Super-huge structure deformation detection method and system, medium and program product

Abstract

The invention discloses an oversize structure deformation detection method and system, a medium and a program product, and the method comprises the steps: collecting multi-source detection data and deformation detection data of a structure in a maximum load period and a minimum load period; calculating a deformation difference value to obtain a deformation difference value range; in the deformation difference value range, the load transportation equipment is controlled to apply and unload a preset load according to a preset load step length; acquiring deformation data under each preset load step length to obtain deformation recovery process data, and determining an elastic deformation interval according to the deformation recovery process data; when the deformation detection data is within the elastic deformation interval, the load transportation equipment is controlled to unload the structure according to a preset unloading step length, and the deformation recovery rate is calculated; and when the deformation recovery rate is lower than a preset threshold value, determining the corresponding deformation area as a key monitoring area. The method and the device are used for improving the accuracy of structure deformation detection, so that the accuracy and the timeliness of structure safety evaluation are improved.

Description

Technical Field

[0001] This application belongs to the field of measurement not dedicated to specific variables, and particularly relates to a deformation detection method, system, medium, and program product for extra-large structures. Background Art

[0002] With the rapid development of infrastructure construction, the number and scale of extra-large structures are continuously increasing. These structures will deform under the influence of natural and human factors during operation. How to detect and evaluate the deformation of structures in real time and accurately is of great significance for ensuring the safe operation of structures and extending their service life. Currently, there are many types of structure deformation detection technologies, including traditional measurement, GPS positioning, InSAR, and fiber optic sensing technologies. However, due to the differences in the spatio-temporal resolution and measurement accuracy of different detection technologies, the detection results are inconsistent, making it difficult to accurately evaluate the actual deformation characteristics of structures.

[0003] In related technologies, according to the characteristic differences in the time and space resolution of different observation technologies, prior data and deformation prediction models can be applied, combined with the M-estimation theory, to establish a consistency evaluation method for the deformation results of multi-source detection of structures, which can effectively solve the problems of inconsistent detection results and detection result fusion, and improve the detection efficiency and accuracy.

[0004] However, when sudden or non-linear deformation occurs in a structure, since the prior data and prediction models are mainly based on historical data and linear assumptions, it is difficult to capture and respond to such abnormal deformation characteristics in a timely manner, which may lead to deviations between the detection results and the actual deformation conditions, thereby reducing the accuracy and timeliness of the safety assessment of the structure. Summary of the Invention

[0005] This application provides a deformation detection method, system, medium, and program product for extra-large structures, which is used to improve the accuracy of structure deformation detection, and further improve the accuracy and timeliness of structure safety assessment.

[0006] In a first aspect, this application provides a deformation detection method for extra-large structures, which collects multi-source detection data and deformation detection data of the structure during the maximum load period and the minimum load period; Calculate the deformation difference of the multi-source detection data during the maximum load period and the minimum load period to obtain the deformation difference range of the structure; Within the deformation difference range, control the load transportation equipment to apply and unload the preset load according to the preset load step; Collect the deformation data at each preset load step to obtain the deformation recovery process data of the structure, and determine the elastic deformation interval of the structure according to the deformation recovery process data; When the deformation detection data is within the elastic deformation range, control the load transportation device to unload the structure according to a preset unloading step length, and calculate the deformation recovery rate; When the deformation recovery rate is lower than the preset threshold, mark the corresponding deformation area as an inelastic deformation area, and determine the inelastic deformation area as the key monitoring area.

[0007] By adopting the above technical solutions, multi-source detection data and deformation detection data of the structure during the maximum and minimum load periods are collected, and the deformation difference range is calculated, so as to accurately grasp the extreme deformation range of the structure under the actual operating conditions. Loading and unloading tests are carried out according to the preset load step length to obtain the deformation recovery process data, which can truly reflect the deformation response characteristics of the structure under different load levels. By analyzing the deformation recovery rate, the inelastic deformation areas existing inside the structure can be identified, and these areas are often the key parts where the structure is damaged or deformed abnormally. Determining the inelastic deformation area as the key monitoring area helps to timely discover the potential risks of the structure and reduce the probability of the overall structural safety being reduced due to local deformation abnormalities. Through in-depth analysis of the deformation mechanism of the structure, the accuracy of evaluating the deformation state of the structure can be improved, and thus the accuracy and timeliness of the structural safety assessment are improved.

[0008] Combined with some embodiments of the first aspect, in some embodiments, calculating the deformation difference between the multi-source detection data during the maximum load period and the minimum load period to obtain the deformation difference range of the structure specifically includes: Divide the multi-source detection data during the maximum load period into several detection cycles according to the time series; Within each detection cycle, calculate the deformation difference ratio between adjacent detection points; When the change trend of the deformation difference ratio is periodic, regard the deformation difference corresponding to the corresponding period as the effective deformation difference; Determine the deformation difference range of the structure according to the statistical distribution characteristics of the effective deformation difference.

[0009] By adopting the above technical solutions, dividing the multi-source detection data during the maximum load period into detection cycles according to the time series and calculating the deformation difference ratio between adjacent detection points can reduce the influence of random fluctuations in the detection data. By identifying the periodic change characteristics of the deformation difference ratio, representative effective deformation differences are screened out, reducing the interference of abnormal data on the judgment of the deformation difference range. Determining the deformation difference range according to the statistical distribution characteristics of the effective deformation difference makes the calculation result of the deformation difference range more objective and reliable, improves the accuracy and reliability of the calculation of the deformation difference range, provides a scientific deformation control interval for subsequent load tests, and makes the whole detection process safer and more controllable.

[0010] In connection with some embodiments of the first aspect, in some embodiments, determining the elastic deformation range of the structure according to the deformation recovery process data specifically includes: Dividing the deformation recovery process data into a number of loading stages according to a preset load step; Calculating the ratio of the deformation increment to the load increment within each loading stage to obtain the deformation-load response coefficient; When the change rate of the deformation-load response coefficient exceeds a preset ratio, determining the corresponding deformation value as the upper limit of elastic deformation; Determining the elastic deformation range according to the upper limit of elastic deformation and the initial deformation value.

[0011] By adopting the above technical solution, dividing the deformation recovery process data into loading stages according to a preset load step and calculating the deformation-load response coefficient can quantitatively characterize the deformation sensitivity of the structure under different load levels. By analyzing the change rate of the deformation-load response coefficient, the critical point at which the structure changes from elastic deformation to inelastic deformation can be accurately identified, thereby determining the upper limit of elastic deformation. Determining the elastic deformation range in combination with the initial deformation value can provide a clear deformation threshold for the load control of the structure, reducing the irreversible damage caused by excessive load on the structure.

[0012] In connection with some embodiments of the first aspect, in some embodiments, after determining the inelastic deformation area as the key monitoring area, the method further includes: Calculating the deformation difference sequence of adjacent monitoring points within the key monitoring area; Performing a sliding time window analysis on the deformation difference sequence to determine the deformation mutation period; Extracting the deformation data before and after the load application within the deformation mutation period and calculating the second derivative of the deformation data to obtain the deformation acceleration; Determining the deformation sensitive area based on the deformation acceleration; Analyzing the deformation response delay characteristics within the deformation sensitive area, obtaining the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and dividing the deformation warning level according to the slope change of the corresponding relationship curve.

[0013] By adopting the above technical solution, the deformation difference sequence between adjacent monitoring points in the key monitoring area is calculated. By analyzing with a sliding time window to identify the deformation mutation period, the abnormal fluctuations of the local deformation of the structure can be captured. Extracting the deformation data before and after the load action and calculating the deformation acceleration during the deformation mutation period can accurately reflect the dynamic response characteristics of the structure to the load change. Based on the deformation acceleration, the deformation-sensitive area is determined, and the deformation response delay characteristics are analyzed, so as to master the sensitivity degree and response speed of each part of the structure to the load change. Through the corresponding relationship curve between the deformation response delay time and the deformation acceleration, combined with the slope change to divide the deformation warning level, the accuracy and real-time performance of the evaluation and hierarchical warning of the deformation risk of the structure can be improved.

[0014] Combined with some embodiments of the first aspect, in some embodiments, analyzing the deformation response delay characteristics in the deformation-sensitive area, obtaining the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and dividing the deformation warning level according to the slope change of the corresponding relationship curve specifically includes: Calculating the deformation acceleration difference within adjacent time windows in the deformation-sensitive area; Identifying the peak of the deformation acceleration difference to obtain the acceleration mutation point; Calculating the time interval between the load action moment corresponding to the acceleration mutation point and the deformation response moment to obtain the first delay time sequence; Matching the first delay time sequence with the deformation acceleration at the corresponding moment to obtain the delay time-acceleration data pair; Performing curve fitting on the delay time-acceleration data pair to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration; Calculating the segmented slope of the corresponding relationship curve to obtain the slope change sequence; Determining the deformation warning level according to the numerical interval of the slope change sequence.

[0015] By adopting the above technical solution, calculating the deformation acceleration difference of adjacent time windows in the deformation-sensitive area, identifying the acceleration mutation point, and calculating the time interval between the load action moment and the deformation response moment to obtain the delay time sequence, matching the delay time sequence with the deformation acceleration and then performing curve fitting to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and then determining the deformation warning level by calculating the segmented slope of the corresponding relationship curve. This enables the system to improve the accuracy of capturing the characteristics of the deformation acceleration change of the structure during the stress process. Introducing the time delay factor into the deformation warning classification process, the system can detect deformation anomalies earlier and improve the timeliness of the warning. The warning level division method based on the segmented slope can refine the division accuracy of the warning level, enhance the reliability of the warning result, and make the warning mechanism more in line with the actual deformation development law of the structure.

[0016] In some embodiments in combination with some embodiments of the first aspect, after determining the deformation warning level, the method further includes: Extracting the deformation time history curve in the key monitoring area, and performing spectral analysis on the deformation time history curve to obtain the deformation frequency distribution; Calculating the amplitude ratio between adjacent frequency bands to obtain the frequency amplitude sequence; Determining the main control frequency band based on the frequency amplitude sequence, and extracting the phase information corresponding to the main control frequency band to obtain the phase change sequence; Analyzing the phase change sequence to obtain the deformation transmission direction; Calculating the amplitude attenuation characteristic during the deformation transmission according to the deformation transmission direction.

[0017] By adopting the above technical solution, performing spectral analysis on the deformation time history curve in the key monitoring area, calculating the amplitude ratio between adjacent frequency bands to obtain the frequency amplitude sequence, determining the main control frequency band and extracting the phase information, analyzing the phase change sequence to obtain the deformation transmission direction, and finally calculating the amplitude attenuation characteristic during the deformation transmission. It can reveal the deformation propagation law of the structure from the frequency domain perspective, accurately grasp the main deformation characteristics of the structure through the identification of the main control frequency band, and use the phase information to track the propagation path of the deformation wave, which can reduce the influence of interference factors such as environmental noise and improve the accuracy of deformation characteristic extraction.

[0018] In some embodiments in combination with some embodiments of the first aspect, calculating the amplitude attenuation characteristic during the deformation transmission according to the deformation transmission direction specifically includes: Extracting the amplitude data of adjacent monitoring points within the main control frequency band, and calculating the amplitude difference between adjacent monitoring points; Establishing the corresponding relationship between the distance of the monitoring points and the amplitude difference; Calculating the first derivative of the corresponding relationship to obtain the amplitude attenuation rate; Determining the deformation transmission attenuation characteristic according to the numerical value of the amplitude attenuation rate.

[0019] By adopting the above technical solution, extracting the amplitude data of adjacent monitoring points within the main control frequency band, calculating the amplitude difference between adjacent monitoring points, establishing the corresponding relationship between the monitoring point distance and the amplitude difference, calculating the first derivative of the corresponding relationship to obtain the amplitude attenuation rate, and finally determining the deformation transmission attenuation characteristic. By analyzing the energy attenuation law of the deformation signal during spatial propagation, it can accurately reflect the deformation response characteristics of each part of the structure. By quantitatively describing the energy loss law during the deformation transmission process, the accuracy of evaluating the deformation sensitivity of each part of the structure can be improved.

[0020] In a second aspect, an embodiment of the present application provides a deformation detection system for a super-large structure. The deformation detection system for a super-large structure includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium including instructions, which when running on a system, cause the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, it causes the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for detecting the deformation of a super-large structure. By collecting multi-source detection data and deformation detection data of the structure during the maximum and minimum load periods, the range of deformation differences can be calculated, and the extreme deformation interval of the structure under actual operating conditions can be accurately grasped. By performing loading and unloading tests according to a preset load step size and obtaining data on the deformation recovery process, the deformation response characteristics of the structure at different load levels can be truly reflected. By analyzing the deformation recovery rate, the inelastic deformation regions inside the structure can be identified, and these regions are often the key parts where the structure is damaged or deformed abnormally. Determining the inelastic deformation regions as key monitoring regions helps to timely discover potential risks of the structure and reduce the probability of the overall structural safety being reduced due to local deformation abnormalities. By deeply analyzing the deformation mechanism of the structure, the accuracy of evaluating the deformation state of the structure can be improved, and further the accuracy and timeliness of the safety assessment of the structure can be improved.

[0024] 2. This application provides a deformation detection method for super-large structures. By calculating the deformation difference sequence between adjacent monitoring points in the key monitoring area and identifying the deformation mutation period through sliding time window analysis, abnormal fluctuations in the local deformation of the structure can be captured. Extracting the deformation data before and after the load application and calculating the deformation acceleration during the deformation mutation period can accurately reflect the dynamic response characteristics of the structure to load changes. Based on the deformation acceleration, the deformation-sensitive area is determined, and the deformation response delay characteristics are analyzed to understand the sensitivity and response speed of each part of the structure to load changes. By using the corresponding relationship curve between the deformation response delay time and the deformation acceleration and combining the slope change to divide the deformation warning level, the accuracy and real-time performance of the assessment and hierarchical warning of the deformation risk of the structure can be improved.

[0025] 3. This application provides a deformation detection method for super-large structures. By performing spectral analysis on the deformation time history curve in the key monitoring area, calculating the amplitude ratio between adjacent frequency bands to obtain the frequency amplitude sequence, determining the main control frequency band and extracting the phase information, analyzing the phase change sequence to obtain the deformation propagation direction, and finally calculating the amplitude attenuation characteristics during the deformation propagation process. It can reveal the deformation propagation law of the structure from the frequency domain perspective, accurately grasp the main deformation characteristics of the structure by identifying the main control frequency band, and use the phase information to track the propagation path of the deformation wave, which can reduce the influence of interference factors such as environmental noise and improve the accuracy of deformation feature extraction. Description of the Drawings

[0026] Figure 1 It is a schematic flowchart of a deformation detection method for super-large structures in an embodiment of this application.

[0027] Figure 2 It is another schematic flowchart of a deformation detection method for super-large structures in an embodiment of this application.

[0028] Figure 3 It is a schematic structural diagram of the physical device of a deformation detection system for super-large structures provided in an embodiment of this application. Detailed Embodiments

[0029] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and appended claims of this application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0031] The following uses an embodiment and combines Figure 1 , to describe a deformation detection method for a super-large structure in the embodiments of the present application: Please refer to Figure 1 , which is a schematic flowchart of a deformation detection method for a super-large structure in the embodiments of the present application.

[0032] S101. Collect multi-source detection data and deformation detection data of the structure during the maximum load period and the minimum load period; In this step, the system performs comprehensive data collection on the super-large structure. First, the system collects its status data through a variety of sensors and monitoring devices when the structure is under the maximum load and the minimum load respectively. These multi-source detection data can include strain data, vibration data, displacement data, etc., reflecting the stress and deformation conditions of the structure under different load conditions. At the same time, the system also specifically collects the deformation detection data of the structure, accurately recording its deformation amount and deformation direction. By comprehensively analyzing the multi-source detection data and deformation data, the stress performance and deformation law of the structure can be comprehensively evaluated.

[0033] In specific implementation, the system can deploy various types of sensors, such as accelerometers and displacement sensors, etc., to achieve multi-parameter monitoring of the structure. These sensors can be embedded in the key parts of the structure or attached to the surface of the structure. At the same time, the system can also use advanced measurement technologies, such as laser ranging, total station, etc., to accurately collect the spatial deformation data of the structure. In addition, the system can also automatically extract and analyze the deformation characteristics of the structure through image recognition and computer vision technologies. Through the fusion analysis of multi-modal data, the system can accurately grasp the deformation response of the structure under different load conditions.

[0034] In order to further improve the intelligent level of data collection, the system can also introduce an adaptive sampling strategy. According to the real-time stress state of the structure and the historical data trend, the system can dynamically adjust the sampling frequency and sampling point position of the sensors, and automatically optimize the data collection scheme. This can not only reduce data redundancy, but also enhance the detection intensity of key areas specifically. In addition, in the face of sudden load changes, the system can also actively trigger the fast sampling mode, densely record the dynamic response process of the structure, and provide high-precision data support for subsequent deformation analysis.

[0035] S102. Calculate the deformation difference of the multi-source detection data during the period of maximum load and the period of minimum load to obtain the deformation difference range of the structure; The system calculates the deformation difference of the multi-source detection data during the period of maximum load and the period of minimum load to obtain the deformation difference range of the structure. Specifically: divide the multi-source detection data during the period of maximum load into several detection cycles according to the time series; Within each detection cycle, calculate the deformation difference ratio between adjacent detection points; When the change trend of the deformation difference ratio shows periodicity, regard the deformation difference of the corresponding period as the effective deformation difference; Determine the deformation difference range of the structure according to the statistical distribution characteristics of the effective deformation difference.

[0036] In this step, the system analyzes the multi-source detection data to calculate the deformation difference of the structure under the maximum load and the minimum load, so as to obtain its deformation difference range. First, the system divides the collected multi-source detection data according to the time series to form several detection cycles. Within each detection cycle, the system calculates the deformation difference ratio between adjacent detection points to reflect the relative size of the local deformation. When these difference ratios show an obvious periodic change trend, it indicates that the deformation data at this time can reflect the true deformation law of the structure under the action of the load, and the system marks it as the effective deformation difference. Finally, the system determines the overall deformation difference range of the structure according to the statistical distribution characteristics of the effective deformation difference.

[0037] In practical applications, the system can adopt a variety of data processing and feature extraction algorithms to accurately identify the periodic characteristics of the deformation difference from the multi-source detection data. For example, the system can use methods such as Fourier transform and wavelet analysis to automatically extract the frequency domain characteristics of the data and identify the periodic components therein. At the same time, the system can also combine machine learning algorithms such as support vector machines and random forests to perform intelligent classification and anomaly detection on the deformation difference. By comprehensively applying these algorithms, the system can accurately extract effective deformation information from complex detection data.

[0038] S103. Within the deformation difference range, control the load transportation equipment to apply and unload the preset load according to the preset load step; In this step, the system controls the load transportation equipment to apply and unload the preset load to the structure according to the calculated deformation difference range, so as to simulate various load conditions that the structure may encounter during actual use. The system increases or decreases the load magnitude applied to the structure step by step according to the preset load step, and at the same time monitors the deformation response of the structure in real time. Through this controlled loading and unloading process, the system can comprehensively evaluate the deformation characteristics and bearing capacity of the structure under different load levels.

[0039] In specific implementation, the system can adopt precise load control technology to ensure that the load applied to the structure meets the preset step requirements. For example, the system can use a servo controller and a high-precision force sensor to monitor and adjust the output force of the load transportation equipment in real time. At the same time, the system can also dynamically adjust the size and duration of the load step according to the actual response of the structure to obtain more detailed and comprehensive deformation data. In addition, the system can simulate various complex load conditions, such as alternating loads, impact loads, etc., to comprehensively evaluate the anti-load performance of the structure.

[0040] S104. Collect the deformation data under each preset load step, obtain the deformation recovery process data of the structure, and determine the elastic deformation interval of the structure according to the deformation recovery process data. The system collects the deformation data under each preset load step, obtains the deformation recovery process data of the structure, and determines the elastic deformation interval of the structure. Specifically: divide the deformation recovery process data into several loading stages according to the preset load step. Calculate the ratio of the deformation increment to the load increment in each loading stage to obtain the deformation-load response coefficient. When the change rate of the deformation-load response coefficient exceeds the preset ratio, determine the corresponding deformation value as the upper limit of elastic deformation. Determine the elastic deformation interval according to the upper limit of elastic deformation and the initial deformation value.

[0041] In this step, the system collects the deformation data of the structure under each preset load step and records its deformation recovery during the unloading process. By analyzing the deformation recovery process data, the system can determine the elastic deformation interval of the structure, that is, the deformation range within which the structure can fully recover to its initial state. First, the system divides the deformation recovery process data into several loading stages according to the preset load step. In each loading stage, the system calculates the ratio of the deformation increment to the load increment to obtain the deformation-load response coefficient. When the change rate of this coefficient exceeds the preset ratio, it indicates that the deformation of the structure begins to deviate from the linear elastic range, and the system marks the deformation value at this time as the upper limit of elastic deformation. Finally, the system determines the elastic deformation interval of the structure according to the initial deformation value and the upper limit of elastic deformation.

[0042] In some cases, the deformation recovery process of the structure may exhibit significant discreteness and uncertainty. For example, due to the inhomogeneity of materials and complex loading conditions, there may be significant differences in the local deformation recovery rates of the structure. To address this uncertainty, the system can introduce probabilistic and statistical methods to perform uncertainty analysis and reliability assessment on the deformation recovery process data. By constructing confidence intervals and probability distribution models, the system can quantitatively describe the uncertain range of the elastic deformation interval, providing a reliable basis for subsequent safety assessment and decision-making optimization. At the same time, the system can also update and adjust the probability model parameters in real time, continuously improving the accuracy and adaptability of the elastic deformation interval estimation.

[0043] S105. When the deformation detection data is within the elastic deformation interval, control the load transportation equipment to unload the structure according to the preset unloading step size, and calculate the deformation recovery rate. In this step, the system monitors the deformation detection data of the structure in real time and determines whether it is within the elastic deformation interval. When the deformation of the structure does not exceed the elastic deformation range, the system controls the load transportation equipment to gradually reduce the load applied to the structure according to the preset unloading step size, and at the same time calculates the deformation recovery rate of the structure. The deformation recovery rate reflects the speed at which the deformation of the structure returns to the initial state during the unloading process and is an important indicator for evaluating the elastic recovery ability of the structure.

[0044] In specific implementation, the system can optimize the setting of the unloading step size according to the material characteristics and loading characteristics of the structure. For example, for a structure with a large stiffness, the system can adopt a larger unloading step size to improve the detection efficiency; while for a structure with a small stiffness and prone to deformation, the system can adopt a smaller unloading step size to obtain more detailed deformation recovery data. At the same time, the system can also dynamically adjust the unloading rate to avoid structural vibration or secondary damage caused by too fast unloading. In addition, the system can introduce a variety of numerical differentiation and curve fitting algorithms to accurately calculate the deformation recovery rate and evaluate the elastic recovery performance of the structure in real time.

[0045] S106. When the deformation recovery rate is lower than the preset threshold, mark the corresponding deformation area as an inelastic deformation area and determine the inelastic deformation area as the key monitoring area.

[0046] In this step, the system determines whether the structure has undergone inelastic deformation based on the calculated deformation recovery rate. When the deformation recovery rate is lower than the preset threshold, it indicates that the structure cannot quickly return to the initial state during the unloading process and may have entered the inelastic deformation stage. At this time, the system marks the corresponding deformation area as an inelastic deformation area and determines it as the key monitoring area for targeted detection and evaluation.

[0047] In practical applications, the system can reasonably set the preset threshold of the deformation recovery rate according to the material properties, stress characteristics and usage requirements of the structure. For example, for steel structures, due to their high elastic modulus, the deformation recovery rate is usually fast, so a higher threshold can be set; while for concrete structures, due to their low elastic modulus, the deformation recovery rate is relatively slow, so a lower threshold can be set. At the same time, the system can also use a self-learning algorithm to continuously optimize and adjust the threshold setting based on historical detection data and expert experience to improve the accuracy and adaptability of inelastic deformation judgment.

[0048] Once the inelastic deformation area is identified, the system can automatically trigger the key monitoring process. The system can increase the density of sensor deployment in the area, improve the frequency of data collection, and achieve refined monitoring of the inelastic deformation area. At the same time, the system can also carry out targeted local deformation analysis, stress-strain analysis, etc., to deeply evaluate the severity and potential risks of inelastic deformation. For areas with severe structural damage, the system can provide timely warnings and provide repair and reinforcement plans. Through key monitoring and analysis, the system can more comprehensively grasp the overall performance status of the structure and provide a reliable decision-making basis for operation, maintenance and safety management.

[0049] In the above embodiment, by collecting multi-source detection data and deformation detection data of the structure during the maximum and minimum load periods, and calculating the deformation difference range, the deformation extreme value interval of the structure under actual operating conditions can be accurately grasped. Loading and unloading tests are carried out according to the preset load step length, and deformation recovery process data is obtained, which can truly reflect the deformation response characteristics of the structure under different load levels. By analyzing the deformation recovery rate, the inelastic deformation areas inside the structure can be identified. These areas are often the key parts where the structure is damaged or deformed abnormally. Determining the inelastic deformation area as a key monitoring area helps to timely discover the potential risks of the structure and reduce the probability of reducing the safety of the overall structure due to local deformation abnormalities. Through in-depth analysis of the deformation mechanism of the structure, the accuracy of assessing the deformation state of the structure can be improved, thereby improving the accuracy and timeliness of the safety assessment of the structure.

[0050] Based on the determination of the inelastic deformation area of ​​the structure, this embodiment further analyzes the deformation characteristics in the key monitoring area to achieve a refined assessment of the deformation state and graded warning. Figure 2 , another method for detecting deformation of a super-large structure in an embodiment of the present application is described: See also Figure 2 , is another flow chart of a method for detecting deformation of an extra-large structure in an embodiment of the present application.

[0051] S201. Calculate the deformation difference sequence between adjacent monitoring points in the key monitoring area; In this step, the system conducts a quantitative analysis of the deformation differences between adjacent monitoring points in the key monitoring area. First, the system divides the key monitoring area into several sub-areas, each of which contains multiple monitoring points. Then, the system calculates the deformation differences between adjacent monitoring points in each sub-area, obtaining a set of time-series data, namely the deformation difference sequence. The deformation difference sequence reflects the relative magnitude and change trend of local deformation in the key monitoring area, providing an important basis for subsequent deformation mutation detection and sensitive area identification.

[0052] In specific implementation, the system can adopt various numerical calculation methods, such as the difference method, interpolation method, etc., to calculate the deformation differences between adjacent monitoring points. At the same time, the system can also introduce a spatial weight factor to weight the deformation differences according to the distance and position relationship between the monitoring points, so as to improve the spatial resolution and sensitivity of the deformation difference analysis. In addition, the system can set multiple time scales and calculate the deformation difference sequences within different time windows to capture the multi-scale characteristics of the deformation evolution in the key monitoring area.

[0053] S202. Conduct a sliding time window analysis on the deformation difference sequence to determine the deformation mutation period; In this step, the system identifies the deformation mutation period from the deformation difference sequence through a sliding time window analysis. First, the system sets a time window with a fixed length and slides this window on the deformation difference sequence to extract the deformation difference subsequence within each window. Then, the system calculates the statistical characteristics of each subsequence, such as the mean, variance, slope, etc., and serializes these characteristic values to form a set of time-varying characteristic curves. Finally, the system analyzes the mutation characteristics of the time-varying characteristic curves to identify the mutation period in the deformation difference sequence. The mutation period usually corresponds to the period when the deformation rate or acceleration in the key monitoring area changes drastically, reflecting the abnormal deformation behavior of the local structure.

[0054] In practical applications, the system can flexibly set the length and step size of the sliding window according to the deformation characteristics of the key monitoring area and the data sampling frequency. For areas with a faster deformation rate, a shorter window length can be adopted to improve the time resolution of mutation detection; while for areas with a slower deformation rate, a longer window length can be adopted to capture the mutation characteristics of the long-term deformation trend. At the same time, the system can also introduce multi-scale analysis methods, such as wavelet transform, to perform multi-scale decomposition on the time-varying characteristic curves and extract the mutation information at different frequency scales. In addition, the system can use machine learning algorithms, such as support vector machines, anomaly detection algorithms, etc., to automatically identify the mutation patterns in the time-varying characteristic curves and improve the discrimination accuracy of the deformation mutation period.

[0055] S203. Extract the deformation data before and after the load application during the deformation mutation period, and calculate the second derivative of the deformation data to obtain the deformation acceleration. In this step, the system extracts the deformation monitoring data of the structure before and after the load application during the deformation mutation period, and calculates the second derivative of the deformation data through numerical differentiation to obtain the deformation acceleration of the structure during this period. The deformation acceleration reflects the change rate of the deformation rate of the structure and is an important indicator for evaluating the deformation development trend and potential risks. By analyzing the magnitude and sign of the deformation acceleration, it can be determined whether obvious deformation anomalies occur in the structure under the load, providing a basis for identifying the deformation-sensitive areas.

[0056] In specific implementation, the system can adopt various numerical differentiation algorithms, such as central difference, multi-step difference, etc., to calculate the second derivative of the deformation data. At the same time, in order to improve the accuracy of acceleration calculation, the system can also introduce data smoothing and denoising techniques, such as moving average, wavelet denoising, etc., to preprocess the original deformation data and weaken the influence of noise and interference. In addition, considering that the sampling interval of the deformation data may be uneven, the system can also adopt methods such as variable-step differential or interpolation differential to adapt to different data sampling strategies and ensure the accuracy and stability of acceleration calculation.

[0057] S204. Determine the deformation-sensitive areas based on the deformation acceleration. In this step, the system determines the deformation-sensitive areas of the structure according to the calculated deformation acceleration. The deformation-sensitive areas refer to the local areas that show obvious deformation anomalies under the load, reflecting the weak links and potential risk points of the structure's deformation response. By identifying the deformation-sensitive areas, targeted key monitoring and safety assessment can be carried out to timely detect and warn of local deformation risks. The system can adopt methods such as threshold judgment or pattern recognition to automatically extract the spatial range of the deformation-sensitive areas according to the magnitude, sign, and distribution characteristics of the deformation acceleration.

[0058] In practical applications, the system can comprehensively consider multiple characteristic parameters of the deformation acceleration, such as peak value, mean value, variance, etc., to construct a multi-dimensional sensitivity evaluation index system. By performing weighted combination and comprehensive scoring on these indexes, the system can quantitatively evaluate the deformation sensitivity degree of each local area and determine the priority and danger level of the deformation-sensitive areas according to the scoring results. At the same time, the system can also introduce spatial clustering algorithms, such as K-means clustering, hierarchical clustering, etc., to automatically group the spatial distribution of the deformation acceleration and identify the core area and edge area of the deformation-sensitive areas. Through spatial clustering analysis, the system can more accurately depict the geometric shape and range of the deformation-sensitive areas, providing a reference for subsequent monitoring point layout and maintenance decision-making.

[0059] S205. Analyze the deformation response delay characteristics in the deformation-sensitive area, obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and divide the deformation warning levels according to the slope change of the corresponding relationship curve.

[0060] The system analyzes the deformation response delay characteristics in the deformation-sensitive area, obtains the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and divides the deformation warning levels according to the slope change of the corresponding relationship curve. Specifically: calculate the difference in deformation acceleration within adjacent time windows in the deformation-sensitive area; Identify the peaks of the difference in deformation acceleration to obtain the acceleration mutation points; Calculate the time interval between the load application moment and the deformation response moment corresponding to the acceleration mutation points to obtain the first delay time series; Match the first delay time series with the deformation acceleration at the corresponding moments to obtain the delay time-acceleration data pairs; Perform curve fitting on the delay time-acceleration data pairs to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration; Calculate the piecewise slopes of the corresponding relationship curve to obtain the slope change sequence; Determine the deformation warning levels according to the numerical intervals of the slope change sequence.

[0061] In this step, the system focuses on analyzing the deformation response delay characteristics in the deformation-sensitive area. Deformation response delay refers to the time required for a structure to produce an obvious deformation response under the action of a load, which reflects the hysteresis effect and damping characteristics of the structure's deformation. By analyzing the corresponding relationship between the deformation response delay time and the deformation acceleration, the dynamic characteristics of the deformation-sensitive area can be revealed, the deformation development trend can be predicted, and the deformation warning levels can be divided accordingly.

[0062] Specifically, the system first calculates the difference in deformation acceleration within adjacent time windows in the deformation-sensitive area and extracts the acceleration mutation points through a peak identification algorithm. Then, the system calculates the time interval between the load application moment and the deformation response moment corresponding to each acceleration mutation point to obtain the deformation response delay time series. Next, the system matches the delay time series with the deformation acceleration values at the corresponding moments to obtain a set of delay time-acceleration data pairs. Finally, the system performs curve fitting on these data pairs to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration.

[0063] In the above embodiments, the deformation difference sequence of adjacent monitoring points in the key monitoring area is calculated. By analyzing with a sliding time window to identify the deformation mutation period, the abnormal fluctuations of the local deformation of the structure can be captured. The deformation data before and after the load application are extracted within the deformation mutation period and the deformation acceleration is calculated, which can accurately reflect the dynamic response characteristics of the structure to the load change. Based on the deformation acceleration, the deformation sensitive area is determined, and the deformation response delay characteristic is analyzed, so as to master the sensitivity degree and response speed of each part of the structure to the load change. Through the corresponding relationship curve between the deformation response delay time and the deformation acceleration, combined with the slope change to divide the deformation warning level, the accuracy and real-time performance of the assessment and hierarchical warning of the deformation risk of the structure can be improved.

[0064] Further, after determining the deformation warning level in step S205 of the above embodiments, the deformation time history curve in the key monitoring area is extracted, and the deformation time history curve is subjected to frequency spectrum analysis to obtain the deformation frequency distribution; Calculate the amplitude ratio between adjacent frequency bands to obtain the frequency amplitude sequence; Based on the frequency amplitude sequence, the main control frequency band is determined, and the phase information corresponding to the main control frequency band is extracted to obtain the phase change sequence; Analyze the phase change sequence to obtain the deformation transmission direction; According to the deformation transmission direction, calculate the amplitude attenuation characteristic during the deformation transmission process. Specifically: extract the amplitude data of adjacent monitoring points within the main control frequency band, and calculate the amplitude difference between adjacent monitoring points; Establish the corresponding relationship between the distance of the monitoring points and the amplitude difference; Calculate the first derivative of the corresponding relationship to obtain the amplitude attenuation rate; Determine the deformation transmission attenuation characteristic according to the numerical value of the amplitude attenuation rate.

[0065] First, the system extracts the deformation time history curve in the key monitoring area, that is, the curve of the deformation amount changing with time. By performing frequency spectrum analysis on the deformation time history curve, such as Fourier transform, etc., the system can obtain the amplitude distribution of the deformation at different frequency components, that is, the deformation frequency distribution. The deformation frequency distribution reflects the periodic characteristics and energy distribution of the deformation at different time scales.

[0066] Then, the system calculates the amplitude ratio between adjacent frequency bands to obtain a sequence of frequency amplitude ratios. The frequency amplitude ratio characterizes the relative contribution degree of different frequency components to the total deformation. By analyzing the distribution characteristics of the frequency amplitude sequence, the system can automatically identify the frequency band that plays a dominant role in the total deformation, that is, the main control frequency band. The main control frequency band usually has a larger amplitude ratio and energy proportion, and plays a key driving role in the deformation evolution process.

[0067] Next, the system extracts the phase information corresponding to the main control frequency band to obtain a sequence of phase changes over time, i.e., the phase change sequence. The phase information reflects the propagation characteristics and dynamic behavior of the deformation in the main control frequency band. By analyzing the trend and mutation characteristics of the phase change sequence, the system can determine the transmission direction and path of the deformation in space and identify the propagation law of the deformation.

[0068] Finally, the system calculates the amplitude attenuation characteristics of the deformation during the transmission process according to the deformation transmission direction. Specifically, the system extracts the amplitude data of adjacent monitoring points within the main control frequency band, calculates the amplitude difference between adjacent monitoring points, and establishes the corresponding relationship between the monitoring point distance and the amplitude difference. By calculating the first derivative of this corresponding relationship, the system can obtain the attenuation rate of the amplitude with the transmission distance, that is, the attenuation speed of the amplitude per unit distance. The magnitude of the attenuation rate reflects the energy dissipation and damping characteristics of the deformation during the transmission process. The larger the attenuation rate, the faster the energy loss during the deformation transmission process and the smaller the spatial influence range of the deformation.

[0069] In specific implementation, the system can adopt various spectrum analysis methods, such as short-time Fourier transform, wavelet transform, etc., to adapt to the characteristics of different deformation time history curves. At the same time, in order to improve the accuracy of frequency amplitude ratio and phase information extraction, the system can also introduce advanced signal processing technologies, such as adaptive filtering, singular spectrum analysis, etc., to perform noise reduction and enhancement processing on the deformation time history curve. In addition, when establishing the corresponding relationship between the amplitude difference and the distance, the system can adopt various regression analysis methods, such as local polynomial regression, Gaussian process regression, etc., to fit complex non-linear attenuation models and improve the accuracy of attenuation rate estimation.

[0070] In the above embodiments, the spectrum analysis is performed on the deformation time history curve in the key monitoring area, the frequency amplitude sequence is obtained by calculating the amplitude ratio between adjacent frequency bands, the main control frequency band is determined and the phase information is extracted, the deformation transmission direction is obtained by analyzing the phase change sequence, and finally the amplitude attenuation characteristics during the deformation transmission process are calculated. It can reveal the deformation propagation law of the structure from the frequency domain perspective, accurately grasp the main deformation characteristics of the structure through the identification of the main control frequency band, and use the phase information to track the propagation path of the deformation wave, which can reduce the influence of interference factors such as environmental noise and improve the accuracy of deformation feature extraction.

[0071] The following describes the system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of a deformation detection system for a super-large structure provided in the embodiments of the present application.

[0072] It should be noted that Figure 3 the structure of the system shown is only an example and should not bring any restrictions to the functions and usage scopes of the embodiments of the present invention.

[0073] As Figure 3 shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303, such as executing the method in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0074] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a Liquid Crystal Display (LCD), a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as required. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as required so that a computer program read from it can be installed into the storage section 308 as required.

[0075] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0076] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0078] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0079] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0080] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0081] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0082] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media that can store program codes, such as ROM, random access memory (RAM), magnetic disks, or optical discs.

Claims

1. A deformation detection method for super-large structures, characterized in that, Including: Collecting multi-source detection data and deformation detection data of the collection structure during the maximum load period and the minimum load period; Calculating the deformation difference value of the multi-source detection data during the maximum load period and the minimum load period to obtain the deformation difference range of the structure; Within the deformation difference range, controlling the load transportation equipment to apply and unload the preset load according to the preset load step; Collecting the deformation data under each preset load step to obtain the deformation recovery process data of the structure, and determining the elastic deformation interval of the structure according to the deformation recovery process data; When the deformation detection data is within the elastic deformation interval, controlling the load transportation equipment to unload the structure according to the preset unloading step and calculating the deformation recovery rate; When the deformation recovery rate is lower than the preset threshold, marking the corresponding deformation area as an inelastic deformation area and determining the inelastic deformation area as the key monitoring area.

2. The method according to claim 1, wherein The calculating the deformation difference value of the multi-source detection data during the maximum load period and the minimum load period to obtain the deformation difference range of the structure specifically includes: Dividing the multi-source detection data during the maximum load period into several detection cycles according to the time series; Calculating the deformation difference ratio between adjacent detection points within each detection cycle; When the change trend of the deformation difference ratio shows periodicity, taking the deformation difference value of the corresponding period as the effective deformation difference value; Determining the deformation difference range of the structure according to the statistical distribution characteristics of the effective deformation difference value.

3. The method according to claim 1, characterized in that, The determining the elastic deformation interval of the structure according to the deformation recovery process data specifically includes: Dividing the deformation recovery process data into several loading stages according to the preset load step; Calculating the ratio of the deformation increment to the load increment within each loading stage to obtain the deformation-load response coefficient; When the change rate of the deformation-load response coefficient exceeds the preset ratio, determining the corresponding deformation value as the upper limit of elastic deformation; Determining the elastic deformation interval according to the upper limit of elastic deformation and the initial deformation value.

4. The method according to claim 1, wherein After determining the inelastic deformation area as the key monitoring area, the method further includes: Calculating the deformation difference sequence of adjacent monitoring points within the key monitoring area; Performing a sliding time window analysis on the deformation difference sequence to determine the deformation mutation period; Extracting the deformation data before and after the load action within the deformation mutation period and calculating the second derivative of the deformation data to obtain the deformation acceleration; Determining the deformation sensitive area based on the deformation acceleration; Analyzing the deformation response delay characteristics within the deformation sensitive area to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and dividing the deformation warning level according to the slope change of the corresponding relationship curve.

5. The method according to claim 4, wherein The analyzing the deformation response delay characteristics within the deformation sensitive area to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration, and dividing the deformation warning level according to the slope change of the corresponding relationship curve specifically includes: Calculating the deformation acceleration difference within adjacent time windows of the deformation sensitive area; Perform peak identification on the difference in deformation acceleration to obtain acceleration mutation points; Calculate the time interval between the load application moment and the deformation response moment corresponding to the acceleration mutation points to obtain the first delay time series; Match the first delay time series with the deformation acceleration at the corresponding moment to obtain delay time acceleration data pairs; Perform curve fitting on the delay time acceleration data pairs to obtain the corresponding relationship curve between the deformation response delay time and the deformation acceleration; Calculate the segmented slope of the corresponding relationship curve to obtain the slope change sequence; Determine the deformation warning level according to the numerical range of the slope change sequence.

6. The method according to claim 4 or 5, characterized in that, After determining the deformation warning level, the method further includes: Extract the deformation time history curve in the key monitoring area and perform spectral analysis on the deformation time history curve to obtain the deformation frequency distribution; Calculate the amplitude ratio between adjacent frequency bands to obtain the frequency amplitude sequence; Determine the main control frequency band based on the frequency amplitude sequence and extract the phase information corresponding to the main control frequency band to obtain the phase change sequence; Analyze the phase change sequence to obtain the deformation transmission direction; Calculate the amplitude attenuation characteristics during the deformation transmission according to the deformation transmission direction.

7. The method according to claim 6, characterized in that The calculating the amplitude attenuation characteristics during the deformation transmission according to the deformation transmission direction specifically includes: Extract the amplitude data of adjacent monitoring points within the main control frequency band and calculate the amplitude difference between the adjacent monitoring points; Establish the corresponding relationship between the distance of the monitoring points and the amplitude difference; Calculate the first derivative of the corresponding relationship to obtain the amplitude attenuation rate; Determine the deformation transmission attenuation characteristics according to the numerical value of the amplitude attenuation rate.

8. A deformation detection system for a super-large structure, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the system, cause the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the system, cause the system to execute the method according to any one of claims 1-7.

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