Method and system for constructing digital twinborn digital intelligence of paving and erecting all-in-one machine

Abstract

The invention relates to the technical field of bridge construction monitoring, and provides a digital twinborn digital-intelligent construction method and system for paving and erecting all-in-one machines. Under the construction operation working condition that a PK type section steel beam and a concrete prefabricated bridge deck slab are overlapped later, according to obtained construction monitoring sensing information of all the paving and erecting all-in-one machines in the main bridge construction process, the construction monitoring sensing information of all the paving and erecting all-in-one machines is obtained; oPC data is integrated to form logic and physical information and construction environment data information of the laying and erecting all-in-one machine, and modular association simulation driving is used for conducting three-dimensional simulation on main beam construction and associating construction monitoring data information through PLC programming data processing. The working posture and physical information state matching of each simulation model and dynamic construction simulation of the construction environment are completed, digital twin digital-intelligent construction of actual physical data is achieved, digital-intelligent development of bridge construction is improved, and the problem that in the prior art, under the PK type steel beam and prefabricated bridge deck slab post-overlapping asynchronous construction condition, the construction efficiency is greatly improved is solved. And the intelligent safety monitoring technology of the construction equipment and the steel beam structure is immature.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge construction and monitoring, and in particular to a digital twin intelligent construction method and system for an integrated laying and erection machine. Background Art

[0002] With the continuous expansion of the construction scale of modern cross-river and cross-sea bridges, long-span cable-stayed bridges mostly adopt steel-concrete composite girders with good span capacity, and carry out the construction of cross-river and cross-sea cable-stayed bridges by means of suspended assembly hoisting with a deck crane. The self-weight of the steel-concrete composite girder will directly determine the lifting capacity of the deck crane, directly affect the steel structure reinforcement of the main parts of the beam segment and the division length of the main bridge segment, and restrict the overall construction progress of the main girder. To reduce the self-weight of the deck crane and the main girder segment, the construction method of post-overlapping a steel beam and a precast concrete bridge deck can be adopted for operation. By hoisting the main girder and the bridge deck in stages, the self-weight requirements of the mechanical equipment and the main girder structure are reduced, and thus the overall control of the material construction input and the construction period is strengthened.

[0003] In the prior art, for the post-overlapping suspended assembly construction of the main bridge of a cross-river or cross-sea cable-stayed bridge, existing conventional equipment such as deck cranes and slewing cranes can complete the laying of N sections of bridge decks after hoisting N sections of girders under their own designed functions or with the assistance of truck cranes. However, such deck cranes are designed with a large weight, have a low overall work efficiency, and a long construction period. As a large-scale lifting equipment, the bridge erection machine bears the huge load of hoisting the beam slices. The acquisition and analysis of the structural stress state of the bridge erection machine are important guarantee measures to ensure the safe operation of the bridge erection machine. The structural monitoring of the conventional processes of the deck crane has been comprehensively applied, but the prior art has not formed a complete construction and monitoring process for the asynchronous construction of the steel beam hoisting and the bridge deck paving in different segments.

[0004] In the prior art, for the post-overlapping suspended assembly construction of the main bridge of a cross-river or cross-sea cable-stayed bridge, a channel steel box girder is mostly used as the main girder structure. At home and abroad, the control of the post-overlapping construction technology of the PK steel girder is poor, and the maturity of the construction monitoring early warning and deduction early warning technology is poor. Moreover, in the existing construction monitoring technology, the main basic information data needs to be extracted and judged manually, and the model analysis needs to be updated by manual real-scene modeling, which has a huge system operation and maintenance cost and time investment, is not conducive to the high-speed and intelligent development of bridge digital twin intelligent construction, and the existing construction monitoring technology cannot reliably realize monitoring early warning and deduction early warning. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a digital twin intelligent construction method and system for an integrated laying and erection machine to overcome the above problems.

[0006] One aspect of the present invention provides a digital twin intelligent construction method for a combined erection and paving machine, which is used for the construction operation condition of the post - laminated PK - type section steel girder and precast concrete bridge deck. The hoisting construction of the PK - type section steel girder and the paving construction of the precast concrete bridge deck are carried out simultaneously, and the construction areas are located in different segment areas, including: Pre - construct a construction structure model, a GIS real - scene site model, a construction equipment simulation model, and a construction dynamic information model; Under the construction operation condition of the post - laminated PK - type section steel girder and precast concrete bridge deck, through the Internet of Things devices set at the key points of each combined erection and paving machine, obtain the construction monitoring and sensing information of each combined erection and paving machine during the main bridge construction process. The construction monitoring and sensing information includes beam segment station information, stress monitoring information, internal force information of the steel girder structure, construction site environment information, equipment inclination information, equipment attitude information, and equipment positioning information; Transmit the construction monitoring and sensing information to the OPC server for OPC data integration to form the logical, physical information, and construction environment data information of the combined erection and paving machine, and transmit the OPC data integration to the PLC programmable logic controller; Through the PLC programmable logic controller, complete the information extraction and processing of the OPC data integration, and respectively feedback the obtained logical, physical information, and construction environment data information to the construction structure model, GIS real - scene site model, construction equipment simulation model, and construction dynamic information model, complete the matching of the working postures and physical information states of each simulation model and the dynamic construction simulation of the construction environment, and realize the digital twin intelligent construction of actual physical data.

[0007] Furthermore, the method further includes: Perform data processing and analysis on the construction monitoring and sensing information under the current stage construction operation condition to form a construction simulation of the post - laminated PK - type section steel girder and precast concrete bridge deck. Combine the construction simulation posture and the measured construction monitoring and sensing information under the next stage construction operation condition, and perform the deduction and prediction of the safety warning for the next stage construction operation.

[0008] Furthermore, during the deduction and prediction of the safety warning for the next stage construction operation, adjust the comparison threshold range of different alarm levels of various safety warnings according to the deviation inertia ratio of the data to eliminate the monitoring error caused by the cumulative loss of components.

[0009] Furthermore, the deduction and prediction of the safety warning for the next stage construction operation includes: The deep learning model of the neural network takes the current physical state of the erection and laying integrated machine and the bridge deck beam slabs to be installed next as the analysis object. According to the hoisting positions of each bridge deck beam slab, the positions of the erection and laying integrated machine, and the stroke data of the moving cylinders, it is obtained whether the position of the equipment and the cylinder stroke meet the requirements for the docking of the beam slabs during the installation process at the current position and state. According to the current attitude of the erection and laying integrated machine and the form of the beam slabs to be installed next, calculate and predict the inclination and deformation states that the erection and laying integrated machine will generate according to stress and structural strain, so as to realize the preliminary judgment of the operation stability of the equipment.

[0010] Furthermore, the method further includes: Obtain the construction monitoring image information of the main bridge; Remotely transmit the construction monitoring image information to the PLC programmable logic controller through the server streaming media application; Actively identify high-altitude falls, fires, and unsafe behaviors of operators in the construction monitoring image information through a preset hazard intelligent identification algorithm.

[0011] Furthermore, the information extraction for completing OPC data integration through the PLC programmable logic controller includes: Clean and filter the construction monitoring sensor information in the OPC data integration, and eliminate deviation-overlimit and non-standard data to eliminate data differences; Synchronously integrate multi-source data through the sensor data interaction strategy to achieve data classification and labeling, ensure the standard consistency of the data, and perform smoothing processing on various types of data respectively through the Kalman filter algorithm, obtain the optimal estimated values of the corresponding data and extract them.

[0012] Furthermore, the information processing for completing OPC data integration through the PLC programmable logic controller includes: Analyze the actual operation state of the erection and laying integrated machine according to the beam segment position information, equipment inclination information, equipment attitude information, and equipment positioning information of the erection and laying integrated machine. By comparing and analyzing the actual operation state of the erection and laying integrated machine with the preset design operation state, realize the safety control and real-time warning of the equipment operation state.

[0013] Furthermore, the obtained logical, physical information, and construction environment data information are respectively fed back to the construction structure model, GIS real-scene site model, construction equipment simulation model, and construction dynamic information model to complete the matching of the working postures and physical information states of each simulation model and the dynamic construction simulation of the construction environment, including: Create model attribute parameters that match the data collected at the construction site for the construction structure model, GIS real - scene site model, construction equipment simulation model, and construction dynamic information model respectively. Through the fusion and correspondence of data and model attribute parameters, realize the digital twin application of actual physical data; Associate the construction dynamic information model with production management data, and associate construction progress information, precast beam - slab process acceptance data, and bridge structure segmented installation team information with the model to achieve multi - dimensional digital twin of the construction dynamic information model; Update and create the actual working scene of bridge construction through satellite three - dimensional map image data and construction environment data information, and realize the automatic driving adjustment of the GIS real - scene site model with the change of satellite images.

[0014] Another aspect of the present invention provides a digital twin intelligent construction system for a combined erection and paving machine, which is used for the construction operation condition of the post - superimposed PK - type section steel beam and precast concrete bridge deck. The hoisting construction of the PK - type section steel beam and the paving construction of the precast concrete bridge deck are carried out simultaneously and the construction areas are located in different segment areas. The system includes functional modules for realizing the digital twin intelligent construction method of the combined erection and paving machine as described in any one of the above, specifically including: A simulation model construction module for pre - constructing a construction structure model, a GIS real - scene site model, a construction equipment simulation model, and a construction dynamic information model; A structure monitoring module for obtaining the construction monitoring sensing information of each combined erection and paving machine during the main bridge construction process through Internet of Things devices set at key points of each combined erection and paving machine under the construction operation condition of the post - superimposed PK - type section steel beam and precast concrete bridge deck. The construction monitoring sensing information includes beam segment station information, stress monitoring information, internal force information of the steel beam structure, construction site environment information, equipment inclination information, equipment attitude information, and equipment positioning information; A data transmission module for transmitting the construction monitoring sensing information to an OPC server for OPC data integration to form the logical, physical information, and construction environment data information of the combined erection and paving machine, and transmitting the OPC data integration to a PLC programmable logic controller; A construction linkage digital twin module for extracting and processing the information of the OPC data integration through the PLC programmable logic controller, and respectively feeding back the obtained logical, physical information, and construction environment data information to the construction structure model, GIS real - scene site model, construction equipment simulation model, and construction dynamic information model, completing the matching of the working postures and physical information states of each simulation model and the dynamic construction simulation of the construction environment, and realizing the digital twin intelligent construction of actual physical data.

[0015] Furthermore, the system further includes: The construction simulation analysis module is used to process and analyze the construction monitoring sensor information under the construction operation conditions of the current stage to form a construction simulation of the post - superimposed construction of the PK - type section steel beam and the precast concrete bridge deck. Combining the construction simulation attitude and the measured construction monitoring sensor information under the construction operation conditions of the next stage, it performs the deduction and prediction of the safety warning for the construction operation of the next stage.

[0016] The digital twin intelligent construction method and system of the erection and laying integrated machine provided by the embodiment of the present invention, under the construction operation conditions of the post - superimposed construction of the PK - type section steel beam and the precast concrete bridge deck, according to the construction monitoring sensor information of each erection and laying integrated machine obtained during the main bridge construction process, and forming the logic, physical information and construction environment data information of the erection and laying integrated machine through OPC data integration, uses modular association simulation drive through PLC programming data processing to perform three - dimensional simulation of the main beam construction and associate construction monitoring data information, completes the matching of the working attitude and physical information state of each simulation model and the dynamic construction simulation of the construction environment, realizes the digital twin intelligent construction of actual physical data, and promotes the intelligent development of bridge construction.

[0017] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically given below. Brief Description of the Drawings

[0018] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. In the drawings: Figure 1 It is a schematic structural diagram of the bridge site working condition system in a digital twin intelligent construction method of an erection and laying integrated machine according to an embodiment of the present invention; Figure 2 It is a flowchart of a digital twin intelligent construction method of an erection and laying integrated machine according to an embodiment of the present invention; Figure 3 It is a flowchart of a digital twin intelligent construction method of an erection and laying integrated machine according to another embodiment of the present invention; Figure 4 It is a schematic diagram of the electronic information transmission structure of a digital twin intelligent construction system of an erection and laying integrated machine according to an embodiment of the present invention; Figure 5 It is a schematic structural diagram of a digital twin intelligent construction system of an erection and laying integrated machine according to an embodiment of the present invention. Detailed Embodiments

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0020] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms used herein (including technical terms and scientific terms) have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless specifically defined.

[0021] An embodiment of the present invention provides a digital twin intelligent construction method for a combined erection and paving machine, which is used for the construction operation condition of the post - superimposed combination of a PK - type section steel girder and a precast concrete bridge deck. The hoisting construction of the PK - type section steel girder and the paving construction of the precast concrete bridge deck are carried out simultaneously, and the construction areas are located in different segment areas, such as Figure 1 As shown, the bridge site condition system in this embodiment covers key technical process control components such as a combined erection and paving machine, the hoisting of N - section steel girders, the paving of precast bridge decks in the N - 2 section, and the predicted hoisting of N - section steel girders. As Figure 2 As shown, the digital twin intelligent construction method for a combined erection and paving machine proposed by the present invention includes the following steps: S11. Pre - construct a construction structure model, a GIS real - scene site model, a construction equipment simulation model, and a construction dynamic information model.

[0022] The simulation model in this embodiment includes a construction structure model, a GIS real-scene site model, a construction equipment simulation model, and a construction dynamic information model, where: The construction structure model, based on various modeling software, creates a BIM model of the complex curved surface main tower structure of the bridge, various forms of PK steel beams, various forms of precast bridge deck slabs, the wet joint structure and reinforcement of the bridge deck, and the bridge protection structure during the construction process, and creates a conversion and recognition module for various format models to complete the integration of the overall building model. The GIS real-scene site model generates a model of the surrounding site environment by means of drone oblique photography technology; for the drone oblique photography modeling of the wide-area water surface, through the front-end data processing of image format, exposure rate, and noise reduction balance, and the back-end data processing parameter module of regional multi-point adjustment and multi-point image matching according to the water area width, accurate and efficient modeling of the wide-area water surface is realized. Further, the satellite three-dimensional map image data is updated and the open-source data of the reservoir water level monitoring is used to automatically identify and obtain the environment, water level, water flow velocity, flow rate, and the positioning information of the 10-nautical-mile main beam transportation at the bridge site, and form a dynamic simulation of the surrounding environment model during the bridge construction process. The construction equipment simulation model includes the structural models of the entire set of equipment such as the structure and track of the new type of bridge deck beam and slab laying and erection machine, winches, jacks, and pump stations, and assigns dynamic measured physical property parameters to the structural models to realize the operation state simulation of the laying and erection machine with data-model interaction. The construction dynamic information model includes associating construction progress attributes and construction project quantity attributes based on the building structure model, superimposing production data information and process quality control information on precast components, and realizing the multi-dimensional information integration of the BIM model.

[0023] S12. Under the construction operation condition of the post-overlapping of the PK-type cross-section steel beam and the precast concrete bridge deck, the construction monitoring and sensing information of each laying and erection machine during the main bridge construction is obtained through the Internet of Things devices set at the key points of each laying and erection machine. Among them, the construction monitoring and sensing information includes beam segment station information, stress monitoring information, internal force information of the steel beam structure, construction site environment information, equipment tilt information, equipment attitude information, and equipment positioning information.

[0024] Specifically, after receiving the confirmation information of the laying and erection machine digital twin intelligent safety monitoring system fed back and connected, the construction monitoring sensor information of each laying and erection machine during the main bridge construction is obtained.

[0025] S13. Transmit the construction monitoring and sensing information to the OPC server for OPC data integration to form the logical, physical information, and construction environment data information of the laying and erection machine, and transmit the OPC data integration to the PLC programmable logic controller.

[0026] Specifically, the Internet of Things devices collect actual on-site data, use the OPC server for data integration, package the data into a standard communication data language, form the data information of the erection and laying machine's logic, physical information, and construction environment, and through the data processing function of the PLC programmable logic, while filtering and noise reduction of the packaged data transmitted by the OPC server to improve data quality, calculate and analyze the collected data through the logic operation module, output the results, and feedback the output physical information to the bridge construction simulation.

[0027] S14. Complete the information extraction and processing of OPC data integration through the PLC programmable logic controller, and respectively feedback the obtained logic, physical information, and construction environment data information to the construction structure model, GIS real-scene site model, construction equipment simulation model, and construction dynamic information model, complete the matching of the working postures and physical information states of each simulation model and the dynamic construction simulation of the construction environment, and realize the digital twin intelligent construction of actual physical data.

[0028] In this embodiment, through the data collection of the Internet of Things devices, the beam segment station information, stress monitoring information, internal force information of the steel beam structure, construction site environment information, equipment tilt information, equipment attitude information, and equipment positioning information are obtained. The logic and physical information of the erection and laying machine are integrated by OPC data, and the information extraction, processing, and output of the data integration are automatically completed through PLC programming data processing, realizing the monitoring of the structure and beam slab state of the erection and laying machine during the construction process.

[0029] In this embodiment, the information extraction of OPC data integration completed by the PLC programmable logic controller in step S14 includes: cleaning and filtering the construction monitoring sensor information in the OPC data integration, clearing the deviation-exceeding and non-standard data to eliminate data differences; synchronously integrating multi-source data through the sensor data interaction strategy to realize the classification labeling of data, ensuring the standard consistency of data, and respectively performing smoothing processing on various types of data through the Kalman filter algorithm to obtain the optimal estimated values of the corresponding data and extract them.

[0030] In this embodiment, the information processing of OPC data integration completed by the PLC programmable logic controller in step S14 includes: analyzing the actual operating state of the erection and laying machine according to the beam segment station information, equipment tilt information, equipment attitude information, and equipment positioning information of the erection and laying machine, and realizing the safety control and real-time warning of the equipment operating state through the comparison and analysis of the actual operating state of the erection and laying machine with the preset designed operating state.

[0031] Specifically, for the station position information of the bridge girder segments, through the cooperation of a 3D high-frequency low-wave distance measuring instrument and a line shape detector, the distance between the erection and laying integrated machine and the center of a single-span bridge construction and the plane position status are monitored and transmitted to the OPC data integration. The data is converted into a standard format and encapsulated for transmission. Further, for the processing of the station position data of the girder segments, during the hoisting operation of the erection and laying integrated machine under different types of PK-shaped cross-section steel girders, safety calculations are carried out on the distance of the erection and laying integrated machine's station position from the bridge center and the beam end to determine the safety valve threshold. Through the PLC programmable logic controller, the measured station position data of the girder segments at the construction site transmitted by the OPC data integration is filtered and noise-reduced. The processed data is compared with the calculated safety valve threshold. If it exceeds the safety valve threshold, a warning is issued, and then the operation position of the erection and laying integrated machine is adjusted until the warning is eliminated.

[0032] Specifically, for the stress monitoring information, the point positions are arranged according to the structural design calculation; a compound variable chord sensor is used to monitor the stress data at the monitoring points and transmit it to the OPC data integration. The data is converted into a standard format and encapsulated for transmission. Further, for the processing of the stress monitoring data, a finite element analysis calculation is carried out on the structural stress state of the PK-shaped cross-section steel girder under the working conditions of hoisting, precast slab installation, and stay cable tensioning. Considering the specified safety factor, the stress thresholds of the diaphragm, web, and anchor box are determined. Through the PLC programmable logic controller, the stress data during the installation of the beam and slab transmitted by the OPC data integration is filtered and noise-reduced. The processed data is compared with the calculated safety valve threshold. If it exceeds the safety valve threshold, a warning is issued, and the postures of the erection and laying integrated machine and the transport barge are adjusted until the stress information of the beam and slab is within a reasonable range to avoid excessive deformation of the beam and slab or the erection and laying integrated machine during the installation process. Even further, for the processing of the stress monitoring data, the stress of the stay cable is also monitored through the PLC programmable logic controller. The stress data of the stay cable during the tensioning process at two stages, namely, after the installation of the beam and slab is completed and after the wet joint of the bridge deck beam and slab is poured, is collected and compared with the tensioning design value to form guidance and verification for the tensioning operation at the construction site. At the same time, the stress of the cable during the construction process is monitored to avoid stress concentration.

[0033] Specifically, for the internal force information of the steel beam structure, the monitoring points are arranged according to the structural design concept and the support positions of the erection and paving machine. The surface stress gauge is used to monitor the load-bearing state of the component, and the data is transmitted to the OPC server for the integration of the internal force data of the erection and paving machine structure. The OPC server is used to convert the data format and perform encapsulated transmission. Specifically, for the processing of the internal force data of the steel beam structure, the finite element stress analysis is carried out on the chord, vertical rod, and the mid-span position of the lower horizontal bracing under the hoisting condition of the erection and paving machine to verify the stability of the structure of the erection and paving machine under the most unfavorable condition. At the same time, the calculated stress of the erection and paving machine under the most unfavorable condition is imported into the PLC programmable logic controller as the safety control threshold. The PLC programmable logic controller filters and denoises the key structural stress data of the erection and paving machine received through the OPC data integration transmission. The processed data is compared with the safety control threshold. When the measured data exceeds the limit, it proves that the construction process has exceeded the most unfavorable condition of the theoretical analysis. At this time, the non-compliance of the construction organization should be dealt with in a timely manner. In this embodiment, the PLC programming data processing can automatically judge whether the monitored information data meets the structural construction bearing capacity, form a four-level early warning, and take appropriate countermeasures according to the early warning information.

[0034] Specifically, for the on-site environmental information, the on-site temperature, wind force, rainfall, air pressure, and air humidity are monitored by environmental information collection devices. The on-site environmental data is integrated through the OPC server 405, the data is converted into a standard format and encapsulated for transmission. Further, for the processing of the on-site environmental data, according to the construction operation conditions, the control indicators for the on-site temperature, wind force, rainfall, air pressure, and air humidity are set. The PLC programmable logic controller filters and denoises the on-site environmental data received through the OPC data integration transmission, compares and analyzes the measured data. When the on-site measured environmental temperature is higher than 35°C or the temperature is higher than 32°C and the relative humidity ≥ 80%RH, a high-temperature early warning is triggered, and the construction site adjusts the working hours or conducts appropriate shift work. When the wind force reaches 10 m / s, a warning for the girder and slab hoisting operation is triggered. When the wind force reaches 13 m / s, a warning for the bridge deck operation is triggered simultaneously. Similarly, a warning for the welding operation is triggered according to the rainfall situation.

[0035] Specifically, the equipment tilt information is collected by installing inclination sensors on the main beam and pillar positions of the track laying machine, collecting the tilt status data of the key structures of the equipment in the X and Y directions, and collecting the working data of the hydraulic sensors and stroke sensors of the hydraulic cylinders of the track laying machine. The tilt data of the main beam and pillar of the track laying machine are integrated through the OPC server, and the data is converted into a standard format and packaged for transmission. Furthermore, the equipment tilt data is processed, and the tilt status threshold of the main beam and pillar of the track laying machine is determined according to the equipment design instructions. The tilt data of the main beam and pillar of the track laying machine at the construction site received from the OPC data integration transmission is filtered and denoised through the PLC programmable logic controller, and the processed data is compared with the threshold, and an alarm is issued when the deviation exceeds the limit.

[0036] Specifically, the equipment posture information is integrated through the OPC server through the hydraulic sensor of the lifting cylinder of the track laying machine, the longitudinal and transverse cylinder stroke sensors, the hydraulic pump station pressure sensor, and the hoisting weight data of the winch, and the data of the track laying machine operation posture is converted into a standard format and packaged for transmission. Furthermore, the equipment posture data is processed by pre-setting the cylinder stroke deviation, cylinder working pressure deviation, winch hoisting weight and other equipment working index parameter thresholds according to the working posture requirements of the track laying machine. The PLC programmable logic controller is used to filter and reduce the noise of the construction site track laying machine operation posture data received from the OPC data integration transmission. The cylinder stroke deviation can reflect the tilt of the track laying machine and the synchronous movement of the main beam and the frame. The cylinder working pressure can reflect the sealing state of the equipment hydraulic system. When the deviation between the measured data and the equipment working parameter threshold exceeds the limit, the system will trigger an alarm.

[0037] Specifically, the equipment positioning information collects the location information of steel beam transport ships and machinery through Beidou positioning tags, and the location information of on-site workers is collected through positioning safety helmets. Combined with the map data embedded in the Wiscada desktop system, visual control of equipment and personnel positioning data in dangerous workplaces is achieved.

[0038] In this embodiment, the obtained logic, physical information and construction environment data information in step S14 are fed back to the construction structure model, GIS real scene site model, construction equipment simulation model and construction dynamic information model respectively, so as to complete the matching of the working posture of each simulation model with the physical information state and the dynamic construction simulation of the construction environment, including: Create model attribute parameters that match the data collected at the construction site for the construction structure model, GIS real-scene site model, construction equipment simulation model, and construction dynamic information model. By integrating the data and model attribute parameters, the digital twin application of actual physical data is realized. Associate the construction dynamic information model with production management data, and associate the construction progress information, precast beam and slab process acceptance data, and bridge structure block installation team information with the model to achieve the multi-dimensional digital twin of the construction dynamic information model; Update and create the actual working scene of bridge construction through satellite three-dimensional map image data and construction environment data information, and realize the automatic drive adjustment of the GIS real scene site model with the change of satellite images.

[0039] In this embodiment, in order to realize the interactive application of the BIM model and the physical data of the Internet of Things multi-source data collection, it can be based on the modular association simulation drive (MKQ), and use logical programming for information processing and operation to realize the association and correspondence between the BIM model parameters and the physical data, complete the matching of the working posture and physical information state of the simulation model, and then conduct a simulation of bridge construction.

[0040] Specifically, the data processing of the modular association simulation drive (MKQ) is to clean and filter the on-site data collected by Internet of Things devices, eliminate deviation over-limit and non-standard data, eliminate data differences, synchronize and integrate multi-source data through the sensor data interaction strategy to achieve data classification and labeling, ensure the standard consistency of the data, and smooth and calculate the data through the Kalman filter algorithm to obtain the optimal estimated value of the data and extract it.

[0041] Specifically, the data and model fusion of the modular association simulation drive (MKQ) creates model attribute parameters that match the data collected at the construction site for the BIM model, such as the lifting cylinder stroke, cylinder pressure, winch lifting weight, and upper beam levelness of the paving and erection machine. Through the fusion and correspondence of the data and model parameters, the digital twin application of actual physical data is realized.

[0042] Specifically, associate the BIM model with production management data, and associate the construction progress information, precast beam and slab process acceptance data, and bridge structure block installation team information with the model to achieve the multi-dimensional digital twin based on the BIM model.

[0043] Specifically, the BIM model is based on the BIM software modeling, and creates the actual working scene of bridge construction through satellite three-dimensional map image + oblique photography real scene modeling technology, meets the automatic drive adjustment of the real scene environment model with the change of satellite images, and conducts a simulation of bridge construction in the form of a PK-type section steel beam + precast concrete bridge deck composite beam based on the BIM information model.

[0044] Specifically, the working scenario updates according to the satellite three-dimensional map image data and the open-source data of reservoir water level monitoring, automatically identifies and obtains the water level, water flow velocity, flow rate at the bridge site, and coordinates the 10-nautical-mile main girder transportation positioning information, integrates relevant network open-source data information with OPC data, automatically completes information data processing through PLC programming, and inputs it into the bridge construction simulation.

[0045] Specifically, it is equipped with a modular associated simulation drive (MKQ) to match the water level states during the flood season and dry season at the bridge site with the simulated postures of the information model, and locally display the relevant coordinated information.

[0046] In the embodiment of the present invention, the oblique photography real-scene modeling is to solve the problem of inaccurate modeling caused by the easy influence of water flow fluctuations and water surface reflections on the oblique photography modeling of the wide-area water surface by drones. The technical parameters of the front-end data processing of image format, exposure rate, and noise reduction balance and the back-end data processing of regional multi-point adjustment and multi-point image matching according to the water area width are debugged to form a real-scene modeling data processing parameter set, realizing the precise and efficient modeling of the wide-area water surface and the integration of the data of the modular associated simulation drive (MKQ) with the real-scene model; In the embodiment of the present invention, in the bridge construction simulation, the target area is set according to the division of the designed main girder segments, and the target area is connected to the construction monitoring sensing information and the construction monitoring image information.

[0047] In the embodiment of the present invention, the bridge construction simulation satisfies the process characteristics that the main construction processes such as the hoisting of the N segments of the steel girder with the PK section, the installation of the precast bridge deck of the N-2 segments, the initial tension of the stay cables, and the casting of the wet joints are not carried out in the same segment area, but satisfy the synchronous progress of multiple processes such as girder erection + deck paving.

[0048] In another embodiment of the present invention, as Figure 3 shown, the method further includes step S15: S15. Process and analyze the construction monitoring sensing information under the construction operation conditions of the current stage to form a construction simulation of the construction operation of the post-overlaid steel girder with the PK section and the precast concrete bridge deck, and combine the construction simulation posture and the measured construction monitoring sensing information under the construction operation conditions of the next stage to perform the deduction and prediction of the safety warning for the construction operation of the next stage.

[0049] In the embodiment of the present invention, in order to analyze the construction simulation of the erection and paving integrated machine and the subsequent construction process, an ecological logic information processing model (ELPML) is formed to realize the deduction and prediction of the safety warning for the next stage of construction and the warning and alarm function. Specifically, through the ecological logic information processing model (ELPML), based on the data processing and analysis of the current stage construction monitoring information, combined with the construction simulation posture and the measured construction monitoring information of the next stage, the deduction and prediction of the safety warning for the next stage of construction are automatically carried out.

[0050] In this embodiment, a clear visual display interface can be built through the wiscada desktop system to integrate sensor positioning, PLC programming data processing, modular associative simulation drive (MKQ), and state logic information processing model (ELPML) data to achieve good visual communication of system information.

[0051] Furthermore, in the process of executing the deduction and prediction of the safety warning of the next stage of construction work, the comparison threshold range of different alarm levels of various safety warnings is adjusted according to the deviation inertia ratio of the data to eliminate the monitoring error caused by the cumulative loss of components.

[0052] Furthermore, the deduction and prediction of the execution of the safety warning of the next stage of construction work includes: using the deep learning model of the neural network to take the current physical state of the track-laying machine and the bridge deck beams to be installed in the next step as analysis objects, according to the hoisting position of each bridge deck beam and the position of the track-laying machine, and the active cylinder stroke data, to obtain whether the position of the equipment and the cylinder stroke when hoisting the next section of the bridge deck beam at the current position and state meet the beam-slab docking requirements during the installation process; according to the current posture of the track-laying machine and the form of the beams to be installed in the next step, the inclination and deformation state of the track-laying machine is predicted according to the stress and structural strain calculation, so as to predict the stability of the equipment operation.

[0053] Specifically, the ecological logic information processing model (ELPML) can also automatically identify the physical information of the subsequently installed steel beams or bridge decks by analyzing the FRID tag data of the beam-slab segments and the recorded data of the installed steel beams and bridge decks, and realize the dynamic deduction of the asynchronous construction of the bridge deck steel beams and bridge decks.

[0054] In another embodiment of the present invention, the method further comprises: Obtain the construction monitoring image information of the main bridge. Specifically, the construction monitoring image information of the main bridge can be obtained through a high-definition digital camera while obtaining the construction monitoring sensor information of each track-laying integrated machine of the main bridge construction. Then, the construction monitoring image information is remotely transmitted to the PLC programmable logic controller through the server streaming media application; and the construction monitoring image information is actively identified through the intelligent identification algorithm of dangerous sources preset by the PLC programmable logic controller for high-altitude falls, fires, and unsafe behaviors of operators, so as to realize the safety supervision and emergency command function of the system in construction sites with greater risks.

[0055] In this embodiment, the construction image data of the construction site is collected by a high-definition digital camera, and through the analysis of the intelligent hazard identification algorithm, the active identification of high-altitude falls, fires, and unsafe behaviors of operators is realized. Specifically, the intelligent hazard identification algorithm analyzes and preprocesses the collected images based on the on-site construction images, including noise reduction, enhancement of image contrast, and color boundary space recognition conversion. Specifically, through the PLC programmable logic controller, the convolutional neural network (CNN) image recognition algorithm is used to identify the flame shape, smoke effect, and reflective characteristics of the clothing in the image, and feedback the dangerous state information of fires and explosions and the non-compliance information of operators not wearing reflective vests or safety helmets, etc. At the same time, human body recognition of the image is carried out, and the FRID tags of the edge protection components and dangerous equipment at the construction site are dynamically tracked to feedback the positional relationship between the personnel and the dangerous operating equipment and dangerous edges, so as to realize early warning of the dangerous state of the construction site and the dangerous behaviors of operators.

[0056] In the embodiment of the present invention, the digital twin intelligent construction method of a paving and erecting integrated machine provided by the present invention further includes a data traceability implementation step, specifically including: The data server and the application server are independently deployed to formulate a data storage and backup strategy, and the system data is backed up monthly according to the capacity of the data server of 1000G. The operation data of the equipment under various working conditions is mined and analyzed to form standard values of the equipment operation state parameters under various working conditions, such as the stationing data and oil cylinder action data of the paving and erecting integrated machine during the installation of bridge deck beams and slabs of each type. A database of construction process monitoring data and equipment parameters is established, and a standardized format template for data output is created to export data reports. The data reports are used as information carriers for information interaction penetration among various specialties and departments, and a professional project production management process control system is created around the working data of the new paving and erecting integrated machine to promote the continuous optimization of the installation construction process of precast bridge beams of cable-stayed bridges. Specifically, the data report is the data of the cable-stayed bridge slab installation construction process in a standard format based on the production operation data of the paving and erecting integrated machine, mainly reflecting the production data such as the phased construction process record, the installed bridge deck beam and slab segments, the number of early warnings and early warning classifications occurred within the stage, the maximum structural internal force, the maximum main girder inclination, and the maximum lifting weight of the paving and erecting integrated machine in this stage.

[0057] In the embodiment of the present invention, refer to Figure 4 , for communication and data transmission, a combination of wired and wireless data acquisition and transmission is adopted, an independent power supply is set, and the data communication integrates a wired transmission + 5G wireless transmission method to achieve a data reception and transmission range of up to 19 kilometers and a millisecond-level feedback speed, accurately and quickly inputting information into the OPC data integration.

[0058] In the embodiments of the present invention, data can be visually deployed through the wiscada desktop system software to process the data of PLC programmable logic controllers or some Internet of Things devices, and develop personalized HMI screens for each system function module.

[0059] The present invention innovatively researches and develops a digital twin intelligent construction method and system for a combined erection and laying machine. For the construction process of post - superimposing PK - type section steel girders and precast concrete bridge decks, which is weak in the prior art, considering that the main construction processes such as hoisting of steel girders, paving of precast bridge decks, staged tensioning of stay cables, and casting of wet joints are not carried out in the same segment area but satisfy the process characteristics of synchronous progress of multiple processes, digital twin intelligent construction is carried out. The safety detection of equipment and steel girder structures, video monitoring information during the construction process are associated and displayed with the digital twin simulation model, realizing that monitoring data drives model simulation and model simulation feeds back monitoring parameters. The manual collection of monitoring data is converted into automatic acquisition, processing, and three - dimensional simulation display, strengthening the timeliness and accuracy of monitoring data and improving the intelligent construction ability of bridge projects.

[0060] The present invention integrates logical and physical information with OPC data, automatically completes the information extraction, processing, and output of the data integration through PLC programming, uses modular - associated simulation drive (MKQ) to match the working postures and physical information of the simulation model, and innovatively researches and develops an ecological logic information processing model (ELPML). Combining the construction simulation postures and the measured construction monitoring information in the next stage, it automatically conducts the deduction and prediction of construction safety warnings in the next stage. While ensuring construction monitoring, it develops the process monitoring deduction of ecological logic processing, achieving the advanced monitoring of construction safety and improving the safety guarantee level and digital information level of project construction.

[0061] The present invention collects environmental information in the way of satellite maps and network messages, and at the same time superimposes a physical information model based on modular - associated simulation drive (MKQ) to make the feedback of on - site working state information more comprehensive.

[0062] The present invention realizes the organic combination of monitoring data and digital twin, realizes the advanced simulation display of working conditions, prediction data feedback, prediction alarms and other states, transforms the safety management idea of bridge construction, and converts the management mode of discovering and solving problems into the management mode of predicting potential safety hazards and conducting advanced investigation and control. It fully guarantees the safety and reliability of bridge digital construction.

[0063] The digital twin intelligent construction method of the integrated girder and deck erection machine provided by the present invention takes a specialized bridge construction equipment that meets the asynchronous installation requirements of precast beams and slabs of long-span bridges as the research object. Correspondingly, in terms of the monitoring of design and operation information, it is not limited to only monitoring the integrated girder and deck erection machine itself, but also covers the monitoring of the positional relationship between the integrated girder and deck erection machine and precast beam and slab segments, the positioning of construction transportation equipment (pilot application of Beidou positioning in the engineering construction process), and the changes in the construction surrounding environment, emphasizing the integrity of construction process control around the operation of the integrated girder and deck erection machine.

[0064] In the technical solution of the present invention, the specialization of the operation monitoring of the integrated girder and deck erection machine adopts Internet of Things monitoring equipment for the production requirements of professional equipment to obtain information data that truly reflects the production status, avoiding data redundancy and the cumbersome operation of the system, and improving the practicality of the system.

[0065] In the technical solution of the present invention, by building an image recognition algorithm suitable for determining dangerous elements at the construction site, the automatic recognition ability of the dangerous behaviors of personnel and the dangerous states of objects at the construction site of long-span bridges through images is strengthened, highlighting the application value of the system in construction safety management.

[0066] In the technical solution of the present invention, by building a driver program that associates physical data with the model, the one-to-one correspondence between the virtual model and physical data is realized, achieving a vivid display of the collected data on the spot and realizing a more realistic digital twin of the construction site.

[0067] In the technical solution of the present invention, by building an ecological logic information processing model, the understanding of the construction process by digital technology is strengthened. The system can automatically identify the next construction content, the models of the precast beams and slabs to be hoisted on the bridge deck next, and determine whether the equipment meets the requirements of the next construction step in combination with the current state of the equipment and give an early warning, achieving the effect of advanced control.

[0068] In the technical solution of the present invention, a more accurate and complete real-scene modeling function is provided. By processing image data and overlaying on-site environmental data (such as water flow rate, water surface width, etc.), a real-scene model that can better reflect the actual on-site environment is formed.

[0069] In the technical solution of the present invention, a more practical system data post-processing function module is provided for storing and backing up process data, forming a professional equipment operation and construction production report, refining key data, and realizing the collaborative sharing of all information of the integrated girder and deck erection machine in the construction production process among all parties involved in project management, all management departments, and all professional personnel, achieving the optimization and improvement of production equipment and production technology.

[0070] For method embodiments, for the sake of simplicity of description, they are all expressed as a series of combinations of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the described order of actions, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0071] Another embodiment of the present invention also provides a digital twin intelligent construction system for a combined girder and erection machine, and the system includes functional modules for implementing the digital twin intelligent construction method of the combined girder and erection machine as described in any one of the above. Figure 5 Schematically shows a structural schematic diagram of a digital twin intelligent construction system for a combined girder and erection machine provided by an embodiment of the present invention. Refer to Figure 5 , a digital twin intelligent construction system for a combined girder and erection machine according to an embodiment of the present invention specifically includes: A simulation model construction module 501, configured to pre-construct a construction structure model, a GIS real-scene site model, a construction equipment simulation model, and a construction dynamic information model; A structure monitoring module 502, configured to, under the construction operation condition of the post-overlapping of the PK-type section steel girder and the precast concrete bridge deck, obtain the construction monitoring sensing information of each combined girder and erection machine during the main bridge construction through the Internet of Things devices arranged at the key points of each combined girder and erection machine. The construction monitoring sensing information includes beam segment station information, stress monitoring information, internal force information of the steel girder structure, construction site environment information, equipment tilt information, equipment attitude information, and equipment positioning information; A data transmission module 503, configured to transmit the construction monitoring sensing information to an OPC server for OPC data integration to form the combined girder and erection machine logic, physical information, and construction environment data information, and transmit the OPC data integration to a PLC programmable logic controller; A construction linkage digital twin module 504, configured to complete the information extraction and processing of the OPC data integration through the PLC programmable logic controller, and respectively feedback the obtained logic, physical information, and construction environment data information to the construction structure model, the GIS real-scene site model, the construction equipment simulation model, and the construction dynamic information model, complete the matching of the working postures and physical information states of each simulation model and the dynamic construction simulation of the construction environment, and realize the digital twin intelligent construction of actual physical data.

[0072] In another embodiment of the present invention, the system further includes a construction simulation analysis module not shown in the drawings. The construction simulation analysis module is used to perform data processing and analysis on the construction monitoring sensing information under the construction operation conditions of the current stage to form a construction simulation of the construction operation of the post-laminated PK-type section steel beam and precast concrete bridge deck, and combine the construction simulation posture and the measured construction monitoring sensing information under the construction operation conditions of the next stage to perform the deduction and prediction of the safety warning for the construction operation of the next stage.

[0073] In another embodiment of the present invention, the system further includes a construction simulation analysis module not shown in the drawings. The construction simulation analysis module is used to perform data processing and analysis on the construction monitoring sensing information under the construction operation conditions of the current stage to form a construction simulation of the construction operation of the post-laminated PK-type section steel beam and precast concrete bridge deck, and combine the construction simulation posture and the measured construction monitoring sensing information under the construction operation conditions of the next stage to perform the deduction and prediction of the safety warning for the construction operation of the next stage.

[0074] In another embodiment of the present invention, the system further includes a video monitoring module not shown in the drawings. The video monitoring module is used to obtain the construction monitoring image information of the main bridge.

[0075] Further, the data transmission module 503 remotely transmits the construction monitoring image information to the PLC programmable logic controller through the server streaming media application; Further, the system further includes an intelligent recognition module not shown in the drawings. The intelligent recognition module is used to actively recognize high-altitude falls, fires, and unsafe behaviors of operators in the construction monitoring image information through a preset hazard intelligent recognition algorithm.

[0076] In the specific implementation process of the embodiments of this system, reference may be made to the above method embodiments, and they have corresponding technical effects In addition, those skilled in the art can understand that although some of the embodiments herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present invention and forms different embodiments. For example, any one of the claimed embodiments can be used in any combination.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital twin digital intelligence construction method for a track laying machine, characterized in that: The method is used for the construction operation condition of post-superposition of PK-section steel beams and precast concrete bridge decks, in which the hoisting construction of PK-section steel beams and the paving construction of precast concrete bridge decks are carried out simultaneously and the construction areas are located in different section areas, including: Pre-build construction structure model, GIS real-scene site model, construction equipment simulation model and construction dynamic information model; Under the construction working condition of the PK-section steel beam and the precast concrete bridge deck being superimposed, the construction monitoring sensor information of each track laying machine during the main bridge construction process is obtained through the IoT devices installed at the key points of each track laying machine. The construction monitoring sensor information includes the beam section position information, stress monitoring information, steel beam structure internal force information, construction site environment information, equipment tilt information, equipment posture information and equipment positioning information; The construction monitoring sensor information is transmitted to the OPC server for OPC data integration to form the track laying machine logic, physical information and construction environment data information, and the OPC data integration is transmitted to the PLC programmable logic controller; The information extraction and processing of OPC data integration is completed through the PLC programmable logic controller, and the obtained logical, physical information and construction environment data information are fed back to the construction structure model, GIS real-scene site model, construction equipment simulation model and construction dynamic information model respectively, to complete the matching of the working posture and physical information status of each simulation model and the dynamic construction simulation of the construction environment, and realize the digital twin digital intelligent construction of actual physical data.

2. The method according to claim 1, characterized in that The method further comprises: The construction monitoring sensor information under the current construction working conditions is processed and analyzed to form a construction simulation of the superposition of the PK-section steel beam and the precast concrete bridge deck. Combined with the construction simulation posture and the measured construction monitoring sensor information under the next stage of construction working conditions, the prediction of the safety warning of the next stage of construction work is carried out.

3. The method according to claim 2, characterized in that In the process of executing the deduction and prediction of the safety warning of construction work in the next stage, the comparison threshold range of different alarm levels of various safety warnings is adjusted according to the deviation inertia ratio of the data to eliminate the monitoring error caused by the cumulative loss of components.

4. The method according to claim 2, characterized in that: The deduction and prediction of the execution of the next stage of construction work safety warning includes: Through the deep learning model of the neural network, the current physical state of the bridge deck slab and the bridge deck beam to be installed in the next step are taken as the analysis objects. According to the hoisting position of each bridge deck beam slab and the position of the bridge deck slab and the active cylinder stroke data of the bridge deck slab, it is determined whether the position of the equipment and the cylinder stroke when hoisting the next section of the bridge deck beam slab at the current position and state meet the beam-slab docking requirements during the installation process; According to the current posture of the track-laying machine and the form of the beam and slab to be installed in the next step, the inclination and deformation state of the track-laying machine are predicted by stress and structural strain calculation, so as to predict the stability of the equipment operation.

5. The method according to claim 1, characterized in that The method further comprises: Obtain monitoring image information of the main bridge construction; Remotely transmit construction monitoring image information to the PLC programmable logic controller through the server streaming media application; The preset intelligent hazard identification algorithm is used to proactively identify high-altitude falls, fires, and unsafe behaviors of workers in construction monitoring image information.

6. The method according to any one of claims 1 to 5, characterized in that: The information extraction of OPC data integration is completed by using a PLC programmable logic controller, including: Clean and filter the construction monitoring sensor information in OPC data integration, remove deviation exceeding limit and non-standard data to eliminate data differences; Through the sensor data interaction strategy, multi-source data are synchronously integrated to realize data classification labeling and ensure the consistency of data standards. Through the Kalman filter algorithm, each type of data is smoothed separately to obtain the optimal estimate of the corresponding data and extract it.

7. The method according to any one of claims 1 to 5, characterized in that: Information processing to complete OPC data integration through PLC programmable logic controller includes: The actual operating status of the track laying machine is analyzed based on the beam section position information, equipment inclination information, equipment posture information and equipment positioning information. By comparing the actual operating status of the track laying machine with the preset design operating status, safe control and real-time early warning of the equipment operating status can be achieved.

8. The method according to any one of claims 1 to 5, characterized in that: The obtained logic, physical information and construction environment data information are fed back to the construction structure model, GIS real-scene site model, construction equipment simulation model and construction dynamic information model respectively, to complete the matching of the working posture of each simulation model with the physical information state and the dynamic construction simulation of the construction environment, including: Create model attribute parameters that match the data collected at the construction site for the construction structure model, GIS real-scene site model, construction equipment simulation model, and construction dynamic information model. By integrating the data and model attribute parameters, the digital twin application of actual physical data is realized. The construction dynamic information model is associated with production management data, and the construction progress information, prefabricated beam and slab process acceptance data, and bridge structure block installation team information are associated with the model to realize the multi-dimensional digital twin of the construction dynamic information model; The actual working scene of bridge construction is created by updating satellite 3D map image data and construction environment data information, and the GIS real-scene site model is automatically driven and adjusted as satellite images change.

9. A digital twin digital intelligent construction system for a track laying machine, characterized in that: The system is used for the construction operation condition of the PK-section steel beam and the precast concrete bridge deck being superimposed. The PK-section steel beam hoisting construction and the precast concrete bridge deck paving construction are carried out simultaneously and the construction areas are located in different section areas, including: The simulation model building module is used to pre-build the construction structure model, GIS real-scene site model, construction equipment simulation model and construction dynamic information model; The structural monitoring module is used to obtain the construction monitoring sensor information of each track laying machine during the main bridge construction process through the Internet of Things devices installed at the key points of each track laying machine under the construction working condition of the PK-section steel beam and the concrete prefabricated bridge deck. The construction monitoring sensor information includes beam section position information, stress monitoring information, steel beam structure internal force information, construction site environment information, equipment tilt information, equipment posture information and equipment positioning information; A data transmission module is used to transmit the construction monitoring sensor information to the OPC server for OPC data integration, to form the logic and physical information of the track laying machine and the construction environment data information, and to transmit the OPC data integration to the PLC programmable logic controller; The construction linkage digital twin module is used to complete the information extraction and processing of OPC data integration through the PLC programmable logic controller, and feed back the obtained logical, physical information and construction environment data information to the construction structure model, GIS real-scene site model, construction equipment simulation model and construction dynamic information model respectively, to complete the matching of the working posture and physical information status of each simulation model and the dynamic construction simulation of the construction environment, to realize the digital twin digital intelligent construction of actual physical data.

10. The system according to claim 9, characterized in that The system further comprises: The construction simulation analysis module is used to process and analyze the construction monitoring sensor information under the current stage of construction working conditions to form a construction simulation of the construction work of the PK-section steel beam and the concrete prefabricated bridge deck after superposition. Combined with the construction simulation posture and the measured construction monitoring sensor information under the next stage of construction working conditions, the deduction and prediction of the safety warning of the next stage of construction work are performed.

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