A Twin Simulation Monitoring and Optimization Method and System for the Entire Bridge Construction Process

Abstract

This invention provides a digital twin simulation monitoring and optimization method and system for the entire bridge construction process. The method includes: making construction monitoring and optimization decisions based on a pre-established digital twin model of the entire bridge construction site, and obtaining decision results; analyzing the impact on construction order caused by directly responding to the decision results at the bridge construction site; when the impact on construction order is below a threshold, instructing the bridge construction site to directly respond to the decision results; otherwise, improving the decision results to minimize the impact on construction order, and instructing the bridge construction site to respond to the improved decision results. This invention uses a digital twin model for real-time monitoring and optimization decision-making throughout the entire bridge construction process, ensuring accurate monitoring of construction progress. By analyzing the impact of directly responding to decisions on construction order, if the impact is below a threshold, a direct response is taken; if it is above the threshold, measures are taken to minimize the impact before responding, ensuring work efficiency and construction safety.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method and system for optimizing the whole process of bridge construction through digital twin simulation monitoring. Background Technology

[0002] Currently, bridge construction typically involves complex steps, multiple stages, and a large number of equipment and personnel, necessitating comprehensive monitoring and optimization throughout the entire construction process. Traditional monitoring and optimization methods largely rely on manual labor, making it difficult to achieve precise real-time monitoring and optimization.

[0003] Furthermore, traditional methods, after determining the decisions regarding construction monitoring and optimization, often involve directly directing the bridge construction site to respond. However, this can disrupt the existing order at the construction site, affecting work efficiency or construction safety. It focuses only on the quality of the decision itself, neglecting its impact on the construction order and failing to implement effective decision improvements when the construction order is disrupted.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the objectives of this invention is to provide a twin simulation monitoring and optimization method for the entire bridge construction process, in order to solve the problems in the background technology.

[0006] This invention provides a twin simulation monitoring and optimization method for the entire bridge construction process, comprising:

[0007] Based on a pre-established digital twin model of the entire bridge construction site, construction monitoring and optimization decisions are made to obtain decision results.

[0008] Analyze the impact on construction order if decisions are directly responded to at the bridge construction site.

[0009] When the impact of construction order is below the threshold, the bridge construction site is instructed to directly respond to the decision result; otherwise, the decision result is improved to minimize the impact of construction order, and the bridge construction site is instructed to respond to the improved decision result.

[0010] Optionally, the pre-construction steps of the digital twin model of the entire simulated bridge construction site include:

[0011] Construction status information is collected in real time by multiple sensors pre-deployed at the bridge construction site.

[0012] Based on the twin simulation platform, a digital twin model of the entire bridge construction site is generated by using real-time collected construction status information and a 3D model of the simulated bridge construction site.

[0013] Optionally, based on a pre-established digital twin model of the entire simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0014] Based on the digital twin model, the mechanical properties, force transmission path and cracking mechanism of the steel-concrete composite section of the bridge are monitored in real time during the construction stage.

[0015] Based on real-time monitoring of mechanical properties, force transmission paths, and cracking mechanisms, decisions are made to optimize the construction procedures of the steel-concrete composite section of the bridge, and the decision results are obtained.

[0016] Optionally, based on a pre-established digital twin model of the entire simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0017] Based on the digital twin model, the stress distribution, deformation pattern and the impact of eccentric load on the structure are monitored in real time during the cantilever construction of the main span steel box girder;

[0018] Based on real-time monitoring of stress distribution, deformation patterns, and the impact of eccentric loading on the structure, decisions are made to optimize the cantilever construction process of the main span steel box girder, and the decision results are obtained.

[0019] Optionally, based on a pre-established digital twin model of the entire simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0020] Based on the digital twin model, the status parameters of the cable tower anchorage area are monitored in real time;

[0021] Based on real-time monitored status parameters, decisions are made to optimize the anti-cracking construction of the cable tower anchorage zone, and the decision results are obtained.

[0022] Optionally, the analysis of the impact on construction order caused by directly responding to the decision results at the bridge construction site includes:

[0023] A local model at the current moment, extracted from the digital twin model, corresponding to the area of ​​the decision-making result to be responded to at the bridge construction site;

[0024] A transient model that predicts how the local model at the current moment will change over multiple future moments;

[0025] Plot the transformation rate curves of the local model at the current moment and the transient model at future moments;

[0026] When the first inflection point in the rate of change curve is a peak, the local model at the current moment is taken as the pre-steady-state model; when the first inflection point is a trough, the transient model that changes at the future moment corresponding to the first inflection point is taken as the pre-steady-state model.

[0027] The decision results are simulated for response propagation in a pre-steady-state model;

[0028] During the response propagation simulation, whenever any affected target in the pre-steady-state model receives the propagation of the decision result and generates a possible response, the impact of the possible response on all affected targets in the execution of construction affairs is taken as the construction order impact.

[0029] Optionally, plotting the transformation rate curves of the local model at the current moment and the transient model changing at future moments includes:

[0030] Calculate the rate of change of the transient model at each future time step; where the rate of change of the transient model at the first future time step is the degree of change of the transient model at the first future time step relative to the local model at the current time step; the rate of change of the transient model at the ith future time step is the degree of change of the transient model at the ith future time step relative to the transient model at the (i-1)th future time step; 2≤i≤N, where N is the total number of future time steps;

[0031] The rate of change of the transient model at each future time point is mapped into the template curve to obtain the rate of change curve; where the horizontal axis of the template curve is time and the vertical axis is the magnitude of the rate of change.

[0032] Optionally, the steps for obtaining the degree of impact of construction order include:

[0033] Match the impact of construction order from the preset impact database.

[0034] Optionally, the improvement of the decision result to minimize the impact on the construction order includes:

[0035] Identify multimodal approaches that support improvements to decision outcomes from pre-steady-state models;

[0036] Based on a multimodal approach, the decision-making results are improved to make them as close as possible to the improvement target.

[0037] The improvement objectives include:

[0038] During the response propagation simulation of the improved decision results in the pre-steady-state model, the possible reactions of each affected target when receiving the propagation of the decision results will not affect its and other affected targets in carrying out construction tasks.

[0039] This invention provides a twin simulation monitoring and optimization system for the entire bridge construction process, comprising:

[0040] The construction monitoring and optimization module is used to make decisions on construction monitoring and optimization based on a pre-established digital twin model of the full-process simulated bridge construction site, and to obtain the decision results.

[0041] The impact analysis module is used to analyze the impact on the construction order at the bridge construction site if the decision results are directly responded to.

[0042] The response command module is used to direct the bridge construction site to directly respond to the decision results when the impact of the construction order is below a threshold; otherwise, it improves the decision results to minimize the impact of the construction order and directs the bridge construction site to respond to the improved decision results.

[0043] The present invention has achieved the following beneficial effects:

[0044] This invention is based on a pre-established digital twin model of a full-process simulated bridge construction site to make decisions on construction monitoring and optimization, thereby achieving precise real-time monitoring and optimization of the entire bridge construction process. Secondly, it analyzes the impact on construction order caused by directly responding to the decision results at the bridge construction site. If the impact is below a threshold, the site is instructed to respond directly; otherwise, improvements are made to minimize the impact on construction order, and the bridge construction site is instructed to respond to the improved decision results to avoid affecting work efficiency or construction safety.

[0045] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart of a twin simulation monitoring and optimization method for the entire bridge construction process, as described in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of a twin simulation monitoring and optimization system for the entire bridge construction process, as described in an embodiment of the present invention. Detailed Implementation

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0051] Example 1:

[0052] This invention provides a twin simulation monitoring and optimization method for the entire bridge construction process, such as... Figure 1 As shown, it includes:

[0053] S1. Based on a pre-established digital twin model of the entire process of bridge construction site simulation, make decisions on construction monitoring and optimization, and obtain decision results.

[0054] S2. Analyze the impact on construction order if the decision-making results are directly responded to at the bridge construction site.

[0055] S3. When the impact of construction order is below the threshold, the bridge construction site is instructed to directly respond to the decision result; otherwise, the decision result is improved to minimize the impact of construction order, and the bridge construction site is instructed to respond to the improved decision result.

[0056] In S1, the digital twin model provides a real-time 3D simulation of the entire bridge construction process at the construction site. Based on this simulation, decisions can be made regarding construction monitoring and optimization, yielding the desired outcomes. When making these monitoring and optimization decisions, a construction monitoring and optimization decision model (pre-trained using a large number of historical bridge construction states labeled with decision results) can be used to make decisions based on the bridge construction states appearing in the digital twin model. The decision results include construction optimization measures that require a response at the bridge construction site.

[0057] In S2, direct response refers to issuing decision results directly to the bridge construction site, ordering relevant personnel to execute them. Impact on construction order refers to the effect of direct response decisions on the construction site's operational order, such as interfering with workers' actual tasks, affecting work continuity, and causing distraction.

[0058] In S3, the impact degree represents the magnitude of the impact of construction order. When the impact degree is below the threshold, it means that the impact degree is small, and the bridge construction site is instructed to directly respond to the decision result. Otherwise, it means that the impact degree is large, and the decision result is improved to minimize the impact of construction order. The bridge construction site is instructed to respond to the improved decision result, so that the bridge construction site responds to the improved decision result, and the original impact of construction order is minimized.

[0059] This invention is based on a pre-established digital twin model of a full-process simulated bridge construction site to make decisions on construction monitoring and optimization, thereby achieving precise real-time monitoring and optimization of the entire bridge construction process. Secondly, it analyzes the impact on construction order caused by directly responding to the decision results at the bridge construction site. If the impact is below a threshold, the site is instructed to respond directly; otherwise, improvements are made to minimize the impact on construction order, and the bridge construction site is instructed to respond to the improved decision results to avoid affecting work efficiency or construction safety.

[0060] Example 2:

[0061] In this embodiment of the invention, the pre-construction step of the digital twin model of the full-process simulated bridge construction site includes:

[0062] Construction status information is collected in real time by multiple sensors pre-deployed at the bridge construction site.

[0063] Based on the twin simulation platform, a digital twin model of the entire bridge construction site is generated by using real-time collected construction status information and a 3D model of the simulated bridge construction site.

[0064] The digital twin model simulating the entire bridge construction site collects real-time construction status information through sensors pre-deployed at the construction site. This real-time information is then combined with a twin simulation platform to generate a corresponding digital twin model. This model reflects the construction status in real time, providing precise data support for monitoring, optimization, and decision-making in bridge construction, enabling dynamic monitoring and optimization of the entire construction process.

[0065] It should be noted that the present invention provides three parallel implementation methods for the technical steps of construction monitoring and construction optimization decision-making based on digital twin models, namely the technical solutions in Embodiment 3, Embodiment 4 and Embodiment 5.

[0066] Example 3:

[0067] In this embodiment of the invention, based on a pre-established digital twin model of a full-process simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0068] Based on the digital twin model, the mechanical properties, force transmission path and cracking mechanism of the steel-concrete composite section of the bridge are monitored in real time during the construction stage.

[0069] Based on real-time monitoring of mechanical properties, force transmission paths, and cracking mechanisms, decisions are made to optimize the construction procedures of the steel-concrete composite section of the bridge, and the decision results are obtained.

[0070] Based on a pre-established digital twin model, the mechanical properties, force transmission paths, and cracking mechanisms of the steel-concrete composite section of a bridge are monitored in real time during the construction phase. By analyzing this real-time monitoring data, the system can dynamically evaluate the performance of the steel-concrete composite section during construction and make optimization decisions based on mechanical properties, force transmission paths, and cracking mechanisms, adjusting construction procedures to ensure construction quality and structural safety, and optimize construction progress and resource utilization.

[0071] Example 4:

[0072] In this embodiment of the invention, based on a pre-established digital twin model of a full-process simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0073] Based on the digital twin model, the stress distribution, deformation pattern and the impact of eccentric load on the structure are monitored in real time during the cantilever construction of the main span steel box girder;

[0074] Based on real-time monitoring of stress distribution, deformation patterns, and the impact of eccentric loading on the structure, decisions are made to optimize the cantilever construction process of the main span steel box girder, and the decision results are obtained.

[0075] Based on a pre-established digital twin model, the system monitors in real time the stress distribution, deformation patterns, and the impact of eccentric loading on the structure during the cantilever construction of the main span steel box girder. By analyzing this real-time data, the system can assess the stress state of the steel box girder during construction and identify potential structural problems. Based on the monitoring results, the system makes optimization decisions for the construction process, adjusts the construction plan, ensures structural safety, construction quality, and efficiency, and ultimately generates optimized construction decisions.

[0076] Example 5:

[0077] In this embodiment of the invention, based on a pre-established digital twin model of a full-process simulated bridge construction site, construction monitoring and optimization decisions are made to obtain decision results, including:

[0078] Based on the digital twin model, the status parameters of the cable tower anchorage area are monitored in real time;

[0079] Based on real-time monitored status parameters, decisions are made to optimize the anti-cracking construction of the cable tower anchorage zone, and the decision results are obtained.

[0080] Based on a pre-established digital twin model, the system monitors the state parameters of the tower's anchorage zone in real time, including key data such as stress and displacement. By analyzing this real-time monitoring data, the system can identify potential crack risks in the tower's anchorage zone and make optimized construction decisions based on the state parameters. Ultimately, the system generates an optimized construction plan to guide construction personnel in taking effective measures to ensure the structural safety and construction quality of the tower's anchorage zone.

[0081] Example 6:

[0082] When analyzing the impact of construction order, it is essential to simulate the propagation of decision-making results within a designated area. Only after different personnel successfully receive the propagated decision-making results will they react according to their respective job responsibilities and status, thus affecting the original work order. However, in actual construction, the successful propagation of decision-making results presupposes a relatively stable state within the area. For example, only in this relatively stable state can personnel within the area focus more on new decisions and respond effectively. Therefore, identifying and determining this relatively stable area becomes a key issue in the analysis of the impact of construction order.

[0083] Therefore, in this embodiment of the invention, the analysis of the impact on construction order caused by directly responding to decision results at the bridge construction site includes:

[0084] A local model at the current moment is extracted from the digital twin model, corresponding to the area of ​​the decision result to be responded to at the bridge construction site. This area refers to the site area where construction optimization measures in the decision result need to be implemented. It has a corresponding local three-dimensional model in the digital twin model, which is extracted as the local model.

[0085] A transient model is used to predict the changes of the local model at the current moment over multiple future moments. When predicting the transient model, a construction change prediction model (trained using a large amount of historical bridge construction site information labeled with construction changes) is used to predict the construction changes of the local model at the current moment over multiple future moments. A local model with one type of construction change at each moment is considered a transient model. These multiple moments can be limited to N unit moments, where N is a positive integer. To ensure that the decision results are responded to on-site as quickly as possible, the transient model used to analyze the impact of construction order cannot occur a long time in the future. Therefore, N should be set so that the future moments are very close to the current moment.

[0086] Plot the transformation rate curves of the local model at the current moment and the transient model at each future moment; the transformation rate curves continuously reflect the relative degree of change between the local model at the current moment and the transient model at the first future moment, and also reflect the relative degree of change between the transient models at each pair of adjacent future moments.

[0087] When the first inflection point in the rate of change curve is a peak, the local model at the current moment is taken as the pre-steady-state model; when the first inflection point is a trough, the transient model corresponding to the future moment at the first inflection point is taken as the pre-steady-state model. When the first inflection point is a peak, it indicates that the curve is initially rising, continuously reflecting an increasingly larger degree of relative change. Therefore, the local model at the current moment represents a relatively stable state in the aforementioned region, and the decision result can be successfully propagated. Conversely, when the first inflection point is a trough, it indicates that the curve is initially falling, continuously reflecting an increasingly smaller degree of relative change. The corresponding transient models represent an increasingly stable region. Therefore, the transient model corresponding to the future moment at the first inflection point represents a relatively stable state in the aforementioned region, and the decision result can be successfully propagated. When the decision result is actually responded to, the aforementioned transient model will also ensure the successful propagation of the decision result at the future moment corresponding to the first inflection point, thereby influencing the construction order.

[0088] The decision results are simulated for response propagation in a pre-steady-state model. When simulating response propagation, the first recipient of the decision results (usually the corresponding on-site manager) is determined. Based on their location and the locations of other on-site personnel, as well as their command issuance habits, the order in which the recipient will issue the decision results to other on-site personnel is determined, thus realizing the response propagation simulation.

[0089] During the response propagation simulation, whenever any affected target in the pre-steady-state model receives the propagation of the decision result and generates a possible response, the impact of this possible response on the execution of construction tasks by all affected targets is considered as the construction order impact. Affected targets can be workers, and a possible response refers to the reaction made by an affected target upon receiving the propagation of the decision result, based on its own job responsibilities and status. Possible responses may affect the execution of construction tasks by all affected targets (including those that have received the propagation of the decision result), and such impacts are considered as the construction order impact.

[0090] This invention predicts the transient model of the local model at the current moment and its changes at multiple future moments. It plots the transformation rate curves of the local model at the current moment and the transient model at each future moment. Based on the case where the first inflection point of the curve is a peak or trough, a pre-steady-state model is determined accordingly. This precision ensures that the determined pre-steady-state model can reflect the relatively stable state of the area in the bridge construction site where the decision result needs to be responded to. This allows the process of simulating the response propagation of the decision result in the pre-steady-state model to most closely resemble the actual process after directly responding to the decision result at the bridge construction site. Finally, the possible reactions of each affected target in the pre-steady-state model when receiving the propagation of the decision result, and their impact on other affected targets in the execution of construction tasks, are taken as the impact on construction order. This greatly improves the accuracy, comprehensiveness, and efficiency of the construction order impact analysis, enhances the applicability of the system, and is also highly intelligent.

[0091] Example 7:

[0092] In this embodiment of the invention, the step of plotting the transformation rate curves of the local model at the current moment and the transient model changing at future moments includes:

[0093] Calculate the rate of change of the transient model at each future time step; where the rate of change of the transient model at the first future time step is the degree of change of the transient model at the first future time step relative to the local model at the current time step; the rate of change of the transient model at the ith future time step is the degree of change of the transient model at the ith future time step relative to the transient model at the (i-1)th future time step; 2≤i≤N, where N is the total number of future time steps;

[0094] The rate of change of the transient model at each future time point is mapped into the template curve to obtain the rate of change curve; where the horizontal axis of the template curve is time and the vertical axis is the magnitude of the rate of change.

[0095] When plotting the rate of change curve, the rate of change of the transient model at each future time step is calculated. This rate of change is then mapped onto a template curve. Based on the time step and the corresponding rate of change, multiple coordinate points are determined on the template curve with time on the horizontal axis and the magnitude of the rate of change on the vertical axis. These coordinate points are then connected sequentially to obtain the rate of change curve. The degree of change of one model relative to another refers to the weighted sum of the differences between each pair of factors influencing the propagation of decision results received by the target in both models. These factors can include personnel working status, personnel spacing, equipment operating status, etc. The weights for the difference weighting calculation can be set according to the degree of influence of each factor type on the propagation of decision results received by the target. During the weighted calculation, the weight of the same factor type is multiplied by the difference between the factors of the two models under that factor type. The results of multiple multiplications are then summed to obtain the degree of change.

[0096] The embodiments of the present invention improve the ability of the plotted rate of change curve to continuously reflect the relative change between the local model at the current moment and the transient model at the first future moment, as well as the relative change between the transient models at two adjacent future moments. This improves the accuracy of the subsequent determination of a pre-steady-state model representing the region to be responded to in the bridge construction site, which has a relatively stable state, and improves the working efficiency of the system.

[0097] Example 8:

[0098] In this embodiment of the invention, the step of obtaining the degree of influence of construction order includes:

[0099] Match the impact of construction order from the preset impact database.

[0100] The impact degree database is pre-set with different impact degrees for different construction order. The impact degree can be set according to the specific impact degree of the construction order. In this way, when determining the impact degree, it can be directly matched from the database.

[0101] Example 9:

[0102] In this embodiment of the invention, the improvement of the decision result to minimize the impact on the construction order includes:

[0103] Identify multimodal approaches that support improvements to decision outcomes from pre-steady-state models;

[0104] Based on a multimodal approach, the decision-making results are improved to make them as close as possible to the improvement target.

[0105] The improvement objectives include:

[0106] During the response propagation simulation of the improved decision results in the pre-steady-state model, the possible reactions of each affected target when receiving the propagation of the decision results will not affect its and other affected targets in carrying out construction tasks.

[0107] In a pre-steady-state model, identifying multimodal approaches to improve decision outcomes refers to the various methods that can be implemented within the model to improve the decision results. Examples include changing the time and order in which different personnel receive the decision results. During response propagation simulations of the improved decision results within the pre-steady-state model, the possible reactions of each affected target upon receiving the propagated decision results, ensuring that these reactions do not negatively impact the execution of construction tasks by themselves or other affected targets, serves as the improvement objective. The decision results are then improved to best approximate this objective, minimizing the impact on construction order and thus enhancing the accuracy of the improvement process.

[0108] Specifically, for example, the timing and order in which different people receive decision results can be changed so that the decision results do not affect people's current work as much as possible during dissemination, and that they receive the decision results during their spare time or when performing less important tasks.

[0109] Example 10:

[0110] This invention provides a twin simulation monitoring and optimization system for the entire bridge construction process, such as... Figure 2 As shown, it includes:

[0111] Construction monitoring and optimization module 1 is used to make decisions on construction monitoring and optimization based on a pre-established digital twin model of the full-process simulated bridge construction site, and to obtain the decision results.

[0112] Impact Analysis Module 2 is used to analyze the impact on construction order if the decision results are directly responded to at the bridge construction site.

[0113] The response command module 3 is used to direct the bridge construction site to directly respond to the decision results when the impact of the construction order is below a threshold; otherwise, it improves the decision results to minimize the impact of the construction order and directs the bridge construction site to respond to the improved decision results.

[0114] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A bridge construction whole-process twin simulation monitoring optimization method, characterized in that, The method comprises the following steps: obtaining a decision result based on a pre-established digital twin model of a whole-process simulation bridge construction site for construction monitoring and construction optimization decision-making; analyzing the construction order impact that the bridge construction site will bring if directly responding to the decision result; when the influence degree of the construction order impact is lower than a threshold, commanding the bridge construction site to directly respond to the decision result; otherwise, improving the decision result to minimize the construction order impact and commanding the bridge construction site to respond to the improved decision result; the analysis of the construction order impact that the bridge construction site will bring if directly responding to the decision result comprises: cutting a local model at the current time corresponding to the region of the bridge construction site to be responded to the decision result from the digital twin model; predicting a transient model of the change of the local model at the current time at future time points; drawing a transformation rate curve of the local model at the current time and the transient model changed at the future time points; when the first inflection point in the transformation rate curve is a wave crest, taking the local model at the current time as a pre-steady-state model; when the first inflection point is a wave trough, taking the transient model changed at the future time corresponding to the first inflection point as a pre-steady-state model; simulating the response propagation of the decision result in the pre-steady-state model; in the response propagation simulation process, whenever any influence target in the pre-steady-state model receives the propagation of the decision result and generates a possible reaction, the influence of the possible reaction on all influence targets in the execution of construction affairs is taken as the construction order impact; the drawing of the transformation rate curve of the local model at the current time and the transient model changed at the future time points comprises: calculating the change rate of the transient model changed at each future time point; wherein the transformation rate of the transient model changed at the first future time point is the change degree of the transient model changed at the first future time point relative to the local model at the current time; the transformation rate of the transient model changed at the i-th future time point is the change degree of the transient model changed at the i-th future time point relative to the transient model changed at the i-1-th future time point; 2≤i≤N, N is the total number of future time points; mapping the change rate of the transient model changed at each future time point into a template curve to obtain a change rate curve; wherein the horizontal axis of the template curve is time, and the vertical axis is the change rate.

2. The whole-process bridge construction twin simulation monitoring optimization method according to claim 1, characterized in that, The pre-construction steps of the digital twin model of the whole-process simulation bridge construction site comprise: real-time collection of construction state information through a plurality of sensors pre-deployed in the bridge construction site; based on a twin simulation platform, generating a digital twin model of a whole-process simulation bridge construction site according to the real-time collected construction state information and a three-dimensional model of the simulated bridge construction site.

3. The whole-process bridge construction twin simulation monitoring optimization method according to claim 1, characterized in that, Based on the pre-established digital twin model of the whole-process simulation bridge construction site, construction monitoring and construction optimization decision-making are performed to obtain a decision result, which comprises: based on the digital twin model, real-time monitoring of the mechanical properties, force transmission path and cracking mechanism of the bridge steel-concrete joint section during the construction stage; based on the real-time monitored mechanical properties, force transmission path and cracking mechanism, making a decision to optimize the construction process of the bridge steel-concrete joint section to obtain a decision result.

4. The bridge construction whole-process twin simulation monitoring optimization method according to claim 1, characterized in that, Based on the pre-established digital twin model of the whole-process simulation bridge construction site, construction monitoring and construction optimization decision are performed, and a decision result is obtained, including: Based on the digital twin model, the stress distribution, deformation law and the influence of eccentric load on the structure during the main span steel box girder cantilever construction process are monitored in real time; Based on the real-time monitored stress distribution, deformation law and the influence of eccentric load on the structure, the decision of optimizing the main span steel box girder cantilever construction process is made, and a decision result is obtained.

5. The bridge construction whole-process twin simulation monitoring optimization method according to claim 1, characterized in that, Based on the pre-established digital twin model of the whole-process simulation bridge construction site, construction monitoring and construction optimization decision are performed, and a decision result is obtained, including: Based on the digital twin model, the state parameters of the cable tower anchorage zone are monitored in real time; Based on the real-time monitored state parameters, the decision of optimizing the cable tower anchorage zone crack prevention construction is made, and a decision result is obtained.

6. The bridge construction whole-process twin simulation monitoring optimization method according to claim 1, characterized in that, The obtaining step of the influence degree of the construction order influence includes: Matching the influence degree of the construction order influence from the pre-set influence degree library.

7. The bridge construction whole-process twin simulation monitoring optimization method according to claim 1, characterized in that, The improvement of the decision result to minimize the construction order influence includes: Determining a multi-modal mode supporting the improvement of the decision result from the pre-steady-state model; Based on the multi-modal mode, the decision result is improved to be closest to the improvement target; Wherein, the improvement target includes: In the process of response propagation simulation of the improved decision result in the pre-steady-state model, the possible reaction of each influence target in the pre-steady-state model when receiving the propagation of the decision result will not affect the execution of the construction affairs of other influence targets.

8. A bridge construction whole-process twin simulation monitoring optimization system, characterized in that, Including: A construction monitoring optimization module is configured to perform construction monitoring and construction optimization decision based on a pre-established digital twin model of a whole-process simulation bridge construction site, and obtain a decision result. An influence analysis module is configured to analyze the construction order influence that the bridge construction site will bring if directly responding to the decision result. A response command module is configured to command the bridge construction site to directly respond to the decision result when the influence degree of the construction order influence is lower than a threshold; otherwise, to improve the decision result to minimize the construction order influence, and to command the bridge construction site to respond to the improved decision result. The analysis of the construction order influence that the bridge construction site will bring if directly responding to the decision result includes: Cutting a local model at the current time corresponding to the region in the bridge construction site to which the decision result is to be responded from the digital twin model; Predicting a transient model of the local model at the current time changing at future time points; Drawing a transformation rate curve of the local model at the current time and the transient model changing at future time points; When the first inflection point in the transformation rate curve is a wave crest, the local model at the current time is taken as a pre-steady-state model; when the first inflection point is a wave trough, the transient model corresponding to the future time point of the first inflection point is taken as a pre-steady-state model; Performing response propagation simulation of the decision result in the pre-steady-state model; In the response propagation simulation process, whenever any influence target in the pre-steady-state model receives the propagation of the decision result and produces a possible reaction, the influence of the possible reaction on all influence targets in the execution of construction affairs is taken as the construction order influence. The transformation rate curve of the local model at the current time and the transient model changing at each future time comprises: calculating the change rate of the transient model changing at each future time; wherein the transformation rate of the transient model changing at the first future time is the change degree of the transient model changing at the first future time relative to the local model at the current time; the transformation rate of the transient model changing at the i-th future time is the change degree of the transient model changing at the i-th future time relative to the transient model changing at the (i-1)-th future time; 2≤i≤N, N is the total number of future times; mapping the change rate of the transient model changing at each future time into a template curve to obtain a change rate curve; wherein the horizontal axis of the template curve is time, and the vertical axis is the change rate.

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