Fabricated building construction progress simulation method and system based on digital twinning

Abstract

This invention discloses a method and system for simulating the construction progress of prefabricated buildings based on digital twins. It pre-constructs a multi-dimensional lightweight simulation verification engine and an assembly performance deviation quantification judgment engine. Based on a BIM model, it builds a digital twin of the assembly benchmark for prefabricated components, pre-setting a benchmark assembly sequence, rigid constraints, a compliance rule library, and a simulation benchmark dataset. It receives assembly sequence adjustment instructions and generates a custom twin, conducting lightweight simulation and deviation quantification judgment through four fixed dimensions, outputting standardized results and compliance solutions. This invention achieves rapid, safe, and compliant verification of assembly sequence adjustments, effectively avoiding potential risks to construction period, safety, and quality, and improving the refinement and intelligence of construction progress management. It is applicable to the entire process management of prefabricated building construction.

Description

Technical Field

[0001] This application relates to the field of construction progress simulation methods, and in particular to a method and system for simulating the construction progress of prefabricated buildings based on digital twins. Background Technology

[0002] Prefabricated buildings represent a core development direction for my country's building industrialization and green building system. Leveraging their advantages of standardized production, prefabricated construction, and low environmental impact, they have been widely adopted in housing construction, municipal engineering, and other fields. The on-site construction progress, structural safety, and assembly quality of prefabricated buildings are strongly correlated with the assembly sequence of prefabricated components. Influenced by factors such as the timing of component arrival, site conditions, the rhythm of work processes, and unforeseen circumstances, the pre-set assembly sequence needs frequent temporary adjustments on the construction site. These adjustments directly impact the temporary structural safety during the construction phase, the accumulation of assembly accuracy, the effectiveness of schedule control, and compliance with regulations, placing extremely high demands on the refinement and intelligence of construction management.

[0003] Currently, for the management of construction progress in prefabricated buildings, existing technologies mostly employ BIM technology for static construction progress simulation, or use general-purpose finite element analysis software for structural safety verification under a fixed assembly sequence. Some solutions introduce digital twin technology to achieve visualized management of the construction process, and there are also related studies on optimizing the assembly sequence, which have achieved digital management of prefabricated construction progress to a certain extent. However, existing technical solutions are mostly based on preset baseline construction plans. For scenarios where temporary adjustments to the assembly sequence frequently occur on construction sites, there is a lack of adaptable and implementable full-process management and verification solutions, making it difficult to meet the actual application needs of construction sites.

[0004] Existing simulation solutions mostly adopt a full-model overall simulation mode, which involves large computational loads and long simulation cycles, making it impossible to achieve rapid verification at the construction site within seconds to minutes, and difficult to adapt to the needs of real-time decision-making on site. Existing solutions lack rigid constraint pre-verification mechanisms and safety red line control rules, failing to intercept illegal assembly sequence adjustments at the source, which can easily lead to structural safety hazards during the construction phase. Existing solutions only perform single-segment verification on the adjusted area, without considering the chain reaction of assembly sequence adjustments on subsequent entire flow sections and floor-level construction, easily leading to a series of control problems such as subsequent schedule loss and cumulative exceedance of assembly accuracy standards. Existing solutions generally use a fixed threshold mode for performance deviation judgment, without differentiating settings according to the importance level of prefabricated components, resulting in insufficient rationality and adaptability of the judgment rules. Existing solutions have a high overall operational threshold, requiring highly skilled simulation technicians to complete the entire process, which cannot meet the needs of front-line management personnel at construction sites, making it difficult to achieve large-scale promotion and application in prefabricated construction scenarios. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing prefabricated building construction progress control technologies, which suffer from long simulation verification cycles, poor on-site adaptability, lack of pre-existing rigid constraints and safety red line control, failure to consider the chain reaction of assembly sequence adjustments on the entire construction process, unreasonable performance deviation judgment rules, and excessively high operational thresholds that make them difficult to implement. This invention provides a prefabricated building construction progress simulation method and system based on digital twins.

[0006] To achieve the above objectives, this application provides a method and system for simulating the construction progress of prefabricated buildings based on digital twins, which adopts the following technical solution:

[0007] In a first aspect, this application discloses a method for simulating the construction progress of prefabricated buildings based on digital twins, comprising:

[0008] Pre-built multi-dimensional lightweight simulation verification engine and assembly performance deviation quantification judgment engine;

[0009] Based on the BIM model of prefabricated building design, a unique digital identity code is assigned to each prefabricated component, and a corresponding full-dimensional parameter set is bound to the digital identity code. The pre-set benchmark assembly sequence, rigid constraint rule library, and compliance rule library are then added to the digital twin of the prefabricated component assembly benchmark, and a full-process simulation benchmark dataset corresponding to the pre-set benchmark assembly sequence is superimposed.

[0010] Based on the digital twin of the assembly benchmark of prefabricated components, the system receives the assembly sequence adjustment instructions of prefabricated components input by the construction personnel, and generates a custom assembly sequence twin by matching the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code.

[0011] By using a pre-built multi-dimensional lightweight simulation verification engine, local lightweight simulation calculations are performed on the adjusted assembly sequence of prefabricated components for a custom assembly sequence twin, so as to output fixed-dimensional simulation data. The fixed dimensions include structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension.

[0012] By using a pre-built assembly performance deviation quantification judgment engine, the simulation data of the custom assembly sequence twin is compared with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component, the performance deviation degree corresponding to each fixed dimension is calculated, and the standardized judgment result is output based on the preset hierarchical judgment threshold system.

[0013] Based on the standardized judgment results, output the corresponding compliance solution and generate a technical review file.

[0014] Preferably, the assembly reference digital twin of the prefabricated component and the multi-dimensional lightweight simulation verification engine further include:

[0015] The full-dimensional parameter set includes geometric parameters of precast components, mechanical property parameters of materials, manufacturing tolerance thresholds, node connection parameters, stress parameters of temporary supports, design boundary conditions, and accidental load parameters during construction.

[0016] The rigid constraint rule base contains non-adjustable strong logic assembly nodes, which are dynamically locked during subsequent assembly sequence adjustments; the compliance rule base contains mandatory requirements of project design documents, structural safety limit thresholds, and allowable deviations in assembly accuracy.

[0017] The pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are generated. Based on historical construction measurement data and structural simulation verification data of similar prefabricated building projects, the training and optimization of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are completed.

[0018] A three-dimensional dimensional chain tolerance transfer model is pre-built, and the three-dimensional dimensional chain tolerance transfer model, component-level reduced-order mechanical model library, and three-dimensional tolerance chain model library are built into the assembly benchmark digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine.

[0019] Preferably, the prefabricated component assembly sequence adjustment instructions are received through a visual interactive interface adapted to outdoor construction site operations. The visual interactive interface has a built-in rigid constraint pre-verification mechanism. When construction personnel modify the assembly sequence and assembly timing nodes of prefabricated components through drag-and-drop operations, the rigid constraint pre-verification mechanism verifies in real time whether the operation touches the strong logic assembly nodes in the rigid constraint rule base. If a strong logic assembly node is touched, the touch operation is intercepted and an early warning prompt is output.

[0020] The preferred rule for calculating fixed-dimensional simulation data is as follows:

[0021] For temporary working conditions during the construction phase, a multi-load coupled nonlinear mechanical simulation is performed. The stress state of the temporary building structure system, the stress of the component nodes, the stress of the temporary support system, and the overall and local overturning stability are calculated under the adjusted assembly sequence of prefabricated components. The accidental load combination conditions of wind load and construction live load during the construction phase are simultaneously superimposed, and the stress simulation data of the temporary structure are output.

[0022] Based on a pre-built three-dimensional dimension chain tolerance transfer model, the changes and cumulative effects of tolerance transfer path caused by the adjusted assembly sequence are simulated, the final assembly accuracy deviation is calculated, and the assembly accuracy simulation data is output.

[0023] Pre-build a project flow rhythm model, and based on the project flow rhythm model, deduce the impact of the adjusted assembly sequence of prefabricated components on the connection between preceding and subsequent processes, the construction rhythm of the flow section, the critical path duration, and the total duration, and output the simulation data of the progress impact.

[0024] The system compares the adjusted assembly sequence of prefabricated components with the compliance rule base item by item, and outputs compliance verification data.

[0025] Preferably, the formula for calculating the performance deviation is:

[0026]

[0027] in,

[0028] The performance deviation of any verification dimension in a fixed dimension;

[0029] Simulation data for a custom assembly sequence twin;

[0030] This serves as the baseline data for the entire simulation process.

[0031] The upper limit of the compliance threshold for the verification dimension;

[0032] Among them, the upper limit of the compliance threshold for the verification dimension is the maximum value allowed by the specifications in the compliance rule library and the design safety limit value; the graded judgment threshold system is a differentiated dynamic threshold set based on the importance level of prefabricated components;

[0033] Among them, the assembly performance deviation quantification judgment engine automatically identifies the importance level of prefabricated components and adaptively matches the corresponding threshold based on the full-dimensional parameter set bound by the unique digital identity code of the prefabricated components.

[0034] Among them, performance deviation includes structural safety performance deviation, cumulative assembly accuracy deviation, process progress adaptation deviation, and standard compliance deviation.

[0035] Preferably, the deviation determination logic of the assembly performance deviation quantification determination engine further includes:

[0036] Based on the rigid constraint rule base, compliance rule base and structural safety limit threshold, a safety red line veto mechanism and a safety red line real-time pre-verification mechanism are configured in the assembly performance deviation quantification judgment engine.

[0037] The real-time pre-verification mechanism for safety red lines is used to simultaneously perform pre-verification of safety red lines during the generation of a custom assembly sequence twin.

[0038] The safety red line veto mechanism is used to prohibit the adjustment of the assembly benchmark digital twin of prefabricated components when any preset safety red line situation occurs, and to generate risk traceability information. The risk traceability information includes the unique digital identity code of the prefabricated component of the non-compliant component, the violation situation, deviation data, and the assembly node location where the risk occurred.

[0039] The preset safety red line scenarios include: the adjusted assembly sequence exceeds the rigid constraint rule library, or the strong logic assembly node that cannot be adjusted is modified; and the structural simulation data exceeds the design safety limit threshold of the yield strength and overturning safety factor of the precast component.

[0040] Preferably, the standardized judgment result is output based on a preset hierarchical judgment threshold system, including:

[0041] Based on feasible judgment results, output a compliant solution for adjusting the assembly sequence, and generate construction handover documents based on the custom assembly sequence twin;

[0042] Based on the optimized and feasible judgment results, targeted quantitative optimization suggestions are output, including assembly sequence fine-tuning scheme, temporary support reinforcement scheme, and precision pre-control scheme.

[0043] For infeasible judgments, the sources of risk and safety hazards are marked, and alternative adjustment schemes are generated based on multi-objective genetic optimization algorithms.

[0044] Preferably, after outputting the corresponding compliance solution based on the standardized judgment results, it includes:

[0045] Collect measured data of precast components, including installation data, stress monitoring data of temporary structures, process execution data, and quality acceptance results;

[0046] By using a custom assembly sequence twin, and based on residual analysis of measured data, structural safety performance simulation data, assembly accuracy cumulative simulation data, and process progress adaptation simulation data, the mechanical parameters, pre-built three-dimensional dimension chain tolerance transfer model, and deviation judgment rules of the assembly reference digital twin of prefabricated components are corrected.

[0047] Based on the incremental learning algorithm, the pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are incrementally updated using the collected measured data.

[0048] The project adjustment plan, structural safety performance simulation data, assembly accuracy cumulative simulation data, process progress adaptation simulation data, standard compliance verification data, and standardization judgment results are summarized to form standardized management and control rules.

[0049] Preferably, before outputting a compliant solution for adjusting the assembly sequence based on feasible judgment results, a full-flow linkage verification and optimization step is performed:

[0050] Based on the adjusted custom assembly sequence twin, using the unique digital identity code of the prefabricated component as an index, the preset baseline assembly sequence, the process logic of each flow section and floor, and the prefabricated component arrival plan are matched to generate the full flow linkage assembly sequence twin corresponding to this adjustment.

[0051] Through a pre-built multi-dimensional lightweight simulation and verification engine, the assembly sequence twin of the entire flow linkage is simulated and calculated, and the following data are output: structural safety performance simulation data, cumulative assembly accuracy simulation data, process progress adaptation simulation data, and standard compliance verification data for each subsequent flow section and floor.

[0052] By using a pre-built assembly performance deviation quantification judgment engine, the simulation data of structural safety performance, the cumulative simulation data of assembly accuracy, the simulation data of process progress adaptation, the verification data of standard compliance, and the simulation benchmark dataset of the assembly benchmark digital twin of prefabricated components are compared with the simulation benchmark dataset to calculate the performance deviation of each subsequent flow section and floor, and trigger real-time pre-verification of the safety red line.

[0053] When all subsequent performance deviations meet the compliance thresholds of the corresponding precast components and do not violate the safety red line, the compliance solution of this adjustment is confirmed to be output normally.

[0054] When any performance deviation of any flow segment or floor exceeds the compliance threshold or touches the safety red line, the current adjustment plan and the assembly sequence of subsequent flow segments are optimized in conjunction with the multi-objective genetic optimization algorithm, the simulation verification is re-triggered, and the final full flow linkage adjustment compliance plan is output.

[0055] Secondly, this application discloses a prefabricated building construction progress simulation system based on digital twins, which applies the prefabricated building construction progress simulation method based on digital twins as described in the first aspect, including:

[0056] The benchmark twin management module is used to assign a unique digital identity code to each prefabricated component based on the prefabricated building design BIM model, bind the corresponding full-dimensional parameter set to the digital identity code, and add the preset benchmark assembly sequence, rigid constraint rule library, and compliance rule library to the prefabricated component assembly benchmark digital twin, and overlay the full-process simulation benchmark dataset corresponding to the preset benchmark assembly sequence.

[0057] The custom twin reconstruction module is used to receive the prefabricated component assembly sequence adjustment instructions input by the construction personnel based on the assembly benchmark digital twin of the prefabricated components. It matches the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code, and generates a custom assembly sequence twin.

[0058] The multi-dimensional lightweight simulation verification engine is used to perform local lightweight simulation calculations on the adjusted assembly sequence of prefabricated components using a pre-built multi-dimensional lightweight simulation verification engine for a custom assembly sequence twin, so as to output simulation data with fixed dimensions, including structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension.

[0059] The assembly performance deviation quantification judgment engine is used to compare the simulation data of the custom assembly sequence twin with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component through the pre-built assembly performance deviation quantification judgment engine, calculate the performance deviation degree corresponding to each fixed dimension, and output standardized judgment results based on the preset hierarchical judgment threshold system.

[0060] The compliance solution output and closed-loop iteration module outputs the corresponding compliance solution based on the standardization judgment results and generates a technical review file.

[0061] Compared with existing technologies, this invention provides a method and system for simulating the construction progress of prefabricated buildings based on digital twins, which has the following beneficial effects:

[0062] 1. By pre-constructing a multi-dimensional lightweight simulation verification engine and an assembly performance deviation quantification engine, combined with fixed-dimensional simulation calculation rules and a differentiated hierarchical judgment threshold system based on the importance level of prefabricated components, lightweight and rapid simulation and accurate deviation quantification judgment of prefabricated component assembly sequence adjustment scenarios in prefabricated buildings are achieved. This solution significantly reduces the amount of simulation calculation through a pre-trained component-level reduced-order mechanical model library, and can conduct local lightweight simulation for the adjustment area of ​​a custom assembly sequence twin, keeping the simulation cycle of a single solution within the range that adapts to real-time decision-making on the construction site, thus lowering the technical application threshold; at the same time, through a full-dimensional parameter set bound to the unique digital identity code of the prefabricated component, the importance level of the prefabricated component is automatically identified and the corresponding judgment threshold is adaptively matched. Compared with the existing fixed threshold judgment mode, this significantly improves the rationality and accuracy of performance deviation judgment, providing a standardized and quantifiable core basis for the compliance judgment of assembly sequence adjustment;

[0063] 2. By employing a rigid constraint rule base, a safety red line veto mechanism, and a real-time safety red line pre-verification mechanism, combined with full-flow linkage verification and optimization steps, a dual safety control system was constructed for the entire process of assembly sequence adjustment. Through the rigid constraint pre-verification mechanism built into the visual interactive interface, violations involving strong logic assembly nodes are intercepted in real time during the assembly sequence adjustment process. This, coupled with the safety red line veto mechanism, mitigates structural safety and compliance risks arising from assembly sequence adjustments from the outset. Simultaneously, by generating a full-flow linkage assembly sequence twin for full-cycle simulation verification, the system overcomes the limitations of existing technologies that only verify single segments of the adjustment area. It comprehensively covers the chain reaction impact of assembly sequence adjustments on subsequent entire flow segments and floor-level construction, predicting and mitigating risks of subsequent project schedule derailment and cumulative assembly accuracy exceeding standards, thus achieving full-cycle safety and compliance control for assembly sequence adjustments.

[0064] 3. By binding prefabricated components with unique digital identification codes throughout the entire data process, employing real-time twin calibration algorithms and incremental learning algorithms, a closed-loop iterative system for prefabricated construction progress control has been constructed. Through residual analysis of on-site measured data and corresponding simulation data of prefabricated components, the mechanical parameters, three-dimensional dimensional chain tolerance transfer model, and deviation judgment rules of the prefabricated component assembly benchmark digital twin can be dynamically corrected. Simultaneously, the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are incrementally updated, continuously optimizing simulation accuracy and judgment accuracy without requiring full retraining. Furthermore, the adjustment plans, simulation data, standardized judgment results, and execution experience accumulated throughout the project lifecycle can be transformed into standardized control rules, which can be reused in new projects of the same type. Combined with the technical review archives generated throughout the entire process, this achieves full traceability and accountability of the construction process, comprehensively improving the standardization and intelligence level of prefabricated building construction progress control. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the steps of a method for simulating the construction progress of prefabricated buildings based on digital twins, according to an embodiment of this application. Figure 2 This is a flowchart illustrating the logical steps of constructing a three-dimensional dimensional chain tolerance transfer model for a digital twin-based method for simulating the construction progress of prefabricated buildings, according to an embodiment of this application. Figure 3 This is a flowchart illustrating the steps of a digital twin-based method for simulating the construction progress of prefabricated buildings, which outputs standardized judgment results based on a preset hierarchical judgment threshold system. Detailed Implementation

[0066] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0067] This application discloses a method and system for simulating the construction progress of prefabricated buildings based on digital twins.

[0068] Firstly, referring to Figure 1 This application discloses a method for simulating the construction progress of prefabricated buildings based on digital twins, including:

[0069] S1, Pre-built multi-dimensional lightweight simulation verification engine and assembly performance deviation quantification judgment engine;

[0070] The multi-dimensional lightweight simulation verification engine is a dedicated simulation calculation unit developed for temporary working conditions and dynamic adjustment of assembly sequence during the construction phase of prefabricated buildings. It has built-in mechanical simulation kernel, tolerance transfer calculation kernel, construction period prediction kernel and compliance verification kernel adapted to the assembly scenario of prefabricated components.

[0071] Among them, the assembly performance deviation quantification judgment engine is a dedicated quantitative analysis unit developed for compliance judgment after assembly sequence adjustment. It has built-in standardized deviation calculation logic and hierarchical judgment rule library, which can realize automatic dimension-by-dimensional comparison of simulation data and benchmark data, deviation quantification calculation and standardized judgment result output, providing quantifiable and traceable core judgment basis for subsequent compliance solution output.

[0072] S2. Based on the BIM model of prefabricated building design, assign a unique digital identity code to each prefabricated component, bind the corresponding full-dimensional parameter set to the digital identity code, and add the preset benchmark assembly sequence, rigid constraint rule library, and compliance rule library to the digital twin of the prefabricated component assembly benchmark, and overlay the full-process simulation benchmark dataset corresponding to the preset benchmark assembly sequence.

[0073] Among them, the prefabricated building design BIM model is a construction drawing-level BIM model that has been jointly submitted and approved by the design unit, construction unit and supervision unit. The prefabricated building design BIM model contains complete geometric information, attribute information and node connection information of prefabricated components, which can fully map the design requirements and technical indicators of the project.

[0074] For digital identity codes, each digital identity code has one and only one corresponding prefabricated component. Once a digital identity code has been assigned to a prefabricated component, it is not allowed to modify the digital identity code assigned to the prefabricated component a second time.

[0075] Furthermore, the prefabricated component assembly benchmark digital twin is a digital twin carrier that maps the project benchmark construction plan. It is constructed by using the approved BIM model as a digital base, and by overlaying the full-process simulation benchmark dataset through preset benchmark assembly sequence, rigid constraint rule library, compliance rule library, and fully restoring the project's preset benchmark construction plan, rigid constraints and compliance requirements.

[0076] Correspondingly, the compliance rule base is a set of rules that contain the compliance requirements of the entire project process, and it is the core basis for determining the compliance of assembly sequence adjustments;

[0077] Among them, the full-process simulation benchmark dataset is a set of benchmark data obtained by pre-completing the full-process simulation calculation based on the preset benchmark assembly sequence and through the pre-built multi-dimensional lightweight simulation verification engine. It is the core benchmark reference for subsequent deviation calculation and compliance judgment after the assembly sequence is adjusted.

[0078] S3. Based on the assembly benchmark digital twin of prefabricated components, receive the prefabricated component assembly sequence adjustment instructions input by the construction personnel, and generate a custom assembly sequence twin by matching the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code.

[0079] Specifically, the precast component assembly sequence adjustment instruction is an instruction input by construction site personnel and project management personnel based on the actual situation such as the timing of component arrival, site conditions, work process connection rhythm, and unexpected situations, to adjust the assembly sequence and timing nodes of precast components.

[0080] Furthermore, upon receiving the adjustment instruction, the system automatically identifies all prefabricated components involved in the adjustment instruction using the unique digital identification code of the prefabricated component as a unique index. It then matches the node connection relationships, stress boundary conditions, and assembly constraint rules between the prefabricated components after the adjustment, ensuring that the adjusted assembly relationships fully match the design requirements and component attributes.

[0081] Among them, the custom assembly sequence twin is a dedicated digital twin generated based on the adjusted assembly sequence and corresponding to the current adjustment scheme. The parameters, constraints and reference data of the prefabricated component assembly benchmark digital twin are fully reused in the non-adjusted areas. The assembly relationship and constraint conditions are reconstructed only for the adjusted areas, which greatly shortens the twin construction cycle and adapts to the use needs of rapid response on the construction site.

[0082] S4. Through a pre-built multi-dimensional lightweight simulation verification engine, local lightweight simulation calculations are performed on the adjusted assembly sequence of prefabricated components for the custom assembly sequence twin, so as to output fixed-dimensional simulation data. The fixed dimensions include structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension.

[0083] Specifically, a local lightweight simulation mode is adopted, which only performs targeted simulation calculations for the areas in the custom assembly sequence twin where the assembly sequence has been adjusted. The non-adjusted areas directly reuse the full-process simulation benchmark dataset, without the need to perform overall simulation of the entire model. This greatly reduces the amount of calculation and shortens the simulation cycle, enabling rapid simulation verification at the construction site at the second to minute level.

[0084] Among them, the structural safety performance verification dimension corresponds to the safety performance simulation of the temporary structural system during the construction phase; the assembly accuracy cumulative verification dimension corresponds to the simulation of the tolerance cumulative effect brought about by the adjustment of the assembly sequence; the process progress adaptation verification dimension corresponds to the simulation of the impact of the adjustment of the assembly sequence on the construction period and process connection; and the standard compliance verification dimension corresponds to the compliance verification of the adjusted assembly sequence.

[0085] S5. By using a pre-built assembly performance deviation quantification judgment engine, the simulation data of the custom assembly sequence twin is compared with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component, the performance deviation degree corresponding to each fixed dimension is calculated, and the standardized judgment result is output based on the preset hierarchical judgment threshold system.

[0086] In practical applications, the full-process simulation benchmark dataset of the assembly benchmark digital twin of prefabricated components is used as a reference. The simulation data of each dimension output by the custom assembly sequence twin is compared dimension by dimension. The performance deviation corresponding to each fixed dimension is calculated through standardized calculation logic, and the degree of deviation of this assembly sequence adjustment relative to the benchmark scheme is quantified.

[0087] The preset graded judgment threshold system is a pre-set set of deviation thresholds corresponding to different compliance levels. Based on the matching results of the calculated performance deviation degree of each dimension and the graded judgment threshold system, a standardized judgment result is output, providing a clear and quantifiable judgment basis for the subsequent compliance solution output.

[0088] S6. Based on the standardized judgment results, output the corresponding compliance solutions and generate technical review files. For different standardized judgment results, output matching compliance solutions, including the formal issuance of feasible solutions, optimization suggestions for solutions that need optimization, risk warnings for infeasible solutions, and recommendations for alternative solutions, to ensure that the adjusted assembly sequence meets the requirements of safety, quality, schedule, and compliance in all dimensions. The file fully records the entire process data of this assembly sequence adjustment, including adjustment instructions, full simulation data, deviation calculation results, judgment results, approval records, etc., to achieve traceability and verification of the entire adjustment process, which can be directly used for project completion archiving and engineering supervision and verification.

[0089] Furthermore, the full-dimensional parameter set includes the geometric parameters of precast components, material mechanical property parameters, manufacturing tolerance thresholds, node connection parameters, temporary support stress parameters, design boundary conditions, and accidental load parameters during construction. The full-dimensional parameter set will not be described in detail here.

[0090] The rigid constraint rule base contains non-adjustable strong logic assembly nodes, which are dynamically locked during subsequent assembly sequence adjustments; the compliance rule base contains mandatory requirements of project design documents, structural safety limit thresholds, and allowable deviations in assembly accuracy.

[0091] Among them, the strong logic assembly nodes are the non-adjustable assembly sequence nodes that directly affect the safety of the main structure and violate the core design logic. Specifically, they include: the assembly sequence nodes of vertical load-bearing prefabricated components from bottom to top, the assembly sequence nodes of core load-bearing components of the transfer layer, and the assembly sequence requirements of core nodes involving the force transmission path of the main structure.

[0092] Furthermore, the compliance rule library serves as the core benchmark for verifying the compliance of assembly sequence adjustments, calculating performance deviations, and determining safety red lines. Its built-in content includes: the mandatory technical requirements specified in the project design documents, the structural safety limit threshold corresponding to the yield strength of the main structural materials and the structural overturning safety factor, the allowable deviation of prefabricated component assembly accuracy specified in the current national prefabricated building construction specifications, and the mandatory indicators for supervision, acceptance, and quality control.

[0093] The pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are generated. Based on historical construction measurement data and structural simulation verification data of similar prefabricated building projects, the training and optimization of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are completed.

[0094] The component-level reduced-order mechanics model library is a collection of lightweight mechanics simulation models categorized by prefabricated component type, covering core prefabricated component types for prefabricated buildings such as prefabricated shear walls, prefabricated columns, composite slabs, prefabricated stairs, and prefabricated beams. The training process is as follows:

[0095] Based on historical construction measurement data and high-precision finite element structural simulation verification data of similar prefabricated building projects, a model reduction algorithm is used to reduce the order of high-precision mechanical models of various prefabricated components. Under the premise of ensuring that the accuracy deviation of simulation calculation does not exceed 5%, the calculation amount and simulation time of single component models are reduced.

[0096] Through iterative training with massive amounts of historical data, the parameters and computational logic of the reduced-order model are continuously optimized, ultimately forming a standardized component-level reduced-order mechanics model library that can be directly called and quickly calculated.

[0097] A pre-built 3D dimensional chain tolerance transfer model is incorporated into the assembly reference digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine. This enables the reference twin to possess complete mechanical simulation and tolerance calculation capabilities, providing complete reference constraints and model support for the generation of custom assembly sequence twins. The built-in multi-dimensional lightweight simulation verification engine allows the engine to directly call the corresponding model and model library for rapid calculation when it receives simulation calculation instructions, without having to rebuild the simulation model for each adjustment.

[0098] Among them, the three-dimensional dimension chain tolerance transfer model is a special tolerance calculation algorithm model developed for the three-dimensional spatial assembly scenario of prefabricated buildings. It is adapted to the three-dimensional spatial assembly scenario of prefabricated buildings, accurately identifies the tolerance transfer path changes under different assembly sequences, quantifies the tolerance cumulative effect in the assembly process of multiple components, and can simultaneously incorporate the coupling influence of multiple factors such as component manufacturing tolerance, installation alignment deviation, and node connection gap, and accurately output the final assembly accuracy deviation value.

[0099] Furthermore, the aforementioned three-dimensional tolerance chain model library is a collection of standardized single-node three-dimensional tolerance closed-loop calculation models that have been pre-trained for mainstream assembly nodes in prefabricated buildings.

[0100] Reference Figure 2 The logic for constructing the three-dimensional dimension chain tolerance transfer model is as follows:

[0101] A1. Basic Data Extraction and Benchmark Unification: Using the unique digital identification code of prefabricated components as the unique index, extract the component's geometric parameters, manufacturing tolerance thresholds, and node connection parameters from the full-dimensional parameter set, establish a three-dimensional benchmark coordinate system for each prefabricated component that is completely unified with the project design / construction benchmark, and complete the collection of basic modeling data;

[0102] A2. Construction of single-node three-dimensional tolerance closed-loop unit: For mainstream prefabricated assembly nodes such as sleeve grouting and grout anchor lap joint, the minimum three-dimensional tolerance closed-loop calculation unit is constructed respectively. The closed loop, component loop and reference loop of each unit are defined. The three-dimensional deviation transmission calculation formula is established based on the space vector algorithm. At the same time, the tolerance thresholds of the rigid constraint rule library and the compliance rule library are bound to ensure that the calculation logic is consistent with the compliance judgment requirements of this application.

[0103] in,

[0104] The closed loop represents the final assembly alignment deviation;

[0105] The constituent loops are the total tolerance elements that affect the deviation;

[0106] The reference ring serves as the assembly reference for the components.

[0107] A3. Assembly sequence-tolerance transfer path coupling mechanism: Establish a one-to-one mapping rule between assembly sequence and tolerance transfer path. The initial transfer path is completely matched with the preset baseline assembly sequence. The calculation results of the preceding nodes are automatically used as the input parameters of the subsequent adjacent nodes. Develop an automatic path reconstruction algorithm. When a custom assembly sequence twin is generated, the tolerance transfer path of the affected area can be automatically reconstructed based on the adjusted assembly sequence without the need for manual remodeling.

[0108] A4. Multi-factor coupling accuracy correction: Combining the temporary support stress parameters and accidental load parameters during construction from the full-dimensional parameter set, the influence coefficient of on-site construction factors on tolerance transfer is fitted, and the calculation results of single-node closed-loop units are dynamically corrected to improve the adaptability of the model to the actual on-site working conditions.

[0109] A5. Accuracy Verification and System Integration: The accuracy of the model is verified by using historical measured assembly data from similar projects to ensure that the calculation deviation rate meets the construction control requirements. The verified and solidified model is then integrated into the assembly benchmark digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine, along with the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library.

[0110] Furthermore, the system receives instructions to adjust the assembly sequence of precast components through a visual interactive interface adapted to outdoor construction site operations. The visual interactive interface has a built-in rigid constraint pre-verification mechanism. When construction personnel modify the assembly sequence and timing nodes of precast components through drag-and-drop operations, the rigid constraint pre-verification mechanism verifies in real time whether the operation touches the strong logic assembly nodes in the rigid constraint rule base. If a strong logic assembly node is touched, the operation is intercepted and a warning prompt is output.

[0111] Specifically, the visual interactive interface synchronously loads the assembly benchmark digital twin of the prefabricated components, displaying all prefabricated components of the project in a dual mode of 3D model + list. Each prefabricated component is marked with a unique digital identification code, and the core information of the component type, building and floor, preset benchmark assembly sequence, and bound full-dimensional parameter set are displayed synchronously. It is fully integrated with the benchmark twin and unique digital identification code system of this application. It is the only interactive entry point for construction personnel and project management personnel on the construction site to input assembly sequence adjustment instructions. All adjustment operations for the assembly sequence and assembly time nodes of prefabricated components are completed through the visual interactive interface.

[0112] Furthermore, the built-in and linkage implementation of the rigid constraint pre-verification mechanism is as follows:

[0113] The core logic of the mechanism is to adopt a real-time pre-verification mode for the operation process, and to conduct compliance verification simultaneously during the process of construction personnel adjusting the assembly sequence, thereby intercepting illegal adjustment operations from the source.

[0114] Linkage rules: Real-time retrieval of the strong logic assembly node locking list built into the rigid constraint rule library. Each strong logic assembly node in the list is bound to the unique digital identity code of the corresponding prefabricated component, clearly indicating the locking status of the node and the range of assembly timing that cannot be adjusted.

[0115] The corresponding implementation method for the assembly sequence adjustment drag-and-drop operation is as follows:

[0116] Basic operation logic: In the visual interactive interface, construction personnel can directly click on the 3D model or list item of the target precast component and modify its position in the assembly sequence list by dragging and dropping, or directly modify the assembly sequence node of the component without inputting complex parameters, thus reducing the operation threshold.

[0117] Auxiliary prompt function: During the drag operation, the visual interactive interface highlights the upstream and downstream components that have assembly constraints with the component, the type of assembly node to which the component belongs, and simultaneously prompts the preset benchmark assembly sequence of the component, allowing construction personnel to intuitively grasp the impact range of the adjustment operation and avoid misoperation.

[0118] The overall execution logic for real-time verification, operation interception, and early warning is as follows:

[0119] Real-time verification trigger: Throughout the entire process of the construction personnel's drag operation, the rigid constraint pre-verification mechanism performs real-time synchronous verification. When each drag action is triggered, it immediately checks whether the prefabricated component being adjusted belongs to the strong logic assembly node in the rigid constraint rule library, and whether the operation has modified the locked assembly sequence or broken the rigid constraint boundary.

[0120] Violation detection: When the verification finds that the operation has modified the assembly order of the strong logic assembly node or exceeded the locking range of the rigid constraint rule base, it is immediately determined to be a violation operation.

[0121] Interception of illegal operations: After a violation is detected, the drag operation is directly intercepted, the precast component to be adjusted is automatically restored to the locked assembly sequence position before the adjustment, and the illegal modification is prohibited from taking effect, thus avoiding the structural safety risks caused by illegal adjustments from the source;

[0122] Warning output: While intercepting the operation, a prominent warning message will pop up on the visual interactive interface, clearly indicating the unique digital identification code of the non-compliant prefabricated component, the component name, the type of strong logic assembly node to which it belongs, and the unmodifiable compliance basis.

[0123] Furthermore, the calculation rules for fixed-dimensional simulation data are as follows:

[0124] S41. Perform multi-load combined coupled nonlinear mechanical simulation for temporary working conditions during the construction phase. Calculate the stress state, component node stress, temporary support system stress, and overall and local overturning stability of the temporary building structure system under the adjusted assembly sequence of prefabricated components. Simultaneously superimpose the accidental load combination of wind load and construction live load during the construction phase, and output the stress simulation data of the temporary structure.

[0125] The multi-load combined coupled nonlinear mechanical simulation working mode is as follows:

[0126] Automatic reconstruction of simulation boundaries: Based on a custom assembly sequence twin, using the unique digital identity code of prefabricated components as an index, the system automatically identifies the temporary structural system boundary and component constraint conditions corresponding to the adjusted assembly sequence, and retrieves the material mechanical properties parameters, temporary support stress parameters, design boundary conditions, and accidental load parameters of the corresponding components from the full-dimensional parameter set.

[0127] Lightweight model call: From the component-level reduced-order mechanical model library, the standardized reduced-order mechanical model of the corresponding prefabricated component is called. There is no need to rebuild the full high-precision finite element model. Only the area where the assembly sequence is adjusted is used for directional simulation calculation.

[0128] Multi-condition coupled simulation calculation: Based on the above multi-load combination rules, coupling effects and nonlinear calculation logic, mechanical simulation of temporary working conditions during the construction stage is carried out. The stress state of the temporary structural system, the stress of the component nodes, the stress of the temporary support system, and the overall and local overturning stability are calculated under the adjusted assembly sequence.

[0129] Standardized output results: Output temporary structural stress simulation data, which is synchronously transmitted to the assembly performance deviation quantification and judgment module for performance deviation calculation and safety red line verification, forming a complete calculation closed loop.

[0130] S42. Based on the pre-built three-dimensional dimension chain tolerance transfer model, simulate the change and cumulative effect of tolerance transfer path brought about by the adjusted assembly sequence, calculate the final assembly accuracy deviation, and output the assembly accuracy simulation data.

[0131] S43. Pre-build a project flow rhythm model, and based on the project flow rhythm model, deduce the impact of the adjusted assembly sequence of prefabricated components on the connection between preceding and subsequent processes, the construction rhythm of the flow section, the critical path duration, and the total duration, and output the progress impact simulation data.

[0132] It should be noted that the project flow rhythm model automatically extrapolates the chain changes in the entire project construction process as the assembly sequence of prefabricated components is adjusted, quantifies the impact of adjusting the assembly sequence of prefabricated components on process connection, flow rhythm, critical path, and total project duration, and provides core calculation support for process progress adaptability verification and full flow linkage optimization.

[0133] The steps for constructing the project flow rhythm model are as follows:

[0134] S431. Basic Data Collection: Import the approved construction organization design, BIM model, and special construction plan for the project, and extract basic data such as the division of flow sections, process logic, work team configuration, and overall project schedule target.

[0135] S432. Solidification of strong constraint rules: Connect with the rigid constraint rule library and compliance rule library to solidify the unadjustable process sequence logic, acceptance node timing, and hard requirements for total project duration, and clarify the inviolable boundaries of the model;

[0136] S433, Cyclic rhythm parameter calibration: Based on historical construction measurement data of similar projects and combined with the actual production capacity of the work teams in this project, calibrate the standard flow rhythm parameters of each process and each type of prefabricated component, and complete the construction of the parameter library;

[0137] S434, Component-Process Binding: Using the unique digital identification code of prefabricated components as an index, complete the one-to-one mapping of a single prefabricated component with the corresponding process, flow section, and time sequence node, synchronously connect with the component arrival plan, and complete the configuration of linkage units;

[0138] S435, Benchmark Verification and Consolidation: Based on the preset benchmark assembly sequence, run the model to complete the benchmark construction period simulation, ensuring that the simulation results are completely consistent with the approved overall construction schedule. After verification, the model is consolidated and built into the benchmark digital twin and simulation verification engine, serving as the sole benchmark for subsequent simulations of the impact of assembly sequence adjustments on the construction period.

[0139] S44. Compare the matching of the adjusted prefabricated component assembly sequence with the compliance rule base item by item, and output the compliance verification data.

[0140] Furthermore, the formula for calculating the performance deviation is:

[0141]

[0142] in,

[0143] The performance deviation of any verification dimension in a fixed dimension;

[0144] Simulation data for a custom assembly sequence twin;

[0145] This serves as the baseline data for the entire simulation process.

[0146] The upper limit of the compliance threshold for the verification dimension;

[0147] Among them, the upper limit of the compliance threshold for the verification dimension is the maximum value allowed by the specifications in the compliance rule library and the design safety limit value; the graded judgment threshold system is a differentiated dynamic threshold set based on the importance level of prefabricated components;

[0148] Among them, the assembly performance deviation quantification judgment engine automatically identifies the importance level of prefabricated components and adaptively matches the corresponding threshold based on the full-dimensional parameter set bound by the unique digital identity code of the prefabricated components.

[0149] Among them, performance deviation includes structural safety performance deviation, cumulative assembly accuracy deviation, process progress adaptation deviation, and standard compliance deviation.

[0150] Furthermore, the deviation determination logic of the assembly performance deviation quantification determination engine further includes:

[0151] Based on the rigid constraint rule base, compliance rule base and structural safety limit threshold, a safety red line veto mechanism and a safety red line real-time pre-verification mechanism are configured in the assembly performance deviation quantification judgment engine.

[0152] In practical applications, the unique digital identity code of the prefabricated component is used as the unique index to retrieve the rigid constraint rules and structural safety limit thresholds of all prefabricated components involved in the adjustment in real time. The system also verifies whether the adjusted assembly sequence touches the preset safety red line. If the verification finds a risk of violation, the process of generating the custom assembly sequence twin is immediately terminated and a warning message is output simultaneously. There is no need to enter the subsequent simulation calculation stage. This significantly reduces the time spent on invalid calculations and intercepts illegal adjustment schemes in advance, meeting the needs of rapid decision-making on construction sites.

[0153] The real-time pre-verification mechanism for safety red lines is used to simultaneously perform pre-verification of safety red lines during the generation of a custom assembly sequence twin.

[0154] The safety red line veto mechanism is used to prohibit the adjustment of the assembly benchmark digital twin of prefabricated components when any preset safety red line situation occurs, and to generate risk traceability information. The risk traceability information includes the unique digital identity code of the prefabricated component of the non-compliant component, the violation situation, deviation data, and the assembly node location where the risk occurred.

[0155] The preset safety red line situations include: the adjusted assembly sequence exceeds the rigid constraint rule base, or the strong logic assembly node that cannot be adjusted is modified;

[0156] In the specific implementation process, as long as the adjustment modifies the strongly logical assembly node that is dynamically locked throughout the rigid constraint rule base and breaks through the hard boundary of the rigid constraint, regardless of the size of the modification or whether it affects the structural stress, the safety red line will be triggered directly.

[0157] If the structural simulation data exceeds the design safety limit threshold of the yield strength and overturning safety factor of the precast components, and the stress of any core component in the output temporary structural stress simulation data exceeds the material yield strength of the corresponding precast component, or the overall / local overturning safety factor of the temporary structural system during the construction stage is lower than the minimum limit required by the design, the safety red line will be directly triggered.

[0158] Furthermore, refer to Figure 3 Based on a preset hierarchical judgment threshold system, standardized judgment results are output, including:

[0159] S51. For feasible judgment results, output a compliant solution for adjusting the assembly sequence and generate construction handover documents based on the custom assembly sequence twin.

[0160] S52. Based on the feasible judgment results after optimization, output targeted quantitative optimization suggestions, including assembly sequence fine-tuning scheme, temporary support reinforcement scheme, and precision pre-control scheme.

[0161] S53. For infeasible judgment results, mark the sources of risk and safety hazards, and generate alternative adjustment schemes based on multi-objective genetic optimization algorithms.

[0162] The corresponding implementation method for the infeasibility determination result is as follows:

[0163] The infeasible determination result corresponds to the determination situation where the performance deviation of any dimension exceeds the infeasible threshold or triggers the safety red line. It is a violation determination result that prohibits implementation.

[0164] S531. In response to this judgment, firstly, based on the risk tracing information and deviation calculation results, clearly mark the risk source, safety hazard level, scope of impact and basis for violation of the current adjustment plan, so that operators know the core reasons why the plan is not feasible.

[0165] S532 is generated based on a multi-objective genetic optimization algorithm. It uses a rigid constraint rule base and a compliance rule base as prerequisite constraints and adapts to the on-site working conditions as the core objective. It automatically generates alternative solutions that meet safety and compliance requirements. All alternative solutions are equipped with complete simulation verification data and compliance judgment results for on-site selection.

[0166] Furthermore, after outputting the corresponding compliance solution based on the standardized judgment results, it includes:

[0167] S61. Collect measured data of precast components, including installation data, temporary structure stress monitoring data, process execution data, and quality acceptance results;

[0168] To better illustrate the instructions, the installation data, temporary structure stress monitoring data, process execution data, and quality acceptance results are briefly described here.

[0169] Installation data: collected through 3D laser scanning and total station measurements, the core of which includes the actual installation position deviation of precast components, node docking accuracy, and embedded part alignment deviation.

[0170] Temporary structure stress monitoring data: Real-time data is collected through IoT stress sensors and strain gauges embedded in the temporary support system and core load-bearing components. The core data includes the actual stress on the temporary support, the actual stress at the component nodes, and the amount of structural deformation.

[0171] Sequence execution data: collected through the project construction management system and process inspection system, the core of which includes the actual assembly completion time of each precast component, the actual connection time between preceding and subsequent processes, and the actual construction rhythm of the flow section.

[0172] Quality acceptance results: Collected through the supervision and acceptance system and the quality assessment system, the core of which includes the acceptance qualification of component installation, the quality assessment results of node construction, and the accuracy acceptance deviation value.

[0173] The above-mentioned collection of measured data is a standard data collection method in the construction industry, and the actual collection methods and processes will not be described in detail here.

[0174] After the actual measurement data is collected, all actual measurement data is cleaned to remove outliers and invalid data, forming a standardized actual measurement dataset. This dataset is then stored in a full-dimensional parameter set bound to the unique digital identity code of the corresponding component, providing a foundation for subsequent analysis and optimization.

[0175] S62. By using a custom assembly sequence twin, based on residual analysis of measured data, structural safety performance simulation data, assembly accuracy cumulative simulation data, and process progress adaptation simulation data, correct the mechanical parameters, pre-built three-dimensional dimension chain tolerance transfer model, and deviation judgment rules of the assembly reference digital twin of prefabricated components.

[0176] Specifically, in step S62, the core is to compare and analyze the actual measured data and the simulation prediction data to correct the systematic deviation between the benchmark twin and the core model, and continuously improve the simulation accuracy and the rationality of the judgment. All comparison benchmark data come from structural safety performance simulation data, assembly accuracy cumulative simulation data, and process progress adaptation simulation data. The analysis carrier is a custom assembly sequence twin.

[0177] In practical applications, the first step is to use the unique digital identification code of the prefabricated component as an index to align the standardized measured dataset with the corresponding simulation data one by one. The least squares method is then used to conduct residual analysis and calculate the relative deviation between the simulation data and the measured data. Based on the results of the residual analysis, targeted corrections are made to the three core contents.

[0178] The three core contents are as follows:

[0179] Correction of mechanical parameters of digital twin for assembly reference of prefabricated components: For components with excessive residuals in structural safety performance simulation, the mechanical properties of the bound materials, the stress parameters of temporary supports, and the design boundary conditions are corrected to make the mechanical properties of the reference twin highly match the actual working conditions on site.

[0180] Correction of the 3D dimension chain tolerance transfer model: For nodes where the cumulative simulation residual of assembly accuracy exceeds the standard, the tolerance transfer coefficient and multi-factor coupling correction parameters of the 3D dimension chain tolerance transfer model are corrected, the calculation logic of the tolerance transfer path is optimized, and the problem of excessive deviation between simulation prediction and actual measurement is solved. After correction, it is synchronously updated to the benchmark twin and the multi-dimensional lightweight simulation verification engine.

[0181] Deviation judgment rule correction: For scenarios where simulation predictions are compliant but on-site acceptance tests are unqualified, the graded judgment threshold system is optimized based on actual measurement data, and the warning thresholds and compliance thresholds for corresponding component types are adjusted to improve the on-site adaptability of standardized judgment results.

[0182] S63. Based on the incremental learning algorithm, the pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are incrementally updated using the collected measured data.

[0183] In step S63, the model library is continuously self-optimized using on-site measured data, which can improve the generalization ability and simulation accuracy of the model library without full retraining. The updated objects are the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library.

[0184] Specifically, the cleaned standardized measured dataset and the residual analysis results of the corresponding simulation data are first organized into an incremental learning training sample set. The sample set is classified and labeled according to component type and node type, which is completely matched with the classification system of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library.

[0185] Furthermore, an incremental learning algorithm is adopted, using the pre-trained component-level reduced-order mechanics model library and 3D tolerance chain model library as the base weights. Incremental training is carried out only for the model branches corresponding to the newly added sample set, updating the model's reduced-order calculation parameters, tolerance transfer coefficients, and deviation correction factors. There is no need to retrain the entire component-level reduced-order mechanics model library and 3D tolerance chain model library, which greatly reduces the computational power consumption and time cost of training.

[0186] Meanwhile, after the incremental update is completed, the simulation accuracy of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library is verified to ensure that the deviation rate between the updated model simulation results and the on-site measured data meets the construction control requirements. The component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library that have passed the verification are simultaneously built into the assembly benchmark digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine, providing more accurate model support for the assembly sequence adjustment simulation of subsequent projects.

[0187] S64. Summarize the project adjustment plan, structural safety performance simulation data, assembly accuracy cumulative simulation data, process progress adaptation simulation data, standard compliance verification data, and standardization judgment results to form standardized management and control rules.

[0188] Specifically, the project collects all assembly sequence adjustment plans throughout the entire project lifecycle, corresponding simulation data across four dimensions, compliance verification data, standardized judgment results, on-site implementation effects, and acceptance results to form a complete project experience dataset.

[0189] Furthermore, the dataset is categorized and summarized to extract key points for assembly sequence adjustment and control under different types of prefabricated projects, different component types, and different working conditions, as well as rules for setting safety thresholds, deviation warning boundaries, and core requirements for compliance verification, ultimately forming reusable standardized control rules.

[0190] Subsequently, the standardized management and control rules are simultaneously stored in the construction knowledge base, and can be directly reused in the construction of the benchmark twin, the setting of the rigid constraint rule base, and the configuration of the hierarchical judgment threshold system for new projects of the same type.

[0191] Furthermore, before outputting a compliant solution for adjusting the assembly sequence based on feasible judgment results, a full-flow linkage verification and optimization process is executed:

[0192] B1. Based on the adjusted custom assembly sequence twin, using the unique digital identity code of the prefabricated component as the index, match the preset baseline assembly sequence, the process logic of each flow section and floor, and the prefabricated component arrival plan to generate the full flow linkage assembly sequence twin corresponding to this adjustment.

[0193] In this step, the preset benchmark assembly sequence is built into the benchmark construction sequence of the prefabricated component assembly benchmark digital twin. The process logic of each flow section and floor is matched with the construction organization design approved by the project and the project flow rhythm model. The prefabricated component arrival plan comes from the component production, transportation and arrival sequence plan of the project material management system.

[0194] Among them, the full-flow linkage assembly sequence twin, unlike the custom assembly sequence twin that only covers the adjustment area, fully covers all assembly processes and components of all flow sections and all floors in the entire construction cycle of the project. It fully restores the temporal chain effect of this partial assembly sequence adjustment on the subsequent full construction process, and provides a complete digital carrier for subsequent full-flow simulation verification.

[0195] B2. Through a pre-built multi-dimensional lightweight simulation verification engine, the assembly sequence twin of the entire flow linkage is simulated and calculated, and the structural safety performance simulation data, assembly accuracy cumulative simulation data, process progress adaptation simulation data, and standard compliance verification data of each subsequent flow section and floor are output.

[0196] Specifically, based on the assembly sequence twin of the entire flow linkage, starting from the assembly sequence adjusted this time, lightweight simulation calculations are performed on the assembly procedures of all subsequent flow sections and all floors according to the construction sequence.

[0197] It should be noted that during the lightweight simulation calculation, the full-process simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component is reused, and targeted simulation calculations are only carried out for the processes and components affected by this adjustment. There is no need to recalculate the entire model. While covering the chain effect of the entire process, the simulation calculation efficiency is ensured and it can meet the needs of rapid decision-making on the construction site.

[0198] B3. By using a pre-built assembly performance deviation quantification judgment engine, the structural safety performance simulation data, assembly accuracy cumulative simulation data, process progress adaptation simulation data, and standard compliance verification data are compared with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component to calculate the performance deviation of each subsequent flow section and floor, and trigger real-time pre-verification of the safety red line.

[0199] Align the simulation data with the benchmark dataset one by one according to the flow segment and floor, calculate the corresponding performance deviation degree dimension by dimension, identify the importance level of the corresponding prefabricated components based on the hierarchical judgment threshold system, and adaptively match the compliance threshold. Simultaneously trigger the real-time pre-verification mechanism of the safety red line, and carry out quality and safety red line verification on the assembly sequence and structural simulation data of the entire flow segment and the entire floor. The priority is higher than the hierarchical threshold judgment rule.

[0200] B4. When all subsequent performance deviations meet the compliance thresholds of the corresponding precast components and do not touch the safety red line, confirm that the compliance solution of this adjustment is output normally.

[0201] After the preset graded judgment threshold system outputs standardized judgment results, the compliance of this adjustment plan throughout the entire cycle and process is officially confirmed. The formal assembly sequence adjustment compliance plan and supporting construction handover documents are output, and the full amount of data of the full-process linkage verification is stored in the technical review archive to achieve full-process traceability.

[0202] B5. When any performance deviation of any flow segment or floor exceeds the compliance threshold or touches the safety red line, based on the multi-objective genetic optimization algorithm, the current adjustment plan and the assembly sequence of subsequent flow segments are optimized in conjunction, the simulation verification is re-triggered, and the final full flow linkage adjustment compliance plan is output.

[0203] In step B5, the triggering condition is: the performance deviation of any dimension of any subsequent flow segment or floor exceeds the compliance threshold, or any link touches the preset safety red line.

[0204] Furthermore, the core algorithm of the linkage optimization is a multi-objective genetic optimization algorithm. It takes the rigid constraint rule library, compliance rule library and safety red line threshold as the prerequisite hard constraints, and takes the minimum impact on the total project duration, the most convenient on-site construction operation and the lowest additional construction cost as the multiple optimization objectives. At the same time, it performs linkage optimization on the local adjustment plan submitted this time and the assembly sequence of the affected subsequent flow sections, rather than just adjusting the current plan, so as to eliminate the chain risk of the entire flow caused by the current adjustment from the root.

[0205] After optimization, the assembly sequence twin is automatically regenerated, triggering full-process simulation verification, deviation judgment and safety red line verification again, until the entire project and all flow segments meet the compliance requirements. Finally, a formal full-flow linkage adjustment compliance plan is output, along with supporting construction handover documents and full verification data, which are stored in the technical review archive, completing the full-cycle compliance control closed loop for this assembly sequence adjustment.

[0206] Secondly, this application discloses a prefabricated building construction progress simulation system based on digital twins, which applies the prefabricated building construction progress simulation method based on digital twins as described in the first aspect, including:

[0207] The benchmark twin management module is used to assign a unique digital identity code to each prefabricated component based on the prefabricated building design BIM model, bind the corresponding full-dimensional parameter set to the digital identity code, and add the preset benchmark assembly sequence, rigid constraint rule library, and compliance rule library to the prefabricated component assembly benchmark digital twin, and overlay the full-process simulation benchmark dataset corresponding to the preset benchmark assembly sequence.

[0208] Set up a BIM model interface that is compatible with mainstream BIM design software. It can import the approved prefabricated building design BIM model and extract the geometric information, node connection information and design attribute information of all prefabricated components to provide basic data for the construction of the assembly benchmark digital twin of prefabricated components.

[0209] Built-in standardized coding rules, according to the rule of project unique code + building number + floor number + component type code + serial number, assign a unique and unmodifiable digital identity code to each prefabricated component throughout the process, and establish a lifelong binding relationship between the code and the corresponding prefabricated component;

[0210] The built-in reference twin construction unit can solidify the preset reference assembly sequence, rigid constraint rule library, and compliance rule library into the digital twin of the assembly reference of prefabricated components, and superimpose the full-process simulation reference dataset corresponding to the preset reference assembly sequence to complete the construction, storage and full life cycle management of the reference twin; at the same time, it supports the pre-construction, embedding and update management of component-level reduced mechanical model library, three-dimensional tolerance chain model library and three-dimensional dimension chain tolerance transfer model.

[0211] The custom twin reconstruction module is used to receive the prefabricated component assembly sequence adjustment instructions input by the construction personnel based on the assembly benchmark digital twin of the prefabricated components. It matches the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code, and generates a custom assembly sequence twin.

[0212] The rigid constraint pre-verification mechanism is linked in real time with the rigid constraint rule library of the benchmark twin management module. When construction personnel modify the assembly sequence and assembly timing nodes of prefabricated components by dragging and dropping, the operation is verified in real time to see if it touches the strong logic assembly node. If it does, the operation is directly intercepted and a warning prompt is output simultaneously, thus intercepting the illegal adjustment command from the source.

[0213] The twin reconstruction unit is set up. After receiving the compliant assembly sequence adjustment instruction, it uses the unique digital identity code of the prefabricated component as the unique index to retrieve the full-dimensional parameter set of the corresponding component from the benchmark twin management module. It automatically matches the node connection relationship, stress boundary conditions and assembly constraint rules between the adjusted prefabricated components and quickly generates a custom assembly sequence twin corresponding to this adjustment scheme.

[0214] The multi-dimensional lightweight simulation verification engine is used to perform local lightweight simulation calculations on the adjusted assembly sequence of prefabricated components using a pre-built multi-dimensional lightweight simulation verification engine for a custom assembly sequence twin, so as to output simulation data with fixed dimensions, including structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension.

[0215] The assembly performance deviation quantification judgment engine is used to compare the simulation data of the custom assembly sequence twin with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component through the pre-built assembly performance deviation quantification judgment engine, calculate the performance deviation degree corresponding to each fixed dimension, and output standardized judgment results based on the preset hierarchical judgment threshold system.

[0216] The system incorporates a built-in safety red line veto mechanism and a real-time safety red line pre-verification mechanism. These mechanisms have the highest priority control rules. Regardless of whether the performance deviation meets the threshold requirements, if the preset safety red line situation is triggered, the solution is directly determined to be infeasible. Simultaneously, tamper-proof risk traceability information is generated, which includes the unique digital identification code of the prefabricated component of the non-compliant component, the violation situation, deviation data, and the assembly node location where the risk occurred.

[0217] The compliance solution output and closed-loop iteration module outputs the corresponding compliance solution based on the standardization judgment results and generates a technical review file.

[0218] The built-in hierarchical solution output unit outputs formal assembly sequence adjustment compliance solutions for feasible judgment results, and generates 3D visualized construction briefing documents based on a custom assembly sequence twin; for optimized feasible judgment results, it outputs targeted quantitative optimization suggestions including assembly sequence fine-tuning solutions, temporary support reinforcement solutions, and precision pre-control solutions; for infeasible judgment results, it clearly marks the sources of risk and safety hazards, and generates alternative adjustment solutions that meet compliance requirements based on a multi-objective genetic optimization algorithm.

[0219] It has a built-in full-flow linkage verification and optimization unit. Before outputting the compliant solution corresponding to the feasible judgment result, it automatically executes the full-flow linkage verification and optimization steps to ensure that the adjustment solution is compliant throughout the entire life cycle and all flow stages.

[0220] The built-in technical review archive management unit can automatically collect the adjustment instructions, full simulation data, deviation calculation results, standardization judgment results, approval records, and compliance plan documents of the entire process of this assembly sequence adjustment, and generate an unalterable technical review archive, supporting full-cycle traceability and as-built archiving of the project.

[0221] The built-in closed-loop iterative optimization unit can collect on-site measured data of precast components. Through residual analysis of measured data and simulation data, it corrects the mechanical parameters of the reference twin, the three-dimensional dimension chain tolerance transfer model, and the deviation judgment rules. Based on the incremental learning algorithm, it completes the incremental update of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library. At the same time, it accumulates the full-cycle data of the project to form reusable standardized management and control rules, realizing the continuous self-optimization of the system and the reuse of experience.

[0222] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for simulating the construction progress of prefabricated buildings based on digital twins, characterized in that, include: Pre-built multi-dimensional lightweight simulation verification engine and assembly performance deviation quantification judgment engine; Based on the BIM model of prefabricated building design, a unique digital identity code is assigned to each prefabricated component, and a corresponding full-dimensional parameter set is bound to the digital identity code. The pre-set benchmark assembly sequence, rigid constraint rule library, and compliance rule library are then added to the digital twin of the prefabricated component assembly benchmark, and a full-process simulation benchmark dataset corresponding to the pre-set benchmark assembly sequence is superimposed. Based on the digital twin of the assembly benchmark of prefabricated components, the system receives the assembly sequence adjustment instructions of prefabricated components input by the construction personnel, and generates a custom assembly sequence twin by matching the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code. By using a pre-built multi-dimensional lightweight simulation verification engine, local lightweight simulation calculations are performed on the adjusted assembly sequence of prefabricated components for a custom assembly sequence twin, so as to output fixed-dimensional simulation data. The fixed dimensions include structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension. By using a pre-built assembly performance deviation quantification judgment engine, the simulation data of the custom assembly sequence twin is compared with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component, the performance deviation degree corresponding to each fixed dimension is calculated, and the standardized judgment result is output based on the preset hierarchical judgment threshold system. Based on the standardized judgment results, output the corresponding compliance solution and generate a technical review file.

2. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 1, characterized in that, The assembly benchmark digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine further include: The full-dimensional parameter set includes geometric parameters of precast components, mechanical property parameters of materials, manufacturing tolerance thresholds, node connection parameters, stress parameters of temporary supports, design boundary conditions, and accidental load parameters during construction. The rigid constraint rule base contains non-adjustable strong logic assembly nodes, which are dynamically locked during subsequent assembly sequence adjustments; the compliance rule base contains mandatory requirements of project design documents, structural safety limit thresholds, and allowable deviations in assembly accuracy. The pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are generated. Based on historical construction measurement data and structural simulation verification data of similar prefabricated building projects, the training and optimization of the component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are completed. A three-dimensional dimensional chain tolerance transfer model is pre-built, and the three-dimensional dimensional chain tolerance transfer model, component-level reduced-order mechanical model library, and three-dimensional tolerance chain model library are built into the assembly benchmark digital twin of prefabricated components and the multi-dimensional lightweight simulation verification engine.

3. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 2, characterized in that: The system receives instructions to adjust the assembly sequence of precast components through a visual interactive interface adapted for outdoor construction site operations. The visual interactive interface has a built-in rigid constraint pre-verification mechanism. When construction personnel modify the assembly sequence and timing nodes of precast components by dragging and dropping, the rigid constraint pre-verification mechanism verifies in real time whether the operation touches the strong logic assembly nodes in the rigid constraint rule base. If a strong logic assembly node is touched, the operation is intercepted and a warning prompt is output.

4. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 2, characterized in that, The calculation rules for fixed-dimensional simulation data are as follows: For temporary working conditions during the construction phase, a multi-load coupled nonlinear mechanical simulation is performed. The stress state of the temporary building structure system, the stress of the component nodes, the stress of the temporary support system, and the overall and local overturning stability are calculated under the adjusted assembly sequence of prefabricated components. The accidental load combination conditions of wind load and construction live load during the construction phase are simultaneously superimposed, and the stress simulation data of the temporary structure are output. Based on a pre-built three-dimensional dimension chain tolerance transfer model, the changes and cumulative effects of tolerance transfer path caused by the adjusted assembly sequence are simulated, the final assembly accuracy deviation is calculated, and the assembly accuracy simulation data is output. Pre-build a project flow rhythm model, and based on the project flow rhythm model, deduce the impact of the adjusted assembly sequence of prefabricated components on the connection between preceding and subsequent processes, the construction rhythm of the flow section, the critical path duration, and the total duration, and output the simulation data of the progress impact. The system compares the adjusted assembly sequence of prefabricated components with the compliance rule base item by item, and outputs compliance verification data.

5. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 4, characterized in that, The formula for calculating performance deviation is: ； in, The performance deviation of any verification dimension in a fixed dimension; Simulation data for a custom assembly sequence twin; This serves as the baseline data for the entire simulation process. The upper limit of the compliance threshold for the verification dimension; The upper limit of the compliance threshold for the verification dimension is the maximum value allowed by the specifications in the compliance rule base and the design safety limit value; the graded judgment threshold system is a differentiated dynamic threshold set based on the importance level of prefabricated components; The assembly performance deviation quantification judgment engine automatically identifies the importance level of prefabricated components and adaptively matches the corresponding threshold based on the full-dimensional parameter set bound to the unique digital identity code of the prefabricated components. Performance deviation includes structural safety performance deviation, cumulative assembly accuracy deviation, process progress adaptation deviation, and standard compliance deviation.

6. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 5, characterized in that, The deviation determination logic of the assembly performance deviation quantification determination engine further includes: Based on the rigid constraint rule base, compliance rule base and structural safety limit threshold, a safety red line veto mechanism and a safety red line real-time pre-verification mechanism are configured in the assembly performance deviation quantification judgment engine. The real-time pre-verification mechanism for safety red lines is used to simultaneously perform pre-verification of safety red lines during the generation of a custom assembly sequence twin. The safety red line veto mechanism is used to prohibit the adjustment of the assembly benchmark digital twin of prefabricated components when any preset safety red line situation occurs, and to generate risk traceability information. The risk traceability information includes the unique digital identity code of the prefabricated component of the non-compliant component, the violation situation, deviation data, and the assembly node location where the risk occurred. The preset safety red line scenarios include: the adjusted assembly sequence exceeds the rigid constraint rule library, or the strong logic assembly node that cannot be adjusted is modified; and the structural simulation data exceeds the design safety limit threshold of the yield strength and overturning safety factor of the precast component.

7. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 1, characterized in that, Based on a preset hierarchical judgment threshold system, standardized judgment results are output, including: Based on feasible judgment results, output a compliant solution for adjusting the assembly sequence, and generate construction handover documents based on the custom assembly sequence twin; Based on the optimized and feasible judgment results, targeted quantitative optimization suggestions are output, including assembly sequence fine-tuning scheme, temporary support reinforcement scheme, and precision pre-control scheme. For infeasible judgments, the sources of risk and safety hazards are marked, and alternative adjustment schemes are generated based on multi-objective genetic optimization algorithms.

8. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 2, characterized in that, After outputting the corresponding compliance solution based on the standardized judgment results, it includes: Collect measured data of precast components, including installation data, stress monitoring data of temporary structures, process execution data, and quality acceptance results; By using a custom assembly sequence twin, and based on residual analysis of measured data, structural safety performance simulation data, assembly accuracy cumulative simulation data, and process progress adaptation simulation data, the mechanical parameters, pre-built three-dimensional dimension chain tolerance transfer model, and deviation judgment rules of the assembly reference digital twin of prefabricated components are corrected. Based on the incremental learning algorithm, the pre-trained component-level reduced-order mechanical model library and the three-dimensional tolerance chain model library are incrementally updated using the collected measured data. The project adjustment plan, structural safety performance simulation data, assembly accuracy cumulative simulation data, process progress adaptation simulation data, standard compliance verification data, and standardization judgment results are summarized to form standardized management and control rules.

9. The method for simulating the construction progress of prefabricated buildings based on digital twins according to claim 7, characterized in that, Before outputting a compliant solution for adjusting the assembly sequence based on feasible judgment results, perform full-flow linkage verification and optimization steps: Based on the adjusted custom assembly sequence twin, using the unique digital identity code of the prefabricated component as an index, the preset baseline assembly sequence, the process logic of each flow section and floor, and the prefabricated component arrival plan are matched to generate the full flow linkage assembly sequence twin corresponding to this adjustment. Through a pre-built multi-dimensional lightweight simulation and verification engine, the assembly sequence twin of the entire flow linkage is simulated and calculated, and the following data are output: structural safety performance simulation data, cumulative assembly accuracy simulation data, process progress adaptation simulation data, and standard compliance verification data for each subsequent flow section and floor. By using a pre-built assembly performance deviation quantification judgment engine, the simulation data of structural safety performance, the cumulative simulation data of assembly accuracy, the simulation data of process progress adaptation, the verification data of standard compliance, and the simulation benchmark dataset of the assembly benchmark digital twin of prefabricated components are compared with the simulation benchmark dataset to calculate the performance deviation of each subsequent flow section and floor, and trigger real-time pre-verification of the safety red line. When all subsequent performance deviations meet the compliance thresholds of the corresponding precast components and do not violate the safety red line, the compliance solution of this adjustment is confirmed to be output normally. When any performance deviation of any flow segment or floor exceeds the compliance threshold or touches the safety red line, the current adjustment plan and the assembly sequence of subsequent flow segments are optimized in conjunction with the multi-objective genetic optimization algorithm, the simulation verification is re-triggered, and the final full flow linkage adjustment compliance plan is output.

10. A prefabricated building construction progress simulation system based on digital twins, employing the prefabricated building construction progress simulation method based on digital twins as described in any one of claims 1-9, characterized in that, include: The benchmark twin management module is used to assign a unique digital identity code to each prefabricated component based on the prefabricated building design BIM model, bind the corresponding full-dimensional parameter set to the digital identity code, and add the preset benchmark assembly sequence, rigid constraint rule library, and compliance rule library to the prefabricated component assembly benchmark digital twin, and overlay the full-process simulation benchmark dataset corresponding to the preset benchmark assembly sequence. The custom twin reconstruction module is used to receive the prefabricated component assembly sequence adjustment instructions input by the construction personnel based on the assembly benchmark digital twin of the prefabricated components. It matches the node connection relationship, stress boundary conditions and assembly constraint rules between the prefabricated components according to the unique digital identity code, and generates a custom assembly sequence twin. The multi-dimensional lightweight simulation verification engine is used to perform local lightweight simulation calculations on the adjusted assembly sequence of prefabricated components using a pre-built multi-dimensional lightweight simulation verification engine for a custom assembly sequence twin, so as to output simulation data with fixed dimensions, including structural safety performance verification dimension, assembly accuracy cumulative verification dimension, process progress adaptation verification dimension, and standard compliance verification dimension. The assembly performance deviation quantification judgment engine is used to compare the simulation data of the custom assembly sequence twin with the simulation benchmark dataset of the assembly benchmark digital twin of the prefabricated component through the pre-built assembly performance deviation quantification judgment engine, calculate the performance deviation degree corresponding to each fixed dimension, and output standardized judgment results based on the preset hierarchical judgment threshold system. The compliance solution output and closed-loop iteration module outputs the corresponding compliance solution based on the standardization judgment results and generates a technical review file.

Images