Bridge construction control method and system based on extreme environment, and storage medium

Abstract

The embodiment of the application discloses a bridge construction control method and system based on an extreme environment, and a storage medium. The method first generates a digital twin model containing virtual-real mapping relationships between bridge structures, construction equipment and extreme environment characteristic items according to structured data and unstructured data extracted from a bridge construction task report, and outputs a bridge construction simulation result. Then, an associated optimization model is constructed based on the bridge structures and the extreme environment characteristic items, and an adaptive adjustment result of a construction configuration item is determined with reference to the simulation result. Finally, a synchronous communication protocol of the construction equipment is determined according to the adjustment result, a device space operation path is adjusted, and the construction equipment is cooperatively controlled based on a risk transmission mechanism during the path adjustment process. The method realizes multi-element deep coupling and whole-chain cooperative control, and improves the stability and reliability of bridge construction in an extreme environment.

Description

Technical Field

[0001] This application relates to the field of computer control technology, and in particular to a bridge construction control method, system and storage medium based on extreme environments. Background Technology

[0002] Bridge construction is a complex systems engineering project involving multidisciplinary and multi-stage collaboration. Its construction quality and efficiency directly affect the overall safety and service life of the bridge. As bridge construction extends to areas with long spans, high piers, complex geological conditions, and extreme environments, the technical challenges faced during construction are becoming increasingly prominent. Currently, digital twin technology has been gradually applied to the field of bridge construction, simulating, monitoring, and optimizing the construction process by constructing a mapping relationship between physical entities and virtual models. In existing technologies, the construction of digital twin models largely relies on structured data such as construction design drawings and real-time sensor data. Some studies have attempted to incorporate unstructured data such as construction logs and technical disclosure documents to enrich the model dimensions, but overall, the characterization of the correlation between multiple factors such as bridge structure, construction equipment, and extreme environments remains insufficient. Furthermore, the application of correlation optimization models in bridge construction often focuses on optimizing a single objective (such as construction schedule or cost). Their input parameters often do not fully integrate the simulation results output by the digital twin model, resulting in insufficient adaptability of the optimization results to the actual construction scenario.

[0003] In terms of construction equipment control, existing technologies mostly employ preset communication protocols to achieve information exchange between devices, guiding equipment operation through planned fixed spatial operating paths. However, in extreme environments (such as strong winds, high temperatures, and high altitudes), the performance of construction equipment and the operating environment undergo dynamic changes, making it difficult for fixed communication protocols and operating paths to adapt to this dynamism. Furthermore, existing collaborative control of construction equipment is largely based on simple command interactions, lacking consideration for risk transmission mechanisms during construction. When a piece of equipment malfunctions, it is difficult to quickly identify the risk transmission path and take effective collaborative control measures, potentially leading to risk spread and affecting overall construction safety.

[0004] Therefore, how to achieve deep coupling and collaborative control of bridge construction in extreme environments to improve the stability and reliability of bridge construction is one of the problems existing in current technologies. Summary of the Invention

[0005] This application provides a bridge construction control method, system, and storage medium based on extreme environments, which is used to achieve deep coupling and collaborative control of bridge construction in extreme environments to improve the stability and reliability of bridge construction.

[0006] This application provides a bridge construction control method based on extreme environments, applied to a bridge construction control system. The method includes: A digital twin model is generated based on structured and unstructured data extracted from bridge construction task reports, which includes the virtual-real mapping relationship between bridge structure, construction equipment and extreme environment features. The bridge construction simulation results are then output through the digital twin model. A correlation optimization model is constructed based on the bridge structure and the extreme environment characteristics. Using the bridge construction simulation results as a reference, the adaptive adjustment results of the construction configuration items are determined through the correlation optimization model; Based on the adaptive adjustment results, the synchronous communication protocol of the construction equipment is determined, and the spatial operation path of the construction equipment is adjusted through the synchronous communication protocol. During the adjustment of the spatial operation path, the construction equipment is coordinated and controlled based on the risk transmission mechanism.

[0007] One embodiment of this application provides a bridge construction control system, including: A processor; a storage device having a computer program stored thereon; a network interface for providing network communication functions; when the computer program is executed by the processor, the processor enables the processor to implement any of the bridge construction control methods based on extreme environments.

[0008] One embodiment of this application provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the bridge construction control method based on extreme environments.

[0009] This application embodiment extracts structured and unstructured data from bridge construction task reports to generate a digital twin model containing the virtual-real mapping relationship between bridge structure, construction equipment, and extreme environment features. Combined with the bridge construction simulation results output by this digital twin model, and linked with an association optimization model constructed based on bridge structure and extreme environment features, the adaptive adjustment results of construction configuration items are determined. Furthermore, based on these adjustment results, the synchronous communication protocol of the construction equipment is determined to adjust the equipment's spatial operation path. Simultaneously, during the path adjustment process, collaborative control of the construction equipment is achieved based on a risk transmission mechanism. This forms a full-chain collaborative control system: data-driven twin modeling, simulation result-guided optimization, configuration adjustment and protocol adaptation, path optimization, and risk collaborative control. This overcomes the technical bottlenecks of traditional bridge construction where digital twin models only focus on single-dimensional mapping, association optimization is disconnected from simulation results, and equipment control lacks protocol adaptation and risk transmission linkage. It achieves deep coupling mapping of multiple elements of bridge structure, construction equipment, and extreme environment, improving the accuracy and dynamic adaptability of construction configuration adjustments, enhancing the optimization efficiency of construction equipment's spatial operation path, and improving the safety of collaborative control, thereby achieving stability and reliability in bridge construction under extreme environments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a bridge construction control method based on extreme environments, provided as an embodiment of this application.

[0012] Figure 2 This is a schematic diagram of the basic structure of a bridge construction control system provided in an embodiment of this application.

[0013] Figure 3 This is a functional block diagram of a bridge construction control device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0015] Please see Figure 1 , Figure 1 This is a flowchart of a bridge construction control method based on extreme environments provided in an embodiment of this application. The method can be executed by the bridge construction control system or jointly by the bridge construction control system and the server. The method may include steps 110-140.

[0016] This application embodiment describes a bridge construction control scenario based on extreme environments (high-altitude, cold, and high-temperature-difference regions). The execution entity is the bridge construction control system, and the specific steps are as follows: Step 110: Generate a digital twin model containing the virtual-real mapping relationship between bridge structure, construction equipment and extreme environment features based on the structured and unstructured data extracted from the bridge construction task report, and output the bridge construction simulation results through the digital twin model.

[0017] In this embodiment, the bridge construction control system first extracts structured and unstructured data from the bridge construction task report. The structured data includes bridge design parameters (such as span, height, and component dimensions), construction equipment models and performance parameters (such as maximum operating radius and maximum load capacity), and historical monitoring data of extreme environments (such as historical statistical data on temperature, wind speed, and air pressure). The unstructured data includes textual descriptions of the construction plan, bridge structure design drawings, construction equipment operation manuals, and on-site survey reports of extreme environments. Then, the control system preprocesses the data, including cleaning, deduplication, and normalization of the structured data, and text segmentation, keyword extraction, and image feature extraction of the unstructured data, resulting in a standardized dataset.

[0018] Furthermore, the control system constructs a digital twin model based on a standardized dataset. This model establishes a virtual-real mapping relationship between the bridge structure, construction equipment, and extreme environmental characteristics, digitally reconstructing the bridge construction scenario in the physical world within a virtual space. For example, the virtual-real mapping relationship of the bridge structure maps the bridge's design parameters and component connection relationships to the geometry and topology in the virtual model; the virtual-real mapping relationship of the construction equipment maps the equipment's performance parameters and operating status to the equipment model and motion state in the virtual model; and the virtual-real mapping relationship of extreme environmental characteristics maps environmental monitoring data and trends to the environmental field and boundary conditions in the virtual model.

[0019] Finally, the control system simulates the bridge construction process using a digital twin model and outputs the bridge construction simulation results, which include the deformation of the bridge structure during construction, stress distribution, the operating trajectory of construction equipment, operation sequence, and the impact of extreme environments on the construction process.

[0020] Step 120: Construct an association optimization model based on the bridge structure and the extreme environment features.

[0021] In this embodiment, the bridge construction control system constructs an associated optimization model based on the bridge structure and extreme environment characteristics obtained in step 110. First, the control system extracts features from the bridge structure and extreme environment characteristics to obtain feature vectors for the bridge structure (such as span, height, component stiffness, and strength) and feature vectors for the extreme environment characteristics (such as temperature, wind speed, air pressure, and humidity). Further, the control system analyzes the interaction between the bridge structure and extreme environment characteristics to construct the objective function and constraints of the associated optimization model. The objective function can be to minimize structural deformation and stress concentration during bridge construction, or to maximize the operating efficiency of the construction equipment. Constraints include strength, stiffness, and stability constraints of the bridge structure; operating range and performance constraints of the construction equipment; and constraints related to the impact of the extreme environment (such as the influence of temperature on material properties and wind speed on equipment operation). Finally, the control system uses a multi-objective optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm) to solve the associated optimization model, obtaining the optimal construction configuration scheme, which includes the selection, placement, and timing of construction equipment.

[0022] Step 121: Deconstruct the structural components of the bridge structure hierarchically and extract structural correlation clues at different levels. The structural correlation clues include the connection relationship between components, the force transmission relationship, and the assembly dependency relationship.

[0023] In this embodiment, the bridge construction control system decomposes the structural components of the bridge structure hierarchically, from the whole to the parts, into levels such as bridge system, subsystem, component, and sub-component. For example, the bridge system is decomposed into subsystems such as superstructure, substructure, and auxiliary structures; the superstructure is decomposed into components such as beams, bridge deck pavement, and expansion joints; and the beams are decomposed into sub-components such as steel bars and concrete. Next, the control system extracts structural correlation clues at different levels, including connection relationships between components such as welded connections, bolted connections, and hinged connections; force transmission relationships such as vertical force transmission, horizontal force transmission, and torque transmission; and assembly dependency relationships such as installing foundation components before installing superstructure components, and tying steel bars before pouring concrete.

[0024] Step 122: Perform scenario-based mapping on the extreme environmental features and extract environmental correlation clues under different construction scenarios. The environmental correlation clues include the distribution relationship, change trend and impact relationship of environmental elements on the structure.

[0025] In this embodiment, the bridge construction control system performs scenario-based mapping of extreme environmental characteristics (such as temperature, wind speed, air pressure, humidity, etc.) to different construction scenarios (such as foundation construction scenario, superstructure construction scenario, bridge deck construction scenario, etc.). For example, in the foundation construction scenario, temperature affects the setting time and strength development of concrete, while wind speed affects the pouring quality and construction safety of concrete; in the bridge deck construction scenario, temperature affects the paving temperature and compaction effect of asphalt mixture, while wind speed affects the smoothness and durability of the bridge deck pavement. Next, the control system extracts environmental correlation clues under different construction scenarios. The distribution relationship of environmental elements includes the spatial distribution pattern of environmental elements (such as the temperature distribution difference in different parts of the bridge, the wind speed distribution difference in different heights of the bridge, etc.); the change trend includes the temporal change pattern of environmental elements (such as daily and seasonal temperature changes, annual and monthly wind speed changes, etc.); the influence relationship on the structure includes the physical effects of environmental elements on the bridge structure (such as structural deformation caused by temperature changes, structural vibration caused by wind speed, etc.) and chemical effects (such as structural corrosion caused by humidity, structural aging caused by salt spray, etc.).

[0026] Step 123: Perform cross-association processing on the structural association clues and the environmental association clues to obtain a structure-environment cross-association logical chain. The cross-association logical chain is used to reflect the dynamic interaction relationship between structural state changes and environmental characteristic fluctuations.

[0027] In this embodiment, the bridge construction control system performs cross-correlation processing on the structural correlation clues extracted in step 121 and the environmental correlation clues extracted in step 122. For example, the control system constructs a structure-environment cross-correlation logical chain by analyzing the interaction between the connection relationships, force transmission relationships, and assembly dependencies between structural correlation clues and the distribution relationships, changing trends, and impact relationships of environmental elements on the structure in environmental correlation clues. For instance, when the ambient temperature changes, the concrete components of the bridge structure will deform due to thermal expansion and contraction. This deformation will be transmitted to other components through the connection relationships between components, thus affecting the stress state of the entire bridge structure. When the wind speed exceeds a certain threshold, the beams of the bridge structure will vibrate. This vibration will affect the foundation structure of the bridge through the force transmission relationships, thus affecting the stability of the bridge.

[0028] Step 124: Obtain real-time constraints on structural state changes during bridge construction. These real-time constraints include structural strength requirements during the construction phase, environmental adaptation requirements, and interference requirements from construction actions.

[0029] In this embodiment, the bridge construction control system acquires real-time constraints on structural state changes during bridge construction. These constraints include: structural strength requirements for different construction stages (e.g., foundation construction, superstructure construction, deck construction) to ensure structural safety; environmental adaptability requirements (e.g., deformation and vibration limits under varying environmental conditions such as temperature, wind speed, and air pressure) to ensure structural stability in extreme environments; and interference requirements (e.g., limiting the impact of construction equipment operation on the bridge structure, such as equipment vibration and load) to ensure smooth construction.

[0030] Step 125: Integrate the real-time constraints into the structure-environment cross-correlation logic chain to obtain the correlation optimization model. The correlation optimization model is used to reflect the dynamic correlation between the bridge structural state, extreme environmental feature changes, and construction process constraints.

[0031] In this embodiment, the bridge construction control system integrates the real-time constraints obtained in step 124 into the structure-environment cross-correlation logic chain obtained in step 123 to obtain an association optimization model. For example, the control system adds structural strength requirements, environmental change adaptation requirements, and construction action interference requirements as constraints to the structure-environment cross-correlation logic chain. By adjusting the parameters and weights in the logic chain, the association optimization model can reflect the dynamic correlation between the bridge structural state, extreme environmental characteristic changes, and construction process constraints. For instance, when the ambient temperature exceeds a certain threshold, the association optimization model will adjust the operation sequence and parameters of the construction equipment according to the structural strength requirements and environmental change adaptation requirements to ensure the safety and stability of the bridge structure.

[0032] Step 130: Using the bridge construction simulation results as a reference, determine the adaptive adjustment results of the construction configuration items through the correlation optimization model.

[0033] In this embodiment, the bridge construction control system uses the bridge construction simulation results obtained in step 110 as a reference, comparing and analyzing the simulation results with the objective function and constraints of the correlation optimization model. If the simulation results meet the objective function and constraints, the construction configuration scheme does not need to be adjusted; if the simulation results do not meet the objective function or constraints, the control system adaptively adjusts the construction configuration items through the correlation optimization model. For example, the control system re-simulates the bridge construction process by adjusting parameters such as the selection, placement, and timing of construction equipment until the simulation results meet the objective function and constraints. Finally, the control system outputs the adaptive adjustment results of the construction configuration items, including the adjusted parameters of the construction equipment and the adjusted timing.

[0034] Step 131: Decompose the bridge construction simulation results in multiple dimensions and extract structural state simulation information, environmental feature simulation information, and construction action simulation information; wherein, the structural state simulation information includes the deformation state, connection state, and stress state of the components, the environmental feature simulation information includes the real-time state, change state, and influence state of environmental elements, and the construction action simulation information includes the operation state, timing state, and coordination state of the equipment.

[0035] In this embodiment, the bridge construction control system decomposes the bridge construction simulation results in multiple dimensions, extracting simulation information from three dimensions: structural state, environmental characteristics, and construction actions. The structural state simulation information is obtained by analyzing the bridge structure deformation data, stress data, and strain data in the simulation results. This includes the deformation state of components (such as elongation, shortening, and bending deformation), connection state (such as stress distribution at connection points and changes in bolt preload), and stress state (such as axial force, bending moment, and shear force). The environmental characteristic simulation information is obtained by analyzing the environmental monitoring data and environmental change trend data in the simulation results. This includes the real-time status of environmental elements (such as current temperature, wind speed, and air pressure), change status (such as the rate of temperature change and the magnitude of wind speed change), and influence status (such as the impact of environmental elements on the deformation and stress of the bridge structure). The construction action simulation information is obtained by analyzing the construction equipment operation data, operation sequence data, and collaborative operation data in the simulation results. This includes the equipment operation status (such as the equipment start-up status, stop status, and operating speed), timing status (such as the start time, end time, and interval time of the equipment operation), and collaborative status (such as the coordination and information exchange between multiple pieces of equipment).

[0036] Step 132: Map the structural state simulation information, the environmental feature simulation information, and the construction action simulation information to the corresponding dimensions of the correlation optimization model, and extract the correlation logic branches in the correlation optimization model corresponding to each simulation information.

[0037] In this embodiment, the bridge construction control system maps structural state simulation information, environmental feature simulation information, and construction action simulation information to the structural dimension, environmental dimension, and construction dimension of the correlation optimization model, respectively. For example, the structural state simulation information is mapped to the structural dimension of the correlation optimization model, and the correlation logic branch corresponding to the structural state simulation information is extracted by matching it with structural parameters and constraints in the model; the environmental feature simulation information is mapped to the environmental dimension of the correlation optimization model, and the correlation logic branch corresponding to the environmental feature simulation information is extracted by matching it with environmental parameters and constraints in the model; the construction action simulation information is mapped to the construction dimension of the correlation optimization model, and the correlation logic branch corresponding to the construction action simulation information is extracted by matching it with construction parameters and constraints in the model.

[0038] Step 1321: Decompose the structural state simulation information of the bridge construction simulation results into component-level, connection-level, and overall-level structural state simulation sub-information.

[0039] In this embodiment, the bridge construction control system decomposes the structural state simulation information into granular components, extracting structural state simulation sub-information at three levels: component level, connection level, and overall level. The component-level structural state simulation sub-information includes the deformation state, connection state, and stress state of individual components; the connection-level structural state simulation sub-information includes the deformation state, connection state, and stress state of the connection points between components; and the overall structural state simulation sub-information includes the deformation state, connection state, and stress state of the entire bridge structure.

[0040] Step 1322: Decompose the environmental feature simulation information into scene sub-information, dynamic environment simulation sub-information, and interference environment simulation sub-information.

[0041] In this embodiment, the bridge construction control system decomposes the environmental feature simulation information into scenarios, classifying it into static environmental simulation sub-information, dynamic environmental simulation sub-information, and disturbance environmental simulation sub-information based on the changing characteristics of environmental elements. Static environmental simulation sub-information includes information on the stable state of environmental elements over a short period (such as the topography and geological conditions of the construction site); dynamic environmental simulation sub-information includes information on the changing state of environmental elements over time (such as daily and seasonal temperature variations, and annual and monthly wind speed variations); and disturbance environmental simulation sub-information includes information on sudden changes in environmental elements (such as sudden strong winds, heavy rain, and earthquakes).

[0042] Step 1323: Perform time-series decomposition on the construction action simulation information to obtain initial action simulation sub-information, process action simulation sub-information, and final action simulation sub-information.

[0043] In this embodiment, the bridge construction control system performs a time-series decomposition of the construction action simulation information, dividing it into initial action simulation sub-information, process action simulation sub-information, and final action simulation sub-information according to the time sequence of the construction process. The initial action simulation sub-information includes the status information of actions such as starting, positioning, and debugging of construction equipment; the process action simulation sub-information includes the status information of actions such as operation, movement, and adjustment of construction equipment; and the final action simulation sub-information includes the status information of actions such as stopping, removal, and maintenance of construction equipment.

[0044] Step 1324: Match the split structural state simulation sub-information, environmental feature simulation sub-information, and construction action simulation sub-information with the hierarchical nodes of the associated optimization model to generate a mapping matrix between simulation information and hierarchical nodes.

[0045] In this embodiment, the bridge construction control system matches the decomposed structural state simulation sub-information, environmental feature simulation sub-information, and construction action simulation sub-information with the hierarchical nodes of the correlation optimization model. For example, the control system first determines the hierarchical nodes of the correlation optimization model, including the model's input nodes, output nodes, intermediate nodes, etc.; further, it matches each simulation sub-information with its corresponding hierarchical node, determining the mapping relationship between the simulation sub-information and the hierarchical node based on the characteristics of the simulation sub-information and the function of the hierarchical node; finally, it generates a mapping matrix between the simulation information and the hierarchical nodes, with the simulation sub-information as rows and the hierarchical nodes as columns, and the elements in the matrix representing the degree of matching between the simulation sub-information and the hierarchical node.

[0046] Step 1325: Extract the associated logical branches covering all simulated sub-information based on the mapping matrix. The associated logical branches include the hierarchical nodes corresponding to each simulated sub-information and the association relationships between the nodes.

[0047] In this embodiment, the bridge construction control system extracts associated logical branches covering all simulated sub-information based on a mapping matrix. For example, the control system first selects the hierarchical node with the highest matching degree for each simulated sub-information according to the matching degree in the mapping matrix; further, it connects these nodes according to the association relationships between the hierarchical nodes to form associated logical branches; finally, it integrates and optimizes the associated logical branches to ensure that they cover all simulated sub-information and that the association relationships between nodes are reasonable and clear.

[0048] Step 133: Based on the deviation information between the bridge construction simulation results and the correlation optimization model, locate the initial correlation nodes that generate the deviations. The initial correlation nodes include structural state deviation nodes, environmental feature deviation nodes, and construction action deviation nodes.

[0049] In this embodiment, the bridge construction control system compares the bridge construction simulation results with the target values ​​of the associated optimization model and calculates the deviation information between the two. Further, the control system locates the initial associated node that caused the deviation based on the deviation information; this node is the source of the deviation. For example, a structural state deviation node refers to a hierarchical node that causes a deviation between the bridge structural state simulation results and the target value (e.g., insufficient stiffness of components, insufficient strength of connection parts, etc.); an environmental characteristic deviation node refers to a hierarchical node that causes a deviation between the environmental characteristic simulation results and the target value (e.g., the predicted value of ambient temperature does not match the actual value, the predicted value of wind speed does not match the actual value, etc.); and a construction action deviation node refers to a hierarchical node that causes a deviation between the construction action simulation results and the target value (e.g., unreasonable operation sequence of construction equipment, improper setting of operating parameters, etc.).

[0050] Step 134: Trace the propagation path of the initial associated node in the associated logic branch, analyze the propagation relationship between different associated nodes, and deduce the critical path of deviation propagation.

[0051] In this embodiment, the bridge construction control system tracks the propagation path of the initial associated node within the associated logical branches. For example, starting from the initial associated node, the control system sequentially tracks the propagation of deviations within the nodes along the node connection relationships in the associated logical branches. During the tracking process, the control system analyzes the propagation relationship of the deviation between different associated nodes, including the direction, intensity, and time of deviation propagation. Furthermore, based on the propagation relationship of the deviation, the control system derives the critical path of deviation propagation, which is the main path of deviation propagation from the initial associated node to the ultimately affected node.

[0052] Step 1341: Classify the deviation information of the bridge construction simulation results in multiple dimensions to obtain structural state deviation, environmental characteristic deviation and construction action deviation.

[0053] In this embodiment, the bridge construction control system classifies the deviation information of the bridge construction simulation results into multiple dimensions, categorizing them into structural state deviation, environmental characteristic deviation, and construction action deviation based on the cause and scope of the deviation. Structural state deviation refers to the deviation between the simulation results and the target values ​​caused by problems inherent in the bridge structure itself (such as insufficient stiffness of components or insufficient strength of connection parts). Environmental characteristic deviation refers to the deviation between the simulation results and the target values ​​caused by changes in environmental factors (such as discrepancies between predicted and actual temperature values ​​or predicted and actual wind speed values). Construction action deviation refers to the deviation between the simulation results and the target values ​​caused by operational problems with the construction equipment (such as unreasonable work sequence or improper setting of operating parameters).

[0054] Step 1342: For each deviation type, traverse the node hierarchy of the associated logic branch, backtracking from the bottom node upwards to locate the initial associated node that caused the deviation, and determine whether the input-output logic of the initial associated node conforms to the preset rules of the association model.

[0055] In this embodiment, the bridge construction control system traverses the node hierarchy of the associated logic branch for each deviation type. For example, starting from the lowest node of the associated logic branch, the control system traces back the input-output logic of each node upwards, checking whether the node's input conforms to preset rules and whether the output is correct. If it is found that the input-output logic of one node does not conform to the preset rules, that node is positioned as the initial associated node for the deviation.

[0056] Step 1343: Based on the dependency relationship between the initial associated node and the adjacent associated nodes, determine the first dependent node and the second dependent node. The first dependent node refers to the node in which the initial node cannot output normally without the node, and the second dependent node refers to the node that only affects the output accuracy of the initial node.

[0057] In this embodiment, the bridge construction control system determines the first dependent node and the second dependent node based on the dependency relationship between the initial associated node and its adjacent associated nodes. For example, the control system analyzes the input sources of the initial associated node to determine which nodes are essential for the normal output of the initial associated node (i.e., the first dependent node) and which nodes only affect the output accuracy of the initial associated node (i.e., the second dependent node). For instance, if the output of the initial associated node depends on the input of one of the nodes, and the initial associated node cannot output normally without the input of that node, then that node is the first dependent node; if the output of the initial associated node is affected by the input of one of the nodes, but the initial associated node can still output without the input of that node, only with reduced output accuracy, then that node is the second dependent node.

[0058] Step 1344: Determine the preferred direction of deviation propagation based on the dependency relationship, trace the propagation path of the first dependent node according to the preferred direction, and simultaneously record the propagation probability of the second dependent node.

[0059] In this embodiment, the bridge construction control system determines the preferred direction of deviation propagation based on the dependency relationship between the initial associated node and adjacent associated nodes. For example, the control system uses the propagation direction of the first dependent node as the preferred direction of deviation propagation because the first dependent node is necessary for the normal output of the initial associated node, and the propagation of deviation in the first dependent node will directly affect the output of the initial associated node. Further, the control system tracks the propagation path of the first dependent node according to the preferred direction and records the propagation of deviation in the first dependent node. Simultaneously, the control system records the propagation probability of the second dependent node, because the second dependent node only affects the output accuracy of the initial associated node, and the propagation of deviation in the second dependent node may indirectly affect the output of the initial associated node.

[0060] Step 1345: Track the propagation process of the deviation in the associated logic branches, record the activation state of each node, and when the propagation path branches out, track the activation order of the nodes in each branch and mark the propagation endpoint of each branch.

[0061] In this embodiment, the bridge construction control system tracks the propagation process of deviations in associated logical branches, recording the activation state of each node (e.g., whether the node is activated by the deviation, the activation time, the activation intensity, etc.). When a branch appears in the propagation path, the control system tracks the node activation order of each branch and records the propagation of the deviation in each branch. Finally, the control system marks the propagation endpoint of each branch, i.e., the final node where the deviation propagates in that branch.

[0062] Step 1346: After integrating the transmission paths of all branches, check whether there are logical conflicts between the transmission paths. If there are conflicts, backtrack the dependency relationship of the initial associated nodes and re-analyze. After correcting the conflicts, form the transmission path of the deviation in the associated logical branches.

[0063] In this embodiment, the bridge construction control system integrates the transmission paths of all branches to obtain the complete transmission path of the deviation in the associated logical branches. Further, the control system checks for logical conflicts between the transmission paths (such as unreasonable node activation order or mismatch between node output and input). If a logical conflict exists, the control system re-analyzes the dependencies of the initial associated nodes, adjusts the relationships between nodes, and corrects the conflict. Finally, the control system forms the final transmission path of the deviation in the associated logical branches.

[0064] Step 135: Based on the critical path of the deviation propagation, reverse the adjustment association nodes of the construction configuration items and generate adaptive adjustment results of the construction configuration items. The adaptive adjustment results include the logical order, adjustment direction and adjustment range of the adjustment association nodes.

[0065] In this embodiment, the bridge construction control system uses the critical path of deviation propagation to reverse-engineer the adjustment-related nodes of construction configuration items. For example, the control system starts from the endpoint of the critical path of deviation propagation and traces backward along the path to determine which adjustments to construction configuration items can eliminate the deviation. Further, the control system generates adaptive adjustment results for the construction configuration items based on the logical order, direction, and scope of the adjustment-related nodes. For instance, if the critical path of deviation propagation shows that an unreasonable operating sequence of construction equipment caused the deviation, the control system will adjust the operating sequence of the construction equipment to conform to the requirements of the correlation optimization model.

[0066] Step 140: Determine the synchronous communication protocol of the construction equipment based on the adaptive adjustment result, adjust the spatial operation path of the construction equipment through the synchronous communication protocol, and perform collaborative control of the construction equipment based on the risk transmission mechanism during the adjustment of the spatial operation path.

[0067] In this embodiment, the bridge construction control system determines the synchronous communication protocol of the construction equipment based on the adaptive adjustment result obtained in step 130. For example, the control system determines the communication method, communication frequency, and communication content between the construction equipment based on the adjustment parameters of the construction configuration items and the adjusted operation sequence, and generates a synchronous communication protocol. Further, the control system sends control commands to the construction equipment through the synchronous communication protocol to adjust the spatial operation path of the construction equipment. During the adjustment of the spatial operation path, the control system performs collaborative control of the construction equipment based on a risk transmission mechanism. This mechanism analyzes the operating status of the construction equipment, environmental changes, and the bridge structural status to identify potential risk factors and take corresponding control measures to ensure the safety and stability of the construction process.

[0068] Step 141: Extract the collaborative operation requirements of construction equipment from the adaptive adjustment results.

[0069] In this embodiment, the bridge construction control system extracts the collaborative operation requirements of construction equipment from the adaptive adjustment results. For example, the control system analyzes the adjustment parameters of the construction configuration items, the adjusted operation sequence, and other information to determine which construction equipment needs to operate collaboratively, and the specific requirements for collaborative operation (such as the order of operations, synchronization of operations, and coordination accuracy of operations). Furthermore, the control system organizes the above collaborative operation requirements into a standardized format as input to the synchronous communication protocol.

[0070] Step 1411: Reversely decompose the adjustment logic chain of the adaptive adjustment result, trace the generation basis of the adjustment instruction node by node, extract the pre-adjustment triggering conditions and post-adjustment influence range of each adjustment node, and generate the dependency relationship network of the adjustment nodes.

[0071] In this embodiment, the bridge construction control system reverse-engineers the adjustment logic chain of the adaptive adjustment result, starting from the final node of the adjustment result and tracing the generation basis of the adjustment command node by node. For example, the control system analyzes the input and output of each adjustment node to determine the reason for the generation of the adjustment command and its scope of influence. Further, the control system extracts the pre-adjustment triggering conditions (such as the completion of one construction device's operation, or another environmental parameter reaching a threshold) and the post-adjustment influence scope (such as the impact on the operation sequence and operating parameters of other construction devices) of each adjustment node, generating a dependency network of the adjustment nodes.

[0072] Step 1412: Based on the dependency network, match the construction equipment operation tasks corresponding to each adjustment node, track the equipment sequence of task execution, and identify the operation connection links that require multiple devices to execute in parallel or serially.

[0073] In this embodiment, the bridge construction control system matches the operation tasks of the construction equipment corresponding to each adjustment node based on the dependency network of adjustment nodes. For example, the control system determines which construction equipment is responsible for performing the operation tasks corresponding to that adjustment node based on the function of the adjustment node and the performance parameters of the construction equipment. Further, the control system tracks the equipment sequence for task execution and identifies the operational connections that require multiple devices to perform in parallel or sequentially. For instance, if one adjustment node requires multiple devices to operate simultaneously, this connection is a parallel operation connection; if one adjustment node requires the previous device to complete its operation before the next device can operate, this connection is a sequential operation connection.

[0074] Step 1413: Determine the direction of coordination constraints for each link based on the operation start sequence, position coordination requirements, and action execution rhythm between the devices in the operation connection link.

[0075] In this embodiment, the bridge construction control system determines the direction of collaborative constraints for each connection link based on the operation startup sequence, positional coordination requirements, and action execution rhythm between equipment in the operation connection process. For example, the control system analyzes the operation startup sequence between equipment to determine which equipment needs to be started first and which needs to be started later; it analyzes the positional coordination requirements between equipment to determine the operating position and relative positional relationship of the equipment; and it analyzes the action execution rhythm between equipment to determine the operating speed and acceleration of the equipment. Further, based on the above analysis results, the control system determines the direction of collaborative constraints (such as temporal constraints, spatial constraints, and action constraints) for each connection link.

[0076] Step 1414: Cross-map the collaborative constraint directions of different connection links, and check whether there are logical contradictions in the collaborative constraints of the same device in different connection links. If there are contradictions, backtrack and adjust the dependency network of the nodes to reorganize the triggering conditions.

[0077] In this embodiment, the bridge construction control system cross-maps the collaborative constraint directions of different connection links to check whether there are logical contradictions in the collaborative constraints of the same equipment in different connection links (such as conflicting operation timing requirements or conflicting operation position requirements of the same equipment in different connection links). If logical contradictions exist, the control system backtracks and adjusts the dependency network of nodes to reorganize the triggering conditions and adjust the collaborative constraint directions to ensure that the collaborative constraint logic of the same equipment is consistent in different connection links.

[0078] Step 1415: Based on the corrected collaborative constraint direction, generate collaborative operation requirements covering all operational connection links.

[0079] In this embodiment, the bridge construction control system generates collaborative operation requirements covering all operational connections based on the modified collaborative constraint directions. For example, the control system organizes the collaborative constraint directions of each connection into a standardized format, including temporal constraints (such as start time, end time, and interval time of the operation), spatial constraints (such as the position coordinates and relative positional relationships of the operation), and motion constraints (such as the speed, acceleration, and force of the operation). Furthermore, the control system integrates these collaborative operation requirements to form a complete collaborative operation requirement document, which serves as input to the synchronous communication protocol.

[0080] Step 142: Perform a three-dimensional correlation mapping of time-space-action for the collaborative operation requirements of the construction equipment, mapping each collaborative operation link to a three-dimensional coordinate system to establish a one-to-one correspondence between requirements and coordinates; analyze the interaction triggering logic of each collaborative operation link in the three-dimensional coordinate system to determine the operation start trigger signal in the time dimension, the position alignment feedback signal in the spatial dimension, and the execution force synchronization signal in the action dimension; based on the interaction triggering signals, deduce the constraint content that the synchronous communication protocol needs to carry, with each constraint content corresponding to a transmission rule and interaction response logic for a trigger signal; according to the correlation matrix of the constraint content, determine whether there is a logical conflict between the transmission rules of different constraint contents, and if there is a conflict, adjust the interaction triggering order of the trigger signals; generate a synchronous communication protocol correlation constraint framework covering the time, space, and action dimensions based on the adjusted constraint content; generate a synchronous communication protocol based on the synchronous communication protocol correlation constraint framework.

[0081] In this embodiment, the bridge construction control system performs a three-dimensional correlation mapping of time-space-action for the collaborative operation requirements of construction equipment. For example, the control system maps each collaborative operation step to a three-dimensional coordinate system, where the time dimension represents the temporal sequence of operations, the spatial dimension represents the position coordinates of the operations, and the action dimension represents the execution intensity of the operations. Further, the control system establishes a one-to-one correspondence between collaborative operation requirements and three-dimensional coordinates, ensuring that each collaborative operation requirement can find its corresponding position in the three-dimensional coordinate system. Next, the control system analyzes the interactive triggering logic of each collaborative operation step in the three-dimensional coordinate system, determining the operation initiation trigger signal in the time dimension (such as a signal indicating the completion of work by one of the devices, or a signal indicating that an environmental parameter has reached a threshold), the position alignment feedback signal in the spatial dimension (such as a deviation signal between the device's position coordinates and the target position coordinates, or a signal indicating the relative positional relationship between devices), and the execution intensity synchronization signal in the action dimension (such as a deviation signal between the device's operating speed and the target speed, or a signal indicating the synchronization of operating intensity between devices).

[0082] Based on the above interactive trigger signals, the control system derives the constraints that the synchronous communication protocol needs to carry. Each constraint corresponds to a transmission rule for the trigger signal and an interactive response logic. For example, the transmission rules for the timing-dimensional operation start trigger signal include the signal transmission method, transmission frequency, and transmission format, while the interactive response logic includes the signal reception method, processing method, and feedback method.

[0083] Furthermore, based on the correlation matrix of the constraint content, the control system determines whether there are logical conflicts in the transmission rules between different constraint content (such as conflicting transmission frequencies of different trigger signals, incompatible transmission formats, etc.). If logical conflicts exist, the control system adjusts the interactive triggering order of the trigger signals to ensure logical consistency in the transmission rules between different constraint content. Finally, the control system generates a synchronous communication protocol correlation constraint framework covering temporal, spatial, and action dimensions based on the adjusted constraint content, and generates a synchronous communication protocol based on this framework.

[0084] Step 1421: Perform a three-dimensional logical decomposition of the synchronous communication protocol association constraint framework in terms of time sequence, space, and action, and decompose it into different types of logical units in each dimension.

[0085] In this embodiment, the bridge construction control system performs a three-dimensional logical decomposition of the synchronous communication protocol's associated constraint framework, considering time, space, and action. Different types of logical units are decomposed from these three dimensions. The logical units in the time dimension include the start time, end time, and interval of the operation; the logical units in the space dimension include the equipment's operating position coordinates, relative positional relationships, and operating range; and the logical units in the action dimension include the equipment's operating speed, acceleration, and force.

[0086] Step 1422: Through the interaction triggering logic between logic units of each dimension, determine that the start of the temporal dimension triggering logic unit needs to be associated with the feedback of the spatial dimension response logic unit, and that the execution of the action dimension coordination logic unit needs to match the rhythm of the temporal dimension triggering logic unit, thus generating a logical coupling link between dimensions.

[0087] In this embodiment, the bridge construction control system analyzes the interactive triggering logic between logical units of various dimensions to determine that the activation of a time-series triggering logical unit requires the feedback of a spatial-dimensional response logical unit (e.g., triggering a time-series operation activation signal when the deviation signal between the equipment's position coordinates and the target position coordinates reaches a preset threshold), and the execution of an action-dimensional coordination logical unit needs to match the rhythm of the time-series triggering logical unit (e.g., the equipment's operating speed needs to be consistent with the frequency of the time-series operation activation signal). Furthermore, based on the above interactive triggering logic, the control system generates inter-dimensional logical coupling links, which represent the interrelationships between logical units in the time, space, and action dimensions.

[0088] Step 1423: Based on the logical coupling links between the dimensions, connect the logical units of each dimension to form a constraint logic dependency network, and take each logical unit as a network node, and the interactive trigger logic as the connection relationship between the network nodes.

[0089] In this embodiment, the bridge construction control system connects logical units of each dimension to form a constraint logic dependency network based on inter-dimensional logical coupling links. For example, the control system treats each logical unit as a network node and the interaction triggering logic as the connection relationship between network nodes to construct a constraint logic dependency network. This network can completely record the mutual dependencies and interaction triggering logic between each logical unit.

[0090] Step 1424: Traverse all network node connection paths of the constraint logic dependency network. When a path branches out, track the triggering order and response relationship of the logic units under each branch and mark the logical endpoint of each branch.

[0091] In this embodiment, the bridge construction control system traverses all network node connection paths of the constraint logic dependency network, recording the triggering order and response relationship of each path. When a path branches, the control system tracks the triggering order and response relationship of the logic units under each branch, and marks the logical endpoint of each branch.

[0092] Step 1425: Extract the triggering preconditions, response time requirements and coordination order rules of each network node from the traversed constraint logic dependency network to generate a protocol interaction logic rule cluster.

[0093] In this embodiment, the bridge construction control system extracts the triggering preconditions (such as triggering a node when the output of one of its logic units reaches a preset threshold), response time requirements between nodes (such as the response time of a node must be within a preset time range), and coordination order rules (such as the triggering order of nodes must follow a preset order) for each network node from the traversed constraint logic dependency network. Furthermore, the control system organizes the above information into a protocol interaction logic rule set.

[0094] Step 1426: Connect the protocol interaction logic rule clusters in the logical order of triggering order, response relationship, and coordination rules to form an uninterrupted protocol interaction logic link; wherein, the output of each protocol interaction logic rule cluster serves as the input of the next protocol interaction logic rule cluster.

[0095] In this embodiment, the bridge construction control system connects protocol interaction logic rule clusters in a logical order of triggering sequence, response relationship, and coordination rules to form an uninterrupted protocol interaction logic link. For example, the control system uses the output of the previous protocol interaction logic rule cluster as the input of the next protocol interaction logic rule cluster, ensuring the continuity and integrity of the protocol interaction logic link.

[0096] Step 1427: Convert the uninterrupted protocol interaction logic link into signal transmission rules and response command sequences to be identified by the construction equipment to obtain the synchronous communication protocol.

[0097] In this embodiment, the bridge construction control system transforms the uninterrupted protocol interaction logic link into signal transmission rules and response command sequences that the construction equipment needs to recognize. For example, the control system transforms information such as logical units, triggering conditions, and response relationships in the protocol interaction logic link into signal formats (such as digital signals, analog signals, etc.) and command sequences (such as start commands, stop commands, adjustment commands, etc.) that the construction equipment can recognize, thus obtaining a synchronous communication protocol.

[0098] Step 143: Based on the synchronous communication protocol, determine the path adjustment tag, which includes the timing adjustment, spatial adjustment and action adjustment of the path, and adjust the spatial operation path of the construction equipment through the path adjustment tag.

[0099] In this embodiment, the bridge construction control system determines path adjustment tags based on a synchronous communication protocol. These tags include timing adjustments (such as adjusting the start and end times of equipment operations), spatial adjustments (such as adjusting the coordinates and relative positions of the equipment's operating location), and motion adjustments (such as adjusting the equipment's operating speed and acceleration). Furthermore, the control system adjusts the spatial operation path of the construction equipment through these path adjustment tags to ensure that the construction equipment operates according to the requirements of the synchronous communication protocol.

[0100] Step 144: Identify risk transmission nodes during the spatial operation path adjustment process, and coordinate the construction equipment in conjunction with the risk transmission mechanism. The coordinated control includes temporal coordinated control, spatial coordinated control, and motion coordinated control.

[0101] In this embodiment, the bridge construction control system identifies risk transmission nodes during the spatial operation path adjustment process. For example, the control system analyzes information such as the operating status of construction equipment, environmental changes, and the bridge structural status to identify potential risk factors (such as equipment operation errors, sudden environmental changes, and excessive bridge structural deformation), and marks the nodes corresponding to these risk factors as risk transmission nodes. Furthermore, the control system combines a risk transmission mechanism to perform coordinated control of the construction equipment. This mechanism analyzes the transmission path and impact range of the risk transmission nodes and takes corresponding control measures (such as adjusting the equipment's operating sequence, spatial path, and action parameters) to ensure the safety and stability of the construction process.

[0102] Step 1441: Collect the operating status of construction equipment, environmental characteristics, and path node status in real time, and generate a real-time dynamic association graph. Each edge in the real-time dynamic association graph represents the interactive association relationship between different states.

[0103] In this embodiment, the bridge construction control system collects real-time data on the operating status of construction equipment (such as operating speed, location coordinates, and working intensity), environmental characteristics (such as temperature, wind speed, and air pressure), and path node status (such as path curvature, slope, and length). Furthermore, the control system integrates this status information to generate a real-time dynamic correlation graph. Each edge in this graph represents the interactive correlation between different states (such as the correlation between the operating status of the equipment and the environmental characteristics, or the correlation between the operating status of the equipment and the path node status).

[0104] Step 1442: Identify abnormal nodes in the real-time dynamic association graph whose activation frequency or association strength deviates from the normal operation scenario, and mark the abnormal nodes as initial risk transmission nodes.

[0105] In this embodiment, the bridge construction control system identifies abnormal nodes in the real-time dynamic correlation graph where the activation frequency or correlation strength deviates from that of the normal operating scenario. For example, the control system compares the real-time collected status information with the status information under the normal operating scenario and calculates the deviation value of the activation frequency or correlation strength. If the deviation value exceeds a preset threshold, the node is marked as an initial risk transmission node.

[0106] Step 1443: Track all output interaction edges of the initial risk transmission node in the real-time dynamic association graph, and mark the transmission activation condition of each interaction edge. The transmission activation condition is: the interaction edge is activated when the state change of the association node reaches a preset logical threshold.

[0107] In this embodiment, the bridge construction control system tracks all output interaction edges of the initial risk transmission node in a real-time dynamic correlation graph and marks the transmission activation condition of each interaction edge. For example, the control system analyzes the state changes of the associated nodes corresponding to each interaction edge and determines that the interaction edge is activated when the state change of the associated node reaches a preset logical threshold (such as the equipment's operating speed exceeding the maximum allowable speed, or the ambient temperature exceeding the maximum allowable temperature).

[0108] Step 1444: Determine the blocking priority of the cooperative control based on the conduction activation condition. The blocking priority is used to block the risk conduction interaction edge with the highest weight that satisfies the conduction activation condition.

[0109] In this embodiment, the bridge construction control system determines the blocking priority of collaborative control based on the conduction activation condition. For example, the control system sorts the risk conduction interaction edges according to the satisfaction weights of the conduction activation conditions (such as the severity of the risk, the probability of occurrence, etc.), and sets the blocking priority of the risk conduction interaction edge with the highest satisfaction weight to the highest.

[0110] Step 1445: Send a collaborative control command to the construction equipment according to the blocking priority, adjust the operation status of the construction equipment to cut off the risk transmission interaction edge, and update the real-time dynamic correlation graph in real time. Iteratively adjust the control command according to the changes in the interaction relationship in the real-time dynamic correlation graph.

[0111] In this embodiment, the bridge construction control system sends collaborative control commands to the construction equipment according to the blocking priority, adjusting the operating status of the construction equipment (such as stopping the equipment's operation, reducing the equipment's operating speed, adjusting the equipment's operating position, etc.) to cut off the risk transmission interaction edge. Furthermore, the control system updates the real-time dynamic correlation graph in real time, iteratively adjusting the control commands based on changes in the interaction relationships in the graph to ensure the safety and stability of the construction process.

[0112] After implementing coordinated control of the construction equipment based on the risk transmission mechanism, the method further includes: Step 150: Process the real-time collected bridge structural status data, construction equipment operation data, and extreme environment characteristic data after collaborative control to obtain multi-source fusion data; extract the structural deformation deviation features, equipment path deviation features, and environmental impact deviation features from the multi-source fusion data and the bridge construction simulation results output by the digital twin model through dimension-by-dimensional deviation feature extraction; based on the correlation between the structural deformation deviation features, equipment path deviation features, and environmental impact deviation features, generate collaborative deviation features that characterize the collaborative relationship between structure, equipment, and environment; integrate the collaborative deviation features into the real-time constraints of the correlation optimization model to update the dynamic interaction relationship of the structure-environment cross-correlation logical chain in the correlation optimization model; based on the updated structure-environment cross-correlation logical chain, adjust the virtual-real mapping relationship between the bridge structure, construction equipment, and extreme environment characteristic items in the digital twin model to obtain the updated digital twin model; use the updated digital twin model as the base model for the next bridge construction simulation to output new bridge construction simulation results.

[0113] In this embodiment of the application, after the bridge construction control system performs collaborative control of the construction equipment based on the risk transmission mechanism, it processes the real-time collected collaborative control data of the bridge structure status, construction equipment operation data and extreme environment characteristic data, such as data cleaning, deduplication and normalization, to obtain multi-source fused data.

[0114] Furthermore, the control system extracts deviation features from the bridge construction simulation results output by the multi-source fusion data and the digital twin model in a dimension-by-dimensional manner, and obtains structural deformation deviation features (such as the deviation between the actual deformation and the simulated deformation of the bridge structure), equipment path deviation features (such as the deviation between the actual operation path and the simulated operation path of the construction equipment), and environmental impact deviation features (such as the deviation between the actual impact and the simulated impact of environmental factors on the bridge structure).

[0115] Next, based on the correlation between the above deviation characteristics, the control system generates collaborative deviation characteristics representing the collaborative relationship between structure, equipment, and environment. Further, the control system integrates these collaborative deviation characteristics into the real-time constraints of the correlation optimization model, updating the dynamic interaction relationships of the structure-environment cross-correlation logical chains in the model. Based on the updated structure-environment cross-correlation logical chains, the control system adjusts the virtual-real mapping relationship between the bridge structure, construction equipment, and extreme environment feature terms in the digital twin model, obtaining an updated digital twin model. Finally, the control system uses the updated digital twin model as the base model for the next bridge construction simulation, outputting new bridge construction simulation results.

[0116] As can be seen, the embodiments of this application achieve dynamic iteration of the digital twin model and the associated optimization model through multi-source data fusion and closed-loop feedback mechanism: first, integrate the real data of the structure, equipment and environment after construction, compare the simulation results to extract multi-dimensional deviations and generate collaborative deviation features; then, integrate them into the constraints of the associated optimization model and update the structure-environment interaction logic; finally, adjust the virtual-real mapping relationship of the digital twin in reverse. This process can continuously correct the deviation between the model and the actual scenario, improve the accuracy of construction simulation, provide a more realistic decision-making basis for the dynamic optimization of bridge construction in extreme environments, and ensure the safety and adaptability of the construction process.

[0117] After the collaborative control of the construction equipment based on the risk transmission mechanism, the method further includes: real-time acquisition of dynamic fluctuation data of extreme environmental characteristics; trend decomposition processing of the dynamic fluctuation data to separate linear trend features and nonlinear mutation features; extraction of precursor fluctuation features from the nonlinear mutation features to obtain environmental mutation precursor features; inputting the environmental mutation precursor features into an association optimization model to deduce potential bridge structural state change trends and construction configuration item deviations through a structure-environment cross-association logic chain; generating construction configuration pre-adjustment instructions based on potential bridge structural state change trends and construction configuration item deviations, the construction configuration pre-adjustment instructions including operation timing adjustment rules, spatial path pre-correction rules, and collaborative response rules for the construction equipment; inputting the construction configuration pre-adjustment instructions into a synchronous communication protocol to convert them into protocol-compatible pre-adjustment signals to be identified by the construction equipment; sending the protocol-compatible pre-adjustment signals to the construction equipment; analyzing the transmission impact of the pre-adjustment signals between construction equipment in conjunction with the risk transmission mechanism to obtain a collaborative execution feasibility label for the protocol-compatible pre-adjustment signals; if the collaborative execution feasibility label indicates that the collaborative execution feasibility is passed, then controlling the construction equipment to execute the protocol-compatible pre-adjustment signals; otherwise, re-deriving the potential bridge structural state change trends and construction configuration item deviations.

[0118] In this embodiment, after the bridge construction control system performs coordinated control of construction equipment based on a risk transmission mechanism, it collects dynamic fluctuation data of extreme environmental characteristics (such as real-time monitoring data of temperature, wind speed, and air pressure) in real time. Further, the control system performs trend decomposition processing on the dynamic fluctuation data, separating linear trend characteristics (such as the slow change trend of environmental factors over time) from nonlinear abrupt change characteristics (such as sudden changes in environmental factors). Then, the control system extracts precursor fluctuation characteristics (such as abnormal fluctuations of environmental factors before the abrupt change) from the nonlinear abrupt change characteristics to obtain the precursor characteristics of environmental abrupt change.

[0119] Then, the control system inputs the precursor features of environmental abrupt changes into the correlation optimization model, and derives potential trends in bridge structural state changes (such as deformation trends and stress trends) and deviations in construction configuration items (such as operational timing deviations and spatial path deviations of construction equipment) through the structure-environment cross-correlation logic chain. Based on the above derivation results, the control system generates pre-adjustment instructions for construction configuration. These instructions include rules for adjusting the operational timing of construction equipment (such as adjusting the start and end times of equipment operations), pre-correction rules for spatial paths (such as adjusting the coordinates and relative positions of equipment operations), and collaborative response rules (such as rules for coordination and information exchange between equipment operations).

[0120] Furthermore, the control system inputs the construction configuration pre-adjustment command into the synchronous communication protocol, converting it into a protocol-compatible pre-adjustment signal to be recognized by the construction equipment. Next, the control system sends the protocol-compatible pre-adjustment signal to the construction equipment, analyzing the transmission impact of the pre-adjustment signal between the construction equipment (such as the impact of the pre-adjustment signal on the equipment's operating status and on the construction process) using a risk transmission mechanism, and obtains a feasibility label for the collaborative execution of the protocol-compatible pre-adjustment signal. If the feasibility label indicates that collaborative execution is feasible, the control system controls the construction equipment to execute the protocol-compatible pre-adjustment signal; otherwise, the control system re-derives the potential trends in bridge structural state changes and deviations in construction configuration items, adjusting the construction configuration pre-adjustment command until the feasibility label indicates that collaborative execution is feasible.

[0121] By applying the above embodiments, proactive control of extreme environmental mutations can be achieved: by collecting environmental fluctuation data in real time, separating linear trends from nonlinear mutations, and extracting precursor features; by combining correlation optimization models to deduce potential structural risks and construction deviations, generating pre-adjustment instructions and converting them into equipment-compatible signals; and finally, by verifying feasibility to realize pre-control execution, this process transforms passive response into proactive early warning, avoids the impact of environmental mutations on construction in advance, reduces work stoppages or safety hazards caused by sudden environmental interference, and improves the risk resistance and continuity of bridge construction in extreme environments.

[0122] This application embodiment extracts structured and unstructured data from bridge construction task reports to generate a digital twin model containing the virtual-real mapping relationship between bridge structure, construction equipment, and extreme environment features. Combined with the bridge construction simulation results output by this digital twin model, and linked with an association optimization model constructed based on bridge structure and extreme environment features, the adaptive adjustment results of construction configuration items are determined. Furthermore, based on these adjustment results, the synchronous communication protocol of the construction equipment is determined to adjust the equipment's spatial operation path. Simultaneously, during the path adjustment process, collaborative control of the construction equipment is achieved based on a risk transmission mechanism. This forms a full-chain collaborative control system: data-driven twin modeling, simulation result-guided optimization, configuration adjustment and protocol adaptation, path optimization, and risk collaborative control. This overcomes the technical bottlenecks of traditional bridge construction where digital twin models only focus on single-dimensional mapping, association optimization is disconnected from simulation results, and equipment control lacks protocol adaptation and risk transmission linkage. It achieves deep coupling mapping of multiple elements of bridge structure, construction equipment, and extreme environment, improving the accuracy and dynamic adaptability of construction configuration adjustments, enhancing the optimization efficiency of construction equipment's spatial operation path, and improving the safety of collaborative control, thereby achieving stability and reliability in bridge construction under extreme environments.

[0123] Please see Figure 2The figure is a schematic diagram of the basic structure of a bridge construction control system 200 provided in an embodiment of this application. The bridge construction control system 200 includes: a processor 201; a storage device 202 on which a computer program 2020 is stored; and a network interface 203 for providing network communication functions. When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the bridge construction control methods based on extreme environments.

[0124] Please see Figure 3 This application provides a functional block diagram of a bridge construction control device, which includes: The digital twin module is used to generate a digital twin model containing the virtual-real mapping relationship between bridge structure, construction equipment and extreme environment features based on structured and unstructured data extracted from bridge construction task reports, and outputs bridge construction simulation results through the digital twin model; The model building module is used to build an associated optimization model based on the bridge structure and the extreme environment features. An adaptive adjustment module is used to determine the adaptive adjustment result of the construction configuration item through the correlation optimization model, using the bridge construction simulation results as a reference. The collaborative control module is used to determine the synchronous communication protocol of the construction equipment based on the adaptive adjustment result, adjust the spatial operation path of the construction equipment through the synchronous communication protocol, and perform collaborative control of the construction equipment based on the risk transmission mechanism during the adjustment of the spatial operation path.

[0125] Based on the above, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the above method are implemented.

[0126] Furthermore, it should be noted that this application also provides a computer program product, which may include a computer program that can be stored in a computer-readable storage medium. The processor of the bridge construction control system reads the computer program from the computer-readable storage medium, and the processor can execute the computer program, causing the bridge construction control system to perform the aforementioned... Figure 1 The methods described in the corresponding embodiments are already known, and therefore will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated. For technical details not disclosed in the computer program product embodiments related to this application, please refer to the description of the method embodiments of this application.

[0127] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

Claims

1. A bridge construction control method based on extreme environments, characterized in that, The method includes: A digital twin model is generated based on structured and unstructured data extracted from bridge construction task reports. This model includes a virtual-to-real mapping relationship between bridge structure, construction equipment, and extreme environment features. The bridge construction simulation results are then output through the digital twin model. The structured data includes bridge design parameters, construction equipment models and performance parameters, and historical monitoring data of the extreme environment. The unstructured data includes textual descriptions of the construction plan, bridge structure design drawings, construction equipment operation manuals, and on-site survey reports of the extreme environment. A correlation optimization model is constructed based on the bridge structure and the extreme environment characteristics. Using the bridge construction simulation results as a reference, the adaptive adjustment results of the construction configuration items are determined through the correlation optimization model; Based on the adaptive adjustment results, the synchronous communication protocol of the construction equipment is determined, and the spatial operation path of the construction equipment is adjusted through the synchronous communication protocol. During the adjustment of the spatial operation path, the construction equipment is coordinated and controlled based on the risk transmission mechanism. The construction of the correlation optimization model based on the bridge structure and the extreme environmental features includes: hierarchically decomposing the structural components of the bridge structure and extracting structural correlation clues at different levels, the structural correlation clues including the connection relationships, force transmission relationships, and assembly dependency relationships between components; performing scenario-based mapping on the extreme environmental features and extracting environmental correlation clues under different construction scenarios, the environmental correlation clues including the distribution relationships, change trends, and impact relationships of environmental elements on the structure; performing cross-correlation processing on the structural correlation clues and the environmental correlation clues to obtain a structure-environment cross-correlation logical chain, the cross-correlation logical chain being used to reflect the dynamic interaction relationship between structural state changes and environmental feature fluctuations; obtaining real-time constraints on structural state changes during bridge construction, the real-time constraints including structural strength requirements, environmental change adaptation requirements, and construction action interference requirements during the construction phase; and integrating the real-time constraints into the structure-environment cross-correlation logical chain to obtain a correlation optimization model, the correlation optimization model being used to reflect the dynamic correlation relationship between the bridge structural state, extreme environmental feature changes, and construction process constraints. The process involves determining the synchronous communication protocol for the construction equipment based on the adaptive adjustment results, adjusting the spatial operation path of the construction equipment using the synchronous communication protocol, and coordinating the control of the construction equipment based on a risk transmission mechanism during the adjustment process. This includes: extracting the collaborative operation requirements of the construction equipment from the adaptive adjustment results; performing a three-dimensional correlation mapping of the collaborative operation requirements of the construction equipment—time-space-action—mapping each collaborative operation link to a three-dimensional coordinate system and establishing a one-to-one correspondence between requirements and coordinates; analyzing the interactive triggering logic of each collaborative operation link in the three-dimensional coordinate system, determining the operation initiation trigger signal in the time dimension, the position alignment feedback signal in the spatial dimension, and the execution force synchronization signal in the action dimension; and deriving the constraints that the synchronous communication protocol needs to carry based on the interactive trigger signals. Each constraint corresponds to a transmission rule and interactive response logic for a trigger signal. Based on the correlation matrix of the constraint content, it is determined whether there are logical conflicts in the transmission rules between different constraint contents. If conflicts exist, the interactive triggering order of the trigger signals is adjusted. A synchronous communication protocol-related constraint framework covering temporal, spatial, and action dimensions is generated based on the adjusted constraint content. A synchronous communication protocol is generated based on the synchronous communication protocol-related constraint framework. Path adjustment tags, including temporal, spatial, and action adjustments, are determined based on the synchronous communication protocol. The spatial operation path of the construction equipment is adjusted using these path adjustment tags. During the spatial operation path adjustment process, risk transmission nodes are identified, and the construction equipment is collaboratively controlled using a risk transmission mechanism. This collaborative control includes temporal collaborative control, spatial collaborative control, and action collaborative control. The process of identifying risk transmission nodes during the spatial operation path adjustment and coordinating the control of the construction equipment using a risk transmission mechanism includes: real-time acquisition of the construction equipment's operating status, environmental characteristics, and path node status, generating a real-time dynamic correlation graph, where each edge represents an interactive relationship between different states; identifying abnormal nodes in the real-time dynamic correlation graph whose activation frequency or correlation strength deviates from the normal operating scenario, and marking these abnormal nodes as initial risk transmission nodes; tracking all output interaction edges of the initial risk transmission nodes in the real-time dynamic correlation graph, marking the transmission activation condition for each interaction edge, where the interaction edge is activated when the state change of the associated node reaches a preset logical threshold; determining the blocking priority of the collaborative control based on the transmission activation condition, where the blocking priority is used to block the risk transmission interaction edge with the highest weight in satisfying the transmission activation condition; sending collaborative control commands to the construction equipment according to the blocking priority, adjusting the operating status of the construction equipment to cut off the risk transmission interaction edge, and updating the real-time dynamic correlation graph in real time, iteratively adjusting the control commands based on changes in the interaction relationships in the real-time dynamic correlation graph.

2. The bridge construction control method based on extreme environments as described in claim 1, characterized in that, The step of using the bridge construction simulation results as a reference and determining the adaptive adjustment results of the construction configuration items through the correlation optimization model includes: The bridge construction simulation results are decomposed in multiple dimensions to extract structural state simulation information, environmental feature simulation information, and construction action simulation information. The structural state simulation information includes the deformation state, connection state, and stress state of the components; the environmental feature simulation information includes the real-time state, change state, and influence state of environmental elements; and the construction action simulation information includes the operation state, timing state, and coordination state of the equipment. The structural state simulation information, the environmental feature simulation information, and the construction action simulation information are respectively mapped to the corresponding dimensions of the correlation optimization model, and the correlation logic branches corresponding to each simulation information in the correlation optimization model are extracted. Based on the deviation information between the bridge construction simulation results and the associated logic branches, the initial associated nodes that generate the deviations are located. The initial associated nodes include structural state deviation nodes, environmental feature deviation nodes, and construction action deviation nodes. Trace the propagation path of the initial associated node in the associated logic branch, analyze the propagation relationship of the deviation between different associated nodes, and deduce the critical path of deviation propagation; Based on the critical path of the deviation propagation, the adjustment association nodes of the construction configuration items are reversed, and the adaptive adjustment results of the construction configuration items are generated. The adaptive adjustment results include the logical order, adjustment direction and adjustment range of the adjustment association nodes.

3. The bridge construction control method based on extreme environments as described in claim 2, characterized in that, The step of mapping the structural state simulation information, the environmental feature simulation information, and the construction action simulation information to the corresponding dimensions of the correlation optimization model, and extracting the correlation logic branches in the correlation optimization model corresponding to each simulation information, includes: The structural state simulation information of the bridge construction simulation results is decomposed into component-level, connection-level, and overall-level structural state simulation sub-information. The environmental feature simulation information is divided into scene sub-information, static environment simulation sub-information, dynamic environment simulation sub-information, and interference environment simulation sub-information. The construction action simulation information is split into time sequence to obtain initial action simulation sub-information, process action simulation sub-information and final action simulation sub-information. The decomposed structural state simulation sub-information, environmental feature simulation sub-information, and construction action simulation sub-information are matched with the hierarchical nodes of the associated optimization model to generate a mapping matrix between simulation information and hierarchical nodes. Based on the mapping matrix, an associated logical branch covering all simulated sub-information is extracted. The associated logical branch contains the hierarchical nodes corresponding to each simulated sub-information and the association relationships between the nodes. The step of tracing the propagation path of the initial associated node in the associated logical branch includes: The deviation information of the bridge construction simulation results is classified in multiple dimensions to obtain structural state deviation, environmental characteristic deviation, and construction action deviation. For each type of deviation, the node hierarchy of the associated logic branch is traversed, and the node is traced back from the bottom node to locate the initial associated node that caused the deviation. The input and output logic of the initial associated node is then judged to conform to the preset rules of the association model. Based on the dependency relationship between the initial associated node and its adjacent associated nodes, the first dependent node and the second dependent node are determined. The first dependent node is the node in which the initial node cannot output normally without it, and the second dependent node is the node that only affects the output accuracy of the initial node. Based on the dependency relationship, the priority direction of deviation propagation is determined, and the propagation path of the first dependent node is tracked according to the priority direction while the propagation probability of the second dependent node is recorded simultaneously. Track the propagation process of deviation in the associated logical branches, record the activation state of each node, and when the propagation path branches out, track the activation order of the nodes in each branch and mark the propagation endpoint of each branch. After integrating the transmission paths of all branches, check whether there are logical conflicts between the transmission paths. If there are conflicts, backtrack the dependencies of the initial associated nodes and re-analyze them. After correcting the conflicts, the transmission paths of the deviations in the associated logical branches are formed.

4. The bridge construction control method based on extreme environments as described in claim 1, characterized in that, The extraction of collaborative operation requirements for construction equipment from the adaptive adjustment results includes: The adjustment logic chain of the adaptive adjustment result is decomposed in reverse, the generation basis of the adjustment instruction is traced node by node, the pre-adjustment triggering conditions and post-adjustment influence range of each adjustment node are extracted, and the dependency relationship network of the adjustment nodes is generated. Based on the dependency network, the construction equipment operation tasks corresponding to each adjustment node are matched, and the equipment sequence of task execution is tracked to identify the operation connection links that require multiple devices to execute in parallel or serially. Based on the operation start sequence, position coordination requirements, and action execution rhythm between devices in the operation connection link, determine the direction of collaborative constraints for each connection link; Cross-mapping the directions of collaborative constraints in different connection links is performed to detect whether there are logical contradictions in the collaborative constraints of the same device in different connection links. If there are contradictions, the dependency network of the nodes is backtracked and the triggering conditions are reorganized. Based on the revised collaborative constraint direction, collaborative operation requirements covering all operational connection links are generated.

5. The bridge construction control method based on extreme environments as described in claim 1, characterized in that, The step of generating a synchronization communication protocol based on the associated constraint framework of the synchronization communication protocol includes: The synchronous communication protocol's associated constraint framework is decomposed into a three-dimensional logical structure of time, space, and action, resulting in different types of logical units in each dimension. By using the interaction trigger logic between logical units of each dimension, it is determined that the activation of the temporal dimension triggering logical unit needs to be associated with the feedback of the spatial dimension response logical unit, and the execution of the action dimension coordination logical unit needs to match the rhythm of the temporal dimension triggering logical unit, thus generating logical coupling links between dimensions. Based on the logical coupling links between the dimensions, the logical units of each dimension are linked together to form a constraint logic dependency network, with each logical unit as a network node and the interactive triggering logic as the connection relationship between the network nodes. Traverse all network node connection paths of the constraint logic dependent network. When a path branches, track the triggering order and response relationship of the logic units under each branch and mark the logical endpoint of each branch. Extract the triggering preconditions, response time requirements and coordination order rules of each network node from the traversed constraint logic dependency network to generate a protocol interaction logic rule set; The protocol interaction logic rule clusters are connected in series according to the logical order of triggering order, response relationship, and coordination rules to form an uninterrupted protocol interaction logic link; wherein, the output of each protocol interaction logic rule cluster serves as the input of the next protocol interaction logic rule cluster. The uninterrupted protocol interaction logic link is transformed into signal transmission rules and response command sequences to be identified by the construction equipment, thus obtaining a synchronous communication protocol.

6. A bridge construction control system, characterized in that, include: A processor; a storage device having a computer program stored thereon; a network interface for providing network communication functions; when the computer program is executed by the processor, the processor enables the processor to implement the bridge construction control method based on extreme environments as described in any one of claims 1-5.

7. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the bridge construction control method based on extreme environments as described in any one of claims 1-5.

Images