Urban road and bridge diagnosis method and system based on digital twinborn technology

Abstract

The invention discloses an urban road bridge diagnosis method and system based on a digital twinborn technology, and relates to the technical field of bridge diagnosis, and the method can perform high-precision analogue simulation on a bridge structure and a bridge support by constructing a digital twinborn model and utilizing multi-source real-time data driving. According to the method, firstly, a bridge 3D skeleton model is constructed through a design drawing of a bridge, and then data integration is carried out by means of an Internet of Things platform, so that thermal environment data, vibration force data and traffic load data are collected in real time. The data are primarily processed and compressed through edge computing nodes, and the multi-source data are uniformly processed through a timestamp correction mechanism, so that the consistency and precision of all input data are ensured. According to the process, the real-time performance and precision of bridge health diagnosis are greatly improved, the health state can be continuously updated in the bridge operation process, and real-time structural analysis and risk assessment are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge diagnosis, and specifically to a method and system for diagnosing urban road bridges based on digital twin technology. Background Art

[0002] As an advanced technical means, digital twin technology has been widely applied in various engineering fields, including intelligent manufacturing, intelligent buildings, intelligent transportation, etc. Especially in the field of urban infrastructure construction, digital twin technology provides new possibilities for maintenance, management, and optimization through real-time data interaction between digital models and physical structures. In this broad application background, the health diagnosis of urban road bridges has become an important application direction of digital twin technology. Bridges, as key infrastructure in transportation hubs, their safety is directly related to safety and the sustainable development of society and economy. Therefore, a method for diagnosing urban road bridges based on digital twin technology has emerged, aiming to comprehensively monitor the health status of bridges using real-time data and simulation models, and accurately identify potential risks and faults.

[0003] The health management and diagnosis of urban road bridges mainly rely on traditional manual inspections and periodic detection methods. Although these methods are effective, they have some significant limitations. For example, traditional manual inspections rely on manual experience and are prone to missing hidden damage problems, especially for parts such as bridge bearings and bridge connections that are difficult to directly observe. Periodic detection can evaluate the health status of bridges regularly, but it cannot monitor the actual stress conditions and dynamic responses of bridges under different environments and traffic loads in real time, resulting in potential faults of bridges not being discovered in a timely manner at the initial stage. In addition, traditional detection means lack comprehensive multi-source data analysis and are difficult to accurately reflect the health status of bridges under the combined action of complex environments and various factors. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method and system for diagnosing urban road bridges based on digital twin technology, and solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: including the following steps:

[0006] S1. By constructing a digital twin model and using multi-source real-time data to drive the simulation of the bridge structure and bridge bearings, while extracting multi-source data sets during the simulation process, and preprocessing the multi-source data sets to obtain standard data sets;

[0007] S2. Based on the obtained standard data sets, calculate and output the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and analyze the hysteresis phenomenon of the bridge bearings under high temperature and traffic braking disturbances;

[0008] S3. Based on the obtained temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, perform comprehensive calculations to output the micro-slip trigger function Stigger, and set the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for a preliminary comparative evaluation with the micro-slip trigger function Stigger;

[0009] S4. Based on the preliminary comparative evaluation, trigger the degradation diagnosis process. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through a standard data set to analyze the stiffness mismatch and energy consumption mismatch of the bridge bearings;

[0010] S5. Based on the structural effective stiffness Keff, perform comprehensive calculations to output the structural degradation risk index Rmod, and set the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for a secondary comparative evaluation with the structural degradation risk index Rmod.

[0011] Preferably, the S1 includes S11, S12, and S13;

[0012] S11. First, through the design drawings of the bridge, construct a 3D skeleton model of the bridge, and construct a digital twin model based on the 3D skeleton model of the bridge and multi-source real-time data driving;

[0013] The multi-source real-time data driving is integrated with the digital twin model by using the Internet of Things platform. Through the sensor group installed around the bridge and the bridge bearings, real-time entity data is collected, and through the edge computing node, the entity data is preliminarily processed and compressed. Then, an asynchronous data alignment and timestamp correction mechanism is adopted to unify the multi-source entity data. The entity data includes thermal environment data, vibration force data, and traffic load data. When transmitting the multi-source real-time data to the digital twin model through the Internet of Things platform, the multi-source real-time data drives the digital twin model through dynamic boundary reconstruction and load input reconstruction;

[0014] The sensor group includes acceleration sensors, thermometers, and traffic monitors;

[0015] S12. Based on the digital twin model driven by multi-source real-time data, load the evolutionary cognition mechanism. Based on the multi-source real-time data driving and the evolutionary cognition mechanism, simulate the bridge and the bridge bearings in the digital twin model;

[0016] The evolutionary cognition mechanism includes thermal-induced structural behavior evolution simulation, structural main modal frequency evolution simulation, and slip hysteresis evolution simulation;

[0017] The simulation of the evolution of thermal-induced structural behavior incorporates the simulation of the stiffness changes caused by the thermal expansion, contraction, and temperature gradient of bridge materials into the digital twin model, integrates time thermal environment data, and conducts the simulation of the evolution of thermal-induced structural behavior;

[0018] The simulation of the evolution of slip hysteresis constructs a physical behavior evolution model of the bridge bearing interface and the bridge based on real-time data, and identifies the phenomena of hysteretic slip, crack initiation, and stick-slip behavior;

[0019] The simulation of the evolution of the main modal frequency of the structure simulates the change process of the macroscopic properties of the structure and establishes a causal mapping relationship with the behavior model.

[0020] Preferably, S13, a digital twin model driven by multi-source real-time data, and in the process of simulating the bridge bearing, multi-source data sets are extracted in real time, and the multi-source data sets are preprocessed to obtain a standard data set;

[0021] The preprocessing includes dimensionless processing of data and timestamp alignment, eliminating the influence of the unit dimension of all parameters in the multi-source data set, and at the same time unifying the time line of the timestamps;

[0022] The standard data set includes the surface temperature Ts(t) of the bridge bearing at time t, the braking event intensity Vbrk(t) at time t, the vertical acceleration Av(t) of the bridge at time t, the phase lag angle Xw(t) at time t, the local energy consumption factor Eloc(t) at time t, the bearing slip-induced imbalance coefficient Qskew(t) at time t, and the main modal frequency Wmod(t) of the structure at time t.

[0023] Preferably, the S2 includes S21 and S22;

[0024] S21, calculate and output the temperature-induced slip tensor field Tslip by extracting the surface temperature Ts(t) of the bridge bearing at different positions at time t and the braking event intensity Vbrk(t) at time t in the standard data set, and analyze the micro-slip occurrence points in the bridge bearing area caused by the combined effect of thermal induction and measurement automatic disturbance;

[0025] The temperature-induced slip tensor field Tslip is calculated and output by the following algorithm formula;

[0026] ;

[0027] In the formula, x represents the spatial position of the bridge 3D skeleton model in the digital twin model, Tslip(x, t) represents the temperature-induced slip tensor field at spatial position x and time t, and ▽ 2 Ts(x, t) represents the Laplacian operator of the temperature field, represents the thermosensitive slip coefficient, d represents the integral symbol, dt represents the time integration variable, and ▽Vbrk(x, t) represents the spatial gradient of the braking event intensity at spatial position x and time t.

[0028] Preferably, in S22, by extracting the vertical acceleration Av(t) of the bridge at time t, the phase lag angle Xw(t) at time t, and the local energy consumption factor Eloc(t) at time t from the standard dataset, the hysteresis response integral tensor Hlag is calculated and output, and the accumulation of inelastic response energy consumption caused by stick-slip and hysteresis behaviors is analyzed between time periods;

[0029] The hysteresis response integral tensor Hlag is calculated and output through the following algorithm formula;

[0030] ;

[0031] In the formula, Hlag(t) represents the hysteresis response integral tensor at time t, t0 represents the initial time, Av 2 (t) represents the second derivative of the vertical acceleration of the bridge at time t, Xw 2 (t) represents the second derivative of the phase lag angle at time t.

[0032] Preferably, the said S3 includes S31 and S32;

[0033] S31, based on the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, performs comprehensive calculation to output the microslip trigger function Stigger, and comprehensively analyzes the thermal environment, automatic perturbation, and vibration hysteresis response;

[0034] The microslip trigger function Stigger is calculated and output through the following algorithm formula;

[0035] ;

[0036] In the formula, Stigger(t) represents the microslip trigger function at time t, represents the upper limit value of the temperature-induced slip tensor field among all points at the spatial position x along the bridge bearing area at time t, Tr represents the trace of the tensor, Tr(Hlag(t)) represents the trace of the hysteresis response integral tensor, represents the hysteresis influence weighting coefficient;

[0037] S32. In the digital twin model, based on the limit values of the material of the bridge bearing to withstand micro-slip and energy consumption, set the second slip hysteresis threshold F2, and set the first slip hysteresis threshold F1 based on the critical value of modal mismatch. Compare and evaluate the micro-slip trigger function Stigger with the set first slip hysteresis threshold F1 and second slip hysteresis threshold F2 preliminarily, judge the influence of the thermal environment, automatic disturbance and vibration hysteresis response on the bridge bearing, and trigger the degradation diagnosis process based on the preliminary comparison and evaluation results. The specific evaluation content is as follows;

[0038] When the micro-slip trigger function Stigger(t) at time t < the first slip hysteresis threshold F1, it indicates that the state of the bridge bearing is normal and there is no obvious stick-slip behavior. At this time, the digital twin model maintains the current status;

[0039] When the first slip hysteresis threshold F1 ≤ the micro-slip trigger function Stigger(t) at time t < the first slip hysteresis threshold F2, it indicates that micro-slip behavior occurs and it is in the initial stage of stick-slip evolution. At this time, increase the detection frequency of the current digital twin model by 50%;

[0040] When the micro-slip trigger function Stigger(t) at time t ≥ the first slip hysteresis threshold F2, it indicates that the coupling analysis of hysteresis and slip is abnormal. At this time, trigger the degradation diagnosis process.

[0041] Preferably, the S4 includes S41 and S42;

[0042] S41. After the preliminary comparison and evaluation trigger the degradation diagnosis process, calculate and output the modal energy consumption mismatch function Emis by extracting the vertical acceleration Av(t) of the bridge at time t, the phase hysteresis angle Xw(t) at time t and the local energy consumption factor Eloc(t) in the standard dataset, and analyze the mismatch rate between the structural stiffness and vibration mode of the bridge bearing;

[0043] The modal energy consumption mismatch function Emis is calculated and output through the following algorithm formula;

[0044] ;

[0045] In the formula, Emis(t) represents the modal energy consumption mismatch function at time t;

[0046] S42. Based on the obtained modal energy consumption mismatch function Emis(t) at time t, calculate and output the structural effective stiffness Keff in combination with the bearing slip-induced imbalance coefficient Qskew(t) at time t, directly associate the energy consumption anomaly with the mismatch of the bridge bearing structure, and form the stiffness degradation channel of the digital twin model;

[0047] The structural effective stiffness Keff is calculated and output through the following algorithm formula;

[0048] ;

[0049] Wherein, Keff(t) represents the effective stiffness of the structure at time t, exp represents the exponential function, and K0 represents the initial modal stiffness.

[0050] Preferably, the S5 includes S51 and S52;

[0051] S51, combining the effective stiffness of the structure Keff(t) at the current output time t with the main modal frequency Wmod(t) of the structure at time t in the standard dataset, and comprehensively calculating and outputting the structural degradation risk index Rmod;

[0052] The structural degradation risk index Rmod is calculated and output through the following algorithm formula;

[0053] ;

[0054] Wherein, Rmod(t) represents the structural degradation risk index at time t, and K0 represents the initial modal stiffness.

[0055] Preferably, S52, collecting the real monitoring dataset of similar bridges, annotating the time series of the structural degradation risk index Rmod corresponding to 7 - 14 days before the structural failure, using the clustering SVM classification method to find the inflection point of the risk interval, setting the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2, and conducting a secondary comparative evaluation analysis on the structural degradation risk index Rmod(t) at time t with the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2. The specific evaluation content is as follows;

[0056] When the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R1, it indicates that the stiffness and mode are stable, the bridge bearing structure is healthy, operating normally, and periodic monitoring is carried out;

[0057] When the first degradation diagnosis threshold R1 ≤ the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R2, it indicates that there is a degradation trend in the stiffness and modal frequency of the bridge bearing structure. At this time, the detection frequency of the current digital twin model is increased by 70%;

[0058] When the structural degradation risk index Rmod(t) at time t ≥ the first degradation diagnosis threshold R2, it indicates that the stiffness and modal drift of the bridge bearing structure are abnormal and there is substantial damage. At this time, load limit is activated, and physical inspection is prompted through the digital twin model.

[0059] A city road and bridge diagnosis system based on digital twin technology, including a digital twin module, a data support stick-slip analysis module, a micro-slip response trigger module, a stiffness and mismatch analysis module, and a degradation comprehensive analysis module;

[0060] The digital twin module constructs a digital twin model and uses multi-source real-time data to drive the simulation of the bridge structure and bridge supports. At the same time, it extracts multi-source data sets during the simulation process and preprocesses the multi-source data sets to obtain a standard data set;

[0061] The data support stick-slip analysis module calculates and outputs the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag based on the obtained standard data set, and analyzes the hysteresis phenomenon of the bridge supports under high temperature and traffic braking disturbances;

[0062] The micro-slip response trigger module comprehensively calculates and outputs the micro-slip trigger function Stigger based on the obtained temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and sets the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for a preliminary comparison and evaluation with the micro-slip trigger function Stigger;

[0063] The stiffness and mismatch analysis module triggers the degradation diagnosis process based on the preliminary comparison and evaluation. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through the standard data set, and analyzes the stiffness mismatch and energy consumption mismatch of the bridge supports;

[0064] The degradation comprehensive analysis module comprehensively calculates and outputs the structural degradation risk index Rmod based on the structural effective stiffness Keff, and sets the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for a secondary comparison and evaluation with the structural degradation risk index Rmod.

[0065] The present invention provides a method and system for diagnosing urban road and bridges based on digital twin technology. It has the following beneficial effects:

[0066] (1) By constructing a digital twin model and leveraging multi-source real-time data for driving, this method can perform high-precision simulation on bridge structures and bridge bearings. First, it constructs a 3D skeleton model of the bridge based on the design drawings of the bridge, and then integrates data through the Internet of Things platform to collect real-time thermal environment data, vibration force data, and traffic load data. These data are preliminarily processed and compressed by edge computing nodes, and unified processing of multi-source data is carried out through a timestamp correction mechanism, thus ensuring the consistency and accuracy of all input data. This process greatly improves the real-time performance and accuracy of bridge health diagnosis, can continuously update the health status during the operation of the bridge, and provides real-time structural analysis and risk assessment.

[0067] (2) Based on real-time data collection and the generation of a standard data set, this method can identify the micro-slip and hysteresis phenomena that occur in bridge bearings under high temperature and traffic braking disturbances through the calculation and output of the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag. These data provide a precise analysis of the stick-slip behavior of bridge bearings, and can timely identify potential micro-slip behaviors and their evolution processes. Through the calculation of the micro-slip trigger function Stigger, combined with the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2, this method can accurately compare and judge whether the bridge bearing has entered a potentially failed state, and timely issue a warning and trigger the degradation diagnosis process. Compared with traditional periodic inspections and manual judgments, this data-driven intelligent warning system significantly improves the detection sensitivity of bridge faults, ensuring that potential faults in the bridge can be detected and effective maintenance measures can be taken in the initial stage.

[0068] (3) By analyzing the modal energy dissipation mismatch function Emis and the structural effective stiffness Keff output from the standard data set, this diagnostic method can deeply analyze the stiffness and energy dissipation mismatch of bridge bearings. Combining these indicators, the calculation of the structural degradation risk index Rmod can help judge the degradation trend of the bridge after long-term loading and give accurate risk predictions. When the calculated Rmod value exceeds the set first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2, targeted intervention measures can be triggered, such as increasing the detection frequency or initiating load limit measures. This new intelligent management method provides a scientific basis for bridge maintenance. Through dynamic degradation assessment and precise maintenance strategies, it can avoid problems such as over-maintenance or untimely maintenance, and optimize the allocation of resources and management costs. Description of the Drawings

[0069] Figure 1 Schematic diagram of the steps of the urban road bridge diagnosis method based on digital twin technology of the present invention;

[0070] Figure 2Schematic diagram of the urban road and bridge diagnosis system based on digital twin technology of the present invention;

[0071] Figure 3 Graph of the micro-slip trigger function Stigger changing with time in the present invention;

[0072] Figure 4 Schematic diagram of the evaluation process of the urban road and bridge diagnosis method based on digital twin technology of the present invention. Detailed implementation manners

[0073] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] Embodiment 1

[0075] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 The present invention provides an urban road and bridge diagnosis method based on digital twin technology. To achieve the above purposes, the present invention is realized through the following technical solutions: including the following steps:

[0076] S1. By constructing a digital twin model and using multi-source real-time data to drive the simulation of the bridge structure and bridge bearings, and at the same time extracting the multi-source data set in the simulation process, and preprocessing the multi-source data set to obtain a standard data set;

[0077] S2. Based on the obtained standard data set, calculate and output the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and analyze the hysteresis phenomenon of the bridge bearings under high temperature and traffic braking disturbances;

[0078] S3. Based on the obtained temperature-induced slip tensor field Tslip and hysteresis response integral tensor Hlag, perform comprehensive calculations to output the micro-slip trigger function Stigger, and set the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for preliminary comparison and evaluation with the micro-slip trigger function Stigger;

[0079] S4. Based on the preliminary comparison and evaluation, trigger the degradation diagnosis process. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through the standard data set, and analyzes the stiffness mismatch situation and energy consumption mismatch situation of the bridge bearings;

[0080] S5. Based on the effective structural stiffness Keff, comprehensively calculate and output the structural degradation risk index Rmod, and set the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for a secondary comparison and evaluation with the structural degradation risk index Rmod.

[0081] In this embodiment, through the bridge diagnosis based on the digital twin technology, integrating multi-source real-time data and simulation technology, a comprehensive assessment of the bridge health state is achieved. First, by constructing a digital twin model and being driven by multi-source real-time data, data from the sensor group of the bridge bearing is collected in real time, and data preprocessing is carried out on it to form a standard data set. This process ensures the unity and accuracy of the data and can provide high-quality input for subsequent analysis. Then, using these standard data sets, the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag are calculated and output, and the hysteresis behavior of the bridge bearing under high temperature and traffic braking disturbances is analyzed in depth. By comprehensively analyzing these data, the micro-slip trigger function Stigger is further calculated, and appropriate first slip hysteresis threshold F1 and second slip hysteresis threshold F2 are set for a preliminary evaluation to detect potential slip failure problems at an early stage. As the evaluation deepens, the degradation diagnosis process is triggered, and the modal energy consumption mismatch function Emis and the effective structural stiffness Keff are calculated through the standard data set to analyze the stiffness and energy consumption mismatch of the bridge bearing and accurately identify the potential risks of structural degradation. Finally, through the secondary evaluation of the structural degradation risk index Rmod and comparison with the preset first degradation diagnosis threshold R1 and second degradation diagnosis threshold R2, the early diagnosis ability of bridge degradation is further improved. Through the implementation of this comprehensive diagnosis method, real-time monitoring and accurate assessment of the bridge health state are achieved, which has significant advantages in detecting bridge problems at an early stage and accurately predicting the bridge degradation trend.

[0082] Embodiment 2

[0083] Please refer to Figure 1 Specifically: S1 includes S11, S12, and S13;

[0084] S11. First, through the design drawings of the bridge, construct a 3D skeleton model of the bridge, and construct a digital twin model based on the 3D skeleton model of the bridge and multi-source real-time data drive;

[0085] Multi-source real-time data-driven integration is achieved by leveraging the Internet of Things platform and digital twin model. Through the bridge and the sensor group installed around the bridge bearings, entity data is collected in real time. And through the edge computing node, preliminary data processing and compression are performed on the entity data. Then, an asynchronous data alignment and timestamp correction mechanism is adopted to uniformly process the multi-source entity data. The entity data includes thermal environment data, vibration force data, and traffic load data. When transmitting the multi-source real-time data to the digital twin model through the Internet of Things platform, a multi-source real-time data-driven digital twin model is carried out through dynamic boundary reconstruction and load input reconstruction;

[0086] The sensor group includes acceleration sensors, thermometers, and traffic monitors;

[0087] S12. Based on the digital twin model driven by multi-source real-time data, a loading evolution cognitive mechanism is carried out. Based on the multi-source real-time data-driven and evolution cognitive mechanism, the bridge and bridge bearings are simulated in the digital twin model;

[0088] The evolution cognitive mechanism includes thermal-induced structural behavior evolution simulation, structural main modal frequency evolution simulation, and slip hysteresis evolution simulation;

[0089] The thermal-induced structural behavior evolution simulation incorporates the simulation of stiffness changes caused by thermal expansion, cold shrinkage, and temperature gradients of bridge materials into the digital twin model, and fuses the time thermal environment data to conduct the thermal-induced structural behavior evolution simulation;

[0090] The slip hysteresis evolution simulation constructs a physical behavior evolution model of the bridge bearing interface and the bridge according to the real-time data to identify phenomena such as hysteretic slip, crack initiation, and stick-slip behavior;

[0091] The structural main modal frequency evolution simulation simulates the change process of the macroscopic properties of the structure and establishes a causal mapping relationship with the behavior model.

[0092] S13. Based on the digital twin model driven by multi-source real-time data, during the simulation of the bridge bearings, multi-source data sets are extracted in real time, and the multi-source data sets are preprocessed to obtain standard data sets;

[0093] The preprocessing includes data dimensionless processing and timestamp alignment, eliminating the influence of the unit dimension of all parameters in the multi-source data set, and at the same time unifying the timestamps to a unified timeline;

[0094] The standard data set includes the surface temperature Ts(t) of the bridge bearing at time t, the braking event intensity Vbrk(t) at time t, the vertical acceleration Av(t) of the bridge at time t, the phase lag angle Xw(t) at time t, the local energy consumption factor Eloc(t) at time t, the bearing slip-induced imbalance coefficient Qskew(t) at time t, and the main structural modal frequency Wmod(t) at time t.

[0095] In this embodiment, the method realizes the precise diagnosis of urban road bridges by constructing a digital twin model and leveraging multi-source real-time data driving. First, a 3D skeleton model of the bridge is constructed through the design drawings of the bridge, which is combined with multi-source real-time data driving. The Internet of Things platform and the sensor group are integrated to collect the entity data of the bridge and the bearing in real time. The edge computing node preliminarily processes, compresses, and unifies the data, eliminates the unit dimension differences between different data sources, and corrects the timestamps to ensure the consistency and reliability of the data. This process not only effectively integrates the thermal environment data, vibration force data, and traffic load data but also provides real-time updates and high-precision simulation inputs for the digital twin model through dynamic boundary reconstruction and load input reconstruction. Supported by the evolutionary cognitive mechanism, the digital twin model can simulate the behaviors of the bridge and the bridge bearing, covering the simulation of the evolution of thermally induced structural behavior, the evolution of the main structural modal frequency, and the evolution of slip hysteresis. This series of simulations can accurately predict the deformation behavior of the bridge under high temperature and traffic load, identify micro-slip, crack initiation, and stick-slip phenomena, and further provide data support for subsequent structural health assessment. By real-time extracting and preprocessing the multi-source data set to obtain the standard data set, the method ensures the consistency of the data and provides a comprehensive information basis for subsequent analysis. This series of steps effectively improves the accuracy and real-time performance of the bridge bearing health diagnosis.

[0096] Embodiment 3

[0097] Please refer to Figure 1 and Figure 4 Specifically: S2 includes S21 and S22;

[0098] S21. Calculate and output the temperature-induced slip tensor field Tslip by extracting the surface temperature Ts(t) of the bridge bearing at time t and the braking event intensity Vbrk(t) at time t at different positions in the standard data set, and analyze the micro-slip occurrence points in the bridge bearing area caused by the combined effect of thermal induction and measurement automatic disturbance;

[0099] The temperature-induced slip tensor field Tslip is calculated and output through the following algorithm formula;

[0100] ;

[0101] where x represents the spatial position of the bridge 3D skeleton model in the digital twin model, Tslip(x, t) represents the temperature-induced slip tensor field at spatial position x and time t, and ▽ 2 Ts(x, t) represents the Laplacian operator of the temperature field, indicating the second-order spatial difference between the temperature at a certain spatial position x and time t and the surrounding temperature in the digital twin model. represents the thermosensitive slip coefficient, that is, the sensitivity of temperature to slip triggering. d represents the integral symbol, dt represents the time integral variable, and ▽Vbrk(x, t) represents the spatial gradient of the braking event intensity at spatial position x and time t, that is, it represents the change in vehicle braking intensity at different positions.

[0102] S22. By extracting the vertical acceleration of the bridge Av(t) at time t, the phase lag angle Xw(t) at time t, and the local energy consumption factor Eloc(t) in the standard dataset, calculate and output the hysteretic response integral tensor Hlag, and analyze the accumulation of inelastic response energy consumption caused by stick-slip and hysteretic behavior between time periods;

[0103] The hysteretic response integral tensor Hlag is calculated and output through the following algorithm formula;

[0104] ;

[0105] where Hlag(t) represents the hysteretic response integral tensor at time t, t0 represents the initial time, and Av 2 (t) represents the second derivative of the vertical acceleration of the bridge at time t, and Xw 2 (t) represents the second derivative of the phase lag angle at time t. The physical meaning of the formula is that when the value of the hysteretic response integral tensor Hlag continues to increase, it indicates that the structural energy response deviates more and more from the ideal elastic path and may have entered the mismatch or decline period; it is an important reference index for judging whether the slip behavior continues to accumulate and cause damage; it can be used to trigger the evaluation of structural degradation or predict the evolution of the next stage.

[0106] In this embodiment, the method comprehensively evaluates the micro-slip and inelastic behavior of urban road bridges under the influence of high temperature and traffic loads by calculating the temperature-induced slip tensor field Tslip and the hysteretic response integral tensor Hlag, and combining multi-source real-time data. First, by extracting the surface temperature Ts of the bridge bearing and the braking event intensity Vbrk in the standard dataset, the temperature-induced slip tensor field Tslip is calculated, and the micro-slip occurrence points in the bridge bearing area under the combined action of thermal induction and traffic braking disturbances are analyzed. Through the Laplace operator, temperature difference, and spatial gradient of braking events, this process accurately identifies the risk areas of slip behavior and early warns of possible structural problems. Secondly, by extracting the vertical acceleration Av of the bridge, the phase hysteresis angle Xw, and the local energy consumption factor Eloc, the hysteretic response integral tensor Hlag is calculated, which measures the energy consumption of the inelastic response of the bridge caused by hysteretic slip. Through the calculation of the second derivative and time integration, it is possible to monitor in real time whether the structure enters the inelastic behavior stage, so as to accurately evaluate the accumulation of slip behavior. As the value of the hysteretic response integral tensor Hlag continues to increase, the bridge bearing may enter the mismatch or decline stage, providing data support for the evaluation of structural degradation in advance. Through these comprehensive calculations, this method can monitor and early warn the micro-slip and inelastic behavior of bridge bearings in real time, and conduct accurate structural health assessment in combination with the evolutionary cognition mechanism. The joint analysis of temperature-induced slip and hysteretic behavior not only enhances the sensitivity to abnormal behavior of bridges, but also effectively improves the early warning ability of structural degradation.

[0107] Embodiment 4

[0108] Please refer to Figure 1 、 Figure 3 and Figure 4 Specifically: S3 includes S31 and S32;

[0109] S31. Based on the temperature-induced slip tensor field Tslip and the hysteretic response integral tensor Hlag, comprehensive calculations are performed to output the micro-slip trigger function Stigger, and comprehensive analysis of the thermal environment, automatic disturbance, and vibration hysteretic response is carried out;

[0110] The micro-slip trigger function Stigger is calculated and output through the following algorithm formula;

[0111] ;

[0112] In the formula, Stigger(t) represents the micro-slip trigger function at time t, represents the upper limit value of the temperature-induced slip tensor field among all points at the spatial position x along the bridge bearing area at time t, Tr represents the trace of the tensor, and Tr(Hlag(t)) represents the trace of the hysteretic response integral tensor, that is, the sum of the diagonal elements, It represents the hysteresis influence weighting coefficient, which controls the relative weight of the hysteresis integral term in the total evaluation. It is dimensionless and obtained by machine learning fitting.

[0113] S32. In the digital twin model, based on the limit values of the material of the bridge bearing to withstand microslip and energy consumption, set the second slip hysteresis threshold F2, and set the first slip hysteresis threshold F1 based on the critical value of modal mismatch. Conduct a preliminary comparative evaluation of the microslip trigger function Stigger with the set first slip hysteresis threshold F1 and second slip hysteresis threshold F2 to judge the influence of the thermal environment, automatic disturbance, and vibration hysteresis response on the bridge bearing, and trigger the degradation diagnosis process based on the preliminary comparative evaluation results. The specific evaluation content is as follows;

[0114] When the microslip trigger function Stigger(t) at time t < the first slip hysteresis threshold F1, it indicates that the state of the bridge bearing is normal and there is no obvious stick-slip behavior. At this time, the digital twin model maintains the current status;

[0115] When the first slip hysteresis threshold F1 ≤ the microslip trigger function Stigger(t) at time t < the first slip hysteresis threshold F2, it indicates that microslip behavior occurs and it is in the initial stage of stick-slip evolution. At this time, increase the detection frequency of the current digital twin model by 50%;

[0116] When the microslip trigger function Stigger(t) at time t ≥ the first slip hysteresis threshold F2, it indicates that the coupling analysis of hysteresis and slip is abnormal. At this time, trigger the degradation diagnosis process;

[0117] Specific example:

[0118] Set ▽ 2 Ts(x, t) = 2.5, = 0.9, ▽Vbrk(x, t) = 12.0, = 0.8

[0119] ;

[0120] Assume that the hysteresis response integral tensor Hlag is 3×3, that is , take the trace, that is, the sum of the main diagonal elements, Tr(Hlag(t)) = 12.5 + 15.8 + 10.1 = 38.4;

[0121] Set = 0.5;

[0122] ;

[0123] Assume that the first slip hysteresis threshold F1 = 25 and the second slip hysteresis threshold F2 = 40.

[0124] In this embodiment, the method comprehensively analyzes the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, calculates the microslip trigger function Stigger in the digital twin model, and comprehensively evaluates the behavior of the bridge bearing under thermal environment, automatic disturbance, and vibration hysteresis response. The microslip trigger function Stigger is a dimensionless index that comprehensively considers the effects of temperature-induced slip and hysteresis response, and combines the trace Tr of the tensor as the hysteresis influence weighting coefficient, providing a dynamic and accurate quantitative basis for evaluating the slip state of the bridge bearing. Through the output of this function, the microslip behavior of the bridge bearing under different environmental conditions can be monitored in real time, and potential slip risks can be identified in a timely manner. Further, by setting the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2, and comparing and analyzing the microslip trigger function with these thresholds, the state of the bridge bearing can be judged and corresponding decisions can be made. When the microslip trigger function Stigger is less than the first slip hysteresis threshold F1, it indicates that the state of the bridge bearing is normal, and the digital twin model remains unchanged; when the value of Stigger is between F1 and F2, it indicates that microslip behavior occurs, and the detection frequency will be increased to more accurately track its evolution; while when Stigger exceeds the second slip hysteresis threshold F2, it indicates that a relatively serious problem of hysteresis and slip coupling has occurred in the bridge bearing, and the degradation diagnosis process will be triggered to further evaluate the health state of the bridge. The implementation of this method can not only monitor the microslip and hysteresis phenomena of the bridge bearing in real time, but also adjust the detection frequency according to the slip degree to ensure a high sensitivity response to small changes. At the same time, through the adaptive update of the digital twin model, this method effectively improves the ability to identify faults in the early stage and the decision-making accuracy in bridge health management.

[0125] Embodiment 5

[0126] Please refer to Figure 1 and Figure 3 Specifically: S4 includes S41 and S42;

[0127] S41. After initially comparing and evaluating to trigger the degradation diagnosis process, by extracting the vertical acceleration Av(t) of the bridge at time t, the phase hysteresis angle Xw(t) at time t, and the local energy consumption factor Eloc(t) at time t from the standard dataset, calculate and output the modal energy consumption mismatch function Emis, and analyze the mismatch rate between the structural stiffness and vibration mode of the bridge bearing;

[0128] The modal energy consumption mismatch function Emis is calculated and output through the following algorithm formula;

[0129] ;

[0130] In the formula, Emis(t) represents the modal energy consumption mismatch function at time t;

[0131] S42. Based on the obtained modal energy consumption mismatch function \(E_{mis}(t)\) at time \(t\), combined with the bearing slip-induced imbalance coefficient \(Q_{skew}(t)\) at time \(t\), calculate and output the effective stiffness \(K_{eff}\) of the structure, directly associate the energy consumption anomaly with the mismatch of the bridge bearing structure, and form the stiffness degradation channel of the digital twin model;

[0132] The effective stiffness \(K_{eff}\) of the structure is calculated and output through the following algorithm formula;

[0133] ;

[0134] In the formula, \(K_{eff}(t)\) represents the effective stiffness of the structure at time \(t\), exp represents the exponential function, \(K_0\) represents the initial modal stiffness, which is a bridge bearing structure design parameter derived through initial finite element modeling and has a dimensionless value.

[0135] In this embodiment, after the preliminary evaluation triggers the degradation diagnosis process, through further calculation of the modal energy consumption mismatch function \(E_{mis}\) and the effective stiffness \(K_{eff}\) of the structure, the relationship between the stiffness mismatch of the bridge bearing and the vibration mode is accurately analyzed. First, by extracting the vertical acceleration \(A_v\), phase lag angle \(X_w\), and local energy consumption factor \(E_{loc}\) of the bridge from the standard dataset, the modal energy consumption mismatch function \(E_{mis}\) is calculated. This function is used to quantify the energy loss of the structure and further analyze the mismatch rate between the stiffness of the bridge bearing structure and the vibration mode. This process provides an important physical basis for subsequent structural health assessment and can accurately identify the energy consumption offset caused by hysteretic behavior. Next, combined with the bearing slip-induced imbalance coefficient \(Q_{skew}\), the effective stiffness \(K_{eff}\) of the structure is obtained through calculation. This parameter reflects the stiffness decay of the bridge bearing. The \(K_{eff}\) calculated through the exponential decay model directly associates the relationship between the energy consumption anomaly and the structural mismatch, providing an accurate description for the stiffness degradation channel of the digital twin model. The real-time change of the effective stiffness of the structure can timely capture the stiffness degradation of the bridge bearing and further promote the execution of the degradation diagnosis process. Through these comprehensive calculations, this method can realize the dynamic monitoring and health assessment of the bridge bearing under the drive of multi-source real-time data, especially the high-precision diagnosis in terms of stiffness mismatch and energy consumption anomaly. This method significantly improves the forward-looking and real-time nature of bridge health management, can identify potential structural problems earlier, and optimize maintenance decisions. Compared with traditional detection methods, this method not only improves the sensitivity of fault warning, but also reduces maintenance costs, extends the service life of the bridge, and improves the safety and stability of the bridge.

[0136] Embodiment 6

[0137] Please refer to Figure 1 and Figure 3 Specifically: S5 includes S51 and S52;

[0138] S51. Combine the effective structural stiffness Keff(t) output at the current time t with the main modal frequency Wmod(t) of the structure at time t in the standard dataset, and comprehensively calculate and output the structural degradation risk index Rmod;

[0139] The structural degradation risk index Rmod is calculated and output through the following algorithm formula;

[0140] ;

[0141] In the formula, Rmod(t) represents the structural degradation risk index at time t, and K0 represents the initial modal stiffness.

[0142] S52. Collect the real monitoring dataset of similar bridges, label the time series of the structural degradation risk index Rmod corresponding to 7 - 14 days before the structural failure, use the clustering SVM classification method to find the inflection point of the risk interval, set the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2, and conduct a secondary comparative evaluation and analysis of the structural degradation risk index Rmod(t) at time t with the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2. The specific evaluation content is as follows;

[0143] When the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R1, it indicates that the stiffness and mode are stable, the bridge bearing structure is healthy, operates normally, and is monitored periodically;

[0144] When the first degradation diagnosis threshold R1 ≤ the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R2, it indicates that there is a degradation trend in the stiffness and modal frequency of the bridge bearing structure. At this time, increase the detection frequency of the current digital twin model by 70%;

[0145] When the structural degradation risk index Rmod(t) at time t ≥ the first degradation diagnosis threshold R2, it indicates that the stiffness and modal drift of the bridge bearing structure are abnormal and there is substantial damage. At this time, start load restriction and prompt physical inspection through the digital twin model.

[0146] In this embodiment, the method calculates the structural degradation risk index Rmod by combining the effective structural stiffness Keff and the main modal frequency Wmod of the structure, realizing the comprehensive evaluation and real-time monitoring of the bridge health state. First, by extracting Keff and Wmod at each time point and calculating the output of the structural degradation risk index Rmod through a formula, this method can quantify the structural degradation risk of the bridge bearings and reflect the degradation trend of the bridge under the influence of thermal environment, traffic load and vibration in real time. This process provides an accurate risk index for the health management of the bridge, enabling the manager to quickly evaluate the health status of the structure based on the value of the structural degradation risk index Rmod. Next, by collecting the real monitoring data sets of similar bridges and annotating the time series of the structural degradation risk index Rmod before structural failure, the risk inflection points of structural degradation are identified using the clustering SVM classification method. By setting the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2, the value of the current structural degradation risk index Rmod can be compared with the thresholds for secondary evaluation. If the structural degradation risk index Rmod is less than R1, it means that the bridge is in a normal state and periodic monitoring continues; if the structural degradation risk index Rmod is between R1 and R2, it means that the bridge begins to show a degradation trend and the detection frequency will be increased; when the structural degradation risk index Rmod is greater than R2, load limit and physical inspection prompts will be initiated to ensure that structural failures can be identified and addressed in a timely manner. The implementation of this method not only improves the sensitivity and early warning ability for the bridge degradation process, but also can dynamically adjust the monitoring frequency through real-time data driving, effectively improving the intelligent level of bridge health management. Compared with the traditional regular inspection method, this method has higher diagnostic sensitivity and accuracy, can identify and intervene in the initial stage of structural degradation, and significantly reduces the maintenance cost and potential safety hazards. Through accurate risk assessment and intelligent intervention measures, the safety and stability of the bridge are effectively guaranteed, and the service life and management efficiency of the bridge are improved.

[0147] Example 7

[0148] Please refer to Figure 1 and Figure 2 , a urban road bridge diagnosis system based on digital twin technology, including a digital twin module, a data bearing stick-slip analysis module, a microslip response trigger module, a stiffness and mismatch analysis module, and a degradation comprehensive analysis module;

[0149] The digital twin module constructs a digital twin model and uses multi-source real-time data to drive the simulation of the bridge structure and bridge bearings. At the same time, a multi-source data set is extracted during the simulation process, and a standard data set is obtained by preprocessing the multi-source data set;

[0150] The data bearing stick-slip analysis module calculates and outputs the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag based on the acquired standard data set, and analyzes the hysteresis phenomenon of the bridge bearing under high temperature and traffic braking disturbances;

[0151] The micro-slip response trigger module comprehensively calculates and outputs the micro-slip trigger function Stigger based on the acquired temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and sets the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for a preliminary comparison and evaluation with the micro-slip trigger function Stigger;

[0152] The stiffness and mismatch analysis module triggers the degradation diagnosis process based on the preliminary comparison and evaluation. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through the standard data set, and analyzes the stiffness mismatch and energy consumption mismatch of the bridge bearing;

[0153] The degradation comprehensive analysis module comprehensively calculates and outputs the structural degradation risk index Rmod based on the structural effective stiffness Keff, and sets the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for a secondary comparison and evaluation with the structural degradation risk index Rmod.

[0154] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A method for diagnosing urban road bridges based on digital twin technology, characterized in that: It includes the following steps: S1. Build a digital twin model, use multi-source real-time data to drive the simulation of the bridge structure and bridge bearings, extract the multi-source data set during the simulation, and preprocess the multi-source data set to obtain a standard data set; S2. Based on the obtained standard data set, calculate and output the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and analyze the hysteresis phenomenon of the bridge bearings under high temperature and traffic braking disturbances; S3. Based on the obtained temperature-induced slip tensor field Tslip and hysteresis response integral tensor Hlag, perform comprehensive calculations to output the micro-slip trigger function Stigger, and set the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for preliminary comparison and evaluation with the micro-slip trigger function Stigger; S4. Based on the preliminary comparison and evaluation, trigger the degradation diagnosis process. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through the standard data set, and analyzes the stiffness mismatch and energy consumption mismatch of the bridge bearings; S5. Based on the structural effective stiffness Keff, perform comprehensive calculations to output the structural degradation risk index Rmod, and set the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for secondary comparison and evaluation with the structural degradation risk index Rmod.

2. The urban road and bridge diagnosis method based on digital twin technology according to claim 1, characterized in that: S1 includes S11, S12, and S13; S11. First, build a 3D skeleton model of the bridge through the design drawings of the bridge, and build a digital twin model based on the 3D skeleton model of the bridge and multi-source real-time data; The multi-source real-time data drive integrates with the digital twin model through the Internet of Things platform. Through the sensor group installed around the bridge and bridge bearings, real-time entity data is collected, and the entity data is preliminarily processed and compressed by the edge computing node. Then, an asynchronous data alignment and timestamp correction mechanism is used to unify the multi-source entity data. The entity data includes thermal environment data, vibration force data, and traffic load data. The multi-source real-time data is transmitted to the digital twin model through the Internet of Things platform, and the multi-source real-time data drives the digital twin model through dynamic boundary reconstruction and load input reconstruction; The sensor group includes acceleration sensors, thermometers, and traffic monitors; S12. Based on the digital twin model driven by multi-source real-time data, load the evolutionary cognitive mechanism. Based on the multi-source real-time data drive and the evolutionary cognitive mechanism, simulate the bridge and bridge bearings in the digital twin model; The evolutionary cognitive mechanism includes thermal-induced structural behavior evolution simulation, structural main modal frequency evolution simulation, and slip hysteresis evolution simulation; The thermal-induced structural behavior evolution simulation incorporates the simulation of the stiffness changes caused by the thermal expansion, cold shrinkage, and temperature gradient of the bridge materials into the digital twin model, and fuses the time thermal environment data to perform the thermal-induced structural behavior evolution simulation; The slip hysteresis evolution simulation constructs a physical behavior evolution model of the bridge bearing interface and the bridge according to real-time data, and identifies the phenomena of hysteresis slip, crack initiation, and stick-slip behavior; The simulation of the evolution of the main modal frequency of the structure is carried out by simulating the change process of the macroscopic properties of the structure and establishing a causal mapping relationship with the behavior model.

3. The urban road and bridge diagnosis method based on digital twin technology according to claim 2, characterized in that: S13. Based on the digital twin model driven by multi-source real-time data, during the simulation of the bridge bearing, multi-source data sets are extracted in real time, and the multi-source data sets are preprocessed to obtain standard data sets. The preprocessing includes dimensionless processing of data and timestamp alignment, eliminating the influence of the unit dimension of all parameters in the multi-source data set, and at the same time unifying the time line of the timestamps. The standard data set includes the surface temperature Ts(t) of the bridge bearing at time t, the braking event intensity Vbrk(t) at time t, the vertical acceleration Av(t) of the bridge at time t, the phase lag angle Xw(t) at time t, the local energy consumption factor Eloc(t) at time t, the skew-induced imbalance coefficient Qskew(t) of the bearing at time t, and the main modal frequency Wmod(t) of the structure at time t.

4. The method for diagnosing urban road bridges based on digital twin technology according to claim 3, wherein: The S2 includes S21 and S22. S21. By extracting the surface temperature Ts(t) of the bridge bearing at time t and the braking event intensity Vbrk(t) at different positions in the standard data set, the temperature-induced slip tensor field Tslip is calculated and output, and the micro-slip occurrence points caused by the combined effect of thermal induction and measurement automatic disturbance in the bridge bearing area are analyzed. The temperature-induced slip tensor field Tslip is calculated and output through the following algorithm formula. ； Where x represents the spatial position of the bridge 3D skeleton model in the digital twin model, Tslip(x, t) represents the temperature-induced slip tensor field at spatial position x and time t, and ▽ 2 Ts(x, t) represents the Laplace operator of the temperature field, represents the thermosensitive slip coefficient, d represents the integral symbol, dt represents the time integration variable, and ▽Vbrk(x, t) represents the spatial gradient of the braking event intensity at spatial position x and time t.

5. The urban road and bridge diagnosis method based on digital twin technology according to claim 4, characterized in that: S22. By extracting the vertical acceleration Av(t) of the bridge at time t, the phase lag angle Xw(t) at time t, and the local energy consumption factor Eloc(t) in the standard data set, the hysteresis response integral tensor Hlag is calculated and output, and the accumulation of inelastic response energy consumption caused by stick-slip and hysteresis behavior during the time period is analyzed. The hysteresis response integral tensor Hlag is calculated and output through the following algorithm formula. ； where \(H_{lag}(t)\) represents the integral tensor of the hysteretic response at time \(t\), \(t_0\) represents the initial time, \(A_v\) 2 \((t)\) represents the second derivative of the vertical acceleration of the bridge at time \(t\), \(X_w\) 2 \((t)\) represents the second derivative of the phase lag angle at time \(t\).

6. The urban road and bridge diagnosis method based on digital twin technology according to claim 1, characterized in that: The S3 includes S31 and S32. S31. Based on the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, the micro-slip trigger function Stigger is calculated and output through comprehensive calculation, and a comprehensive analysis of the thermal environment, automatic disturbance, and vibration hysteresis response is carried out. The micro-slip trigger function Stigger is calculated and output through the following algorithm formula. ； where, Stigger(t) represents the micro-slip trigger function at time t, represents the upper limit value of the temperature-induced slip tensor field among all points at the spatial position x in the bridge bearing area at time t, Tr represents the trace of the tensor, and Tr(Hlag(t)) represents the trace of the hysteretic response integral tensor, represents the hysteresis influence weighting coefficient; S32. In the digital twin model, based on the limit values of the material of the bridge bearing to withstand micro-slip and energy consumption, the second slip hysteresis threshold F2 is set, and the first slip hysteresis threshold F1 is set based on the modal mismatch critical value. The micro-slip trigger function Stigger is preliminarily compared and evaluated with the set first slip hysteresis threshold F1 and second slip hysteresis threshold F2 to judge the influence of the thermal environment, automatic disturbance, and vibration hysteresis response on the bridge bearing, and based on the preliminary comparison and evaluation results, the degradation diagnosis process is triggered. The specific evaluation content is as follows. When the micro-slip trigger function Stigger(t) at time t < the first slip hysteresis threshold F1, it indicates that the state of the bridge bearing is normal and there is no obvious stick-slip behavior. At this time, the digital twin model maintains the current status. When the first slip hysteresis threshold F1 ≤ the micro-slip trigger function Stigger(t) at time t < the first slip hysteresis threshold F2, it indicates the occurrence of micro-slip behavior and is in the initial stage of stick-slip evolution. At this time, the detection frequency of the current digital twin model is increased by 50%; When the micro-slip trigger function Stigger(t) at time t ≥ the first slip hysteresis threshold F2, it indicates an anomaly in the coupling analysis of hysteresis and slip. At this time, the degradation diagnosis process is triggered.

7. The method for diagnosing urban road bridges based on digital twin technology according to claim 6, characterized in that: S4 includes S41 and S42; S41. After initially comparing and evaluating to trigger the degradation diagnosis process, by extracting the vertical acceleration Av(t) of the bridge at time t, the phase hysteresis angle Xw(t) at time t, and the local energy consumption factor Eloc(t) at time t from the standard dataset, calculate and output the modal energy consumption mismatch function Emis, and analyze the mismatch rate between the structural stiffness of the bridge bearing and the vibration mode; The modal energy consumption mismatch function Emis is calculated and output through the following algorithm formula; ； In the formula, Emis(t) represents the modal energy consumption mismatch function at time t; S42. Based on the obtained modal energy consumption mismatch function Emis(t) at time t, combine the bearing slip-induced imbalance coefficient Qskew(t) at time t to calculate and output the effective structural stiffness Keff, directly associate the energy consumption anomaly with the mismatch of the bridge bearing structure, and form the stiffness degradation channel of the digital twin model; The effective structural stiffness Keff is calculated and output through the following algorithm formula; ； In the formula, Keff(t) represents the effective structural stiffness at time t, exp represents the exponential function, and K0 represents the initial modal stiffness.

8. The urban road and bridge diagnosis method based on digital twin technology according to claim 1, characterized in that: S5 includes S51 and S52; S51. Combine the currently output effective structural stiffness Keff(t) at time t with the structural main modal frequency Wmod(t) at time t in the standard dataset for comprehensive calculation and output the structural degradation risk index Rmod; The structural degradation risk index Rmod is calculated and output through the following algorithm formula; ； In the formula, Rmod(t) represents the structural degradation risk index at time t, and K0 represents the initial modal stiffness.

9. The method for diagnosing urban road bridges based on digital twin technology according to claim 8, wherein: S52. Collect the real monitoring dataset of similar bridges, label the time series of the structural degradation risk index Rmod corresponding to 7 - 14 days before the structural failure, use the clustering SVM classification method to find the inflection point of the risk interval, set the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2, and conduct a secondary comparative evaluation analysis of the structural degradation risk index Rmod(t) at time t with the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2. The specific evaluation content is as follows; When the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R1, it indicates that the stiffness and mode are stable, the bridge bearing structure is healthy, operates normally, and is monitored periodically; When the first degradation diagnosis threshold R1 ≤ the structural degradation risk index Rmod(t) at time t < the first degradation diagnosis threshold R2, it indicates that there is a degradation trend in the stiffness and modal frequency of the bridge bearing structure. At this time, the detection frequency of the current digital twin model is increased by 70%; When the structural degradation risk index Rmod(t) at time t ≥ the first degradation diagnosis threshold R2, it indicates that the stiffness and modal drift of the bridge bearing structure are abnormal and there are substantial damages. At this time, load limitation is initiated, and a physical inspection is prompted through the digital twin model.

10. A city road and bridge diagnosis system based on digital twin technology, which is applied to the city road and bridge diagnosis method based on digital twin technology according to any one of claims 1-9, and is characterized in that: It includes a digital twin module, a data bearing stick-slip analysis module, a micro-slip response trigger module, a stiffness and mismatch analysis module, and a degradation comprehensive analysis module; The digital twin module constructs a digital twin model and uses multi-source real-time data to drive the simulation of the bridge structure and bridge bearings. At the same time, it extracts multi-source data sets during the simulation process and preprocesses the multi-source data sets to obtain a standard data set; The data bearing stick-slip analysis module calculates and outputs the temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag based on the obtained standard data set, and analyzes the hysteresis phenomenon of the bridge bearings under high temperature and traffic braking disturbances; The micro-slip response trigger module comprehensively calculates and outputs the micro-slip trigger function Stigger based on the obtained temperature-induced slip tensor field Tslip and the hysteresis response integral tensor Hlag, and sets the first slip hysteresis threshold F1 and the second slip hysteresis threshold F2 for a preliminary comparison and evaluation with the micro-slip trigger function Stigger; The stiffness and mismatch analysis module triggers the degradation diagnosis process based on the preliminary comparison and evaluation. The degradation diagnosis process calculates and outputs the modal energy consumption mismatch function Emis and the structural effective stiffness Keff through the standard data set, and analyzes the stiffness mismatch and energy consumption mismatch of the bridge bearings; The degradation comprehensive analysis module comprehensively calculates and outputs the structural degradation risk index Rmod based on the structural effective stiffness Keff, and sets the first degradation diagnosis threshold R1 and the second degradation diagnosis threshold R2 for a secondary comparison and evaluation with the structural degradation risk index Rmod.

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