Intelligent protection system for corrosion prevention of steel bridge

Abstract

The application relates to the technical field of steel bridge corrosion prevention, and discloses a steel bridge corrosion prevention intelligent protection system, which comprises an electrochemical protection system, a steel bridge monitoring system and a central monitoring platform; the electrochemical protection system comprises a solar cell panel, a small wind turbine, an electricity storage device, a transistor constant potential instrument and an auxiliary anode rod, and is used for keeping the steel bridge in a negative protection state; the steel bridge monitoring system is used for collecting monitoring data of the steel bridge in real time and transmitting the monitoring data to the central monitoring platform; the central monitoring platform is used for receiving data sent by the steel bridge monitoring system, constructing a steel bridge structure digital twin model and a health state prediction model; and the electrochemical protection system is further dynamically optimized based on data analysis results provided by the central monitoring platform. The application realizes dynamic optimization of electrochemical negative protection, and through real-time monitoring and prediction of the structure health state, intelligent early warning and maintenance decision are carried out, corrosion is effectively prevented and controlled, and the operation safety and maintenance efficiency of the bridge are improved.

Description

Technical Field

[0001] This invention relates to the field of steel bridge corrosion protection technology, and specifically to an intelligent protection system for steel bridge corrosion protection. Background Technology

[0002] Corrosion is a common quality issue in steel bridge construction. Under natural conditions, rust removal and renovation are typically required every few years, using methods such as applying anti-rust and anti-corrosion paint to maintain normal operation. This not only incurs maintenance and operating costs and affects the normal operation of the bridge, but also damages the bridge structure itself. Furthermore, in recent years, safety accidents involving damaged steel bridges have occurred frequently, posing numerous safety hazards to society and people's lives and property. Therefore, it is urgent to utilize modern technology to add protective devices to steel bridge structures and to establish safety monitoring platforms, ensuring that steel bridges are always in a good and safe operating condition. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, the present invention aims to provide an intelligent anti-corrosion protection system for steel bridges. This system effectively prevents and controls corrosion problems in steel structure bridges, improves the safety performance and durability of steel bridges, ensures their long-term stable use, and enhances the accuracy and efficiency of maintenance work through intelligent means, thereby reducing potential safety risks and economic losses.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent protection system for corrosion prevention of steel bridges, comprising:

[0005] Electrochemical protection system, steel bridge monitoring system, and central monitoring platform;

[0006] The electrochemical protection system includes solar panels, a small wind turbine, an energy storage device, a transistor potentiostat, and an auxiliary anode rod, used to keep the steel bridge in a negative electrode protection state.

[0007] The steel bridge monitoring system is used to collect monitoring data of steel bridges in real time and transmit it to the central monitoring platform.

[0008] The central monitoring platform is used to receive data sent by the steel bridge monitoring system to construct a digital twin model of the steel bridge structure and a health status prediction model to provide real-time feedback on the electrochemical corrosion prevention effect and the health status of the steel bridge structure.

[0009] The electrochemical protection system is also dynamically optimized based on the data analysis results provided by the central monitoring platform.

[0010] Furthermore, solar panels and small wind turbines work together to power the energy storage device, ensuring that the device is always in a state of saturation.

[0011] The energy storage device converts the power supplied by the solar panels and small wind turbine into a safe DC voltage. The voltage is supplied to one end of the auxiliary anode rod in the anode well through the positive terminal of the transistor potentiostat. The other end of the auxiliary anode rod is connected to the root of the steel bridge pier via a wire and fixed to the steel support at one end of the steel bridge.

[0012] The negative terminal of the transistor potentiostat is connected to the other end of the steel bridge pier via wire two and a reference electrode. At the other end of the steel bridge pier, wire two is connected to the steel support at the other end of the steel bridge via the reference electrode and fixed thereon.

[0013] Furthermore, the steel bridge monitoring system includes:

[0014] The current detector module is connected to the conductor of the electrochemical protection system through a current transformer to monitor and record the magnitude of the conductor current in real time.

[0015] The voltage detector module is connected to the output terminal of the transistor potentiostat, the steel pier, and the steel support via a voltage divider resistor or a voltage transformer to directly measure the voltage value applied to the steel bridge structure by the electrochemical protection system.

[0016] The potential monitoring module involves evenly embedding several potential monitoring instruments at the bottom of the steel bridge according to its length. These instruments directly contact the surface of the steel bridge structure through a reference electrode to accurately measure the potential value of each monitoring point relative to the reference electrode.

[0017] The steel bridge stress monitoring module embeds or attaches stress monitoring instruments to key stress-bearing parts of the steel bridge to monitor changes in structural stress.

[0018] The steel bridge deformation monitoring module involves embedding several deformation monitoring instruments at different parts of the steel bridge to monitor the deformation of each part of the steel bridge in real time.

[0019] The steel bridge steel component corrosion monitoring module involves installing a real-time online steel structure corrosion monitoring instrument on the easily corroded parts of the steel bridge to monitor the corrosion rate on the surface and inside of the steel structure.

[0020] Furthermore, the central monitoring platform includes:

[0021] The data acquisition module is used to acquire monitoring data from the steel bridge monitoring system, as well as the geometric structure and material properties data of the steel bridge in real time.

[0022] The data processing module is used to clean, verify, integrate, and store the received raw data;

[0023] The model building module analyzes the physical state, electrochemical system protection status, and structural health status of steel bridges in real time through the construction of a digital twin model of the steel bridge structure and a health status prediction model.

[0024] The protection and control module dynamically optimizes the operating parameters of the electrochemical protection system based on the model analysis results.

[0025] The early warning module, based on the analysis results of the digital twin model and health status prediction model of the steel bridge structure, immediately issues an early warning signal when the corrosion risk, structural damage, or health status indicators exceed the preset threshold.

[0026] The user module provides a graphical user interface to display real-time data, simulation images, early warning information, and health status, and supports users in querying, analyzing data, and performing operations.

[0027] The communication module is used to coordinate communication and data exchange between various modules.

[0028] Furthermore, constructing the health status prediction model includes the following steps:

[0029] Setting I t Let V be the magnitude of the conductor current in the electrochemical protection system at time t. t Let be the voltage value applied to the steel bridge structure by the electrochemical protection system at time t. Let σ be the potential value of the i-th monitoring point relative to the reference electrode at time t, where i = 1, 2, ..., n, and n is the total number of monitoring points of the potential monitor. t Let be the change in structural stress of the steel bridge at time t. Let R be the deformation of the j-th part at time t, where j = 1, 2, ..., m, and m is the total number of monitoring points of the deformation monitoring instrument. t Let be the corrosion rate of the steel structure surface and interior at time t;

[0030] Constructing time series feature vectors In the digital twin model of the steel bridge structure, whenever new monitoring data is acquired, it is updated in real time and cleaned, standardized, or normalized to construct an updated time-series feature vector. The input is fed into the LSTM model for training to obtain the trained LSTM model;

[0031] The trained LSTM model is used as a health status prediction model, and the updated time series feature vectors are used. In the input health status prediction model, the output predicted health status index is Y. t+τ ;

[0032] Y t+τ =LSTM(X) t ;θ)

[0033] Where θ represents the parameters of the LSTM model, which are obtained through training with historical data, and τ represents a future time period;

[0034] Let the health status index be Y. t+τ As a predicted value, a health status threshold Y is set. thres An alert is triggered when the predicted value exceeds the threshold.

[0035] Furthermore, a digital twin model of the steel bridge structure is established based on all the data from the data acquisition module;

[0036] The predicted values ​​of the health status prediction model are combined with the digital twin model of the steel bridge structure to update the health status information of the corresponding steel bridge structure in the digital twin model of the steel bridge structure in real time.

[0037] Furthermore, the electrochemical protection system also performs dynamic optimization based on the data analysis results provided by the central monitoring platform, including:

[0038] Based on the predicted values ​​from the health status prediction model and real-time monitoring data, the magnitude of the conductor current, the voltage applied to the steel bridge structure, the potential value of each monitoring point relative to the reference electrode, the number of auxiliary anodes, and the distance between the auxiliary anodes and the protective structure are dynamically adjusted.

[0039] Furthermore, the steel bridge monitoring system also includes a video acquisition module, which is used to install multiple video monitors in the accessible area to capture images of people or objects that illegally enter the perimeter in real time and issue automatic warnings.

[0040] The beneficial effects of this invention are as follows:

[0041] 1. Data is collected in real time through the steel bridge monitoring system. The central monitoring platform constructs a digital twin model and a health status prediction model. Based on data analysis, the electrochemical protection strategy is dynamically adjusted to effectively prevent and control corrosion risks.

[0042] 2. It can predict potential corrosion risks, provide early warnings and guide the optimization of electrochemical protection systems, reduce the frequency of unexpected shutdowns and maintenance, and improve the operating efficiency and safety of steel bridges.

[0043] 3. Construct digital twin models and health status prediction models for steel bridge structures to achieve comprehensive and precise management of steel bridge structures, promote scientific and timely maintenance decisions, and reduce the passivity and uncertainty of traditional anti-corrosion maintenance. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a structural schematic diagram of an intelligent anti-corrosion protection system for steel bridges provided in an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of an electrochemical protection system. Detailed Implementation

[0047] 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.

[0048] like Figures 1 to 2 As shown, the present invention provides an intelligent protection system for corrosion prevention of steel bridges, comprising:

[0049] Electrochemical protection system, steel bridge monitoring system, and central monitoring platform;

[0050] The electrochemical protection system includes solar panels, a small wind turbine, an energy storage device, a transistor potentiostat, and an auxiliary anode rod, used to keep the steel bridge in a negative electrode protection state.

[0051] The steel bridge monitoring system is used to collect monitoring data of steel bridges in real time and transmit it to the central monitoring platform.

[0052] The central monitoring platform is used to receive data sent by the steel bridge monitoring system to build a digital twin model of the steel bridge structure and a health status prediction model to provide real-time feedback on the electrochemical corrosion prevention effect and the health status of the steel bridge structure.

[0053] The electrochemical protection system is also dynamically optimized based on data analysis results provided by the central monitoring platform.

[0054] It should be noted that since most steel structure bridges are located in outdoor natural environments, solar panels and small wind turbines are used to jointly power the energy storage device, ensuring that the energy storage device is always in a state of saturation. The energy storage device converts the power supplied by the solar panels and small wind turbines into a safe DC voltage, and energizes one end of the auxiliary anode rod in the anode well through the positive terminal of the transistor potentiostat. The other end of the auxiliary anode rod is connected to the root of the steel pier through a wire and fixed to the steel support at one end of the steel bridge.

[0055] The negative terminal of the transistor potentiostat is connected to the other end of the steel bridge pier via wire two and a reference electrode. At the other end of the steel bridge pier, wire two is connected to the steel support at the other end of the steel bridge via the reference electrode and fixed thereon.

[0056] Specifically, the steel bridge monitoring system includes:

[0057] The current detection module is connected to the conductors of the electrochemical protection system via a current transformer to monitor and record the conductor current in real time. This ensures a stable current flows through the steel bridge to achieve the protection purpose, while avoiding over-protection and damage to the anti-corrosion layer. The voltage detection module is connected to the output of the transistor potentiostat, the piers, and the steel supports via voltage divider resistors or voltage transformers to directly measure the voltage applied to the steel bridge structure by the electrochemical protection system. This ensures voltage stability and control within safe limits, guaranteeing the safety of the steel bridge during use. The potential monitoring module, based on the length of the steel bridge, evenly embeds several potential monitoring instruments at the bottom of the bridge. These instruments directly contact the surface of the steel bridge structure via reference electrodes to accurately measure the potential value of each monitoring point relative to the reference electrode, ensuring the overall basic stability of the steel bridge. The system includes: a steel bridge stress monitoring module (embedded or affixed to key stress-bearing parts of the steel bridge to monitor structural stress changes and ensure overall structural stress stability); a steel bridge deformation monitoring module (embedded with deformation monitoring instruments at different locations to monitor deformation in real time and ensure overall structural deformation remains within a relatively stable range); a steel bridge steel component corrosion monitoring module (installed with real-time online corrosion monitoring instruments at easily corroded parts of the steel bridge to monitor corrosion rates on the surface and inside of the steel structure and ensure corrosion levels do not exceed predetermined thresholds); and a video acquisition module (installed with multiple video monitors in accessible areas to capture and automatically warn of unauthorized intrusions).

[0058] Preferably, the central monitoring platform includes: a data acquisition module for real-time acquisition of monitoring data from the steel bridge monitoring system, as well as the geometric structure and material properties data of the steel bridge; a data processing module for cleaning, verifying, integrating, and storing the received raw data; a model building module for constructing a digital twin model of the steel bridge structure and a health status prediction model to reflect the physical state, electrochemical system protection status, and structural health status of the steel bridge in real time; a protection control module for dynamically optimizing the operating parameters of the electrochemical protection system based on data analysis results; an early warning module for immediately issuing early warning signals and providing maintenance suggestions or optimization solutions once potential corrosion risks, structural damage, or health status indicators exceeding preset thresholds are detected, based on the results of the digital twin model and health status prediction model of the steel bridge structure; a user module for providing a graphical user interface to display real-time data, simulation images, early warning information, and health status assessment reports, supporting users to query, analyze data, and perform operations; and a communication module for coordinating communication and data exchange between the modules.

[0059] Furthermore, constructing a health status prediction model includes the following steps:

[0060] Setting I t Let V be the magnitude of the conductor current in the electrochemical protection system at time t. t Let be the voltage value applied to the steel bridge structure by the electrochemical protection system at time t. Let σ be the potential value of the i-th monitoring point relative to the reference electrode at time t, where i = 1, 2, ..., n, and n is the total number of monitoring points of the potential monitor. t Let be the change in structural stress of the steel bridge at time t. Let R be the deformation of the j-th part at time t, where j = 1, 2, ..., m, and m is the total number of monitoring points of the deformation monitoring instrument. t Let be the corrosion rate of the steel structure surface and interior at time t;

[0061] Constructing time series feature vectors In the digital twin model of the steel bridge structure, whenever new monitoring data is acquired, it is updated in real time and cleaned, standardized, or normalized to construct an updated time-series feature vector. The input is fed into the LSTM model for training to obtain the trained LSTM model;

[0062] The trained LSTM model is used as a health status prediction model, and the updated time series feature vectors are used. In the input health status prediction model, the output predicted health status index is Y. t+τ ;

[0063] Y t+τ =LSTM(X) t ;θ)

[0064] Where θ represents the parameters of the LSTM model, which are obtained through training with historical data, and τ represents a future time period;

[0065] Let the health status index be Y. t+τ As a predicted value, a health status threshold Y is set. thres An alert is triggered when the predicted value exceeds the threshold.

[0066] Specifically, a digital twin model of the steel bridge structure is established based on all the data from the data acquisition module;

[0067] The predicted values ​​of the health status prediction model are combined with the digital twin model of the steel bridge structure to update the health status information of the corresponding steel bridge structure in the digital twin model in real time.

[0068] Specifically, the electrochemical protection system also undergoes dynamic optimization based on data analysis results provided by the central monitoring platform, including:

[0069] Based on the predicted values ​​from the health status prediction model and real-time monitoring data, the magnitude of the conductor current, the voltage applied to the steel bridge structure, the potential value of each monitoring point relative to the reference electrode, the number of auxiliary anodes, and the distance between the auxiliary anodes and the protective structure are dynamically adjusted.

[0070] Example 2

[0071] Based on Example 1, this example provides the following optimizations when increased corrosion is observed in a certain section of a steel bridge:

[0072] Based on a digital twin model of the steel bridge structure, this embodiment observed that real-time monitoring data showed a significant accelerating trend in the corrosion rate at a monitoring point on the surface of the beam segment with span number SP5 in the middle of the steel bridge, increasing from an initial 0.1 mm / m to the current 0.03 mm / m. The health status prediction model further predicts that the corrosion rate will continue to increase at a rate of 0.01 mm / m over the next three months. Calculations indicate that the current protective current density in this area is 20 mA / m. 2 To effectively prevent the spread of corrosion, the current needs to be increased to 30 mA / m.

[0073] Next, using the digital twin model of the steel bridge structure to display the steel bridge structure and auxiliary anode layout, the current auxiliary anode configuration of the SP5 beam segment was identified. The current distribution effect after adding auxiliary anodes and adjusting the potentiostat parameters was simulated in the digital twin model, calculating the required new current value and the adjustment range of the conductor current. Based on the actual structure of the steel bridge, the appropriate location for adding auxiliary anodes was determined to ensure that the stability of the steel bridge structure was not affected. Finally, based on the simulation results, a set of auxiliary anodes was added to the SP5 beam segment, and the potentiostat parameters were adjusted in real time in the twin model, increasing the conductor current in this area from 150A to 200A to ensure it reaches 30mA / m. 2 The current density effectively suppresses the corrosion rate at the monitoring point.

[0074] Example 3

[0075] Based on Example 1, this example provides the following optimizations when it is observed that the monitoring point potential reading is consistently lower than the ideal protection potential range:

[0076] Based on a digital twin model of the steel bridge structure, this embodiment monitored that the potential reading at monitoring point P1 was consistently below the ideal protection potential range of -800mV to -1200mV. The health status prediction model, based on historical data analysis, weather forecasts, and other environmental monitoring data such as humidity, temperature, and air pollutant levels, predicted that the corrosion risk at point P1 would further increase in the upcoming harsh environment. Then…

[0077] In the digital twin model of the steel bridge structure, the distribution of auxiliary anodes around monitoring point P1 was located, and the uneven distribution of current density was analyzed. The model was used to predict the minimum current density requirement of point P1 under the ideal protection potential. Different auxiliary anode addition schemes and position adjustments were simulated in the digital twin model of the steel bridge structure, and their impact on the potential value of point P1 was observed. Finally, the scheme of adding a set of auxiliary anodes 5 meters away from point P1 was selected. The voltage setting of the potentiostat was adjusted to the ideal value for potential recovery through model calculation. Specifically, the voltage of the potentiostat was adjusted from -1.1V to -1.2V to ensure that the potential value of monitoring point P1 returns to the protection range.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart protection system for corrosion prevention of steel bridges, characterized in that, include: Electrochemical protection system, steel bridge monitoring system, and central monitoring platform; The electrochemical protection system includes solar panels, a small wind turbine, an energy storage device, a transistor potentiostat, and an auxiliary anode rod, used to keep the steel bridge in a negative protection state. The steel bridge monitoring system is used to collect monitoring data of steel bridges in real time and transmit it to the central monitoring platform. The central monitoring platform is used to receive data sent by the steel bridge monitoring system to construct a digital twin model of the steel bridge structure and a health status prediction model to provide real-time feedback on the electrochemical corrosion prevention effect and the health status of the steel bridge structure. The electrochemical protection system is also dynamically optimized based on the data analysis results provided by the central monitoring platform. The steel bridge monitoring system includes: The current detector module is connected to the conductor of the electrochemical protection system through a current transformer to monitor and record the magnitude of the conductor current in real time. The voltage detector module is connected to the output terminal of the transistor potentiostat, the steel pier, and the steel support via a voltage divider resistor or a voltage transformer to directly measure the voltage value applied to the steel bridge structure by the electrochemical protection system. The potential monitoring module involves evenly embedding several potential monitoring instruments at the bottom of the steel bridge according to its length. These instruments directly contact the surface of the steel bridge structure through a reference electrode to accurately measure the potential value of each monitoring point relative to the reference electrode. The steel bridge stress monitoring module embeds or attaches stress monitoring instruments to key stress-bearing parts of the steel bridge to monitor changes in structural stress. The steel bridge deformation monitoring module involves embedding several deformation monitoring instruments at different parts of the steel bridge to monitor the deformation of each part of the steel bridge in real time. The steel bridge steel component corrosion monitoring module involves installing a real-time online steel structure corrosion monitoring instrument on the easily corroded parts of the steel bridge to monitor the corrosion rate on the surface and inside of the steel structure.

2. The intelligent protection system for corrosion prevention of steel bridges as described in claim 1, characterized in that, Solar panels and small wind turbines work together to power the energy storage device, keeping it in a state of full charge. The energy storage device converts the power supplied by the solar panels and small wind turbine into a safe DC voltage. The voltage is supplied to one end of the auxiliary anode rod in the anode well through the positive terminal of the transistor potentiostat. The other end of the auxiliary anode rod is connected to the root of the steel bridge pier via a wire and fixed to the steel support at one end of the steel bridge. The negative terminal of the transistor potentiostat is connected to the other end of the steel bridge pier via wire two and a reference electrode. At the other end of the steel bridge pier, wire two is connected to the steel support at the other end of the steel bridge via the reference electrode and fixed thereon.

3. The intelligent protection system for corrosion prevention of steel bridges as described in claim 1, characterized in that, The central monitoring platform includes: The data acquisition module is used to acquire monitoring data from the steel bridge monitoring system, as well as the geometric structure and material properties data of the steel bridge in real time. The data processing module is used to clean, verify, integrate, and store the received raw data; The model building module analyzes the physical state, electrochemical system protection status, and structural health status of steel bridges in real time through the construction of a digital twin model of the steel bridge structure and a health status prediction model. The protection and control module dynamically optimizes the operating parameters of the electrochemical protection system based on the model analysis results. The early warning module, based on the analysis results of the digital twin model and health status prediction model of the steel bridge structure, immediately issues an early warning signal when the corrosion risk, structural damage, or health status indicators exceed the preset threshold. The user module provides a graphical user interface to display real-time data, simulation images, early warning information, and health status, and supports users in querying, analyzing data, and performing operations. The communication module is used to coordinate communication and data exchange between various modules.

4. The intelligent protection system for corrosion prevention of steel bridges as described in claim 3, characterized in that, Constructing the health status prediction model includes the following steps: set up Let be the magnitude of the conductor current of the electrochemical protection system at time t. Let be the voltage value applied to the steel bridge structure by the electrochemical protection system at time t. Let be the potential value of the i-th monitoring point relative to the reference electrode at time t. Where n is the total number of monitoring points of the potential monitoring instrument. Let be the change in structural stress of the steel bridge at time t. Let be the deformation of the j-th part at time t. Where m is the total number of monitoring points of the deformation monitoring instrument. Let be the corrosion rate of the steel structure surface and interior at time t; Constructing time series feature vectors In the digital twin model of the steel bridge structure, whenever new monitoring data is acquired, it is updated in real time and cleaned, standardized, or normalized to construct an updated time series feature vector. The input is fed into the LSTM model for training to obtain the trained LSTM model; The trained LSTM model is used as a health status prediction model, and the updated time series feature vectors are used. The input to the health status prediction model is the output predicted health status index. ; in The parameters of the LSTM model are obtained through training on historical data. For a future moment; The health status index is As a predicted value, a health status threshold is set. An alert is triggered when the predicted value exceeds the threshold.

5. The intelligent protection system for corrosion prevention of steel bridges as described in claim 3, characterized in that, A digital twin model of the steel bridge structure is established based on all the data from the data acquisition module; The predicted values ​​of the health status prediction model are combined with the digital twin model of the steel bridge structure to update the health status information of the corresponding steel bridge structure in the digital twin model of the steel bridge structure in real time.

6. The intelligent protection system for corrosion prevention of steel bridges as described in claim 3, characterized in that, The electrochemical protection system also undergoes dynamic optimization based on data analysis results provided by the central monitoring platform, including: Based on the predicted values ​​from the health status prediction model and real-time monitoring data, the magnitude of conductor current, the voltage applied to the steel bridge structure, the potential value of each monitoring point relative to the reference electrode, the number of auxiliary anodes, and the distance between the auxiliary anodes and the protective structure are dynamically adjusted.

7. The intelligent protection system for corrosion prevention of steel bridges as described in claim 4, characterized in that, The steel bridge monitoring system also includes a video acquisition module, which is used to install multiple video monitors in the accessible area to capture images of people or objects that illegally enter the perimeter and issue automatic warnings.

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