A safety regulation method and system for super-high bridge tower based on time-varying effect of concrete

Abstract

This invention proposes a safety control method and system for ultra-high bridge towers based on the time-varying effect of concrete, belonging to the technical field of bridge tower control systems. Addressing the problems in existing technologies such as insufficient prediction accuracy of long-term internal force redistribution caused by concrete shrinkage and creep, and insufficient consideration of the coupling effect between multiple risk factors and bridge tower concrete shrinkage and creep, resulting in low accuracy in predicting the state of bridge towers and a lack of guidance for maintenance control, this invention integrates multi-source heterogeneous data to calculate the impact indices of concrete shrinkage and creep, dynamic ice impact, static ice pressure, concrete freeze-thaw cycles, and ice wedging. A machine learning-based comprehensive risk assessment model is then used to fuse these indices, generating a comprehensive risk report containing risk levels and control recommendations. This invention significantly improves the accuracy of single risk factor assessment, enhances the comprehensive prediction accuracy of complex coupled risks, and strengthens the system's decision-making robustness and maintenance efficiency in complex scenarios with multiple concurrent risks.

Description

Technical Field

[0001] This invention relates to the field of bridge tower control system technology, and in particular to a method and system for safety control of ultra-high bridge towers based on the time-varying effect of concrete. Background Technology

[0002] With the rapid development of long-span bridge construction, the long-term safety and durability of concrete super-high bridge towers, as major load-bearing components, are receiving increasing attention. Real-time monitoring and safety assessment of bridge tower structures can effectively identify potential structural risks, provide data support for preventative maintenance, and is of great value in ensuring bridge operational safety and extending service life. In recent years, structural health monitoring technology based on the time-varying effects of concrete has provided a new technical approach for the accurate perception and intelligent early warning of bridge tower safety status, and has broad application prospects.

[0003] However, traditional methods often focus on single-factor or static load analysis, failing to fully consider the coupling effect of the inherent shrinkage and creep of concrete materials with complex environmental loads. This results in insufficient accuracy in predicting the long-term redistribution of internal forces caused by concrete shrinkage and creep. When faced with the impact of dynamic ice on bridge towers, the pressure exerted by static ice layers, the damage caused by cyclic freeze-thaw cycles in concrete, and the crack propagation caused by ice wedging into structural gaps, the coupling effect of multiple risk factors with the shrinkage and creep of bridge tower concrete is not adequately considered. This makes it difficult to comprehensively reflect the overall safety status of bridge towers in real-world environments, leading to low accuracy in predicting bridge tower conditions and a lack of guiding principles for bridge tower maintenance and control.

[0004] Therefore, there is an urgent need to develop a safety control method and system for ultra-high bridge towers based on the time-varying effect of concrete with higher precision and accuracy. Summary of the Invention

[0005] To address this, the present invention provides a method and system for safety control of ultra-high bridge towers based on the time-varying effect of concrete, which overcomes the problems in the prior art of insufficient prediction accuracy of long-term internal force redistribution caused by concrete shrinkage and creep, insufficient consideration of the coupling effect of multiple risk factors and bridge tower concrete shrinkage and creep, resulting in low accuracy of bridge tower state prediction and lack of guiding basis for bridge tower maintenance and control.

[0006] To achieve the above objectives, this invention provides a method for safety control of ultra-high bridge towers based on the time-varying effect of concrete, comprising:

[0007] S1, acquire multi-source data from multiple heterogeneous data sources and structural and historical data of the target bridge tower;

[0008] S2, Based on the structural data of the target bridge tower, calculate the concrete shrinkage and creep influence index;

[0009] S3. Based on the multi-source data, calculate the dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index.

[0010] S4, integrate the shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index to generate a comprehensive risk report;

[0011] S5. Based on the comprehensive risk report, perform maintenance and control on the target bridge tower.

[0012] Furthermore, the multi-source data includes:

[0013] A time-series data stream from physical sensors, which include at least a temperature sensor, an ice pressure sensor, an accelerometer, a strain gauge, a tiltmeter, and a GLONASS receiver;

[0014] Visual data from an image acquisition device, the visual data including at least high-definition images, infrared thermal imaging data, and three-dimensional point cloud data.

[0015] Furthermore, the calculation of the concrete shrinkage and creep influence index based on the structural data of the target bridge tower includes:

[0016] S21, Based on the strain time series data of the historical data, the instantaneous elastic strain and long-term time-varying strain are separated;

[0017] S22, based on concrete material data and load history data, uses a shrinkage and creep prediction model to calculate the theoretical shrinkage strain and creep coefficient;

[0018] S23. The calculation results are compared with the measured long-term time-varying strain to obtain a quantitative value that characterizes the deviation between the actual development level and the model prediction.

[0019] S24. Based on the quantized value, the equivalent nodal force increment caused by shrinkage and creep in the next time period is calculated using a recursive formula fitted by an exponential function.

[0020] S25. Based on the degree of influence of the equivalent nodal force increment on the internal forces of the structure, the concrete shrinkage and creep influence index is finally determined.

[0021] Furthermore, the calculation of the dynamic ice impact index, static ice pressure index, concrete freeze-thaw impact index, and ice wedging impact index based on the multi-source data includes:

[0022] S31, based on the time-series data stream of accelerometers and strain gauges, identify moving ice impact events by setting dynamic thresholds, calculate the energy of a single impact based on the spectrum and amplitude characteristics of the impact signal, accumulate the total impact energy within a preset time window, and generate a moving ice impact influence index based on the total impact energy;

[0023] S32, based on the temperature data of the temperature sensor, predict the ice layer thickness within the target period, calculate the predicted static ice pressure acting on the bridge tower within the target period through a thermodynamic-structural mechanics coupling model, and generate a static ice pressure influence index based on the predicted static ice pressure.

[0024] S33, based on infrared thermal imaging data and time-series data from concrete internal temperature / humidity sensors, calculate the temperature distribution field and water saturation of concrete in the bridge tower water level fluctuation zone, predict the degree of concrete strength reduction based on the freeze-thaw damage constitutive model, and generate a concrete freeze-thaw impact index based on the degree of reduction.

[0025] S34. Based on high-definition image data, the geometric features of bridge tower joints and existing cracks are identified through a computer vision model. Combined with the temperature drop trend, the predicted value of crack width change is calculated. Based on the predicted value of crack width change, an ice body wedging influence index is generated.

[0026] Furthermore, the method of identifying dynamic ice impact events by setting a dynamic threshold includes:

[0027] S311, calculate the signal energy characteristics of the accelerometer and strain gauge within the sliding time window;

[0028] S312, quantize the signal energy characteristics into a dynamic identification threshold; wherein, the dynamic identification threshold is M times the standard deviation of the signal amplitude within the previous sliding time window, and M is a preset sensitivity coefficient;

[0029] S313, when the amplitude or energy characteristics of the signal exceed the dynamic identification threshold, it is determined to be a potential collision event;

[0030] S314, Analyze the signal waveform of the potential impact event to verify whether its duration and rise edge characteristics conform to the preset typical pattern of dynamic ice impact.

[0031] S315, when the signal characteristics pass the verification, confirm and record the event as a valid dynamic ice impact event, and extract its time domain and frequency domain characteristic parameters.

[0032] Furthermore, the signal waveform of the potential impact event is analyzed to verify whether its duration and rise edge characteristics conform to the preset typical pattern of dynamic ice impact, including:

[0033] S314a, extract the signal envelope of the potential impact event, calculate the time span from when the signal amplitude exceeds the first threshold to when it falls back to the second threshold, and verify whether the time span is within the reasonable duration range of the typical mode of dynamic ice impact.

[0034] S314b, extract the rising edge phase of the signal, calculate the time difference from the signal start point to the peak point as the rise time, and calculate the first derivative of the signal amplitude with time during the rise time as the average slope, and verify whether the rise time and the average slope fall within the characteristic range of the preset typical mode of dynamic ice impact.

[0035] S314c, Perform frequency domain transformation on the signal waveform, analyze its spectral energy distribution, and verify whether there are characteristic frequency components related to ice breakage and impact.

[0036] S314d, taking into account the verification results of the time span, rise time and average slope and spectral energy distribution, when all verification results meet the preset typical mode of dynamic ice impact, the potential event is determined to have passed the verification.

[0037] Furthermore, the calculation of the predicted crack width change based on the temperature decrease trend includes:

[0038] S341, based on high-definition image data, identifies and locates existing cracks in the water level fluctuation zone and obtains their initial width;

[0039] S342, based on time-series data from a temperature sensor, acquires the temperature and rate of temperature decrease of the water within the existing crack;

[0040] S343, Establish a linear expansion model for water freezing inside the crack, and predict the volume expansion of water inside the crack based on the temperature decrease trend.

[0041] S344, Calculate the expansion pressure on the crack wall caused by the expansion of water body based on the volume expansion amount;

[0042] S345, Calculate the predicted change in crack width based on the expansion pressure and concrete material data.

[0043] Furthermore, the integrated shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index generate a comprehensive risk report, including:

[0044] S41, the concrete shrinkage and creep influence index, dynamic ice impact index, static ice pressure influence index, concrete freeze-thaw influence index and ice wedging influence index are quantified into a multi-dimensional risk feature vector.

[0045] S42, input the multidimensional risk feature vector into the preset comprehensive risk assessment model to generate a comprehensive risk score;

[0046] S43. Based on the comprehensive risk score, determine the current risk level and generate a comprehensive risk report that includes the risk level, the dominant risk type, and control recommendations.

[0047] Furthermore, the thermodynamic-structural mechanics coupled model is a mathematical model for calculating the static ice pressure caused by temperature changes, wherein the static ice pressure is the pressure exerted by the ice layer on the bridge tower.

[0048] The freeze-thaw damage constitutive model is a mathematical model based on the properties of concrete that can describe the degradation of the mechanical properties of concrete under freeze-thaw cycles.

[0049] This invention also provides a safety control system for ultra-high bridge towers based on the time-varying effect of concrete. The system employs any one of the safety control methods for ultra-high bridge towers based on the time-varying effect of concrete, specifically including:

[0050] The multi-source data acquisition module acquires multi-source data from multiple heterogeneous data sources, as well as structural and historical data of the target bridge tower.

[0051] The time-varying effect analysis module is used to calculate the concrete shrinkage and creep influence index based on the structural data of the target bridge tower.

[0052] The environmental load assessment module, connected to the time-varying effect analysis module, is used to calculate the dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index based on the multi-source data.

[0053] The risk fusion decision module, connected to the environmental load assessment module, is used to fuse the shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index to generate a comprehensive risk report.

[0054] The intelligent control execution module, connected to the risk fusion decision module, is used to perform maintenance control on the target bridge tower based on the comprehensive risk report.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] Firstly, by establishing a multi-dimensional and multi-level intelligent verification mechanism for dynamic ice impact events, the accuracy and precision of risk identification are significantly improved. Compared with traditional single-threshold judgment methods, this invention innovatively introduces a complete verification chain based on signal envelope time span verification, rising edge time domain feature analysis, frequency domain energy distribution verification, and comprehensive confidence assessment. By extracting the signal envelope through Hilbert transform and accurately defining the event time boundary, transient noise and continuous vibration interference are effectively eliminated. Through dual-parameter analysis of rising time and average slope, the intensity of impact is quantitatively differentiated, improving the accuracy of event type discrimination. Through fast Fourier transform and characteristic frequency band energy analysis, verification is conducted from the physical mechanism of ice breakage, enhancing the physical credibility of event identification. Finally, by weighted fusion of multi-dimensional verification results for comprehensive judgment, a high-confidence event identification closed loop is constructed. Through collaborative operational technical means, the technical challenges of high false alarm rates and high false negative rates in dynamic ice impact events under complex environments are systematically solved, improving the accuracy and precision of comprehensive risk assessment.

[0057] Secondly, this invention further improves the accuracy of risk prediction and assessment by introducing physical mechanism models and intelligent algorithms. For complex risks such as static ice pressure, concrete freeze-thaw damage, and ice wedging, this invention does not rely on empirical formulas but establishes mathematical models based on physical mechanisms, including a thermodynamic-structural mechanics coupling model, a freeze-thaw damage constitutive model, and a linear expansion model considering geometric constraints. Simultaneously, the comprehensive risk assessment model trained using machine learning can deeply explore the nonlinear relationships and coupling effects among multiple risk factors, thereby generating a more scientific and accurate comprehensive risk score and report. The dual guarantee of mechanism models and data-driven approaches makes the system's prediction of the bridge tower's state under complex time-varying and environmental coupling effects more realistic, significantly improving the accuracy of the assessment results.

[0058] Third, this invention possesses strong robustness and intelligent decision-making capabilities in complex scenarios such as concurrent multiple risks and time-varying environments. Through the designed risk fusion and control execution mechanism, it can effectively handle complex working conditions involving multiple intertwined factors such as dynamic ice impact, static ice pressure, freeze-thaw damage, ice wedging, and shrinkage creep. The system can intelligently determine the urgency of risk handling and generate a combined optimized maintenance strategy package through dynamic priority ranking and measure coordination mechanisms. In addition, based on the closed-loop management concept, the system can dynamically adjust the evaluation results and subsequent strategies according to the real-time feedback data after the implementation of control measures, ensuring that the system's decision-making remains stable, reliable, and efficient even when environmental conditions and structural states are constantly changing, demonstrating excellent engineering practicality and scenario adaptability. Attached Figure Description

[0059] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating a method for safety control of ultra-high bridge towers based on the time-varying effect of concrete, according to an embodiment of the present invention.

[0061] Figure 2 This is a structural block diagram of a safety control system for ultra-high bridge towers based on the time-varying effect of concrete, according to an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0063] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0064] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0065] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0066] Example 1

[0067] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for safety control of ultra-high bridge towers based on the time-varying effect of concrete, comprising:

[0068] S1, acquire multi-source data from multiple heterogeneous data sources and structural and historical data of the target bridge tower;

[0069] Multi-source data, including:

[0070] A time-series data stream from physical sensors, which include at least a temperature sensor, an ice pressure sensor, an accelerometer, a strain gauge, a tiltmeter, and a GLONASS receiver;

[0071] Visual data from an image acquisition device, the visual data including at least high-definition images, infrared thermal imaging data, and three-dimensional point cloud data.

[0072] In one possible implementation, the multi-source data mainly includes two categories: physical sensor time-series data streams and visual data, which are transmitted to a central data processing server via wired or wireless networks.

[0073] Specifically, a distributed sensor network will be deployed in the target bridge tower and its surrounding waters, including:

[0074] Temperature sensors are deployed inside the concrete of the bridge tower, on the surface of the structure, in nearby water and in the air to monitor the core temperature of the concrete, surface temperature, water temperature and air temperature, and collect data at a frequency of minutes.

[0075] The ice pressure sensor, which is a resistance strain gauge pressure sensor, is installed in an array on the water-facing side of the bridge tower in the water level fluctuation zone and the expected ice height. It directly measures the static pressure of the ice layer on the structure and the instantaneous impact force of the moving ice.

[0076] Accelerometers: Triaxial accelerometers are installed at different heights on the bridge towers to collect vibration signals of the structure at a frequency of 100Hz, which is used to capture transient events such as impacts from moving ice.

[0077] Strain gauges are pre-embedded or surface-mounted in key sections of bridge towers, such as the tower base and crossbeam connections, to monitor the micro-strain of concrete under load and temperature. The data acquisition frequency is synchronized with that of accelerometers.

[0078] Inclinometers, installed at the top and middle of the bridge towers, are used to monitor changes in the tilt attitude of the bridge towers under loads such as wind and ice.

[0079] A GLONASS receiver is installed at the top of the bridge tower, along with a high-precision GLONASS reference station and monitoring station, to monitor the absolute displacement of the bridge tower in three-dimensional space.

[0080] The raw time-series data streams collected by the aforementioned sensors are cleaned, denoised, and standardized with unified timestamps to form regular time-series data.

[0081] Specifically, visual data is acquired through a combination of fixed installation and mobile inspection, including:

[0082] High-definition images are obtained by installing high-definition, fog-penetrating monitoring PTZ cameras along the bridge tower or on the tower itself to continuously record and capture videos of the bridge tower surface, water level, and surrounding ice conditions. The images are used for ice condition observation and preliminary identification of surface damage.

[0083] Infrared thermal imaging data: Using drones equipped with infrared thermal imagers, bridge towers, especially areas with fluctuating water levels, are scanned regularly. The acquired thermal images are used to analyze the temperature distribution field on the concrete surface and identify areas of thermal anomalies caused by different moisture contents.

[0084] The three-dimensional point cloud data is generated by using a drone equipped with LiDAR to scan the bridge tower from all directions, producing a high-precision three-dimensional point cloud model of the bridge tower. This model is used to accurately quantify the geometric shape of the bridge tower and to detect subtle deformations such as spalling and crack width changes on the concrete surface by comparing point clouds from different periods.

[0085] The structural and historical data of the bridge towers are digitally obtained from the bridge's design, construction, and operation and maintenance archives. Specifically, this includes the bridge tower's geometric dimensions, design drawings, concrete strength grade, elastic modulus, reinforcement information, load history information, strain and deformation history information, and environmental history information.

[0086] This invention achieves a comprehensive characterization of bridge tower status by fusing time-series data streams from physical sensors with multimodal visual data. Through cross-validation and fusion analysis of multi-source heterogeneous data, it significantly improves the accuracy of diagnostic results, building a solid and reliable sensing foundation for the entire ultra-high bridge tower safety control system. It is the core guarantee for realizing intelligent and precise structural health management and operation and maintenance.

[0087] S2, based on the structural data of the target bridge tower, calculate the concrete shrinkage and creep influence index, including:

[0088] S21, Based on the strain time series data of the historical data, the instantaneous elastic strain and long-term time-varying strain are separated;

[0089] S22, based on concrete material data and load history data, uses a shrinkage and creep prediction model to calculate the theoretical shrinkage strain and creep coefficient;

[0090] S23. The calculation results are compared with the measured long-term time-varying strain to obtain a quantitative value that characterizes the deviation between the actual development level and the model prediction.

[0091] S24. Based on the quantized value, the equivalent nodal force increment caused by shrinkage and creep in the next time period is calculated using a recursive formula fitted by an exponential function.

[0092] S25. Based on the degree of influence of the equivalent nodal force increment on the internal forces of the structure, the concrete shrinkage and creep influence index is finally determined.

[0093] In one possible implementation, strain components are separated from strain history information using digital signal processing techniques. Specifically, instantaneous elastic strain caused by traffic loads, wind loads, etc., is extracted by high-frequency filtering, and long-term time-varying strain caused by shrinkage and creep effects is separated by low-frequency filtering.

[0094] Based on concrete material data, such as strength grade and mix proportion, and load history, a standard predictive model is used to calculate the theoretical shrinkage strain value and creep coefficient. The relative deviation between the measured long-term time-varying strain value and the theoretical long-term time-varying strain value is calculated. Based on the relative deviation value, an exponential function fitting recursive algorithm is used to calculate the equivalent nodal force increment. The equivalent nodal force increment is applied to the bridge tower finite element model. The variation amplitude of internal forces in key sections (such as the tower base) is analyzed. The internal force variation is normalized into a concrete shrinkage and creep influence index according to preset rules. The higher the influence index, the more significant the impact of shrinkage and creep on structural safety.

[0095] This invention solves the technical problem of accurately separating time-varying components in structural strain response by using digital signal filtering and separation technology, achieving high-precision separation of instantaneous elastic strain and long-term time-varying strain, and providing a clean data foundation for independent analysis of shrinkage and creep effects;

[0096] By comparing and analyzing theoretical values ​​derived from the standard model with measured data, the technical problem of discrepancies between theoretical prediction models and actual material properties was solved, enabling precise quantification of the actual shrinkage and creep development of concrete. Through a recursive algorithm using exponential function fitting, the technical problems of large data storage and low computational efficiency in traditional creep calculations were solved, significantly improving the computational efficiency of long-term time-varying effect analysis. Furthermore, through finite element analysis of equivalent nodal force increments, the technical problem of difficulty in quantifying the impact of shrinkage and creep on structural safety was solved, enabling the use of a normalized index to intuitively characterize the impact of shrinkage and creep on structural safety.

[0097] S3, based on the aforementioned multi-source data, calculate the dynamic ice impact index, static ice pressure index, concrete freeze-thaw impact index, and ice wedging impact index, including:

[0098] S31, based on the time-series data stream of accelerometers and strain gauges, identify moving ice impact events by setting dynamic thresholds, calculate the energy of a single impact based on the spectrum and amplitude characteristics of the impact signal, accumulate the total impact energy within a preset time window, and generate a moving ice impact influence index based on the total impact energy;

[0099] Identifying dynamic ice impact events by setting dynamic thresholds, including:

[0100] S311, calculate the signal energy characteristics of the accelerometer and strain gauge within the sliding time window;

[0101] S312, quantize the signal energy characteristics into a dynamic identification threshold; wherein, the dynamic identification threshold is M times the standard deviation of the signal amplitude within the previous sliding time window, and M is a preset sensitivity coefficient;

[0102] S313, when the amplitude or energy characteristics of the signal exceed the dynamic identification threshold, it is determined to be a potential collision event;

[0103] S314, Analyze the signal waveform of the potential impact event to verify whether its duration and rise edge characteristics conform to the preset typical pattern of dynamic ice impact.

[0104] S315, when the signal characteristics pass the verification, confirm and record the event as a valid dynamic ice impact event, and extract its time domain and frequency domain characteristic parameters.

[0105] In one possible implementation, the sliding time window is set to 5 minutes with a window overlap rate of 50%. Within each window, the root mean square value, peak-to-average ratio, and kurtosis of the acceleration and strain data are calculated. The background vibration level is calculated, taking into account the average energy and fluctuation of the signal. The dynamic threshold is the product of the sensitivity coefficient and the signal amplitude in the previous sliding time window. The sensitivity coefficient is adjusted according to environmental conditions. For example, a smaller coefficient is used during periods of severe ice conditions to increase sensitivity, while a larger coefficient is used during periods of rapid water flow to reduce the false alarm rate.

[0106] When the signal amplitude suddenly increases and exceeds the dynamic threshold, or the signal energy increases significantly in a short period of time, the start time, peak time and end time of the event are recorded. Multi-dimensional analysis and verification are performed on each potential event. When the signal passes all verification conditions, it is confirmed as a valid ice impact event, and the key feature parameters of the event are extracted. The key feature parameters specifically include: impact intensity and duration, frequency characteristics and energy distribution, waveform characteristics and spatial location information. All feature parameters are stored in the database.

[0107] When the event is confirmed as an ice impact event, the energy of a single impact is calculated by numerically integrating the acceleration signal and the high-frequency strain gauge signal over the duration of the event. A weighted synthesis method is used to combine the energy of the acceleration signal and the energy of the strain signal. The acceleration weighting coefficient is 0.6 and the strain weighting coefficient is 0.4. The preset time window is set to 24 hours, and the energy of all valid impact events is accumulated. The cumulative energy value at each time scale is recorded, and the impact frequency and average single impact energy are statistically analyzed.

[0108] The calculated cumulative energy value is compared with three preset energy thresholds. These three thresholds are set according to the design load-bearing capacity of the bridge tower and correspond to different risk levels: a concern threshold of 10% of the design load-bearing capacity, a warning threshold of 30% of the design load-bearing capacity, and an alarm threshold of 50% of the design load-bearing capacity. When the cumulative energy value does not exceed the concern threshold, the impact index is calculated proportionally, with a value ranging from 0 to 0.3. Specifically, the index value is equal to 0.3 multiplied by the ratio of the cumulative energy to the concern threshold. This means that at this stage, the impact is at a minor level.

[0109] When the accumulated energy exceeds the attention threshold but does not reach the warning threshold, the impact index is calculated using a piecewise interpolation method. The index baseline value starts at 0.3, plus 0.4 multiplied by a scaling factor, which is the ratio of the portion of the accumulated energy exceeding the attention threshold to the difference between the warning threshold and the attention threshold. Thus, the index value will vary between 0.3 and 0.7, reflecting a moderate level of impact.

[0110] When the accumulated energy continues to increase, exceeding the warning threshold but still within the alarm threshold range, the impact index further increases. At this point, the index baseline value starts at 0.7, plus 0.3 multiplied by another scaling factor, which is the ratio of the portion of accumulated energy exceeding the warning threshold to the difference between the alarm threshold and the warning threshold. The index value at this stage is between 0.7 and 0.9, indicating a more severe impact.

[0111] Finally, when the accumulated energy exceeds the alarm threshold, the system directly sets the impact index to the maximum value of 1.0, indicating that the impact has reached a severe level and immediate countermeasures are required.

[0112] This invention solves the technical problem that traditional binary judgment cannot quantify risk levels by using piecewise linear index generation technology. By setting multiple threshold levels based on design bearing capacity, it improves the accuracy of the dynamic ice impact index, thereby improving the accuracy and precision of the comprehensive risk report.

[0113] The signal waveforms of the potential impact events are analyzed to verify whether their duration and rise edge characteristics conform to the preset typical pattern of dynamic ice impact, including:

[0114] S314a, extract the signal envelope of the potential impact event, calculate the time span from when the signal amplitude exceeds the first threshold to when it falls back to the second threshold, and verify whether the time span is within the reasonable duration range of the typical mode of dynamic ice impact.

[0115] S314b, extract the rising edge phase of the signal, calculate the time difference from the signal start point to the peak point as the rise time, and calculate the first derivative of the signal amplitude with time during the rise time as the average slope, and verify whether the rise time and the average slope fall within the characteristic range of the preset typical mode of dynamic ice impact.

[0116] S314c, Perform frequency domain transformation on the signal waveform, analyze its spectral energy distribution, and verify whether there are characteristic frequency components related to ice breakage and impact.

[0117] S314d, taking into account the verification results of the time span, rise time and average slope and spectral energy distribution, when all verification results meet the preset typical mode of dynamic ice impact, the potential event is determined to have passed the verification.

[0118] In one possible implementation, the signal envelope is extracted using Hilbert transform, and after smoothing, the event time boundary is precisely defined. The criteria are: the event start point corresponds to twice the initial background noise level of the envelope, and the end point corresponds to the envelope falling back to 1.3 times the background noise level and remaining stable. The calculated time span must be within a reasonable range of 0.1-2.0 seconds. Events that are too short may be electrical noise, while events that are too long may be other types of continuous vibration.

[0119] The first threshold can be implemented in a range of 1.5 to 2.5 times the background noise, with a preferred value of 2.0 times; the second threshold can be implemented in a range of 1.1 to 1.5 times the background noise, with a preferred value of 1.3 times.

[0120] Specifically, the rising edge signal from the starting point to the peak point is extracted, and the time difference and average slope between the peak point and the starting point are calculated. Valid dynamic ice impact events require a rise time in the range of 0.01-0.2 seconds and an average slope in the range of 10-100 amplitude / second. The rise time and average slope together reflect the severity of the impact and can effectively distinguish ice impact from other interferences such as mechanical vibration.

[0121] Fast Fourier Transform was performed on the complete event signal, focusing on the energy distribution in the low-frequency band (2Hz-10Hz), mid-frequency band (10Hz-20Hz), and high-frequency band (20Hz-30Hz). A real dynamic ice impact event should show obvious characteristic peaks in the 5-15Hz range, and the energy proportion in the low-frequency band should be greater than 40%. This characteristic is closely related to the physical process of ice breaking and can verify the nature of the event from a mechanistic perspective.

[0122] Duration compliance accounts for 30% of the weight, rising edge compliance accounts for 40%, and frequency domain compliance accounts for 30%. When all validation items pass and the total confidence level is greater than 85%, it is determined to be a valid dynamic ice impact event. The entire validation process forms a complete logical chain from coarse screening to fine analysis, which improves the accuracy of dynamic ice impact time identification and provides a high-quality data foundation for subsequent energy calculation and risk assessment.

[0123] This invention addresses the technical problem of traditional methods failing to accurately define the time boundary of impact events through Hilbert transform envelope extraction and time span calculation techniques. It achieves precise quantification of event duration, effectively eliminating interference from instantaneous electrical noise and continuous environmental vibration, thus improving the accuracy of duration-based screening of moving ice events and providing a reliable time-domain feature foundation for subsequent analysis. Furthermore, it employs a dual-parameter verification technique using rise time and average slope to overcome the insufficient discriminative power of a single feature parameter in distinguishing ice impact from mechanical vibration. By precisely extracting the rise edge signal and performing slope analysis, it achieves a quantitative assessment of impact severity, improving... The system improved the accuracy of distinguishing different types of impact events and significantly reduced the false positive rate. By using Fast Fourier Transform and frequency band energy distribution analysis techniques, it solved the technical problem that the nature of an event could not be verified from a mechanistic perspective based solely on time-domain features. The feature frequency identification method based on the physical characteristics of ice body breakage enabled secondary verification of impact events from a frequency domain perspective, improving the overall confidence of event identification and providing a deeper physical basis for the judgment results. The establishment of a weighted confidence comprehensive evaluation system solved the technical problem that the verification results from a single aspect might be biased. Through multi-dimensional feature fusion analysis, the system's practicality and reliability were significantly improved.

[0124] S32, based on the temperature data of the temperature sensor, predict the ice layer thickness within the target period, calculate the predicted static ice pressure acting on the bridge tower within the target period through a thermodynamic-structural mechanics coupling model, and generate a static ice pressure influence index based on the predicted static ice pressure.

[0125] In one possible implementation, continuous temperature time-series data is acquired by temperature sensors deployed on the water surface, the atmosphere, and the bridge tower surface. An unsteady-state heat conduction equation is used to calculate the heat exchange balance of the water body, focusing on the impact of the rate of temperature decrease on the heat dissipation of the water body. Based on the cumulative negative temperature method, the temperature data for the prediction period is input into the ice layer growth model, and the ice layer thickness change curve for the next 24 hours is output. The prediction results are periodically corrected by the actual measurement data of the radar ice measuring instrument to ensure the prediction accuracy.

[0126] Based on the temperature drop and the thermal expansion coefficient of ice, the thermal stress generated when the ice layer is constrained is calculated, a bridge tower-ice layer interaction model is established, the constraining effect of bridge tower stiffness on ice layer expansion is analyzed, and considering the non-uniformity of the ice layer and the geometry of the bridge tower, the static ice pressure distribution along the height of the bridge tower is calculated, and the maximum static ice pressure value and its location are identified as key parameters for risk assessment.

[0127] Based on the bridge tower design data, the attention threshold, early warning threshold, and alarm threshold for static ice pressure were determined. A piecewise linear interpolation method was used to map the predicted static ice pressure to the influence index range of 0-1.

[0128] This invention solves the technical problem that traditional empirical formulas cannot accurately reflect the time-varying effects of temperature and the dynamic response of structures by establishing a coupled thermodynamic-structural-mechanical model. This model organically combines temperature monitoring data with the thermal expansion characteristics of ice layers and the structural stiffness of bridge towers, enabling accurate prediction of the static ice pressure on bridge towers and improving the accuracy and precision of predicted safety for bridge towers facing static ice pressure scenarios.

[0129] S33, based on infrared thermal imaging data and time-series data from concrete internal temperature / humidity sensors, calculate the temperature distribution field and water saturation of concrete in the bridge tower water level fluctuation zone, predict the degree of concrete strength reduction based on the freeze-thaw damage constitutive model, and generate a concrete freeze-thaw impact index based on the degree of reduction.

[0130] The thermodynamic-structural mechanics coupled model is a mathematical model used to calculate the static ice pressure caused by temperature changes, where the static ice pressure is the pressure exerted by the ice layer on the bridge tower;

[0131] The freeze-thaw damage constitutive model is a mathematical model based on the properties of concrete that can describe the degradation of the mechanical properties of concrete under freeze-thaw cycles.

[0132] In one possible implementation, infrared thermal imaging data and internal temperature sensor data are combined, and a three-dimensional temperature field of the bridge tower water level fluctuation zone is constructed using a spatial interpolation method. By calculating the temperature gradient inside the concrete, key areas prone to freeze-thaw damage are identified. Based on temperature and humidity sensor data, the pore water saturation of the concrete is estimated using a capillary suction model to determine whether the concrete temperature in each area is within the critical range of freeze-thaw cycles from -3℃ to 3℃.

[0133] Based on temperature field data and concrete pore water saturation, the damage caused to concrete by the current freeze-thaw cycle is calculated. By establishing a damage-strength relationship model, the reduction in concrete compressive strength and elastic modulus is predicted. The number of freeze-thaw cycles throughout the winter is counted to assess the cumulative degradation effect of concrete performance. Based on the current reduction in concrete compressive strength and elastic modulus, the remaining service life of concrete in key parts is predicted.

[0134] Based on the reduction in concrete compressive strength and modulus of elasticity, freeze-thaw damage is classified into three levels: slight, moderate, and severe. The levels are then normalized to an impact index of 0-1, reflecting the severity of the freeze-thaw damage.

[0135] This invention addresses the lag in traditional methods that rely solely on apparent phenomena to assess freeze-thaw damage by employing multi-source data fusion analysis. By combining infrared thermal imaging data with internal temperature and humidity sensor data, a three-dimensional temperature field and saturation distribution model of concrete is constructed, enabling early identification and precise location of freeze-thaw damage and improving the timeliness of freeze-thaw damage risk identification. Furthermore, the invention utilizes a freeze-thaw damage constitutive model to overcome the inability of traditional experience-based judgments to quantify concrete performance degradation. Based on damage mechanics theory, a damage degree-strength relationship model enables quantitative prediction of concrete strength reduction, improving the accuracy and precision of freeze-thaw damage assessment and providing reliable data support for maintenance decisions.

[0136] S34. Based on high-definition image data, the geometric features of bridge tower joints and existing cracks are identified through a computer vision model. Combined with the temperature drop trend, the predicted value of crack width change is calculated. Based on the predicted value of crack width change, an ice body wedging influence index is generated.

[0137] The predicted value of crack width change is calculated based on the temperature decrease trend, including:

[0138] S341, based on high-definition image data, identifies and locates existing cracks in the water level fluctuation zone and obtains their initial width;

[0139] S342, based on time-series data from a temperature sensor, acquires the temperature and rate of temperature decrease of the water within the existing crack;

[0140] S343, Establish a linear expansion model for water freezing inside the crack, and predict the volume expansion of water inside the crack based on the temperature decrease trend.

[0141] S344, Calculate the expansion pressure on the crack wall caused by the expansion of water body based on the volume expansion amount;

[0142] S345, Calculate the predicted change in crack width based on the expansion pressure and concrete material data.

[0143] In one possible implementation, an improved U-Net convolutional neural network architecture is adopted. This network has been trained on tens of thousands of bridge crack images and has a powerful feature extraction capability. The crack length and branching situation are accurately calculated through the skeleton extraction algorithm. Three-dimensional reconstruction technology is used to obtain crack depth information by combining multi-view images and establish a crack spatiotemporal database to record the initial state, historical change trend and spatial distribution characteristics of each crack.

[0144] Based on the Kriging interpolation method, a three-dimensional temperature field of the crack region is constructed, the temperature drop rate and gradient distribution are calculated, and the temperature change trend in the next 24 hours is predicted, providing a time window for ice wedging early warning.

[0145] The initial volume of water within the crack is determined by calculating the crack's geometric dimensions. Based on the natural expansion characteristics of water during freezing, a standard volume expansion rate is adopted, incorporating the effect of the temperature drop rate. The faster the temperature drops, the faster the freezing process. Simultaneously, a time-dependent growth function is introduced to describe the asymptotic characteristics of the freezing process. The calculation results are corrected by a crack geometric constraint function, which considers the restriction effect of the actual crack shape on the free expansion of the ice. The initial volume of water within the crack is multiplied together with the standard volume expansion rate, the temperature drop rate, the time-dependent growth function, and the crack geometric constraint function to calculate the volume expansion of water within the crack.

[0146] The calculated volumetric expansion is converted into an equivalent pressure load. The ability of concrete to restrain expansion is comprehensively analyzed, including the elastic modulus and strength characteristics of concrete. Through the principle of mechanical equilibrium, a quantitative relationship between the expansion volume and the generated pressure is established, and finally the maximum expansion pressure value and its distribution characteristics formed on the crack wall are determined.

[0147] Based on linear elastic fracture mechanics, the change in crack width is related to expansion pressure, crack geometry parameters, and concrete material properties. A simplified calculation model is constructed as follows:

[0148] The change in crack width is equal to (expansion pressure multiplied by initial maximum width multiplied by length) divided by (elastic modulus of concrete multiplied by moment of inertia of crack section), where the moment of inertia of crack section (rectangular section) is equal to (depth multiplied by cube of initial maximum width) divided by twelve.

[0149] The predicted crack width is equal to the initial maximum width plus the crack width change, while setting reasonable boundaries: if the predicted crack width is less than the initial maximum width, it is determined to be caused by abnormal conditions such as temperature fluctuations, and the initial maximum width is taken as the predicted crack width; if the predicted crack width exceeds the maximum allowable crack width of the concrete structure, the allowable value for bridge tower concrete is set to 0.3mm, and it is marked as a high-risk warning point.

[0150] The ice wedging impact index is calculated by dividing the predicted crack width by the maximum allowable crack width of the concrete structure, with the result being a maximum of 1.

[0151] This invention addresses the problems of low efficiency, strong subjectivity, and difficulty in detecting early micro-cracks in traditional manual inspections by using a deep learning-based computer vision model. It achieves automated, high-precision identification and quantification of bridge tower cracks, improving crack identification efficiency. By establishing a linear expansion model that considers geometric constraints, it solves the technical problem of traditional methods simplifying ice expansion to uniform expansion and ignoring the influence of crack morphology, enabling accurate prediction of the volume expansion of water freezing within cracks. Through fracture mechanics theory and material constitutive models, it solves the technical problem that traditional empirical formulas cannot quantify and predict crack propagation behavior, enabling accurate prediction of crack width changes based on mechanical principles. This improves the accuracy and precision of crack propagation trend prediction, providing a scientific basis for preventive maintenance.

[0152] S4, by integrating the shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index, a comprehensive risk report is generated, including:

[0153] S41, the concrete shrinkage and creep influence index, dynamic ice impact index, static ice pressure influence index, concrete freeze-thaw influence index and ice wedging influence index are quantified into a multi-dimensional risk feature vector.

[0154] S42, input the multidimensional risk feature vector into the preset comprehensive risk assessment model to generate a comprehensive risk score;

[0155] S43. Based on the comprehensive risk score, determine the current risk level and generate a comprehensive risk report that includes the risk level, the dominant risk type, and control recommendations.

[0156] In one possible implementation, the shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index are arranged in a fixed order to form a five-dimensional vector. The numerical range of each dimension is normalized to the interval [0, 1], with 1 representing the highest risk.

[0157] The model employs ensemble learning algorithms, such as random forests and gradient boosting trees, to train a classification or regression model. During the training phase, this model learns from a large amount of historical data, including various risk vectors and their corresponding actual structural damage or safety status. It can uncover the complex nonlinear relationships and weighting ratios among the five risk indices. When the feature vectors generated by S41 are input into the model, the model outputs a comprehensive risk score. The comprehensive risk score is not a simple average of the five indices, but rather a holistic and weighted judgment made by the model based on the inherent patterns it has learned regarding the severity of the current risk combination.

[0158] Based on the comprehensive risk score, the risk level is classified according to preset thresholds, and a comprehensive risk report is generated. The comprehensive risk report specifically includes:

[0159] Risk level, to clarify the current overall safety status;

[0160] Dominate risk types, identify and point out risk factors that exceed the allowable range, such as the current primary risk being the ice wedging effect and the secondary risk being static ice pressure, to achieve precise source tracing;

[0161] The regulation recommendations are based on the risk level and dominant risk type. They match and output specific and actionable recommendations from the pre-set maintenance strategy knowledge base. For example, if the risk is high and the dominant risk is ice wedging, it is recommended to perform pressure grouting pretreatment on the crack at position X and to strengthen the inspection of the area in the next 48 hours.

[0162] This invention, through feature vector fusion, changes the traditional approach of assessing various risks separately and arriving at fragmented conclusions, achieving a unified consideration of all risk factors. By using machine learning models to replace simple empirical formulas or manual weighting, the comprehensive assessment becomes more objective and better able to uncover complex correlations, improving the accuracy and predictability of risk assessment. The generated report not only informs how high the risk is, but also indicates where the risk comes from and what should be done, directly transforming monitoring data into clear maintenance management instructions, greatly improving operation and maintenance efficiency and safety.

[0163] S5. Based on the comprehensive risk report, perform maintenance and control on the target bridge tower.

[0164] Specifically, step S5 is the value realization stage of this method. Its core logic lies in transforming the comprehensive risk report generated in S4 into specific and executable maintenance control instructions. Its innovation is particularly reflected in its ability to intelligently prioritize and coordinate measures when facing complex working conditions with multiple concurrent risk factors, rather than simply piling up disposal plans.

[0165] In one possible implementation, risks are categorized into immediate threats, such as static ice pressure that is about to exceed design values, and progressive damage, such as ongoing freeze-thaw damage. The system assesses the level of structural damage that each risk may cause individually and in conjunction with concrete shrinkage and creep. For example, the system might prioritize extremely high static ice pressure (which could immediately lead to structural instability) over moderate ice cracking (which could worsen over the next few days). Accordingly, resources would be allocated primarily to mitigating the highest priority risks.

[0166] A knowledge graph of maintenance measures was established and integrated. This knowledge graph defines the relationships between different control measures, such as synergy, mutual exclusion, and sequence. When the risks of dynamic ice impact and freeze-thaw damage coexist, two suggestions are output simultaneously:

[0167] 1) Repair the peeling areas to address freeze-thaw damage;

[0168] 2) Add impact protection armor to the repaired area to prevent future impacts.

[0169] Based on the risk combination, the optimal strategy package is generated from the measure library, rather than a single measure. For example, when facing a comprehensive risk rating caused by multiple factors such as shrinkage and creep, freeze-thaw cycles, etc., the generated report will not only recommend strengthening monitoring, but may be a combination strategy: increase the monitoring frequency of the entire area by 20% in the next week, carry out preventive repairs on freeze-thaw scabbing points in area B, and arrange a special inspection for creep deformation in area A next month.

[0170] Based on new monitoring data after the measures are implemented, the risk status is reassessed, and subsequent strategies are adjusted. For example, when the system issues an alarm-level command to initiate icebreaking operations due to static ice pressure, it monitors pressure sensor data in real time. Once the pressure drops below a safe threshold, the system automatically downgrades the risk level and shifts the focus of control to other persistent risks, such as monitoring changes in ice wedging into cracks.

[0171] This invention addresses the technical problems of delayed response and conflicting measures in traditional maintenance when faced with multiple concurrent risks through dynamic priority ranking and coordinated measures mechanisms. It enables intelligent decision-making based on the timeliness and severity of risks, shortening emergency response time. Furthermore, through a progressive response and closed-loop management mechanism, it solves the technical problem of lack of continuous tracking and dynamic adjustment in traditional one-off responses, achieving dynamic strategy optimization based on real-time monitoring data and reducing waste of maintenance resources. Finally, through a resource optimization allocation model, it addresses the technical problems of unreasonable resource allocation and lack of focus in traditional maintenance, improving resource utilization efficiency.

[0172] Example 2

[0173] like Figure 2As shown, the present invention also provides a safety control system for ultra-high bridge towers based on the time-varying effect of concrete, which adopts any of the ultra-high bridge tower safety control methods based on the time-varying effect of concrete described in Example 1, specifically including:

[0174] The multi-source data acquisition module acquires multi-source data from multiple heterogeneous data sources, as well as structural and historical data of the target bridge tower.

[0175] The time-varying effect analysis module is used to calculate the concrete shrinkage and creep influence index based on the structural data of the target bridge tower.

[0176] The environmental load assessment module, connected to the time-varying effect analysis module, is used to calculate the dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index based on the multi-source data.

[0177] The risk fusion decision module, connected to the environmental load assessment module, is used to fuse the shrinkage and creep impact index, dynamic ice impact index, static ice pressure impact index, concrete freeze-thaw impact index, and ice wedging impact index to generate a comprehensive risk report.

[0178] The intelligent control execution module, connected to the risk fusion decision module, is used to perform maintenance control on the target bridge tower based on the comprehensive risk report.

[0179] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A safety regulation method for super-high bridge towers based on time-varying effects of concrete, characterized in that, The method comprises the following steps: S1, obtaining multi-source data from multiple heterogeneous data sources and structural data and historical data of a target bridge tower; S2, calculating a concrete shrinkage and creep influence index based on the structural data of the target bridge tower; S3, calculating a dynamic ice impact influence index, a static ice pressure influence index, a concrete freeze-thaw influence index and an ice body wedge influence index based on the multi-source data; S4, fusing the shrinkage and creep influence index, the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index and the ice body wedge influence index to generate a comprehensive risk report; S5, based on the comprehensive risk report, carrying out maintenance control on the target bridge tower; The method for calculating the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index and the ice body wedge influence index based on the multi-source data comprises the following steps: S31, based on the time series data stream of the accelerometer and the strain gauge, a dynamic threshold is set to identify a dynamic ice impact event, the single impact energy is calculated based on the frequency spectrum and amplitude characteristics of the impact signal, and the total impact energy is accumulated in a preset time window, and the dynamic ice impact influence index is generated based on the total impact energy; S32, predicting the ice layer thickness in a target period based on the temperature data of the temperature sensor, calculating the predicted static ice pressure acting on the bridge tower in the target period through a thermodynamics-structural mechanics coupling model, and generating the static ice pressure influence index based on the predicted static ice pressure; S33, based on the infrared thermal imaging data and the time series data of the concrete internal temperature / humidity sensor, the temperature distribution field and the water saturation of the concrete in the water level change area of the bridge tower are calculated, the freeze-thaw damage constitutive model is used to predict the strength reduction degree of the concrete, and the concrete freeze-thaw influence index is generated based on the reduction degree; S34, based on the high-definition image data, the geometric characteristics of the bridge tower joint and existing cracks are identified through a computer vision model, the crack width change prediction value is calculated combined with the temperature drop trend, and the ice body wedge influence index is generated based on the crack width change prediction value.

2. The method for safety regulation of super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, The multi-source data comprises: time series data streams from physical sensors, the physical sensors at least including temperature sensors, ice pressure sensors, accelerometers, strain gauges, inclinometers and GLONASS receivers; visual data from image acquisition devices, the visual data at least including high-definition images, infrared thermal imaging data and three-dimensional point cloud data.

3. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, The method for calculating the concrete shrinkage and creep influence index based on the structural data of the target bridge tower comprises the following steps: S21, based on the strain time series data of the historical data, the instantaneous elastic strain and the long-term time-varying strain are separated; S22, based on the material data and the load history data of the concrete, a shrinkage and creep prediction model is used to calculate the theoretical shrinkage strain and the creep coefficient; S23, the calculation result is compared with the measured long-term time-varying strain to obtain a quantitative value representing the actual development degree and the deviation of the model prediction; S24, based on the quantitative value, a recursive formula of an exponential function fitting is used to calculate the equivalent node force increment caused by shrinkage and creep in the next period. S25, determining the concrete shrinkage and creep influence index according to the influence degree of the equivalent node force increment on the internal force of the structure.

4. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, The dynamic threshold is set to identify the dynamic ice impact event, comprising: S311, calculating the signal energy features of the accelerometer and the strain gauge in the sliding time window; S312, quantifying the signal energy features into a dynamic identification threshold; wherein the dynamic identification threshold is M times of the standard deviation of the signal amplitude in the previous sliding time window, wherein M is a preset sensitivity coefficient; S313, determining a potential impact event when the amplitude or energy features of the signal exceed the dynamic identification threshold; S314, analyzing the signal waveform of the potential impact event to verify whether the duration and rising edge features thereof conform to the preset typical mode of the dynamic ice impact; S315, confirming and recording the event as an effective dynamic ice impact event when the signal features pass the verification, and extracting the time domain and frequency domain feature parameters thereof.

5. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 4, characterized in that, S314a, extracting the signal envelope of the potential impact event, calculating the time span of the signal amplitude from exceeding the first threshold to falling back to the second threshold, and verifying whether the time span is within the reasonable duration interval of the preset typical mode of the dynamic ice impact; S314b, intercepting the rising edge stage of the signal, calculating the time difference from the starting point of the signal to the peak point as the rising time, and calculating the first derivative of the signal amplitude with respect to time in the rising time as the average slope, and verifying whether the rising time and the average slope fall within the feature range of the preset typical mode of the dynamic ice impact; S314c, performing frequency domain transformation on the signal waveform, analyzing the frequency spectrum energy distribution thereof, and verifying whether there is a characteristic frequency component related to ice body breaking and impact; S314d, comprehensively verifying the results of the time span, the rising time and the average slope, and the frequency spectrum energy distribution, and determining that the potential impact event passes the verification when all the verification results conform to the preset typical mode of the dynamic ice impact. The crack width change prediction value is calculated based on the temperature drop trend, comprising:

6. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, S341, identifying and positioning the existing cracks in the water level change area based on the high-definition image data, and obtaining the initial width thereof; S342, obtaining the temperature and temperature drop rate of the water body in the existing cracks based on the time series data of the temperature sensor; S343, establishing a linear expansion model of the water body in the cracks, and predicting the volume expansion amount of the water body in the cracks based on the temperature drop trend; S344, calculating the expansion pressure generated by the water body expansion on the crack wall surface according to the volume expansion amount; S345, calculating the predicted change value of the crack width according to the expansion pressure and the material data of the concrete. The shrinkage and creep influence index, the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index, and the ice body wedge influence index are fused to generate a comprehensive risk report, comprising:

7. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, ​ S41, quantifying the concrete shrinkage and creep influence index, the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index and the ice body wedge influence index into a multi-dimensional risk feature vector; S42, inputting the multi-dimensional risk feature vector into a preset comprehensive risk assessment model to generate a comprehensive risk score; S43, determining the current risk level based on the comprehensive risk score, and generating a comprehensive risk report containing the risk level, the dominant risk type and the regulation suggestion.

8. The method for safety regulation of a super-high bridge tower based on time-varying effect of concrete according to claim 1, characterized in that, Comprise: The thermodynamic-structural mechanics coupling model is a mathematical model for calculating the static ice pressure caused by temperature change, wherein the static ice pressure is the pressure of the ice layer on the bridge tower; The freeze-thaw damage constitutive model is a mathematical model based on the characteristics of concrete, which can describe the degradation of mechanical properties of concrete under the action of freeze-thaw cycle.

9. A super-high bridge tower safety regulation system based on time-varying effect of concrete, characterized in that, The system adopts the safety regulation method of super-high bridge tower based on time-varying effect of concrete according to any one of claims 1 to 8, and specifically comprises: A multi-source data acquisition module acquires multi-source data from multiple heterogeneous data sources and structural data and historical data of the target bridge tower; A time-varying effect analysis module is used to calculate the concrete shrinkage and creep influence index based on the structural data of the target bridge tower; An environmental load assessment module is connected with the time-varying effect analysis module, and is used to calculate the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index and the ice body wedge influence index based on the multi-source data; A risk fusion decision module is connected with the environmental load assessment module, and is used to fuse the shrinkage and creep influence index, the dynamic ice impact influence index, the static ice pressure influence index, the concrete freeze-thaw influence index and the ice body wedge influence index to generate a comprehensive risk report; An intelligent regulation execution module is connected with the risk fusion decision module, and is used to regulate and control the target bridge tower based on the comprehensive risk report.

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