National and provincial road bridge heavy-load full-link cooperative monitoring and control system and method

Abstract

The invention provides a national and provincial road bridge heavy-load full-link cooperative monitoring and control system and method, and the system comprises a heavy-load source docking and prediction module, a dynamic deployment and control module, a multi-source data fusion and damage evaluation module and a cooperative management closed-loop module which are connected in sequence. Through docking enterprise transportation data and predicting a time window for a vehicle to arrive at a bridge, a sensing device is actively scheduled to carry out accurate monitoring according to the time window; through fusion of vehicle load and bridge structure response data, the degree of damage caused by overload is quantitatively calculated based on highway bridge engineering specification logic, and targeted risk assessment is performed on special bridges such as single-column piers and low-rating bridges. And finally, automatically generating a graded early warning and repairing scheme according to an evaluation result, and linking multiple departments such as traffic, maintenance, emergency and the like to form a disposal closed loop. According to the invention, full-link cooperation from the source to management is realized, the problems of monitoring blind areas and disjunction of damage attribution and response are solved, and the bridge heavy load safety prevention and control capability is remarkably improved.

Description

Technical Field

[0001] This invention relates to the technical field of bridge structural health monitoring and heavy traffic load control, and in particular to a collaborative monitoring and control system and method for heavy traffic load across the entire chain of national and provincial highway bridges. Background Technology

[0002] National and provincial highway bridges, as core nodes in regional freight networks, bear heavy transportation burdens. Statistics show that small and medium-span bridges account for over 80% of national and provincial highways, bearing the repeated passage of heavy-duty trucks over long periods and at high frequencies. Under continuous heavy traffic loads, coupled with the combined effects of complex factors in the outdoor environment such as temperature variations, freeze-thaw cycles, and vehicle vibrations, bridge structures are highly susceptible to cumulative damage such as crack propagation and stiffness reduction, seriously threatening structural and traffic safety. To address bridge safety issues caused by heavy transportation, the industry has developed several technologies; however, these existing solutions are isolated and have failed to form a collaborative and effective end-to-end management system. Specifically, the limitations of existing technologies are reflected in the following three aspects: Firstly, in terms of overload identification, existing technologies typically employ dynamic weighing systems or visual recognition systems deployed at road cross-sections. For example, this involves embedding piezoelectric or bending plate sensors, or using cameras that fuse visible light and infrared light to dynamically measure the weight of passing vehicles and identify license plates. While these technologies can achieve basic overload determination and vehicle recording, their monitoring range has a significant "source blind spot." Specifically, these methods only focus on the instant a vehicle passes through on the road; their technical logic does not extend upstream to the loading sources of goods, such as mines and concrete mixing plants. Therefore, they cannot obtain and correlate key data such as the transportation plans and loading documents of the source enterprises. This directly results in the system being unable to trace the enterprise affiliation of overloaded vehicles, and even less able to implement planned control and accountability at the loading stage. This leaves overload management at a passive stage of "discovery on the road and punishment afterward," failing to achieve source prevention.

[0003] Secondly, in bridge health monitoring, existing technologies generally employ the method of deploying various sensors on the bridge structure. For example, deflectometers, strain gauges, crack monitoring cameras, or vibration sensors are installed, and intelligent algorithms may be used to analyze the collected data in order to identify structural damage. However, these methods typically focus only on the bridge's own physical response, forming an independent monitoring loop. The core flaw lies in the complete separation between the damage data acquired by the monitoring system (such as crack development and stiffness changes) and the external load data that induces the damage (especially specific, real-time information on overloaded vehicles). Therefore, even if the system alarms and indicates an anomaly in the bridge, management personnel cannot accurately determine whether the anomaly is caused by overloading, or by which type of overloading behavior. This results in a lack of precise load-induced basis for damage assessment, thus affecting the scientific rigor and timeliness of maintenance decisions.

[0004] Thirdly, in terms of data fusion and early warning management, existing technologies mostly rely on general communication technologies and computing platforms. For example, 4G / 5G or BeiDou are used for data transmission, preliminary processing is performed at the edge, and fixed thresholds are often set to trigger early warnings. This conventional approach has significant shortcomings: First, its early warning logic is often "one-size-fits-all," failing to fully consider the individual differences of different bridges in terms of design load, structural form, technical condition, and importance. It does not incorporate professional design specifications and load-bearing capacity assessment models for bridge engineering, resulting in insufficient accuracy and specificity of early warnings, and is prone to false alarms or missed alarms for special bridges. Second, the design of the early warning process is usually isolated. After the early warning information is issued, there is a lack of standardized handling procedures that link with relevant departments such as road administration, traffic police, and source industry authorities, failing to form a complete management closed loop from risk perception to on-site intervention, thus greatly reducing the effectiveness of early warnings.

[0005] Furthermore, some existing technologies attempt to achieve partial integration. For example, some solutions try to indirectly estimate the traffic load on bridges by integrating public travel data such as vehicle GPS trajectories and toll station passage records. However, such methods neither connect to accurate bridge structural response monitoring data to verify the impact of the load nor connect to data from the loading source companies to trace responsibility. Their early warning design still lacks differentiation and may miss traffic data for some national and provincial road sections, failing to fundamentally solve the problems of accurate assessment and collaborative management.

[0006] In summary, existing technical solutions generally suffer from a fundamental limitation: they each optimize on a single technical path—"vehicle weighing," "bridge sensing and monitoring," or "general data integration"—without data sharing or logical connections between them, failing to construct a comprehensive collaborative system covering the entire chain from "heavy load source to transportation route to bridge structure to management response." Due to the lack of deep integration of cross-domain data and intelligent analysis models based on bridge expertise, current safety management of heavy-load bridges faces systemic bottlenecks, manifested as "lack of source control, difficulty in damage attribution, insufficient early warning accuracy, and disconnected management response." Therefore, there is an urgent need for a new technical solution that can connect the entire business chain, achieve intelligent data linkage and cross-departmental business collaboration, transforming passive response into proactive and precise prevention and control, and effectively improving the safety assurance capabilities of national and provincial highway bridges under heavy traffic. Summary of the Invention

[0007] The technical problem to be solved by this invention is: In view of the technical problems existing in the prior art, this invention provides a full-link collaborative monitoring and control system and method for heavy load bridges on national and provincial highways, aiming to achieve full-link collaboration from source to management, overcome the problems of monitoring blind spots, damage attribution and response disconnection, and significantly improve the safety prevention and control capabilities of heavy load bridges.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A collaborative monitoring and control system for heavy-load bridges on national and provincial highways, comprising: The heavy-load source docking and prediction module is used to obtain vehicle transportation plan data of heavy-load source enterprises through a standardized interface, and predict the expected time window for the target truck to arrive at the target bridge based on the transportation plan data, road network information and historical trajectory data. The dynamic deployment module is used to generate deployment instructions based on the expected time window to remotely activate or adjust the operating parameters of the sensing devices deployed in the area associated with the target bridge. The multi-source data fusion and damage assessment module is used to receive and fuse vehicle load data from the sensing device and structural response data from the structural sensors deployed on the target bridge, and calculate the quantitative damage index caused by the target truck to the target bridge based on the preset highway bridge engineering specification logic. The collaborative management closed-loop module is used to determine the risk level based on the quantitative damage index or the vehicle load data, generate early warning information including the risk level, treatment suggestions and repair plan, and push it to the management department terminal corresponding to the risk level.

[0009] As a further improvement of the present invention: the heavy load source docking and prediction module is docked with the transportation management system (TMS) of the heavy load source enterprise to obtain data including vehicle identification, actual load, planned departure time, destination and planned route. Based on the pre-route, a three-dimensional association index is established between the transportation entity, the vehicle, and the infrastructure node; A spatiotemporal prediction model is used to predict the path and time for the vehicle to reach the target bridge.

[0010] As a further improvement of the present invention: the deployment instructions generated by the dynamic deployment module are used for: When it is predicted that the target truck is about to arrive, the sampling frequency of the target bridge-associated dynamic weighing unit and structural strain sensor is increased; If the expected arrival time is in a low-light environment, turn on the auxiliary lighting equipment of the visual perception unit; If there are curves or blind spots in the approach area of ​​the target bridge, the mobile blind spot monitoring unit deployed in that area will be activated.

[0011] As a further improvement of the present invention: the multi-source data fusion and damage assessment module includes: The data calibration unit is used to calibrate the estimated load data, dynamic weighing data, and structural sensor data affected by ambient temperature and humidity from visual recognition. The load-damage coupling calculation unit has a built-in load-structure response coupling model based on bridge engineering code logic and bridge historical condition assessment. It is used to calculate the theoretical structural response value corresponding to the vehicle load data according to the span, material parameters and technical condition rating of the target bridge, and compare it with the structural response data to quantify the degree of cumulative damage to the bridge structure caused by overload.

[0012] As a further improvement of the present invention: the load-damage coupling calculation unit is configured to: determine the dynamic allowable overload limit based on the technical condition rating and service environment of the target bridge; calculate the overload amount of the actual load exceeding the limit; and calculate the theoretical increment of crack width and vertical displacement based on the overload amount and the standard formula.

[0013] As a further improvement of the present invention: the multi-source data fusion and damage assessment module further includes a special bridge-targeted assessment unit, used for: When the target bridge is identified as a single-column pier structure, the structural anti-overturning stability state analysis is performed based on the real-time vehicle load data and its lateral distribution information on the bridge deck, and the corresponding overturning risk level signal is generated according to the analysis results. When the technical condition rating of the target bridge is found to be lower than the preset safety threshold, enhanced monitoring and trend analysis are performed on the damage indicators of crack propagation and vertical displacement of the main beam. Based on the damage development trend obtained from the analysis, a graded early warning strategy and repair and treatment plan are dynamically matched.

[0014] As a further improvement of the present invention, the overturning stability analysis of the single-column pier structure includes: calculating the ratio of the overturning moment to the anti-overturning moment based on the real-time load data of the vehicle and its lateral eccentricity, as well as the geometric and material parameters of the pier column, and classifying the risk level based on the ratio.

[0015] As a further improvement of the present invention: the collaborative management closed-loop module divides the risk levels into three levels: Level 1 warning corresponds to general overloading, which is pushed to the traffic law enforcement department with a vehicle interception suggestion; Level 2 warning corresponds to severe overloading or structural damage, which is pushed to the traffic law enforcement and bridge maintenance departments with a temporary traffic restriction suggestion; Level 3 warning corresponds to extreme overloading or major structural risks, which is pushed to the traffic law enforcement, bridge maintenance and emergency management departments and triggers on-site audible and visual alarms. Based on the type and degree of the quantitative damage indicators, the repair process and priority are automatically matched from the preset standard repair scheme library; Regularly generate correlation analysis reports, which include heavy-load vehicle flow statistics, damage trend analysis, and source tracing information of high-risk enterprises.

[0016] This invention also provides a method for the collaborative monitoring and control of heavy-load bridges on national and provincial highways based on a system for the collaborative monitoring and control of heavy-load bridges across the entire chain, comprising the following steps: Step S1: Obtain vehicle transportation plan data from heavy-duty source enterprises and predict the estimated time window for the target truck to arrive at the target bridge; Step S2: Based on the estimated time window, dynamically adjust the operating parameters of the sensing devices deployed in the area associated with the target bridge; Step S3: Integrate the collected vehicle load data and the structural response data of the target bridge, and calculate and quantify the damage index based on the bridge engineering specifications. Step S4: Determine the risk level based on the quantitative damage index or the vehicle load data, generate corresponding early warning information, and push it to the relevant management department.

[0017] As a further improvement to the method of the present invention: the quantitative damage index calculated based on bridge engineering specifications in step S3 includes: Based on the technical condition rating and service environment of the target bridge, its permissible overload limit is determined; Calculate the amount by which the vehicle load data exceeds the permissible overload limit; Based on the overload, the theoretical increments in crack width and vertical displacement caused to the target bridge are calculated using standard formulas.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, by constructing a full-link collaborative architecture of "source data docking and trajectory prediction → dynamic deployment → multi-source data fusion and professional damage assessment → hierarchical collaborative management," for the first time deeply couples traffic management data with bridge engineering specifications, achieving a fundamental shift from passive, fragmented, single-link monitoring to proactive, precise, and closed-loop systematic control. Specifically, this technology enables the system to proactively perceive and predict high-risk heavy-load vehicles, achieving on-demand precise deployment and monitoring resource optimization; it establishes a quantitative causal relationship between load and structural damage based on national standards, enabling accurate attribution and quantitative assessment of bridge damage; it implements targeted risk assessments for special bridges such as single-column piers and low-rated bridges, significantly reducing false alarm rates; and it automatically transforms technical warnings into cross-departmental collaborative tasks with clear handling instructions, forming a complete management closed loop of "monitoring-early warning-handling-repair-optimization," thereby achieving significant comprehensive technical effects in improving overload traceability, reducing monitoring costs, improving assessment accuracy, and shortening emergency response time. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the heavy-load full-link collaborative monitoring and control system for provincial highway bridges in China, as described in an embodiment of the present invention.

[0020] Figure 2 This is a logic diagram for assessing the tilting risk of a single-column pier bridge in an embodiment of the present invention.

[0021] Figure 3 This is a diagram illustrating the architecture of a comprehensive collaborative monitoring and control system for heavy-load bridges on provincial highways in China, as described in a specific embodiment of the present invention.

[0022] Figure 4 This is a flowchart illustrating the linkage between the heavy load source, trajectory, and bridge in an embodiment of the present invention. Detailed Implementation

[0023] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, this embodiment provides a full-link collaborative monitoring and control system for heavy-load bridges on national and provincial highways, including: The heavy-load source docking and prediction module is used to obtain vehicle transportation plan data of heavy-load source enterprises through a standardized interface, and predict the expected time window for the target truck to arrive at the target bridge based on the transportation plan data, road network information and historical trajectory data. The dynamic deployment module is used to generate deployment commands based on the expected time window to remotely activate or adjust the operating parameters of the sensing devices deployed in the target bridge-related area; The multi-source data fusion and damage assessment module is used to receive and fuse vehicle load data from sensing devices and structural response data from structural sensors deployed on the target bridge, and calculate the quantitative damage index caused by the target truck to the target bridge based on the preset highway bridge engineering specification logic. The collaborative management closed-loop module is used to determine the risk level based on quantitative damage indicators or vehicle load data, generate early warning information including risk level, handling suggestions and repair plans, and push it to the management department terminal corresponding to the risk level.

[0025] This embodiment addresses the problems in existing technologies, such as the lack of source control over heavy loads, the disconnect between overload and bridge damage data, insufficient targeted early warning for specific bridges, and the broken management loop. It constructs an integrated architecture of "heavy load source data docking - trajectory prediction - dynamic deployment" to fill the gap in source control, integrates a multi-source data fusion engine based on bridge engineering specifications to improve damage assessment accuracy, designs targeted assessment logic for specific bridges to reduce early warning misjudgment rate, and forms a complete management loop of "overload prevention - real-time assessment - multi-departmental collaboration - repair recommendations." This promotes the industry's upgrade from "passive early warning" to "proactive prevention and control," providing scientific support for the safe operation and maintenance of national and provincial highway bridges.

[0026] In this embodiment, the heavy load source docking and prediction module docks with the transportation management system (TMS) of the heavy load source enterprise to obtain data including vehicle identification, actual load, planned departure time, destination and planned route. Based on the pre-route, a three-dimensional association index is established between the transportation entity, the vehicle, and the infrastructure node; A spatiotemporal prediction model is used to predict the path and time for vehicles to reach the target bridge.

[0027] In this embodiment, the standardized interface design follows "JT / T697.7-2013 Road Transport Vehicle Satellite Positioning System Part 7: Data Exchange", and interfaces with the Transportation Management System (TMS) of heavy-duty enterprises such as mines and mixing plants. It collects six types of core data: "unique vehicle identifier, actual load, planned departure time, destination, and expected route", and establishes an association index according to "enterprise ID-vehicle ID-expected bridge ID" to remove redundant information of empty vehicles. Trajectory prediction and dynamic deployment use LSTM or neural network models. Inputs include enterprise transportation plans, national and provincial road network topology, historical trajectories of the same enterprise in the past 3 months, and real-time traffic data. The system trains the probability of "enterprise-bridge" route selection and outputs "a list of bridges that must be passed + the estimated arrival time window".

[0028] In this embodiment, the deployment instructions generated by the dynamic deployment module are used for: When the target truck is predicted to arrive, increase the sampling frequency of the target bridge's associated dynamic weighing unit and structural strain sensor; If the expected arrival time is in a low-light environment, turn on the auxiliary lighting equipment of the visual perception unit; If there are curves or blind spots in the approach area of ​​the target bridge, the mobile blind spot monitoring unit deployed in that area will be activated.

[0029] In practical implementation, when the system predicts that the target truck will arrive at the target bridge within one hour, it will automatically trigger a dynamic deployment command to precisely adjust the working mode of the bridge-related sensing equipment, achieving optimization from "all-weather monitoring" to "on-demand targeted monitoring." Specifically, the system sends a command to the target bridge's sensing layer to increase the sampling frequency of the dynamic weighing unit and structural strain sensor from the conventional mode to a high-frequency acquisition mode. If the predicted arrival time is in morning fog, night, or other low-light environments, the laser-assisted lighting equipment of the dual-light vision unit is simultaneously activated to ensure image recognition quality. If there are curves or monitoring blind spots on the target bridge's approach road, the mobile blind spot monitoring unit deployed in that area is remotely activated to form continuous and uninterrupted monitoring coverage. Through the above-mentioned on-demand, hierarchical equipment control strategy, the system significantly reduces overall monitoring energy consumption and equipment wear while ensuring the quality of key data acquisition, achieving intelligent optimization of monitoring resources.

[0030] In this embodiment, the multi-source data fusion and damage assessment module includes: The data calibration unit is used to calibrate the estimated load data, dynamic weighing data, and structural sensor data affected by ambient temperature and humidity from visual recognition.

[0031] Specifically, tire deformation and axle height are identified by dual-light vision units (roadtop visible light + roadside infrared) to preliminarily estimate the load. Environmental interference is eliminated by combining dynamic weighing data with linear regression. Bridge sensor data (deflection / strain / cracks) are combined with temperature and humidity parameters for correction, and environmental misjudgment is avoided by referring to the "Technical Specifications for Highway Bridge Structural Safety Monitoring System".

[0032] The load-damage coupling calculation unit has a built-in load-structure response coupling model based on the logic of bridge engineering specifications and the assessment of the bridge's historical condition. It is used to calculate the theoretical structural response value corresponding to the vehicle load data according to the target bridge's span, material parameters, and technical condition rating, and compare it with the structural response data to quantify the degree of cumulative damage to the bridge structure caused by overloading.

[0033] In this embodiment, the load-damage coupling calculation unit is configured to: determine the dynamic allowable overload limit based on the technical condition rating and service environment of the target bridge; calculate the overload amount exceeding the limit for the actual load; and calculate the theoretical increment of crack width and vertical displacement based on the standard formula according to the overload amount.

[0034] In a more specific application example, the load-damage coupling calculation follows the "General Specifications for Design of Highway Bridges and Culverts" JTGD60-2015 and the "Standard for Technical Condition Assessment of Highway Bridges" JTG / TH21-2011. The stiffness coefficient is determined based on the bridge span, beam height, and material strength, and a "real load-main beam deflection" correlation is established. Allowable overload limits are set according to the bridge technical condition rating (Class I to Class V). When the limit is exceeded, the crack width increment and vertical displacement are calculated according to the standard formula (for low-rated bridges, the influence of initial cracks and historical deflection are additionally considered) to quantify the degree of structural damage.

[0035] In this embodiment, the multi-source data fusion and damage assessment module also includes a special bridge-targeted assessment unit, used for: When the target bridge is identified as a single-column pier structure, the structural overturning stability analysis is performed based on the real-time vehicle load data and its lateral distribution information on the bridge deck, and the corresponding overturning risk level signal is generated based on the analysis results. When the technical condition rating of the target bridge is found to be lower than the preset safety threshold, enhanced monitoring and trend analysis are performed on the damage indicators of crack propagation and vertical displacement of the main beam. Based on the damage development trend obtained from the analysis, a graded early warning strategy and repair and treatment plan are dynamically matched.

[0036] In this embodiment, the overturning stability analysis of the single-column pier structure includes: calculating the ratio of the overturning moment to the anti-overturning moment based on the real-time load data of the vehicle and its lateral eccentricity, as well as the geometric and material parameters of the pier column, and classifying the risk level based on the ratio.

[0037] In specific application embodiments, a comprehensive technical system encompassing collaborative data acquisition, fusion analysis, targeted assessment, and closed-loop management is constructed to achieve proactive prevention and control from the source to the disposal stage. The system first collaboratively acquires multi-source data through a heavy-load source sensing subsystem, a bridge structure sensing subsystem, and a dual-light vision sensing subsystem. Linear regression and environmental parameter correction methods are used to calibrate the load data and structural sensing data to ensure data accuracy. The dual-light vision sensing subsystem includes a road-top rapid-capture camera and a roadside infrared thermal imager, which assists in calibrating load data by identifying truck tire deformation and axle height. Subsequently, based on the "General Specifications for Highway Bridge and Culvert Design" and the "Standards for Technical Condition Assessment of Highway Bridges," the system constructs a load-structure response coupled calculation model, establishing a quantitative correlation between actual load and main beam deflection, and between overload and crack increment. Based on the bridge's real-time technical condition rating, a dynamic permissible overload limit is determined, achieving a precise quantitative assessment of the degree of structural damage.

[0038] In the targeted assessment phase for special bridges, specifically for single-column pier bridges, the system implements the following procedures based on the "Specifications for Overturning Resistance Design of Highway Bridges": Figure 2The closed-loop risk assessment logic shown is as follows: Real-time data collection of vehicle load and lateral position, pier geometry and material parameters; parallel calculation of overturning moment and anti-overturning moment, and determination of their ratio; based on this ratio, a three-level judgment is made: if the upper limit of the safety threshold is exceeded, a red warning is triggered and a "no passage + detour guidance" instruction is output; if it is within the critical range, a yellow warning is triggered and a "speed limit + no overtaking" instruction is output; if it is below the safety threshold, normal monitoring is maintained. Through this automated process from data input and standardized verification to risk classification and control instruction output, the system achieves real-time quantitative assessment of overturning risk and differentiated traffic control. For bridges with a technical condition rating of Class III or below, the system implements a different set of specialized monitoring logic: it focuses on tracking sensitive indicators such as crack width increment, main beam displacement, and bearing settlement; if the crack increment exceeds the limit, it automatically matches the recommended repair process such as epoxy resin grouting; if the main beam displacement continues to increase, it pushes load test suggestions; if multiple indicators exceed the standard at the same time, the system automatically downgrades the bridge's technical condition rating and generates temporary load limit suggestions, thereby achieving proactive early warning and maintenance intervention for cumulative structural damage.

[0039] The system employs a three-tiered early warning mechanism based on the degree of overloading and structural risk: Level 1 warnings for general overloading are sent to traffic enforcement departments with interception recommendations; Level 2 warnings for severe overloading or structural damage are sent to both traffic and maintenance departments with temporary traffic restriction recommendations; and Level 3 warnings for extreme overloading or major structural risks are sent to both traffic, maintenance, and emergency management departments, triggering on-site audible and visual alarms and emergency repair dispatch. Simultaneously, the system automatically matches standardized repair plans based on damage type, generating a daily "Heavy Load Damage Report" and a monthly "Correlation Analysis Report," archiving heavy load traffic flow, damage trends, and high-risk enterprise data to continuously optimize deployment strategies and maintenance plans. Through this end-to-end collaborative solution, the system achieves significant technical effects, reducing the heavy load missed detection rate by over 60%, reducing damage assessment errors from ±20% to within ±5%, reducing the early warning misjudgment rate for single-column piers and low-rated bridges by over 50%, and shortening the average emergency response time from 4 hours to within 30 minutes, thus promoting a fundamental shift in bridge safety management from passive response to proactive prevention.

[0040] In the process of generating repair recommendations and managing data archives, the intelligent repair scheme matching stage automatically matches repair schemes recommended by standards such as the "Specifications for Design of Highway Bridge Strengthening" based on specific damage types such as bridge cracks, displacement, and bearing failure. This clarifies key technological points, applicable scenarios, and implementation priorities (urgent / routine / preventative), providing precise guidance for maintenance work. Routine data archiving includes the daily generation of the "Daily Report on Bridge Heavy Load Damage Monitoring," summarizing daily heavy-load traffic data, abnormal indicator records, and handling status, as well as the monthly generation of the "Monthly Report on the Correlation Analysis between Heavy Load and Bridge Damage," which deeply analyzes heavy-load traffic characteristics, damage development trends, and a list of high-risk transportation companies. All data is categorized and archived on the bridge health monitoring platform, providing solid data support for subsequent enforcement and control optimization, maintenance plan formulation, and structural safety assessment.

[0041] In this embodiment, the collaborative management closed-loop module classifies risk levels as follows: Level 1 warning corresponds to general overloading, which is pushed to the traffic enforcement department along with a vehicle interception suggestion; Level 2 warning corresponds to severe overloading or structural damage, which is simultaneously pushed to the traffic enforcement and bridge maintenance departments along with a temporary traffic restriction suggestion; Level 3 warning corresponds to extreme overloading or major structural risks, which is simultaneously pushed to the traffic enforcement, bridge maintenance and emergency management departments, and triggers on-site audible and visual alarms. Based on the type and degree of quantitative damage indicators, the repair process and priority are automatically matched from the preset standard repair solution library; Regularly generate correlation analysis reports, which include heavy-load vehicle flow statistics, damage trend analysis, and source tracing information for high-risk enterprises.

[0042] In this embodiment, the source docking and prediction module connects with the enterprise transportation management system through a standardized interface or a blockchain data sharing mode based on a consortium blockchain to obtain vehicle transportation plan data and establish a three-dimensional correlation index. Then, it uses a spatiotemporal prediction model to estimate the time window for trucks to arrive at the target bridge. The dynamic deployment module remotely controls the sensing equipment based on the prediction results, including increasing the sampling frequency of dynamic weighing and strain sensors, turning on visual auxiliary lighting under low light conditions, and activating mobile blind spot filling units in curve blind spots. The multi-source data fusion and damage assessment module quantifies the cumulative structural damage caused by overload through a load-damage coupling model, performs a special anti-overturning assessment based on moment ratio calculation for single-column pier bridges, and strengthens the monitoring and trend warning of crack and displacement indicators for low-rated bridges. The collaborative management closed-loop module triggers multi-level warnings based on damage and overload levels, and links multiple departments such as transportation, maintenance, and emergency response through SMS, dedicated APP, and Beidou short message methods. It synchronously matches standardized repair plans and generates damage reports and optimization analysis reports, realizing closed-loop management of the entire process from data collection and professional assessment to collaborative disposal.

[0043] The following example, using the above system in a specific application embodiment for identifying overloaded trucks on national and provincial highway bridges, analyzing load-bearing capacity and monitoring lightweighting, further illustrates the present invention: This embodiment uses a provincial highway bridge cluster as an application scenario, including one Class III low-rated simply supported beam bridge (assumed to be at K120+500) and one single-column pier continuous beam bridge (assumed to be at K125+300). The specific parameters are as follows: Simply supported beam bridge: span 20m, main beam is C50 concrete, technical condition rating is Class III, initial crack width is 0.15mm, allowable mid-span deflection is 5mm; Single-column pier continuous beam bridge: main span 30m, single-column pier cross-section dimensions 1.2m×0.8m, concrete density 25kN / m³, overturning safety factor allowable value ≥1.3; Heavy load sources: Two mines (Mine A and Mine B) and one concrete mixing plant (Station C) in the area are all equipped with a transport management system (TMS).

[0044] I. System Deployment and Parameter Settings (a) Deployment of sensing layer equipment The overall architecture of the system and the deployment of each subsystem are as follows: Figure 3 As shown.

[0045] Heavy-duty source sensing subsystem: Standardized data interfaces (following JT / T697.7-2013) are deployed at the entrances and exits of mines A, B, and C to connect to the TMS system; lightweight dynamic weighing units (range 0-150t, accuracy ±0.3%FS) are deployed 500m from the exit of each enterprise. Bridge structure sensing subsystem: Simply supported beam bridge: Deflection sensors (range ±30mm, accuracy ±0.1mm) and strain sensors (range ±1500με, resolution 0.1με) are installed at mid-span, and crack sensors (measurement range 0-2mm, accuracy ±0.01mm) are installed at the bottom of the main beam. Single-column pier bridges: Inclination sensors (range ±5°, accuracy ±0.01°) are installed on the pier top, and deflection and strain sensors with the same parameters as those for simply supported beam bridges are installed at the mid-span and supports. Dual-light visual perception subsystem: A road-top high-speed capture camera (4K resolution, 30fps) and a roadside infrared thermal imager (temperature measurement range -20℃~80℃, resolution 384×288) are deployed 300m from the entrances of the two bridges. Mobile blind spot monitoring unit: A mobile monitoring terminal is deployed at the approach curve of a simply supported beam bridge (where the sight distance is less than 100m) to meet the needs of blind spot monitoring.

[0046] (II) Parameter settings for the data layer and analysis layer Database construction: Heavy-duty truck basic database: Stores information on 86 heavy-duty trucks from 3 companies (license plate, approved load capacity 31t, vehicle dimensions), and associates it with the index "company ID-vehicle ID-predicted bridge ID"; Bridge foundation database: Input the structural parameters (span, beam height, material strength), technical condition rating, and initial defect records of two bridges; Analysis layer model parameters: The trajectory prediction engine uses a 3-layer LSTM model (6 input dimensions, 64 hidden layer neurons, 100 iterations), and the training data consists of 1200 historical traffic trajectories of 86 trucks over the past 3 months. Multi-source data fusion engine: The stiffness coefficient of the load-response coupling model is based on the elastic modulus of C50 concrete, which is 3.45 × 10⁻⁶. 4 Based on MPa calculation, the permissible overload limit for low-rated bridges is reduced by 20% (i.e., 24.8t) according to the "Technical Condition Assessment Standard for Highway Bridges" JTG / TH21-2011. Special bridge assessment engine: The overturning resistance calculation of single-column piers is set within the safe range according to the "Specifications for Overturning Resistance Design of Highway Bridges" JTG / T3360-01-2018 (overturning moment / anti-overturning moment ≤ 0.8). The allowable limit for crack increment of low-rated bridges is 0.1mm (cumulative ≤ 0.25mm).

[0047] II. Specific Implementation Steps (I) Heavy-load source data docking and trajectory prediction The entire process of data linkage and collaborative control logic from the source of heavy load to the bridge structure, such as... Figure 4 As shown.

[0048] Data Acquisition: Real-time data is obtained from the A-mine TMS system via a standardized interface. Example data is as follows: Vehicle ID "Cloud A" "XXXX1", actual load 42t, planned departure time 14:00, destination industrial park, expected to pass through "K120+500 simple supported beam bridge" and "K125+300 single column pier bridge"; Data cleaning: GPS location confirmed the vehicle's route as "Mine A → K120+500 Bridge → K125+300 Bridge → Industrial Park". Empty vehicle data was removed, and a related index was created: "Enterprise ID: Mine A - Vehicle ID: Cloud A". XXXX1-Bridge ID:1,2”; Trajectory prediction: Input the enterprise's transportation plan, the topology of the national and provincial road network (bridge spacing 5km, speed limit 60km / h), and real-time traffic conditions (no congestion) into the LSTM model, and output the prediction results: It is expected to arrive at bridge K120+500 at 14:12 and bridge K125+300 at 14:20, with a time window error of ±5 minutes.

[0049] (II) Dynamic Deployment Activation When the system predicts "Cloud A" When the truck "XXXX1" arrives at the K120+500 bridge within one hour, a deployment command will be automatically sent: The sampling frequency of the dynamic weighing unit and strain sensor for simply supported beam bridges has been increased from 1 time / 2 seconds to 1 time / 0.5 seconds; Since the expected arrival time is 14:12 (sunny day, with ample ambient light), the laser auxiliary lighting will not be turned on at this time. Activate the mobile blind spot filling unit at the curve and turn on the infrared thermal imager to enhance the recognition effect of subsequent road sections at night.

[0050] (III) Multi-source data acquisition and calibration Data collection: Load data: The dual-light vision unit identified a tire deformation coefficient of 0.85 and a 5cm decrease in axle height, with a preliminary estimated load of 43t; the dynamic weighing unit simultaneously collected data of 41.8t. Structural data: Initial mid-span deflection of the simply supported beam bridge: 1.2 mm; initial strain: 350 με; crack sensor reading: 0.15 mm. Environmental parameters: Temperature 28℃, Humidity 65%; Data calibration: Load data: Linear regression correction was used, and the fitting formula was y=0.98x+0.5 (x is the visual estimate, y is the calibrated load). Substituting the values, the calibrated load was 42.3t. Structural data: According to the "Technical Specification for Safety Monitoring System of Highway Bridge Structure", the strain of C50 concrete at 28℃ does not need to be corrected and the original data can be directly retained.

[0051] (iv) Multi-source data fusion and damage quantification Load-deflection correlation calculation: Stiffness coefficient of simply supported beam bridge k = EI / L³ (E = 3.45 × 10⁻⁶) 4 MPa, I=0.08m 4 (L=20m), the calculated k=1380kN / m; according to the relationship of "actual load-main beam deflection" Δf=P / k, substituting P=42.3t (converted to 423kN), we get Δf=0.307m (30.7mm). Adding the initial deflection of 1.2mm, the total deflection is 31.9mm (not exceeding the allowable value of 50mm). Overload - Crack Increment Calculation: The bridge's allowable overload limit is 24.8t, and the actual overload is 42.3 - 24.8 = 17.5t. According to the standard formula Δw = 0.001 × ΔP (ΔP is the overload, in tons), the crack increment is calculated to be 0.0175mm, and the cumulative crack width is 0.1675mm (not exceeding the allowable limit of 0.25mm). Damage assessment: Based on the combined deflection and crack data, the current damage level of this simply supported beam bridge is determined to be "medium risk," requiring continuous monitoring.

[0052] (v) Targeted assessment of special bridges Overturning resistance assessment of single-column pier bridges The detailed process for assessing the lateral tilt risk of a single-column pier bridge is as follows: Figure 3 As shown.

[0053] When "Cloud A" When the truck "XXXX1" reached the K125+300 single-column pier bridge: Input parameters: actual load 42.3t, wheel center distance from bridge centerline 1.5m, single column pier self-weight G=1.2×0.8×8×25=192kN (pier height 8m), pier column cross-section width 0.8m; Standard verification: Calculated according to the "Specifications for Overturning Resistance Design of Highway Bridges": Overturning moment Mtilt = P × e = 423 kN × 1.5 m = 634.5 kN m; Overturning moment Mresistance = G × b / 2 = 192 kN × 0.4 m = 76.8 kN m; The torque ratio Mtilt / Mresistance = 8.26; Risk assessment: The ratio of 8.26 far exceeds the safe range (≤0.8), triggering a red alert.

[0054] Subsequent assessment of low-rated simply supported beam bridges After one month of operation, the system detected a crack increase of 0.1 mm and a cumulative width of 0.25 mm in the simply supported beam bridge (reaching the allowable limit). Automatic matching repair solution: Epoxy resin grouting (process: crack cleaning → drying → grouting, priority level 1); The warning was sent to the transportation and maintenance departments, suggesting that "the cracks be repaired within 15 days and heavy-duty trucks (≤20t) be restricted from passing during the repair period."

[0055] (vi) Closed-loop management of the entire process Tiered early warning push Red Alert (Single-Pillar Bridge): Simultaneously pushed to the Transportation Bureau, Maintenance Center, and Emergency Management Bureau, containing the information "License Plate Cloud A". XXXX1, actual load 42.3t, single column pier overturning moment ratio 8.26, posing a risk of lateral tilting; attached is the handling suggestion of "prohibiting passage + dispatching patrol vehicles to guide to the S312 detour route"; on-site audible and visual alarms are activated to remind truck drivers to unload at the nearest location; Medium-risk warning (simply supported beam bridge): The message was sent to the maintenance center, stating that "the cumulative width of the cracks on bridge K120+500 is 0.25mm, which has reached the allowable limit," and included an "epoxy resin grouting repair plan."

[0056] Repair suggestions and data archiving Repair Implementation: The maintenance department completed the crack repair of the simply supported beam bridge according to the system's recommended plan, and the crack width was reduced to 0.08mm after the repair; the single-column pier bridge was under traffic control and no overloaded vehicles passed through; Data archiving: Daily Heavy Load Damage Report: Records traffic data of 86 trucks from 3 companies; crack increase of 0.0175mm on bridge K120+500; no risk events on bridge K125+300. Monthly Correlation Analysis Report: Statistics show that 12% of the vehicles in Mine A are overloaded (high-risk enterprise), and the damage trend of the K120+500 bridge is stable. It is recommended to optimize the frequency of control at the exit of Mine A.

[0057] Implementation effect verification This embodiment achieved the following results after a 3-month trial run: The rate of missed detections under heavy load was reduced from 35% to 12% using traditional methods, and the accuracy of source tracing reached 100%. The bridge damage assessment error is controlled within ±4%, which is a significant improvement over existing technologies (±20%). The false alarm rate for single-column pier bridges was 0%, and the false alarm rate for low-rated bridges dropped to 8%. The average emergency response time for Level 3 early warning has been shortened to 25 minutes, a significant improvement over the traditional manual coordination model (4 hours).

[0058] Those skilled in the art can adjust the equipment deployment and model parameters according to the actual bridge parameters and the distribution of heavy-load enterprises in the region, without departing from the core protection scope of this invention.

[0059] This embodiment also provides a method for full-link collaborative monitoring and control of heavy-load bridges on national and provincial highways based on the above system, including the following steps: Step S1: Obtain vehicle transportation plan data from heavy-duty source enterprises and predict the estimated time window for the target truck to arrive at the target bridge; Step S2: Based on the expected time window, dynamically adjust the operating parameters of the sensing devices deployed in the area associated with the target bridge; Step S3: Integrate the collected vehicle load data and the structural response data of the target bridge, and calculate and quantify the damage index based on the bridge engineering specifications. Step S4: Determine the risk level based on quantitative damage indicators or vehicle load data, generate corresponding early warning information, and push it to relevant management departments.

[0060] In this embodiment, the damage index is quantitatively calculated based on the logic of bridge engineering specifications, including: The permissible overload limit is determined based on the technical condition rating and service environment of the target bridge. Calculate the amount of overload that exceeds the permissible overload limit for vehicle load data; Based on the overload, the theoretical increment of crack width and vertical displacement caused to the target bridge is calculated using standard formulas.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A collaborative monitoring and control system for heavy-load bridges on national and provincial highways, characterized in that, include: The heavy-load source docking and prediction module is used to obtain vehicle transportation plan data of heavy-load source enterprises through a standardized interface, and predict the expected time window for the target truck to arrive at the target bridge based on the transportation plan data, road network information and historical trajectory data. The dynamic deployment module is used to generate deployment instructions based on the expected time window to remotely activate or adjust the operating parameters of the sensing devices deployed in the area associated with the target bridge. The multi-source data fusion and damage assessment module is used to receive and fuse vehicle load data from the sensing device and structural response data from the structural sensors deployed on the target bridge, and calculate the quantitative damage index caused by the target truck to the target bridge based on the preset highway bridge engineering specification logic. The collaborative management closed-loop module is used to determine the risk level based on the quantitative damage index or the vehicle load data, generate early warning information including the risk level, treatment suggestions and repair plan, and push it to the management department terminal corresponding to the risk level.

2. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 1, characterized in that, The heavy-load source docking and prediction module docks with the transportation management system (TMS) of the heavy-load source enterprise to obtain data including vehicle identification, actual load, planned departure time, destination and planned route. Based on the pre-route, a three-dimensional association index is established between the transportation entity, the vehicle, and the infrastructure node; A spatiotemporal prediction model is used to predict the path and time for the vehicle to reach the target bridge.

3. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 1, characterized in that, The deployment commands generated by the dynamic deployment module are used for: When it is predicted that the target truck is about to arrive, the sampling frequency of the target bridge-associated dynamic weighing unit and structural strain sensor is increased; If the expected arrival time is in a low-light environment, turn on the auxiliary lighting equipment of the visual perception unit; If there are curves or blind spots in the approach area of ​​the target bridge, the mobile blind spot monitoring unit deployed in that area will be activated.

4. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 1, characterized in that, The multi-source data fusion and damage assessment module includes: The data calibration unit is used to calibrate the estimated load data, dynamic weighing data, and structural sensor data affected by ambient temperature and humidity from visual recognition. The load-damage coupling calculation unit has a built-in load-structure response coupling model based on bridge engineering code logic and bridge historical condition assessment. It is used to calculate the theoretical structural response value corresponding to the vehicle load data according to the span, material parameters and technical condition rating of the target bridge, and compare it with the structural response data to quantify the degree of cumulative damage to the bridge structure caused by overload.

5. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 4, characterized in that, The load-damage coupling calculation unit is configured to: determine the dynamic allowable overload limit based on the technical condition rating and service environment of the target bridge; calculate the overload amount exceeding the limit based on the actual load; and calculate the theoretical increment of crack width and vertical displacement based on the overload amount and the standard formula.

6. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 1, characterized in that, The multi-source data fusion and damage assessment module also includes a special bridge-targeted assessment unit, used for: When the target bridge is identified as a single-column pier structure, the structural anti-overturning stability state analysis is performed based on the real-time vehicle load data and its lateral distribution information on the bridge deck, and the corresponding overturning risk level signal is generated according to the analysis results. When the technical condition rating of the target bridge is found to be lower than the preset safety threshold, enhanced monitoring and trend analysis are performed on the damage indicators of crack propagation and vertical displacement of the main beam. Based on the damage development trend obtained from the analysis, a graded early warning strategy and repair and treatment plan are dynamically matched.

7. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 6, characterized in that, The overturning stability analysis of the single-column pier structure includes: calculating the ratio of the overturning moment to the anti-overturning moment based on the real-time load data of the vehicle and its lateral eccentricity, as well as the geometric and material parameters of the pier column, and classifying the risk level based on the ratio.

8. The heavy-load full-link collaborative monitoring and control system for national and provincial highway bridges according to claim 1, characterized in that, The collaborative management closed-loop module classifies risk levels as follows: Level 1 warning corresponds to general overloading, which is pushed to the traffic enforcement department along with a vehicle interception suggestion; Level 2 warning corresponds to severe overloading or structural damage, which is simultaneously pushed to the traffic enforcement and bridge maintenance departments along with a temporary traffic restriction suggestion; Level 3 warning corresponds to extreme overloading or major structural risks, which is simultaneously pushed to the traffic enforcement, bridge maintenance and emergency management departments, and triggers on-site audible and visual alarms. Based on the type and degree of the quantitative damage indicators, the repair process and priority are automatically matched from the preset standard repair scheme library; Regularly generate correlation analysis reports, which include heavy-load vehicle flow statistics, damage trend analysis, and source tracing information of high-risk enterprises.

9. A method for full-link collaborative monitoring and control of heavy-load bridges on national and provincial highways based on the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: Step S1: Obtain vehicle transportation plan data from heavy-duty source enterprises and predict the estimated time window for the target truck to arrive at the target bridge; Step S2: Based on the estimated time window, dynamically adjust the operating parameters of the sensing devices deployed in the area associated with the target bridge; Step S3: Integrate the collected vehicle load data and the structural response data of the target bridge, and calculate and quantify the damage index based on the logic of highway bridge engineering specifications; Step S4: Determine the risk level based on the quantitative damage index or the vehicle load data, generate corresponding early warning information, and push it to the relevant management department.

10. The method according to claim 9, characterized in that, The step S3, which involves calculating and quantifying damage indicators based on highway bridge engineering specifications, includes: Based on the technical condition rating and service environment of the target bridge, its permissible overload limit is determined; Calculate the amount by which the vehicle load data exceeds the permissible overload limit; Based on the overload, the theoretical increments in crack width and vertical displacement caused to the target bridge are calculated using standard formulas.

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