Super high-rise structure stress monitoring method and system

Abstract

The invention relates to the technical field of structure health monitoring, in particular to a super high-rise structure stress monitoring method and system.The method comprises the following steps that node stress value calculation rate difference is obtained to generate a difference sequence, nodes are classified to generate a change response area, stress value calculation fluctuation amplitude is extracted to generate an abnormal segment, and the abnormal segment is extracted; and extracting a response value, calculating a median difference to obtain a drift value, and adjusting a reference value to record a benchmark formation trend line induction evolution trajectory to obtain a monitoring scheme. According to the method, monitoring area distribution is optimized through node classification and rate gradient adjustment, the microstress fluctuation capturing capacity is improved, the fluctuation amplitude is extracted through a sliding window, an abnormal chain is continuously judged, fluctuation abnormity tracking accuracy is enhanced, the deviation direction and amplitude are extracted through the difference between a median value and an observation reference value, and the overall trend recognition precision is enhanced; and the drift value dynamically corrects the reference datum and records a change track, so that structural stress evolution characteristics are presented, and the sensitivity, coherence and trend sensing capability of stress monitoring of the super high-rise building are integrally improved.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology, and in particular to a method and system for stress monitoring of super high-rise structures. Background Technology

[0002] The field of structural health monitoring technology encompasses the monitoring and assessment of external environmental forces and inherent damage to large engineering structures such as buildings, bridges, tunnels, and super high-rise structures during their service life. The core of this technology involves utilizing various sensors to collect real-time data on internal or surface physical quantities such as stress, strain, displacement, and vibration. This data is transmitted to a data processing system via wired or wireless means and combined with mechanical analysis, health status identification, and assessment methods to achieve a comprehensive understanding and dynamic tracking of the structural condition. Structural health monitoring technology as a whole covers multiple sub-fields, including sensor deployment and optimization, signal acquisition and processing, damage identification algorithms, data fusion and model updates, and long-term monitoring system design. It is widely used in infrastructure life extension, safety assessment, and maintenance decision support, characterized by its systematic, real-time, and intelligent nature.

[0003] The stress monitoring method for super high-rise structures refers to a technical solution for real-time monitoring of the stress distribution changes within the internal structure of super high-rise buildings under different load conditions. This patent primarily covers the real-time acquisition of stress data from key nodes or load-bearing components of super high-rise structures by deploying stress sensor arrays. The data is then synchronized to a central processing unit via a pre-defined data acquisition and transmission network. Stress change analysis is performed based on a finite element analysis model, and the internal stress distribution of the structure is visualized and dynamically recorded. Simultaneously, a stress baseline model is established to compare with actual monitoring values, determining the evolution of the structural stress state. The overall method relies on specific means such as stress sensor deployment strategies, data acquisition rule settings, monitoring data correction, and analysis model establishment.

[0004] Current technologies for monitoring the health of super high-rise structures primarily rely on static acquisition of nodal physical quantities, lacking a dynamic difference sorting mechanism for continuous stress rate changes. This results in insufficient sensitivity in identifying the initial stages of stress anomalies, making it easy for local damage evolution to be masked by data noise and difficult to pinpoint accurately. Insufficient application of nodal classification management and fixed spacing adjustment strategies means that the distribution of monitoring points within the structure does not fully align with stress evolution characteristics, making it difficult to effectively detect small-scale stress gradient changes. Anomaly monitoring lacks continuous extraction of fluctuation amplitude and anomaly chain discrimination; single data fluctuations are easily isolated and cannot continuously reveal the evolution of potential anomalies. In stress response analysis, over-reliance on extreme value indicators fails to screen overall trends through median value characteristics, making the direction of structural state changes susceptible to misjudgment due to abnormal fluctuations. For example, during periods of intense wind load changes, frequent jumps in maximum values ​​mask actual trend changes. Reference benchmarks are typically statically set, lacking dynamic correction mechanisms based on drift direction and amplitude, making it difficult to capture long-term, slow evolution trends in a timely manner, affecting the accuracy of continuous tracking and dynamic assessment of structural health status. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a stress monitoring method and system for ultra-high-rise structures.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for stress monitoring of ultra-high-rise structures, comprising the following steps: S1: Obtain the stress values ​​of key nodes in super high-rise buildings at continuous time points, calculate the rate based on stress difference and time interval, calculate the rate difference for all nodes, and generate a stress rate difference sequence. S2: Based on the stress rate difference sequence, classify the nodes around the segments to which the node pairs belong, and combine the circumferential direction of the core tube and the axial direction of the outer frame column. Calculate the rate difference between the node pairs and generate the response zone of the measuring point change. S3: Based on the change response zone of the measuring point, collect the nodal stress value within a set time interval, extract the maximum and minimum values ​​using a sliding window, calculate the fluctuation amplitude, compare it with the benchmark value to determine the out-of-tolerance situation, and generate an abnormal record segment. S4: Based on the time range corresponding to the abnormal record segment, extract the nodal stress response value caused by the associated load, calculate the difference between the median value and the observed reference value, and obtain the structural stress drift value; S5: Based on the obtained structural stress drift value, dynamically adjust the current stage reference value in terms of amplitude, record the adjusted baseline state, form a stress trend line of time series, and summarize the evolution trajectory of the partition over time to obtain a stress monitoring scheme for super high-rise structures.

[0007] As a further aspect of the present invention, the stress rate difference sequence includes a node combination sequence, a rate difference set, and a sorting priority; the measurement point change response zone includes circumferentially distributed nodes in the core tube, axially distributed nodes in the outer frame columns, the rate gradient change direction, and fixed spacing adjustment parameters; the abnormal record segment includes fluctuation amplitude values, stability benchmark values, out-of-tolerance marking results, and continuous abnormal segment numbers; the structural stress drift value includes maximum node stress value, minimum node stress value, median node stress value, offset direction index, and change amplitude value; and the ultra-high-rise structure stress monitoring scheme includes reference value adjustment records, a change trend line sequence, and a segment evolution trajectory set.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the stress values ​​of key component nodes at continuous time points, calculate the ratio of stress difference between adjacent time points to time interval, and obtain the node stress rate value of the node. S102: Call the node stress rate value of the node, perform difference calculation on a time point-by-time basis, analyze the rate difference of each pair of nodes, and integrate them into a node pair rate difference sequence; S103: Based on the absolute value of the node pair rate difference sequence, sort the nodes by the absolute value, extract the top-ranked node pairs, retain the difference values ​​in the original time order, and generate a stress rate difference sequence.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the stress rate difference sequence, extract the first and second ranked node pairs, call the distribution information of adjacent nodes of the steel structure tower column segment to which the node pairs belong, and divide the adjacent nodes into a circumferential distribution node set and an axial distribution node set according to the geometric features of the core tube circumferential direction and the outer frame column axial direction. S202: Traverse all node pairs in the circumferential distribution node set and the axial distribution node set, calculate the rate difference of each pair of nodes, compare the mean absolute value of the rate difference of node pairs in the two sets, identify the set category of the rate change direction, and generate the principal gradient direction category. S203: Based on the node distribution characteristics of the main gradient direction category, perform fixed-distance expansion or contraction operations on adjacent measurement points along the direction, record the adjusted spatial coverage of measurement points, and generate a measurement point change response area.

[0010] As a further aspect of the present invention, the formula for calculating the rate difference between each pair of nodes is as follows: ; in, This represents the difference in rate values ​​between each pair of nodes. The mean absolute value of the rate difference between circumferentially distributed node pairs. The mean absolute value of the rate difference between axially distributed node pairs. The range of the absolute value of the rate difference between circumferentially distributed node pairs. The range of the absolute value of the rate difference between axially distributed node pairs. Represents the total number of circumferentially distributed node pairs. This represents the total number of axially distributed node pairs.

[0011] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Call the stress sampling value of the change response zone of the measuring point, divide the sliding window according to the set time interval, extract the maximum and minimum values ​​of the stress sampling value in each window, perform the subtraction operation between the two, and generate the fluctuation amplitude value corresponding to the time window. S302: Calculate the difference between the fluctuation amplitude value of the time window and the preset stability benchmark value. If the fluctuation amplitude value exceeds the range of the stability benchmark value, mark the time window as out of tolerance. Integrate all time window marking states to generate an out of tolerance marking sequence. S303: Traverse the marking states of adjacent time windows in the out-of-tolerance marking sequence, count the segment numbers of consecutive identical marking states, extract the start and end positions of consecutive out-of-tolerance or consecutive non-out-of-tolerance segments, and generate abnormal record segments.

[0012] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the time range of the abnormal record segment, extract all node stress response values ​​corresponding to the load event within the time period, sort the stress value of each node according to the time sequence, record the maximum value, minimum value and the sorted median value respectively, and generate a node stress response set. S402: Call the maximum, minimum and median values ​​of the nodes in the nodal stress response set, perform a subtraction operation between the median value of each node and the preset observation reference value, if the difference is greater than zero, mark it as a positive offset, and if it is less than zero, mark it as a negative offset, and generate a set of nodal stress offset directions; S403: Based on the marked state of the set of nodal stress offset directions, calculate the average absolute value of the difference between positive and negative offsets, take the two average values ​​as the positive drift amplitude and negative drift amplitude respectively, integrate the direction and amplitude data, and generate the structural stress drift value.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the positive and negative direction and amplitude of the structural stress drift value, perform an upward or downward incremental adjustment on the current stage reference value, record the adjustment amplitude and adjustment time after each adjustment, and generate a reference state sequence sorted by time. S502: Call the adjustment magnitude and time data of the baseline state sequence, connect the reference values ​​after each adjustment in chronological order, calculate the slope of the difference between reference values ​​at adjacent time points, and construct trend line data reflecting the changes in reference values; S503: Integrate the slope direction and amplitude of the differentiated time periods in the trend line data, classify the starting and ending time points of segments with the same slope change direction according to time segments, summarize the evolution paths of all segments, and generate a stress monitoring scheme for super high-rise structures.

[0014] As a further aspect of the present invention, the formula for calculating the slope of the difference between reference values ​​at adjacent time points is as follows: ; in, The slope representing the difference between reference values ​​at adjacent time points. Representing the The adjusted reference value Represents the preceding order The reference value after the adjustment ( For dynamic offset and ), Represents crossing The cumulative time difference of each adjustment Representing the The absolute value of the magnitude of the adjustment. Represents the preceding order The absolute value of the magnitude of the adjustment. Representing the The squared value at the next adjustment point. Represents the preceding order The squared value at the next adjustment point. Represents a scaling factor based on nonlinear compensation of the baseline state sequence. This indicates the normalized product of the two calculation results (i.e., ), This indicates signed addition operations (i.e.) ).

[0015] A stress monitoring system for super high-rise structures, comprising: The stress rate analysis module obtains the stress values ​​of key nodes at continuous time points, calculates the ratio of the stress difference between adjacent time points to the time interval to generate the stress change rate of the nodes, calculates the absolute value of the rate difference of all node combinations and sorts them to generate a stress rate difference sequence. Based on the stress rate difference sequence, the segment classification response module extracts the segment number of the node pair, classifies the node category by combining the circumferential direction of the core tube and the axial direction of the outer frame column, calculates the rate difference of the node pair, and generates the response zone of the measuring point change. The fluctuation anomaly identification module collects nodal stress values ​​within a set time interval based on the change response zone of the measuring point, extracts the maximum and minimum values ​​using a sliding window, calculates the fluctuation amplitude and compares it with the benchmark value, and generates anomaly record segments. The drift quantification assessment module extracts the nodal stress response values ​​caused by the associated loads based on the time range corresponding to the abnormal record segments, calculates the difference between the median value and the observed reference value, and generates structural stress drift values. The benchmark dynamic adjustment module dynamically adjusts the current stage reference value based on the structural stress drift value, records the adjusted benchmark state to form a time series stress trend line, summarizes the changes of the partitions over time, and obtains a stress monitoring scheme for super high-rise structures.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the distribution of monitoring areas is optimized by node classification and rate gradient adjustment to improve the ability to capture micro-stress fluctuations. The fluctuation amplitude is extracted by a sliding window and abnormal chains are continuously identified to enhance the accuracy of fluctuation anomaly tracking. The difference between the median value and the observed reference value is used to extract the offset direction and amplitude, thereby enhancing the overall trend recognition accuracy. The drift value is dynamically corrected to correct the reference benchmark and the change trajectory is recorded to present the structural stress evolution characteristics. Overall, the sensitivity, continuity and trend perception capabilities of stress monitoring for super high-rise buildings are improved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 A method for stress monitoring of super high-rise structures includes the following steps: S1: Obtain the stress value changes of continuous nodes in key parts of super high-rise buildings, calculate the rate value based on the stress value difference and time interval at continuous time points, calculate the rate value difference for each pair of nodes to form a difference sequence, call the sorting mechanism to sort the difference sequence by size, extract the node pairs in the top column of the sorting, and generate the stress rate difference sequence. S2: Based on the first and second node pairs in the stress rate difference sequence, focus on the steel structure tower column segment, classify adjacent nodes in the segment according to the circumferential distribution direction of the core tube and the axial arrangement direction of the outer frame column, calculate the rate difference between any two points in the classification, compare the obtained gradient values ​​and identify the direction of change, and adjust the fixed spacing of the measuring points according to the determined direction to generate the measuring point change response zone. S3: Call the stress sampling value of the response zone of the measurement point change, slide the sampling window at the set time interval to extract the maximum and minimum values ​​in each segment, calculate the fluctuation amplitude based on the two, judge the difference between the amplitude value and the stability benchmark value, mark whether each segment is out of tolerance, and at the same time count the segment numbers with the same consecutive marking in adjacent segments to generate abnormal record segments. S4: Extract the nodal stress response values ​​caused by the load event based on the time range of the abnormal record segment, extract the maximum, minimum and median values ​​of the nodes respectively, perform difference calculation on the median value and the observed reference value, determine the offset direction and change range, and obtain the structural stress drift value. S5: Based on the magnitude and direction of the structural stress drift value, the reference value at the current stage is adjusted upward and downward, the baseline state after each adjustment is recorded, the trend line arranged in chronological order is constructed, the evolution trajectory of the section is summarized, and the stress monitoring scheme for super high-rise structures is obtained.

[0025] The stress rate difference sequence includes a node combination sequence, a set of rate difference values, and a sorting priority. The measurement point change response zone includes circumferentially distributed nodes in the core tube, axially distributed nodes in the outer frame columns, the direction of rate gradient change, and fixed spacing adjustment parameters. The abnormal record segments include fluctuation amplitude values, stability benchmark values, out-of-tolerance marking results, and continuous abnormal segment numbers. The structural stress drift values ​​include maximum stress values ​​at nodes, minimum stress values ​​at nodes, median stress values ​​at nodes, offset direction indicators, and change amplitude values. The stress monitoring scheme for super high-rise structures includes reference value adjustment records, change trend line sequences, and a set of segment evolution trajectories.

[0026] The specific steps of S1 are as follows: S101: Obtain the stress values ​​of key component nodes at continuous time points, calculate the ratio of stress difference between adjacent time points to time interval, and obtain the node stress rate value of the node. In stress monitoring of critical bridge support nodes, sensors continuously collect stress data at a fixed sampling frequency (e.g., 100Hz), recording a timestamp and corresponding stress value every Δt = 0.1 seconds. For example, at t1 = 0 seconds, σ1 = 150MPa is obtained, and at t2 = 0.1 seconds, σ2 = 153MPa. The difference calculation module between σ2 and σ1 is called to perform a subtraction operation Δσ = σ2 − σ1 = 3MPa. Simultaneously, the timestamp difference calculation module is called to calculate the time interval Δt = 0.1 seconds. Δσ is then input into the rate calculation module, and a division operation is performed to obtain the node stress rate value of 30MPa / s at time t2. The data collection continues until t3 = 0.2 seconds, at which σ3 = 156MPa is obtained. The above process is repeated to calculate Δσ = 156MPa from t2 to t3. Given 3 MPa and Δt = 0.1 seconds, the resulting rate is 30 MPa / s. All rate values ​​are stored in array [30, 30] in chronological order. In the scenario of a robotic arm joint node, if an aluminum alloy node has σ1 = 80 MPa at t1 = 0 seconds and σ2 = 85 MPa at t2 = 0.05 seconds, the calculated Δt = 0.05 seconds, Δσ = 5 MPa, and the rate is 100 MPa / s. If a carbon fiber suspension node for an automobile has σ1 = 200 MPa at t1 = 0.2 seconds and σ2 = 208 MPa at t2 = 0.25 seconds, Δt = 0.05 seconds, Δσ = 8 MPa, and the calculated rate is 160 MPa / s. All rate calculation results are arranged in ascending order by timestamp to form the stress rate time series of the node for subsequent analysis.

[0027] S102: Call the node stress rate value, perform difference calculation on a time point-by-time basis, analyze the rate difference of each pair of nodes, and integrate them into a node pair rate difference sequence. For a monitoring scenario involving nodes A and B of a wind turbine blade, the system retrieves the real-time rate values ​​of node A (25 MPa / s) and node B (20 MPa / s) from the database at t1 = 10 seconds. It then executes the difference calculation module to calculate the rate difference value D1 = 25 - 20 = 5 MPa / s at the current time point. At t2 = 10.1 seconds, it reads the updated rate values ​​of node A (28 MPa / s) and node B (23 MPa / s), calculating D2 = 28 - 23 = 5 MPa / s. If the rate sequence of node C in an aero-engine turbine is [50, 55, 60] MPa / s, and that of node D is [45, 50, 55] MPa / s, the system retrieves the rate values ​​point by point in chronological order, calculating the difference value 5(50 - 45) at t1. ), at t2 5 (55−50), at t3 5 (60−55), integrated into the sequence [5, 5, 5]. In automobile chassis monitoring, if the rate sequence of steel node E is [120, 125, 130] MPa / s and node F is [115, 120, 125] MPa / s, the system iterates through each time point, performs subtraction to obtain the difference sequence [5, 5, 5]. All difference values ​​are stored in an independent array in the order of timestamps to form the node pair rate difference sequence. If a certain building steel structure node pair fluctuates in the time series, such as the difference value sequence [−3, 7, −10, 5], the system will retain the sign and store [−3, 7, −10, 5] in the time order. The sign of the difference sequence can be used for subsequent directional analysis.

[0028] S103: Based on the absolute value of the node pair rate difference sequence, sort the node pairs in the top column, retain the difference values ​​in the original time order, and generate the stress rate difference sequence. The difference sequence of a steel structure node pair in a high-rise building is [10, 15, 8, 20, 5]. The system calls the sorting module to sort the absolute values ​​of the differences in descending order as 20 (4th in the original position), 15 (2nd), 10 (1st), 8 (3rd), 5 (5th). A threshold is set at a difference value ≥ 15 MPa / s or the top 30% of items are extracted according to business rules (total sequence length 5, top 30% is 2 items). 20 and 15 are selected and mapped to their corresponding time points according to the original time sequence, resulting in the sequence [15 (t2), 20 (t4)]. In automotive chassis monitoring, if the difference sequence is [12, 18, 7, 25, 3], sorted by absolute value as 25 (t4), 18 (t2), 12 (t1), 7 (t3), 3 (t4), 18 (t2), 12 (t1), 7 (t3), 3 (t4), 15 (t2), 10 (t1), 8 (t3), 20 (t4), 20 (t5 ... t5), set the threshold to a difference value ≥18MPa / s or the top 20% (take 1 item), filter out 25, retain its original timestamp to generate the sequence [25(t4)]. The threshold setting is based on historical data statistics (such as the minimum difference value in the chassis node failure case of this model is 18MPa) or dynamic rules (such as business requirements to only monitor the top 20% of high-risk differences). If the historical safety threshold of a certain aero-engine node pair is 12MPa / s, then set the screening threshold to a difference value ≥12MPa / s, extract 14 and 16 from the sequence [9, 14, 11, 16, 8] to generate the sequence [14(t2), 16(t4)]. All the filtered difference values ​​are recombined in the original time order to form a highly significant stress rate difference sequence.

[0029] The specific steps of S2 are as follows: S201: Extract the first and second node pairs based on the stress rate difference sequence, call the distribution information of adjacent nodes of the steel structure tower column segment to which the node pair belongs, and divide the adjacent nodes into a circumferential distribution node set and an axial distribution node set according to the geometric features of the core tube circumferential direction and the outer frame column axial direction. In the monitoring of super high-rise buildings, the first node pair of the stress rate difference sequence is extracted (e.g., nodes A and B, with a difference of 25 MPa / s). The adjacent node database of the tower column segment to which the node belongs is called to obtain the node coordinate data. The core tube circumferential node set is based on the center (15m, 5m) and all nodes within a radius of 3 meters are filtered (e.g., node C coordinates 12m, 5m, 30m, node D coordinates 10m, 5m, 30m). The outer frame column axial node set is filtered along the z-axis direction and nodes are distributed at intervals of 2 meters (e.g., node E coordinates 14m, 5m, 32m, node F coordinates 16m, 5m, 34m). In the bridge steel tower scenario, the circumferential radius of the core tube is set to 5 meters, and the axial nodes are distributed every 3 meters along the longitudinal direction of the main beam. The coordinates of node G (20m, 8m, 40m, circumferential) and node H (25m, 8m, 43m, axial) are called to complete the division of the two types of node sets.

[0030] S202: Traverse all node pairs in the circumferential and axial distribution node sets, calculate the rate difference for each pair of nodes, compare the mean absolute value of the rate difference between node pairs in the two sets, identify the set category of the rate change direction, and generate the principal gradient direction category. Formula for calculating the rate difference between each pair of nodes: ; in, This represents the difference in rate values ​​between each pair of nodes. The mean absolute value of the rate difference between circumferentially distributed node pairs. The mean absolute value of the rate difference between axially distributed node pairs. The range of the absolute value of the rate difference between circumferentially distributed node pairs. The range of the absolute value of the rate difference between axially distributed node pairs. Represents the total number of circumferentially distributed node pairs. Represents the total number of axially distributed node pairs; Parameter definition and data source: and Mean absolute value of the rate difference between circumferentially distributed node pairs and the mean absolute value of the velocity difference between axially distributed node pairs The values ​​were obtained through experimental monitoring. Taking a rotating machinery system as an example, there are 10 pairs of circumferentially distributed nodes and 8 pairs of axially distributed nodes. The absolute values ​​of their rate differences are as follows: Zhou Xiang: ; ; Axial direction: ; .

[0031] and : Range of absolute values ​​of velocity differences between circumferentially distributed node pairs and the absolute value range of the velocity difference between axially distributed node pairs The range (maximum value - minimum value) is calculated using monitoring data: Circumferential range ; Axial range .

[0032] and The total number of circumferential and axially distributed node pairs is determined by the system design parameters. , .

[0033] Formula calculation process: Absolute value of the difference between the means: ; Sum of ranges: ; Weighting coefficients: ; Directional difference factor H: ; Parameter rationality verification: and The numerical range (0.2-1.8 m / s) conforms to the normal range of velocity fluctuation in industrial fluid machinery (the literature states that the velocity fluctuation range is usually ≤2.0 m / s). and The values ​​(10 and 8) conform to the sensor deployment density standard (the number of node pairs is usually 5-20). Weighting coefficient The design is based on the moderating effect of the difference in the number of nodes on the directional difference factor, and the weight decreases when the total number of nodes increases.

[0034] Meaning of numerical results: Directional difference factor The value approaching 0 indicates that the difference in the direction of rate change between the circumferential and axial distribution node pairs is small, but the circumferential mean is... Higher than the axial mean Based on the principal gradient direction determination rule (prioritizing those with higher mean values), the principal gradient direction category is generated as circumferential.

[0035] S203: Based on the node distribution characteristics of the main gradient direction category, perform fixed-interval expansion or contraction operations on adjacent measurement points along the direction, record the adjusted spatial coverage of measurement points, and generate the measurement point change response area; For the determination result that the main gradient direction is axial, the original measuring point spacing of 2 meters is expanded to 3 meters along the z-axis of the tower column. After adjustment, the coverage range is extended from z=30m to 51m (new measuring point z=51m). If the shrinkage operation is performed, the spacing is reduced to 1.5 meters, and the coverage range is reduced to 30m-46.5m. In the case of circumferential expansion of bridges, the circumferential radius is increased from 3 meters to 4 meters, and the coordinates of the new measuring points are 16m, 5m, and 30m (1 meter outside the original 3-meter radius boundary). In the monitoring of aero-engine casing, the main gradient direction is circumferential. The original circumferential measuring point interval angle is expanded from 15 degrees to 20 degrees, and the new measuring point angle positions are 120 degrees and 140 degrees. The spatial coverage range after adjustment is recorded.

[0036] The specific steps for S3 are as follows: S301: Call the stress sampling value of the change response zone of the measuring point, divide the sliding window according to the set time interval, extract the maximum and minimum values ​​of the stress sampling value in each window, perform the subtraction operation between the two, and generate the fluctuation amplitude value corresponding to the time window. In the monitoring of the response zone of the measuring point change, the stress sampling value [200, 210, 195] MPa of measuring point P1 during the time window t1-t3 (0-30 seconds) is called. The maximum value of 210 MPa and the minimum value of 195 MPa within the window are extracted. The subtraction operation 210-195=15 MPa is performed and recorded as the fluctuation amplitude value of 15 MPa in window 1. The sampling value [210, 195, 208] in window t2-t4 (10-40 seconds) has a maximum value of 210 and a minimum value of 195, with a fluctuation amplitude of 15 MPa. The sampling value [195, 208, 190] in window t3-t5 (20-50 seconds) has a maximum value of 210. 08, minimum value 190, fluctuation amplitude 18MPa. In the monitoring of the robotic arm joint measuring point J1, the sampling value of time window 1 (0-5 seconds) is [85, 90, 82], with a fluctuation amplitude of 8MPa. The sampling value of window 2 (3-8 seconds) is [90, 82, 95], with a fluctuation amplitude of 13MPa. All window fluctuation amplitudes are stored in the array [15, 15, 18] or [8, 13]. If the sampling value of a certain wind turbine blade measuring point W1 window is [120, 115, 125], with a fluctuation amplitude of 10MPa, this process generates a fluctuation amplitude sequence by traversing the sampling data in each time window and calling the extreme value calculation module item by item.

[0037] S302: Calculate the difference between the fluctuation amplitude value of the time window and the preset stability benchmark value. If the fluctuation amplitude value exceeds the range of the stability benchmark value, mark the time window as out of tolerance. Integrate all time window marking states to generate an out of tolerance marking sequence. The stability benchmark value is set to 15MPa (based on the maximum fluctuation value of the 95% confidence interval in the historical data of fatigue test of bridge steel structure materials). The fluctuation amplitude of window 1 is 15MPa. It is compared with the benchmark value. The difference is 0MPa, which is marked as normal (0). The fluctuation amplitude of window 3 is 18MPa. The difference is 3MPa, which is marked as out of tolerance (1). The out-of-tolerance mark sequence [0, 0, 1] is generated. In the monitoring of the robotic arm, the benchmark value is set to 10MPa (according to the upper limit of fluctuation allowed by the aluminum alloy joint design specification). The fluctuation amplitude of window 1 is 8MPa, which is not out of tolerance (marked 0). The fluctuation amplitude of window 2 is 13MPa, which is out of tolerance (marked 1). The sequence [0, 1] is generated. If the benchmark value of the turbine measuring point of the aero-engine is set to 20MPa (based on the creep test data of high temperature alloy materials), the fluctuation amplitude of window [18, 22, 25] generates the mark sequence [0, 1, 1]. The threshold setting rules include: material characteristic data (such as the fluctuation range corresponding to the yield strength of steel), equipment design parameters (maximum load fluctuation of the robotic arm joint), and industry standards (EN61400 specification for wind turbine blades).

[0038] S303: Traverse the marking status of adjacent time windows in the out-of-tolerance marking sequence, count the segment numbers of consecutive segments with the same marking status, extract the start and end positions of consecutive out-of-tolerance or consecutive non-out-of-tolerance segments, and generate abnormal record segments. Traverse the bridge out-of-tolerance marker sequence [0, 0, 1, 1, 0], starting the detection from window 1. Window 1-2 is marked as 0, recorded as continuous non-out-of-tolerance segment 1 (start t1, end t2). Window 3-4 is marked as 1, recorded as continuous out-of-tolerance segment 2 (start t3, end t4). Window 5 is marked as 0, recorded as segment 3 (start t5, end t5). In the robotic arm monitoring sequence [0, 1, 1, 0, 1], segment 1 (t1, non-out-of-tolerance), ... Segment 2 (t2-t3, out of tolerance), segment 3 (t4, not out of tolerance), segment 4 (t5, out of tolerance). In the aero-engine sequence [0, 1, 1], the segment that does not exceed the tolerance (t1) and the segment that exceeds the tolerance (t2-t3) are recorded. If a building structure sequence is [1, 1, 0, 1, 1], the segment that exceeds the tolerance (t1-t2), segment 2 (t4-t5), and segment that does not exceed the tolerance (t3) are generated. All segments are arranged in ascending order of start time to generate abnormal record segments.

[0039] The specific steps of S4 are as follows: S401: Based on the time range of the abnormal record fragments, extract all nodal stress response values ​​corresponding to the load events within the time period, sort the stress values ​​of each node according to the time sequence, record the maximum value, minimum value and the median value after sorting, and generate a nodal stress response set. During the bridge structural anomaly recording segment t3-t4, the stress response values ​​[205, 210, 200, 195] MPa of node N1 corresponding to measuring point P1 were retrieved, sorted by time as [195, 200, 205, 210]. The maximum value was 210 MPa, the minimum value was 195 MPa, and the median value was 202.5 MPa (the average of the 2nd and 3rd values ​​after sorting). The stress values ​​of node N2 [180, 185, 190, 185] had a median value of 185 MPa after sorting. During the abnormal period t2-t3 of the arm joint, the stress value of node J1 is [85, 90, 95] with a median of 90 MPa. If the stress value of node W1 of a wind turbine blade during the abnormal period is [120, 125, 130] with a median of 125 MPa, the maximum, minimum, and median values ​​of all nodes are stored in the dataset. For example, the N1 dataset is {max: 210, min: 195, mid: 202.5}, and W1 is {max: 130, min: 120, mid: 125}.

[0040] S402: Call the maximum, minimum and median values ​​of the nodes in the nodal stress response set, perform a subtraction operation between the median value of each node and the preset observation reference value. If the difference is greater than zero, it is marked as a positive offset, and if it is less than zero, it is marked as a negative offset, generating a set of nodal stress offset directions. The historical normal operating condition median value of bridge node N1 is set to 200 MPa (based on data statistics from the past year). The median value of N1 is called, which is 202.5 MPa. The subtraction operation is performed: 202.5 − 200 = 2.5 MPa. The difference is greater than zero and marked as a positive offset. The median value of node N2, which is 185 MPa, is compared with the reference value of 180 MPa (design allowable benchmark). The difference is 5 MPa and marked as a positive offset. The median value of robotic arm node J1, which is 90 MPa, is compared with the reference value of 95 MPa (aluminum alloy fatigue threshold). The difference is −5 MPa and marked as a negative offset. In wind turbine blade node W1, the median value of 125 MPa is compared with the reference value of 130 MPa (corresponding value of blade rated load). The difference is −5 MPa and marked as a negative offset. The offset direction set [N1: positive, N2: positive, J1: negative, W1: negative] is generated.

[0041] S403: Based on the marked state of the nodal stress offset direction set, calculate the mean absolute value of the difference between positive and negative offsets, take the two mean values ​​as the positive drift amplitude and negative drift amplitude respectively, integrate the direction and amplitude data, and generate the structural stress drift value. The positive offset difference of bridge node N1 is 2.5 MPa, and the positive offset of N2 is 5 MPa. The average positive drift amplitude is calculated to be (2.5+5) / 2=3.75 MPa. The absolute value of the negative offset difference of robotic arm node J1 is 5 MPa, and the absolute value of the negative offset difference of wind power node W1 is 5 MPa. The average negative drift amplitude is (5+5) / 2=5 MPa. If the positive offset difference of aero-engine node K1 is 8 MPa, and the negative offset difference of K2 is 3 MPa, with a positive average of 8 MPa and a negative average of 3 MPa, the integrated structural stress drift values ​​are {positive: 3.75 MPa, negative: 5 MPa} or {positive: 8 MPa, negative: 3 MPa}.

[0042] The specific steps of S5 are as follows: S501: Based on the positive and negative direction and amplitude of the structural stress drift value, perform incremental adjustments upward or downward to the reference value of the current stage. After each adjustment, record the adjustment amplitude and adjustment time to generate a baseline state sequence sorted by time. Based on the bridge structure stress drift values ​​(positive 3.75 MPa, negative 5 MPa), the current reference value of 200 MPa was adjusted. A positive drift of 3.75 MPa triggered an upward adjustment of 2 MPa (the adjustment range was set to 50% of the drift value based on the material fatigue coefficient), and the adjusted reference value was recorded as 202 MPa (time t1 = 2023-01-10). Subsequently, a negative drift of 5 MPa triggered a downward adjustment of 3 MPa (the adjustment range was 60% of the drift value), and the reference value was updated to 199 MPa (t2 = 2023-01-20). In the robotic arm joint monitoring, the initial parameters... The reference value is 95MPa. A positive drift of 8MPa triggers an upward adjustment of 4MPa to 99MPa (t1=10:00), and a negative drift of 3MPa triggers a downward adjustment of 2MPa to 97MPa (t2=10:30). A reference state sequence is generated: [(202MPa, t1), (199MPa, t2)] or [(99MPa, t1), (97MPa, t2)]. The adjustment range rules are based on the following: for steel structures, a single adjustment is allowed not to exceed 60% of the drift value (material safety margin), and for robotic arm joints, a single adjustment is allowed not to exceed 50% of the drift value (aluminum alloy plastic deformation limit).

[0043] S502: Call the adjustment magnitude and time data of the baseline state sequence, connect the reference values ​​after each adjustment in chronological order, calculate the slope of the difference between reference values ​​at adjacent time points, and construct trend line data reflecting the changes in reference values; The specific formula for calculating the slope of the difference between reference values ​​at adjacent time points is as follows: ; in, The slope representing the difference between reference values ​​at adjacent time points. Representing the The adjusted reference value Represents the preceding order The reference value after the adjustment ( For dynamic offset and ), Represents crossing The cumulative time difference of each adjustment Representing the The absolute value of the magnitude of the adjustment. Represents the preceding order The absolute value of the magnitude of the adjustment. Representing the The squared value at the next adjustment point. Represents the preceding order The squared value at the next adjustment point. Represents a scaling factor based on nonlinear compensation of the baseline state sequence. This indicates the normalized product of the two calculation results (i.e., ), This indicates signed addition operations (i.e.) ); Parameter source and assignment rules: : No. The adjusted reference value is obtained through real-time monitoring of the baseline state sequence, such as the steady-state value after equipment voltage adjustment. (Setting) According to the system log records, V, V ( This is a dynamic offset, automatically set to 2 by the system based on the previous adjustment frequency.

[0044] Crossing The cumulative time difference of each adjustment According to the device logs, , Time difference Hours converted to seconds Second.

[0045] : No. The absolute value of the adjustment amplitude is collected by a sensor. Adjustment range V.

[0046] Prequel The absolute value of the magnitude of the adjustment. and hour, V (derived from historical monitoring data).

[0047] : No. The squared value at the next adjustment point. (Time point) Decimal values ​​in hours Hour).

[0048] Prequel The squared value at the next adjustment point. .

[0049] The scaling factor for nonlinear compensation is set based on the stability index of the baseline state sequence. Stability index Calculated using historical data variance (The reasonable range is) ), .

[0050] Formula step-by-step calculation process: calculate : ; calculate : ; Perform normalized product : ; Calculate the cube root term : ; Superposition operation : ; Correlation between numerical results and steps: This result indicates that the combined slope parameter Reflecting the dynamic change intensity of the reference value after the third adjustment, its core contribution comes from the superposition of the time cumulative effect (cubic root term) and the amplitude-time normalization term. The numerical results are directly used as trend line data points to connect the slope characteristics of adjacent adjustment nodes, thus completing the trend line construction.

[0051] S503: Integrate the slope direction and amplitude of the differentiated time periods in the trend line data, classify the start and end time points of the segments with the same slope change direction according to the time segment, summarize the evolution path of all segments, and generate a stress monitoring scheme for super high-rise structures. Integrating the bridge trend line data, the slope from t1 to t2 is 0.3 MPa / day (positive), and the slope from t2 to t3 is -0.5 MPa / day (negative), dividing it into two segments: segment 1 (t1-t2, positive) and segment 2 (t2-t3, negative). In the robotic arm trend line, the slope from t1 to t2 is -4 MPa / hour, and the slope from t2 to t3 is 2 MPa / hour (positive), dividing it into segment 1 (negative) and segment 2 (positive). If the wind turbine tower trend line has a continuous positive slope of 1 MPa / day... (t1-t2) and 1.2MPa / day (t2-t3) are merged into a single positive segment (t1-t3), generating a monitoring scheme containing a list of segments. For example, the bridge scheme is [segment 1 (start t1, end t2, positive), segment 2 (start t2, end t3, negative)], and the robotic arm scheme is [segment 1 (10:00-10:30, negative), segment 2 (10:30-11:00, positive)]. The evolution path summarization rule is that adjacent time periods with the same slope direction are merged, otherwise they are split.

[0052] A stress monitoring system for super high-rise structures, comprising: The stress rate analysis module obtains the stress values ​​of key nodes at continuous time points, calculates the ratio of the stress difference between adjacent time points to the time interval to generate the stress change rate of the nodes, calculates the absolute value of the rate difference of all node combinations and sorts them to generate a stress rate difference sequence. The segment classification response module extracts the segment number of the node pair based on the stress rate difference sequence, classifies the node category by combining the circumferential direction of the core tube and the axial direction of the outer frame column, calculates the rate difference of the node pair, and generates the response zone of the measuring point change. The fluctuation anomaly identification module is based on the change response zone of the measuring point. It collects the nodal stress values ​​within a set time interval, uses a sliding window to extract the maximum and minimum values, calculates the fluctuation amplitude and compares it with the benchmark value, and generates anomaly record segments. The drift quantification assessment module extracts the nodal stress response values ​​caused by associated loads based on the time range corresponding to the abnormal record segments, calculates the difference between the median value and the observed reference value, and generates structural stress drift values. The benchmark dynamic adjustment module dynamically adjusts the reference value at the current stage based on the structural stress drift value, records the adjusted benchmark state to form a time series stress trend line, summarizes the changes of the zone over time, and obtains a stress monitoring scheme for super high-rise structures.

[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for stress monitoring of super high-rise structures, characterized in that, Includes the following steps: S1: Obtain the stress values ​​of key nodes in super high-rise buildings at continuous time points, calculate the rate based on stress difference and time interval, calculate the rate difference for all nodes, and generate a stress rate difference sequence. S2: Based on the stress rate difference sequence, classify the nodes around the segments to which the node pairs belong, and combine the circumferential direction of the core tube and the axial direction of the outer frame column. Calculate the rate difference between the node pairs and generate the response zone of the measuring point change. S3: Based on the change response zone of the measuring point, collect the nodal stress value within a set time interval, extract the maximum and minimum values ​​using a sliding window, calculate the fluctuation amplitude, compare it with the benchmark value to determine the out-of-tolerance situation, and generate an abnormal record segment. S4: Based on the time range corresponding to the abnormal record segment, extract the nodal stress response value caused by the associated load, calculate the difference between the median value and the observed reference value, and obtain the structural stress drift value; S5: Based on the obtained structural stress drift value, dynamically adjust the current stage reference value in terms of amplitude, record the adjusted baseline state, form a stress trend line of time series, and summarize the evolution trajectory of the partition over time to obtain a stress monitoring scheme for super high-rise structures.

2. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The stress rate difference sequence includes a node combination sequence, a rate difference set, and a sorting priority. The measurement point change response zone includes circumferentially distributed nodes in the core tube, axially distributed nodes in the outer frame columns, the rate gradient change direction, and fixed spacing adjustment parameters. The abnormal record segment includes fluctuation amplitude values, stability benchmark values, out-of-tolerance marking results, and continuous abnormal segment numbers. The structural stress drift value includes the maximum stress value of the node, the minimum stress value of the node, the median stress value of the node, the offset direction index, and the change amplitude value. The stress monitoring scheme for ultra-high-rise structures includes reference value adjustment records, a change trend line sequence, and a segment evolution trajectory set.

3. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the stress values ​​of key component nodes at continuous time points, calculate the ratio of stress difference between adjacent time points to time interval, and obtain the node stress rate value of the node. S102: Call the node stress rate value of the node, perform difference calculation on a time point-by-time basis, analyze the rate difference of each pair of nodes, and integrate them into a node pair rate difference sequence; S103: Based on the absolute value of the node pair rate difference sequence, sort the nodes by the absolute value, extract the top-ranked node pairs, retain the difference values ​​in the original time order, and generate a stress rate difference sequence.

4. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the stress rate difference sequence, extract the first and second ranked node pairs, call the distribution information of adjacent nodes of the steel structure tower column segment to which the node pairs belong, and divide the adjacent nodes into a circumferential distribution node set and an axial distribution node set according to the geometric features of the core tube circumferential direction and the outer frame column axial direction. S202: Traverse all node pairs in the circumferential distribution node set and the axial distribution node set, calculate the rate difference of each pair of nodes, compare the mean absolute value of the rate difference of node pairs in the two sets, identify the set category of the rate change direction, and generate the principal gradient direction category. S203: Based on the node distribution characteristics of the main gradient direction category, perform fixed-distance expansion or contraction operations on adjacent measurement points along the direction, record the adjusted spatial coverage of measurement points, and generate a measurement point change response area.

5. The method for monitoring stress in super high-rise structures according to claim 4, characterized in that, The formula for calculating the rate difference between each pair of nodes is as follows: ； in, This represents the difference in rate values ​​between each pair of nodes. The mean absolute value of the rate difference between circumferentially distributed node pairs. The mean absolute value of the rate difference between axially distributed node pairs. The range of the absolute value of the rate difference between circumferentially distributed node pairs. The range of the absolute value of the rate difference between axially distributed node pairs. Represents the total number of circumferentially distributed node pairs. This represents the total number of axially distributed node pairs.

6. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Call the stress sampling value of the change response zone of the measuring point, divide the sliding window according to the set time interval, extract the maximum and minimum values ​​of the stress sampling value in each window, perform the subtraction operation between the two, and generate the fluctuation amplitude value corresponding to the time window. S302: Calculate the difference between the fluctuation amplitude value of the time window and the preset stability benchmark value. If the fluctuation amplitude value exceeds the range of the stability benchmark value, mark the time window as out of tolerance. Integrate all time window marking states to generate an out of tolerance marking sequence. S303: Traverse the marking states of adjacent time windows in the out-of-tolerance marking sequence, count the segment numbers of consecutive identical marking states, extract the start and end positions of consecutive out-of-tolerance or consecutive non-out-of-tolerance segments, and generate abnormal record segments.

7. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the time range of the abnormal record segment, extract all node stress response values ​​corresponding to the load event within the time period, sort the stress value of each node according to the time sequence, record the maximum value, minimum value and the sorted median value respectively, and generate a node stress response set. S402: Call the maximum, minimum and median values ​​of the nodes in the nodal stress response set, perform a subtraction operation between the median value of each node and the preset observation reference value, if the difference is greater than zero, mark it as a positive offset, and if it is less than zero, mark it as a negative offset, and generate a set of nodal stress offset directions; S403: Based on the marked state of the set of nodal stress offset directions, calculate the average absolute value of the difference between positive and negative offsets, take the two average values ​​as the positive drift amplitude and negative drift amplitude respectively, integrate the direction and amplitude data, and generate the structural stress drift value.

8. The method for monitoring stress in super high-rise structures according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the positive and negative direction and amplitude of the structural stress drift value, perform an upward or downward incremental adjustment on the current stage reference value, record the adjustment amplitude and adjustment time after each adjustment, and generate a reference state sequence sorted by time. S502: Call the adjustment magnitude and time data of the baseline state sequence, connect the reference values ​​after each adjustment in chronological order, calculate the slope of the difference between reference values ​​at adjacent time points, and construct trend line data reflecting the changes in reference values; S503: Integrate the slope direction and amplitude of the differentiated time periods in the trend line data, classify the starting and ending time points of segments with the same slope change direction according to time segments, summarize the evolution paths of all segments, and generate a stress monitoring scheme for super high-rise structures.

9. The method for monitoring stress in super high-rise structures according to claim 8, characterized in that, The formula for calculating the slope of the difference between reference values ​​at adjacent time points is as follows: ； in, The slope representing the difference between reference values ​​at adjacent time points. Representing the The adjusted reference value Represents the preceding order The reference value after the adjustment ( For dynamic offset and ), Represents crossing The cumulative time difference of each adjustment Representing the The absolute value of the magnitude of the adjustment. Represents the preceding order The absolute value of the magnitude of the adjustment. Representing the The squared value at the next adjustment point. Represents the preceding order The squared value at the next adjustment point. Represents a scaling factor based on nonlinear compensation of the baseline state sequence. This indicates the normalized product of the two calculation results (i.e., ), This indicates signed addition operations (i.e.) ).

10. A stress monitoring system for ultra-high-rise structures, characterized in that, A method for monitoring stress in a super high-rise structure according to any one of claims 1-9, wherein the system comprises: The stress rate analysis module obtains the stress values ​​of key nodes at continuous time points, calculates the ratio of the stress difference between adjacent time points to the time interval to generate the stress change rate of the nodes, calculates the absolute value of the rate difference of all node combinations and sorts them to generate a stress rate difference sequence. Based on the stress rate difference sequence, the segment classification response module extracts the segment number of the node pair, classifies the node category by combining the circumferential direction of the core tube and the axial direction of the outer frame column, calculates the rate difference of the node pair, and generates the response zone of the measuring point change. The fluctuation anomaly identification module collects nodal stress values ​​within a set time interval based on the change response zone of the measuring point, extracts the maximum and minimum values ​​using a sliding window, calculates the fluctuation amplitude and compares it with the benchmark value, and generates anomaly record segments. The drift quantification assessment module extracts the nodal stress response values ​​caused by the associated loads based on the time range corresponding to the abnormal record segments, calculates the difference between the median value and the observed reference value, and generates structural stress drift values. The benchmark dynamic adjustment module dynamically adjusts the current stage reference value based on the structural stress drift value, records the adjusted benchmark state to form a time series stress trend line, summarizes the changes of the partitions over time, and obtains a stress monitoring scheme for super high-rise structures.

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