Existing operation station shield receiving construction step sequence optimization method based on digital twinning

Abstract

The invention discloses an existing operation station shield receiving construction step sequence optimization method and system based on digital twinning, and the method comprises the steps: collecting the associated data of an existing operation station, constructing a digital twinning basic model, achieving the full-factor digital mapping of entity engineering, and carrying out the parameterization and lightweight optimization. And establishing dynamic association between the model component and the original data. Dynamic monitoring and construction process data are collected in real time and are uniformly accessed through an integrated platform, and a real-time synchronization mechanism of the data and a model is established after preprocessing and screening. Based on the model and integrated data, setting a multi-dimensional optimization constraint, embedding a dynamic optimization algorithm, simulating, deducing and optimizing a construction step sequence by taking safety, high efficiency and low risk as targets, and generating an initial scheme; and when a preset trigger event occurs, re-optimization is carried out, and an adaptive scheme is output. And the scheme is pushed to a construction execution end, an execution state is tracked, actual and preset data deviations are compared, and an early warning mechanism is established. And smooth construction of the existing operation station is ensured.

Description

Technical Field

[0001] This invention belongs to the field of digital technology in rail transit engineering, and more specifically, relates to a method and system for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins. Background Technology

[0002] In the construction of rail transit projects, existing operating stations have complex structures and varied construction environments, presenting numerous technical challenges. Shield tunneling at existing operating stations typically involves multiple layers of intersecting underground structures, requiring coordination of the construction schedules of different lines. Furthermore, these stations are often located in the heart of the city, surrounded by dense buildings and crisscrossing underground pipelines, resulting in significantly limited construction space.

[0003] The complexity of geological conditions further exacerbates the construction difficulties. Some existing operating stations are located in strata with unfavorable geological conditions such as liquefiable soil and shallow methane, which can easily lead to risks such as foundation pit collapse and structural deformation, placing stringent requirements on the rationality of the construction sequence. Traditional construction sequence formulation relies heavily on experience and is based on static design data, making it difficult to fully consider the dynamic changes in the geological environment, surrounding environment, and construction process, resulting in a lack of flexibility and adaptability in the construction sequence.

[0004] During construction, when unexpected situations arise such as sudden changes in geological conditions, exceeding limits in monitoring data, or disturbances in the surrounding environment, traditional methods struggle to quickly adjust the construction sequence, easily leading to construction delays and increased safety risks. Furthermore, due to the lack of digital tools, optimizing the construction sequence fails to achieve multi-objective collaboration, often failing to balance construction safety, efficiency, and risk control, potentially resulting in resource waste or unforeseen safety hazards.

[0005] With the expansion of rail transit construction scale and the improvement of construction standards, traditional construction sequence planning methods can no longer meet the high precision, high safety, and high efficiency requirements of shield tunneling at existing operating stations. There is an urgent need for a technical solution that can integrate multi-source data, respond to dynamic changes in real time, and achieve precise optimization of construction sequence to solve the problems of static planning, poor adaptability, and insufficient multi-objective coordination in traditional methods. This will ensure the smooth progress of shield tunneling at existing operating stations and improve the overall quality and efficiency of rail transit engineering construction. Summary of the Invention

[0006] This invention aims to solve the problems of static planning, poor adaptability, and insufficient multi-objective coordination in the traditional method of formulating the construction sequence of shield tunneling at existing rail transit stations. Through digital twin modeling, multi-source data integration, dynamic optimization algorithms, and closed-loop management, it achieves accurate simulation, real-time adjustment, and efficient optimization of the construction sequence, taking into account construction safety, efficiency, and risk control, and ensuring the smooth progress of shield tunneling construction at existing operating stations.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for optimizing the construction sequence of shield tunneling receiving at existing operating stations based on digital twins, comprising: S1. Collect existing operational station related data, construct a digital twin basic 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 basic 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; embed dynamic optimization algorithms to simulate and optimize the construction sequence of shield tunneling at existing operating stations with the goals of construction safety and reliability, efficiency optimization and risk control, and generate an initial optimization scheme; when a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme. S4. Push the optimized plan to the construction execution end to guide the on-site construction; track the execution status of the plan in real time based on the digital twin model, compare and analyze the deviation between the actual construction data and the preset data of the optimized plan, and establish a deviation early warning mechanism.

[0008] Furthermore, 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.

[0009] Furthermore, the basic model in S1 covers the structural system, geological environment system, surrounding environment system, and design constraint system.

[0010] Furthermore, the process in S1 that dynamically associates the model components with various types of original 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.

[0011] Furthermore, 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.

[0012] Furthermore, the constraints in S3 include structural safety constraints, deformation control constraints, construction process constraints, environmental safety constraints, and design specification constraints.

[0013] Furthermore, the process of simulating and optimizing the construction sequence of the shield tunneling receiving 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.

[0014] Furthermore, 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.

[0015] As a second aspect of the present invention, a system for optimizing the construction sequence of shield tunneling receiving at existing operating stations based on digital twins is also provided, comprising: The twin model construction and association unit is used to collect associated data from existing operating stations, construct a digital twin basic 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 basic 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; embeds a dynamic optimization algorithm to simulate and optimize the construction steps of the shield receiving of existing operating stations with the goals of construction safety and reliability, efficiency optimization and risk control, and generates an initial optimization scheme; when a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme. 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, it tracks the scheme execution status in real time, compares and analyzes the deviation between the actual construction data and the preset data of the optimized scheme, and establishes a deviation early warning mechanism.

[0016] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor as described in any one of the claims, a method for optimizing the construction sequence of shield tunneling at an existing operating station based on digital twins.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The present invention provides a method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins. This method constructs a digital twin model by collecting associated data from existing operating stations, achieving a full-element digital mapping of the physical project. Through parametric configuration and lightweight optimization, it establishes a dynamic association between model components and various original data. This technical feature achieves a precise correspondence between the physical project and the digital model, allowing the model to completely replicate the structural system, geological environment, surrounding environment, and design constraints of the shield tunneling construction at the existing operating station. This provides a high-precision digital carrier for subsequent construction sequence optimization, ensuring that the basic data upon which the optimization calculation relies is comprehensive and realistic. It avoids deviations in the optimization scheme caused by the disconnect between the model and the physical entity, laying a data and model foundation for the scientific optimization of the construction sequence.

[0018] 2. The present invention provides a method for optimizing the construction sequence of shield tunneling receiving at existing operating stations based on digital twins. This method establishes an integrated data transmission and integration platform to aggregate dynamic monitoring data and construction process data in real time. After preprocessing and filtering, a real-time synchronization mechanism between the data and the digital twin model is established. This technical feature enables efficient integration and dynamic updating of multi-source data, allowing the digital twin model to reflect changes in the geological environment, structural state, and surrounding environment during construction in real time. This ensures that the optimization of the construction sequence is always based on the latest actual engineering data, avoiding mismatches between the optimization scheme and the on-site conditions due to data lag, and improving the timeliness and accuracy of the construction sequence optimization.

[0019] 3. The present invention provides a digital twin-based method for optimizing the construction sequence of shield tunneling at existing operating stations. This method sets multi-dimensional optimization constraints, embeds a dynamic optimization algorithm to simulate and generate an initial plan, and automatically initiates a re-optimization process to output an adaptive plan upon encountering a preset trigger event. Simultaneously, it tracks the execution status of the plan and establishes a deviation early warning mechanism. This technical feature achieves dynamic optimization and closed-loop management of the construction sequence, ensuring that the optimized plan meets multiple objectives such as structural safety, efficiency optimization, and risk control, while also adapting to real-time changes in the project's dynamics. It effectively avoids spatiotemporal conflicts, geological risks, and environmental disturbances during construction, guaranteeing the safe, reliable, and efficient progress of shield tunneling at existing operating stations. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method for optimizing the construction sequence of shield tunneling at existing operating stations based on digital twins, according to an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the relative relationship between the tunnel boring machine receiving project and the high-speed railway station according to an embodiment of the present invention. Figure 3 This is a schematic diagram of a high-speed railway station model after the roof slab has been removed, according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the vertical displacement of the station structure according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the horizontal displacement of the station structure according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1 This embodiment 1 provides a method for optimizing the construction sequence of shield tunneling reception at existing operating stations based on digital twins, including: S1. Collect existing operational station related data, construct a digital twin basic 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 basic 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; embed dynamic optimization algorithms to simulate and optimize the construction sequence of shield tunneling at existing operating stations with the goals of construction safety and reliability, efficiency optimization and risk control, and generate an initial optimization scheme; when a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme. S4. Push the optimized plan to the construction execution end to guide the on-site construction; track the execution status of the plan in real time based on the digital twin model, compare and analyze the deviation between the actual construction data and the preset data of the optimized plan, and establish a deviation early warning mechanism.

[0023] This embodiment 1 further elaborates on the above steps.

[0024] (1) Twin model construction and association In the shield tunneling construction of existing rail transit stations, the accurate correlation between the model and the data is the core foundation for optimizing the construction sequence. However, traditional modeling methods often result in a lack of reliable support for subsequent optimization due to the disconnect between the model and the data. Therefore, it is necessary to establish a scientific model construction and dynamic correlation process.

[0025] First, data related to existing operating stations was collected. This data covers four main categories: structural design, geological environment, surrounding environment, and design standards. Structural design data includes the dimensional parameters, component types, reinforcement information, and connection details of the main structure and enclosure structure, which is the core basis for building the model structural framework. 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. Surrounding environment data includes the foundation form and structural type of surrounding buildings, distance data from existing operating stations, specifications, depth, direction, and relocation plans of underground pipelines, and topographic data. Design standard data includes seismic fortification intensity, civil defense resistance level, waterproofing level, structural safety level, and durability design requirements.

[0026] Based on this data, a digital twin foundation model is constructed to fully replicate the existing operating station's structural system, geological environment system, surrounding environment system, and design constraint system, achieving a full-element digital mapping of the physical project. Subsequently, the foundation model is parametrically configured and lightweight optimized, allowing each component of the model to update its state through parameter adjustments, while reducing the model's computational load and adapting to subsequent real-time optimization needs.

[0027] After the model is built, it is necessary to establish dynamic relationships between the model components and various types of raw data. This process is achieved through multi-step logical deduction. The specific process 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 include component strain increment, displacement trend and crack resistance performance index, which 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 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; 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 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; 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. This verification equation is used to check the correlation accuracy between the model components and the original data in real time; when the deviation exceeds the threshold, the coupling coefficient is automatically corrected. , This ensures that the dynamic relationships always conform to the actual situation of the physical project, providing accurate and reliable model support for the simulation and optimization calculation of the construction sequence.

[0028] For example, taking the shield tunneling receiving construction at an existing operating station as an example, in the model building and dynamic correlation process, relevant data such as the station's structural design, geological environment, surrounding environment, and design standards are first collected. Based on this data, a digital twin basic model is built. Figure 2 The presented model depicting the relative relationship between the tunnel boring machine (TBM) receiving project and the high-speed railway station replicates the spatial location, structural boundaries, and connection relationships between the TBM receiving area and the main structure of the high-speed railway station. This provides an accurate spatial benchmark for mapping the surrounding environment and structural systems within the model. Figure 3 The model of the high-speed railway station after the roof slab was removed details the structural morphology under key construction procedures, clarifying the component distribution and load transfer path changes in the roof slab removal area. This provides a direct basis for the initial setting of the structural stress state in the model's parametric configuration. Subsequently, displacement monitoring data collected during construction was correlated with the model. Figure 4 Schematic diagram of vertical displacement of station structure Figure 5 The horizontal displacement diagram visually presents the displacement amplitude and distribution patterns of key station components at different construction stages. These displacement data, as measured values ​​of the model component state parameters, are combined with the mechanical properties of the soil and rock layers and groundwater parameters in the geological environment to determine the geological environment-structure coupling coefficient. By logically superimposing the spatial distribution of the influence of geological and environmental factors on component displacement in different regions, the model can accurately simulate displacement change trends that match the measured data. Finally, a comparison is made. Figure 4 , Figure 5 The deviation between the displacement value calculated by the model and the actual displacement data measured on site is used to verify the correlation accuracy between the model components and the original data. If the deviation exceeds the threshold defined by the existing operating station shield tunneling construction quality acceptance standard, the coupling coefficient is automatically corrected. This ensures that the digital twin model can accurately replicate the structural stress and deformation state of the existing operating station during shield tunneling construction, providing reliable support for the simulation and optimization calculation of subsequent construction steps.

[0029] (2) Multi-source data integration and synchronization During the shield tunneling process at existing operating stations, dynamic factors such as changes in geological conditions, structural stress adjustments, and disturbances in the surrounding environment 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 unable to provide timely support for step sequence optimization. Therefore, it is necessary to establish a mechanism for real-time integration of multi-source data and model synchronization.

[0030] First, a dynamic monitoring system was deployed, setting up monitoring points and equipment for key construction stages and risk points. The collected dynamic monitoring data covers multiple core indicators. Among them, displacement, settlement, stress, and strain data of the retaining structure directly reflect the stability of the foundation pit support system; surface settlement data is used to assess the degree of ground disturbance caused by construction; axial force data of support components can promptly detect safety hazards such as support overload; groundwater level data is related to foundation pit anti-buoyancy and seepage control; harmful gas concentration data ensures the safety of construction personnel; settlement and tilt data of surrounding buildings are key to controlling environmental risks; and structural construction strength data ensures that the quality of pouring and other processes meets standards. Simultaneously, construction process data is collected, including excavation operation parameters such as excavation depth, area, and rate; process execution data such as support installation and dismantling sequence and concrete pouring parameters; and management data such as resource allocation information and equipment operation status data, comprehensively covering the entire construction execution process.

[0031] Subsequently, an integrated data transmission and integration platform was built to achieve unified access to dynamic monitoring data and construction process data, breaking down barriers between different data sources and forming a complete construction data chain. After data integration, preprocessing is required. First, outlier removal eliminates invalid data caused by equipment malfunctions and external interference. Then, format standardization converts heterogeneous data output from different devices and systems into a unified format for easier subsequent processing. Next, accuracy verification is performed, comparing the data against monitoring equipment calibration standards and construction data recording specifications to ensure data errors are within acceptable limits. Finally, noise reduction filters out random interference signals in the data, improving data quality. After preprocessing, a real-time synchronization and update mechanism between the data and the digital twin model is established, allowing processed valid data to be fed back to the model in real time. This enables the model to dynamically replicate the actual structural state, geological environment, and construction progress during construction, providing accurate and timely data support for the dynamic optimization of subsequent construction steps.

[0032] The core of establishing a real-time synchronous update mechanism for data and digital twin models is to achieve dynamic binding and immediate feedback between pre-processed effective data and various elements of the model, ensuring that the model always accurately matches the actual construction process.

[0033] First, based on the parametric characteristics of the model components, specific data mapping rules are set for each type of data, clarifying the model-related objects and update dimensions corresponding to different types of data. For example, monitoring data such as displacement and stress of the retaining structure are directly mapped to the geometric and mechanical state parameters of the corresponding retaining components in the model; construction process data such as excavation depth and support installation sequence are associated with the construction progress node parameters and work space state parameters of the model; data such as groundwater level and settlement of surrounding buildings are synchronously updated with geological environment parameters and surrounding environment impact parameters in the model, achieving a one-to-one correspondence between data and model elements.

[0034] Secondly, a low-latency data transmission channel is established. After data preprocessing, valid data is pushed to the digital twin model management platform via a real-time communication protocol. The platform has a built-in data parsing module that automatically identifies the data type, source, and associated model objects, triggering model parameter updates according to preset mapping rules. For continuously changing monitoring data (such as displacement and stress time series data), an incremental update method is adopted, synchronizing only the data changes to the model to reduce data transmission and computational load. For discrete construction process data (such as support removal and concrete pouring completion signals), an immediate full update method is adopted to ensure that the model reflects the status changes of key construction nodes in a timely manner.

[0035] Simultaneously, a model update verification mechanism is established to automatically verify the rationality and consistency of the updated model parameters after data synchronization. For example, it verifies the matching between the stress data of retaining components and geological environmental parameters to avoid model distortion due to data anomalies; it checks the construction progress data against the temporal logic of the work units in the model to ensure that the model's construction status is consistent with the actual on-site progress. If the verification detects data conflicts or parameter anomalies, the system automatically triggers an early warning and suspends model updates until manual verification or data correction is performed before resuming synchronization, ensuring the accuracy of model updates.

[0036] In addition, a model update visualization feedback function is set up to present the data synchronization process and model status changes in real time, allowing managers to intuitively grasp the synchronization status between the model and the actual project. Through this series of mechanisms, the entire process from data preprocessing to model parameter updates is automated and executed with low latency. This enables the digital twin model to dynamically and accurately replicate the real-time situation of structural status, geological environment changes, and construction progress during actual construction, providing reliable model support for the dynamic optimization of construction steps.

[0037] (3) Step sequence optimization and scheme generation In the shield tunneling construction of existing operating stations, the rationality of the construction sequence directly determines the construction safety and efficiency. However, the geological, structural, and environmental conditions change dynamically during the construction process. The traditional method of relying on experience to formulate the sequence is difficult to adapt to changes in real time, which can easily lead to safety risks or inefficiency. Therefore, it is necessary to carry out sequence simulation optimization and dynamic adjustment based on digital twin models and integrated data.

[0038] First, multi-dimensional optimization constraints are clearly defined to establish rigid boundaries for step-by-step optimization. Structural safety constraints specify requirements for component strength and stiffness to prevent component damage during construction; deformation control constraints limit the allowable deformation of the retaining structure, ground surface, and surrounding buildings to prevent safety hazards caused by deformation; construction process constraints adhere to industry standards such as pre-support excavation and layered excavation to ensure compliance; environmental safety constraints manage the impact of construction on the surrounding ecology and facilities; and design specification constraints are based on seismic and civil defense standards to ensure that construction meets engineering design requirements. All these constraints are determined using digital twin models and integrated data to ensure they closely align with the actual engineering situation.

[0039] Next, the construction sequence simulation and optimization calculations were carried out. The specific process 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; The total number of work units covers core operations such as excavation zoning, support erection, support replacement and dismantling, structural pouring, and interface connection. For the first The basic duration of each work unit is determined by simulation using a digital twin model based on the workload, construction machinery efficiency, and geological conditions. 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 (such as when the support erection needs to be carried out after excavation), and zero when there is no dependency and they can be operated in parallel. This is the temporal correlation coefficient, which takes the value of 1 when there is a process dependency and 0 when there is no dependency. Its value is determined by the construction process specifications and the operational logic constraints in the digital twin model. This model provides a basis for schedule calculation for step sequence optimization by quantifying temporal relationships, and is dynamically correlated with the state parameters of model components and the duration of operation units. It will adjust in real time according to the stress and deformation feedback of the components.

[0040] 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 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.

[0041] 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 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.

[0042] 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. , , By resolving parameters such as these, the function is recalculated to output an adaptive optimization scheme, ensuring that the construction sequence is accurately adapted to the dynamic changes of the project.

[0043] When a preset trigger event occurs, the adaptive optimization process is automatically initiated, and the output process of the adaptive optimization scheme 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 extent of the event's impact is determined by a digital twin model combined with the event type (such as geological mutation or exceeding monitoring limits). 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 parameter values ​​before the event occurred; the integral term calculates the rate of change of 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. This model is directly linked to the dynamic data in the digital twin model to ensure the accuracy of the impact quantification. 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, including core parameters such as work duration, construction scope, and time interval; 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 component status exceeds the safe range, a positive value is used, requiring an increase in parameter adjustment to enhance the optimization effect; conversely, a negative value is used, and fine-tuning the parameters can maintain the safe state. This model ensures that the correction of construction sequence parameters and the impact of events are accurately matched by quantifying the adjustment amount.

[0044] 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.

[0045] For adjustment schemes that meet all constraints, their execution effects are simulated and deduced through a digital twin model. The scheme with the greatest safety redundancy, the most reasonable construction period, and the lowest risk is selected as the adaptive optimization scheme and pushed to the construction execution end. At the same time, the scheme parameters are fed back to the digital twin model to achieve dynamic synchronization between the model and the scheme, providing a basis for subsequent construction and possible further optimization.

[0046] (4) Implementation of the plan and early warning of deviations In the shield tunneling construction of existing operating stations, the effective implementation and dynamic management of optimized plans are key to ensuring construction quality. If plans are only formulated without real-time tracking and deviation control, the plans may fail due to on-site execution deviations or changes in working conditions, leading to safety risks or project delays. Therefore, it is necessary to build a closed-loop management process for plan delivery, execution tracking, and deviation early warning.

[0047] Once the optimized plan is finalized, it is transmitted to the construction execution end via data transmission channels. The execution end includes the on-site dispatch center, construction machinery control system, and mobile devices for workers. The plan details the specific requirements for each work unit, including excavation depth, area, and rate; specific timing for support installation and dismantling; concrete mix design, slump, and pouring rate; and the allocation of personnel, machinery, and other resources, as well as equipment operating parameters. On-site construction personnel carry out operations according to the plan, machinery operators receive parameter instructions through the control system, and dispatchers coordinate all aspects based on the plan to ensure construction proceeds according to the optimized sequence.

[0048] Simultaneously, based on the digital twin model, the execution status of the plan is tracked in real time, and a real-time construction data acquisition link is established. The collected actual construction data forms corresponding comparison dimensions with the preset data in the optimized plan. Among them, construction progress data such as excavation depth, area, and rate are compared with the preset progress nodes in the plan; monitoring data such as retaining structure displacement, stress, and support axial force are compared with the preset safety control thresholds in the plan; and process data such as concrete pouring parameters and resource allocation information are compared with the preset process and management requirements in the plan. The digital twin model is dynamically updated based on the real-time collected data, intuitively presenting the execution status such as construction progress, structural status, and resource usage, realizing visualized tracking of the plan execution process.

[0049] Based on this, a deviation early warning mechanism is established, setting allowable deviation ranges for each comparison dimension. These ranges are determined according to construction quality acceptance standards, structural safety control requirements, and project schedule management objectives. The system automatically compares actual data with preset data, and triggers an early warning immediately when a deviation exceeds the allowable range. For example, if the actual excavation rate is too fast, causing the retaining structure displacement to approach the threshold, or if the concrete slump deviates significantly from the preset value, the early warning information will be pushed to the dispatch center and relevant personnel terminals in real time. After the early warning is triggered, the cause of the deviation is identified. If it is due to inadequate execution, on-site rectification is urged; if it is due to changes in working conditions, the impact is analyzed using a digital twin model, providing a basis for whether to initiate a re-optimization of the plan and ensuring that construction remains under control.

[0050] Example 2 Please refer to Figure 6 This embodiment 2 provides a system for optimizing the construction sequence of shield tunneling reception at existing operating stations based on digital twins, including: The twin model construction and association unit is used to collect associated data from existing operating stations, construct a digital twin basic 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 basic 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; embeds a dynamic optimization algorithm to simulate and optimize the construction steps of the shield receiving of existing operating stations with the goals of construction safety and reliability, efficiency optimization and risk control, and generates an initial optimization scheme; when a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme. 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, it tracks the scheme execution status in real time, compares and analyzes the deviation between the actual construction data and the preset data of the optimized scheme, and establishes a deviation early warning mechanism.

[0051] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement any step of an existing operation station shield tunneling receiving construction sequence optimization method based on digital twins.

[0052] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0054] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the construction sequence of a shield receiving operation at an existing operating station based on digital twinning, characterized in that, Comprise: S1. Collecting associated data of existing operating station, constructing a digital twin basic model based on the associated data, completing full-factor digital mapping of entity existing operating station shield receiving construction project; establishing a model parameter association mechanism, parameterizing configuration and lightweight optimization of the basic model, making the model components dynamically associated with various types of original data; S2. Real-time collection of dynamic monitoring data in the construction process, gathering dynamic monitoring data and construction process data through the establishment of an integrated data transmission and integration platform, completing unified access of multi-source data; at the same time, pre-processing the integrated data, filtering the data based on data effectiveness and consistency; at the same time, establishing a real-time synchronization updating mechanism for data and digital twin model; S3. Based on the digital twin model and integrated data, setting multi-dimensional optimization constraint conditions; Embedding dynamic optimization algorithm, taking construction safety and reliability, efficiency optimization, and risk controllability as the goal, simulating and optimizing the construction sequence of the existing operating station construction shield receiving construction, generating an initial optimization scheme; When a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme; S4. Push the optimization scheme to the construction execution end to guide the on-site construction; Based on the digital twin model, real-time tracking of the scheme execution state, comparative analysis of the deviation between the actual construction data and the preset data of the optimization scheme, and establishment of a deviation warning mechanism.

2. The method according to claim 1, wherein the method is characterized in that, The associated data in S1 includes structural design data, geological environment data, surrounding environment data, and design standard data; Among them, the structural design data includes the size parameters, component types, reinforcement information, and connection structure data of the main structure and enclosure structure; the geological environment data includes the distribution characteristics of rock and soil layers, special rock and soil physical and mechanical properties, groundwater types and water level parameters, adverse geological distribution range and characteristic data; the surrounding environment data includes the foundation form, structure type, distance data from the existing operating station of the surrounding buildings, the specification, buried depth, trend, 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.

3. The method according to claim 1, wherein the method is characterized in that, The basic model in S1 covers the structure system, geological environment system, surrounding environment system, and design constraint system.

4. The method according to claim 1, wherein the method is characterized in that, The process of making the model components dynamically associated with various types of original data in S1 is as follows: First, establish a dynamic evolution equation for component state parameters to represent the change law of model component state with time and space, the equation is as follows: ， wherein, represent the first class model component state parameter, directly reflecting the real-time working state of the component; is the time rate of change, used to characterize the dynamic evolution rate of the component state with the construction process; is the geological environment parameter corresponding to the position of the first class model component, reflecting the influence degree of the geological condition on the stress of the component; is the surrounding environment parameter affecting the first state parameter, reflecting the response law of the environment to the construction disturbance; represents the design standard limiting parameter, defining the safe working boundary of the component; is the time rate of change of the design standard parameter, adapting to the dynamic demand of design optimization adjustment in the construction process; is the geological environment-structure coupling coefficient, 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, reflecting the interaction strength of the environmental deformation and the structural stress; is the gradient operator, used to describe the spatial distribution rate of change of , ; Second, supplement the regional association of parameters through the spatial distribution equation to ensure the spatial coupling consistency of model components and original data, the equation is as follows: ， wherein, is the three-dimensional spatial coordinate of the existing operating station; is the initial value of the state parameter of the first class model component, determined by the component design parameter and the initial construction working condition; is the initial value of the state parameter of the first class model component, determined by the component design parameter and the initial construction working condition; is the spatial influence volume corresponding to the first , are respectively the geological environment parameters and the surrounding environment parameters at the spatial coordinate ; the integral term is used to superimpose the comprehensive influence of the geological environment and the surrounding environment on the component state parameter within the spatial range, to ensure accurate matching of the model component state and the original data of different regions; Finally, ensure the effectiveness of the association relationship through the coupling verification equation, the equation is as follows: ， wherein, is the calculated value of the component state parameter and the measured value of the deviation; 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 allowed threshold value, which is determined by the existing operating station shield receiving construction quality acceptance standard and structure safety control requirements.

5. The method according to claim 1, wherein the method is characterized in that, The dynamic monitoring data in S2 includes displacement, settlement, stress, and strain data of the enclosure structure, ground settlement data, axial force data of support components, groundwater level data, harmful gas concentration data, settlement and inclination data of surrounding buildings, and structure construction intensity data; 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. The preprocessing includes outlier rejection, format standardization, precision verification and noise reduction processing.

6. The method according to claim 1, wherein the method is characterized in that, The constraint conditions in S3 include structural safety constraints, deformation control constraints, construction process constraints, environmental safety constraints and design specification constraints.

7. The method according to claim 1, wherein the method is characterized in that, The process of simulating and optimizing the construction step sequence of the existing operating station in S3 is as follows: First, a construction step sequence timing correlation model is established to quantify the timing logic relationship of each work unit, as follows: ， wherein, is the total duration of the construction step sequence; is the total number of operation units; is the base duration of the th operation unit; is the time interval correction amount between the th operation unit and the th operation unit, which is positive when there is a process dependency between the two operation units, and zero when there is no dependency and the operation units can be performed in parallel; is the time sequence correlation coefficient, which is 1 when there is a process dependency, and 0 when there is no dependency. Second, a construction step sequence safety constraint model is constructed to convert multi-dimensional safety requirements into mathematical constraint conditions, as follows: ， 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 the coordinated optimization of construction safety, efficiency and risk, as follows: ， 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.

8. The method according to claim 1, wherein the method is characterized in that, The output process of the adaptive optimization scheme in S3 is as follows: First, a trigger event influence quantification model is established to accurately define the interference range and degree of events on the construction step sequence, as follows: ， wherein, is the comprehensive influence index of the triggering event, quantifying the disturbance intensity of the event on the construction system; is the spatial range of the triggering event influence; is the time of occurrence of the triggering event, is the time when the influence tends to be stable; , , are the geological environment parameters, the surrounding environment parameters, and the design standard parameters at different times after the occurrence of the event, respectively; , , are the parameter baseline values before the occurrence of the event; the integral term calculates the parameter change rate through normalization, superimposes the influence of time and space dimensions, and comprehensively represents the disturbance degree of the event on the construction sequence. Second, an optimization scheme adjustment amount calculation model is constructed to dynamically correct the construction step sequence parameters based on the influence index, 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 scheme is selected and output through a scheme feasibility verification model, as follows: ， wherein, , is the component stress and displacement amount generated by the parameter-adjusted jth work unit after the event occurs; , , , is the component state parameter, the geological environment parameter, and the surrounding environment parameter updated 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 allowable risk accumulation value.

9. A system for optimizing the construction procedure of a shield receiving operation at an existing operation station based on digital twinning, characterized in that, It includes: Twin model construction and associated units are used to collect associated data of existing operating stations, and a digital twin basic model is constructed based on the associated data to complete full-factor digital mapping of the entity existing operating station shield receiving construction project; A model parameter association mechanism is established to parameterize and optimize the basic model, so that the model components are dynamically associated with various raw data; Multi-source data integration and synchronization unit is used to collect dynamic monitoring data in the construction process in real time, and through the construction of an integrated data transmission and integration platform, dynamic monitoring data and construction process data are gathered to complete unified access of multi-source data; At the same time, the integrated data is preprocessed based on data effectiveness and consistency; At the same time, a real-time synchronization updating mechanism of data and digital twin model is established; Step sequence optimization and scheme generation unit is used to set multi-dimensional optimization constraint conditions based on digital twin model and integrated data; Embedding dynamic optimization algorithm, taking construction safety and reliability, efficiency optimization and risk control as the goal, simulating and optimizing the construction step sequence of the existing operating station, generating an initial optimization scheme; When a preset trigger event occurs, the algorithm automatically starts the re-optimization process and outputs an adaptive optimization scheme; The scheme execution and deviation warning unit is used to push the optimization scheme to the construction execution end to guide the on-site construction; Based on the digital twin model, the scheme execution state is tracked in real time, and the deviation between the actual construction data and the preset data of the optimization scheme is compared and analyzed to establish a deviation warning mechanism.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to perform the method of claim 1-8.

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