycles. and The structural stress state at different time points is recorded in real time using a high-precision strain sensing unit. Then, the stress difference between nodes at each time point is calculated, and the average stress difference within the monitoring period is obtained. It is used to establish a benchmark scale for stress changes and to extract real-time pressure change data of the hydraulic system to calculate the hydraulic differential. The system pressure normalization module corrects the stress level to reflect the load adjustment effect of the support arm, while collecting nodal surface temperature data to calculate the temperature gradient difference. The stress offset effect is corrected using the thermal expansion coefficient of the structural material to compensate for stress errors under high-temperature conditions. Then, the friction coefficient correction term between nodes is calculated using a friction monitoring device. This is used to characterize the nonlinear effect of the contact resistance between the guide rail and the support arm on force transmission. The stress difference, hydraulic difference, temperature difference, and friction correction term are multiplied by weighting coefficients respectively. , , , Furthermore, by using historical monitoring data and employing the minimum mean square error method to determine the weight combination, it can dynamically balance multiple physical influencing factors when calculating the improved stress dependence rate between nodes. This reflects the structural health status between the guide rail nodes and hydraulic support arms in the complex working environment of the aerial building factory, enabling quantitative diagnosis of structural load transfer characteristics and potential abnormal trends.

[0025] The specific steps for generating a set of structural degradation trajectories are as follows: Based on the structural health confidence distribution set, the confidence value sequence of the main bearing arm and the rotary support shaft is extracted, the change rate is determined by calculating the confidence difference of adjacent time points, and the direction judgment and amplitude normalization are performed according to the cycle index, forming the time sequence confidence difference record, and generating the confidence change difference value set; Based on the confidence change difference value set, the confidence drop rate threshold node is screened, the degradation trend is determined by rearranging the confidence sequence and calculating the average change rate of adjacent sections, and the fluctuation interval range and stable cycle length are calculated in each sequence, generating the degradation node sequence set; Based on the degradation node sequence set, the difference operation is performed on the node confidence curve and the morphological offset value is calculated, the nodes with similar morphology are aggregated by comparing the distance between curves, the long short-term memory network is used for time-dependent prediction and sequence fitting of the confidence curve, and the time synchronization balance calculation is performed in the group to form the continuous degradation trajectory sequence, generating the structure degradation trajectory set; Based on the structural health confidence distribution set, the confidence value sequence of the main bearing arm and the rotary support shaft is extracted, the confidence change rate is calculated by using time series difference algorithm, the time sampling interval is set to 0.1 seconds, the confidence value range is pre-defined as 0 to 1, the difference order is 1, the end points are filled with mirror image, 5 sample points are added at both ends of the sequence, the instantaneous change is obtained by calculating the confidence value difference of adjacent time points, and the change rate array is formed by taking the absolute value of the change and taking the mean value, the direction is identified by the sign function, the amplitude is normalized to the interval 0 to 1 by the minimum maximum normalization method, the direction mark and amplitude remapping are performed on each time slice according to the cycle index, the confidence change rate time sequence is output, the confidence difference sequence is recorded in time order, and the confidence change difference value set is generated; Based on the confidence change difference value set, the degradation nodes are determined by threshold screening and moving average method, the confidence drop rate threshold is pre-set to 0.05, the sliding window length is 10 sample points, and the step length is 5 sample points, the window average is performed on each node confidence change sequence, the average drop rate in the window is extracted and compared with the threshold, the threshold node index is marked, the threshold node confidence sequence is rearranged and aligned in time order, the average change rate is calculated for adjacent sections, the interval length is set to 3 seconds, and the time span is increased by a fixed step, the variance of each sequence is calculated and the fluctuation interval range is recorded, the cycle length is calculated by the first peak distance of the autocorrelation function and rounded to the stable cycle length, the degradation node index and its corresponding average change rate and cycle length record are output, and the degradation node sequence set is generated; Based on the set of degenerate node sequence, the first order difference and morphological distance calculation method is performed on the node confidence curve, the difference step is 1, the end point is filled by forward copying, the morphological offset value calculation adopts Euclidean distance and is normalized in time dimension, the distance matrix is calculated for all node curves and the clustering operation is performed, the clustering method is hierarchical clustering algorithm, the shortest distance is taken as the link method, the number of clusters is set to 5, the morphologically similar nodes are divided into the same set, then the long short-term memory network is used for time-dependent prediction and sequence fitting, the network structure includes input layer dimension 1, hidden layer 2 layers, each layer unit number 64, output layer dimension 1, the activation function adopts hyperbolic tangent function, the optimizer selects Adam, the learning rate is 0.001, the batch size is 32, the training rounds are 100, the loss function adopts mean square error, the time synchronization balance calculation is performed on the confidence curve in each clustering group, the balance strategy is interpolation to the uniform time axis length 1000 sample points, the continuous degradation trajectory sequence is generated by stacking according to the time index, and the structural degradation trajectory set is generated.

[0026] The long short-term memory network takes the time sequence of the confidence value of each node in the set of degenerate node sequences as input, constructs the confidence change rate, fluctuation amplitude and period index value of each node into a sequence vector according to time sequence, and takes the confidence difference between adjacent time periods as a time step input unit. Through the state transmission between the network in front and back time steps, the influence of historical confidence change on the current node state is accumulated and expressed. State memory and output mapping operations are performed between each time step. Then the output sequence is continuously expanded in time and regressed in value to obtain the degradation change sequence of the corresponding node. The degradation sequences of each node are arranged synchronously according to the time index and integrated into a trajectory to generate a structural degradation trajectory set. The long short-term memory network is according to the formula:

[0027] Among them: represents the improved hidden state output of the air building factory at time t, represents the activation function, represents the input weight matrix, represents the input feature vector of the degenerate node confidence curve at time t, represents the state transmission weight matrix, represents the hidden state vector at the previous time t-1, represents the bias term, represents the morphological skewness weight coefficient, represents the morphological skewness index, represents the curvature energy item weight coefficient, represents the curvature energy density, represents the aggregation condensation item weight coefficient, represents the aggregation condensation coefficient, denotes a time synchronization item weight coefficient, denotes a time synchronization balance offset; The execution process is as follows: firstly, the confidence curve sequence of each guide rail node and the hydraulic support arm is collected, the rate of change of the node confidence value with time is calculated through continuous monitoring periods, and the morphological offset is obtained, the differential result is spliced with the morphological features to form an input feature vector , which is then input into the input layer and mapped to the hidden layer state space through the input weight matrix , depicting the time sequence change trend of the node degradation feature, and the hidden state of the previous moment is transmitted to the current moment through the state transmission weight matrix to maintain the time dependence of the degradation process and add a bias term to eliminate systematic prediction bias and introduce a morphological skew index The third central moment of the confidence curve is normalized to measure the asymmetry of the curve, and the curvature energy density is calculated The second-order differential square mean value is used to represent the curve fluctuation intensity, and the inverse of the normalized distance between the node curve and the intra-group Fréchet mean curve is used to calculate the aggregation coefficient to reflect the intra-group concentration of the curve, and then the time synchronization balance offset is obtained in the local least squares alignment manner to correct the time misalignment between curves, and the four innovative parameters are multiplied by their corresponding weights and summed with the main term to input the activation function , which nonlinearly maps all terms to output the improved hidden state at the current moment , and the degradation trajectory sequence is formed by iterating through consecutive time steps, which constitutes the result degradation trajectory set of the air building factory, which can be used to identify the structural performance degradation trend and potential degradation risk, and realize dynamic prediction and reliability evaluation of the overall structural health state.

[0028] The specific steps of generating the degradation phase distribution set are as follows: Based on the structural degradation trajectory set, the slope change rate of the guide rail segment and the support node curve is calculated, the degradation rate distribution is judged by comparing the curve morphological difference in adjacent periods, and the curve turning points and trend paragraphs are located in the time interval to generate the degradation boundary interval set; Based on the degradation boundary interval set, the rate change of each interval and the period attenuation value are statistically segmented, the average attenuation period length is obtained through linear segment difference value operation, and the interval attenuation ratio is sequentially arranged to form a phase division table, and the degradation phase distribution set is generated; Based on the structural degradation trajectory set, the piecewise linear regression algorithm is used to calculate the curve slope change rate of the guide rail segment and the support node. The input data is the time sequence of the node degradation trajectory, the time sampling interval is set to 0.1 seconds, the segment interval length is set to 50 sample points, the linear regression process is independently executed for each time period, the time average and confidence value average of each interval sample are calculated, the offset of each sample point relative to the time average is calculated, and the product sum and square sum of the offset are accumulated, then the slope value is obtained by dividing the two, the intercept is obtained by subtracting the slope multiplied by the time average from the confidence value average, the slope sequence is formed by arranging the slope of each interval in turn, the slope change rate sequence is obtained by dividing the slope difference of adjacent intervals by the interval time interval, the unit is megapascal per second, the curve patterns of adjacent cycles are compared, the dynamic time warping algorithm is used to perform pattern matching, the Euclidean distance is set as the distance measurement method, the constraint step is 2 sampling points, and the time matching error tolerance is 0.05 seconds. The shortest distance path is recorded by performing column-by-column scanning on the shape difference matrix, and the local minimum value region in the path is marked as a high similarity interval. The turning points are detected in the time sequence by using the second derivative sign change, and the turning point detection threshold is set to 1.2 times the average change rate of the derivative. The time period between consecutive turning points is divided into trend intervals, the trend direction is determined according to the positive and negative of the slope and is recorded in time sequence, and the degradation boundary interval set is generated. Based on the degradation boundary interval set, the linear segment difference value and cycle calculation algorithm is used to perform segment statistics on the rate change and cycle attenuation values of each interval. The input is the start and end time of the degradation boundary interval, the slope sequence and the confidence value sequence. First, calculate the slope difference between the first and last of each interval to record the rate change, with the unit of megapascal per second. Then, calculate the average change of the confidence value of each interval by time length to obtain the cycle attenuation value. The cycle division length is set to 10 seconds. The average ratio of the rate change and the cycle attenuation value in each cycle is recorded. The difference between the rate change and the cycle attenuation of each interval is linearly superimposed and averaged to obtain the average attenuation cycle length, with the unit of seconds. The average attenuation length of all intervals is arranged in time sequence, and the interval attenuation ratio sequence is generated. The attenuation ratio is defined as the proportion of the attenuation length of a single interval to the total cycle length, with a value range of 0 to 1. Finally, the attenuation ratios of all intervals are arranged in order to form a stage index list. The stage numbers are sequentially ordered and saved to a two-dimensional array structure to generate the degradation stage distribution set.

[0029] The specific steps of generating the structural residual life index are as follows: Based on the degradation stage distribution set, the stage confidence drop rate and cycle length ratio of the bearing arm and the rotary frame are calculated. The degradation stage boundary is identified by the rate threshold value, and the end time of the cycle node is arranged in order to generate the life critical stage set. Based on the life critical stage set, the termination time is multiplied by the confidence decline rate to form the life attenuation table, and the cumulative attenuation is multiplied by the cycle distribution parameter to generate the structure residual life index; Based on the degradation stage distribution set, the threshold segmentation algorithm is used to calculate the confidence decline rate and cycle length ratio of the bearing arm and the rotary frame stage. The input data includes the confidence value sequence and the time index sequence. The confidence decline rate is calculated by dividing the confidence difference at the beginning and end of the stage by the corresponding cycle time length. The time resolution is set to 0.1 seconds, and the result unit is confidence value per second. The decline rate of each stage is calculated and recorded. The cycle length is calculated as the cycle length column according to the stage start and end time difference. Then, the rate ratio sequence is generated by the ratio operation of the rate and cycle length. The threshold is set to 1.3 times the average value of the decline rate. The threshold is determined by comparing each item in the rate ratio sequence. The determination rule is that the value greater than the threshold is marked as a degradation boundary point, and the value less than or equal to the threshold is marked as a stable stage. The time index of the marked boundary point is extracted to obtain the termination time of the corresponding cycle node. The termination time sequence is arranged in time order, numbered and output as a time sequence to generate the life critical stage set. Based on the life critical stage set, the termination time is multiplied by the confidence decline rate to form the life attenuation table, and the cumulative attenuation is multiplied by the cycle distribution parameter to generate the structure residual life index.

[0030] Please refer to Figure 2 , the air building factory structure health monitoring system is used to execute the above-mentioned air building factory structure health monitoring method. The system comprises: Load response acquisition module: based on the load change rate and vibration offset of the main bearing arm, lifting guide rail and cantilever frame, the node force difference and displacement gradient are calculated, the direction is determined by comparing the reaction force distribution and load increment, the node force matrix is formed, and the structure stress dynamic sequence set is generated. The time sequence dependent calculation module calculates the guide rail node and the hydraulic support arm dependent rate based on the structural stress dynamic sequence set, compares the period stress difference and the reaction force difference ratio, establishes the node time sequence dependent relationship by using a dynamic Bayesian network, updates the state matrix and smooths the confidence sequence, and generates a structural health confidence distribution set; The confidence evolution inference module calculates the confidence change rate and selects the descending nodes based on the structural health confidence distribution set, rearranges the confidence sequence to calculate the change interval, predicts the confidence curve change trend by using a long short-term memory network, forms a continuous degradation sequence, and generates a structural degradation trajectory set. The degradation phase recognition module extracts the curve slope and the inflection point based on the structural degradation trajectory set, compares the interval rate to judge the degradation boundary and fits the decay period, and generates a degradation phase distribution set. The life quantitative evaluation module calculates the confidence decline rate and the period length ratio based on the degradation phase distribution set, selects the termination time point, and substitutes it into the expression to calculate the decay length, and obtains a structural remaining life index.

[0031] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to obtain equivalent embodiments applied to other fields. However, any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the present application.

Claims

1. A method for monitoring the structural health of a factory built in the air, characterized in that, Includes the following steps: S1: Based on the load change rate and vibration offset of the main load-bearing arm, lifting guide rail, and cantilever frame, calculate the nodal stress difference and displacement gradient, compare the load increase direction and reaction force distribution offset, form a time-series stress response matrix, and generate a set of dynamic stress sequence of the structure. S2: Based on the dynamic sequence set of structural stress, calculate the stress dependence rate of guide rail nodes and hydraulic support arm, establish node temporal dependence relationship using dynamic Bayesian network, compare the difference of periodic load and the difference of reaction force change, correct the node confidence level, update the state matrix and smooth the confidence sequence, and generate structural health confidence distribution set. S3: Based on the aforementioned structural health confidence distribution set, compare the confidence change rates of the main bearing arm and the slewing support axis, screen out nodes that exceed the threshold for decline, calculate the curve difference and compare the morphological similarity, use a long short-term memory network to predict the trend of confidence curve changes, divide the degradation clusters to generate a unified curve, and generate a set of structural degradation trajectories. S4: Based on the set of structural degradation trajectories, extract the slope, inflection point and span of the guide rail segment and support node curves, compare the interval rate to determine the degradation boundary, fit the linear rate of change and the periodic decay length to form a set of degradation stage distributions; S5: Based on the degradation stage distribution set, calculate the confidence decline rate and period length ratio of the bearing arm and the slewing frame stages, screen out the overthreshold stage to extract the termination time point, substitute it into the confidence expression to calculate the decay time, and obtain the structural remaining life index.

2. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The set of dynamic stress sequences of the structure includes load change sequences, stress distribution sequences, and displacement gradient sequences. The set of confidence distributions of structural health includes node confidence value sequences, state transition matrices, and smooth confidence curves. The set of structural degradation trajectories includes degradation curve sequences, degradation node sets, and confidence rate of change curves. The set of degradation stage distributions includes stage boundary location sets, degradation rate sets, and period decay interval sets. The remaining life index of the structure includes remaining life values, life decay equation parameters, and time distribution ranges.

3. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The specific steps for generating the dynamic force sequence set of the structure are as follows: Based on the load change rate and vibration offset of the main load-bearing arm, lifting guide rail, and cantilever frame, the force value and displacement difference of the nodes are calculated. The force direction is determined by the moment balance calculation between nodes. The cumulative displacement change over a continuous period is used to form a time-series distribution table and generate a node force difference matrix. Based on the node stress difference matrix, the load change and reaction force distribution values ​​of each node are proportionally converted, and linearly superimposed and grouped according to the monitoring time axis to form a continuous mapping matrix of node stress difference in the time dimension, which is defined as the time-series stress response matrix. Based on the time-series stress response matrix, the rate of change of force and the distribution of displacement gradient of each node in the continuous monitoring period are calculated, and a stress-displacement response correspondence table is established with the time series as the index to form the overall stress response sequence relationship and generate a set of dynamic stress sequence of the structure.

4. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The specific steps for generating the structural health confidence distribution set are as follows: Based on the dynamic sequence set of structural stress, the stress and load changes of the guide rail nodes and hydraulic support arms are collected in the continuous monitoring cycle. The dependence direction is determined by calculating the force difference between nodes and comparing it with the cycle average value. The difference and ratio conversion is performed in time intervals. A dynamic Bayesian network is used to establish the temporal conditional dependence relationship and state transfer structure between nodes, and a set of node stress dependence rates is generated. Based on the set of nodal stress dependence rates, the difference between periodic load and reaction force is numerically compared. The magnitude of state change is determined by synchronously calculating the difference between nodal force and reaction force. Linear correction and proportional reduction are performed on the node confidence values ​​that deviate from the limit to generate a node confidence correction matrix. Based on the node confidence correction matrix, the node confidence values ​​are arranged in time series and interval smoothing is performed. The confidence curve balance adjustment is completed by the built-in confidence value averaging and extreme value removal in the sliding segment. A continuous mapping relationship between periods is established in the sequence to generate a structural health confidence distribution set.

5. The method for monitoring the structural health of a building-in-the-sky factory according to claim 4, characterized in that, The dynamic Bayesian network first uses monitoring data from guide rail nodes, hydraulic support arms, and main load-bearing arms as input sources. It constructs a node set based on stress, load, and vibration changes within continuous monitoring cycles. Using the stress difference and load ratio between the previous and current cycles for each node as condition variables, it establishes temporal dependency connections between node states. By calculating the conditional probabilities of adjacent nodes and sorting them by time index, a state transition chain structure is formed. Within each monitoring cycle, the conditional probability distribution is corrected based on the updated observations, ensuring that nodes remain dynamically correlated in the time series. This results in a temporal probability structure containing node dependencies and state transfer directions, generating a set of node stress dependency rates.

6. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The specific steps for generating the set of structural degradation trajectories are as follows: Based on the aforementioned structural health confidence distribution set, the confidence value sequence of the main bearing arm and the slewing support shaft is extracted. The rate of change is determined by calculating the confidence difference between adjacent time points, and direction judgment and amplitude normalization are performed according to the periodic index to form a time series confidence difference record and generate a confidence change difference set. Based on the set of confidence change differences, nodes with confidence decline rates exceeding the threshold are screened, and the degradation trend is determined by rearranging their confidence sequences and calculating the average rate of change of adjacent segments. The fluctuation range and the length of the stable period are measured in each sequence to generate a set of degradation node sequences. Based on the degenerate node sequence set, differential operations are performed on the node confidence curves and morphological offset values ​​are calculated. By comparing the distances between curves, a set of nodes with similar morphologies is aggregated. A long short-term memory network is used to perform time-dependent prediction and sequence fitting on the confidence curves. Time-synchronous balancing calculations are performed within the group to form a continuous degenerate trajectory sequence, generating a structural degenerate trajectory set.

7. The method for monitoring the structural health of a building-in-the-sky factory according to claim 6, characterized in that, The Long Short-Term Memory (LSTM) network takes the time series of confidence values ​​of each node in the degenerate node sequence set as input. It constructs a sequence vector by the confidence change rate, fluctuation amplitude, and period index value of each node in chronological order, and uses the confidence difference between adjacent time periods as the input unit of the time step. Through the state transfer between consecutive time steps, the influence of historical confidence changes on the current node state is accumulated and expressed. State memory and output mapping operations are performed between each time step. Then, continuous time expansion and numerical regression are performed on the output sequence to obtain the degenerate change sequence of the corresponding node. Finally, the degenerate sequences of each node are synchronously arranged and integrated according to the time index to generate a set of structural degenerate trajectories.

8. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The specific steps for generating the degradation stage distribution set are as follows: Based on the set of structural degradation trajectories, the slope change rate of the guide rail segment and support node curves is calculated. The degradation rate distribution is determined by comparing the curve shape differences in adjacent periods. The curve inflection points and trend segments are located within the time interval to generate a set of degradation boundary intervals. Based on the set of degradation boundary intervals, the rate change and period decay value of each interval are statistically analyzed. The average decay period length is obtained by linear segment difference calculation. The interval decay ratios are arranged in order to form a stage division table, generating a degradation stage distribution set.

9. The method for monitoring the structural health of a building-in-the-sky factory according to claim 1, characterized in that, The specific steps for generating the remaining life index of the structure are as follows: Based on the degradation stage distribution set, the ratio of the stage confidence descent rate to the cycle length of the bearing arm and the slewing frame is calculated. The degradation stage boundary is identified by the rate threshold, and the termination time of the cycle node is arranged in order to generate a life critical stage set. Based on the set of critical lifetime stages, the termination time and the confidence decline rate are multiplied over time to form a lifetime decay table. The cumulative decay amount is then multiplied and added with the periodic distribution parameter to generate the structural remaining lifetime index.

10. A structural health monitoring system for aerial building factories, characterized in that, The method for monitoring the structural health of a building-in-the-sky factory according to any one of claims 1-9, wherein the system comprises: Load response acquisition module: Based on the load change rate and vibration offset of the main load arm, lifting guide rail, and cantilever frame, calculate the force difference and displacement gradient of the nodes, determine the direction by comparing the reaction force distribution with the load increase, form the node force matrix, and generate a set of dynamic sequence of structural forces. The temporal dependency calculation module calculates the dependency rate between the guide rail nodes and the hydraulic support arm based on the dynamic stress sequence set of the structure, compares the ratio of periodic stress difference to reaction force difference, establishes the temporal dependency relationship of the nodes using a dynamic Bayesian network, updates the state matrix and smooths the confidence sequence, and generates a structural health confidence distribution set. Confidence evolution inference module: Based on the structural health confidence distribution set, calculate the confidence change rate and screen the declining nodes, rearrange the confidence sequence to calculate the change interval, use a long short-term memory network to predict the change trend of the confidence curve, form a continuous degradation sequence, and generate a set of structural degradation trajectories; Degradation stage identification module: Based on the set of degradation trajectories of the structure, extract the curve slope and inflection point, compare the interval rate to determine the degradation boundary and fit the decay period to generate a set of degradation stage distributions; Lifetime quantitative assessment module: Based on the degradation stage distribution set, calculate the confidence decline rate to cycle length ratio, screen the termination time point and substitute it into the expression to calculate the decay duration, and obtain the structural remaining life index.

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