Bridge engineering full life cycle intelligent collaborative management system and method based on large language model

Abstract

The application provides a bridge engineering full-life-cycle intelligent collaborative management system and method based on a large language model, relates to the technical field of bridge monitoring, and quantitatively analyzes internal defects of a structure by introducing a temperature-strain correlation coefficient, generates a comprehensive stress tensor by combining three factors of temperature, wind speed and traffic load, respectively analyzes and comprehensively generates a current real-time dynamic equivalent stress evaluation index of a bridge sub-region, the dynamic equivalent stress evaluation index reflects the risk size of the sub-region, and by comparison with a threshold value, the current risk level of the sub-region can be output, and precise dynamic evaluation of a local sub-region of a bridge is realized.

Description

Technical Field

[0001] This invention relates to the field of bridge monitoring technology, specifically to an intelligent collaborative management system and method for the entire lifecycle of bridge engineering based on a large language model. Background Technology

[0002] A bridge is an engineering structure that spans obstacles, designed to connect two places, provide transportation routes, and facilitate the flow of people and goods. Bridges come in various structural forms, commonly including beam bridges, arch bridges, suspension bridges, and cable-stayed bridges, each type selected based on the span distance, terrain conditions, and functional requirements. This invention focuses on planar bridges as the monitoring object. As critical transportation hubs, the safety and durability of bridges directly affect the smooth flow of transportation and public safety. Bridge maintenance and management encompass the entire process of design, construction, operation, and upkeep, and modern technology allows for the monitoring of bridge data using sensors.

[0003] Traditional bridge management relies heavily on manual inspections and experience-based judgments, making it difficult to conduct dynamic, real-time analysis of bridge hazards, especially in terms of stress state and internal damage monitoring of bridge structures. This makes it difficult to quantify and analyze these hazards, and to achieve detailed risk assessments and early warnings for specific areas of the bridge.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent collaborative management system and method for the entire lifecycle of bridge engineering based on a large language model, so as to solve the problems mentioned in the background art.

[0006] A bridge engineering full lifecycle intelligent collaborative management system based on a large language model includes:

[0007] Association unit, data acquisition unit, analysis unit, evaluation unit;

[0008] The correlation unit is used to divide the bridge deck into several sub-regions and analyze the correlation coefficients between bridge temperature and strain.

[0009] The data acquisition unit is used to monitor sub-areas and collect temperature parameters, wind speed parameters, and traffic load parameters.

[0010] The analysis unit is used to obtain the stress in the sub-region, including temperature stress, wind stress, and load stress, and to generate a stress tensor.

[0011] The assessment unit is used to perform correlation analysis on the stress tensor, generate equivalent stress, perform correlation analysis on the equivalent stress and correlation coefficient, generate a dynamic equivalent stress assessment index, and output risk warning reports, maintenance priority assessments, and decision recommendations.

[0012] This invention also provides a method for intelligent collaborative management of the entire lifecycle of bridge engineering based on a large language model, used to execute an intelligent collaborative management system for the entire lifecycle of bridge engineering based on a large language model. Specific steps include:

[0013] S1. Divide the bridge deck into several sub-regions. Under conditions of no wind and no traffic load, acquire the strain and temperature data of the sub-regions at the current moment multiple times. Perform correlation analysis on the strain and temperature data to generate a correlation coefficient. The correlation coefficient is used to reflect the degree of correlation between bridge temperature and strain.

[0014] S2. Obtain the current bearing pressure data of the bridge sub-area, monitor the bridge sub-area, and collect temperature parameters, wind speed parameters, and traffic load parameters. The temperature parameter is used to reflect the temperature value at the bridge at the current time, the wind speed parameter is used to reflect the wind speed value in three directions at the bridge at the current time, and the traffic load parameter is used to reflect the load weight on the bridge deck at the current time.

[0015] S3. Obtain the stress on the bridge sub-region at the current moment. The stress includes temperature stress, wind stress, and load stress. Perform correlation analysis on the temperature parameters to generate temperature stress, perform correlation analysis on the wind speed parameters to generate wind stress, and perform correlation analysis on the traffic load parameters to generate load stress.

[0016] S4. Perform a comprehensive analysis of the stress on the sub-region to generate a stress tensor, which is used to reflect the stress situation of the bridge at the current moment.

[0017] S5. Perform correlation analysis on the stress tensor to generate equivalent stress. Perform correlation analysis on the equivalent stress and correlation coefficient to generate a dynamic equivalent stress assessment index. Compare the dynamic equivalent stress assessment index with a threshold and output a risk warning report, maintenance priority assessment and decision-making suggestions. The dynamic equivalent stress assessment index is used to reflect the current stress assessment magnitude of the bridge.

[0018] Furthermore, the sub-regions are numbered, and strain values ​​are collected at different temperatures under windless and traffic-free conditions. These values ​​are then standardized to generate strain and temperature data. The standardization formula is as follows: Where X represents the original data, μ represents the mean of the original data, σ represents the standard deviation of the original data, and Z represents the standardized data values. The strain data includes the strain value measured in the j-th measurement of the i-th sub-region. mean strain The temperature value measured in the j-th measurement of the i-th sub-region Average temperature The superscript i is used to index the sub-regions, where there are N sub-regions and M measurements are performed.

[0019] Furthermore, a correlation analysis was performed on the strain and temperature data to generate a correlation coefficient r. i The formula used is:

[0020]

[0021] Correlation coefficient r i Used to reflect the correlation between temperature and strain values ​​in the i-th sub-region, and to reflect the size of pores and cracks inside the bridge section in the i-th sub-region.

[0022] Furthermore, the temperature parameter T i Given the current temperature value in the i-th sub-region, perform correlation analysis on the temperature parameters to generate the temperature strain ∈ 1 / i for the i-th sub-region. i The formula used is:

[0023] ∈ i =α(T) i -T0)

[0024] Where T0 is the average temperature of the area where the bridge is located that year, α is the linear thermal expansion coefficient of the bridge, and temperature strain ∈ i Used to reflect the strain that the bridge should theoretically produce in the i-th sub-region at the current temperature;

[0025] With respect to temperature strain ∈ i Perform correlation analysis to generate the temperature strain stress σ of the i-th sub-region. i The formula used is:

[0026] σ i =E*∈ i

[0027] Where E is the elastic modulus of the bridge material, and σ is the temperature strain stress. i This represents the temperature stress experienced by the current bridge sub-region due to temperature changes.

[0028] Furthermore, the wind speed parameter v i Given the current wind speed in the i-th sub-region, perform correlation analysis on the wind speed parameters to generate the wind force F in the i-th sub-region. i The formula used is:

[0029]

[0030] Where ρ is the air density, C i Let A be the drag coefficient of the i-th sub-region. i Let F be the windward area of ​​the i-th sub-region, and let F be the wind force. i The wind force and wind speed parameter v represent the magnitude of the wind force experienced by the i-th sub-region. i The formula used is:

[0031]

[0032] The bridge is placed in a three-dimensional XYZ coordinate system, with the X-axis pointing towards the bridge's axis, the Y-axis perpendicular to the bridge's axis and parallel to the bridge surface, and the Z-axis perpendicular to the XOY plane. i,x v represents the magnitude of the wind force along the X-axis in the i-th sub-region. i,y v represents the magnitude of the wind force along the Y-axis in the i-th sub-region. i,z The magnitude of the wind force experienced by the i-th sub-region along the Z-axis;

[0033] For wind speed parameter v i Wind force F i Correlation analysis is performed to generate wind stress, which includes the X-axis wind stress ω. i,x Y-axis wind stress ω i,y and Z-axis wind stress ω i,z The formula used is:

[0034]

[0035] Among them, A i,x Let A be the cross-sectional area along the X-axis of the i-th sub-region. i,y Let A be the cross-sectional area along the Y-axis of the i-th sub-region. i,z Let ω be the cross-sectional area along the Z-axis of the i-th sub-region, and ω be the wind stress along the X-axis. i,x Used to reflect the magnitude of stress generated in the i-th sub-region under wind action in the X-axis direction, and the Y-axis wind stress ω. i,y The Z-axis wind stress ω is used to reflect the magnitude of the stress generated in the i-th sub-region under the action of wind in the Y-axis direction. i,z It is used to reflect the magnitude of the stress generated in the i-th sub-region under the action of wind in the Z-axis direction.

[0036] Furthermore, the traffic load parameter m i Let m be the total mass of vehicles on the bridge deck in the i-th sub-region. i Perform correlation analysis to generate load stress τ i The formula used is:

[0037]

[0038] Among them, S i Let be the area of ​​the bridge deck in the i-th sub-region, and τ be the load stress. i This is used to reflect the stress exerted by vehicles on the bridge in the i-th sub-region under vehicle traffic conditions.

[0039] Furthermore, a comprehensive analysis of the stresses experienced by the sub-regions is performed to generate the stress tensor γ. i The formula used is:

[0040]

[0041] Stress tensor γ i This reflects the stress condition of the i-th sub-region of the bridge, and the stress tensor γ i Decomposed into the stress γ along the X-axis of the i-th sub-region i,x The Y-axis stress γ experienced by the i-th sub-region i,y The stress γ along the Z-axis in the i-th sub-region i,z .

[0042] Furthermore, regarding the stress tensor γ i Perform correlation analysis to generate equivalent stress DY i The formula used is:

[0043]

[0044] Equivalent stress DY i Used to reflect the equivalent stress value of the i-th sub-region under the current temperature, wind speed, and load conditions;

[0045] Equivalent stress DY i Correlation analysis was performed with the correlation coefficient to generate the dynamic equivalent effect assessment index DYP. i The formula used is:

[0046]

[0047] ε is the correlation weighting factor, used to control the influence of the correlation coefficient on the dynamic equivalence assessment index. The larger the value, the greater the influence. i Representing equivalent stress, the larger the value, the greater the stress on the sub-region, and the higher the risk of the bridge sub-region, i.e., DY. i With DYP i There is a positive correlation; the greater the equivalent effect, the greater the safety risk index. i This represents the degree of linear correlation between temperature and strain; the closer to 1, the stronger the correlation and the fewer internal defects. iThe smaller the value of r, the larger the internal defects of the sub-region, and the higher the risk of the bridge sub-region. i With DYP i There is a negative correlation, r i The larger the value of r, the lower the safety risk index. i As the density decreases, internal cracks and defects grow exponentially, contributing more to the risk. Therefore, a power-law amplification of the contribution is employed, using the dynamic equivalent stress assessment index DYP. i The risk index of a sub-region is used to reflect the comprehensive correlation coefficient and equivalent stress in the analysis. The larger the dynamic equivalent stress assessment index, the worse the safety of the sub-region.

[0048] The dynamic equivalent stress assessment index DYP i By comparing with the threshold θ and analyzing the stress limit threshold using historical data from other bridges, the dynamic equivalent stress assessment index DYP is determined. i Compare with this value, and output a risk warning report, maintenance priority assessment, and decision-making recommendations. When DYP i When ≥θ, the sub-region's security level is level two, triggering a risk warning report and increasing the maintenance priority of that sub-region. Maintenance decision recommendations are then output based on a large language model query. When DYP i When the value is greater than or equal to θ, the security level of the sub-region is Level 1, and the sub-region is in a normal state.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] This invention introduces a temperature-strain correlation coefficient to quantify internal structural defects and combines temperature, wind speed, and traffic load to generate a comprehensive stress tensor. After analysis and synthesis, a real-time dynamic equivalent stress assessment index for the bridge sub-region is generated. The dynamic equivalent stress assessment index reflects the risk level of the sub-region. By comparing it with a threshold, the current risk level of the sub-region can be output, thus achieving accurate dynamic assessment of local sub-regions of the bridge. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the process of the present invention;

[0052] Figure 2 This is a flowchart illustrating the modules of the present invention;

[0053] Figure 3 This is a fitting curve of the equivalent stress-dynamic equivalent stress evaluation index of the present invention;

[0054] Figure 4 This is a curve showing the correlation coefficient and dynamic equivalent stress evaluation index of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0056] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0057] Example:

[0058] Please see Figure 1-4 The present invention provides a technical solution:

[0059] Reference Figure 1 This invention provides an intelligent collaborative management method for the entire lifecycle of bridge engineering based on a large language model. The invention uses planar bridges as the monitoring object, and the specific steps include:

[0060] Step 1: In order to perform segmented processing on the bridge and improve the accuracy of the analysis, the bridge deck is divided into several sub-regions. Under the conditions of no wind and no traffic load, the strain and temperature data of the sub-regions at the current moment are obtained multiple times. From the correlation between temperature and strain, a correlation coefficient is generated. The correlation coefficient is used to reflect the degree of correlation between bridge temperature and strain.

[0061] Sub-regions were numbered. To control for other variables, strain values ​​were collected at different temperatures under windless and traffic-free conditions during data acquisition. Temperature-strain values ​​were recorded and standardized to generate strain and temperature data. The standardization formula is as follows: Where X represents the original data, μ represents the mean of the original data, σ represents the standard deviation of the original data, and Z represents the standardized data values. The strain data includes the strain value measured in the j-th measurement of the i-th sub-region. and the average strain of M measurements Temperature data includes the temperature value measured j times in the i-th sub-region. and the average temperature of M measurements The superscript 'i' is used to index sub-regions, where there are N sub-regions and M measurements. Standardized data can be analyzed using a unified set of units.

[0062] Correlation analysis was performed on strain and temperature data to generate a correlation coefficient r. i The formula used is:

[0063]

[0064] The correlation coefficient, calculated using the correlation degree method, reflects the association between two independent variables. The correlation coefficient r... i The correlation coefficient is used to reflect the degree of correlation between temperature and strain values ​​in the i-th sub-region. When the correlation coefficient between strain and temperature data is close to 1, it indicates a linear correlation between the two data points, representing a very high correlation. This means that the internal porosity and cracks of the bridge in this sub-region are minimal, and the porosity and cracks do not affect the correlation between the two. When the correlation coefficient is far from 1, it indicates that there are more internal porosity and cracks in the bridge in the sub-region, affecting the linear correlation between temperature and strain and reducing its correlation coefficient. Therefore, the correlation coefficient can reflect the size of internal porosity and cracks in the i-th sub-region of the bridge; that is, the closer the correlation coefficient is to 1, the smaller the internal porosity and cracks in the sub-region. In bridge structures, temperature changes cause thermal expansion and contraction of materials, resulting in strain. Ideally, when the bridge material is uniform and undamaged, temperature and strain exhibit a strong linear relationship because temperature change is the main driving force for strain. When there are pores and cracks inside a bridge, the mechanical response and thermal conductivity of the structure will change. Pores and cracks, as defects, weaken the overall stiffness and continuity of the material, resulting in non-uniform or delayed strain response. Cracks break in the strain transmission path, and the linear relationship between strain and temperature is disrupted.

[0065] Step 2: In order to analyze the real-time monitoring data of the sub-region and analyze the safety of the sub-region, obtain the current pressure data of the bridge sub-region, including monitoring the bridge sub-region and collecting temperature parameters, wind speed parameters, and traffic load parameters. Temperature parameters, wind speed parameters, and traffic load parameters affect the real-time stress of the sub-region. Temperature parameters are used to reflect the temperature value of the bridge at the current moment, wind speed parameters are used to reflect the wind speed values ​​in three directions at the bridge at the current moment, and traffic load parameters are used to reflect the load weight of the bridge deck at the current moment.

[0066] Step 3: Obtain the stress on the bridge sub-region at the current moment. The stress includes temperature stress, wind stress, and load stress. Perform correlation analysis on the temperature parameters to generate temperature stress, perform correlation analysis on the wind speed parameters to generate wind stress, and perform correlation analysis on the traffic load parameters to generate load stress.

[0067] The strain value of a bridge is most affected by three factors: temperature, wind force, and vehicle load. Therefore, this invention analyzes these three factors separately and decomposes the wind force to obtain more accurate stress values.

[0068] Temperature parameter T i Given the current temperature value in the i-th sub-region, perform correlation analysis on the temperature parameters to generate the temperature strain ∈ 1 / i for the i-th sub-region. i The formula used is:

[0069] ∈ i =α(T) i -T0)

[0070] Where T0 is the average temperature of the area where the bridge is located that year, α is the linear thermal expansion coefficient of the bridge, which can be obtained from a table, and temperature strain ∈ i The strain that the bridge should theoretically produce in the i-th sub-region at the current temperature is represented by a theoretically calculated value.

[0071] With respect to temperature strain ∈ i Perform correlation analysis to generate the temperature strain stress σ of the i-th sub-region. i The formula used is:

[0072] σ i =E*∈ i

[0073] Where E is the elastic modulus of the bridge material, and σ is the temperature strain stress. i This represents the temperature stress experienced by the current bridge sub-region due to temperature changes.

[0074] Wind speed parameter v i Given the current wind speed in the i-th sub-region, perform correlation analysis on the wind speed parameters to generate the wind force F in the i-th sub-region. i The formula used is:

[0075]

[0076] Where ρ is the air density, C i The drag coefficient of the i-th sub-region can be obtained by looking up a table, A i Given the windward area of ​​the i-th sub-region, the bridge can be modeled and analyzed using modeling software, with the wind force F... i The wind force and wind speed parameter v represent the magnitude of the wind force experienced by the i-th sub-region. i The formula used is:

[0077]

[0078] The bridge is placed in a three-dimensional XYZ coordinate system, with the X-axis pointing towards the bridge's axis, the Y-axis perpendicular to the bridge's axis and parallel to the bridge surface, and the Z-axis perpendicular to the XOY plane. i,x v represents the magnitude of the wind force along the X-axis in the i-th sub-region. i,y v represents the magnitude of the wind force along the Y-axis in the i-th sub-region. i,z Let X be the magnitude of the wind force along the Z-axis of the i-th sub-region. To analyze the triaxial force of the wind, the wind speed parameter is decomposed into three axes: X-axis, Y-axis, and Z-axis.

[0079] For wind speed parameter v i Wind force F i Correlation analysis is performed to generate wind stress, which includes the X-axis wind stress ω. i,x Y-axis wind stress ω i,y and Z-axis wind stress ω i,z The formula used is:

[0080]

[0081] Among them, A i,x Let A be the cross-sectional area along the X-axis of the i-th sub-region. i,y Let A be the cross-sectional area along the Y-axis of the i-th sub-region. i,z Let ω be the cross-sectional area along the Z-axis of the i-th sub-region, and ω be the wind stress along the X-axis. i,x Used to reflect the magnitude of stress generated in the i-th sub-region under wind action in the X-axis direction, and the Y-axis wind stress ω. i,y The Z-axis wind stress ω is used to reflect the magnitude of the stress generated in the i-th sub-region under the action of wind in the Y-axis direction. i,z It is used to reflect the magnitude of the stress generated in the i-th sub-region under the action of wind in the Z-axis direction.

[0082] Traffic load parameter m i The total mass of vehicles on the bridge deck in the i-th sub-region is obtained by measuring pressure sensors, and the traffic load parameter m is... i Perform correlation analysis to generate load stress τ i The formula used is:

[0083]

[0084] Among them, S i Let be the area of ​​the bridge deck in the i-th sub-region, and τ be the load stress. i This is used to reflect the stress on the bridge in the i-th sub-region caused by vehicles under traffic conditions, i.e., under normal bridge use conditions.

[0085] Step 4: Perform a comprehensive analysis of the stress on the sub-region to generate a stress tensor. The stress tensor is used to reflect the stress situation of the bridge at the current moment.

[0086] A comprehensive analysis of the stresses experienced by the sub-regions is performed to generate the stress tensor γ. i The formula used is:

[0087]

[0088] It is a 3x3 symmetric matrix containing normal stress and shear stress components, σ i Let τ be the shear stress component along the x-axis of this sub-region. i Let ω be the normal stress of this subregion in the XOY plane. i,x ω i,y ω i,z The stress tensor γ is generated by combining the triaxial shear stress components of the wind force in the sub-region. i .

[0089] Stress tensor γ i This reflects the stress condition of the i-th sub-region of the bridge, and the stress tensor γ i Decomposed into the stress γ along the X-axis of the i-th sub-region i,x The Y-axis stress γ experienced by the i-th sub-region i,y The stress γ along the Z-axis in the i-th sub-region i,z Among them, γ i,x =σ i +ω i,x γ i,y =ω i,y γ i,z =τ i +ω i,z .

[0090] Step 5: Perform correlation analysis on the stress tensor to generate equivalent stress. Perform correlation analysis on the equivalent stress and correlation coefficient to generate a dynamic equivalent stress assessment index. Compare the dynamic equivalent stress assessment index with the threshold and output a risk warning report, maintenance priority assessment and decision-making suggestions. The dynamic equivalent stress assessment index is used to reflect the current stress assessment of the bridge.

[0091] With respect to the stress tensor γ i Perform correlation analysis to generate equivalent stress DY i The formula used is:

[0092]

[0093] When a subregion is subjected to stress, a complex three-dimensional stress state is generated within it, including three normal stress components and six shear stress components. In practical engineering, to determine whether the subregion has yielded, a scalar index is usually needed to simplify the multiaxial stress state into an equivalent uniaxial stress value. Plastic yielding of materials is not caused by simple volumetric stress, but mainly by tangential deformation. According to this criterion, the effect of any complex stress state in the subregion can be expressed by an "equivalent" one-dimensional stress value. In the calculation, when calculating the inner product of the shear stress tensor, the square term of the principal stress difference is used.

[0094] Therefore, the equivalent stress DY i Used to reflect the equivalent stress value of the i-th sub-region under the current temperature, wind speed, and load conditions;

[0095] Equivalent stress DY i Correlation analysis was performed with the correlation coefficient to generate the dynamic equivalent effect assessment index DYP. i The formula used is:

[0096]

[0097] Reference Figure 3 and Figure 4 The present invention relates to the equivalent stress DY i Statistical analysis was performed on the correlation coefficient data, and a dynamic equivalent stress assessment index (DYP) was generated by fitting the data. i Obtain the Dynamic Equivalent Stress Assessment Index (DYP) i The calculation formula is shown in Table 1.

[0098] Table 1: Equivalent Stress DY i and correlation coefficient statistics

[0099] Serial Number Equivalent stress Correlation coefficient Dynamic Equivalent Stress Assessment Index 1 1.25 0.97 1.25 2 2.1 0.63 2.1 3 0.85 0.88 0.85 4 1.75 0.73 1.75 5 0.95 0.92 0.95 6 2.5 0.65 2.5 7 1 0.85 1 8 1.4 0.79 1.4 9 2.9 0.61 2.9 10 0.75 0.94 0.75 11 1.6 0.7 1.6 12 1.1 0.8 1.1 13 0.9 0.96 0.9 14 1.5 0.67 1.5 15 2.2 0.75 2.2 16 1.05 0.9 1.05 17 0.65 0.98 0.65 18 2 0.62 2 19 1.85 0.77 1.85 20 0.8 0.87 0.8 21 1.95 0.69 1.95 22 1.15 0.83 1.15 23 2.3 0.64 2.3 24 0.7 0.95 0.7 25 1.2 0.76 1.2 26 2.4 0.68 2.4 27 1 0.91 1 28 1.3 0.74 1.3 29 1.7 0.86 1.7 30 0.6 0.93 0.6

[0100] exist Figure 3 From this, we can conclude that there is a linear relationship between equivalent stress and the dynamic equivalent stress assessment index. The greater the equivalent stress, the greater the stress in the sub-region, the more dangerous the sub-region, and the higher the value of the dynamic equivalent stress assessment index. Figure 4 The correlation coefficient and the dynamic equivalent stress assessment index have an exponential decreasing relationship. The larger the correlation coefficient, the smaller the dynamic equivalent stress assessment index. The correlation coefficient contributes more to the risk than the equivalent stress.

[0101] ε is the correlation weighting factor, used to control the influence of the correlation coefficient on the dynamic equivalence assessment index. The larger the value, the greater the influence. i Representing equivalent stress, the larger the value, the greater the stress on the sub-region, and the higher the risk of the bridge sub-region, i.e., DY. iWith DYP i There is a positive correlation; the greater the equivalent effect, the greater the safety risk index. i This represents the degree of linear correlation between temperature and strain; the closer to 1, the stronger the correlation and the fewer internal defects. i The smaller the value of r, the larger the internal defects of the sub-region, and the higher the risk of the bridge sub-region. i With DYP i There is a negative correlation, r i The larger the value of r, the lower the safety risk index. i As the density decreases, internal cracks and defects grow exponentially, contributing more to the risk. Therefore, a power-law amplification of the contribution is employed, using the dynamic equivalent stress assessment index DYP. i The risk index of a sub-region is used to reflect the comprehensive correlation coefficient and equivalent stress in the analysis. The larger the dynamic equivalent stress assessment index, the worse the safety of the sub-region.

[0102] The dynamic equivalent stress assessment index DYP i By comparing with the threshold θ and analyzing the stress limit threshold using historical data from other bridges, the dynamic equivalent stress assessment index DYP is determined. i Compare with this value, and output a risk warning report, maintenance priority assessment, and decision-making recommendations. When DYP i When the risk level is ≥θ, the safety level of the sub-region is Level 2, triggering a risk warning report and increasing the maintenance priority of that sub-region. Maintenance decision suggestions are then generated based on a large language model query, providing reference for staff by inputting the current risk level of the sub-region into the large language model. i When the value is greater than or equal to θ, the security level of the sub-region is Level 1, and the sub-region is in a normal state.

[0103] Reference Figure 2 A bridge engineering full lifecycle intelligent collaborative management system based on a large language model includes:

[0104] Association unit, data acquisition unit, analysis unit, evaluation unit;

[0105] The correlation unit is used to divide the bridge deck into several sub-regions and analyze the correlation coefficients between bridge temperature and strain;

[0106] The data acquisition unit is used to monitor sub-areas and collect temperature, wind speed, and traffic load parameters.

[0107] The analysis unit is used to obtain the stress in the sub-region, including temperature stress, wind stress, and load stress, and to generate the stress tensor.

[0108] The assessment unit is used to perform correlation analysis on the stress tensor, generate equivalent stress, perform correlation analysis on the equivalent stress and correlation coefficient, generate a dynamic equivalent stress assessment index, and output risk warning reports, maintenance priority assessments, and decision recommendations.

[0109] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0110] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent collaborative management of the entire lifecycle of bridge engineering based on a large language model, characterized in that: The specific steps include: S1. Divide the bridge deck into several sub-regions. Under conditions of no wind and no traffic load, acquire the strain and temperature data of the sub-regions at the current moment multiple times. Perform correlation analysis on the strain and temperature data to generate a correlation coefficient. The correlation coefficient is used to reflect the degree of correlation between bridge temperature and strain. S2. Obtain the current bearing pressure data of the bridge sub-area, monitor the bridge sub-area, and collect temperature parameters, wind speed parameters, and traffic load parameters. The temperature parameter is used to reflect the temperature value at the bridge at the current time, the wind speed parameter is used to reflect the wind speed value in three directions at the bridge at the current time, and the traffic load parameter is used to reflect the load weight on the bridge deck at the current time. S3. Obtain the stress on the bridge sub-region at the current moment. The stress includes temperature stress, wind stress, and load stress. Perform correlation analysis on the temperature parameters to generate temperature stress, perform correlation analysis on the wind speed parameters to generate wind stress, and perform correlation analysis on the traffic load parameters to generate load stress. S4. Perform a comprehensive analysis of the stress on the sub-region to generate a stress tensor, which is used to reflect the stress situation of the bridge at the current moment. S5. Perform correlation analysis on the stress tensor to generate equivalent stress. Perform correlation analysis on the equivalent stress and correlation coefficient to generate a dynamic equivalent stress assessment index. Compare the dynamic equivalent stress assessment index with a threshold and output a risk warning report, maintenance priority assessment and decision-making suggestions. The dynamic equivalent stress assessment index is used to reflect the current stress assessment magnitude of the bridge. Sub-regions were numbered, and strain values ​​were collected from each sub-region at different temperatures under windless and traffic-free conditions. These values ​​were then standardized to generate strain and temperature data. The standardization formula is as follows: Where X is the original data, The mean of the original data. Z represents the standard deviation of the original data, and Z represents the standardized data values. The strain data includes the strain values ​​measured in the j-th measurement of the i-th sub-region. and the average strain of M measurements The temperature data includes the temperature value measured j times in the i-th sub-region. and the average temperature of M measurements The superscript i is used to index the sub-regions, where there are N sub-regions and M measurements are performed. Correlation analysis was performed on strain and temperature data to generate correlation coefficients. The formula used is: Correlation coefficient Used to reflect the degree of correlation between temperature and strain values ​​in the i-th sub-region, and to reflect the size of pores and cracks inside the bridge section in the i-th sub-region; With respect to stress tensor Perform correlation analysis to generate equivalent forces. The formula used is: Equivalent stress This is used to reflect the equivalent stress value of the i-th sub-region under the current temperature, wind speed, and load conditions, and the stress tensor is used to... Decomposed into the stress along the X-axis of the i-th sub-region The stress along the Y-axis in the i-th sub-region The stress along the Z-axis in the i-th sub-region ; Equivalent stress Correlation analysis was performed with the correlation coefficient to generate a dynamic equivalence assessment index. The formula used is: Dynamic Equivalent Stress Assessment Index The risk index of a sub-region is used to reflect the comprehensive correlation coefficient and equivalent stress in the analysis. The larger the dynamic equivalent stress assessment index, the worse the safety of the sub-region. Dynamic equivalent stress assessment index With threshold The comparison is performed, and a risk warning report, maintenance priority assessment, and decision-making recommendations are output. When the sub-region's security level is level two, a risk warning report is triggered, the maintenance priority of that sub-region is increased, and maintenance decision suggestions are output based on a large language model query; when At this time, the security level of the sub-region is level one, and the sub-region is in a normal state.

2. The intelligent collaborative management method for the entire lifecycle of bridge engineering based on a large language model as described in claim 1, characterized in that: The temperature parameter Given the current temperature value in the i-th sub-region, perform correlation analysis on the temperature parameters to generate the temperature strain in the i-th sub-region. The formula used is: in, The average temperature of the area where the bridge is located that year. The linear thermal expansion coefficient of the bridge, temperature strain Used to reflect the strain that the bridge should theoretically produce in the i-th sub-region at the current temperature; Temperature strain Perform correlation analysis to generate the temperature strain stress of the i-th sub-region. The formula used is: Where E is the elastic modulus of the bridge material, and temperature strain stress is... This represents the temperature stress experienced by the current bridge sub-region due to temperature changes.

3. The intelligent collaborative management method for the entire lifecycle of bridge engineering based on a large language model as described in claim 2, characterized in that: The wind speed parameters Given the current wind speed in the i-th sub-region, perform correlation analysis on the wind speed parameters to generate the wind force for the i-th sub-region. The formula used is: in, air density, Let be the drag coefficient of the i-th sub-region. Let be the windward area of ​​the i-th sub-region, and be the wind force. The wind force and wind speed parameter are the magnitudes of the winds experienced by the i-th sub-region. The formula used is: The bridge is placed in a three-dimensional XYZ coordinate system, with the X-axis pointing towards the bridge's axis, the Y-axis perpendicular to the bridge's axis and parallel to the bridge surface, and the Z-axis perpendicular to the XOY plane. Let represent the magnitude of the wind force experienced by the i-th sub-region along the X-axis. Let represent the magnitude of the wind force along the Y-axis in the i-th sub-region. The magnitude of the wind force experienced by the i-th sub-region along the Z-axis; For wind speed parameters Wind power Correlation analysis is performed to generate wind stress, which includes X-axis wind stress. Y-axis wind stress and Z-axis wind stress The formula used is: in, Let be the cross-sectional area of ​​the i-th sub-region along the X-axis. Let be the cross-sectional area of ​​the i-th sub-region along the Y-axis. Let be the cross-sectional area along the Z-axis of the i-th sub-region, and be the wind stress along the X-axis. Used to reflect the magnitude of stress generated in the i-th sub-region under wind action in the X-axis direction, and wind stress in the Y-axis direction. Used to reflect the magnitude of stress generated in the i-th sub-region under wind action in the Y-axis direction, Z-axis wind stress It is used to reflect the magnitude of the stress generated in the i-th sub-region under the action of wind in the Z-axis direction.

4. The intelligent collaborative management method for the entire lifecycle of bridge engineering based on a large language model as described in claim 1, characterized in that: The traffic load parameters Given the total mass of vehicles on the bridge deck in the i-th sub-region, the traffic load parameters are... Perform correlation analysis to generate load stress. The formula used is: in, Let be the area of ​​the bridge deck in the i-th sub-region, and the load stress be... This is used to reflect the stress exerted by vehicles on the bridge in the i-th sub-region under vehicle traffic conditions.

5. The intelligent collaborative management method for the entire life cycle of bridge engineering based on a large language model according to any one of claims 2-4, characterized in that: A comprehensive analysis of the stresses experienced by the sub-regions is performed to generate the stress tensor. The formula used is: Stress Tensor This reflects the stress situation in the i-th sub-region of the bridge.

6. A bridge engineering lifecycle intelligent collaborative management system based on a large language model, used to execute the bridge engineering lifecycle intelligent collaborative management method based on a large language model as described in claim 1, characterized in that, include: Association unit, data acquisition unit, analysis unit, evaluation unit; The correlation unit is used to divide the bridge deck into several sub-regions and analyze the correlation coefficients between bridge temperature and strain. The data acquisition unit is used to monitor sub-areas and collect temperature parameters, wind speed parameters, and traffic load parameters. The analysis unit is used to obtain the stress in the sub-region, including temperature stress, wind stress, and load stress, and to generate a stress tensor. The assessment unit is used to perform correlation analysis on the stress tensor, generate equivalent stress, perform correlation analysis on the equivalent stress and correlation coefficient, generate a dynamic equivalent stress assessment index, and output risk warning reports, maintenance priority assessments, and decision recommendations.

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