A bridge whole life cycle refined account management method based on BIM digital twinning

Abstract

The present application relates to a kind of based on BIM digital twinning bridge whole life cycle refinement account management method, comprising: establishing bridge overall BIM model, generates global unique ID;Multiple precision model file set is generated;Setting standardized process library and relational database;Real-time switching of color state is carried out;Show core account data;Automatic generation multidimensional visual report;Generate comprehensive early warning information;Push the matter prompt corresponding to it.The present application realizes PC end, mobile phone end, paper archival material three end homologous linkage, by component unique code and consistency check formula;Rely on process completion engineering quantitative calculation and digital twinning color mapping, component completion automatic color change, time lag or quality anomaly real-time early warning, whole bridge construction progress is directly visual, greatly improve the efficiency of site control;Component interactive one-key call full account, pouring person, date, construction method, material consumption, team, acceptance information is clear and traceable, quality responsibility can be defined.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction and digital transportation infrastructure technology, and in particular to a method for refined ledger management of the entire life cycle of bridges based on BIM digital twins. Background Technology

[0002] During the construction of highway bridge projects, management personnel need to frequently review component construction records, track progress, verify material usage, and trace quality responsibility. Currently, this is mainly achieved through the following methods:

[0003] First, BIM models are only used for design display or static viewing. Due to the redundancy of model panels and their large size, they cannot be used normally on mobile phones, tablets, or in environments with weak networks. Furthermore, after being lightweighted, component numbers and attribute information are often lost, making it impossible to link with construction business data.

[0004] Second, the construction electronic ledger, BIM model data, and on-site paper construction records are filled out separately and stored in a scattered manner, lacking a unified component coding association mechanism. This leads to a disconnect between online models, mobile phone data, and paper archives, making it difficult to trace the source of defects and assign responsibility in the later stages.

[0005] Third, construction progress management relies on manual marking on drawings or forms, and cannot automatically drive the model to change color according to the degree of completion of the process. The digital twin platform only displays fixed scenes and lacks dynamic linkage capabilities.

[0006] Fourth, querying information such as the person who poured the components, the amount of materials used, and the acceptance records requires flipping through a large number of paper documents or switching between multiple software programs, which is inefficient and prone to errors and omissions. Summary of the Invention

[0007] To address the challenges of implementing BIM models on-site, fragmented data across multiple platforms, cumbersome record-keeping, insufficient progress visualization, and the inability to achieve closed-loop traceability between online and offline systems, this invention aims to provide a BIM-based digital twin-based method for refined record-keeping throughout the entire bridge lifecycle. This method enables intuitive visualization of the bridge construction progress, significantly improves on-site management efficiency, ensures traceability and definition of quality responsibilities, and comprehensively enhances the refined management capabilities throughout the bridge's entire lifecycle.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a method for refined ledger management of the entire life cycle of bridges based on BIM digital twins, the method comprising the following sequential steps:

[0009] (1) Use REVIT software to establish the overall bridge BIM model of the target project, split the bridge components into the smallest units, generate a globally unique ID for each component after splitting, and bind the globally unique ID to the extended attributes of the overall bridge BIM model, the corresponding field of the electronic ledger database, the mobile offline data package and the traceability identifier of the paper archive.

[0010] (2) Based on the grid compression algorithm, the adaptation level is calculated according to the performance of the terminal device and the network bandwidth parameters. Lightweight BIM models of different precisions are distributed according to the adaptation level. A lossless mapping table of component IDs between the lightweight BIM model and the overall bridge BIM model is established to generate a set of multi-precision model files for subsequent digital twin scene loading and calling.

[0011] (3) Set up a standardized process library, collect construction process ledger data in real time and store it in a relational database, and design data tables for the relational database based on the standardized process library; establish a two-way binding relationship between all construction process ledger data and the corresponding components based on the primary key association through a globally unique ID;

[0012] (4) Load the multi-precision model file set, and select the appropriate lightweight BIM model from the multi-precision model file set according to the terminal device performance and network bandwidth through the adaptive loading formula. Combined with the construction process ledger data, calculate the completion degree of each component and the construction period deviation. According to the preset mapping logic, drive the corresponding precision lightweight BIM model to switch the color status in the digital twin scene in real time, and use the color status to represent the construction progress and quality status of the current component.

[0013] (5) In the digital twin scenario, in response to the user's click or touch command on the displayed BIM component, the core ledger data bound to the component is retrieved from the relational database and displayed in a structured pop-up window based on the globally unique ID of the clicked component. The output ledger information set is generated and the current color status is displayed overlaid and output in the form of a pop-up window. The ledger information set displays the current component status according to the mapping relationship. The core ledger data includes the person in charge of pouring, pouring date, construction method, material usage, work team and acceptance record.

[0014] (6) Based on the actual consumption and planned usage in the construction process ledger data, combined with the completion degree and schedule deviation of each component process, automatically generate a multi-dimensional visualization report, set a preset deviation threshold, and mark items whose deviation exceeds the preset deviation threshold in the multi-dimensional visualization report;

[0015] (7) Using the time sequence completion records, material consumption data, team personnel configuration, and schedule deviation in the construction process ledger data as input features, call the pre-trained AI algorithm model set to perform multi-dimensional intelligent analysis, compare the analysis results with the preset deviation threshold, and generate comprehensive early warning information including progress delay warning, material overconsumption list, team efficiency evaluation report and quality defect identification results.

[0016] (8) Based on the comprehensive early warning information, the corresponding reminders are accurately pushed to specific personnel in three modes: one-time timed, periodic timed, and process node triggered, based on the tags of personnel position, section and permission.

[0017] Step (1) specifically refers to: first, importing the overall bridge BIM model into the system, then breaking down the overall bridge BIM model into the smallest management units of pile foundation, pier, cap beam, box girder, and support; and using a hash encryption algorithm to generate a globally unique ID for each component, which is a 32-bit hexadecimal string.

[0018] Step (2) specifically refers to: calculating the adaptation level based on the multi-terminal model adaptive loading formula:

[0019] ;

[0020] in, To adapt to the level value; The performance score for the terminal device is given, with a value ranging from 0 to 1. The network bandwidth is scored, with a value ranging from 0 to 1. and All are weighting coefficients, satisfying ;

[0021] like If the terminal is a PC, then a high-definition model is distributed. A high-definition model refers to a lightweight BIM model that retains all faces and textures.

[0022] like If a medium model is distributed, it refers to a lightweight BIM model with a triangular mesh compression of 50% to 70% and a texture resolution reduced to 512×512.

[0023] like If a simplified model is distributed, it refers to a lightweight BIM model that retains only the outer contour and component IDs.

[0024] In step (3), the standardized process library includes data on rebar tying, formwork installation, concrete pouring, curing, prestressing tensioning, and concealed works acceptance.

[0025] In step (4), the preset mapping logic is as follows: gray is displayed when the component process has not started, yellow is displayed when construction is in progress, green is displayed when all processes are completed and there are no overdue quality abnormalities, red warning is displayed when the construction schedule is delayed, and orange is displayed when there are quality inspection abnormalities; the formula for calculating the degree of completion of the component process is:

[0026] ;

[0027] in, is the degree of completion of the component's process; n is the total number of processes contained in the component; Let be the weight of the k-th process, satisfying ; This represents the completion status of the k-th process, where 1 represents completion and 0 represents incompleteness.

[0028] In step (5), the ledger information set is as follows:

[0029] ;

[0030] in, Each component is uniquely coded; For the date of pouring; The name or employee number of the operator; Description of construction process; This refers to the amount of steel reinforcement used. This refers to the amount of concrete used. The name of the construction team; This is the conclusion of the acceptance test; It is a collection of ledger information.

[0031] In step (6), the preset deviation threshold includes the schedule deviation threshold, the material loss rate threshold, and the cost deviation threshold.

[0032] In step (7), the AI ​​algorithm model set includes an LSTM neural network for project timeline prediction, an isolated forest algorithm for identifying data anomalies and material overconsumption, a K-means clustering algorithm for classifying work team efficiency, and a CNN image classification network for identifying construction defects such as honeycomb, pitting, and exposed rebar on concrete surfaces.

[0033] In step (8), the three modes are a one-time timed mode, a periodic cycle timed mode, and a process node triggering mode. The one-time timed mode is: the user sets a single future time point, and when the time point is reached, a comprehensive early warning information is pushed to the relevant person in charge. The periodic cycle timed mode is: according to a preset cycle of daily, weekly, or monthly, the progress summary and comprehensive early warning information of each component are pushed to the person in charge of each section regularly. The process node triggering mode is: when the completion status of a certain process changes from incomplete to completed, the construction reminder of the next process is automatically triggered and pushed to the terminal of the responsible team of the corresponding process.

[0034] As can be seen from the above technical solution, the beneficial effects of this invention are as follows: First, it achieves unified linkage between PC, mobile, and paper-based archives, completely solving the management problems of fragmented data and inconsistent information through unique component codes and consistency verification formulas; Second, relying on the quantitative calculation of completed work processes and digital twin color mapping, components automatically change color upon completion, providing real-time warnings for delays or quality anomalies, making the overall bridge construction progress intuitively visible and significantly improving on-site management efficiency; Third, it allows for interactive one-click retrieval of the entire ledger for components, clearly showing the pourer, date, construction method, material usage, work team, and acceptance information, making quality responsibility traceable and definable; Fourth, based on construction process ledger data, material consumption data, work team personnel configuration, and schedule deviations, it can call... The pre-trained AI algorithm model set performs multi-dimensional intelligent analysis, automatically generating progress delay warnings, material overconsumption lists, team efficiency evaluation reports, and quality defect identification results. This transforms construction ledger data from static records into analyzable, predictable, and decision-making-supporting data resources, reducing the cost of manual statistics and analysis and improving the level of intelligent bridge construction management. Fifth, BIM lightweight technology and multi-terminal adaptive loading formulas are fully compatible with PCs, mobile phones, weak networks, and low-configuration devices, enabling BIM technology to truly penetrate the construction front line, with strong implementation and wide applicability. Sixth, the combination of timed and targeted precise message push, full-cycle data archiving, and paper-based linkage constructs a closed-loop management system from construction to operation and maintenance, comprehensively improving the refined management capabilities of the entire bridge life cycle. Attached Figure Description

[0035] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0036] like Figure 1 As shown, a method for refined ledger management of the entire lifecycle of bridges based on BIM digital twins is presented. This method includes the following sequential steps:

[0037] (1) Use REVIT software to establish the overall bridge BIM model of the target project, split the bridge components into the smallest units, generate a globally unique ID for each component after splitting, and bind the globally unique ID to the extended attributes of the overall bridge BIM model, the corresponding field of the electronic ledger database, the mobile offline data package and the traceability identifier of the paper archive.

[0038] (2) Based on the grid compression algorithm, the adaptation level is calculated according to the performance of the terminal device and the network bandwidth parameters. Lightweight BIM models of different precisions are distributed according to the adaptation level. A lossless mapping table of component IDs between the lightweight BIM model and the overall bridge BIM model is established to generate a set of multi-precision model files for subsequent digital twin scene loading and calling.

[0039] (3) Set up a standardized process library, collect construction process ledger data in real time and store it in a relational database, and design data tables for the relational database based on the standardized process library; establish a two-way binding relationship between all construction process ledger data and the corresponding components based on the primary key association through a globally unique ID;

[0040] (4) Load the multi-precision model file set, and select the appropriate lightweight BIM model from the multi-precision model file set according to the terminal device performance and network bandwidth through the adaptive loading formula. Combined with the construction process ledger data, calculate the completion degree of each component and the construction period deviation. According to the preset mapping logic, drive the corresponding precision lightweight BIM model to switch the color status in the digital twin scene in real time, and use the color status to represent the construction progress and quality status of the current component.

[0041] (5) In the digital twin scenario, in response to the user's click or touch command on the displayed BIM component, the core ledger data bound to the component is retrieved from the relational database and displayed in a structured pop-up window based on the globally unique ID of the clicked component. The output ledger information set is generated and the current color status is displayed overlaid and output in the form of a pop-up window. The ledger information set displays the current component status according to the mapping relationship. The core ledger data includes the person in charge of pouring, pouring date, construction method, material usage, work team and acceptance record.

[0042] (6) Based on the actual consumption and planned usage in the construction process ledger data, combined with the completion degree and schedule deviation of each component process, automatically generate a multi-dimensional visualization report, set a preset deviation threshold, and mark items whose deviation exceeds the preset deviation threshold in the multi-dimensional visualization report;

[0043] (7) Using the time sequence completion records, material consumption data, team personnel configuration, and schedule deviation in the construction process ledger data as input features, call the pre-trained AI algorithm model set to perform multi-dimensional intelligent analysis, compare the analysis results with the preset deviation threshold, and generate comprehensive early warning information including progress delay warning, material overconsumption list, team efficiency evaluation report and quality defect identification results.

[0044] (8) Based on the comprehensive early warning information, the corresponding reminders are accurately pushed to specific personnel in three modes: one-time timed, periodic timed, and process node triggered, based on the tags of personnel position, section and permission.

[0045] Step (1) specifically refers to: first, importing the overall bridge BIM model into the system, then breaking down the overall bridge BIM model into the smallest management units: pile foundations, piers, cap beams, box girders, and supports; and using a hash encryption algorithm to generate a globally unique ID for each component, which is a 32-bit hexadecimal string. For example, the ID of pier #7 of a certain bridge is “3F7A2B9C…E4D8”, achieving one-item-one-code full-scene association.

[0046] Step (2) specifically refers to: calculating the adaptation level based on the multi-terminal model adaptive loading formula:

[0047] ;

[0048] in, To adapt to the level value; The performance score for the terminal device is given, with a value ranging from 0 to 1. The network bandwidth is scored, with a value ranging from 0 to 1. and All are weighting coefficients, satisfying ;

[0049] like If the terminal is a PC, then a high-definition model is distributed. A high-definition model refers to a lightweight BIM model that retains all faces and textures.

[0050] like If a medium model is distributed, it refers to a lightweight BIM model with a triangular mesh compression of 50% to 70% and a texture resolution reduced to 512×512.

[0051] like If a simplified model is distributed, it refers to a lightweight BIM model that retains only the outer contour and component IDs.

[0052] In step (3), the standardized process library includes data on rebar tying, formwork installation, concrete pouring, curing, prestressing tensioning, and concealed works acceptance.

[0053] In step (4), the preset mapping logic is as follows: gray is displayed when the component process has not started, yellow is displayed when construction is in progress, green is displayed when all processes are completed and there are no overdue quality abnormalities, red warning is displayed when the construction schedule is delayed, and orange is displayed when there are quality inspection abnormalities; the formula for calculating the degree of completion of the component process is:

[0054] ;

[0055] in, is the degree of completion of the component's process; n is the total number of processes contained in the component; Let be the weight of the k-th process, satisfying ; This represents the completion status of the k-th process, where 1 represents completion and 0 represents incompleteness.

[0056] In step (5), the ledger information set is as follows:

[0057] ;

[0058] in, Each component is uniquely coded; For the date of pouring; The name or employee number of the operator; Description of construction process; This refers to the amount of steel reinforcement used. This refers to the amount of concrete used. The name of the construction team; This is the conclusion of the acceptance test; It is a collection of ledger information.

[0059] In step (6), the preset deviation threshold includes the schedule deviation threshold, the material loss rate threshold, and the cost deviation threshold.

[0060] In step (7), the AI ​​algorithm model set includes an LSTM neural network for project timeline prediction, an isolated forest algorithm for identifying data anomalies and material overconsumption, a K-means clustering algorithm for classifying work team efficiency, and a CNN image classification network for identifying construction defects such as honeycomb, pitting, and exposed rebar on concrete surfaces.

[0061] In step (8), the three modes are a one-time timed mode, a periodic cycle timed mode, and a process node triggering mode. The one-time timed mode is: the user sets a single future time point, and when the time point is reached, a comprehensive early warning information is pushed to the relevant person in charge. The periodic cycle timed mode is: according to a preset cycle of daily, weekly, or monthly, the progress summary and comprehensive early warning information of each component are pushed to the person in charge of each section regularly. The process node triggering mode is: when the completion status of a certain process changes from incomplete to completed, the construction reminder of the next process is automatically triggered and pushed to the terminal of the responsible team of the corresponding process.

[0062] Example 1

[0063] A cross-river bridge project consists of 120 pile foundations, 48 ​​piers, 24 cap beams, and 120 box girders. During the foundation pouring phase, the on-site quality inspector clicked on the "7# Pier Foundation" component in the digital twin scene using a mobile app. A pop-up window displayed: the component's unique code is "QZ-07-CT-089", the pouring date is 2026-05-20, the worker is "Li Si", the construction process is "pumping", the amount of steel reinforcement is 18.2t, the amount of concrete is 320m³, the work team is "Steel Reinforcement Team Two", and the acceptance conclusion is "qualified". Simultaneously, because the foundation has completed all four processes—steel reinforcement binding, formwork installation, concrete pouring, and curing—with weights of 0.25, 0.20, 0.40, and 0.15 respectively, according to the component process completion calculation formula, the component's process completion rate is 100%. The component's color in the digital twin scene automatically changes from yellow to green.

[0064] The project manager, viewing the digital twin scenario on a PC, discovered that a certain pier's progress was still marked yellow and had exceeded the planned schedule by 3 days due to a delay in the arrival of reinforcing steel. The system calculated the schedule deviation based on construction process log data, combined it with an LSTM neural network to predict the schedule timeline, generated a progress delay warning, and pushed it to the relevant personnel.

[0065] The on-site data clerk can print the paper acceptance log for the pier with a single click. The paper document automatically generates an A4 form containing a component ID QR code. One year later, during the operation and maintenance phase, staff scan the QR code, and the system performs reverse matching of the hash value to directly retrieve the BIM model, construction procedure log data, and acceptance records, completing the source tracing and responsibility determination for defects.

[0066] One day, a photo of the concrete surface was uploaded to the site. The CNN model automatically identified a honeycomb defect area of ​​approximately 0.2 m². The system immediately sent an orange alert to the quality manager: "Honeycomb was found after the formwork was removed from pier #7. Please rectify and retake the photo within 24 hours." At the same time, the process node triggered a timed reminder two hours before the rectification deadline to ensure a closed loop.

[0067] The above embodiments demonstrate that the present invention achieves lightweight loading of BIM models, real-time mapping of component status in digital twin scenarios, binding of multi-terminal data from the same source, one-click query of construction ledgers, AI-assisted early warning, and online and offline closed-loop traceability, significantly improving the level of refined management of the entire life cycle of bridges.

[0068] In summary, this invention achieves seamless integration across PC, mobile, and paper-based archives. Through unique component codes and consistency verification formulas, it completely resolves the management challenges of fragmented data and inconsistent information. Relying on quantifiable calculations of completed work processes and digital twin color mapping, completed components automatically change color, providing real-time alerts for delays or quality anomalies, making the entire bridge construction progress readily visible and significantly improving on-site management efficiency. Interactive one-click access to the full inventory ledger for each component clearly shows the pourer, date, construction method, material usage, work team, and acceptance information, ensuring traceability and definition of quality responsibility. Based on construction process ledger data, material consumption data, work team personnel configuration, and schedule deviations, it invokes pre-trained AI algorithm models. The system performs multi-dimensional intelligent analysis, automatically generating early warnings for delayed progress, lists of excessive material consumption, team performance evaluation reports, and quality defect identification results. This transforms construction ledger data from static records into analyzable, predictable, and decision-making-supporting data resources, reducing the cost of manual statistics and analysis and improving the level of intelligent bridge construction management. BIM lightweight technology and multi-terminal adaptive loading formulas are fully compatible with PCs, mobile phones, weak network, and low-configuration devices, enabling BIM technology to truly penetrate the construction front line, with strong implementation and wide applicability. Timed and targeted precise message push, full-cycle data archiving, and paper-based linkage combine to build a closed-loop management system for the entire process from construction to operation and maintenance, comprehensively improving the refined management capabilities of the entire bridge life cycle.

[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for refined ledger management of the entire life cycle of bridges based on BIM digital twins, characterized by: The method includes the following steps in sequence: (1) Use REVIT software to establish the overall bridge BIM model of the target project, split the bridge components into the smallest units, generate a globally unique ID for each component after splitting, and bind the globally unique ID to the extended attributes of the overall bridge BIM model, the corresponding field of the electronic ledger database, the mobile offline data package and the traceability identifier of the paper archive. (2) Based on the grid compression algorithm, the adaptation level is calculated according to the performance of the terminal device and the network bandwidth parameters. Lightweight BIM models of different precisions are distributed according to the adaptation level. A lossless mapping table of component IDs between the lightweight BIM model and the overall bridge BIM model is established to generate a set of multi-precision model files for subsequent digital twin scene loading and calling. (3) Set up a standardized process library, collect construction process ledger data in real time and store it in a relational database, and design data tables for the relational database based on the standardized process library; establish a two-way binding relationship between all construction process ledger data and the corresponding components based on the primary key association through a globally unique ID; (4) Load the multi-precision model file set, and select the appropriate lightweight BIM model from the multi-precision model file set according to the terminal device performance and network bandwidth through the adaptive loading formula. Combined with the construction process ledger data, calculate the completion degree of each component and the construction period deviation. According to the preset mapping logic, drive the corresponding precision lightweight BIM model to switch the color status in the digital twin scene in real time, and use the color status to represent the construction progress and quality status of the current component. (5) In the digital twin scenario, in response to the user's click or touch command on the displayed BIM component, the core ledger data bound to the component is retrieved from the relational database and displayed in a structured pop-up window based on the globally unique ID of the clicked component. The output ledger information set is generated and the current color status is displayed overlaid and output in the form of a pop-up window. The ledger information set displays the current component status according to the mapping relationship. The core ledger data includes the person in charge of pouring, pouring date, construction method, material usage, work team and acceptance record. (6) Based on the actual consumption and planned usage in the construction process ledger data, combined with the completion degree and schedule deviation of each component process, automatically generate a multi-dimensional visualization report, set a preset deviation threshold, and mark items whose deviation exceeds the preset deviation threshold in the multi-dimensional visualization report; (7) Using the time sequence completion records, material consumption data, team personnel configuration, and schedule deviation in the construction process ledger data as input features, call the pre-trained AI algorithm model set to perform multi-dimensional intelligent analysis, compare the analysis results with the preset deviation threshold, and generate comprehensive early warning information including progress delay warning, material overconsumption list, team efficiency evaluation report and quality defect identification results. (8) Based on the comprehensive early warning information, the corresponding reminders are accurately pushed to specific personnel in three modes: one-time timed, periodic timed, and process node triggered, based on the tags of personnel position, section and permission.

2. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: Step (1) specifically refers to: first, importing the overall bridge BIM model into the system, then breaking down the overall bridge BIM model into the smallest management units of pile foundation, pier, cap beam, box girder, and support; and using a hash encryption algorithm to generate a globally unique ID for each component, which is a 32-bit hexadecimal string.

3. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: Step (2) specifically refers to: calculating the adaptation level based on the multi-terminal model adaptive loading formula: ； in, To adapt to the level value; The performance score for the terminal device is given, with a value ranging from 0 to 1. The network bandwidth is scored, with a value ranging from 0 to 1. and All are weighting coefficients, satisfying ; like If the terminal is a PC, then a high-definition model is distributed. A high-definition model refers to a lightweight BIM model that retains all faces and textures. like If a medium model is distributed, it refers to a lightweight BIM model with a triangular mesh compression of 50% to 70% and a texture resolution reduced to 512×512. like If a simplified model is distributed, it refers to a lightweight BIM model that retains only the outer contour and component IDs.

4. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (3), the standardized process library includes data on rebar tying, formwork installation, concrete pouring, curing, prestressing tensioning, and concealed works acceptance.

5. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (4), the preset mapping logic is as follows: gray is displayed when the component process has not started, yellow is displayed when construction is in progress, green is displayed when all processes are completed and there are no overdue quality abnormalities, red warning is displayed when the construction schedule is delayed, and orange is displayed when there are quality inspection abnormalities; the formula for calculating the degree of completion of the component process is: ； in, is the degree of completion of the component's process; n is the total number of processes contained in the component; Let be the weight of the k-th process, satisfying ; This represents the completion status of the k-th process, where 1 represents completion and 0 represents incompleteness.

6. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (5), the ledger information set is as follows: ； in, Each component is uniquely coded; For the date of pouring; The name or employee number of the operator; Description of construction process; This refers to the amount of steel reinforcement used. This refers to the amount of concrete used. The name of the construction team; This is the conclusion of the acceptance test; It is a collection of ledger information.

7. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (6), the preset deviation threshold includes the schedule deviation threshold, the material loss rate threshold, and the cost deviation threshold.

8. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (7), the AI ​​algorithm model set includes an LSTM neural network for project timeline prediction, an isolated forest algorithm for identifying data anomalies and material overconsumption, a K-means clustering algorithm for classifying work team efficiency, and a CNN image classification network for identifying construction defects such as honeycomb, pitting, and exposed rebar on concrete surfaces.

9. The method for refined ledger management of the entire life cycle of bridges based on BIM digital twins as described in claim 1, characterized in that: In step (8), the three modes are a one-time timed mode, a periodic cycle timed mode, and a process node triggering mode. The one-time timed mode is: the user sets a single future time point, and when the time point is reached, a comprehensive early warning information is pushed to the relevant person in charge. The periodic cycle timed mode is: according to a preset cycle of daily, weekly, or monthly, the progress summary and comprehensive early warning information of each component are pushed to the person in charge of each section regularly. The process node triggering mode is: when the completion status of a certain process changes from incomplete to completed, the construction reminder of the next process is automatically triggered and pushed to the terminal of the responsible team of the corresponding process.

Images