An integrated data management platform for bridge construction, management and maintenance

Abstract

The embodiment of the present application relates to the technical field of transportation infrastructure construction and management, and discloses a bridge construction, management and maintenance integrated data management platform. The bridge construction, management and maintenance integrated data management platform provided by the embodiment of the present application includes a bridge construction parameter input module, a bridge operation status acquisition module, a bridge status analysis and evaluation module, a bridge simulation model construction module, a bridge simulation model display module, a simulation model parameter interaction module, and a bridge maintenance suggestion generation module. The bridge construction, management and maintenance integrated data management platform of the embodiment of the present application closely combines the construction, management and maintenance of bridges through an integrated management method, and realizes the full-chain management from data entry, status monitoring, status evaluation, simulation to maintenance suggestion generation, which is conducive to improving the overall level of bridge management, reducing management costs, and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation infrastructure construction and management, and in particular to an integrated data management platform for bridge construction, management and maintenance. Background Art

[0002] With the rapid development of modern transportation networks, bridges, as important transportation hubs connecting various regions, are increasingly valued for their safety and durability. Traditional bridge management methods often focus on parameter control and construction quality supervision during the construction phase, while the status monitoring and maintenance management of bridges during operation are relatively lagging behind. This management model is difficult to fully and timely reflect the actual operating status of bridges. Especially in the face of extreme weather conditions (such as ice and snow disasters), bridge corrosion, damage and other problems may rapidly intensify, posing a serious threat to traffic safety.

[0003] In order to overcome the limitations of traditional bridge management methods, in recent years, with the continuous advancement of technologies such as the Internet of Things, big data, and artificial intelligence, integrated data management platforms for bridge construction, management, and maintenance have emerged. Such platforms aim to achieve real-time monitoring, accurate evaluation, and scientific maintenance of bridge conditions by integrating data information from the entire life cycle of bridge construction, operation, and maintenance. However, most existing bridge data management platforms focus on static entry and basic monitoring of bridge structural parameters, and lack in-depth analysis and management of the dynamic corrosion process of bridges in complex environments. Summary of the invention

[0004] The main purpose of the present invention is to provide an integrated data management platform for bridge construction, management and maintenance, aiming to solve the technical problem in the prior art of lack of in-depth analysis and management of the dynamic corrosion process of bridges in complex environments.

[0005] To achieve the above objectives, an embodiment of the present application provides an integrated bridge construction, management and maintenance data management platform, the data management platform comprising:

[0006] A bridge construction parameter input module, including a bridge information input interface, wherein the bridge information input interface is used to input construction parameters of a target bridge, wherein the construction parameters include at least bridge length, bridge pier span, bridge pavement width, and guardrail height parameters;

[0007] The bridge operation status acquisition module includes a bridge monitoring drone, wherein the bridge monitoring drone is provided with a road surface environment status acquisition sensor and a guardrail status acquisition sensor, wherein the road surface environment status acquisition sensor is used to acquire a real-time change image of snow accumulation on the bridge road surface after the drone spreads snow melting agent at high altitude, and the guardrail status acquisition sensor is used to acquire static images of the bridge guardrail at different time periods;

[0008] A bridge status analysis and evaluation module is used to analyze the corrosion status of the bridge pavement and the bridge guardrail based on the real-time changing image of snow accumulation on the bridge pavement and the static image of the bridge guardrail, wherein the corrosion status includes the corrosion type or the corrosion degree;

[0009] A bridge simulation model building module is used to build a bridge corrosion state simulation model according to input bridge construction parameters, bridge pavement and bridge guardrail corrosion state, and the bridge corrosion state simulation model is used to simulate the real corrosion state of the bridge under different conditions;

[0010] A bridge simulation model display module, including a local terminal and a mobile terminal, is used to display the bridge corrosion state simulation model;

[0011] A simulation model parameter interaction module, used to manually correct the model parameters of the bridge corrosion state simulation model, wherein the model parameters include the estimated bridge corrosion speed and / or corrosion degree;

[0012] The bridge maintenance suggestion generation module is used to automatically generate corresponding management suggestions and maintenance suggestions according to the corrosion status of the bridge pavement and bridge guardrail and the corrected estimated bridge corrosion speed and / or corrosion degree. The management suggestions include the monitoring frequency of the bridge monitoring drone, and the maintenance suggestions include maintenance plans and maintenance cycles.

[0013] In one possible implementation, the data management platform also includes an atmospheric environment status detection sensor and a drone management module. The atmospheric environment status detection sensor is used to detect the atmospheric environment status at the current geographical location of the target bridge, and the drone management module is used to control the flight trajectory and flight altitude of the drone according to the atmospheric environment status.

[0014] In a possible implementation, the bridge status analysis and evaluation module includes an image preprocessing submodule and a corrosion status analysis submodule. The image preprocessing submodule is used to preprocess the collected image to obtain a target image, and the corrosion status analysis submodule is used to analyze the target image to obtain the corrosion status of the bridge pavement and guardrail, and output the evaluation results of the corrosion type or corrosion degree.

[0015] In one possible implementation, the data management platform also includes a bridge traffic flow monitoring sensor and a vehicle traffic guidance lighting device, which is used to monitor and record vehicle traffic data on the bridge in real time, wherein the vehicle traffic data includes vehicle flow, vehicle type distribution, and bridge load conditions; the vehicle traffic guidance lighting device is used to guide vehicles to enter the left lane or the right lane of the bridge pavement according to the vehicle traffic data to balance the load on the left and right sides of the bridge pavement.

[0016] In a possible implementation, the data management platform further includes a processor, and the processor is configured to implement the following steps of the data management method:

[0017] Obtain vehicle traffic data on the left and right lanes of the target bridge in real time;

[0018] Determine the load difference between the left and right sides of the target bridge according to the vehicle traffic data of the left lane and the vehicle traffic data of the right lane of the target bridge;

[0019] The vehicle traffic guidance lighting equipment is controlled to turn on according to the load difference between the left and right sides of the target bridge to guide at least some vehicles to travel in a direction to reduce the load difference.

[0020] In one possible implementation, the road environment status acquisition sensor includes a first high-definition camera, which is arranged at the bottom of the bridge monitoring drone, and the guardrail status acquisition sensor includes a second high-definition camera, which is arranged on both sides of the bridge monitoring drone.

[0021] In a possible implementation, the data management platform further includes a processor, and the processor is configured to implement the following steps of the data management method:

[0022] After the UAV spreads snow-melting agent at high altitude, multiple frames of images of real-time changes in snow accumulation on the bridge pavement are collected;

[0023] By using image segmentation technology, the bridge pavement target area is extracted from the image to obtain the bridge pavement target image;

[0024] Through image deep learning technology, image analysis is performed on the target image of the bridge pavement to obtain the time series data of snow thickness;

[0025] The amount of de-icing agent is estimated based on the time series data of the snow thickness to obtain the amount of de-icing agent spread by the drone in the target area;

[0026] The expected corrosion state of the bridge pavement is determined according to the amount of deicing agent spread by the UAV in the target area, wherein the expected corrosion degree of the bridge pavement is positively correlated with the amount of deicing agent.

[0027] In a possible implementation, estimating the amount of de-icing agent according to the time series data of snow thickness to obtain the amount of de-icing agent spread by the drone in the target area includes:

[0028] Obtaining the rate of change and absolute amount of reduction of snow thickness according to the time series data of snow thickness;

[0029] The change rate of the snow thickness and the absolute amount of reduction are input into the pre-built snow-melting agent quantity estimation model to obtain the amount of snow-melting agent spread by the drone in the target area. The snow-melting agent quantity estimation model satisfies the following expression:

[0030] Q=Q0+13.8R+2.03ΔH, where R is the rate of change of snow thickness, ΔH is the absolute reduction in snow thickness, and Q0 is the magnitude control amount of the de-icing agent.

[0031] In a possible implementation, the data management platform further includes a processor, and the processor is further configured to implement the following steps of the data management method:

[0032] Obtain high-resolution still images of bridge guardrails collected by drones;

[0033] Using semantic segmentation technology to perform semantic analysis on the high-resolution static image to obtain a preprocessed image of the bridge guardrail;

[0034] Performing grayscale processing on the preprocessed image of the bridge guardrail to obtain a first target area and a second target area of ​​the preprocessed image;

[0035] Performing contrast enhancement processing on the first target area and the second target area to obtain a target image of the bridge guardrail;

[0036] The corrosion state of the bridge guardrail is analyzed by performing corrosion state analysis on the target image of the bridge guardrail to obtain the corrosion state of the bridge guardrail.

[0037] In a possible implementation, the performing corrosion state analysis on the target image of the bridge guardrail to obtain the corrosion state of the bridge guardrail includes:

[0038] Calculating the area ratio value of the corresponding area according to the first target area and the second target area;

[0039] The one with the smaller area ratio value between the first target area and the second target area is determined as the corrosion area, and the corrosion degree is determined according to the area ratio value of the corrosion area.

[0040] Different from the prior art, the bridge construction, management and maintenance integrated data management platform provided by the embodiment of the present application includes a bridge construction parameter input module, a bridge operation status acquisition module, a bridge status analysis and evaluation module, a bridge simulation model construction module, a bridge simulation model display module, a simulation model parameter interaction module, and a bridge maintenance suggestion generation module. Through the bridge construction parameter input module, the basic construction information of the bridge can be systematically input to provide basic data support for subsequent analysis, evaluation and simulation. The bridge operation status acquisition module uses drone and sensor technology to achieve real-time and remote monitoring of the bridge operation status, effectively improving the efficiency and accuracy of data acquisition. The bridge status analysis and evaluation module can accurately analyze the corrosion status of the bridge pavement and guardrail based on the collected image data, including the judgment of the corrosion type and degree, providing a scientific basis for the subsequent simulation model construction and maintenance suggestion generation. The bridge simulation model construction module can construct a bridge corrosion status simulation model based on the input construction parameters and corrosion status, and display it on the local terminal and mobile terminal through the bridge simulation model display module, so that bridge management personnel can intuitively understand the real corrosion status of the bridge under different conditions. The simulation model parameter interaction module allows manual correction of the parameters of the bridge corrosion state simulation model, such as the estimated bridge corrosion speed and corrosion degree, etc., which enhances the adaptability and accuracy of the model and can better reflect the actual corrosion situation of the bridge. The bridge maintenance suggestion generation module can automatically generate corresponding management suggestions and maintenance suggestions based on the corrosion state of the bridge pavement and guardrails and the corrected estimated bridge corrosion speed and / or corrosion degree. This not only improves the pertinence and efficiency of maintenance work, but also helps to extend the service life of the bridge and ensure its safety. In this way, the integrated data management platform for bridge construction, management and maintenance of the embodiment of the present application closely combines the construction, management and maintenance of the bridge through an integrated management method, and realizes the full-chain management from data entry, status monitoring, status assessment, simulation to maintenance suggestion generation, which is conducive to improving the overall level of bridge management, reducing management costs, and improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0042] Figure 1 This is a schematic diagram of the structure of the integrated data management platform for bridge construction, management and maintenance in some embodiments of the present application;

[0043] Figure 2This is a flowchart of the steps of the data management method in some embodiments of the present application;

[0044] Figure 3 A flowchart of the steps of the data management method in other embodiments of the present application;

[0045] Figure 4 This is a schematic diagram of the module structure of the data management device in other embodiments of the present application.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0049] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0050] With the rapid development of modern transportation networks, bridges, as important transportation hubs connecting various regions, are increasingly valued for their safety and durability. Traditional bridge management methods often focus on parameter control and construction quality supervision during the construction phase, while the status monitoring and maintenance management of bridges during operation are relatively lagging behind. This management model is difficult to fully and timely reflect the actual operating status of bridges. Especially in the face of extreme weather conditions (such as ice and snow disasters), bridge corrosion, damage and other problems may rapidly intensify, posing a serious threat to traffic safety.

[0051] In order to overcome the limitations of traditional bridge management methods, in recent years, with the continuous advancement of technologies such as the Internet of Things, big data, and artificial intelligence, integrated data management platforms for bridge construction, management, and maintenance have emerged. Such platforms aim to achieve real-time monitoring, accurate evaluation, and scientific maintenance of bridge conditions by integrating data information from the entire life cycle of bridge construction, operation, and maintenance. However, most existing bridge data management platforms focus on static entry and basic monitoring of bridge structural parameters, and lack in-depth analysis and management of the dynamic corrosion process of bridges in complex environments.

[0052] In view of the above problems, this application proposes an integrated data management platform for bridge construction, management and maintenance, which includes:

[0053] The bridge construction parameter input module 100 includes a bridge information input interface, wherein the bridge information input interface is used to input construction parameters of a target bridge, wherein the construction parameters include at least bridge length, bridge pier span, and bridge width parameters;

[0054] The bridge operation status acquisition module 200 includes a bridge monitoring drone, wherein the bridge monitoring drone is provided with a road surface environment status acquisition sensor and a guardrail status acquisition sensor, wherein the road surface environment status acquisition sensor is used to acquire a real-time change image of snow accumulation on the bridge road surface after the drone spreads snow melting agent at high altitude, and the guardrail status acquisition sensor is used to acquire static images of the bridge guardrail at different time periods;

[0055] The bridge state analysis and evaluation module 300 is used to analyze the corrosion state of the bridge pavement and the bridge guardrail according to the real-time change image of snow accumulation on the bridge pavement and the static image of the bridge guardrail, wherein the corrosion state includes the corrosion type or the corrosion degree;

[0056] A bridge simulation model building module 400 builds a bridge corrosion state simulation model according to input bridge construction parameters, bridge pavement and bridge guardrail corrosion states, wherein the bridge corrosion state simulation model is used to simulate the actual corrosion state of the bridge under different conditions;

[0057] The bridge simulation model display module 500 includes a local terminal and a mobile terminal, and is used to display the bridge corrosion state simulation model;

[0058] A simulation model parameter interaction module 600 is used to manually correct the model parameters of the bridge corrosion state simulation model, wherein the model parameters include the estimated bridge corrosion speed and / or corrosion degree;

[0059] The bridge maintenance suggestion generation module 700 is used to automatically generate corresponding management suggestions and maintenance suggestions according to the corrosion status of the bridge pavement and bridge guardrails and the corrected estimated bridge corrosion speed and / or corrosion degree. The management suggestions include the monitoring frequency of the bridge monitoring drone, and the maintenance suggestions include maintenance plans and maintenance cycles.

[0060] Specifically, the bridge construction parameter input module 100 provides a bridge information input interface, which is convenient for users to input the construction parameters of the target bridge, such as key information such as bridge length, pier span and bridge width. The bridge operation status acquisition module 200 uses a bridge monitoring drone equipped with a road surface environment status acquisition sensor and a guardrail status acquisition sensor. After spreading snow melting agent at high altitude, the road surface environment status acquisition sensor collects real-time images of snow changes on the bridge road surface. The guardrail status acquisition sensor is responsible for collecting static images of bridge guardrails at different time periods to monitor their state changes. The bridge state analysis and evaluation module 300 uses advanced analysis algorithms to evaluate the corrosion state of the bridge road surface and guardrails, including the type and degree of corrosion, based on the real-time change images of snow on the bridge road surface and the static images of the bridge guardrails. The bridge simulation model construction module 400 uses the input bridge construction parameters and the corrosion state data of the bridge road surface and guardrails to construct a bridge corrosion state simulation model. The model can simulate the real corrosion state of the bridge under different conditions and provide a scientific basis for decision-making. The bridge simulation model display module 500 includes a local terminal and a mobile terminal, which are used to display the bridge corrosion status simulation model, so that managers can view the bridge status anytime and anywhere. The simulation model parameter interaction module 600 allows manual correction of the parameters of the bridge corrosion status simulation model, such as the estimated bridge corrosion speed and / or corrosion degree. Through manual intervention, the accuracy of model prediction is improved to better guide bridge management. The bridge maintenance suggestion generation module 700 automatically generates management suggestions and maintenance suggestions based on the corrosion status of the bridge pavement and guardrails and the corrected estimated bridge corrosion speed and / or corrosion degree. Management suggestions include the monitoring frequency of bridge monitoring drones to ensure that potential problems are discovered in a timely manner. Maintenance suggestions cover maintenance plans and maintenance cycles, providing clear guidance for bridge maintenance.

[0061] In the embodiment of the present application, the bridge construction, management and maintenance integrated data management platform can systematically input the basic construction information of the bridge through the bridge construction parameter input module, and provide basic data support for subsequent analysis, evaluation and simulation. The bridge operation status acquisition module uses drone and sensor technology to realize real-time and remote monitoring of the bridge operation status, effectively improving the efficiency and accuracy of data acquisition. The bridge status analysis and evaluation module can accurately analyze the corrosion status of the bridge pavement and guardrail based on the collected image data, including the judgment of the corrosion type and corrosion degree, and provide a scientific basis for the subsequent simulation model construction and maintenance suggestion generation. The bridge simulation model construction module can construct a bridge corrosion state simulation model according to the input construction parameters and corrosion state, and display it on the local terminal and mobile terminal through the bridge simulation model display module, so that bridge management personnel can intuitively understand the real corrosion state of the bridge under different conditions. The simulation model parameter interaction module allows manual correction of the parameters of the bridge corrosion state simulation model, such as the estimated speed and degree of bridge corrosion, etc., which enhances the adaptability and accuracy of the model and can better reflect the actual corrosion situation of the bridge. The bridge maintenance suggestion generation module can automatically generate corresponding management suggestions and maintenance suggestions based on the corrosion status of the bridge pavement and guardrails and the corrected estimated bridge corrosion speed and / or corrosion degree. This not only improves the pertinence and efficiency of maintenance work, but also helps to extend the service life of the bridge and ensure its safety. In this way, the integrated data management platform for bridge construction, management and maintenance of the embodiment of the present application closely combines the construction, management and maintenance of bridges through an integrated management method, and realizes full-chain management from data entry, status monitoring, status assessment, simulation to maintenance suggestion generation, which is conducive to improving the overall level of bridge management, reducing management costs, and improving management efficiency.

[0062] In one embodiment, the data management platform of the present application also includes an atmospheric environment status detection sensor 800 and a drone management module 900. The atmospheric environment status detection sensor 800 is used to detect the atmospheric environment status at the current geographical location of the target bridge, and the drone management module 900 is used to control the flight trajectory and flight altitude of the drone according to the atmospheric environment status.

[0063] The atmospheric environment state detection sensor 800 can be set on the bridge. The configuration of the atmospheric environment state detection sensor 800 enables the platform to detect the atmospheric environment state of the current geographical location of the target bridge in real time, including key parameters such as temperature, humidity, wind speed, wind direction, and air quality. This information is essential for evaluating the corrosion rate of the bridge structure in the atmospheric environment, predicting the safety of the bridge under extreme weather conditions, and formulating targeted maintenance strategies. The drone management module 900 can intelligently adjust the flight trajectory and flight altitude of the drone according to the atmospheric environment state to ensure that the drone performs efficient bridge monitoring and data collection under safe conditions. For example, in strong winds or severe weather conditions, the drone can automatically adjust the flight altitude and route to avoid collision or damage while ensuring the continuity and accuracy of data collection.

[0064] In one embodiment, the bridge state analysis and evaluation module 300 includes an image preprocessing submodule 310 and a corrosion state analysis submodule 320. The image preprocessing submodule 310 is used to preprocess the collected image to obtain a target image, and the corrosion state analysis submodule 320 is used to analyze the target image to obtain the corrosion state of the bridge pavement and guardrail, and output the evaluation result of the corrosion type or corrosion degree.

[0065] Specifically, the image preprocessing submodule 310 mainly performs a series of preprocessing operations on the collected bridge image to improve the image quality, reduce noise interference, and extract useful feature information, thereby generating a target image for subsequent analysis. The preprocessing operations include but are not limited to denoising (removing noise points in the image through a filtering algorithm to improve the clarity of the image), enhancement (adjusting the brightness, contrast and other parameters of the image to make the details in the image more prominent), cropping (removing irrelevant parts of the image and retaining only key areas such as the bridge pavement and guardrails), scaling and rotation (appropriately scaling and rotating the image to ensure the consistency of the image size and angle), feature extraction (using an image processing algorithm to extract feature information such as edges and textures in the image), etc. The corrosion state analysis submodule 320 receives the target image output by the image preprocessing submodule 310, and analyzes the image using machine learning, deep learning or image processing algorithms to evaluate the corrosion state of the bridge pavement and guardrails.

[0066] In one embodiment, the data management platform of the present application also includes a bridge traffic flow monitoring sensor 1000 and a vehicle traffic guidance lighting device 1100. The bridge traffic flow monitoring sensor 1000 is used to monitor and record vehicle traffic data on the bridge in real time, and the vehicle traffic data includes vehicle flow, vehicle type distribution and bridge load conditions; the vehicle traffic guidance lighting device 1100 is used to guide vehicles to enter the left lane or the right lane of the bridge pavement according to the vehicle traffic data to balance the load on the left and right sides of the bridge pavement.

[0067] Specifically, the embodiment of the present application sets road test detection sensors at the entrance and exit positions of the bridge to detect the type and number of vehicles entering and exiting the left lane or the right lane, so as to analyze the load on the left and right sides of the bridge pavement. When the difference in the number of vehicles entering and exiting the left lane per unit time (i.e., the number of vehicles staying in the left lane of the bridge pavement) is much greater than the difference in the number of vehicles entering and exiting the right lane (i.e., the number of vehicles staying in the right lane of the bridge pavement), it means that the load on the left lane of the bridge pavement is large, and the load on the right lane of the bridge pavement is small. In order to balance this unbalanced load distribution, the vehicle traffic guidance lighting device 1100 configured in the embodiment of the present application will be intelligently adjusted to give priority to guiding vehicles to enter the bridge from the right lane. For example, when necessary, the entry of new vehicles into the left lane can be temporarily restricted by light signals (such as setting a restriction period of 2 minutes), so as to promote the reasonable distribution of loads on the left and right sides of the bridge pavement.

[0068] In one embodiment, the data management platform of the present application further includes a processor, and the processor is configured to implement the following steps of the bridge construction, management and maintenance integrated data management method:

[0069] Step S100, obtaining vehicle traffic data on the left lane and the right lane of the target bridge in real time;

[0070] Step S200, determining the load difference between the left and right sides of the target bridge according to the vehicle traffic data of the left lane and the vehicle traffic data of the right lane of the target bridge;

[0071] Step S300: Control the vehicle traffic guidance lighting equipment to turn on according to the load difference between the left and right sides of the target bridge to guide at least some vehicles to travel in a direction that reduces the load difference.

[0072] Specifically, firstly, road test detection sensors are installed at the entrance and exit of the bridge respectively. The road test detection sensors can accurately record the types and numbers of vehicles entering and exiting the left and right lanes, providing basic data for subsequent analysis. Then, by comparing the vehicle traffic data (entry and exit data) of the left lane and the right lane, especially combining the vehicle model distribution and vehicle weight information (the vehicle model weight database that needs to be pre-set or obtained), the load difference between the left and right sides of the bridge is calculated. Finally, based on the load difference between the left and right sides of the target bridge, the vehicle traffic guidance lighting equipment is intelligently controlled to guide the direction of vehicle travel. When a significant imbalance in the load on the left and right sides of the bridge is detected, the vehicle traffic guidance lighting equipment will be intelligently adjusted according to the algorithm, and the corresponding light signals will be turned on to guide the vehicle to enter the bridge from the lane with a lighter load, or the new vehicle on the lane with a heavier load will be temporarily restricted from entering. For example, if the left lane is overloaded, the guidance lighting equipment will light up a signal to give priority to guiding vehicles to pass through the right lane, or temporarily close the entrance to the left lane to balance the load on the left and right sides of the bridge. In this way, not only can the structural damage caused by long-term excessive unilateral load on the bridge be reduced, but the overall traffic efficiency and safety of the bridge can also be improved.

[0073] It should be noted that the bridge monitoring drone in this application cleverly combines dual functions, aiming to comprehensively improve the safety and operational efficiency of the bridge. First, it has the ability to conduct comprehensive status monitoring of the bridge. Through the high-definition camera and various precision sensors on board, the drone can capture subtle changes in the bridge structure, road conditions and surrounding environment in real time, providing accurate data support for the safety assessment and maintenance of the bridge. Secondly, the drone also undertakes the task of rapid snow melting when the bridge is snowy. In winter or severe weather conditions, snow accumulation on bridges may seriously affect traffic safety and traffic efficiency. To this end, the drone is equipped with a special snow melting agent spreading system, which can accurately and efficiently spread snow melting agents at high altitudes, quickly melt the snow, and restore the normal traffic capacity of the bridge. This function not only greatly reduces the workload of manual snow removal, but also significantly improves the efficiency of snow melting, providing a strong guarantee for the safe operation of the bridge in winter.

[0074] In order to improve the ability to conduct comprehensive status monitoring of bridges, in one embodiment, the road surface environment status acquisition sensor includes a first high-definition camera, and the first high-definition camera is arranged at the bottom of the bridge monitoring drone. The first high-definition camera can directly and clearly capture various details of the bridge road surface, including but not limited to cracks, potholes, water accumulation and foreign objects, etc., to provide intuitive and accurate information for the maintenance and safety assessment of the bridge road surface. The guardrail status acquisition sensor includes a second high-definition camera, and the second high-definition camera is arranged on both sides of the bridge monitoring drone. The second high-definition camera can capture subtle changes in the guardrail in all directions and without blind spots, such as rust, breakage or missing, so as to timely warn of potential safety hazards and provide strong support for the maintenance and repair of bridge guardrails.

[0075] It can be understood that the deicing agent itself is corrosive due to its chemical properties, and the more deicing agent there is, the more corrosive it is to the bridge. Therefore, in one embodiment, the data management platform also includes a processor, and the processor is configured to implement the following steps of the bridge construction, management and maintenance integrated data management method:

[0076] Step S400, collecting multiple frames of images of real-time changes in snow accumulation on the bridge pavement after the drone spreads snow-melting agent at high altitude;

[0077] Step S500: using image segmentation technology to extract the bridge pavement target area from the image to obtain a bridge pavement target image;

[0078] Step S600: Using image deep learning technology, image analysis is performed on the target image of the bridge pavement to obtain time series data of snow thickness;

[0079] Step S700, estimating the amount of de-icing agent according to the time series data of snow thickness to obtain the amount of de-icing agent spread by the drone in the target area;

[0080] Step S800: determining the expected corrosion state of the bridge pavement according to the amount of deicing agent spread by the drone in the target area, wherein the expected corrosion degree of the bridge pavement is positively correlated with the amount of deicing agent.

[0081] Specifically, after the drone completes the task of spreading snowmelt at high altitude, the image acquisition process is immediately started. The high-definition camera mounted on the drone continuously captures multiple frames of images of real-time changes in snow on the bridge pavement. These images provide a valuable data basis for subsequent analysis. Then, the image segmentation technology is used to accurately extract the target area of ​​the bridge pavement from the acquired images. This can eliminate background interference and ensure the accuracy and efficiency of subsequent analysis. A clear target image of the bridge pavement is obtained through image segmentation. Then, the image deep learning technology is used to perform in-depth image analysis on the target image of the bridge pavement. Through the pre-trained deep learning model, the thickness of the snow is accurately identified and measured, and the time series data of the snow thickness is generated. These data reflect the melting process of the snow under the action of the snowmelt. Then, based on the time series data of the snow thickness, an intelligent algorithm is used to estimate the amount of snowmelt. By comparing the historical data of different snow melting speeds and the amount of snowmelt used, the amount of snowmelt spread by the drone in the target area can be estimated more accurately. Finally, based on the amount of deicing agent used, the expected corrosion state of the bridge pavement (the possible degree of corrosion in the future) can be determined by using the known positive correlation between corrosivity and the amount of deicing agent. It can also provide early warning of potential corrosion risks and provide a scientific basis for bridge maintenance.

[0082] In one embodiment, the step S700: estimating the amount of de-icing agent according to the time series data of the snow thickness to obtain the amount of de-icing agent spread by the drone in the target area includes: obtaining the rate of change of the snow thickness and the absolute amount of reduction according to the time series data of the snow thickness; inputting the rate of change of the snow thickness and the absolute amount of reduction into a pre-constructed de-icing agent amount estimation model to obtain the amount of de-icing agent spread by the drone in the target area, and the de-icing agent amount estimation model satisfies the following expression:

[0083] Q=Q0+13.8R+2.03ΔH, where R is the rate of change of snow thickness, ΔH is the absolute reduction in snow thickness, and Q0 is the magnitude control amount of the de-icing agent.

[0084] Specifically, firstly, the time series data of snow thickness is used to calculate the rate of change of snow thickness (R) and the absolute amount of snow thickness reduction (ΔH). These two parameters reflect the speed and degree of snow melting under the action of deicing agent, and are important bases for estimating the amount of deicing agent used. Then, the calculated rate of change of snow thickness (R) and the absolute amount of snow thickness reduction (ΔH) are used as inputs and imported into a pre-built deicing agent quantity estimation model. This model is based on a large amount of historical data and scientific experiments, and can accurately predict the amount of deicing agent required under given snow melting conditions. The specific expression of the model is: Q = Q0+13.8R+2.03ΔH, where Q represents the estimated amount of deicing agent, and Q0 is a basic control quantity for adjusting the magnitude of the deicing agent quantity, which can be preset according to factors such as bridge type, pavement material, and climatic conditions. In this way, through this estimation process, the present application can more accurately grasp the amount of deicing agent spread by the drone in the target area, thereby providing strong data support for subsequent bridge pavement corrosion state prediction and maintenance decisions. The introduction of this embodiment not only improves the accuracy of using snow-melting agents, but also helps to reduce the potential corrosion risk to bridge pavement.

[0085] In one embodiment, the data management platform also includes a processor, and the processor is further configured to implement the following steps of the integrated data management method for bridge construction, management and maintenance: obtaining a high-resolution static image of a bridge guardrail collected by a drone; performing semantic analysis on the high-resolution static image using semantic segmentation technology to obtain a preprocessed image of the bridge guardrail; performing grayscale processing on the preprocessed image of the bridge guardrail to obtain a first target area and a second target area of ​​the preprocessed image; performing contrast enhancement processing on the first target area and the second target area to obtain a target image of the bridge guardrail; performing corrosion status analysis on the target image of the bridge guardrail to obtain the corrosion status of the bridge guardrail.

[0086] Specifically, the processor first acquires high-resolution static images of the bridge guardrail collected by the drone. These images capture every detail of the guardrail with extremely high clarity, providing a solid foundation for subsequent semantic analysis. Then the processor uses semantic segmentation technology to perform in-depth semantic analysis on these high-resolution static images. This step aims to accurately extract relevant information about the bridge guardrail from the complex image background, thereby generating a preprocessed image of the bridge guardrail. The preprocessed image removes unnecessary background interference, making the details of the guardrail more prominent. The processor then grayscales the preprocessed image of the bridge guardrail. This step converts the image into a grayscale mode so that different areas in the image (such as the corroded and non-corroded parts of the guardrail) have a sharp contrast in brightness. Through grayscale processing, the first target area (such as the corroded area) and the second target area (such as the non-corroded area) in the preprocessed image can be clearly identified. In order to further enhance the contrast between the first target area and the second target area, the processor performs contrast enhancement processing on the two areas. This makes the corroded area more conspicuous, which facilitates the subsequent corrosion state analysis. Finally, the processor will perform a detailed corrosion status analysis on the target image of the bridge guardrail. By comparing the features of the rusted area in the target image (such as area, shape, color, etc.), the processor can accurately assess the corrosion status of the bridge guardrail and generate corresponding analysis data.

[0087] In one embodiment, the step of: performing a corrosion status analysis on the target image of the bridge guardrail to obtain the corrosion status of the bridge guardrail includes: calculating the area ratio value of the corresponding area based on the first target area and the second target area; determining the one with the smaller area ratio value between the first target area and the second target area as the corrosion area, and determining the degree of corrosion based on the area ratio value of the corrosion area.

[0088] Specifically, the processor first calculates the area ratio values ​​of the corresponding areas based on the first target area (such as the rusted area) and the second target area (such as the non-rusted area) in the target image of the bridge guardrail. Then the processor compares the area ratio values ​​of the first target area and the second target area to determine which area has a smaller area ratio value. Since the proportion of the corroded area in the image is usually smaller than that of the non-corroded area, and the corroded area usually represents the corroded part of the guardrail, the area with a smaller area ratio value is determined as the corroded area. Finally, the processor determines the degree of corrosion based on the area ratio value of the corroded area. Specifically, a series of area ratio value thresholds can be set to divide the degree of corrosion into different levels (such as slight corrosion, moderate corrosion, severe corrosion, etc.). By comparing the area ratio value of the corroded area with these thresholds, the processor can accurately assess the corrosion status of the bridge guardrail and generate a corresponding analysis report.

[0089] After obtaining the corrosion status data of the bridge pavement and bridge guardrail, the bridge simulation model construction module can construct a bridge corrosion status simulation model based on the input bridge construction parameters and corrosion status data, and display it on local terminals and mobile terminals through the bridge simulation model display module, so that bridge managers can intuitively understand the actual corrosion status of the bridge under different conditions.

[0090] Based on this, the integrated data management platform for bridge construction, management and maintenance of the embodiment of the present application closely combines the construction, management and maintenance of bridges through an integrated management method, and realizes full-chain management from data entry, status monitoring, status assessment, simulation to maintenance suggestion generation, which is conducive to improving the overall level of bridge management, reducing management costs and improving management efficiency.

[0091] An embodiment of the present application also provides an integrated data management device for bridge construction, management and maintenance, including a memory 101 and a processor 102, wherein the memory 101 is used to store program code, and the processor 102 is used to call the program code to execute the integrated data management method for bridge construction, management and maintenance as described above.

[0092] Among them, the processor 102 is used to provide computing and control capabilities to control the bridge construction, management and maintenance integrated data management device to perform corresponding tasks, for example, controlling the bridge construction, management and maintenance integrated data management device to perform the bridge construction, management and maintenance integrated data management method in any of the above-mentioned method embodiments, the method comprising: collecting multiple frames of images of real-time changes in snow accumulation on the bridge pavement after the drone spreads snow-melting agent at high altitude; using image segmentation technology to extract the target area of ​​the bridge pavement from the image to obtain the target image of the bridge pavement; using image deep learning technology to perform image analysis on the target image of the bridge pavement to obtain time series data of snow thickness; estimating the amount of snow-melting agent based on the time series data of snow thickness to obtain the amount of snow-melting agent spread by the drone in the target area; determining the corrosion state of the bridge pavement based on the amount of snow-melting agent spread by the drone in the target area, wherein the degree of corrosion of the bridge pavement is positively correlated with the amount of snow-melting agent.

[0093] The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0094] The memory 101 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the data management method in the embodiments of the present application. The processor 102 can implement the data management method in any of the above method embodiments by running the non-transitory software programs, instructions and modules stored in the memory 101.

[0095] Specifically, the memory 101 may include a volatile memory (VM), such as a random access memory (RAM); the memory 101 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD) or other non-transitory solid-state storage device; the memory 101 may also include a combination of the above types of memories.

[0096] In summary, the integrated data management equipment for bridge construction, management and maintenance of the present application adopts the technical solution of any one of the above-mentioned data management method embodiments, and therefore, has at least the beneficial effects brought by the technical solution of the above-mentioned embodiments, which will not be described one by one here.

[0097] The embodiment of the present application also provides a computer-readable storage medium, such as a memory including a program code, and the program code can be executed by a processor to complete the data management method in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0098] The present application also provides a computer program product, which includes one or more program codes stored in a computer-readable storage medium. The processor of the bridge construction, management and maintenance integrated data management device reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the data management method provided in the above embodiment.

[0099] A person skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by hardware associated with a program code, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0100] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0102] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A bridge construction, management and maintenance integrated data management platform, characterized in that: The data management platform includes: A bridge construction parameter input module, including a bridge information input interface, wherein the bridge information input interface is used to input construction parameters of a target bridge, wherein the construction parameters include at least bridge length, bridge pier span, bridge pavement width, and guardrail height parameters; The bridge operation status acquisition module includes a bridge monitoring drone, wherein the bridge monitoring drone is provided with a road surface environment status acquisition sensor and a guardrail status acquisition sensor, wherein the road surface environment status acquisition sensor is used to acquire a real-time change image of snow accumulation on the bridge road surface after the drone spreads snow melting agent at high altitude, and the guardrail status acquisition sensor is used to acquire static images of the bridge guardrail at different time periods; A bridge status analysis and evaluation module is used to analyze the corrosion status of the bridge pavement and the bridge guardrail based on the real-time changing image of snow accumulation on the bridge pavement and the static image of the bridge guardrail, wherein the corrosion status includes the corrosion type or the corrosion degree; A bridge simulation model building module is used to build a bridge corrosion state simulation model according to input bridge construction parameters, bridge pavement and bridge guardrail corrosion state, and the bridge corrosion state simulation model is used to simulate the real corrosion state of the bridge under different conditions; A bridge simulation model display module, including a local terminal and a mobile terminal, is used to display the bridge corrosion state simulation model; A simulation model parameter interaction module, used to manually correct the model parameters of the bridge corrosion state simulation model, wherein the model parameters include the estimated bridge corrosion speed and / or corrosion degree; A bridge maintenance suggestion generation module, used to automatically generate corresponding management suggestions and maintenance suggestions according to the corrosion status of the bridge pavement and bridge guardrail and the corrected estimated bridge corrosion speed and / or corrosion degree, wherein the management suggestions include the monitoring frequency of the bridge monitoring drone, and the maintenance suggestions include the maintenance plan and maintenance cycle; The data management platform further includes a processor, and the processor is configured to implement the following steps of the data management method: After the UAV spreads snow-melting agent at high altitude, multiple frames of images of real-time changes in snow accumulation on the bridge pavement are collected; By using image segmentation technology, the bridge pavement target area is extracted from the image to obtain the bridge pavement target image; Through image deep learning technology, image analysis is performed on the target image of the bridge pavement to obtain the time series data of snow thickness; The amount of de-icing agent is estimated based on the time series data of the snow thickness to obtain the amount of de-icing agent spread by the drone in the target area; The expected corrosion state of the bridge pavement is determined according to the amount of deicing agent spread by the UAV in the target area, wherein the expected corrosion degree of the bridge pavement is positively correlated with the amount of deicing agent.

2. The bridge construction, management and maintenance integrated data management platform according to claim 1, characterized in that: The data management platform also includes an atmospheric environment state detection sensor and a drone management module. The atmospheric environment state detection sensor is used to detect the atmospheric environment state at the current geographical location of the target bridge, and the drone management module is used to control the flight trajectory and flight altitude of the drone according to the atmospheric environment state.

3. The bridge construction, management and maintenance integrated data management platform according to claim 1 is characterized in that: The bridge status analysis and evaluation module includes an image preprocessing submodule and a corrosion status analysis submodule. The image preprocessing submodule is used to preprocess the collected image to obtain a target image, and the corrosion status analysis submodule is used to analyze the target image to obtain the corrosion status of the bridge pavement and guardrail, and output the evaluation results of the corrosion type or corrosion degree.

4. The bridge construction, management and maintenance integrated data management platform according to claim 1, characterized in that: The data management platform also includes bridge traffic flow monitoring sensors and vehicle traffic guidance lighting equipment, which are used to monitor and record vehicle traffic data on the bridge in real time. The vehicle traffic data includes vehicle flow, vehicle type distribution and bridge load conditions; the vehicle traffic guidance lighting equipment is used to guide vehicles to enter the left lane or right lane of the bridge pavement according to the vehicle traffic data to balance the load on the left and right sides of the bridge pavement.

5. The bridge construction, management and maintenance integrated data management platform as claimed in claim 4, characterized in that: The data management platform further includes a processor, and the processor is configured to implement the following steps of the data management method: Obtain vehicle traffic data on the left and right lanes of the target bridge in real time; Determine the load difference between the left and right sides of the target bridge according to the vehicle traffic data of the left lane and the vehicle traffic data of the right lane of the target bridge; The vehicle traffic guidance lighting equipment is controlled to turn on according to the load difference between the left and right sides of the target bridge to guide at least some vehicles to travel in a direction to reduce the load difference.

6. The bridge construction, management and maintenance integrated data management platform according to claim 1, characterized in that: The road environment status acquisition sensor includes a first high-definition camera, which is arranged at the bottom of the bridge monitoring drone. The guardrail status acquisition sensor includes a second high-definition camera, which is arranged on both sides of the bridge monitoring drone.

7. The bridge construction, management and maintenance integrated data management platform according to claim 1, characterized in that: The method of estimating the amount of snow-melting agent according to the time series data of snow thickness to obtain the amount of snow-melting agent spread by the drone in the target area includes: Obtaining the rate of change and absolute amount of reduction of snow thickness according to the time series data of snow thickness; The change rate of the snow thickness and the absolute amount of reduction are input into the pre-built snow-melting agent quantity estimation model to obtain the amount of snow-melting agent spread by the drone in the target area. The snow-melting agent quantity estimation model satisfies the following expression: Q=Q0+13.8R+2.03ΔH, where R is the rate of change of snow thickness, ΔH is the absolute reduction in snow thickness, and Q0 is the magnitude control amount of the de-icing agent.

8. The bridge construction, management and maintenance integrated data management platform according to claim 1, characterized in that: The data management platform also includes a processor, and the processor is further configured to implement the following steps of the data management method: Obtain high-resolution still images of bridge guardrails collected by drones; Using semantic segmentation technology to perform semantic analysis on the high-resolution static image to obtain a preprocessed image of the bridge guardrail; Performing grayscale processing on the preprocessed image of the bridge guardrail to obtain a first target area and a second target area of ​​the preprocessed image; Performing contrast enhancement processing on the first target area and the second target area to obtain a target image of the bridge guardrail; The corrosion state of the bridge guardrail is analyzed by performing corrosion state analysis on the target image of the bridge guardrail to obtain the corrosion state of the bridge guardrail.

9. The bridge construction, management and maintenance integrated data management platform as claimed in claim 8, characterized in that: The step of performing corrosion state analysis on the target image of the bridge guardrail to obtain the corrosion state of the bridge guardrail includes: Calculating the area ratio value of the corresponding area according to the first target area and the second target area; The one with the smaller area ratio value between the first target area and the second target area is determined as the corrosion area, and the corrosion degree is determined according to the area ratio value of the corrosion area.

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