Method for optimizing construction step sequence of existing operation station shield receiving based on digital twinning

Abstract

The application discloses a kind of based on digital twinning existing operation station shield receiving construction step sequence optimization method and system, method includes: collection existing operation station associated data, constructs digital twinning basic model, realizes entity engineering full factor digital mapping, by parameterization and light weight optimization, the dynamic association of model component and original data is established.Model and real-time synchronization mechanism of data are established by real-time acquisition dynamic monitoring and construction process data, unified access through integrated platform, after pre-processing screening.Model and real-time synchronization mechanism of data are established by real-time acquisition dynamic monitoring and construction process data, unified access through integrated platform, after pre-processing screening.The initial scheme is generated by setting multidimensional optimization constraint based on model and integrated data, embedding dynamic optimization algorithm, simulating and optimizing construction step sequence with safety, efficiency, low risk as target;When preset trigger event occurs, then re-optimization, output adaptive scheme.Scheme is pushed to construction execution end, and the execution state is tracked, and the deviation between actual and preset data is compared, and early warning mechanism is established.The existing operation station construction is smoothly promoted.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of digital technology of rail transit engineering, and more particularly relates to a method and system for optimizing the construction step sequence of a shield receiving construction of an existing operating station based on digital twinning. BACKGROUND

[0002] In rail transit engineering construction, the structure of an existing operating station is complex, the construction environment is variable, and many technical challenges are faced. Shield receiving construction of an existing operating station usually involves the intersection of multiple underground structures, requires coordination of the construction sequence of different lines, and is often located in the core area of a city, with dense surrounding buildings and structures and a complex network of underground pipelines, and the construction space is obviously limited.

[0003] The complexity of geological conditions further increases the difficulty of construction, and some existing operating stations have unfavorable geology such as liquefied soil and shallow marsh gas in the stratum, which can easily cause risks such as foundation pit collapse and structural deformation, and strict requirements are placed on the rationality of the construction step sequence. Traditional construction step sequence formulation relies on experience and judgment, and is planned based on static design data, which is difficult to fully consider the dynamic changes in the geological environment, the surrounding environment, and the construction process, resulting in a lack of flexibility and adaptability in the construction step sequence.

[0004] During construction, when sudden situations such as sudden changes in geological conditions, monitoring data exceeding limits, and disturbances in the surrounding environment occur, traditional methods are difficult to quickly adjust the construction step sequence, which can easily cause construction delays and increase safety risks. At the same time, due to the lack of digital tools, construction step sequence optimization is difficult to achieve multi-objective coordination, and often cannot balance construction safety, efficiency, and risk control, which can lead to resource waste or safety hazards.

[0005] With the expansion of the scale of rail transit construction and the improvement of construction standards, the traditional method of formulating the construction step sequence has been difficult to meet the high precision, high safety, and high efficiency requirements of shield receiving construction of an existing operating station, and a technical solution is needed that can integrate multi-source data, respond to dynamic changes in real time, and achieve precise optimization of the construction step sequence, to solve the problems of static planning, poor adaptability, and insufficient multi-objective coordination in traditional methods, to ensure the smooth progress of shield receiving construction of an existing operating station, and to improve the overall quality and efficiency of rail transit engineering construction. SUMMARY

[0006] The present application aims to solve the problems of static planning, poor adaptability, and insufficient multi-objective coordination in the traditional method of formulating the construction step sequence of a rail transit existing operating station shield receiving construction, and through digital twinning modeling, multi-source data integration, dynamic optimization algorithms, and closed-loop control, to achieve precise simulation, real-time adjustment, and efficient optimization of the construction step sequence, to balance construction safety, efficiency, and risk control, and to ensure the smooth progress of shield receiving construction of an existing operating station.

[0007] In view of the above defects or improvement needs of the prior art, as a first aspect of the present application, the present application provides a method for optimizing the construction step sequence of a shield receiving construction at an existing operating station based on digital twinning, comprising:

[0008] S1. Collecting associated data of the existing operating station, constructing a digital twinning basic model based on the associated data, completing full-factor digital mapping of the shield receiving construction project of the entity existing operating station, establishing a model parameter association mechanism, parameterizing and optimizing the basic model, and dynamically associating the model components with various types of original data;

[0009] S2. Real-time collection of dynamic monitoring data during the construction process, integration of dynamic monitoring data and construction process data through the establishment of an integrated data transmission and integration platform, completion of multi-source data unified access, pre-processing of the integrated data, filtering of the data based on data effectiveness and consistency, and establishment of a real-time synchronous updating mechanism for the data and the digital twinning model;

[0010] S3. Based on the digital twinning model and the integrated data, setting multi-dimensional optimization constraint conditions, embedding a dynamic optimization algorithm, taking construction safety and reliability, efficiency optimization, and risk control as the target, simulating and deducing the construction step sequence of the shield receiving construction at the existing operating station, and generating an initial optimization scheme; when a preset triggering event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme;

[0011] S4. Pushing the optimization scheme to the construction execution end to guide the on-site construction; based on the digital twinning model, tracking the scheme execution state in real time, comparing and analyzing the deviation between the actual construction data and the preset data of the optimization scheme, and establishing a deviation warning mechanism.

[0012] Further, the associated data in S1 includes structural design data, geological environment data, surrounding environment data, and design standard data.

[0013] The structural design data includes size parameters, component types, reinforcement information, and connection structure data of the main structure and the enclosure structure; the geological environment data includes rock-soil layer distribution characteristics, special rock-soil physical and mechanical properties, groundwater type and water level parameters, adverse geological distribution range and characteristic data; the surrounding environment data includes the foundation form, structure type, and distance data of the surrounding buildings relative to the existing operating station, the specification, buried depth, orientation, and relocation scheme data of underground pipelines, and topographic and geomorphic data; the design standard data includes seismic fortification intensity, civil defense resistance level, waterproof grade, structure safety level, and durability design requirement data.

[0014] Further, the basic model in S1 covers the structure system, the geological environment system, the surrounding environment system, and the design constraint system.

[0015] Further, the process of dynamically associating the model components with various types of raw data in S1 is as follows:

[0016] First, the dynamic evolution equation of the component state parameter is established to represent the change rule of the model component state with time and space, and the equation is as follows:

[0017] ,

[0018] wherein, represents the first state parameter of the first type of model component, which directly reflects the real-time working state of the component; is the time change rate of , which is used to represent the dynamic evolution rate of the component state with the construction process; is the geological environment parameter corresponding to the position of the first type of model component, which reflects the influence degree of the geological condition on the component stress; is the surrounding environment parameter affecting the first state parameter, which reflects the response rule of the environment to the construction disturbance; represents the design standard limiting parameter, which defines the safe working boundary of the component; is the time change rate of the design standard parameter, which adapts to the dynamic demand of design optimization adjustment in the construction process; is the geological environment-structure coupling coefficient, which is determined by the friction characteristics and bonding strength of the contact surface between the rock-soil layer and the component; is the surrounding environment-structure coupling coefficient, which reflects the interaction strength between the environment deformation and the structure stress; is the gradient operator, which is used to describe the spatial distribution change rate of , ;

[0019] Secondly, the regional association of the parameters is supplemented through the spatial distribution equation to ensure the spatial coupling consistency of the model component and the raw data, and the equation is as follows:

[0020] ,

[0021] wherein, is the three-dimensional spatial coordinate of the existing operating station; is the initial value of the first state parameter of the first type of model component, which is determined by the component design parameter and the initial working condition of the construction; is the spatial influence volume corresponding to the first type of model component; , are the spatial coordinates The integral term is used to superimpose the comprehensive influence of the geological environment and the surrounding environment in the space range on the component state parameter, to ensure that the model component state is accurately matched with the original data of different regions.

[0022] Finally, the coupling verification equation is used to ensure the effectiveness of the correlation, and the equation is as follows:

[0023] ,

[0024] wherein, is the deviation of the calculated value of the component state parameter from the measured value; is the component state parameter value calculated by the above dynamic evolution equation and the spatial distribution equation; is the measured value of the component state obtained by the monitoring system; is the deviation threshold value, which is determined by the existing operating station shield construction quality acceptance standard and structure safety control requirement.

[0025] Further, the dynamic monitoring data in S2 includes displacement, settlement, stress, strain data of the enclosure structure, ground settlement data, axial force data of the supporting component, groundwater level data, harmful gas concentration data, settlement and inclination data of the surrounding buildings, and structure construction intensity data.

[0026] The construction process data includes excavation depth, excavation area, excavation rate, support installation and removal timing, concrete pouring parameters, resource allocation information, and equipment operation state data.

[0027] The preprocessing includes outlier rejection, format standardization, precision verification, and noise reduction processing.

[0028] Further, the constraint conditions in S3 include structure safety constraints, deformation control constraints, construction process constraints, environmental safety constraints, and design specification constraints.

[0029] Further, the process of simulating and optimizing the construction step sequence of the existing operating station shield in S3 is as follows:

[0030] First, a construction step sequence timing correlation model is established to quantify the timing logic relationship of each work unit, and the formula is as follows:

[0031] ,

[0032] wherein, is the total duration of the construction step sequence; is the total number of work units; is the basic duration of the th work unit; is the basic duration of the The time interval correction amount of the first work unit and the second work unit is positive when the two work units have process dependency, and is zero when there is no dependency and the work units can be performed in parallel. The time interval correction amount of the first work unit and the second work unit is positive when the two work units have process dependency, and is zero when there is no dependency and the work units can be performed in parallel. The time interval correction amount of the first work unit and the second work unit is positive when the two work units have process dependency, and is zero when there is no dependency and the work units can be performed in parallel.

[0033] Secondly, a construction step sequence safety constraint model is constructed to convert multi-dimensional safety requirements into mathematical constraint conditions, and the formula is as follows:

[0034] ,

[0035] Among them, The component stress generated by the first work unit at the space-time coordinate The component stress generated by the first work unit at the space-time coordinate ; The stress limit value determined based on the component state parameter ; The displacement amount caused by the first work unit; The displacement amount caused by the first work unit; The displacement allowance value determined based on the geological environment parameter , the surrounding environment parameter ; The displacement allowance value determined based on the geological environment parameter , the surrounding environment parameter ; The displacement allowance value determined based on the geological environment parameter , the surrounding environment parameter ; The minimum stability coefficient determined based on the design standard parameter ;

[0036] Finally, a multi-objective optimization objective function is established to realize the collaborative optimization of construction safety, efficiency and risk, and the formula is as follows:

[0037] ,

[0038] Among them, the first target item integrates the safety deviation of stress, displacement and stability through normalization processing, and the smaller the value is, the greater the safety redundancy is; the second target item The total construction period reflects the optimization demand of construction efficiency; the third target item is the construction risk cumulative value, The risk intensity at time , which is composed of geological risk, environmental risk and operation conflict risk, is calculated based on the real-time updated geological, environmental and construction data in the digital twin model.

[0039] Further, the output process of the adaptive optimization scheme in S3 is:

[0040] Firstly, a trigger event influence quantification model is established to accurately define the interference range and degree of the event on the construction step sequence, and the formula is as follows:

[0041] ,

[0042] in, To trigger the comprehensive impact index of the event, the intensity of the event's interference with the construction system is quantified; The spatial range affected by the triggering event; To trigger the event at the time of occurrence, This is the moment when the influence tends to stabilize; , , These are geological environmental parameters, surrounding environmental parameters, and design standard parameters at different times after the event. , , These are the baseline values ​​of the parameters before the event occurred; the integral term calculates the rate of change of the parameters through normalization, and superimposes the effects of time and space dimensions to comprehensively characterize the degree of interference of the event on the construction sequence.

[0043] Secondly, a calculation model for the adjustment amount of the optimization scheme is constructed, and the construction sequence parameters are dynamically corrected based on the influence index. The formula is as follows:

[0044] ,

[0045] in, For the first The parameter adjustment amount for each work unit; The preset maximum permissible impact index is determined by the construction safety level and risk tolerance threshold. For the first The parameter limit values ​​of each work unit are determined based on structural safety constraints and process requirements; The first time before the incident occurred The current parameter values ​​of each work unit; This is a function value of the component's state parameters after the event occurs, reflecting the component's current working state; The safety threshold for component state parameters; For a sign function, when When the time is positive, the parameter adjustment amount needs to be increased to enhance the optimization effect; conversely, when the time is negative, fine-tuning the parameter can maintain a safe state.

[0046] Finally, the optimal solution is selected and output using the feasibility verification model, as shown in the following formula:

[0047] ,

[0048] in, , After parameter adjustment, the first The stress and displacement of components generated by each work unit; , , a component state parameter, a geological environment parameter, and a surrounding environment parameter updated after an event occurs; an adjusted total construction duration, a preset maximum allowable duration; an adjusted risk intensity at time t, a maximum allowable risk accumulation value.

[0049] As a second aspect of the present application, a shield receiving construction step sequence optimization system for an existing operating station based on digital twinning is also provided, comprising:

[0050] a twinning model construction and association unit configured to collect associated data of the existing operating station, construct a digital twinning basic model based on the associated data, complete full-factor digital mapping of a shield receiving construction project of the entity existing operating station, establish a model parameter association mechanism, and perform parameterized configuration and lightweight optimization on the basic model to form dynamic association between model components and various types of original data;

[0051] a multi-source data integration and synchronization unit configured to collect dynamic monitoring data in a construction process in real time, gather dynamic monitoring data and construction process data through an integrated data transmission and integration platform to complete unified access of multi-source data, pre-process the integrated data, filter the data based on data validity and consistency, and establish a real-time synchronization update mechanism for the data and the digital twinning model;

[0052] a step sequence optimization and scheme generation unit configured to set multi-dimensional optimization constraint conditions based on the digital twinning model and the integrated data, embed a dynamic optimization algorithm, simulate and deduce a shield receiving construction step sequence for the existing operating station and perform optimization calculation to generate an initial optimization scheme with the construction safety and reliability, efficiency optimization, and risk controllability as the target, automatically start a re-optimization process when a preset triggering event occurs, and output an adaptive optimization scheme;

[0053] a scheme execution and deviation early warning unit configured to push the optimization scheme to a construction execution end to guide on-site construction, track a scheme execution state in real time based on the digital twinning model, compare and analyze deviations between actual construction data and preset data of the optimization scheme, and establish a deviation early warning mechanism.

[0054] As a third aspect of the present application, a computer readable storage medium having a computer program stored thereon is also provided, and the computer program is executed by a processor to implement any one of the above-described shield receiving construction step sequence optimization methods for the existing operating station based on digital twinning.

[0055] Overall, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0056] 1. The digital twin-based construction step sequence optimization method for shield receiving construction at an existing operating station according to the present application, which constructs a digital twin basic model by collecting relevant data of the existing operating station, completes full-factor digital mapping of the entity project, and establishes dynamic association between model components and various original data through parameterization configuration and lightweight optimization. This technical feature realizes accurate correspondence between the entity project and the digital model, enables the model to completely reproduce the structural system, geological environment, surrounding environment, and design constraints of the shield receiving construction at the existing operating station, provides a high-precision digital carrier for subsequent construction step sequence optimization, ensures that the basic data relied on by optimization calculation are comprehensive and practical, avoids deviation of the optimization scheme caused by disconnection between the model and the entity, and lays a data and model foundation for scientific optimization of the construction step sequence.

[0057] 2. The digital twin-based construction step sequence optimization method for shield receiving construction at an existing operating station according to the present application, which realizes efficient integration and dynamic updating of multi-source data by building an integrated data transmission and integration platform, real-time gathering of dynamic monitoring data and construction process data, and establishing a real-time synchronization mechanism between data and the digital twin model after pre-processing and screening. This technical feature enables the digital twin model to reflect the changes in the geological environment, structural state, and surrounding environment in real time during the construction process, ensures that the construction step sequence optimization is always based on the latest actual engineering data, avoids mismatch between the optimization scheme and the site conditions caused by data lag, and improves the timeliness and accuracy of the construction step sequence optimization.

[0058] 3. The digital twin-based construction step sequence optimization method for shield receiving construction at an existing operating station according to the present application, which realizes dynamic optimization and closed-loop control of the construction step sequence by setting multi-dimensional optimization constraint conditions, embedding a dynamic optimization algorithm to simulate and deduce the construction step sequence and generate an initial scheme, automatically starting a re-optimization process to output an adaptive scheme when a preset trigger event occurs, and tracking the scheme execution state and establishing a deviation warning mechanism. This technical feature ensures that the optimization scheme meets the multi-objective requirements of structural safety, efficiency optimization, and risk control, and can adapt to engineering dynamic changes in real time, effectively avoiding the influence of time-space conflicts, geological risks, and environmental disturbances during the construction process, and ensuring the safe and reliable and efficient advancement of the shield receiving construction at the existing operating station. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The flowchart of the digital twin-based construction step sequence optimization method for shield receiving construction at an existing operating station according to the present application;

[0060] Figure 2 The relative relationship model between the shield receiving project and the high-speed rail station according to the present application;

[0061] Figure 3 The high-speed rail station model after the roof is removed according to the present application;

[0062] Figure 4 Fig. 2 is a schematic diagram of vertical displacement of a station structure according to an embodiment of the present application;

[0063] Figure 5 Fig. 3 is a schematic diagram of horizontal displacement of a station structure according to an embodiment of the present application;

[0064] Figure 6 Fig. 4 is a schematic diagram of a system unit according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0066] Embodiment 1

[0067] Please refer to Figure 1 The embodiment 1 provides a shield receiving construction step sequence optimization method for an existing operating station based on digital twinning, comprising:

[0068] S1. Collecting associated data of the existing operating station, constructing a digital twinning basic model based on the associated data, completing full-factor digital mapping of the shield receiving construction project of the entity existing operating station, establishing a model parameter association mechanism, parameterizing and optimizing the basic model, and dynamically associating the model components with various raw data;

[0069] S2. Real-time collection of dynamic monitoring data in the construction process, integration of dynamic monitoring data and construction process data through the construction of an integrated data transmission and integration platform, completion of unified access of multi-source data, pre-processing of the integrated data, filtering of the data based on data effectiveness and consistency, and establishment of a real-time synchronous updating mechanism of the data and the digital twinning model;

[0070] S3. Based on the digital twinning model and the integrated data, setting multi-dimensional optimization constraint conditions, embedding a dynamic optimization algorithm, taking construction safety and reliability, efficiency optimization and risk controllability as the target, simulating and deducing the construction step sequence of the existing operating station construction shield receiving, and generating an initial optimization scheme; when a preset triggering event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme;

[0071] S4. Pushing the optimization scheme to the construction execution end to guide the on-site construction, real-time tracking of the scheme execution state based on the digital twinning model, comparison and analysis of the deviation between the actual construction data and the preset data of the optimization scheme, and establishment of a deviation early warning mechanism.

[0072] This embodiment 1 further expands the above steps.

[0073] (1) Twin model construction and association

[0074] In the construction of receiving shield in the existing operation station of rail transit, the precise association of model and data is the core basis to realize the optimization of construction step sequence. However, the traditional modeling method often causes the disconnection between model and data, resulting in the lack of reliable support for subsequent optimization, so a scientific model construction and dynamic association process needs to be established.

[0075] Firstly, the associated data of the existing operation station is collected, which covers four categories of structure design, geological environment, surrounding environment and design standard. The structure design data includes the size parameters, component type, reinforcement information and connection structure data of the main structure and enclosure structure, which is the core basis for building the model structure framework; the geological environment data includes the distribution characteristics of rock and soil layers, special rock and soil physical and mechanical properties, groundwater type and water level parameters, distribution range and characteristic data of adverse geology; the surrounding environment data includes the foundation form, structure type, distance data from the existing operation station, specification, buried depth, trend and relocation scheme data of underground pipelines, and topography data; the design standard data includes seismic fortification intensity, civil defense resistance level, waterproof grade, structure safety level and durability design requirement data.

[0076] Based on these data, a digital twin basic model is constructed to completely replicate the structure system, geological environment system, surrounding environment system and design constraint system of the existing operation station, realizing the full-factor digital mapping of the entity project. Then, the basic model is parameterized configured and optimized to reduce the model operation load, and to adapt to the subsequent real-time optimization requirements.

[0077] After the model is constructed, dynamic association between model components and various original data needs to be established, which is realized through multi-step logical deduction. The specific process is as follows:

[0078] Firstly, the dynamic evolution equation of component state parameters is established to represent the change law of model component state with time and space, and the equation is as follows:

[0079] ,

[0080] Among them, represents the th state parameter of the th component of the th model, which specifically includes component strain increment, displacement trend and crack resistance performance index, directly reflecting the real-time working state of the component; is the time variation rate, which is used to represent the dynamic evolution rate of component state with construction process; is the time variation rate, which is used to represent the dynamic evolution rate of component state with construction process; The geological environment parameters corresponding to the location of the model component include the permeability coefficient of the soil and rock layer, pore water pressure and liquefaction intensity, reflecting the degree of influence of geological conditions on the stress of the component. To affect the first The surrounding environmental parameters of the state parameters include the foundation stiffness of surrounding buildings, the elastic modulus of underground pipelines, and the distance attenuation characteristics, reflecting the response law of the environment to construction disturbances. It indicates the design standard limiting parameters, including the threshold of the elastic modulus of the structure for seismic fortification, the ultimate bearing capacity of civil defense resistance, and the seepage control standards for waterproofing level, defining the safe working boundary of the components; To adapt to the dynamic needs of design optimization and adjustment during construction, the time variation rate of the design standard parameters is determined. The geological environment-structure coupling coefficient is determined by the friction characteristics and bond strength of the contact surface between the soil layer and the component, and characterizes the efficiency of the transfer of geological forces to the component. The surrounding environment-structure coupling coefficient reflects the intensity of the interaction between environmental deformation and structural stress, and is determined by the stiffness matching relationship between the environmental medium and the structure. Gradient operator, used to describe , The rate of change in spatial distribution reflects the differences in the distribution of parameters in different areas of existing operating stations;

[0081] Secondly, the regional correlation of parameters is supplemented by a spatial distribution equation to ensure the spatial coupling consistency between the model components and the original data. The equation is as follows:

[0082] ,

[0083] in, The three-dimensional spatial coordinates of the existing operating station are used to accurately locate the physical location of components and data; For the first Class model component The initial values ​​of the status parameters are determined by the component design parameters and the initial construction conditions; For the first The spatial influence volume corresponding to the model component, that is, the three-dimensional spatial range covered by the force and deformation influence of the component; , Spatial coordinates The geological environment parameters and surrounding environment parameters at the location reflect the spatial heterogeneity of the parameter distribution; the integral term is used to superimpose the comprehensive influence of the geological environment and surrounding environment on the component state parameters within the spatial range, ensuring that the model component state is accurately matched with the original data of different regions;

[0084] Finally, the validity of the association is ensured by coupling the verification equation, as follows:

[0085] ,

[0086] wherein, is the deviation of the calculated value of the component state parameter from the measured value; is the value of the component state parameter calculated by the dynamic evolution equation and the spatial distribution equation; is the measured value of the component state obtained by the monitoring system; is the deviation threshold value, which is determined by the construction quality acceptance standard and the structure safety control requirement of the existing operating station shield; through the verification equation, the correlation accuracy of the model component and the original data is checked in real time, and when the deviation exceeds the threshold value, the coupling coefficient is automatically corrected , , to ensure that the dynamic correlation relationship always conforms to the actual situation of the entity project, and to provide accurate and reliable model support for the simulation and optimization calculation of the construction sequence.

[0087] For example, taking the shield receiving construction of an existing operating station as an example, in the model building and dynamic correlation process, the relevant data such as the structure design, geological environment, surrounding environment and design standards of the station are collected first, and a digital twin basic model is built based on these data, Figure 2 The relative relationship model between the shield receiving project and the high-speed rail station is presented, which reproduces the spatial position, structure boundary and connection relationship between the shield receiving area and the main structure of the high-speed rail station, providing an accurate spatial reference for the mapping of the surrounding environment system and the structure system in the model, Figure 3 The high-speed rail station model after the roof removal is displayed, which refines the structure form under the key construction process, and clearly shows the component distribution and load transfer path change in the roof removal area, providing a direct basis for the initial setting of the structure stress state in the model parameterization configuration; then the displacement monitoring data collected during the construction process is associated with the model, Figure 4 The vertical displacement diagram of the station structure, Figure 5 The horizontal displacement diagram intuitively presents the displacement amplitude and distribution law of the key components of the station at different construction stages. These displacement data are used as the measured values of the model component state parameters, and the geotechnical layer mechanical properties and groundwater parameters in the geological environment are used to determine the geological environment-structure coupling coefficient. Through the spatial distribution logic, the influence of different regional geology and environmental factors on the component displacement is superimposed, so that the model can accurately simulate the displacement change trend that matches the measured data; finally, Figure 4 , Figure 5The deviation of the displacement value calculated by the model from the actual displacement data is used to check the correlation accuracy of the model components and the original data. If the deviation exceeds the threshold defined in the existing operational station shield receiving construction quality acceptance standard, the coupling coefficient is automatically corrected to ensure that the digital twin model can accurately replicate the structural stress and deformation state of the existing operational station during shield receiving construction, providing reliable support for subsequent simulation and optimization calculations of construction steps.

[0088] (2) Multi-source data integration and synchronization

[0089] During the shield receiving construction process of the existing operational station, dynamic factors such as changes in geological conditions, structural stress adjustment, and surrounding environmental disturbances continuously affect construction safety and efficiency. Traditional data management methods are scattered and lagging, making it difficult to reflect the actual construction situation in real time and providing timely support for step optimization. Therefore, a multi-source data real-time integration and model synchronization mechanism needs to be established.

[0090] First, deploy a dynamic monitoring system, set up monitoring points and equipment for key construction links and risk points, and collect dynamic monitoring data covering multiple core indicators. Among them, the displacement, settlement, stress, and strain data of the enclosure structure directly reflect the stability of the foundation pit support system, the ground settlement data are used to evaluate the disturbance degree of construction on the ground, the axial force data of the support member can timely discover safety hazards such as support overload; groundwater level data are related to foundation pit anti-floating and seepage control, harmful gas concentration data ensure the safety of construction personnel, settlement and inclination data of surrounding buildings are the key to environmental risk prevention and control, and structural construction intensity data ensure that the quality of pouring and other processes meets the standards. At the same time, construction process data are collected synchronously, including excavation depth, area, rate, and other excavation operation parameters, support installation and removal timing, concrete pouring parameters, and other process execution data, as well as resource allocation information, equipment operation status data, and other management data, covering the entire construction execution process.

[0091] Then, build an integrated data transmission and integration platform, through which dynamic monitoring data and construction process data are uniformly accessed, breaking down the barriers between different data sources and forming a complete construction data link. After data integration, preprocessing is needed. First, invalid data caused by equipment failure and external interference are removed through outlier rejection. Then, heterogeneous data output by different devices and systems are converted to a unified format through format standardization, making it easier for subsequent processing. Next, precision verification is carried out to ensure that the data error is within the allowable range by comparing the monitoring equipment calibration standard and the construction data recording specification. Finally, random interference signals in the data are filtered through noise reduction processing to improve data quality. After preprocessing, a real-time synchronization update mechanism between data and digital twin model is established, allowing the processed valid data to be fed back to the model in real time, so that the model can dynamically replicate the actual situation of structure state, geological environment, and construction progress during the construction process, providing accurate and timely data support for the dynamic optimization of subsequent construction steps.

[0092] The core is to realize the dynamic binding and instant feedback of the pre-processed effective data and each element of the model, and to ensure that the model is always accurately matched with the entity construction process.

[0093] Firstly, based on the parameterization characteristics of the model components, a dedicated data mapping rule is set for each type of data, and the corresponding model associated object and update dimension of different types of data are determined. For example, the displacement and stress monitoring data of the enclosure structure are directly mapped to the geometric state parameters and mechanical state parameters of the corresponding enclosure component in the model; the excavation depth and support installation timing of the construction process data are associated with the construction progress node parameters and work space state parameters of the model; and the groundwater level and surrounding building settlement data are used to update the geological environment parameters and surrounding environmental impact parameters in the model, realizing one-to-one correspondence between data and model elements.

[0094] Secondly, a low-latency data transmission channel is built, and after the data preprocessing is completed, the effective data is pushed to the digital twin model management platform through the real-time communication protocol. The platform has a built-in data analysis module that automatically identifies data types, sources, and associated model objects, and triggers model parameter updates according to the pre-set mapping rules. For continuous monitoring data such as displacement and stress time series data, an incremental update method is used to synchronize only the data changes to the model, reducing data transmission and computation load; for discrete construction process data such as support removal and concrete pouring completion signals, an instant full update method is used to ensure that the model reflects the state changes of key construction nodes in a timely manner.

[0095] At the same time, a model update verification mechanism is established to automatically verify the rationality and consistency of the updated model parameters after the data is synchronized to the model. For example, the matching of the stress data of the enclosure component and the geological environment parameters is verified to avoid model state distortion caused by abnormal data; the timing logic of the construction progress data and the work unit in the model is checked to ensure that the model construction state is consistent with the actual progress. If data conflicts or parameter abnormalities are found during the verification, the system automatically triggers an alarm and suspends model updates until manual verification or data correction is completed before resuming synchronization, ensuring the accuracy of model updates.

[0096] In addition, a model update visualization feedback function is set up to present the data synchronization process and model state changes in real time, making it easy for managers to intuitively understand the synchronization status of the model and the physical engineering. Through this series of mechanisms, the full-process automation and low-latency execution of data from preprocessing to model parameter update are realized, enabling the digital twin model to dynamically and accurately replicate real-time conditions such as structural state, geological environment changes, and construction progress in the entity construction process, providing reliable model support for dynamic optimization of construction sequences.

[0097] (3) Step sequence optimization and scheme generation

[0098] In the construction of receiving shield in existing operating stations, the rationality of construction step sequence directly determines the safety and efficiency of construction. However, the dynamic changes of geological conditions, structures, and environment during construction make it difficult for traditional experience-based step sequence formulation to adapt to changes in real time, which may lead to safety risks or low efficiency. Therefore, step sequence simulation optimization and dynamic adjustment need to be carried out based on digital twin models and integrated data.

[0099] Firstly, multi-dimensional optimization constraints are defined to set rigid boundaries for step sequence optimization. The structural safety constraint specifies the requirements of component strength and stiffness to avoid component damage during construction. The deformation control constraint limits the allowable deformation of enclosure structures, ground surface, and surrounding buildings to prevent safety hazards caused by deformation. The construction technology constraint follows industry standards such as supporting before excavation and layered excavation to ensure compliance with the construction process. The environmental safety constraint controls the impact of construction on the surrounding ecology and facilities. The design specification constraint ensures that construction meets the requirements of engineering design according to standards such as seismic resistance and civil defense. These constraint conditions are determined based on digital twin models and integrated data to ensure that they are consistent with the actual engineering.

[0100] Then, construction step sequence simulation and optimization calculation are carried out, and the specific process is as follows:

[0101] Firstly, a construction step sequence timing correlation model is established to quantify the timing logic relationship of each work unit. The formula is as follows:

[0102] ,

[0103] Among them, is the total duration of the construction step sequence; is the total number of work units, including excavation partition, support erection, support replacement and removal, structure pouring, and interface connection; is the basic duration of the th work unit, determined by the work volume, construction machinery efficiency, and geological conditions through digital twin model simulation; is the timing interval correction value of the th work unit and the th work unit, which is positive when there is process dependency (such as support erection after excavation) and zero when there is no dependency and the work units can be performed in parallel; is the timing correlation coefficient, which is 1 when there is process dependency and 0 when there is no dependency. Its value is determined by construction technology specifications and work logic constraints in the digital twin model. This model quantifies the timing relationship to provide a basis for step sequence optimization, and dynamically correlates with the model component state parameters. The duration of the work unit will be adjusted in real time based on component stress and deformation feedback.

[0104] Secondly, a construction sequence safety constraint model is constructed, transforming multi-dimensional safety requirements into mathematical constraints, as shown in the following formula:

[0105] ,

[0106] in, For the first Each work unit in spatiotemporal coordinates The stress generated in the component at the location; Based on component state parameters The determined stress limit value is directly related to the crack resistance and load-bearing capacity of the component; For the first The displacement caused by each work unit includes the displacement of the foundation pit retaining wall, the displacement of surrounding buildings and pipelines; Based on geological environmental parameters Surrounding environmental parameters The determined allowable displacement values ​​reflect geological stability and environmental protection requirements; For the first The foundation pit stability coefficient during the execution of each work unit; Based on design standard parameters The determined minimum stability coefficient covers the safety thresholds corresponding to earthquake resistance and civil defense; this constraint model is directly linked to the dynamic data in the digital twin model to ensure that the optimization process always meets the safety requirements.

[0107] Finally, a multi-objective optimization objective function is established to achieve synergistic optimization of construction safety, efficiency, and risk, as shown in the following formula:

[0108] ,

[0109] The first objective term integrates the safety deviations of stress, displacement, and stability through normalization; a smaller value indicates a greater safety redundancy. The second objective term... The first objective is the total construction period, reflecting the need for optimized construction efficiency; the second objective is the cumulative value of construction risks. for The risk intensity at any given moment is a superposition of geological risks (such as liquefied soil disturbance), environmental risks (such as disturbance from surrounding buildings), and operational conflict risks. Its calculation is based on real-time updated geological, environmental, and construction data in a digital twin model.

[0110] The function is solved using a multi-objective optimization algorithm, iteratively adjusting the timing, spatial layout, and parameter configuration of each work unit to generate an initial optimization scheme; when a preset trigger event occurs, the objective function is updated in real time. , , With the same parameters, the function output of adaptive optimization scheme is recalculated to ensure the precise adaptation of construction step sequence to the dynamic changes of the project.

[0111] When the preset trigger event occurs, the adaptive optimization process is automatically started, and the output process of the adaptive optimization scheme is as follows:

[0112] Firstly, the quantitative model of trigger event influence is established to accurately define the interference range and degree of the event on the construction step sequence, and the formula is as follows:

[0113] ,

[0114] Among them, is the comprehensive influence index of the trigger event, which quantifies the interference intensity of the event on the construction system; is the spatial range of the trigger event influence, which is determined by the digital twin model combined with the type of the event (such as geological mutation, monitoring overrun); is the time when the trigger event occurs, is the time when the influence tends to be stable; , , are the geological environment parameters, surrounding environment parameters, and design standard parameters at different times after the event occurs; , , are the parameter baseline values before the event occurs; the integral term calculates the parameter change rate through normalization, superimposes the influence of time and space dimensions, and fully represents the interference degree of the event on the construction step sequence. This model directly associates the dynamic data in the digital twin model to ensure the accuracy of the influence quantification;

[0115] Secondly, the calculation model of optimization scheme adjustment amount is constructed, and the construction step sequence parameters are dynamically corrected based on the influence index, and the formula is as follows:

[0116] ,

[0117] Among them, is the parameter adjustment amount of the th work unit, including the core parameters such as work duration, construction range, and time interval; is the preset maximum allowed influence index, which is determined by the construction safety level and risk tolerance threshold; is the parameter limit value of the th work unit, which is determined based on the structural safety constraints and process requirements; is the current parameter value of the th work unit before the event occurs; is the function value of the component state parameter after the event occurs, reflecting the current working state of the component; is the safety threshold of the component state parameter; is a symbol function, when is positive, the parameter adjustment amount needs to be increased to strengthen the optimization effect, otherwise it is negative, and the parameter can be fine-tuned to maintain a safe state; the model quantifies the adjustment amount to ensure that the construction step sequence parameter correction and event influence are accurately matched.

[0118] Finally, the optimal scheme is selected and output by the scheme feasibility verification model, and the formula is as follows:

[0119] ,

[0120] Among them, , is the stress and displacement of the component generated by the first operation unit after parameter adjustment; , , are the updated component state parameters, geological environment parameters, and surrounding environment parameters after the event occurs; is the adjusted total construction period, is the preset maximum allowable construction period; is the risk intensity at time t after adjustment, is the maximum allowed risk accumulation value.

[0121] For adjustment schemes that meet all the constraints, the digital twin model is used to simulate and deduce their execution effects, and the scheme with the largest safety redundancy, the most reasonable construction period, and the lowest risk is selected as the adaptive optimization scheme. It is pushed to the construction execution end, and the scheme parameters are fed back to the digital twin model to complete the dynamic synchronization of the model and the scheme, providing a basis for subsequent construction and possible re-optimization.

[0122] (4) Scheme execution and deviation warning

[0123] In the construction of receiving shield in the existing operating station, the effective landing and dynamic control of the optimization scheme are the key links to ensure the construction quality. If only the scheme is developed without real-time tracking and deviation control, the scheme may fail due to on-site execution deviation or changes in working conditions, leading to safety risks or delays in construction period, so a closed-loop management process of scheme pushing, execution tracking, and deviation warning needs to be established.

[0124] After the optimization scheme is determined, it is pushed to the construction execution end through a data transmission channel. The execution end includes a dispatch center, a construction machinery control system, and a mobile terminal of a worker at a construction site. The scheme content is refined to specific requirements of each work unit, including the depth, area, and rate of excavation, the specific time of support installation and removal, the proportioning, slump, and pouring rate of concrete pouring, as well as the configuration scheme of personnel, machinery, and other resources and the equipment operation parameters. The site construction personnel carry out work according to the scheme, the mechanical operator receives parameter instructions through the control system, and the dispatcher coordinates each link according to the scheme to ensure that the construction is pushed forward according to the optimized sequence.

[0125] At the same time, the execution state of the scheme is tracked in real time based on the digital twin model, a construction data real-time acquisition link is built, and the actual construction data acquired and the preset data in the optimization scheme form corresponding comparison dimensions. Among them, the construction progress data such as excavation depth, area, and rate are compared with the progress nodes preset in the scheme; the monitoring data such as the displacement and stress of the enclosure structure and the support axial force are compared with the safety control threshold preset in the scheme; the process data such as the concrete pouring parameters and resource configuration information are compared with the process and management requirements preset in the scheme. The digital twin model is dynamically updated according to the real-time acquired data, and the execution conditions such as construction progress, structure state, and resource use are intuitively presented, realizing the visual tracking of the execution process of the scheme.

[0126] On this basis, a deviation early warning mechanism is established, and the deviation allowed range of each comparison dimension is set. The range is determined according to the construction quality acceptance standard, the structure safety control requirement, and the construction period management target. By automatically comparing the actual data with the preset data through the system, when the deviation exceeds the allowed range, the system immediately triggers an early warning. For example, if the actual excavation rate is too fast, the displacement of the enclosure structure approaches the threshold, or the slump of the concrete pouring deviates too much from the preset value, early warning information will be pushed to the dispatch center and the terminal of the relevant worker in real time. After the early warning is triggered, the cause of the deviation is determined. If it is due to inadequate execution, the site rectification is urged. If it is due to changes in working conditions, the influence is analyzed in combination with the digital twin model to provide a basis for whether to start the scheme re-optimization, ensuring that the construction is always in a controllable state.

[0127] Embodiment 2

[0128] For reference Figure 6 The embodiment 2 provides a shield receiving construction sequence optimization system for an existing operation station based on a digital twin, which comprises:

[0129] The twin model construction and correlation unit is configured to collect correlation data of an existing operation station, construct a digital twin basic model based on the correlation data, and complete full-factor digital mapping of a shield receiving construction project of the entity existing operation station; a model parameter correlation mechanism is established to perform parameterized configuration and lightweight optimization on the basic model, so that model components are dynamically correlated with various raw data;

[0130] The multi-source data integration and synchronization unit is configured to collect dynamic monitoring data in a construction process in real time, gather dynamic monitoring data and construction process data by building an integrated data transmission and integration platform, complete unified access of multi-source data, pre-process the integrated data, filter the data based on data effectiveness and consistency, and establish a real-time synchronization update mechanism of the data and the digital twin model;

[0131] The step sequence optimization and scheme generation unit is configured to set multi-dimensional optimization constraint conditions based on the digital twin model and the integrated data, embed a dynamic optimization algorithm, simulate and optimize calculation of a construction step sequence of an existing operation station based on construction safety and reliability, efficiency optimization, and risk controllability, generate an initial optimization scheme, and automatically start a re-optimization process and output an adaptive optimization scheme when a preset trigger event occurs.

[0132] The scheme execution and deviation early warning unit is configured to push the optimization scheme to a construction execution end to guide on-site construction, track a scheme execution state in real time based on the digital twin model, compare and analyze deviations between actual construction data and preset data of the optimization scheme, and establish a deviation early warning mechanism.

[0133] Embodiment 3

[0134] The embodiment 3 further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement any step of the method for optimizing a construction step sequence of an existing operation station based on a digital twin.

[0135] The computer readable storage medium can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0136] For the computer readable storage medium provided in the present application, refer to the above method embodiments, and the present application will not be repeated here.

[0137] Those skilled in the art can understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for optimizing the construction sequence of shield tunneling receiving at existing operating stations based on digital twins, characterized in that, include: S1. Collect existing operational station related data, construct a digital twin model based on the related data, and complete the full-element digital mapping of the shield tunneling construction project of the existing operational station; establish a model parameter association mechanism, perform parameterized configuration and lightweight optimization of the digital twin model, so that the model components and various original data form a dynamic association. S2. Real-time collection of dynamic monitoring data during construction, and aggregation of dynamic monitoring data and construction process data through the construction of an integrated data transmission and integration platform to achieve unified access of multi-source data; at the same time, the integrated data is preprocessed and filtered based on data validity and consistency; and a real-time synchronization and update mechanism for data and digital twin model is established. S3. Based on the digital twin model and integrated data, set multi-dimensional optimization constraints; By embedding dynamic optimization algorithms, with the goals of construction safety and reliability, efficiency optimization and risk control, the construction sequence of shield tunneling at existing operating stations is simulated and optimized to generate an initial optimization scheme. When a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization solution; S4. Push the optimized plan to the construction execution end to guide the on-site construction; Based on the digital twin model, the execution status of the plan is tracked in real time, and the deviation between the actual construction data and the preset data of the optimized plan is compared and analyzed to establish a deviation early warning mechanism. The process of simulating and optimizing the construction sequence of the shield tunneling receiving machine at the existing operating station in S3 is as follows: First, a construction step sequence temporal relationship model is established to quantify the temporal logical relationship of each work unit, as shown in the following formula: in, The total construction period is the sequence of construction steps; This represents the total number of work units; For the first The basic duration of each work unit; For the first The first work unit and the first The timing interval correction for each work unit is positive when there is a process dependency between the two work units, and zero when there is no dependency and they can be operated in parallel. This is the time-series correlation coefficient, which is 1 when there is a process dependency and 0 when there is no dependency. Secondly, a construction sequence safety constraint model is constructed, transforming multi-dimensional safety requirements into mathematical constraints, as shown in the following formula: in, For the first Each work unit in spatiotemporal coordinates The stress generated in the component at the location; Based on component state parameters Determined stress limit value; For the first The displacement caused by each work unit; Based on geological environmental parameters Surrounding environmental parameters Determined allowable displacement value; For the first The foundation pit stability coefficient during the execution of each work unit; Based on design standard parameters A definite minimum stability coefficient; Finally, a multi-objective optimization objective function is established to achieve synergistic optimization of construction safety, efficiency, and risk, as shown in the following formula: The first objective term integrates the safety deviations of stress, displacement, and stability through normalization; a smaller value indicates a greater safety redundancy. The second objective term... The first objective is the total construction period, reflecting the need for optimized construction efficiency; the second objective is the cumulative value of construction risks. for The risk intensity at any given moment is a superposition of geological risk, environmental risk, and operational conflict risk, and its calculation is based on real-time updated geological, environmental, and construction data in a digital twin model.

2. The method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins according to claim 1, characterized in that, The associated data in S1 includes structural design data, geological environment data, surrounding environment data, and design standard data; The structural design data includes the dimensional parameters, component types, reinforcement information, and connection details of the main structure and enclosure structure; the geological environment data includes the distribution characteristics of soil and rock layers, special physical and mechanical properties of soil and rock, groundwater type and water level parameters, and the distribution range and characteristics of adverse geological conditions; the surrounding environment data includes the foundation type and structural type of surrounding buildings, distance data from existing operating stations, specifications, depth, direction, and relocation plans of underground pipelines, and topographic data; the design standard data includes seismic fortification intensity, civil defense resistance level, waterproofing level, structural safety level, and durability design requirements.

3. The method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins according to claim 1, characterized in that, The digital twin model in S1 covers the structural system, geological environment system, surrounding environment system, and design constraint system.

4. The method for optimizing the construction sequence of shield tunneling reception at an existing operating station based on digital twins as described in claim 1, characterized in that, The process in S1 that dynamically links the model components with various types of raw data is as follows: First, the dynamic evolution equations of the component state parameters are established to characterize the changes in the model component state with time and space. The equations are as follows: in, Representing the The first class model component The status parameters directly reflect the real-time working status of the component; for The time change rate is used to characterize the dynamic evolution rate of the component state as the construction process progresses; In order to be with the first The geological environment parameters corresponding to the location of the model component reflect the degree of influence of geological conditions on the stress of the component; To affect the first The surrounding environmental parameters of the state parameters reflect the response pattern of the environment to construction disturbances; These parameters, defined by design standards, define the safe operating boundaries of components. To adapt to the dynamic needs of design optimization and adjustment during construction, the time variation rate of the design standard parameters is determined. The geological environment-structure coupling coefficient is determined by the frictional characteristics and bond strength of the contact surface between the soil layer and the component. The surrounding environment-structure coupling coefficient reflects the intensity of the interaction between environmental deformation and structural forces. Gradient operator, used to describe , The rate of change in the spatial distribution; Secondly, the regional correlation of parameters is supplemented by a spatial distribution equation to ensure the spatial coupling consistency between the model components and the original data. The equation is as follows: in, The three-dimensional spatial coordinates of the existing operating station; For the first Class model component The initial values ​​of the status parameters are determined by the component design parameters and the initial construction conditions; For the first The spatial influence volume of class model components; , Spatial coordinates Geological environment parameters and surrounding environment parameters at the location; the integral term is used to superimpose the comprehensive influence of the geological environment and surrounding environment on the component state parameters within the spatial range, ensuring that the model component state is accurately matched with the original data of different regions; Finally, the validity of the association is ensured by coupling the verification equation, as follows: in, The deviation between the calculated and measured values ​​of the component's state parameters; These are the component state parameter values ​​calculated using the aforementioned dynamic evolution equation and spatial distribution equation; These are the measured values ​​of the component status obtained through the monitoring system; The allowable deviation threshold is determined by the existing operating station's shield tunneling construction quality acceptance standards and structural safety control requirements.

5. The method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins according to claim 1, characterized in that, The dynamic monitoring data in S2 includes displacement, settlement, stress, and strain data of the enclosure structure, surface settlement data, axial force data of supporting components, groundwater level data, harmful gas concentration data, settlement and tilt data of surrounding buildings, and structural construction strength data. The construction process data includes excavation depth, excavation area, excavation rate, support installation and removal sequence, concrete pouring parameters, resource allocation information, and equipment operation status data. The preprocessing includes outlier removal, format standardization, accuracy verification, and noise reduction.

6. The method for optimizing the construction sequence of shield tunneling reception at an existing operating station based on digital twins according to claim 1, characterized in that, The constraints in S3 include structural safety constraints, deformation control constraints, construction process constraints, environmental safety constraints, and design specification constraints.

7. The method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins according to claim 1, characterized in that, The output process of the adaptive optimization scheme in S3 is as follows: First, establish a quantitative model of the impact of triggering events to accurately define the scope and degree of interference of events on the construction sequence. The formula is as follows: in, To trigger the comprehensive impact index of the event, the intensity of the event's interference with the construction system is quantified; The spatial range affected by the triggering event; To trigger the event at the time of occurrence, This is the moment when the influence tends to stabilize; , , These are geological environmental parameters, surrounding environmental parameters, and design standard parameters at different times after the event. , , These are the baseline values ​​of the parameters before the event occurred; the integral term calculates the rate of change of the parameters through normalization, and superimposes the effects of time and space dimensions to comprehensively characterize the degree of interference of the event on the construction sequence. Secondly, a calculation model for the adjustment amount of the optimization scheme is constructed, and the construction sequence parameters are dynamically corrected based on the influence index. The formula is as follows: in, For the first The parameter adjustment amount for each work unit; The preset maximum permissible impact index is determined by the construction safety level and risk tolerance threshold. For the first The parameter limit values ​​of each work unit are determined based on structural safety constraints and process requirements; The first time before the incident occurred The current parameter values ​​of each work unit; This is a function value of the component's state parameters after the event occurs, reflecting the component's current working state; The safety threshold for component state parameters; For a sign function, when When the time is positive, the parameter adjustment amount needs to be increased to enhance the optimization effect; conversely, when the time is negative, fine-tuning the parameter can maintain a safe state. Finally, the optimal solution is selected and output using the feasibility verification model, as shown in the following formula: in, , After parameter adjustment, the first The stress and displacement of components generated by each work unit; , , The updated component status parameters, geological environment parameters, and surrounding environment parameters are updated after the event occurs. The adjusted total construction period is as follows: This is the preset maximum allowable construction period; To adjust the risk intensity at time t, This represents the maximum permissible cumulative risk value.

8. A system for optimizing the construction sequence of shield tunneling receiving at existing operating stations based on digital twins, characterized in that, include: The twin model construction and association unit is used to collect associated data of existing operating stations, construct a digital twin model based on the associated data, and complete the full-element digital mapping of the shield tunneling construction project of the existing operating station; establish a model parameter association mechanism to perform parameterized configuration and lightweight optimization of the digital twin model, so that the model components and various original data form a dynamic association. The multi-source data integration and synchronization unit is used to collect dynamic monitoring data in real time during the construction process. By building an integrated data transmission and integration platform, it gathers dynamic monitoring data and construction process data to achieve unified access of multi-source data. At the same time, it preprocesses the integrated data and filters the data based on data validity and consistency. It also establishes a real-time synchronization and update mechanism between the data and the digital twin model. The step sequence optimization and scheme generation unit is used to set multi-dimensional optimization constraints based on the digital twin model and integrated data; By embedding dynamic optimization algorithms, with the goals of construction safety and reliability, efficiency optimization and risk control, the construction sequence of shield tunneling at existing operating stations is simulated and optimized to generate an initial optimization scheme. When a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization solution; The scheme execution and deviation early warning unit is used to push the optimized scheme to the construction execution end to guide the on-site construction. Based on the digital twin model, the execution status of the plan is tracked in real time, and the deviation between the actual construction data and the preset data of the optimized plan is compared and analyzed to establish a deviation early warning mechanism. The process of simulating and optimizing the construction sequence of shield tunneling at existing operating stations in the step optimization and scheme generation unit is as follows: First, a construction step sequence temporal relationship model is established to quantify the temporal logical relationship of each work unit, as shown in the following formula: in, The total construction period is the sequence of construction steps; This represents the total number of work units; For the first The basic duration of each work unit; For the first The first work unit and the first The timing interval correction for each work unit is positive when there is a process dependency between the two work units, and zero when there is no dependency and they can be operated in parallel. This is the time-series correlation coefficient, which is 1 when there is a process dependency and 0 when there is no dependency. Secondly, a construction sequence safety constraint model is constructed, transforming multi-dimensional safety requirements into mathematical constraints, as shown in the following formula: in, For the first Each work unit in spatiotemporal coordinates The stress generated in the component at the location; Based on component state parameters Determined stress limit value; For the first The displacement caused by each work unit; Based on geological environmental parameters Surrounding environmental parameters Determined allowable displacement value; For the first The foundation pit stability coefficient during the execution of each work unit; Based on design standard parameters A definite minimum stability coefficient; Finally, a multi-objective optimization objective function is established to achieve synergistic optimization of construction safety, efficiency, and risk, as shown in the following formula: The first objective term integrates the safety deviations of stress, displacement, and stability through normalization; a smaller value indicates a greater safety redundancy. The second objective term... The first objective is the total construction period, reflecting the need for optimized construction efficiency; the second objective is the cumulative value of construction risks. for The risk intensity at any given moment is a superposition of geological risk, environmental risk, and operational conflict risk, and its calculation is based on real-time updated geological, environmental, and construction data in a digital twin model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-7: an optimization method for the construction sequence of shield tunneling at an existing operating station based on digital twins.

Images