BIM (Building Information Modeling)-based visual analysis system for whole-process management of defects of river-crossing bridge

Abstract

The invention relates to the technical field of bridge defect analysis systems, in particular to a BIM (Building Information Modeling)-based river-crossing bridge defect whole-process management visual analysis system, which comprises a data acquisition module, a standard disease library module, a BIM model management module, a disease management module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module, a BIM database module and a BIM database module, the disease management module is in communication connection with the data acquisition module, the standard disease library module and the BIM model management module, the maintenance disposal module is in communication connection with the disease management module, and the data analysis module is in communication connection with the disease management module and the maintenance disposal module. Generating a disease distribution and type proportion analysis chart; and the visual display module is in communication connection with the BIM model management module, the disease management module and the data analysis module, and the system realizes whole-process management from data acquisition to analysis decision through integration of modular design and advanced technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge defect analysis systems, and more specifically, to a BIM-based visualization analysis system for the whole process management of defects in a cross-river bridge. Background Art

[0002] In recent years, with the rapid development of infrastructure construction in my country, the safe operation and long-term maintenance of cross-river bridges as important transportation hubs have become the focus of industry attention. Traditional bridge defect management methods mainly rely on manual inspections and paper records, which have many problems. First, data collection efficiency is low, and inspectors need to manually record a large amount of information, which is prone to omissions or errors. Secondly, data storage and management are chaotic, and paper records are difficult to effectively archive and retrieve, resulting in low utilization of historical data. Furthermore, the defect analysis process is highly subjective, and different engineers may give different evaluation results for the same defect, lacking a unified standard.

[0003] With the development of information technology, some digital management systems have begun to be applied in the field of bridge maintenance. These systems usually include spreadsheet records and simple database management functions, which improve the efficiency of data management to a certain extent. However, such systems still have many shortcomings. For example, the lack of intuitive visual display makes it difficult to quickly locate the defect location; the data analysis ability is limited and it is impossible to explore deep rules and trends; the system operates in isolation and is difficult to effectively integrate with other maintenance management processes.

[0004] Recently, the widespread application of Building Information Modeling (BIM) technology in the engineering field has brought new possibilities for bridge defect management. Some studies have attempted to introduce BIM technology into bridge maintenance management, but most of them remain at the concept verification stage and have not yet formed a complete solution. Although these initial attempts have demonstrated the potential of BIM technology, there are still many problems. For example, the BIM model is not closely related to the actual defect data, making it difficult to achieve real-time updates; the lack of a standardized defect classification and evaluation system affects the consistency and comparability of the data; and the lack of data analysis and prediction capabilities makes it difficult to provide strong support for long-term maintenance decisions.

[0005] In addition, the existing bridge defect management systems generally have problems such as unfriendly user interface, poor system scalability, and insufficient data security, which seriously restrict the application and promotion of the system in actual engineering.

[0006] In view of the above problems, there is an urgent need for a system that can integrate BIM technology, big data analysis, and artificial intelligence advanced technology to achieve full-process management and visual analysis of cross-river bridge defects. The system should be able to provide standardized data collection, real-time visual display, intelligent defect analysis, and scientific maintenance decision support, while also having good scalability and data security. Summary of the invention

[0007] The present invention is proposed to meet the above needs. The present invention provides a visual analysis system for the whole process management of cross-river bridge defects based on BIM, aiming to solve the problems of low data collection efficiency, insufficient analysis ability, low visualization and poor system integration in the prior art.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The BIM-based visual analysis system for the whole process management of cross-river bridge defects includes:

[0010] Data acquisition module for:

[0011] Collect disease information through mobile APP or PC;

[0012] Obtain disease photos, types, locations, and discovery time data;

[0013] The standard disease library module is connected to the data acquisition module for:

[0014] Store disease classification information based on national and industry standards;

[0015] Provide standardized descriptions of disease types, grades, and characteristics;

[0016] The BIM model management module is connected to the standard disease library module for:

[0017] Manage the BIM model of the cross-river bridge;

[0018] Provide model views with different levels of accuracy according to disease management needs;

[0019] The disease management module is connected to the data acquisition module, the standard disease library module and the BIM model management module for:

[0020] Receiving disease information collected by the data collection module;

[0021] Classifying and rating disease information based on the standards of the standard disease library module;

[0022] Marking the location of the defect on the model provided by the BIM model management module;

[0023] The maintenance and treatment module is in communication with the disease management module and is used to:

[0024] Develop a repair plan for damage;

[0025] Record maintenance process and results;

[0026] The data analysis module is in communication with the disease management module and the maintenance and treatment module and is used to:

[0027] Statistical analysis of disease data;

[0028] Generate disease distribution and type ratio analysis charts;

[0029] The visualization display module is in communication with the BIM model management module, the disease management module and the data analysis module, and is used to:

[0030] Visually display the location and information of the damage on the BIM model;

[0031] Display data analysis results and statistical charts.

[0032] Preferably, the data acquisition module comprises:

[0033] Mobile terminal collection unit, used to collect on-site disease information through mobile device APP;

[0034] PC-side collection unit, used to input disease information through the PC-side system interface;

[0035] The data synchronization unit is used to synchronize the data collected by the mobile terminal to the system central database in real time.

[0036] Preferably, the standard disease library module comprises:

[0037] Classification management unit, used to manage the defect information according to the multi-level structure of professional type-component-component-defect type-level;

[0038] Characterization unit, which provides quantitative and qualitative characterization of each disease type and class;

[0039] The update and maintenance unit is used to dynamically update and improve the standard disease library based on actual needs and expert review results.

[0040] Preferably, the BIM model management module includes:

[0041] Model storage unit, used to store the complete BIM model of the cross-river bridge;

[0042] The precision grading unit is used to generate model views of different precision levels according to the LOD1 to LOD4 standards;

[0043] Lightweight processing unit, used to perform lightweight processing on BIM models to meet the needs of online display.

[0044] Preferably, the disease management module comprises:

[0045] An information receiving unit, used for receiving and storing collected disease information;

[0046] Classification and rating unit, used to automatically classify and rate diseases according to a standard disease library;

[0047] Position marking unit, used to accurately mark the location of the defect on the BIM model;

[0048] The review processing unit is used to manually review and confirm the disease information.

[0049] Preferably, the maintenance and treatment module comprises:

[0050] The plan formulation unit is used to formulate corresponding maintenance plans according to the type and severity of the disease;

[0051] Process recording unit, used to record the operation steps and intermediate results during the maintenance process;

[0052] Quality acceptance unit, used to conduct quality acceptance and evaluation of maintenance results;

[0053] The tracking and monitoring unit is used to follow up and monitor the repaired defects.

[0054] Preferably, the data analysis module comprises:

[0055] Statistical analysis unit, used to conduct multi-dimensional statistical analysis of disease data;

[0056] Trend prediction unit, used to analyze disease development trends based on historical data;

[0057] The report generation unit is used to automatically generate disease analysis reports.

[0058] Preferably, the visual display module includes:

[0059] Model interaction unit, used to realize the rotation, scaling, and roaming interactive operations of BIM models;

[0060] Defect marking unit, used to mark the location and severity of defects on the BIM model with different colors and icons;

[0061] Data chart unit is used to visually display disease statistics and analysis results in the form of charts.

[0062] As a preference, it also includes:

[0063] User authority management module, used to manage system access rights and operation rights of different users;

[0064] Data security module, used for encrypted storage and secure transmission of system data;

[0065] The system log module is used to record all operation logs of the system to facilitate later auditing and problem tracking.

[0066] Preferably, the system is deployed using a cloud server, provides an access interface through Web services, and supports simultaneous online operations by multiple users and real-time synchronization of data.

[0067] Compared with the prior art, the beneficial effects of the present invention are embodied in the following aspects:

[0068] The system of the present invention realizes the whole process management from data collection to analysis and decision-making through modular design and integration of advanced technologies. The core advantages of the system are reflected in the following aspects:

[0069] First of all, the present invention realizes the efficiency and standardization of data collection. Through the mobile APP and PC interface, the system supports multiple ways of defect information entry, which greatly improves the efficiency of data collection. At the same time, the system integrates a standardized defect classification library to ensure the consistency and comparability of data. This not only improves the data quality, but also lays the foundation for subsequent analysis work.

[0070] Secondly, the present invention deeply integrates BIM technology with defect management to achieve intuitive visual display. Through the BIM model management module, the system associates abstract defect data with specific bridge structures, making the defect location and severity clear at a glance. This intuitive visualization method greatly improves managers' understanding and grasp of bridge conditions.

[0071] Furthermore, the present invention introduces advanced data analysis technology, which significantly enhances the intelligence level of the system. Through the data analysis module, the system can automatically identify defect types, predict development trends, and generate maintenance suggestions. These functions not only reduce the workload of engineers, but also provide strong support for scientific decision-making.

[0072] In addition, the present invention adopts modularization and microservice architecture design, which greatly improves the scalability and maintainability of the system. The functional modules communicate through standardized interfaces, which not only ensures the overall performance of the system, but also allows the functions to be flexibly added or modified according to actual needs.

[0073] Finally, the present invention attaches great importance to data security and system reliability. Through strict permission management, data encryption and logging measures, the system ensures the security of sensitive information. At the same time, the cloud service deployment method improves the availability and stability of the system.

[0074] In summary, the system of the present invention has effectively solved the problems existing in the prior art through a number of innovations, and provided a comprehensive, efficient and intelligent solution for the defect management of the cross-river bridge. This can not only significantly improve the efficiency of daily maintenance and reduce maintenance costs, but also provide reliable decision-making support for the long-term safe operation of the bridge. More importantly, the design concept and technical solution of this system have a wide range of applicability and are expected to be promoted and applied to other types of large-scale infrastructure management, making an important contribution to the intelligent management of my country's infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flowchart of the overall system of the present invention.

[0076] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0077] Figure 3 It is a logic block diagram of the standard disease library module of the present invention.

[0078] Figure 4 It is a logic block diagram of the BIM model management module of the present invention.

[0079] Figure 5 It is a logic block diagram of the disease management module of the present invention.

[0080] Figure 6 It is a logic block diagram of the maintenance processing module of the present invention.

[0081] Figure 7 It is a logic block diagram of the data analysis module of the present invention.

[0082] Figure 8 It is a logic block diagram of the visual display module of the present invention. DETAILED DESCRIPTION

[0083] like Figure 1-8 As shown, the present invention provides a visual analysis system for the whole process management of cross-river bridge defects based on BIM. The system aims to solve the problems of non-standardized data, incomplete processes, insufficient analysis, etc. in traditional bridge defect management, and realizes the visualization, refinement and intelligence of bridge defect management through advanced information technology means.

[0084] Specifically, the system of the present invention includes the following modules:

[0085] The data collection module 1 is used to collect disease information through a mobile APP or PC, and obtain data such as disease photos, types, locations, and discovery time. Preferably, the module can support offline data collection and automatically synchronize data after the network is restored to meet the needs of on-site work. For example, inspectors can use mobile devices to take photos of cracks on the bridge site and enter information such as crack length and width.

[0086] The standard defect library module 2 is connected to the data acquisition module 1 for storing defect classification information based on national standards and industry standards, and providing standardized descriptions of defect types, grades and characteristics. In one embodiment of the present invention, the standard defect library adopts a multi-level structure of professional type-component-component-disease type-grade, making the defect classification more detailed and comprehensive. For example, for the asphalt concrete pavement of the bridge deck system, the defect type of "pothole" can be defined and further divided into three grades: slight, medium and severe, where the slight grade is defined as "total area < 3% of the bridge deck area".

[0087] The BIM model management module 3 is connected to the standard defect library module 2 for managing the BIM model of the cross-river bridge and providing model views of different accuracy levels (LOD1 to LOD4) according to the defect management requirements. Preferably, this module adopts lightweight processing technology to ensure the smooth display of large BIM models in a web environment. For example, for a cable-stayed bridge with a span of 1,000 meters, the LOD1 level may only show the outlines of the main beams and towers, while the LOD4 level can accurately display the position of each prestressed steel bar.

[0088] The defect management module 4 is in communication with the data acquisition module 1, the standard defect library module 2 and the BIM model management module 3. The module receives the defect information collected by the data acquisition module 1, classifies and rates the defect information based on the standards of the standard defect library module 2, and marks the defect location on the model provided by the BIM model management module 3. In the system of the present invention, the defect management module 4 also includes an intelligent auxiliary unit, which uses a machine learning algorithm to automatically identify the type and severity of the defect based on the defect photo. For example, for a photo showing a crack on the bridge deck, the system can automatically calculate the crack width and perform a preliminary classification based on a preset threshold (such as a width > 0.3 mm for a medium crack).

[0089] The repair and treatment module 5 is in communication with the defect management module 4 and is used to formulate a defect repair plan and record the repair process and results. Preferably, the module includes an expert knowledge base that can automatically generate preliminary repair suggestions based on the type and severity of the defect. For example, for minor cracks on the bridge deck, the system may recommend grouting; for severe structural cracks, carbon fiber reinforcement may be recommended.

[0090] The data analysis module 6 is connected to the disease management module 4 and the maintenance and treatment module 5 for statistical analysis of disease data and generating analysis charts such as disease distribution and type proportion. In one embodiment of the present invention, the module uses a time series analysis algorithm to predict the development trend of diseases. For example, by analyzing the disease data of the past five years, the system can predict the possible development of various diseases in the next two years, providing a basis for formulating preventive maintenance strategies.

[0091] The visualization display module 7 is connected to the BIM model management module 3, the defect management module 4 and the data analysis module 6 for visually displaying the defect location and information on the BIM model, and displaying the data analysis results and statistical charts. Preferably, the module supports multi-dimensional data visualization, including advanced visualization methods such as heat maps and 3D bar graphs. For example, the system can visually display the density of defects in various parts of the bridge through heat maps, and the red area indicates the area where defects frequently occur and needs special attention.

[0092] In a preferred embodiment of the present invention, the data acquisition module 1 includes the following units:

[0093] The mobile terminal collection unit 11 is used to collect on-site disease information through a mobile device APP. This unit supports offline working mode and adapts to the situation where the on-site network conditions are poor. For example, the inspectors can use a smartphone or tablet computer to take pictures of the disease at the bridge site and fill in the relevant information through a specially developed APP.

[0094] The PC terminal acquisition unit 12 is used to input disease information through the PC terminal system interface. This unit is mainly used for background data sorting and supplementation. For example, a technician can input detailed disease description, analysis report and other information through the PC terminal system in the office.

[0095] The data synchronization unit 13 is used to synchronize the data collected by the mobile terminal to the system central database in real time. This unit adopts an incremental synchronization strategy and only transmits newly added or modified data to improve synchronization efficiency. For example, when the inspection personnel return to the office area and connect to the WiFi network, the APP will automatically upload all disease data collected that day to the central server.

[0096] In another preferred embodiment of the present invention, the standard disease library module 2 includes the following units:

[0097] The classification management unit 21 is used to manage the defect information according to the multi-level structure of professional type-component-component-disease type-level. This structural design makes the defect classification more systematic and standardized. For example, for the professional type of "bridge deck system", it can be further subdivided into components such as "bridge deck pavement" and "expansion joint", and "bridge deck pavement" can be subdivided into components such as "asphalt concrete pavement" and "cement concrete pavement".

[0098] The feature description unit 22 is used to provide quantitative and qualitative feature descriptions of each disease type and level. This unit standardizes the disease characteristics to facilitate communication and understanding among different personnel. For example, for bridge deck cracks, minor cracks can be defined as "width < 0.3mm, length < 1m", medium cracks as "0.3mm ≤ width < 1mm, 1m ≤ length < 3m", and severe cracks as "width ≥ 1mm or length ≥ 3m".

[0099] The update maintenance unit 23 is used to dynamically update and improve the standard defect library according to actual needs and expert review results. This ensures the applicability and advancement of the defect library. For example, when a new bridge structure or new material appears, the corresponding defect classification standard can be discussed and formulated through an expert review meeting, and then updated to the standard defect library by the system administrator through this unit.

[0100] The system of the present invention realizes the whole process management and visual analysis of the defects of the cross-river bridge through the collaborative work of the above modules and units. The workflow of the system is as follows: First, the inspection personnel use the mobile terminal acquisition unit 11 of the data acquisition module 1 to collect the defect information on site; then, this information is uploaded to the central database through the data synchronization unit 13; then, the defect management module 4 classifies and rates the defects based on the standards of the standard defect library module 2; then, the maintenance and disposal module 5 formulates a maintenance plan according to the defect situation; at the same time, the data analysis module 6 performs statistical analysis on the defect data; finally, all this information is intuitively presented on the BIM model through the visual display module 7.

[0101] This whole-process management method has significant advantages. First, it realizes the standardization and structuring of disease data, providing a reliable data basis for subsequent analysis and decision-making. Second, through the application of BIM technology, disease information is intuitively associated with bridge structure, which makes it easier for managers to fully understand the condition of the bridge. Third, the system's data analysis function can reveal the law of disease development and assist in the formulation of preventive maintenance strategies. Finally, the visualization function greatly improves the readability of information and the efficiency of decision-making.

[0102] For example, for a 15-year-old cable-stayed bridge, the system may find that the concrete cracks near the main cables have an increasing trend year by year. By analyzing historical data, the system may derive the following prediction model:

[0103] N(t)=N0+αt 2

[0104] Among them, N(t) is the number of cracks after t years, N0 is the initial number of cracks, and α is the growth coefficient. Assuming that N0=5$ and α=0.2 are obtained through data fitting, the system can predict the number of cracks after 5 years as follows:

[0105] N(5)=5+0.2*5 2 =10

[0106] Based on this prediction, the system will issue an early warning to managers, recommending a comprehensive inspection and necessary reinforcement of the main cable area within the next 2-3 years.

[0107] In summary, the system of the present invention provides a comprehensive, efficient and intelligent solution for defect management of cross-river bridges by integrating multiple advanced technologies. It can not only improve the efficiency of daily maintenance, but also provide strong support for the formulation of long-term maintenance strategies for bridges, which is of great significance for ensuring the safe operation and extending the service life of cross-river bridges. In a preferred embodiment of the present invention, the BIM model management module 3 includes the following units:

[0108] The model storage unit 31 is used to store the complete BIM model of the cross-river bridge. The unit adopts distributed storage technology to ensure efficient access and management of large model data. For example, for a cross-sea bridge with a total length of 3 kilometers, its complete BIM model may reach a scale of tens of GB. Through distributed storage, the system can quickly load the required model part according to the user's access needs without loading the entire model at one time.

[0109] The accuracy grading unit 32 generates model views of different accuracy levels according to the LOD1 to LOD4 standards. This grading mechanism can flexibly adjust the display accuracy of the model according to the needs of the user and the performance of the equipment. For example, when performing an overview of the entire bridge, the system may use a model at the LOD1 level to display only the main structure of the bridge; while when checking specific components, it may switch to the LOD4 level to display fine structural details.

[0110] The lightweight processing unit 33 performs lightweight processing on the BIM model to meet the needs of online display. The unit adopts advanced geometric simplification algorithms and texture compression technology to significantly reduce the amount of data while ensuring the visual effect of the model. Preferably, the system of the present invention adopts a simplification algorithm based on importance, and can dynamically adjust the degree of simplification according to the importance of the component and the observation distance. For example, for minor components in the distance, the system may simplify them into simple geometric bodies, while for key components in the vicinity, a higher geometric accuracy is retained.

[0111] The disease management module 4 of the present invention includes the following units:

[0112] The information receiving unit 41 is used to receive and store the collected damage information. The unit adopts a high-concurrency data processing architecture and can process damage information from multiple sources at the same time. For example, during a large-scale bridge inspection, dozens of inspectors may upload damage data at the same time. The unit can ensure timely reception and correct storage of the data.

[0113] The classification and rating unit 42 automatically classifies and rates the defects according to the standard defect library. The unit uses machine learning algorithms, especially convolutional neural networks (CNNs), to intelligently identify and classify defect images. For example, for a photo showing a crack on a bridge deck, the system may use the following algorithm flow:

[0114] 1. Image preprocessing: noise reduction, enhancement and other processing are performed on the original image.

[0115] 2. Feature extraction: Use a pre-trained CNN model (such as ResNet50) to extract image features.

[0116] 3. Classification: Input the extracted features into the trained classifier to obtain the disease type and severity.

[0117] Preferably, the accuracy of the algorithm can reach more than 90%, greatly improving the efficiency and consistency of disease classification.

[0118] The location marking unit 43 accurately marks the location of the defect on the BIM model. This unit uses image recognition and spatial positioning technology to map the location of the defect in the two-dimensional photo to the three-dimensional BIM model. For example, when an inspector uploads a photo of a crack in a bridge pier, the system will automatically analyze the reference objects in the photo (such as the bridge pier number), and combine the GPS positioning information to accurately mark the crack at the corresponding position of the BIM model.

[0119] The audit processing unit 44 manually audits and confirms the disease information. This unit sets up a multi-level audit mechanism to ensure the accuracy of the disease information. For example, for a disease determined by the system to be a serious level, it may require the audit and confirmation of at least two senior engineers.

[0120] In one embodiment of the present invention, the maintenance handling module 5 includes the following units:

[0121] The plan formulation unit 51 formulates a corresponding maintenance plan based on the type and severity of the disease. This unit integrates the expert knowledge base and case library, and can automatically generate preliminary maintenance suggestions based on historical maintenance experience. For example, for minor cracks on the bridge deck, the system may recommend the use of epoxy resin infusion; while for severe structural cracks, it may recommend carbon fiber reinforcement.

[0122] The process recording unit 52 records the operation steps and intermediate results in the maintenance process. This unit uses a structured data model to record in detail the time, personnel, materials, methods and other information of each maintenance operation. For example, for a bridge pier reinforcement project, the system will record the entire process from surface treatment, steel bar rust removal to concrete pouring, including details such as the material ratio used and the curing time.

[0123] The quality acceptance unit 53 performs quality acceptance and evaluation on the repair results. The unit sets up standardized acceptance procedures and evaluation indicators to ensure the consistency of repair quality. Preferably, the system of the present invention adopts automatic acceptance technology based on image recognition. For example, for crack repair projects, the system can automatically calculate the change in crack width and evaluate the repair effect by analyzing high-definition images before and after repair.

[0124] The tracking and monitoring unit 54 performs follow-up tracking and monitoring of the repaired defects. The unit is equipped with an intelligent reminder function, which automatically generates a follow-up inspection plan based on the defect type and repair method. For example, for a newly repaired bridge deck crack, the system may recommend a re-inspection 1 month, 3 months, or 6 months after the repair, and automatically send a reminder to the relevant personnel.

[0125] The data analysis module 6 of the present invention includes the following units:

[0126] The statistical analysis unit 61 performs multi-dimensional statistical analysis on the disease data. The unit uses advanced statistical analysis methods, such as multivariate analysis, cluster analysis, etc., to mine valuable information from massive data. For example, the system may find that the frequent occurrence of diseases in a certain section of the bridge is significantly correlated with local climate conditions.

[0127] The trend prediction unit 62 analyzes the development trend of the disease based on historical data. The unit uses time series analysis and machine learning algorithms to predict the future development of the disease. Preferably, the system of the present invention adopts a long short-term memory network (LSTM) model, which can effectively capture the long-term trend and short-term fluctuation of the disease development. For example, for the development of bridge deck cracks, the system may establish the following prediction model:

[0128] L(t)=L0+αt+βsin(ωt+φ)

[0129] Among them, L(t) is the crack length at time t, L0 is the initial length, α is the linear growth rate, and the term βsin(ωt+φ) represents seasonal fluctuations. By fitting historical data, the system can obtain the optimal estimate of these parameters, thereby accurately predicting the future development of cracks.

[0130] The report generation unit 63 is used to automatically generate a disease analysis report. The unit integrates natural language processing technology and can convert complex data analysis results into easy-to-understand text descriptions. For example, the system may generate the following report fragment: "According to the data analysis of the past 5 years, the number of cracks in the main cable area has shown an accelerating growth trend and is expected to increase by 50% in the next 2 years. It is recommended to conduct a comprehensive inspection and preventive maintenance of the main cable area during the next annual maintenance."

[0131] The system of the present invention realizes comprehensive management and in-depth analysis of defects of cross-river bridges through the collaborative work of these modules and units. The system can not only efficiently process daily disease information, but also reveal potential problems and development trends through advanced data analysis technology, providing strong support for long-term maintenance decisions of bridges.

[0132] For example, through long-term data accumulation and analysis, the system may find that there is a significant correlation between bridge fatigue damage and traffic flow. Based on this finding, management departments can formulate more scientific traffic control strategies, such as restricting the passage of heavy vehicles during specific periods of time, thereby delaying the fatigue damage process of bridges.

[0133] In summary, the system of the present invention provides a comprehensive, intelligent and efficient solution for defect management of cross-river bridges by integrating advanced technologies such as BIM technology, artificial intelligence, and big data analysis. It can not only improve the efficiency of daily maintenance, but also provide a scientific basis for the formulation of long-term maintenance strategies for bridges, which is of great significance for ensuring the safe operation and extending the service life of cross-river bridges. In a preferred embodiment of the present invention, the visualization display module 7 includes the following units:

[0134] The model interaction unit 71 is used to implement interactive operations such as rotation, scaling, and roaming of the BIM model. The unit uses advanced three-dimensional graphics rendering technology to ensure that large BIM models can be displayed smoothly on various terminal devices. Preferably, the system of the present invention adopts a rendering solution based on WebGL to support high-performance three-dimensional interaction in ordinary browsers. For example, the user can easily rotate the entire bridge model through a mouse or touch screen, or quickly zoom in on a specific component for detailed observation.

[0135] The defect marking unit 72 is used to mark the location and severity of defects on the BIM model with different colors and icons. The unit is designed with an intuitive visual coding system so that users can quickly identify defects of different types and degrees. For example, the system may use green, yellow, and red to represent minor, moderate, and severe defects, respectively, and use different icons to represent different types of defects such as cracks, corrosion, and deformation. Preferably, the system of the present invention supports dynamic updates of defect annotations. When new defects are entered or the status of existing defects changes, the annotations on the BIM model will be updated in real time.

[0136] The data chart unit 73 is used to intuitively display disease statistics and analysis results in the form of charts. This unit integrates a variety of advanced visualization technologies and can generate a variety of chart types to meet the needs of users at different levels. For example, for management users, the system may generate a line chart of the overall disease trend; for technical personnel, it may provide a more detailed bar chart or scatter chart to show the specific distribution of various diseases. Preferably, the system of the present invention supports interactive charts, and users can obtain more detailed information by clicking or hovering.

[0137] The system of the present invention also includes a user authority management module 8, which is used to manage the system access rights and operation rights of different users. This module adopts a role-based access control (RBAC) model, which can flexibly define and manage user rights. For example, the system may define multiple roles such as inspectors, engineers, and administrators, each of which has different operation rights. Preferably, the system of the present invention supports fine-grained authority control, which can be accurate to the access rights of specific functional modules or even single data fields. This sophisticated authority management ensures the security of data and also improves the availability of the system.

[0138] The data security module 9 is used to encrypt, store and securely transmit system data. Considering the sensitivity and importance of bridge disease data, this module adopts multiple encryption measures. In terms of data storage, the system uses the AES-256 algorithm to encrypt important data; in terms of data transmission, the TLS1.3 protocol is used to ensure communication security. Preferably, the system of the present invention also implements an end-to-end encryption mechanism, so that even system administrators cannot directly access the user's original data. In addition, the module also includes data backup and recovery functions, and regularly backs up important data off-site to ensure that the system can be quickly restored even in extreme cases.

[0139] The system log module 10 is used to record all operation logs of the system, which is convenient for later auditing and problem tracking. This module adopts distributed log collection and storage technology, which can efficiently handle a large number of concurrent log write requests. Each log record contains detailed information such as operation time, operator, operation type, and operation content. Preferably, the system of the present invention implements an intelligent log analysis function, which can automatically detect abnormal operation modes and discover potential security issues in a timely manner. For example, if the system detects a large number of failed login attempts in a short period of time, it will automatically send an alarm to the administrator.

[0140] In one embodiment of the present invention, the system is deployed using a cloud server, and provides an access interface through a Web service, supporting simultaneous online operations by multiple users and real-time data synchronization. This deployment method has significant advantages: first, the elastic scaling characteristics of cloud services can cope with fluctuations in access volume and ensure the stability of the system; second, the distributed characteristics of cloud services improve the reliability of the system, and even if a node fails, it will not affect the overall service; finally, the global deployment capability of cloud services enables the system to provide low-latency access experience for users distributed in different geographical locations.

[0141] Preferably, the system of the present invention adopts a microservice architecture to decouple each functional module into an independent service. This architectural design improves the maintainability and scalability of the system. For example, when a specific function needs to be upgraded, only the corresponding microservice needs to be updated without affecting the operation of other parts. In addition, the microservice architecture also supports a more flexible expansion strategy, which can perform targeted resource allocation according to the load conditions of different services.

[0142] The system of the present invention realizes the comprehensive informatization and intelligentization of defect management of cross-river bridges through the collaborative work of these modules and units. The system not only provides an intuitive visual interface to facilitate users to quickly understand the bridge status, but also ensures the security of sensitive information through strict authority management and data security measures. At the same time, the perfect log recording and analysis functions provide strong support for the continuous optimization and troubleshooting of the system.

[0143] For example, during a large-scale bridge inspection, multiple inspection teams can upload defect information using mobile devices at the same time. The system processes this data in real time, updates the defect annotations on the BIM model, and generates real-time statistical reports. Managers can view the inspection progress and results at any time through the web interface without having to wait for the summary report after all inspections are completed. If serious defects are found, the system will immediately send an alert to the relevant person in charge to ensure that necessary disposal measures are taken in a timely manner.

[0144] In summary, the system of the present invention provides a comprehensive, efficient and safe solution for defect management of cross-river bridges by integrating advanced information technology. It can not only significantly improve the efficiency of daily maintenance, but also provide reliable data support and analysis tools for long-term maintenance decisions of bridges. This is of great significance for ensuring the safe operation of cross-river bridges, extending their service life, and optimizing maintenance costs. At the same time, the design concept and technical solutions of this system are also widely applicable and can be promoted and applied to other types of large-scale infrastructure management, making positive contributions to the intelligent management of my country's infrastructure.

[0145] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. The visual analysis system for the whole process management of defects of cross-river bridges based on BIM is characterized by: include: Data acquisition module for: Collect disease information through mobile APP or PC; Obtain disease photos, types, locations, and discovery time data; The standard disease library module is connected to the data acquisition module for: Store disease classification information based on national and industry standards; Provide standardized descriptions of disease types, grades, and characteristics; The BIM model management module is connected to the standard disease library module for: Manage the BIM model of the cross-river bridge; Provide model views with different levels of accuracy according to disease management needs; The disease management module is connected to the data acquisition module, the standard disease library module and the BIM model management module for: Receiving disease information collected by the data collection module; Classifying and rating disease information based on the standards of the standard disease library module; Marking the location of the defect on the model provided by the BIM model management module; The maintenance and treatment module is in communication with the disease management module and is used to: Develop a repair plan for damage; Record maintenance process and results; The data analysis module is in communication with the disease management module and the maintenance and treatment module and is used to: Statistical analysis of disease data; Generate disease distribution and type ratio analysis charts; The visualization display module is in communication with the BIM model management module, the disease management module and the data analysis module, and is used to: Visually display the location and information of the damage on the BIM model; Display data analysis results and statistical charts.

2. The system according to claim 1, characterized in that The data acquisition module comprises: Mobile terminal collection unit, used to collect on-site disease information through mobile device APP; PC-side collection unit, used to input disease information through the PC-side system interface; The data synchronization unit is used to synchronize the data collected by the mobile terminal to the system central database in real time.

3. The system according to claim 1, characterized in that The standard disease library module includes: Classification management unit, used to manage the defect information according to the multi-level structure of professional type-component-component-defect type-level; Characterization unit, which provides quantitative and qualitative characterization of each disease type and class; The update and maintenance unit is used to dynamically update and improve the standard disease library based on actual needs and expert review results.

4. The system according to claim 1, characterized in that The BIM model management module includes: Model storage unit, used to store the complete BIM model of the cross-river bridge; The precision grading unit is used to generate model views of different precision levels according to the LOD1 to LOD4 standards; Lightweight processing unit, used to perform lightweight processing on BIM models to meet the needs of online display.

5. The system according to claim 1, characterized in that The disease management module includes: An information receiving unit, used for receiving and storing collected disease information; Classification and rating unit, used to automatically classify and rate diseases according to a standard disease library; Position marking unit, used to accurately mark the location of the defect on the BIM model; The review processing unit is used to manually review and confirm the disease information.

6. The system according to claim 1, characterized in that The maintenance processing module includes: The plan formulation unit is used to formulate corresponding maintenance plans according to the type and severity of the disease; Process recording unit, used to record the operation steps and intermediate results during the maintenance process; Quality acceptance unit, used to conduct quality acceptance and evaluation of maintenance results; The tracking and monitoring unit is used to follow up and monitor the repaired defects.

7. The system according to claim 1, characterized in that The data analysis module includes: Statistical analysis unit, used to conduct multi-dimensional statistical analysis of disease data; Trend prediction unit, used to analyze disease development trends based on historical data; The report generation unit is used to automatically generate disease analysis reports.

8. The system according to claim 1, characterized in that The visual display module includes: Model interaction unit, used to realize the rotation, scaling, and roaming interactive operations of BIM models; Defect marking unit, used to mark the location and severity of defects on the BIM model with different colors and icons; Data chart unit is used to visually display disease statistics and analysis results in the form of charts.

9. The system according to claim 1, characterized in that Also includes: User authority management module, used to manage system access rights and operation rights of different users; Data security module, used for encrypted storage and secure transmission of system data; The system log module is used to record all operation logs of the system to facilitate later auditing and problem tracking.

10. The system according to claim 1, characterized in that The system is deployed using a cloud server, provides an access interface through Web services, and supports simultaneous online operations by multiple users and real-time data synchronization.

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