Dynamic evaluation and early warning platform for safety risk of in-service bridge based on big data driving

Abstract

The invention discloses an in-service bridge safety risk dynamic assessment and early warning platform based on big data driving, and relates to the technical field of bridge engineering safety monitoring. According to the method, multi-dimensional data acquisition is realized through multiple types of sensors and multi-step preprocessing, a coupling influence matrix is constructed, dynamic weight adaptive fusion is realized in combination with reinforcement learning, and rapid and accurate adaptation of a digital twin model is realized by adopting federated learning and a two-stage calibration mode. Maintenance resources are integrated to construct a digital map, an integrated disposal scheme is generated, full-life-cycle cost-oriented pre-control is realized through risk loss quantification and multi-objective optimization, and a visual decision is realized based on multi-form display and accurate pushing. The platform realizes full-process closed-loop management from data acquisition, fusion analysis, model calibration and early warning adaptation to cost optimization, effectively improves bridge safety risk identification accuracy, early warning response efficiency and management and maintenance decision scientificity, and provides reliable support for dynamic safety management of in-service bridges.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering safety monitoring technology, specifically to a big data-driven dynamic assessment and early warning platform for the safety risks of in-service bridges. Background Technology

[0002] As core hubs of transportation infrastructure networks, the safe and stable operation of bridges in service directly impacts regional traffic flow, public safety, and sustainable socio-economic development. With extended service life, surging traffic volumes, and frequent impacts from extreme weather and complex disasters such as ship collisions, bridge structures are prone to various types of damage, including fatigue, corrosion, and foundation scour. Furthermore, the evolution of these damages exhibits non-linear and dynamic characteristics, posing a continuous threat to bridge safety. Traditional bridge safety assessments largely rely on manual inspections and fixed threshold warnings, which have limitations such as limited data collection dimensions, insufficient consideration of the coupled effects of multiple disasters, and delayed assessment results, making them ill-suited to the dynamic safety management needs of complex service environments.

[0003] While some existing bridge monitoring systems based on big data attempt to integrate multi-source data, they generally suffer from core technical shortcomings: a lack of dynamic adaptability to data fusion in multi-hazard coupled scenarios, and rigid weight settings leading to insufficient assessment accuracy; early warning mechanisms are not deeply integrated with maintenance resources and traffic scenarios, resulting in a lack of practicality in response plans; digital twin model calibration relies on single-bridge data, and insufficient cross-regional data collaboration leads to long calibration cycles and poor adaptability; at the same time, the entire life-cycle maintenance cost is not included in risk pre-control decisions, which easily leads to over-maintenance or under-maintenance, ultimately resulting in a comprehensive technical challenge of untimely identification of bridge safety risks, low early warning response efficiency, and poor maintenance economy.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a big data-driven dynamic assessment and early warning platform for the safety risks of in-service bridges, in order to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical issues, this invention provides a big data-driven dynamic assessment and early warning platform for the safety risks of in-service bridges, comprising a data acquisition module, a multi-hazard coupling dynamic weight adaptive fusion module, a digital twin model rapid calibration module, a maintenance resource linkage early warning adaptation module, a life-cycle cost-oriented pre-control module, and a visualization decision-making module.

[0007] The multi-hazard coupling dynamic weight adaptive fusion module has a built-in disaster type structural response damage probability coupling influence matrix construction unit and reinforcement learning weight adjustment unit. The disaster type structural response damage probability coupling influence matrix construction unit establishes the correlation between multiple disaster types and bridge structure response and damage probability. The reinforcement learning weight adjustment unit dynamically adjusts the fusion weights of each data source according to real-time monitoring data and disaster scenario changes.

[0008] The life-cycle cost-oriented pre-control module includes a risk loss model construction unit, a maintenance cost model construction unit, and a multi-objective optimization unit. The risk loss model construction unit quantifies the potential losses corresponding to different safety risk levels, the maintenance cost model construction unit calculates the maintenance cost of the bridge throughout its life cycle, and the multi-objective optimization unit generates the maintenance pre-control scheme with the optimal safety cost based on the risk loss model and the maintenance cost model.

[0009] The data acquisition module collects bridge structural response data, environmental data, and disaster data; the digital twin model rapid calibration module enables rapid calibration of the bridge's digital twin model; the maintenance resource linkage early warning adaptation module matches early warning levels with maintenance resources; and the visualization decision-making module displays assessment results and early warning information. By integrating core technologies of multi-hazard coupling dynamic weight fusion and full life cycle cost optimization, it can accurately identify hidden risks in complex disaster scenarios and achieve a dynamic balance between safety assurance and cost control, significantly improving the scientific nature of the platform's assessment and the economic efficiency of maintenance decisions.

[0010] Furthermore, the data acquisition module includes a structural response monitoring unit, an environmental monitoring unit, a disaster monitoring unit, and a data transmission unit. The structural response monitoring unit is deployed at key stress-bearing parts of the bridge to collect bridge strain, displacement, and vibration data. The environmental monitoring unit collects temperature, humidity, precipitation, and wind speed data. The disaster monitoring unit collects data on rainstorm erosion and ship collisions. The data transmission unit uses 5G communication technology to transmit the collected data to the back-end processing system in real time. This achieves comprehensive coverage and real-time transmission of multi-dimensional key data, ensuring the integrity and timeliness of platform data input, and providing a solid data foundation for subsequent fusion analysis, model calibration, and early warning decision-making.

[0011] Furthermore, the data acquisition module also includes a data preprocessing unit, which comprises an outlier removal subunit, a data denoising subunit, and a data standardization subunit. The outlier removal subunit uses statistical testing methods to remove outlier data from the acquired data. The data denoising subunit uses wavelet transform technology to reduce data noise. The data standardization subunit converts data from different dimensions into a unified standard format. This effectively filters out interference information in the data, improves data quality and consistency, reduces the impact of invalid data on the evaluation model and early warning results, and ensures the accuracy of subsequent technical modules.

[0012] Furthermore, the rapid calibration module for the digital twin model includes a cross-regional data collaboration unit, a basic model construction unit, and a regional adaptation parameter adjustment unit. The cross-regional data collaboration unit adopts a federated learning framework to achieve cross-regional sharing of calibration parameters for bridges of the same type while protecting data privacy. The basic model construction unit establishes a standard digital twin basic model based on general bridge structural parameters. The regional adaptation parameter adjustment unit adjusts the basic model parameters in conjunction with local bridge monitoring data to complete rapid model calibration. This overcomes the limitations of single-bridge data dependence, achieves cross-regional resource sharing while ensuring data privacy, significantly shortens the model calibration cycle, and improves the adaptability and evaluation accuracy of the digital twin model for different bridges.

[0013] Furthermore, the cross-regional data collaboration unit includes a data encryption subunit, a parameter extraction subunit, and a parameter sharing subunit. The data encryption subunit encrypts the bridge data transmitted across regions. The parameter extraction subunit extracts key parameters for model calibration from the encrypted data. The parameter sharing subunit establishes a secure sharing channel to realize the cross-regional transmission of calibration parameters. This constructs a secure and reliable cross-regional data collaboration mechanism, which fully utilizes external data resources while preventing data leakage risks and ensuring the security and stability of the parameter sharing process.

[0014] Furthermore, the maintenance resource linkage early warning adaptation module includes a maintenance resource digital map construction unit, an early warning level determination unit, and a resource demand matching unit. The maintenance resource digital map construction unit integrates maintenance team location, equipment reserve, and material inventory data to establish a dynamically updated maintenance resource digital map. The early warning level determination unit determines the bridge safety risk early warning level based on the assessment results. The resource demand matching unit matches the corresponding level of maintenance resource demand based on the early warning level and the maintenance resource digital map. This achieves precise alignment between the early warning level and maintenance resources, clarifies the resource demand for different risk levels, provides a scientific basis for maintenance scheduling, and improves the rationality and targeting of resource allocation.

[0015] Furthermore, the maintenance resource linkage early warning adaptation module also includes a disposal plan generation unit. The disposal plan generation unit generates an integrated disposal plan based on the resource matching results of the early warning level and the actual situation of the bridge, thereby forming a closed-loop mechanism of "early warning-matching-disposal", clarifying the specific execution path and priority, shortening the disposal response time, and improving the efficiency and operability of bridge safety risk disposal.

[0016] Furthermore, the multi-objective optimization unit includes an objective function construction subunit, a constraint setting subunit, and an optimization algorithm execution subunit. The objective function construction subunit establishes a function with the objectives of achieving safety standards and minimizing costs. The constraint setting subunit clarifies the safety threshold and maintenance resource constraints of the bridge structure. The optimization algorithm execution subunit uses an intelligent optimization algorithm to solve for the optimal maintenance pre-control scheme. The implementation path of the whole life cycle cost optimization is refined to ensure that the maintenance pre-control scheme simultaneously meets safety requirements and cost constraints, thereby improving the scientific nature and feasibility of the scheme.

[0017] Furthermore, the visualization decision-making module includes an assessment result display unit, an early warning information push unit, and a decision-making scheme presentation unit. The assessment result display unit intuitively displays the bridge safety status assessment data in the form of charts. The early warning information push unit pushes graded early warning signals to relevant management departments and maintenance units. The decision-making scheme presentation unit displays maintenance pre-control schemes and resource scheduling suggestions. This enables the intuitive presentation and accurate push of assessment results, early warning information, and decision-making schemes, facilitating relevant personnel to quickly grasp the bridge safety status and improve the efficiency and accuracy of management and maintenance decisions.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. The multi-hazard coupling dynamic weight adaptive fusion technology quantifies the coupling relationship between disasters and structural responses and combines reinforcement learning to achieve real-time dynamic adjustment of fusion weights. This breaks through the technical limitations of fixed weights, significantly improves the accuracy of risk identification in complex disaster scenarios, and effectively captures hidden damage risks that are difficult to detect by traditional methods.

[0020] 2. The rapid calibration technology of digital twin models with cross-regional data collaboration enables parameter sharing of bridges of the same type while ensuring data privacy. Combining the two-level mode of basic model and regional adaptation parameter optimization, it significantly shortens the model calibration cycle, improves the model's adaptability to different bridges, and successfully solves the problem of assessment accuracy caused by insufficient data for small and medium-sized bridges.

[0021] 3. The maintenance resource linkage early warning and adaptation technology, through the full-chain design of dynamic resource digital map, multi-indicator scientific classification, quantitative matching model and integrated disposal plan, realizes deep linkage between early warning and disposal, completely changing the status quo of traditional early warning only remaining at the level of signal prompts, and greatly improving the efficiency and operability of risk response.

[0022] 4. The life-cycle cost-oriented pre-control technology deeply couples risk loss quantification with life-cycle cost accounting. Through multi-objective optimization, it achieves a dynamic balance between safety assurance and cost control, avoiding the problems of over-maintenance or under-maintenance, and significantly improving the long-term economy and comprehensive benefits of bridge maintenance.

[0023] 5. The visualization decision-making module, through multi-format display, precise push notifications, and structured solution presentation, achieves efficient transmission of assessment, early warning, and decision-making information, providing intuitive and convenient operation support for management and maintenance personnel, and further enhancing the practical value and promotion prospects of the overall platform. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the principle of a big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 This invention provides a technical solution: a big data-driven dynamic assessment and early warning platform for the safety risks of in-service bridges, comprising a data acquisition module, a multi-hazard coupled dynamic weight adaptive fusion module, a digital twin model rapid calibration module, a maintenance resource linkage early warning adaptation module, a life-cycle cost-oriented pre-control module, and a visualization decision-making module. These modules work collaboratively to achieve closed-loop management of the entire process, from multi-source data acquisition, intelligent fusion analysis, dynamic assessment and calibration, accurate early warning adaptation to cost optimization and pre-control.

[0027] I. Implementation Background and Existing Technological Deficiencies: As a core component of transportation infrastructure, the safety status of in-service bridges directly affects traffic safety and socio-economic stability. With increasing service life, traffic volume, and the influence of complex environments such as extreme weather, bridge structures are prone to fatigue damage, corrosion, and aging. Traditional assessment methods suffer from deficiencies such as limited data collection, delayed assessment, disconnect between early warning and response, and failure to consider life-cycle costs, making it difficult to meet the needs of dynamic safety management.

[0028] The following section uses a cross-river concrete continuous beam bridge as a specific application scenario to elaborate on the implementation steps of each module. This cross-river bridge has a main span of 180 meters, a service life of 12 years, and an average daily traffic flow of over 8,000 vehicles. It faces multiple risks, including erosion from torrential rains, collisions with ships, and corrosion from high temperatures and humidity. Previously, the traditional method of manual inspection combined with fixed threshold early warning had problems such as long assessment cycles, insufficient identification of hidden risks, and strong subjectivity in maintenance decisions. The application of this platform will completely solve these pain points.

[0029] II. Data Acquisition Module: Data acquisition is the foundation of the platform's operation. Its core objective is to obtain multi-dimensional data on bridge structural response, environmental impact, and disaster effects, providing comprehensive, real-time, and high-quality data input for subsequent integrated analysis, assessment, and early warning. Existing technologies often focus on single structural parameters or environmental factors, lacking a systematic approach, experiencing high data transmission latency, and failing to design specific acquisition schemes for multi-hazard scenarios, leading to biased subsequent assessment results. This module achieves comprehensive, real-time, and reliable data acquisition through multi-unit collaborative acquisition, high-speed transmission, and precise preprocessing. Specific implementation steps are as follows:

[0030] Step 1: Deployment and Data Acquisition of Structural Response Monitoring Units: The structural response of a bridge directly reflects its stress state and health level. Response data from key stress-bearing areas are the core basis for identifying structural damage. Existing technologies mostly employ decentralized monitoring, with deployment locations lacking scientific planning, easily overlooking key damage areas. The specific technical approach is as follows: Based on the results of bridge structural mechanics simulation analysis, stress concentration areas such as the mid-span of the main span, supports, the bottom of piers, and the web of the box girder are identified as key monitoring areas. Fiber optic strain sensors, GNSS displacement sensors, and accelerometers are deployed at each location. The strain sensor range is adapted to the maximum strain value of the bridge, the displacement sensor sampling frequency is set to 10 Hz, and the accelerometer captures the bridge's vibration response.

[0031] Example: Four fiber optic strain sensors are symmetrically deployed at the bottom of the box girder in the middle of the main span of a cross-river bridge, with a spacing of 2 meters; three GNSS displacement sensors are deployed at the bottom of the piers to monitor horizontal and vertical displacement; and acceleration sensors are deployed on the web of the box girder to collect strain, displacement and vibration data under traffic load in real time.

[0032] Compared to existing decentralized monitoring methods, targeted deployment in key areas enables more accurate structural response data collection, effectively reduces invalid data, significantly improves the sensitivity of damage identification, and greatly increases the probability of identifying hidden damage.

[0033] Step 2: Environmental Monitoring Unit Deployment and Data Acquisition: Environmental factors are crucial external conditions affecting the degradation of bridge structural performance. Parameters such as temperature, humidity, precipitation, and wind speed can accelerate structural corrosion and cracking. Existing technologies often neglect the correlation between environmental parameters and structural damage, resulting in assessment models lacking environmental impact considerations. Specific technical measures include deploying environmental monitoring terminals on both sides of the bridge deck, the top of the piers, and the bridge towers, integrating temperature sensors, humidity sensors, rain gauges, and wind speed sensors. The temperature sensors cover the local extreme temperature range, the humidity sensors have an accuracy of 1% relative humidity, the rain gauges record precipitation amount and intensity, and the wind speed sensors capture instantaneous and average wind speeds. All sampling frequencies are 5 Hz.

[0034] Example: An environmental monitoring terminal is deployed every 50 meters on both sides of a cross-river bridge, for a total of 8 terminals, and one terminal is deployed on the top of each pier. The terminals collect real-time data on ambient temperature, relative humidity, precipitation, and wind speed on the bridge deck, and record the temperature difference between the bridge deck and the inside of the box girder in summer, as well as the changes in the intensity of heavy rainfall.

[0035] Multi-location, multi-parameter environmental data acquisition fully captures spatiotemporal variation characteristics, providing support for analyzing the coupling relationship between the environment and structural damage. Compared with existing single-parameter environmental data acquisition, the comprehensiveness of environmental impact assessment is greatly improved, and the accuracy of predicting structural performance degradation trends is significantly enhanced.

[0036] Step 3: Deployment and Data Acquisition of Disaster Monitoring Units: Bridges are susceptible to disasters such as erosion from torrential rains and collisions with ships. These disasters can easily lead to sudden structural damage. Current technology lacks specialized disaster monitoring equipment, making it difficult to capture the disaster process and its impact in real time. Specific technical measures include: deploying ultrasonic water level gauges and scour depth sensors near the bridge pier foundations to monitor foundation scour; deploying millimeter-wave radar sensors on both sides of the navigation opening to monitor ship position, speed, and heading; and connecting to the local earthquake monitoring network to obtain seismic motion parameters. The scour depth sensors use underwater detection, and the millimeter-wave radar has a detection range covering 500 meters on both sides of the navigation opening.

[0037] Example: A scour depth sensor is deployed near the foundations of each of the four piers of a cross-river bridge, with the probe installed 2 meters above the foundation; millimeter-wave radar sensors are deployed on the piers on both sides of the navigation channel, triggering high-frequency data acquisition when a ship approaches a safe distance.

[0038] Specialized disaster monitoring equipment enables real-time capture of disaster effects. Compared with existing methods that lack specialized monitoring, the quantitative analysis of the impact of disasters on bridges is more accurate, the response speed to sudden disasters is significantly improved, and the risk of damage expansion is greatly reduced.

[0039] Step 4: Data Transmission by the Data Transmission Unit: Collected data needs to be transmitted to the backend processing system in real time. Existing technologies mostly use 4G or wired transmission, which suffers from high latency, high deployment costs, and poor stability in harsh environments, making it difficult to meet the real-time requirements of dynamic evaluation. The specific technical solution is to construct a transmission network using 5G communication technology. The data transmission unit integrates a 5G communication module, edge computing nodes, and a data caching module. Raw data is first initially screened by the edge computing nodes, and then transmitted to the backend cloud platform via dedicated 5G slices. The local caching module stores nearly 72 hours of raw data to prevent data loss.

[0040] Example: Data collected by various monitoring sensors on a cross-river bridge is filtered for invalid data by edge computing nodes on the bridge deck, and then transmitted to the cloud platform via dedicated 5G slices. The transmission latency is controlled at the millisecond level, and the data is automatically retransmitted after network interruption.

[0041] 5G communication technology significantly reduces transmission latency, dedicated slicing ensures stability, and edge computing nodes reduce invalid data transmission. Compared with existing 4G or wired transmission, the real-time performance and reliability of data transmission are significantly improved, and the timeliness of data input to the back-end processing system is significantly improved.

[0042] Step 5: Data Optimization in the Data Preprocessing Unit: The raw data contains outliers, noise, and inconsistent formats, directly affecting the accuracy of subsequent analysis. Existing preprocessing techniques are simplistic, ineffective, and lack standardized approaches. Specific technical measures include: the data preprocessing unit comprises outlier removal, data denoising, and data standardization subunits. Outlier removal employs the Grubbs test, determining and removing outliers by calculating the deviation of the data from the mean, combined with the standard deviation and critical value. Data denoising uses db4 wavelet 3-level decomposition to separate signal and noise before reconstructing the data. Data standardization uses Z-score standardization to convert data from different dimensions into a standard format with a mean of 0 and a standard deviation of 1.

[0043] Example: Outliers in the strain data of a cross-river bridge due to sensor malfunction were removed using the Grubbs test; interference noise in the vibration data was processed by wavelet transform to obtain a clean signal; finally, Z-score standardization was used to convert the strain, displacement, temperature and other data into a unified format to prepare for subsequent fusion analysis.

[0044] Multi-step collaborative preprocessing effectively removes outliers, reduces noise, and standardizes the format. Compared with existing single preprocessing methods, data quality is significantly improved, invalid data interference is greatly reduced, and more reliable data input is provided for subsequent modules, indirectly improving the accuracy of evaluation results.

[0045] Publicly available technical documents include an IoT-based bridge health monitoring system and a multi-source data acquisition device for bridge structures. These documents only implement basic data acquisition and simple transmission, lacking specific monitoring solutions for multi-hazard scenarios, and their preprocessing procedures lack systematicity. This module's unique technical approach lies in its precise deployment of key components based on structural mechanics simulation, integration of multi-hazard specialized monitoring equipment, a transmission mechanism combining 5G-dedicated slicing transmission and edge computing, and a preprocessing process that integrates Grubbs' test, wavelet transform, and Z-score standardization. Compared to publicly available documents, this module achieves comprehensive acquisition of multi-dimensional data on structure, environment, and hazards, significantly improving the real-time performance and stability of data transmission, and substantially enhancing preprocessing accuracy. It effectively addresses the problems of partial data acquisition, delayed transmission, and poor data quality in existing technologies, laying a solid foundation for the efficient operation of subsequent modules.

[0046] III. Adaptive Multi-Hazard Coupling Dynamic Weight Fusion Module: Multi-source data fusion is a core component of accurate bridge safety risk assessment. Bridges often face the coupled effects of multiple hazards. Existing technologies mostly employ fixed-weight fusion, failing to consider the cumulative effects of coupling, resulting in incomplete fusion results that fail to reflect the true safety status. This module achieves adaptive multi-source data fusion by constructing a multi-hazard coupling effect matrix and a reinforcement learning dynamic weight adjustment mechanism, improving the accuracy of fusion in complex scenarios. Specific implementation steps are as follows:

[0047] Step 1: Construction of the Coupling Influence Matrix of Disaster Type-Structural Response-Damage Probability: Different disasters have varying degrees of impact on bridge structural responses, and the damage probabilities under coupling effects are not simply the sum of individual disasters. Existing technologies lack quantitative analysis of this coupling relationship, resulting in a lack of scientific basis for setting fusion weights. Specific technical methods include: identifying disaster types such as rainstorm erosion, ship collisions, and wind loads, and clarifying the structural response parameters corresponding to each disaster, such as foundation displacement and erosion depth corresponding to rainstorm erosion. Based on historical monitoring data, structural mechanics simulation results, and disaster statistics, a coupling influence matrix of disaster type-structural response-damage probability is constructed. Matrix elements represent the correlation value between a specific disaster and its response, indicating the bridge's damage probability level. The correlation value is determined using the analytic hierarchy process (AHP), ranging from 0 to 1.

[0048] Example: A cross-river bridge faces three main hazards: rainstorm erosion, ship collision, and wind load. Corresponding response parameters include foundation erosion depth, impact point strain, and bridge vibration amplitude. Damage probability levels are categorized as low, medium, and high. Using the analytic hierarchy process (AHP), the coupling influence matrix shows a correlation of 0.7 between rainstorm erosion and foundation erosion depth (medium damage probability) and 0.85 between ship collision and impact point strain (high damage probability), clearly quantifying the correlation between different hazard coupling scenarios.

[0049] The coupling influence matrix quantifies the coupling relationship of multiple disasters. Compared with existing methods that lack coupling analysis, it provides a scientific basis for weight adjustment. The adaptability of fused data to disaster coupling scenarios is significantly improved, and the accuracy of damage identification under multiple disaster superposition is greatly improved.

[0050] Step 2: Dynamic Calculation of Weights in the Reinforcement Learning Weight Adjustment Unit: The bridge structure response and disaster scenario change dynamically. Fixed weights cannot adapt to these changes, leading to insufficient real-time performance and accuracy of the fusion results. Existing technologies have not achieved dynamic adaptive weight adjustment. The specific technical approach is as follows: A weight adjustment model is constructed using a deep Q-network reinforcement learning algorithm. The state space is defined as a state vector composed of the correlation degree values ​​of real-time monitoring data and the coupling influence matrix; the action space is defined as the weight adjustment amount of each data source, where the weight vector sums to 1 and is not less than 0, and each adjustment increment does not exceed 10%; the reward function is determined by the matching accuracy and error value between the fusion result and the actual damage state; the model is trained using historical data, and the optimal weight adjustment amount is output based on the current state during real-time operation.

[0051] Example: The data source for a cross-river bridge includes strain, displacement, scour depth, vibration, and environmental data, with initial weights equal. When heavy rain is detected, the correlation between rainstorm scour and foundation scour depth in the coupling influence matrix is ​​0.7. The model output is adjusted, increasing the weights of strain and displacement data and decreasing the weight of scour depth data. When a ship approaches, the weights are adjusted again, increasing the weights of vibration and strain data to ensure the fusion result accurately reflects the risk.

[0052] The reinforcement learning algorithm enables dynamic adaptive adjustment of fusion weights. Compared with the existing fixed-weight fusion method, the fusion result can match disaster scenarios and structural response changes in real time. The fusion accuracy under multiple disaster coupling is significantly improved, the ability to identify hidden risks is greatly enhanced, and the evaluation error of complex scenarios is significantly reduced.

[0053] The publicly available technical document describes a bridge safety monitoring and analysis system based on big data. While it discloses a technical solution for multi-source data fusion using a fixed matrix, it fails to consider the coupling effects of multiple disasters, sets weights to fixed values, and lacks dynamic adjustment capabilities. This module's unique approach lies in constructing a disaster type-structural response-damage probability coupling influence matrix and a dynamic weight adjustment mechanism based on deep Q-network reinforcement learning. Compared to the publicly available document, this module quantifies the coupling relationships of multiple disasters for the first time, achieving real-time adaptive weight adjustment. This overcomes the limitations of fixed weights in existing technologies, addresses the issue of one-sided fusion results in multi-disaster coupling scenarios, significantly improves the relevance and accuracy of the fused data, provides high-quality data support for subsequent dynamic assessments, and greatly enhances the reliability of assessments in complex disaster scenarios.

[0054] IV. Rapid Calibration Module for Digital Twin Models: The digital twin model is the core carrier for dynamic assessment of bridge safety risks, and its accuracy directly determines the reliability of the assessment results. In existing technologies, model calibration relies on long-term monitoring data of a single bridge, and cross-regional data sharing is not possible, leading to long calibration cycles and decreased accuracy when data is insufficient. This makes it difficult to meet the assessment needs of a large number of in-service bridges, especially small and medium-sized bridges. This module achieves rapid and accurate calibration of digital twin models through cross-regional data collaboration and a calibration mode that combines a basic model with regional adaptation. The specific implementation steps are as follows:

[0055] Step 1: Data Sharing in Cross-Regional Data Collaboration Units: Bridges of the same type share similar structural parameters and damage evolution patterns. Cross-regional data sharing can compensate for the lack of data from a single bridge. However, existing technologies lack collaborative sharing mechanisms under data privacy protection, resulting in ineffective utilization of data resources. The specific technical approach is as follows: A federated learning framework is used to construct cross-regional data collaboration units, including data encryption, parameter extraction, and parameter sharing sub-units. The data encryption sub-unit uses a homomorphic encryption algorithm to process monitoring data and model parameters; the parameter extraction sub-unit extracts key parameters such as structural stiffness and damping coefficients from locally encrypted data using a backpropagation algorithm; the parameter sharing sub-unit establishes a secure channel, where each regional node uploads encrypted parameters to the central server for aggregation and then feeds them back to each node.

[0056] Example: A federated learning network is formed by nodes in the area of ​​a cross-river bridge and nodes in the areas of similar bridges in three surrounding cities. Each node encrypts its local monitoring data, trains its local model, extracts key parameters, encrypts them, and uploads them to the central server. The server aggregates the global parameters and feeds them back to each node. The node of the cross-river bridge updates its local model based on the global parameters, thus achieving cross-regional data collaboration.

[0057] The federated learning framework enables cross-regional parameter sharing while ensuring data privacy. Compared with the existing single-bridge data calibration method, it significantly improves the utilization rate of data resources, effectively makes up for the shortcomings of single-bridge data, greatly increases the richness of data sources for model calibration, and provides rich parameter support for rapid calibration.

[0058] Step 2: Establishing Standard Models for Basic Model Building Units: Existing digital twin models are mostly customized single-bridge models, resulting in low modeling efficiency and a lack of unified standards, which is not conducive to cross-regional collaborative calibration. This step improves modeling efficiency and versatility by establishing a general basic model. Specific technical means include: based on bridge structural design specifications, general structural mechanics models, and common parameters of similar bridges, determining the general structural form of the bridge, extracting common parameters such as beam cross-sectional dimensions and material elastic modulus, constructing a finite element model using the finite element analysis method, and integrating structural mechanics, temperature, and fluid field modules to achieve multi-physics collaborative simulation.

[0059] Example: For a continuous beam structure of a cross-river bridge, common parameters of similar bridges are extracted. The beam body is made of C50 concrete with an elastic modulus of 3.45 × 10⁴ MPa. The main span is 180 meters. A standard finite element model is constructed, discretized into 800 elements, and a multiphysics module is integrated to simulate the structural response under vehicle load, temperature change and water scouring.

[0060] The standard basic model avoids the repetitive work of customized modeling for single bridges. Compared with the existing customized modeling methods, the modeling efficiency is greatly improved and the cycle is significantly shortened. The unified standard provides a basis for cross-regional collaborative calibration, significantly improves the model's versatility, and greatly increases the reuse rate of bridge models of the same type.

[0061] Step 3: Model Calibration of the Regional Adaptation Parameter Adjustment Unit: The basic model is a general model and cannot reflect the individual characteristics of a single bridge. Parameters need to be adjusted based on local monitoring data to achieve accurate matching between the model and the actual bridge. The specific technical approach is as follows: Using global parameters obtained through cross-regional collaboration and local real-time monitoring data as input, and taking the mean square error between the model output and the actual monitoring data as the calibration target, a particle swarm optimization algorithm is used to optimize and adjust parameters such as material properties and structural geometry, iteratively minimizing the error.

[0062] Example: The mid-span displacement of the main span output by the digital twin basic model of a cross-river bridge deviated from the actual monitored value, with a mean square error of 0.08. Particle swarm optimization (PSO) was used to adjust the model parameters, setting the inertia weight to 0.7, the acceleration coefficient to 2, the number of particles to 50, and iterating 100 times. By the 60th iteration, the error had decreased to below 0.01, and the model output closely matched the actual monitored value, completing rapid calibration.

[0063] The particle swarm optimization algorithm combines cross-regional global parameters and local monitoring data to achieve rapid and accurate adaptation of the basic model. Compared with the existing calibration method that relies on long-term data from a single bridge, the calibration cycle is significantly shortened and the model accuracy is significantly improved. It is suitable for small and medium-sized bridges with insufficient data accumulation and expands the application scope of digital twin models.

[0064] The publicly available technical documents disclose an indirect evaluation method for bridge digital twin models and a bridge health monitoring system based on digital twins. These documents reveal techniques for constructing digital twin models and calibration based on single-bridge data, but do not address cross-regional data collaboration. Calibration relies on single-bridge data, resulting in long cycles and limited accuracy. This module's unique technical approach lies in its cross-regional data collaboration mechanism within a federated learning framework, and its two-level calibration mode combining a basic model with regional adaptation parameter adjustments. Compared to the publicly available documents, this module is the first to apply federated learning to bridge digital twin model calibration, achieving cross-regional data sharing while protecting privacy and addressing the issue of insufficient single-bridge data. The two-level calibration mode balances model universality and personalization, significantly shortening the calibration cycle and improving model accuracy. It is particularly suitable for the dynamic evaluation needs of a large number of in-service small and medium-sized bridges, overcoming existing technological limitations and providing a feasible solution for the large-scale application of bridge digital twin models.

[0065] V. Maintenance Resource Linkage and Early Warning Adaptation Module: The core objective of early warning is to promote timely risk management. Existing tiered early warning technologies are based solely on structural response thresholds, failing to integrate real-time scenario data such as maintenance resource distribution and traffic flow. This leads to a disconnect between early warning signals and response capabilities, resulting in no response or resource mismatch. This module constructs a digital map of maintenance resources, establishes a dynamic matching mechanism between early warning and resources, and generates an integrated response plan, achieving closed-loop management of early warning and response. Specific implementation steps are as follows:

[0066] Step 1: Resource Integration of the Digital Map Construction Unit for Maintenance Resources: Maintenance resources include maintenance teams, equipment, and materials, which are scattered and dynamically changing. Existing technologies lack systematic integration and dynamic management, making it difficult to quickly locate available resources after an early warning. Specific technical means include: integrating the maintenance resource database of bridge management departments, road traffic data of traffic management departments, and resource scheduling data of maintenance units to construct a dynamically updated digital map of maintenance resources. Core data includes the location, number of personnel, and professional qualifications of maintenance teams; the type, location, and working status of maintenance equipment; the types, inventory, and storage location of maintenance materials; and traffic conditions of traffic routes. Geographic Information System (GIS) technology is used to associate geographic location information and establish a distributed resource attribute database, updated every 15 minutes.

[0067] Example: Within a 50-kilometer radius of a cross-river bridge, there are three professional maintenance teams located in areas A, B, and C, equipped with bridge inspection vehicles, crack repair equipment, etc.; two maintenance material warehouses store materials such as steel bars and concrete; and the real-time traffic status of three main traffic routes is displayed. Using Geographic Information System (GIS) technology, this resource information is marked on a digital map, showing that maintenance teams in area A are available, warehouse 1 has sufficient waterproofing materials, and route 1 is unobstructed, updated every 15 minutes.

[0068] The digital map of maintenance resources enables centralized integration and dynamic management of dispersed resources. Compared with existing resource management methods, it significantly improves resource query efficiency, quickly locates available resources, and significantly shortens resource search time. It provides accurate resource information support for early warning response and greatly reduces preparation time for maintenance resource scheduling.

[0069] Step 2: Risk Classification of the Early Warning Level Determination Unit: The early warning level is the basis for resource allocation and response priority. Existing technologies mostly rely on single structural parameter thresholds for determination, without combining fusion analysis results and damage probability, resulting in a lack of scientific rigor in the classification. The specific technical approach is as follows: Based on the fusion results of multi-hazard coupled dynamic weights and the evaluation results of the digital twin model, combined with the damage probability level, the degree of structural response exceeding the standard, the damage probability level, and the duration of disaster impact are set as indicators for determining the early warning level. A fuzzy comprehensive evaluation method is used to construct an evaluation matrix, and the weight of each indicator is determined using the entropy weight method. The early warning levels are divided into three levels: Level 1 (low risk, requiring enhanced monitoring); Level 2 (medium risk, requiring time-limited response); and Level 3 (high risk, requiring immediate response).

[0070] Example: A cross-river bridge, after fusion analysis and model evaluation, has strain values ​​exceeding the standard by 15%, indicating a moderate probability of damage. The impact of the rainstorm disaster will last for 2 hours. Using the entropy weight method, the weights for the degree of structural response exceeding the standard are 0.4, the damage probability level is 0.3, and the duration of the disaster impact is 0.3. The fuzzy comprehensive evaluation result is a level-two warning, requiring response within 24 hours.

[0071] The fuzzy comprehensive evaluation method based on multiple indicators enables scientific determination of early warning levels. Compared with the existing single threshold classification method, the accuracy of classification is significantly improved, the misjudgment rate of risk level is greatly reduced, and a reliable basis is provided for resource matching and priority setting, making subsequent resource scheduling more targeted.

[0072] Step 3: Supply and Demand Matching of Resource Requirements: Existing technologies lack a quantitative matching mechanism between early warning levels and maintenance resource requirements, resulting in untargeted resource allocation and potential resource shortages or waste. Specific technical measures include: establishing a matching model between early warning levels and maintenance resource requirements. Level 1 early warning requires basic monitoring equipment and 1-2 technicians, with a response time of 72 hours; Level 2 early warning requires detection equipment, small repair equipment, and 3-5 professionals, with a response time of 24 hours; Level 3 early warning requires large-scale detection equipment, emergency repair equipment, sufficient supplies, and a professional team of at least 8 people, with a response time of 2 hours. Based on a digital map of maintenance resources, shortest path algorithms and resource availability filtering algorithms are used to select maintenance resources that meet the requirements.

[0073] Example: A cross-river bridge triggers a Level II warning, requiring inspection equipment, crack repair equipment, and four professionals, with a response time of within 24 hours. Based on a digital map, maintenance teams in Area A are identified as available and equipped with the necessary equipment and personnel. The shortest path is calculated using a shortest path algorithm, resulting in a distance of 35 kilometers and an estimated travel time of 45 minutes, meeting the response requirements. Therefore, maintenance teams in Area A are selected as the ideal resource.

[0074] The resource demand matching model enables precise matching between early warning levels and maintenance resources. Compared with the existing blind scheduling method, resource utilization is significantly improved, waste is greatly reduced, response time is significantly shortened, resource shortage or waste is avoided, and the efficiency of maintenance resource scheduling is significantly improved.

[0075] Step 4: Integrated Solution Development in the Disposal Plan Generation Unit: Existing technologies only provide early warning signals without offering specific disposal plans, resulting in a lack of clear guidance for maintenance units, leading to low efficiency and poor results. The specific technical approach is to generate an integrated disposal plan based on the early warning level, resource matching results, and the actual condition of the bridge. This plan includes the disposal objectives, steps, technical requirements, detailed resource allocation, dispatch path, time nodes, and safety precautions. After the plan is generated, it is pushed to relevant units and personnel through a visual decision-making module.

[0076] Example: The integrated handling plan for a level-two early warning of a cross-river bridge clearly states that the objective is to repair the cracks in the web of the box girder. The steps are divided into four stages: detection and location, cleaning and material preparation, grouting construction, and maintenance monitoring. The resource allocation is 4 professional personnel, 1 detection device, 1 grouting device, and 200 kg of grouting material. The dispatch route is Area A - Section 1 - Bridge, with an estimated travel time of 45 minutes. The time nodes and safety precautions for each stage are clearly defined.

[0077] The integrated response plan clearly defines the response process, resource allocation, and time requirements. Compared with the existing approach that only provides early warning signals, the response is significantly more targeted and practical, greatly improving efficiency, reducing confusion in the response process, ensuring timely and effective control of risks, and significantly reducing the risk of damage escalation.

[0078] The publicly available technical documents outline a three-tiered early warning scheme for bridge displacement monitoring and a bridge maintenance resource scheduling system. While these documents disclose the basic technologies for early warning grading and resource scheduling, they lack deep integration between early warning and maintenance resources, and are deficient in a mechanism for generating integrated response plans. This module's unique technical approach lies in its dynamically updated digital map construction of maintenance resources, early warning grading based on multi-indicator fuzzy comprehensive evaluation, a quantitative matching model between early warning levels and resource requirements, and an integrated response plan generation mechanism. Compared to the publicly available documents, this module achieves a closed-loop process for early warning, from grading to resource matching and response execution, addressing the industry pain point of disconnect between early warning and response in existing technologies. It significantly improves the accuracy of maintenance resource scheduling and the efficiency of response, providing a new technical solution for rapid response to bridge safety risks, and greatly enhancing the efficiency of the closed-loop response to early warning and response.

[0079] VI. Life Cycle Cost-Oriented Pre-Control Module: Bridge safety risk pre-control not only needs to ensure structural safety but also needs to consider the rationality of maintenance costs. Existing technologies only focus on risk identification and early warning, without considering the bridge's life cycle maintenance costs. This leads to problems of over-maintenance or under-maintenance in pre-control measures, making it difficult to balance safety and economy. This module constructs a risk loss model and a maintenance cost model, and uses a multi-objective optimization algorithm to generate a safety-cost optimal maintenance pre-control scheme. The specific implementation steps are as follows:

[0080] Step 1: Loss Quantification in the Risk Loss Model Construction Unit: Different safety risk levels correspond to different potential losses, including structural repair and traffic disruption losses. Existing technologies lack systematic quantification, resulting in a lack of scientific basis for cost considerations in pre-control schemes. Specific technical methods include: identifying direct and indirect losses from risk losses. Direct losses include structural repair costs and equipment damage compensation costs; indirect losses include traffic disruption losses, environmental impact losses, and social impact losses. Structural repair costs are quantified based on the degree of damage, repair processes and materials, labor, and equipment rental costs; traffic disruption losses are quantified based on average daily traffic flow, average toll fees, and disruption time; personal injury losses are quantified with reference to compensation standards; and environmental and social impact losses are quantified using an expert scoring method.

[0081] Example: A box girder web crack of 5 meters length corresponds to a Level 3 warning for a cross-river bridge. The repair process is grouting plus carbon fiber reinforcement. The total structural repair cost, including materials, labor, and equipment rental, is 57,000 yuan. The average daily traffic flow is 8,000 vehicles, with an average toll of 20 yuan per vehicle. The estimated traffic interruption is 12 hours, resulting in a traffic interruption loss of 80,000 yuan. Other losses, quantified by experts, are 20,000 yuan, bringing the total risk loss to 157,000 yuan.

[0082] The risk loss model enables the systematic quantification of potential losses. Compared with existing methods that lack loss quantification, it provides a clear quantitative basis for optimizing the cost of pre-control schemes, making the balance between safety and cost more operable, the cost consideration of pre-control schemes more accurate, and avoiding blind cost estimation.

[0083] Step 2: Full-cycle cost accounting for the maintenance cost model construction unit: Existing technologies often calculate the cost of a single maintenance operation, failing to consider the total maintenance cost over the entire bridge's life cycle, leading to an incomplete economic assessment of the pre-control scheme. The specific technical approach is as follows: Based on the bridge's full life cycle theory, the entire life cycle is divided into four stages: operation, maintenance, major repair, and decommissioning, and a maintenance cost model is constructed accordingly. Operation costs include daily inspections and monitoring equipment maintenance costs; maintenance costs include minor repairs and material replacement costs; major repair costs include large-scale repairs and structural reinforcement costs; and decommissioning costs include demolition and waste disposal costs. The net present value method is used to discount the costs of each stage to the valuation year.

[0084] Example: A cross-river bridge is expected to have a total lifespan of 50 years. The operating period is 20 years with a cost of 720,000 yuan, the maintenance period is 20 years with a cost of 1,700,000 yuan, the overhaul period is 8 years with a cost of 4,000,000 yuan, and the decommissioning period is 2 years with a cost of 700,000 yuan. Using a benchmark rate of return of 6%, the costs of each stage are converted into net present value to obtain the net present value of the total lifespan maintenance cost.

[0085] The full life cycle cost model enables comprehensive accounting of maintenance costs. Compared with the existing single-cost accounting method, the cost assessment is more comprehensive and can reflect the long-term economics of the prevention and control plan. It provides complete cost data support for the safety-cost balance, and the economic assessment of the prevention and control plan is more scientific, avoiding long-term waste caused by short-term cost considerations.

[0086] Step 3: Generation of the Optimal Pre-control Scheme for the Multi-Objective Optimization Unit: The pre-control scheme must simultaneously meet the objectives of safety compliance and minimum cost. Existing technologies have not achieved multi-objective collaborative optimization, resulting in schemes that are either overly safe and overly costly, or underly costly and insufficiently safe. The specific technical approach is to construct a multi-objective optimization model using a non-dominated sorting genetic algorithm. The optimization objectives are safety compliance and minimum life-cycle maintenance cost. The safety objective is that the probability of structural damage does not exceed the allowable value, and the cost objective is to minimize the net present value of life-cycle maintenance cost. Constraints include that structural response parameters do not exceed design limits, maintenance resource usage does not exceed the total available amount, and the implementation time of pre-control measures does not exceed the allowable time. The algorithm optimizes the parameters of the pre-control scheme, generating a Pareto optimal solution set, and the optimal scheme is selected based on the management department's preferences.

[0087] Example: A cross-river bridge has an allowable damage probability of 0.2, with constraints including strain not exceeding 1500 microstrains, displacement not exceeding 5 cm, maintenance personnel not exceeding 8 people, and repair time not exceeding 24 hours. Five optimization schemes are generated using a non-dominated sorting genetic algorithm. The optimal pre-control scheme is selected based on the following: maintenance time is the 8th year of operation, a simple grouting process is used, the damage probability is 0.18, and the net present value of cost is 4.5 million yuan.

[0088] The multi-objective optimization model achieves synergistic optimization of safety and cost. Compared with the existing single-objective pre-control scheme, the scientificity and economy of the scheme are significantly improved. It avoids the problems of over-maintenance or under-maintenance, extends the service life of bridges while reducing long-term maintenance costs, and greatly improves the comprehensive benefits of bridges throughout their entire life cycle.

[0089] The publicly available technical documents disclose a detection interval decision system and a bridge maintenance cost optimization method based on a sliding window dynamic fuzzy neural network. While these documents reveal the techniques for detection interval optimization and maintenance cost accounting, they do not deeply couple life-cycle costs with safety risk pre-control and lack a multi-objective collaborative optimization mechanism. This module's unique technical approach lies in its multi-factor system quantification model of risk loss, its multi-stage life-cycle cost accounting model, and its safety-cost dual-objective optimization mechanism based on a non-dominated sorting genetic algorithm. Compared to the publicly available documents, this module achieves, for the first time, deep coupling of life-cycle costs and safety risks. Through multi-objective optimization, it finds the optimal balance, resolving the disconnect between safety and economy in existing technologies. The long-term feasibility and economy of the pre-control scheme are significantly improved, providing a new scientific basis for bridge maintenance decisions and greatly enhancing the overall benefits of bridge management and maintenance.

[0090] VII. Visualized Decision-Making Module: Assessment results, early warning information, and pre-control plans need to be presented clearly and intuitively to relevant management departments and maintenance units. Existing visualization technologies mostly only display monitoring data, lacking an integrated presentation of assessment results, early warning signals, and decision-making plans, resulting in low information transmission efficiency and hindering rapid decision-making. This module, through multi-unit collaboration, achieves intuitive display, accurate delivery, and clear presentation of assessment results, early warning information, and decision-making plans. Specific implementation steps are as follows:

[0091] Step 1: Data Visualization of the Assessment Results Display Unit: The assessment results are of complex data types, and existing technologies lack intuitive visualization methods, making it difficult for relevant personnel to quickly grasp the bridge's safety status. Specific technical solutions include a combination of chart visualization and 3D model visualization to display the assessment results. Chart visualization includes line graphs showing the trend of structural response time changes, bar charts showing the degree of damage in different parts, pie charts showing the impact ratio of each disaster type, and heat maps showing the stress distribution of the structure. 3D model visualization is based on a digital twin model, which annotates the location, degree, and structural response values ​​of damage in real time, and supports model rotation, scaling, and sectioning operations. The visualization interface adopts a browser-server architecture, supports multi-terminal access, and is laid out in three areas: a data overview area, a detailed analysis area, and a 3D model area.

[0092] Example: In the visualization interface of the assessment results of a cross-river bridge, the data overview area displays the warning level as Level II, the damage probability as 0.18, and the main influencing disaster as rainstorm erosion; the detailed analysis area displays the mid-span displacement change of the main span over the past 72 hours through a line graph, the bar chart displays the damage degree of 5 key parts, and the heat map shows the stress concentration area of ​​the box girder web; the 3D model area displays the digital twin model of the bridge, with web cracks marked as red lines, and supports rotation and sectioning for viewing.

[0093] Multi-format visualization transforms complex assessment data into intuitive information. Compared with existing single data display methods, the efficiency of information transmission is greatly improved, relevant personnel can quickly grasp the safety status of the bridge, the information understanding time is significantly shortened, intuitive support is provided for decision-making, and the efficiency of decision-making is greatly improved.

[0094] Step 2: Precise Delivery of Early Warning Information: Early warning information needs to be delivered to relevant units and personnel in a timely manner. Existing technologies often use a single channel for delivery, resulting in inaccurate targeting and potential delays in receiving information from key personnel. Specific technical measures include: establishing a multi-channel, precise early warning information delivery mechanism. Delivery channels include mobile applications, SMS, email, WeChat official accounts, and platform system messages, supporting simultaneous delivery across multiple channels. Delivery recipients are set based on user role permissions, including bridge management department heads, maintenance unit heads, on-site maintenance personnel, and traffic control department personnel. Different roles receive information with varying levels of detail. The delivery mechanism supports tiered delivery: Level 1 warnings are delivered to monitoring personnel and management departments; Level 2 warnings are delivered to management departments, maintenance units, and on-site personnel; and Level 3 warnings are simultaneously delivered to traffic control departments.

[0095] Example: A cross-river bridge triggers a Level II warning. Information is simultaneously pushed through multiple channels. The head of the management department receives the warning level, assessment results, and cost optimization plan; the head of the maintenance team receives the warning level, resource matching results, and dispatch route; and the on-site maintenance personnel receive the warning level, specific handling procedures, and safety precautions.

[0096] A multi-channel, precise push mechanism ensures that early warning information is delivered to key personnel in a timely manner. Compared with the existing single-channel, generalized push method, the timeliness and accuracy of information delivery are significantly improved, the time for key personnel to receive information is greatly shortened, early warning information is avoided, and early warning response is ensured to be initiated quickly, with response start time significantly advanced.

[0097] Step 3: Presentation of the Decision-Making Solution Unit: The maintenance pre-control plan needs to be clearly presented to decision-makers and implementers. Current technology lacks structured presentation, leading to low efficiency in plan understanding and execution. Specific technical measures include: presenting the maintenance pre-control plan in a structured, step-by-step manner, including a plan overview, implementation steps, resource allocation, timelines, technical requirements, safety precautions, and expected results. A combination of text and graphics will be used, with flowcharts for implementation steps, tables for resource allocation, standard references for technical requirements, and data comparison charts for expected results. Online approval and feedback will be supported, allowing decision-makers to directly approve and submit modification suggestions, which will be synchronized with the maintenance unit in real time.

[0098] Example: In a maintenance and control plan for a cross-river bridge, the plan overview clearly defines the repair objectives and core measures; the implementation steps are illustrated with a flowchart showing the entire process of inspection, cleaning, grouting, and maintenance; resource allocation is listed in a table outlining the quantity and type of personnel, equipment, and materials; time nodes are shown in a Gantt chart displaying the start and end times of each step; technical requirements reference highway bridge maintenance standards; safety precautions are accompanied by warning sign images; and expected results are compared with damage probabilities and structural response values ​​before and after repair using a bar chart. Decision-makers approve the plan online and propose adding traffic control personnel, which the maintenance unit receives and modifies in real time.

[0099] The structured, graphic-text-based presentation of the solutions makes them easier to understand and implement. The online approval function shortens the solution iteration cycle. Compared with the existing text-based solution presentation method, the efficiency and accuracy of solution execution are significantly improved, the preparation time for solution understanding and execution is greatly shortened, ensuring that the pre-control solutions are quickly implemented and the efficiency of implementation is significantly improved.

[0100] The publicly available technical documents outline a bridge health monitoring visualization system and an early warning information delivery method. While these documents disclose the basic technologies for visualizing monitoring data and delivering early warning information, they lack an integrated presentation of assessment results, early warning information, and decision-making solutions, and are deficient in structured solution display and precise role-based delivery mechanisms. This module's unique technical approach lies in its multi-format visualization combining charts and 3D models, precise multi-channel delivery based on role-based permissions, and structured text-and-image presentation of decision-making solutions along with an online approval mechanism. Compared to the publicly available documents, this module achieves integrated information display, delivery, and decision-making, significantly improving information transmission and solution execution efficiency. It provides efficient support for rapid decision-making and handling of bridge safety risks, resulting in a substantial increase in overall decision-making and handling efficiency.

Claims

1. A big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges, characterized by: It includes a data acquisition module, a multi-hazard coupling dynamic weight adaptive fusion module, a digital twin model rapid calibration module, a maintenance resource linkage early warning adaptation module, a full life cycle cost-oriented pre-control module, and a visualization decision-making module; The multi-hazard coupling dynamic weight adaptive fusion module has a built-in disaster type structural response damage probability coupling influence matrix construction unit and reinforcement learning weight adjustment unit. The disaster type structural response damage probability coupling influence matrix construction unit establishes the correlation between multiple disaster types and bridge structure response and damage probability. The reinforcement learning weight adjustment unit dynamically adjusts the fusion weights of each data source according to real-time monitoring data and disaster scenario changes. The life-cycle cost-oriented pre-control module includes a risk loss model construction unit, a maintenance cost model construction unit, and a multi-objective optimization unit. The risk loss model construction unit quantifies the potential losses corresponding to different safety risk levels, the maintenance cost model construction unit calculates the maintenance cost of the bridge throughout its life cycle, and the multi-objective optimization unit generates the maintenance pre-control scheme with the optimal safety cost based on the risk loss model and the maintenance cost model. The data acquisition module collects bridge structural response data, environmental data, and disaster data; the digital twin model rapid calibration module enables rapid calibration of the bridge's digital twin model; the maintenance resource linkage early warning adaptation module matches early warning levels with maintenance resources; and the visualization decision-making module displays assessment results and early warning information.

2. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 1, characterized in that: The data acquisition module includes a structural response monitoring unit, an environmental monitoring unit, a disaster monitoring unit, and a data transmission unit. The structural response monitoring unit is deployed at key stress-bearing parts of the bridge to collect bridge strain, displacement, and vibration data. The environmental monitoring unit collects temperature, humidity, precipitation, and wind speed data. The disaster monitoring unit collects data on rainstorm erosion and ship collisions. The data transmission unit uses 5G communication technology to transmit the collected data to the back-end processing system in real time.

3. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 2, characterized in that: The data acquisition module also includes a data preprocessing unit, which includes an outlier removal subunit, a data denoising subunit, and a data standardization subunit; the outlier removal subunit uses statistical testing methods to remove outlier data from the acquired data. The data noise reduction subunit uses wavelet transform technology to reduce data noise; The data standardization subunit converts data from different dimensions into a unified standard format.

4. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 1, characterized in that: The rapid calibration module for the digital twin model includes a cross-regional data collaboration unit, a basic model construction unit, and a regional adaptation parameter adjustment unit. The cross-regional data collaboration unit adopts a federated learning framework to achieve the sharing of calibration parameters for bridges of the same type across regions while protecting data privacy; the basic model construction unit establishes a standard digital twin basic model based on common bridge structural parameters; and the regional adaptation parameter adjustment unit adjusts the basic model parameters in combination with local bridge monitoring data to complete rapid model calibration.

5. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 4, characterized in that: The cross-regional data collaboration unit includes a data encryption subunit, a parameter extraction subunit, and a parameter sharing subunit. The data encryption subunit encrypts the bridge data transmitted across regions. The parameter extraction subunit extracts key parameters for model calibration from the encrypted data. The parameter sharing subunit establishes a secure sharing channel to realize the cross-regional transmission of calibration parameters.

6. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 1, characterized in that: The maintenance resource linkage early warning adaptation module includes a maintenance resource digital map construction unit, an early warning level determination unit, and a resource demand matching unit; the maintenance resource digital map construction unit integrates maintenance team location, equipment reserve, and material inventory data to establish a dynamically updated maintenance resource digital map; The early warning level determination unit determines the bridge safety risk early warning level based on the assessment results; The resource demand matching unit matches the corresponding level of maintenance resource demand based on the early warning level and the digital map of maintenance resources.

7. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 6, characterized in that: The maintenance resource linkage early warning adaptation module also includes a disposal plan generation unit. The disposal plan generation unit generates an integrated disposal plan based on the early warning level resource matching results and the actual situation of the bridge, which includes risk level disposal priority resource scheduling path.

8. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 1, characterized in that: The multi-objective optimization unit includes an objective function construction subunit, a constraint setting subunit, and an optimization algorithm running subunit. The objective function construction subunit establishes a function with the objectives of achieving safety standards and minimizing costs. The constraint setting subunit clarifies the safety threshold of the bridge structure and the constraints on maintenance resources. The optimization algorithm running subunit uses an intelligent optimization algorithm to solve for the optimal maintenance pre-control scheme.

9. The big data-driven dynamic assessment and early warning platform for safety risks of in-service bridges as described in claim 1, characterized in that: The visualization decision-making module includes an assessment result display unit, an early warning information push unit, and a decision-making scheme presentation unit. The assessment result display unit visually displays the bridge safety status assessment data in the form of charts. The early warning information push unit pushes graded early warning signals to relevant management departments and maintenance units. The decision-making scheme presentation unit displays maintenance pre-control schemes and resource scheduling suggestions.

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