Road disease intelligent detection method and system based on large model

Abstract

The invention discloses an intelligent road disease detection method and system based on a large model, and relates to the technical field of road maintenance engineering.The system collects road state data in real time through path planning and sensor group arrangement and transmits the data to an intelligent road disease detection system for data processing; forming a structure response data set and a vehicle load data set, calculating a periodic vibration accumulation index cap through the structure response data set to perform road state evaluation, executing an impact promotion analysis instruction when an evaluation result is a normal state, calculating an excitation fatigue response index sho through the vehicle load data set, road structure evolution evaluation is carried out in combination with the crack evolution kinetic potential index CEP obtained through calculation, and an optimization instruction is generated according to a road structure evolution evaluation result. According to the invention, full-period dynamic monitoring and intelligent decision making of road diseases are realized, the road fatigue and crack expansion trend can be identified in advance, the maintenance strategy is optimized, and the road use safety and management efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road maintenance engineering, in particular to a road disease intelligent detection method and system based on a large model. BACKGROUND

[0002] With the rapid development of deep learning and artificial intelligence technology, large models have shown breakthrough capabilities in computer vision, pattern recognition and complex scene analysis. Applying large models to road disease intelligent detection can enable the system to perceive and infer road conditions with high precision in different environments, different vehicle types and different weather conditions, realizing the cognitive leap from local data to global situation. Especially in the study of road structure and its evolution trend, large models can integrate multi-dimensional sensor data to capture small vibration energy accumulation, impact superposition effect and crack propagation trend, providing a solid technical foundation for dynamic assessment of road health and early intervention. This technical direction is driving the transformation of road maintenance mode from post-repair to predictive maintenance.

[0003] Traditional road structure health assessment relies on periodic detection and manual inspection, often focusing on visible surface damage, making it difficult to fully capture the cumulative fatigue state and structural evolution trend of the road interior. This approach not only has limited detection frequency and delayed response, but also makes it difficult to dynamically track key factors such as vibration energy accumulation and vehicle impact superposition, resulting in a lack of foresight in road maintenance. When faced with heavy freight transport, complex weather and long-term high-intensity traffic, existing technologies are not easy to effectively quantify the energy accumulation characteristics, impact effects and crack evolution trends in the early stages of road disease, thereby affecting the accuracy of early warning and maintenance decisions. SUMMARY

[0004] To overcome the deficiencies of the prior art, the present application provides a road disease intelligent detection method and system based on a large model, which solves the problems in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a road disease intelligent detection system based on a large model, comprising a data acquisition module, an energy analysis module, an impact analysis module, a comprehensive cumulative analysis module and a maintenance strategy module;

[0006] The data acquisition module is used to label the road in sections, set up a sensor group at the labeling point to collect road state data in real time, and then transmit the data to the road disease intelligent detection system for data processing to obtain a structure response data set and a vehicle load data set;

[0007] The energy analysis module is configured to calculate a periodic vibration cumulative index cum based on the structural response data set, and perform road state evaluation with a preset road vibration warning threshold Ec, and execute an impact triggering analysis instruction when the evaluation result is normal;

[0008] The impact effect analysis module is configured to execute the impact triggering analysis instruction, calculate an excitation fatigue response index sho based on the vehicle load data set, and analyze the superimposed influence of the impact wave effect on the road fatigue;

[0009] The comprehensive cumulative analysis module is configured to calculate a crack evolution dynamic potential index CEP based on the periodic vibration cumulative index cum and the excitation fatigue response index sho, and perform road structure evolution evaluation with a crack propagation warning threshold Cy and a crack propagation control threshold Cz;

[0010] The maintenance strategy module is configured to receive an optimization instruction of the road structure evolution evaluation in real time, and execute a safety adjustment.

[0011] Preferably, the data acquisition module comprises a path planning unit, a data acquisition unit and a data processing unit.

[0012] The path planning unit is configured to extract a road design blueprint from a highway bureau database, obtain basic information of each road through the road design blueprint, mark the starting point and the ending point of the road slope and the starting point and the ending point of the curve, and then mark each five-hundred-meter straight and gentle road segment by segment.

[0013] The basic information comprises a road slope, a curve angle and a bearing capacity.

[0014] Preferably, the data acquisition unit is configured to set a sensor group at each marked point to acquire road state data in real time.

[0015] The sensor group comprises a three-way acceleration sensor, a quartz piezoelectric sensor, a laser vehicle detector, a laser speedometer, an embedded optical fiber sensing network and a surface stress sensor.

[0016] The three-way acceleration sensor is configured to be embedded below the surface of asphalt or concrete by 5 cm, and placed in the main traffic trace area of the vehicle to directly collect the vertical vibration acceleration av and the horizontal shear acceleration ah of the road surface.

[0017] The quartz piezoelectric sensor is configured to be laid transversely at a depth of 6 cm along the driving direction of the vehicle to collect the vehicle mass zl in real time.

[0018] The laser vehicle detector is configured to be fixed on a roadside pole with a height of 2.5 m along the advancing direction of the vehicle to identify the vehicle axle number zs in real time, and collect the instantaneous speed vv of the vehicle in real time.

[0019] The laser speedometer is used for being fixed on a roadside vertical pole with a height of 2.5 meters, and collecting the vehicle acceleration ve in real time;

[0020] The embedded optical fiber sensing network is used for being longitudinally and parallelly arranged 3 centimeters below the road asphalt layer and the concrete base layer, and when the road appears a crack, the embedded optical fiber sensing network is used for positioning and collecting the crack width change rate dc in real time;

[0021] The surface stress sensor is used for being arranged at 2 centimeters of the road surface layer, and collecting the road surface layer stress su in real time;

[0022] The data processing unit is used for transmitting the road state data to the road disease intelligent detection system through a wireless network in real time for data processing, and obtaining a structure response data set and a vehicle load data set;

[0023] The data processing is used for pre-processing the road state data, and then performing data analysis, the pre-processing includes filling missing values and dimensionless processing, and the data analysis includes vibration main frequency analysis and axle load quality analysis;

[0024] The missing value filling is completed by a missing value interpolation technique for the road state data with sampling interruption and communication packet loss, and the dimensionless processing is performed by a Max-Min maximum minimization method to remove the dimension influence of the road state data;

[0025] The vibration main frequency analysis is used for converting the pre-processed road surface vertical vibration acceleration av and road surface horizontal shear acceleration ah into frequency signals f according to a fast Fourier transform, obtaining a complete vibration spectrum, and extracting frequency peak values in the complete vibration spectrum, and calculating the vibration main frequency fv by a weighted average method, specifically: In the formula, i represents an i-th discrete frequency point, f i represents a frequency value of the i-th frequency point, A(f i ) represents a spectrum amplitude value of the signal at the i-th frequency point;

[0026] The axle load quality analysis is used for performing division calculation according to the vehicle quality zl and the vehicle axle number zs to obtain the vehicle axle load quality Mv, specifically: Mv=zl / zs;

[0027] The structure response data set includes the road surface vertical vibration acceleration av, the road surface horizontal shear acceleration ah and the vibration main frequency fv;

[0028] The vehicle load data set includes the vehicle axle load quality Mv, the vehicle instantaneous acceleration ve, the crack width change rate dc, the vehicle speed vv and the road surface layer stress su.

[0029] Preferably, the energy analysis module includes a vibration energy analysis unit and a road state evaluation unit;

[0030] The vibration energy analysis unit is used for correlation calculation according to the structure response data set, analyzes the chronic fatigue influence caused by long-term vehicle passing on the road, and forms a periodic vibration cumulative index cum, specifically as follows: In the formula, t0 represents a starting time point of a monitoring period, T represents a terminal time point of the monitoring period, av(t), ah(t) and fv(t) respectively represent a road surface vertical vibration acceleration, a road surface horizontal shear acceleration and a vibration main frequency at t time, and dt represents a time differential.

[0031] Preferably, the road state evaluation unit is used for calculating a mean value u of the periodic vibration cumulative index cum when the road is in a normal state in a historical half year according to a statistical method cum and a standard deviation σ cum , and setting a road vibration early warning threshold Ec based on the mean value u cum and the standard deviation σ cum , specifically as follows: Ec = u cum + σ cum The road state is evaluated based on the periodic vibration cumulative index cum obtained in real time, and a specific evaluation scheme is as follows:

[0032] When the periodic vibration cumulative index cum is less than the road vibration early warning threshold Ec, it indicates that the road is in a normal state, and at this time, normal road maintenance and normal inspection are maintained.

[0033] When the periodic vibration cumulative index cum is greater than or equal to the road vibration early warning threshold Ec, it indicates that the road is in a fatigue state, and at this time, an impact triggering analysis instruction is executed.

[0034] Preferably, the impact effect analysis module is used for executing the impact triggering analysis instruction when the road state evaluation indicates that the road is in a fatigue state.

[0035] The impact triggering analysis instruction is used for correlation calculation according to a vehicle load data set, analyzes the superimposed influence of the shock wave effect caused by traffic behavior on road fatigue and crack expansion, and forms an excitation fatigue response index sho, specifically as follows: In the formula, t0 represents a starting time point of a monitoring period, T represents a terminal time point of the monitoring period, ln represents a logarithmic function, ve(t), dc(t), vv(t) and su(t) respectively represent a vehicle instantaneous acceleration, a crack width change rate, a vehicle speed and a road surface layer stress at t time, and dt represents a time differential.

[0036] Preferably, the comprehensive cumulative analysis module includes a dynamic potential analysis unit and an evolution evaluation unit.

[0037] The dynamic potential analysis unit is used to nonlinearly fuse the periodic vibration cumulative index cum and the excitation fatigue response index sho, comprehensively analyze the overall risk of road fatigue and crack expansion, and form a crack evolution dynamic potential index CEP, specifically as follows: In the formula, ln represents a logarithmic function, cum(t) and sho(t) represent the periodic vibration cumulative index and the excitation fatigue response index at time t respectively, t0 represents the starting time point of the monitoring period, T represents the ending time point of the monitoring period, and dt represents a time differential.

[0038] Preferably, the evolution evaluation unit is used to sort the crack evolution dynamic potential indexes CEP in the past six months from small to large, and preset the Q75 quantile point as a crack expansion early warning threshold Cy and the Q95 quantile point as a crack expansion control threshold Cz by the quantile method, and then perform road structure evolution evaluation with the real-time obtained joint instability risk index UDR, and the specific evaluation scheme is as follows:

[0039] When the crack evolution dynamic potential index CEP is less than the crack expansion early warning threshold Cy, it indicates that the vehicle impact is within the standard range, and a first optimization instruction is generated at this time;

[0040] When the crack expansion early warning threshold Cy is less than or equal to the crack evolution dynamic potential index CEP and the crack evolution dynamic potential index CEP is less than or equal to the crack expansion control threshold Cz, it indicates that the vehicle impact causes pavement cracking, and a second optimization instruction is generated at this time;

[0041] When the crack evolution dynamic potential index CEP is greater than the crack expansion control threshold Cz, it indicates that the vehicle impact causes pavement damage, and a third optimization instruction is generated at this time, informing the relevant departments to close and repair the damaged road section, implement traffic diversion, and set warning signs.

[0042] Preferably, the maintenance strategy module is used to receive the optimization instruction of the road structure evolution evaluation in real time, and perform safety adjustment through a road disease intelligent detection system, specifically as follows:

[0043] The first optimization instruction: informs the relevant departments to maintain the road and keep normal road operation without limiting the vehicle;

[0044] The second optimization instruction: informs the relevant departments to repair the road and limit the vehicle to 30% of the maximum speed and reduce 30% of the standard load;

[0045] The third optimization instruction: informs the relevant departments to close and repair the damaged road section, implement traffic diversion, and set warning signs.

[0046] A road disease intelligent detection method based on a large model, comprising the following steps:

[0047] S1, the road is marked section by section, and a sensor group is set at the marking point to collect road state data in real time, which is then transmitted to the road disease intelligent detection system for data processing to obtain a structure response data set and a vehicle load data set;

[0048] S2, according to the structure response data set, a periodic vibration cumulative index cum is calculated, and a road state evaluation is performed with a preset road vibration early warning threshold Ec, and when the evaluation result is normal, an impact triggering analysis instruction is executed;

[0049] S3, the impact triggering analysis instruction is executed, the excitation fatigue response index sho is calculated through the vehicle load data set, and the superimposed influence of the impact wave effect on the road fatigue is analyzed;

[0050] S4, according to the periodic vibration cumulative index cum and the excitation fatigue response index sho, a crack evolution dynamic potential index CEP is calculated, and a road structure evolution evaluation is performed with a crack expansion early warning threshold Cy and a crack expansion control threshold Cz;

[0051] S5, real-time receiving of the optimization instruction of the road structure evolution evaluation is performed, and safety adjustment is executed.

[0052] The application provides a road disease intelligent detection method and system based on a large model.

[0053] (1) The system data acquisition module marks the slope start and end points, the bend start and end points and the straight line segments through the road design blueprint, sets a sensor group at the marking point, accurately collects road state data, and then transmits the road state data to the road disease intelligent detection system through a wireless network for missing value filling and dimensionless processing, and then performs vibration main frequency analysis and axle load quality analysis to obtain a structure response data set and a vehicle load data set. The design realizes high-frequency monitoring of the road stress state, and lays a basic data support for subsequent disease diagnosis.

[0054] (2) The system energy analysis module calculates the periodic vibration cumulative index cum by integrating the structural response data set, which is used to quantify the chronic fatigue effect caused by long-term traffic load, and assesses the road state with the road vibration warning threshold Ec formed by historical statistics, realizes the early identification of road fatigue state, and executes the impact triggering analysis instruction when the road is in fatigue state. Subsequently, the impact analysis module executes the impact triggering analysis instruction, calculates the excitation fatigue response index sho using the vehicle load data set, reveals the superimposed influence of impact wave effect caused by vehicle sudden braking, sudden acceleration and other behaviors on road fatigue, and solves the technical deficiency that traditional methods cannot dynamically analyze sudden impact behavior. Further, the comprehensive cumulative analysis module is used to form the crack evolution dynamic index CEP by combining the periodic vibration cumulative index cum and the excitation fatigue response index sho, and to assess the road structure evolution with the preset crack expansion warning threshold Cy and crack expansion control threshold Cz, realizing the quantitative discrimination of the whole process from the growth of micro cracks to the expansion of serious cracks, so that the analysis process is no longer limited to single index judgment, but has the ability to predict the dynamic evolution trend.

[0055] (3) The maintenance strategy module of the system converts the road structure evolution assessment result output by the comprehensive cumulative analysis module into three types of optimization instructions. When the first optimization instruction is triggered, the normal road operation is maintained without limiting vehicles. When the second optimization instruction is triggered, vehicle speed limit, load limit and local repair measures are triggered. When the third optimization instruction is triggered, the highest level instruction of closed repair and traffic diversion is generated immediately. Through this top-down closed loop process, the system realizes real-time monitoring, dynamic evaluation and hierarchical response of road diseases, upgrades the traditional road management mode from "manual patrol and passive repair" to "intelligent detection, active warning and precise maintenance" comprehensive management mode, and effectively improves the road service life and public traffic safety level. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A flowchart of the road disease intelligent detection system based on a large model according to the present application;

[0057] Figure 2 A flowchart of the road disease intelligent detection system based on a large model according to the present application;

[0058] Figure 3 A flowchart of the road structure evolution assessment according to the present application. DETAILED DESCRIPTION

[0059] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0060] Embodiment 1

[0061] Please refer to Figure 1 The present application provides a road disease intelligent detection system based on a large model. To achieve the above purpose, the present application is implemented by the following technical solutions: comprising a data acquisition module, an energy analysis module, an impact analysis module, a comprehensive cumulative analysis module and a maintenance strategy module.

[0062] The data acquisition module is used for segmental labeling of the road, and sets a sensor group at the labeling point to collect road state data in real time, and then transmits the data to the road disease intelligent detection system for data processing to obtain a structure response data group and a vehicle load data group.

[0063] The energy analysis module is used for calculating a periodic vibration cumulative index cum according to the structure response data group, and performing road state evaluation with a preset road vibration early warning threshold Ec, and executing an impact triggering analysis instruction when the evaluation result is normal.

[0064] The impact analysis module is used for executing the impact triggering analysis instruction, calculating an excitation fatigue response index sho through the vehicle load data group, and analyzing the superimposed influence of the impact wave effect on the road fatigue.

[0065] The comprehensive cumulative analysis module is used for calculating a crack evolution dynamic potential index CEP according to the periodic vibration cumulative index cum and the excitation fatigue response index sho, and performing road structure evolution evaluation with a crack expansion early warning threshold Cy and a crack expansion control threshold Cz.

[0066] The maintenance strategy module is used for receiving an optimization instruction of the road structure evolution evaluation in real time, and executing safety adjustment.

[0067] In this embodiment, the data acquisition module labels the road section by section, and arranges multiple types of sensor groups at the labeled positions to collect road state data in real time. The road state data is transmitted in real time to the road disease intelligent detection system through a wireless network, and after filling in the missing values and dimensionless processing, vibration frequency analysis and axle load quality analysis are performed to obtain the structure response data set and vehicle load data set, and the continuous and refined monitoring of the road operation state is completed, providing basic data support for subsequent disease detection. The energy analysis module accumulates and calculates the vibration energy caused by long-term traffic load based on the structure response data set to form the periodic vibration accumulation index cum, and then compares it with the preset road vibration warning threshold Ec to evaluate the road state, realize the early identification of chronic fatigue, and execute the impact triggering analysis instruction when the road state evaluation is in the fatigue state. Subsequently, the impact analysis module executes the impact triggering analysis instruction, calculates the excitation fatigue response index sho based on the vehicle load data set, and captures the superposition effect of the sudden impact wave on the road fatigue. Finally, the comprehensive cumulative analysis module integrates the periodic vibration accumulation index cum and the excitation fatigue response index sho to generate the crack evolution dynamic potential index CEP, and compares it with the crack expansion warning threshold Cy and the crack expansion control threshold Cz to evaluate the road structure evolution. This analysis method of sub-modules and sub-indices breaks through the limitations of relying on manual patrol, single measurement or static index judgment in the prior art, and can realize dynamic and multi-dimensional road health evaluation. The maintenance strategy module converts the road structure evolution evaluation conclusion into different levels of maintenance optimization instructions, covering multiple response measures from daily maintenance, speed and load limit repair, to closed repair and traffic diversion. Compared with the prior art, the system not only significantly improves the timeliness of disease discovery and the scientificity of evaluation, but also moves the maintenance measures forward, realizing the transformation from "damage repair after damage" to "risk control before risk". This improvement ultimately brings the comprehensive effect of prolonging the road life, reducing the maintenance cost and improving the driving safety, and promotes the upgrading of road disease detection and maintenance management to initiative, intelligence and high efficiency.

[0068] Embodiment 2

[0069] Please refer to Figure 1 , specifically: the data acquisition module includes a path planning unit, a data acquisition unit and a data processing unit;

[0070] The path planning unit is used to extract the road design blueprint from the highway bureau database, obtain the basic information of each road through the road design blueprint, and label the road slope starting point and ending point, the bend starting point and ending point, and then label each five hundred meters of straight and flat road section by section;

[0071] The basic information includes road slope, bend angle and bearing capacity.

[0072] The data acquisition unit is used for setting a sensor group at each marking point to collect road state data in real time;

[0073] The sensor group comprises a three-way acceleration sensor, a quartz piezoelectric sensor, a laser vehicle detector, a laser speedometer, an embedded optical fiber sensing network and a surface stress sensor;

[0074] The three-way acceleration sensor is used for being embedded below 5 cm of an asphalt or concrete surface layer, being arranged in a main traffic trace area of a vehicle and directly collecting a vertical vibration acceleration av and a horizontal shear acceleration ah of the road surface, which represent an up-and-down vibration intensity and a transverse and tangential pulling force of the road surface to the ground when the vehicle passes through;

[0075] The quartz piezoelectric sensor is used for being transversely laid at a depth of 6 cm of the lane along a driving direction of the vehicle to collect a vehicle mass zl in real time, which represents a mass applied to the road surface by the vehicle;

[0076] The laser vehicle detector is used for being fixed on a roadside pole with a height of 2.5 m along a driving direction of the vehicle to identify a vehicle axle number zs in real time and collect a vehicle instantaneous speed vv in real time;

[0077] The laser speedometer is used for being fixed on the roadside pole with the height of 2.5 m to collect a vehicle acceleration ve in real time, which represents an impact intensity of the road surface when the vehicle suddenly brakes and accelerates;

[0078] The embedded optical fiber sensing network is used for being longitudinally and parallelly arranged below 3 cm of an asphalt layer and a concrete base layer of the road, and when a crack occurs on the road, the embedded optical fiber sensing network is used for positioning and collecting a crack width change rate dc in real time, which represents a change speed of an existing crack width with time;

[0079] The surface stress sensor is used for being arranged at 2 cm of a surface layer of the road to collect a surface layer stress su of the road in real time, which represents an actual stress value of the vehicle load transmitted to the surface layer of the road;

[0080] The data processing unit is used for transmitting the road state data to a road disease intelligent detection system through a wireless network in real time to process the data, and obtain a structure response data group and a vehicle load data group;

[0081] The data processing is used for pre-processing the road state data and then performing data analysis, the pre-processing comprises missing value filling and dimensionless processing, and the data analysis comprises vibration main frequency analysis and axle load mass analysis;

[0082] The missing value filling is performed by a missing value interpolation technique to complete the processing of the road state data with sampling interruption and communication packet loss, and the dimensionless processing is performed by a Max-Min maximum and minimum method to remove the dimension influence of the road state data;

[0083] The vibration main frequency analysis is used to convert the pretreated road surface vertical vibration acceleration av and road surface horizontal shear acceleration ah into frequency signals f according to fast Fourier transform, obtain complete vibration spectrum, and extract frequency peaks in the complete vibration spectrum, and the vibration main frequency fv is calculated by weighted average method, which represents the frequency of road vibration caused by the vehicle passing, and specifically: In the formula, i represents the i-th discrete frequency point, f i represents the frequency value of the i-th frequency point, A(f i ) represents the spectrum amplitude of the signal at the i-th frequency point.

[0084] The axle load quality analysis is used to obtain the vehicle axle load quality Mv by division calculation according to the vehicle quality zl and the number of vehicle axles zs, which represents the mass of a single vehicle axle exerted on the road, and specifically: Mv = zl / zs.

[0085] The structure response data set includes road surface vertical vibration acceleration av, road surface horizontal shear acceleration ah and vibration main frequency fv.

[0086] The vehicle load data set includes vehicle axle load quality Mv, vehicle instantaneous acceleration ve, crack width change rate dc, vehicle speed vv and road surface layer stress su.

[0087] In this embodiment, the path planning unit is based on the design blueprint of the highway bureau database, and systematically marks the starting and ending points of road slope, the starting and ending points of curve and the segmented positions of straight line segment, to provide accurate positioning for sensor layout. The data acquisition unit lays three-axis acceleration sensors, quartz piezoelectric sensors, laser vehicle detectors, laser speedometers, embedded optical fiber sensing networks and surface stress sensors at the marked points, and collects road state data in real time, ensuring the comprehensiveness and real-time nature of the monitoring data. The data processing unit transmits the road state data to the road disease intelligent detection system in real time through a wireless network for missing value interpolation and Max-Min dimensionless processing, and extracts the vibration main frequency fv based on fast Fourier transform, and calculates the vehicle axle load quality Mv, so as to generate the structure response data set and the vehicle load data set. The implementation of this module not only realizes the quantitative capture of the road under long-term traffic load and instantaneous impact, but also eliminates the data scale difference and the influence of data interruption in the collection through preprocessing, and improves the stability and scientificity of subsequent analysis. Compared with the existing method which relies on manual inspection and scattered measurement, this design significantly improves the automation, data continuity and timeliness of road state monitoring, provides high-quality input data for subsequent energy analysis and disease evaluation, and ultimately promotes the upgrade of road health management from "after-event response" to "real-time perception and active intervention", realizes the prolongation of road life, the precision of maintenance decision and the overall improvement of public safety.

[0088] Embodiment 3

[0089] Please refer to Figure 1 Specifically: the energy analysis module includes a vibration energy analysis unit and a road condition assessment unit;

[0090] The vibration energy analysis unit is used to perform correlation calculations based on the structural response data set to analyze the chronic fatigue effects of long-term vehicle traffic on the road, in order to form the periodic vibration cumulative index cum, specifically: In the formula, t0 represents the start time of the monitoring period, T represents the end time of the monitoring period, av(t), ah(t), and fv(t) represent the vertical vibration acceleration, horizontal shear acceleration, and dominant vibration frequency of the road surface at time t, respectively, and dt represents the time integral quantity. The overall vibration intensity is represented by the squares of the vertical and horizontal accelerations combined into a single vibration vector magnitude, emphasizing the total contribution of omnidirectional vibration energy to road fatigue. It reflects the imbalance between vertical and horizontal vibrations. Used to reduce the energy accumulation effect of short-term high frequencies, thus emphasizing low-frequency long-period oscillations; integral sign. Integrating over time and superimposing the vibrational energy demonstrates the accumulation of vibrations over a long period.

[0091] The road condition assessment unit is used to calculate the mean value of the cumulative periodic vibration index cum when the road was in normal condition over the past six months using statistical methods. cum and standard deviation σ cum And based on the mean u cum and standard deviation σ cum The preset road vibration warning threshold Ec is set as follows: Ec = u cum +σ cum Then, the road condition is assessed by comparing the real-time acquired periodic vibration cumulative index cum. The specific assessment scheme is as follows:

[0092] When the cumulative index of periodic vibration cum is less than the road vibration warning threshold Ec, it indicates that the road is in a normal state. At this time, normal road maintenance and inspection should be maintained.

[0093] When the cumulative index of periodic vibration cum is greater than or equal to the road vibration warning threshold Ec, it indicates that the road is in a state of fatigue, and at this time the impact-triggered analysis command is executed.

[0094] In this embodiment, the energy analysis module is composed of a vibration energy analysis unit and a road state evaluation unit, forming a complete technical chain from vibration energy accumulation calculation to road fatigue state judgment. Through the vibration energy analysis unit, the system uses the pavement vertical vibration acceleration av, pavement horizontal shear acceleration ah and vibration main frequency fv in the structural response data set to construct an integral model to calculate the periodic vibration cumulative index cum, quantize the vibration in different directions into a unified vibration vector module, and highlight the real impact of low-frequency, long-period vibration on road chronic fatigue through integral superposition, effectively weakening the interference brought by short-time high-frequency noise.

[0095] Formula derivation basis: The core idea of this formula is derived from the "energy accumulation" concept in classical mechanics and structural fatigue theory; based on the principle of dynamic vector module, the pavement vertical and horizontal acceleration is integrated into Reflects the all-directional mechanical action, in line with the real state of road stress, fv(t) in the denominator is derived from signal processing theory, commonly used frequency weighting to suppress high-frequency signal influence, Derived from the non-uniform stress theory of structural mechanics in signal processing, commonly used to capture vertical and horizontal stress imbalance, the integral is derived from the Miner cumulative damage theory, this formula upgrades "linear accumulation" to "adjusted accumulation", forming a long-term trend value.

[0096] The formula reflects the chronic damage of all-directional vibration energy to road structure during long-term vehicle operation. The pavement vertical vibration acceleration av, pavement horizontal shear acceleration ah and vibration main frequency fv generated when the vehicle passes through the pavement are energyized and vectorized, and then through time integration, the periodic vibration cumulative index cum is formed, Square the pavement vertical acceleration and horizontal shear acceleration and then take the square root to form a synthetic vibration intensity vector, which describes the all-directional vibration intensity of the road under the action of the vehicle in the form of vector, and measures the up-down compression, lateral pulling and shearing effect of the vehicle impact on the pavement, Reflects the imbalance degree of vertical and horizontal vibration, which is added to the denominator to adjust and increase the sensitivity of fatigue signal, The physical meaning of integral operation is energy accumulation, that is, the instantaneous vibration intensity is accumulated in the whole monitoring period, which describes the index of long-period cumulative fatigue effect. Through Unify the vertical and horizontal vibration into the same numerical framework to form a vibration intensity vector that can be directly calculated, divided by Equal to let high-frequency interference reduce weight, let the vibration imbalance increase weight, so as to obtain a vibration value which truly reflects the contribution degree of fatigue damage. The time accumulation integrates the adjusted vibration value to the whole period, and the period vibration cumulative index cum value is generated by accumulating second by second, minute by minute and hour by hour. The larger the period vibration cumulative index cum value is, the more serious the low-frequency, long-period and directionally uneven vibration is, and the deeper the chronic fatigue of the road structure is.

[0097] The road state evaluation unit sets the road vibration early warning threshold Ec based on the mean value u and the standard deviation s of the period vibration cumulative index cum of the historical half-year normal state road cum and standard deviation s cum , and evaluates the road state by comparing the real-time obtained period vibration cumulative index cum with the road vibration early warning threshold Ec, accurately judges whether the road enters the fatigue state, and then triggers the subsequent impact triggering analysis instruction. The design of this module not only realizes the quantitative monitoring and dynamic early warning of road fatigue risk, but also avoids the delay and misjudgment caused by traditional manual inspection or single-point testing, so that the fatigue trend can be captured in the early stage, thereby significantly improving the foresight, scientificity and timeliness of road maintenance, and laying a solid foundation for subsequent disease analysis and maintenance decision-making.

[0098] The specific implementation case is shown in Table 1.

[0099] Table 1: Period vibration cumulative index cum operation implementation example table

[0100]

[0101] Example 4

[0102] Please refer to Figure 1 Specifically, the impact effect analysis module is used to execute the impact triggering analysis instruction when the road state evaluation is that the road is in the fatigue state.

[0103] The impact triggering analysis instruction is used to perform correlation calculation according to the vehicle load data set, analyze the superimposed influence of the shock wave effect caused by the traffic behavior on the road fatigue and crack expansion, and form the excitation fatigue response index sho. Specifically: In the formula, t0 represents the starting time point of the monitoring period, T represents the ending time point of the monitoring period, ln represents the logarithmic function, ve(t), dc(t), vv(t) and su(t) respectively represent the vehicle instantaneous acceleration, crack width change rate, vehicle speed and road surface layer stress at time t, dt represents the time differential quantity, and Mv*|ve(t)|*dc(t) represents the coupling relationship between the shock wave and the vehicle behavior, surface stress. The square root of the vehicle speed is used to buffer the effect, and ln(1+su(t)) is used in logarithmic form to attenuate excessive stress, thus suppressing false alarms caused by noise spikes. (Integral symbol) This is used to accumulate the impact effect, resulting in long-term fatigue superposition.

[0104] In this embodiment, the impact analysis module is automatically triggered after the road condition is assessed as fatigued. It executes the impact-induced analysis command and performs correlation calculations based on the vehicle axle load mass Mv, vehicle instantaneous acceleration ve, crack width change rate dc, vehicle speed vv, and pavement surface stress su from the vehicle load data set to form the excitation fatigue response index sho. During the calculation, Mv*|ve(t)|*dc(t) is established as a coupling term between the shock wave, vehicle behavior, and surface stress. The vehicle speed is buffered by taking the square root to avoid data distortion caused by high-speed sporadic events. At the same time, ln(1+su(t)) is used to logarithmically attenuate high stress values ​​to reduce the risk of misjudgment caused by noise surges. Finally, the long-term cumulative analysis of the impact effect is achieved through an integration process. The implementation of this module enables precise quantification of the combined effects of sudden impact behavior and chronic fatigue. This not only improves the sensitivity and accuracy of road fatigue diagnosis, but also incorporates dynamic factors such as "sudden braking, sudden acceleration, and heavy load impact," which were previously difficult to capture, into the evaluation system. This promotes the transformation of road defect management from single fatigue identification to dynamic impact and fatigue complex effect analysis, significantly improving the foresight of road defect early warning and the pertinence of maintenance decisions.

[0105] The formula derivation is based on: Mv*|ve(t)|*dc(t) originates from the mechanical principle of Newton's second law. The axle load mass Mv of the vehicle represents the mass acting on the road surface. The instantaneous acceleration ve(t) of the vehicle corresponds to the instantaneous magnitude of the dynamic impact force. Multiplying by the crack width change rate dc(t) is a fatigue state correction, indicating that this impact force is amplified at the crack and triggers a more obvious structural response. ln(1+su(t)) originates from the logarithmic compression technique in signal processing and statistics, which smooths and compresses the maxima to eliminate the dominant effect of extreme outliers on the results. The integral part... Derived from the fatigue accumulation theory, fatigue damage is the cumulative result of all loads applied and is used to describe the fatigue energy deposition over a continuous period of time.

[0106] In the formula, the cumulative fatigue response value of the road under the action of the vehicle shock wave in the monitoring period t0 to T represents the accumulation of the superimposed effects of different traffic behaviors and load states on the road fatigue and crack propagation in time. Mv represents the static mass of the vehicle when passing through a single axle, which is the basis of force, |ve(t)| represents the instantaneous dynamic behavior of the vehicle such as sudden braking and sudden acceleration, and the absolute value processing ensures that the impact effect of sudden stop and sudden acceleration on the road, dc(t) reflects the dynamic change trend of the existing cracks of the road, which is a sensitive window of fatigue, and the multiplication of Mv*|ve(t)|*dc(t) indicates that the structural stress at the crack will be further excited under the action of vehicle load and impact behavior; The square root processing makes the buffering effect of speed non-linearly weaken, su(t) reflects the real strength of the current vehicle load transmitted to the surface layer of the road, ln(1+su(t)) compresses the surface layer stress su into a logarithmic curve, so that the extreme stress still has an impact, but not to the extent of distortion of the index, thereby ensuring the stability and robustness of the excitation fatigue response index sho, and the integral symbol All the impact behaviors and fatigue effects in a period are continuously superimposed to obtain a long-period fatigue response index sho, which can reflect the complete chain of vehicle behavior, shock wave effect and road fatigue evolution.

[0107] The specific implementation cases are shown in Table 2.

[0108] Table 2: Implementation example table of excitation fatigue response index sho

[0109]

[0110] Example 5

[0111] Please refer to Figure 1 and Figure 3 , specifically: the comprehensive cumulative analysis module includes a dynamic potential analysis unit and an evolution evaluation unit;

[0112] The dynamic potential analysis unit is used to nonlinearly fuse the periodic vibration cumulative index cum and the excitation fatigue response index sho, and comprehensively analyze the overall risk of road fatigue and crack propagation, to form a crack evolution dynamic potential index CEP, specifically: In the formula, ln represents the logarithmic function, cum(t) and sho(t) represent the periodic vibration cumulative index and the excitation fatigue response index at time t respectively, t0 represents the starting time point of the monitoring period, T represents the termination time point of the monitoring period, dt represents the time differential, cum(t) 2 + sho(t) 2The square of the periodic vibration energy and the shock wave fatigue effect is superimposed to ensure that the contributions of both to the total risk are independent but equally important, and there is no problem of linear superposition diluting the effect of one side. The integral is used for accumulation, and the square root is used to prevent the numerical value of the square superposition from being too large. The logarithmic function ln is used to suppress the extreme value, so that when the road fatigue risk is extremely high, the index can still be controlled.

[0113] The evolution evaluation unit is used to sort the crack evolution dynamic potential index CEP in the last half year from small to large, and preset the Q75 quantile point as the crack propagation early warning threshold Cy and the Q95 quantile point as the crack propagation control threshold Cz by quantile method, and then perform road structure evolution evaluation with the real-time obtained joint instability risk index UDR. The specific evaluation scheme is as follows:

[0114] When the crack evolution dynamic potential index CEP is less than the crack propagation early warning threshold Cy, it indicates that the vehicle impact is within the standard range, and the first optimization instruction is generated at this time;

[0115] When the crack propagation early warning threshold Cy is less than or equal to the crack evolution dynamic potential index CEP and is less than or equal to the crack propagation control threshold Cz, it indicates that the vehicle impact causes pavement cracking, and the second optimization instruction is generated at this time;

[0116] When the crack evolution dynamic potential index CEP is greater than the crack propagation control threshold Cz, it indicates that the vehicle impact causes pavement damage, and the third optimization instruction is generated at this time, informing the relevant departments to close and repair the damaged road section, implement traffic diversion, and set warning signs.

[0117] Formula derivation basis: integral part Based on the fatigue accumulation theory, it is proposed that "structural fatigue damage can be calculated by superimposing load cycle energy", and the core idea is that the stress and energy generated by different load levels can be accumulated in the time scale. The energy calculation formula of the reference signal is The periodic vibration cumulative index cum and the excitation fatigue response index sho are regarded as two types of independent fatigue signals, representing low-frequency vibration energy and high-frequency impact energy, respectively. Replace x(t) with the energy sum of the two types of signals In vector mechanics and signal science, square root is used to calculate the "total modulus" to avoid the extreme value of a signal directly dominating the final value, and the following formula is obtained In numerical analysis, the logarithmic function ln is a common extreme value compression function used to handle the non-linear amplification problem of energy data.

[0118] In this embodiment, the comprehensive cumulative analysis module is designed by the progressive design of the dynamic analysis unit and the evolution evaluation unit. The dynamic analysis unit performs nonlinear fusion on the periodic vibration cumulative index cum and the excitation fatigue response index sho to generate a crack evolution dynamic index CEP that can quantify the combined effect of long-term vibration and sudden impact. This not only reflects the chronic fatigue accumulation effect of long-term low-frequency vibration energy on the road, but also synchronously includes the instantaneous destructive power caused by sudden braking, sudden acceleration, and other impact behaviors, so that the analysis process has adaptability in the whole cycle and all scenarios, avoiding risk underestimation or overestimation caused by a single index.

[0119] The formula emphasizes its independent contribution to road damage and avoids the cancellation of positive and negative vibrations. The integral sign represents the time-domain accumulation of the accumulated fatigue effect in the entire monitoring period, reflecting the gradual accumulation of road structure risks over a long period of time. When the road enters an extremely high fatigue state, the values of the periodic vibration cumulative index cum and the excitation fatigue response index sho will increase sharply, After squaring and adding, the square root is taken, indicating that the damage effects of the periodic vibration cumulative index cum and the excitation fatigue response index sho are independent vectors, maintaining equal importance in synthesizing the total risk and not being linearly superimposed and weakened. The introduction of the logarithmic function ln suppresses the infinite amplification of extreme values, ensuring that the index value is still calculable and controllable in high-risk situations. The periodic vibration cumulative index cum(t) is caused by the accumulation of road vibration energy caused by long-term vehicle traffic, reflecting the accumulation of low-frequency vibration energy on the road surface caused by long-term vehicle traffic. The excitation fatigue response index sho(t) is formed by the sudden impact wave caused by sudden deceleration and acceleration behavior, reflecting the fatigue stimulation of sudden deceleration and acceleration instantaneous impact wave on the road surface. Although the time scales of the two are different, the damage mechanisms are consistent, both through stress concentration and micro-crack expansion to gradually weaken the road structure. By joint analysis of the long-term chronic fatigue periodic vibration cumulative index cum and the short-term impact fatigue excitation fatigue response index sho, the overall dynamic of the crack development trend is presented, the crack evolution dynamic index CEP is obtained, and the time when the road enters the crack acceleration expansion stage is quantified.

[0120] The evolution evaluation unit sorts all crack evolution dynamic potential indexes CEP in the past half year from small to large, sets the Q75 quantile point as the crack expansion early warning threshold Cy and the Q95 quantile point as the crack expansion control threshold Cz through quantile statistics, and performs road structure evolution evaluation on the real-time obtained crack evolution dynamic potential index CEP. Dynamic hierarchical evaluation of road structure state is realized. In combination with real-time monitoring data, this module can automatically generate different grade optimization instructions from daily maintenance, speed and load limitation repair to closure and diversion, so that road disease detection not only stops at state judgment, but also extends to executable intervention decision. Through this process, the system realizes the joint diagnosis, hierarchical early warning and active disposal of chronic fatigue risk and impact damage, significantly improves the foresight, accuracy and response efficiency of road management, effectively prolongs the service life of the road, reduces the maintenance cost, and comprehensively improves the safety and continuity of traffic operation.

[0121] The specific implementation case is shown in Table 3.

[0122] Table 3: Crack evolution dynamic potential index CEP operation implementation example table

[0123]

[0124] Example 6

[0125] Please refer to Figure 1 Specifically, the maintenance strategy module is configured to receive the optimization instruction of the road structure evolution evaluation in real time, and execute the safety adjustment through the road disease intelligent detection system, specifically as follows.

[0126] The first optimization instruction: notify the relevant departments to maintain the road and keep normal road operation without limiting the vehicle;

[0127] The second optimization instruction: notify the relevant departments to repair the road and limit the vehicle by 30% of the maximum speed and reduce the standard load by 30%;

[0128] The third optimization instruction: notify the relevant departments to close the damaged road section for repair, implement traffic diversion and set warning signs.

[0129] In this embodiment, the maintenance strategy module converts different risk levels into first, second and third optimization instructions by receiving the optimization instructions of the road structure evolution evaluation output by the real-time comprehensive cumulative analysis module, and automatically executes safety adjustment by the road disease intelligent detection system; when the evaluation is within the standard range, the first optimization instruction is generated, the maintenance department is notified to carry out routine maintenance, and the normal traffic is maintained; when the road appears crack expansion trend, the second optimization instruction is generated, the speed limit and load limit are required, and the repair is carried out simultaneously; when the road damage has affected the traffic safety, the third optimization instruction is generated, the closed repair is implemented, and the traffic is dredged. Through this hierarchical disposal system, the system realizes the closed-loop management from data monitoring, state evaluation and maintenance instruction execution, achieves the purpose of early intervention, dynamic risk control and accurate allocation of maintenance resources of road diseases, significantly improves the response speed, safety and economic benefit of road maintenance work, and promotes the upgrading of road management from post-maintenance to active prediction and pre-intervention.

[0130] Embodiment 7

[0131] Please refer to Figure 2 A road disease intelligent detection method based on a large model, comprising the following steps:

[0132] S1, label the road section by section, set sensors at the labeling points to collect road state data in real time, and then transmit the data to the road disease intelligent detection system for data processing to obtain a structure response data set and a vehicle load data set;

[0133] S2, calculate the periodic vibration cumulative index cum according to the structure response data set, and perform road state evaluation with the preset road vibration warning threshold Ec, and execute the impact induced analysis instruction when the evaluation result is normal;

[0134] S3, execute the impact induced analysis instruction, calculate the excitation fatigue response index sho through the vehicle load data set, and analyze the superimposed influence of the impact wave effect on the road fatigue;

[0135] S4, calculate the crack evolution dynamic potential index CEP according to the periodic vibration cumulative index cum and the excitation fatigue response index sho, and perform road structure evolution evaluation with the crack expansion warning threshold Cy and the crack expansion control threshold Cz;

[0136] S5, receive the optimization instructions of the road structure evolution evaluation in real time, and execute safety adjustment.

[0137] Although the embodiments of the present application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A road defect intelligent detection system based on a large model, characterized in that: It includes a data acquisition module, an energy analysis module, an impact analysis module, a comprehensive cumulative analysis module, and a maintenance strategy module; The data acquisition module is used to mark the road segment by segment, and set up a sensor group at the marked point to collect road status data in real time, and then transmit it to the intelligent road defect detection system for data processing to obtain structural response data group and vehicle load data group. The energy analysis module is used to calculate the periodic vibration cumulative index cum based on the structural response data set, and to evaluate the road condition with the preset road vibration early warning threshold Ec. When the evaluation result is normal, the impact triggering analysis command is executed. The impact analysis module is used to execute impact-triggered analysis commands, calculate the excitation fatigue response index sho through vehicle load data sets, and analyze the superimposed effect of shock wave on road fatigue. The comprehensive cumulative analysis module is used to calculate the crack evolution dynamic index CEP based on the periodic vibration cumulative index cum and the excitation fatigue response index sho, and to evaluate the road structure evolution by comparing it with the crack propagation early warning threshold Cy and the crack propagation control threshold Cz. The maintenance strategy module is used to receive optimization instructions from road structure evolution assessment in real time and perform safety adjustments.

2. The intelligent road defect detection system based on a large model according to claim 1, characterized in that: The data acquisition module includes a path planning unit, a data acquisition unit, and a data processing unit; The path planning unit is used to extract road design blueprints from the highway bureau's database, obtain basic information about each road through the road design blueprints, and mark the starting and ending points of road slopes, curves, and then mark straight and gentle road sections every 500 meters as a base. The basic information includes road slope, curve angle, and load-bearing capacity.

3. The intelligent road defect detection system based on a large model according to claim 2, characterized in that: The data acquisition unit is used to set up sensor groups at each marked point to collect road condition data in real time. The sensor group includes a triaxial acceleration sensor, a quartz piezoelectric sensor, a laser vehicle detector, a laser velocimeter, an embedded fiber optic sensing network, and a surface stress sensor. The triaxial acceleration sensor is used to be embedded 5 cm below the surface of asphalt or concrete and placed in the main vehicle track area to directly collect the vertical vibration acceleration av and the horizontal shear acceleration ah of the road surface. The quartz piezoelectric sensor is laid laterally along the vehicle's direction of travel at a depth of 6 cm in the lane to collect the vehicle's mass zl in real time. The laser vehicle detector is fixed on a roadside pole 2.5 meters high along the vehicle's direction of travel to identify the number of axles zs in real time and collect the vehicle's instantaneous speed vv in real time. The laser velocimeter is fixed on a roadside pole 2.5 meters high to collect vehicle acceleration ve in real time; The embedded fiber optic sensor network is used to be longitudinally and parallelly deployed 3 cm below the asphalt layer and concrete base layer of the road, and to locate and collect the crack width change rate dc in real time when cracks appear in the road. The surface stress sensor is installed 2 cm below the surface of the road to collect the surface stress su in real time. The data processing unit is used to transmit road condition data to the intelligent road defect detection system in real time via wireless network for data processing, and to obtain structural response data set and vehicle load data set. The data processing is used to preprocess the road condition data before performing data analysis. The preprocessing includes filling in missing values ​​and dimensionless processing. The data analysis includes vibration dominant frequency analysis and axle load mass analysis. The missing value filling process completes the road status data due to sampling interruption and communication packet loss using missing value interpolation technology, and the dimensionless processing removes the dimensional influence of the road status data using the Max-Min method. The vibration dominant frequency analysis is used to convert the preprocessed vertical vibration acceleration av and horizontal shear acceleration ah of the road surface into frequency signals f based on the Fast Fourier Transform, obtain the complete vibration spectrum, extract the frequency peaks in the complete vibration spectrum, and calculate the vibration dominant frequency fv using the weighted average method. Specifically: In the formula, i represents the i-th discrete frequency point, and f i A(f) represents the frequency value at the i-th frequency point. i () represents the spectral amplitude of the signal at the i-th frequency point; The axle load mass analysis is used to calculate the vehicle axle load mass Mv by dividing the vehicle mass zl and the number of axles zs, specifically: Mv = zl / zs; The structural response data set includes the road surface vertical vibration acceleration av, the road surface horizontal shear acceleration ah, and the dominant vibration frequency fv; The vehicle load data set includes vehicle axle load mass Mv, vehicle instantaneous acceleration ve, crack width change rate dc, vehicle speed vv, and road surface stress su.

4. The intelligent road defect detection system based on a large model according to claim 3, characterized in that: The energy analysis module includes a vibration energy analysis unit and a road condition assessment unit; The vibration energy analysis unit is used to perform correlation calculations based on the structural response data set to analyze the chronic fatigue effects of long-term vehicle traffic on the road, in order to form the periodic vibration cumulative index cum, specifically: In the formula, t0 represents the start time of the monitoring cycle, T represents the end time of the monitoring cycle, av(t), ah(t) and fv(t) represent the vertical vibration acceleration, horizontal shear acceleration and dominant vibration frequency of the road surface at time t, respectively, and dt represents the time integral quantity.

5. The intelligent road defect detection system based on a large model according to claim 4, characterized in that: The road condition assessment unit is used to calculate the mean value of the cumulative periodic vibration index cum when the road was in normal condition over the past six months using statistical methods. cum and standard deviation σ cum And based on the mean u cum and standard deviation σ cum The preset road vibration warning threshold Ec is set as follows: Ec = u cum +σ cum Then, the road condition is assessed by comparing the real-time acquired periodic vibration cumulative index cum. The specific assessment scheme is as follows: When the cumulative index of periodic vibration cum is less than the road vibration warning threshold Ec, it indicates that the road is in a normal state. At this time, normal road maintenance and inspection should be maintained. When the cumulative index of periodic vibration cum is greater than or equal to the road vibration warning threshold Ec, it indicates that the road is in a state of fatigue, and at this time the impact-triggered analysis command is executed.

6. The intelligent road defect detection system based on a large model according to claim 5, characterized in that: The impact analysis module is used to execute impact-triggered analysis commands when the road condition assessment indicates that the road is in a fatigue state. The impact-triggered analysis command is used to perform correlation calculations based on vehicle load data sets to analyze the superimposed impact of shock wave effects caused by traffic behavior on road fatigue and crack propagation, in order to form an excitation fatigue response index sho, specifically: In the formula, t0 represents the start time of the monitoring cycle, T represents the end time of the monitoring cycle, ln represents the logarithmic function, ve(t), dc(t), vv(t) and su(t) represent the instantaneous vehicle acceleration, crack width change rate, vehicle speed and road surface stress at time t, respectively, and dt represents the time differential quantity.

7. The intelligent road defect detection system based on a large model according to claim 6, characterized in that: The integrated cumulative analysis module includes a dynamic analysis unit and an evolution evaluation unit; The dynamic analysis unit is used to nonlinearly fuse the periodic vibration cumulative index cum and the excitation fatigue response index sho to comprehensively analyze the overall risk of road fatigue and crack propagation, so as to form the crack evolution dynamic index CEP, specifically: In the formula, ln represents the logarithmic function, cum(t) and sho(t) represent the cumulative exponent of periodic vibration and the excitation fatigue response exponent at time t, respectively, t0 represents the starting time of the monitoring period, T represents the ending time of the monitoring period, and dt represents the time differential quantity.

8. The intelligent road defect detection system based on a large model according to claim 7, characterized in that: The evolution assessment unit is used to sort the crack evolution momentum index (CEP) over the past six months from smallest to largest, and to preset the Q75 quantile as the crack propagation early warning threshold (Cy) and the Q95 quantile as the crack propagation control threshold (Cz) using the quantile method. Then, it is used to assess the road structure evolution with the real-time acquired joint instability risk index (UDR). The specific assessment scheme is as follows: When the crack evolution momentum index CEP < crack propagation warning threshold Cy, it means that the vehicle impact is within the standard range, and the first optimization instruction is generated at this time. When the crack propagation warning threshold Cy ≤ crack evolution momentum index CEP ≤ crack propagation control threshold Cz, it indicates that the road surface cracks due to vehicle impact, and a second optimization instruction is generated at this time. When the crack evolution momentum index CEP > crack expansion control threshold Cz, it indicates that the road surface is damaged due to vehicle impact. At this time, a third optimization instruction is generated to notify relevant departments to close and repair the damaged road section, implement traffic diversion, and set up warning signs.

9. The intelligent road defect detection system based on a large model according to claim 8, characterized in that: The maintenance strategy module is used to receive optimization instructions from the road structure evolution assessment in real time, and to perform safety adjustments through the intelligent road defect detection system, as detailed below; The first optimization instruction is to notify relevant departments to maintain the road and keep it in normal operation without restricting vehicles. The second optimization instruction is to notify the relevant departments to repair the road and limit the maximum speed of vehicles by 30% and reduce the standard load by 30%. The third optimization instruction is to notify relevant departments to close and repair the damaged road section, implement traffic control, and set up warning signs.

10. A road defect intelligent detection method based on a large model, applied to the road defect intelligent detection system based on a large model as described in any one of claims 1-9, characterized in that: Includes the following steps: S1. Mark the road segment by segment, and set up sensor groups at the marked points to collect road status data in real time, and then transmit the data to the intelligent road defect detection system for data processing to obtain structural response data groups and vehicle load data groups. S2. Calculate the periodic vibration cumulative index cum based on the structural response data set, and evaluate the road condition with the preset road vibration early warning threshold Ec. If the evaluation result is normal, execute the impact triggering analysis command. S3. Execute the impact-triggered analysis command, calculate the excitation fatigue response index sho through the vehicle load data set, and analyze the superimposed effect of the shock wave effect on road fatigue. S4. Calculate the crack evolution dynamic index CEP based on the periodic vibration cumulative index cum and the excitation fatigue response index sho, and evaluate the road structure evolution by comparing it with the crack propagation early warning threshold Cy and the crack propagation control threshold Cz. S5. Receive optimization instructions from road structure evolution assessment in real time and execute safety adjustments.

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