An ultra-high-rise construction monitoring method, device and medium based on digital twinning

Abstract

This invention discloses a method, equipment, and medium for super high-rise construction monitoring based on digital twins, relating to the field of digital twin technology. The method includes: acquiring construction operation data from the super high-rise construction site and constructing a digital twin construction model; updating the boundary conditions of the digital twin construction model through a set of construction disturbance events to generate a disturbance-driven twin model; verifying the consistency between the virtual and real data of the model simulation state records in the construction state set and the disturbance-driven twin model to generate a twin credibility evaluation set; improving the local monitoring resolution of high-risk areas to generate twin resolution configuration results; performing local high-precision monitoring of the credible construction twin based on the twin resolution configuration results; and outputting super high-rise construction monitoring results. This invention, by improving the local monitoring resolution and generating twin resolution configuration results, avoids the waste of computational resources caused by global high-precision monitoring, thereby improving the efficiency of monitoring resource utilization and the response accuracy of high-risk areas.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method, equipment and medium for super high-rise construction monitoring based on digital twins. Background Technology

[0002] As urban construction develops towards high density, intensification, and three-dimensionality, the construction of super high-rise buildings is characterized by long construction cycles, complex structural systems, dense overlapping processes, and significant dynamic changes in the construction environment. Traditional construction monitoring technologies mainly rely on structural sensors, video surveillance, BIM models, construction management platforms, and manual inspections to collect and analyze data on structural deformation, equipment operation, construction progress, environmental conditions, and work space occupancy. In recent years, digital twin technology has been gradually applied to the field of building construction, enabling the presentation of the status changes of construction objects, equipment operation relationships, process progress, and space occupancy status in virtual space. This provides a new technological foundation for risk identification, status simulation, and decision support during the construction of super high-rise buildings.

[0003] However, existing technologies still have the following shortcomings: Existing technologies mostly focus on synchronously displaying on-site collected data into the digital twin model, and rarely consider the impact of construction disturbance events on the boundary conditions of the digital twin model. This makes it easy for the model to lag in simulation constraints under conditions such as changes in high-altitude wind environment, equipment operation deviation, process switching, and sudden changes in work space occupation. Existing technologies usually assume that the digital twin model has high credibility and lack a mechanism to verify the consistency between the actual on-site state and the model simulation state, making it difficult to detect model state distortion, local simulation deviation, or data mapping errors in a timely manner. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for super high-rise construction monitoring based on digital twins to solve the problem of simulation constraint lag.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for monitoring the construction of super high-rise buildings based on digital twins, comprising, Acquire construction operation data from the construction site of super high-rise buildings, construct a digital twin construction model, and input the construction operation data into the digital twin construction model to identify construction objects, map construction coordinates, and collect object states, thereby generating a construction state set; The construction state set is subjected to state change detection to generate a construction disturbance event set. The boundary condition disturbance of the digital twin construction model is updated by the construction disturbance event set to generate a disturbance-driven twin model. The consistency between the virtual and real is verified by the model simulation state records in the construction state set and the disturbance-driven twin model to generate a twin credibility evaluation set. Based on the twin credibility evaluation set, the low credibility state region in the disturbance-driven twin model is located, and the state correction of the low credibility state region is performed to generate a credible construction twin. A condition risk assessment is performed on the trusted construction twin to identify areas of concentrated risk. The local monitoring resolution of these areas is then improved to generate twin resolution configuration results. Based on these twin resolution configuration results, local high-precision monitoring is performed on the trusted construction twin to output monitoring results for super high-rise construction.

[0007] As a preferred embodiment of the digital twin-based super high-rise construction monitoring method of the present invention, the specific steps for generating the construction status set are as follows: The construction operation data includes structural response data, construction equipment operation data, construction procedure data, construction environment data, component status data, work space data, construction object identification, and spatial location identification; Obtain basic construction information, including super high-rise building design information, construction procedure plan, construction equipment configuration, and model environment benchmark parameters; Based on the design information, construction procedure plan and construction equipment configuration of super high-rise buildings, a construction object and construction coordinate system are established, and the state attributes of the construction object are initialized to form an initial digital twin construction model. The model environment baseline parameters are mapped to the initial digital twin construction model, and correlation verification is performed to form a digital twin construction model. The construction operation data is divided into multiple object operation data groups according to the construction object identifier. The object operation data groups are mapped to the construction coordinate system in the digital twin construction model according to the spatial location identifier to generate object coordinate mapping data. Extract the structural response status, equipment operation status, process execution status, environmental effect status, component installation status, and work space occupancy status from the same object's coordinate mapping data, and organize them according to the construction object identifier and construction coordinate system to generate a construction status set.

[0008] As a preferred embodiment of the digital twin-based super high-rise construction monitoring method of the present invention, the specific steps for generating the construction disturbance event set are as follows: The object state records in the construction state set are compared with the model simulation state records in the digital twin construction model to generate state change data. Disturbance events are marked on the state change data to generate construction disturbance events, which are then aggregated into a construction disturbance event set.

[0009] As a preferred embodiment of the super high-rise construction monitoring method based on digital twins described in this invention, the specific steps for generating the disturbance-driven twin model are as follows: The event type is determined for construction disturbance events in the construction disturbance event set. When the construction disturbance event belongs to the structural response change type, the boundary parameters of the structural response data are transformed to generate structural response boundary update data. When a construction disturbance event is classified as a change in equipment operation, boundary parameter transformation is performed on the construction equipment operation data to generate updated equipment operation boundary data. When a construction disturbance event belongs to the construction stage change type, the boundary parameters of the construction process data are transformed to generate construction stage boundary update data. When the construction disturbance event belongs to the type of environmental action change, the boundary parameters of the construction environment data are transformed to generate environmental action boundary update data; When a construction disturbance event is classified as a change in area occupancy, boundary parameters are transformed on the work space data to generate updated area occupancy boundary data. The structural response boundary update data, equipment operation boundary update data, construction phase boundary update data, environmental action boundary update data, and area occupancy boundary update data are collected to form a boundary condition update package; The simulation constraint relationships and object state relationships in the digital twin construction model are refreshed based on the boundary condition update package, generating a disturbance-driven twin model.

[0010] As a preferred embodiment of the super high-rise construction monitoring method based on digital twins described in this invention, the specific steps for generating the twin reliability evaluation set are as follows: Extract object status records from the construction status set, and extract model simulation status records from the disturbance-driven twin model that are under the same construction object identifier and the same construction coordinate system as the object status records, forming a virtual-real status comparison record; Perform state deviation analysis on the virtual-to-real state comparison records to generate virtual-to-real deviation data; The credibility evaluation value of the virtual-real state comparison record is calculated based on the virtual-real deviation data, and the credibility evaluation value is summarized according to the construction object identification and construction coordinate system to generate a twin credibility evaluation set.

[0011] As a preferred embodiment of the super high-rise construction monitoring method based on digital twins described in this invention, the specific steps for generating a reliable construction twin are as follows: According to the construction object identification and construction coordinate system, the confidence evaluation value that is lower than the preset confidence condition is matched with the model simulation state record in the disturbance-driven twin model and aggregated to form a low confidence state region. State deviation correction is performed on the actual state data in the low-confidence state region and the model simulation state record in the perturbation-driven twin model to generate correction amount; A reliable construction twin is obtained by updating the model simulation state record of the disturbance-driven twin model with the correction amount.

[0012] As a preferred embodiment of the digital twin-based super high-rise construction monitoring method of the present invention, the specific steps for generating the twin resolution configuration result are as follows: The number of construction objects, the complexity of their status, and the duration of the risk in the high-risk area are used as parameters to improve the resolution of local monitoring. Based on the local monitoring resolution enhancement parameters, configure the model update frequency, object partitioning granularity, status verification frequency, and local accuracy for high-risk areas, and generate twin resolution configuration results.

[0013] As a preferred embodiment of the super high-rise construction monitoring method based on digital twins described in this invention, the specific steps for outputting the super high-rise construction monitoring results are as follows: Based on the twin resolution configuration results, the risk concentration area is divided into local high-precision monitoring areas, and the status of the local high-precision monitoring areas is refreshed to generate local refresh status data. The partial refresh state data is split into fine-grained objects to generate local subdivided state data; Continuous virtual-real verification is performed on local subdivided state data to generate local high-precision reliable state data; The system summarizes local high-precision reliable state data, construction disturbance event set, twin reliability evaluation set, and twin resolution configuration results to output the monitoring results of super high-rise construction.

[0014] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the super high-rise construction monitoring method based on digital twins as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the super high-rise construction monitoring method based on digital twin as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By identifying construction objects, mapping construction coordinates, and aggregating object states, a construction state set is generated, which improves the organization, spatial correlation, and model usability of construction monitoring data. By refreshing the simulation constraint relationships and object state relationships in the digital twin construction model, a disturbance-driven twin model is generated, which improves the reliability of model simulation and the authenticity of construction state analysis. By performing state correction, a credible construction twin is generated, which improves the local authenticity of the digital twin construction model and the credibility of construction monitoring results. By improving the local monitoring resolution, a twin resolution configuration result is generated, which avoids the waste of computing resources caused by global high-precision monitoring and improves the efficiency of monitoring resource utilization and the response accuracy of risk areas. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a construction monitoring method for super high-rise buildings based on digital twins.

[0019] Figure 2 This is a flowchart for improving the resolution of local monitoring.

[0020] Figure 3 This is a flowchart for virtual-real consistency verification and status correction.

[0021] Figure 4 A flowchart for building a digital twin construction model.

[0022] Figure 5 This is a diagram illustrating an application scenario for monitoring construction of super high-rise buildings. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for super high-rise construction monitoring based on digital twins, including the following steps: S1: Obtain construction operation data from the super high-rise construction site, construct a digital twin construction model, and input the construction operation data into the digital twin construction model to identify construction objects, map construction coordinates, and collect object states, thereby generating a construction state set.

[0027] S1.1: Obtain basic construction information, establish the construction object and construction coordinate system based on the design information of the super high-rise building, the construction procedure plan and the configuration of construction equipment, and initialize the status attributes of the construction object to form an initial digital twin construction model; map the model environment benchmark parameters to the initial digital twin construction model and perform correlation verification to form a digital twin construction model.

[0028] Structural response data is collected by deploying strain gauges, accelerometers, and displacement measuring instruments at key structural nodes; construction equipment operation data is collected through the operation logs and sensor data of the construction machinery control terminal; construction procedure data is collected through construction plan records, on-site operation logs, and automated construction recording devices; construction environment data is obtained by measuring temperature, humidity, wind speed, and air pressure through environmental monitoring instruments; component status data is collected through laser scanners and manual inspection records; work space data is obtained through drone aerial photography and 3D scanners; and construction object identification and spatial location identification are collected through unique identification signs and spatial positioning sensors deployed on the construction site. The structural response data, construction equipment operation data, construction procedure data, construction environment data, component status data, work space data, construction object identification, and spatial location identification are categorized and organized according to timestamps, floor locations, and construction objects to form construction operation data.

[0029] For example, the super high-rise construction project involves a 68-story steel-concrete composite structure with 4 underground floors. The monitoring period covers the core tube and outer frame construction area from the 32nd to the 36th floor. The data acquisition cycle for construction operation is 1 second. Structural response data includes displacement measurements from 0.6mm to 8.5mm, strain measurements from 35με to 210με, and vibration amplitudes from 0.02m / s² to 0.18m / s². Construction equipment operation data includes tower crane operating speeds from 0.3m / s to 1.2m / s. The lifting capacity ranges from 1.8t to 8.0t, and the equipment operating radius ranges from 12m to 48m. The construction environment data includes temperature from 18℃ to 32℃, humidity from 45%RH to 82%RH, wind speed from 1.5m / s to 9.8m / s, and air pressure from 99.1kPa to 101.3kPa. The component status data includes component installation offset from 2mm to 18mm. The operating space data includes material stacking area from 12m² to 46m² and equipment operating area from 20m² to 68m².

[0030] By utilizing architectural design documents, construction organization designs, and construction site layout records, structural information, construction sequence plans, construction equipment configurations, and model environment baseline parameters for the super high-rise building are obtained to form basic construction information. The construction floors are divided into multiple construction object layers. Each construction object layer establishes sub-object units according to the floor distribution. Based on the construction sequence plan and construction equipment configuration, structural response units, equipment operation units, sequence execution units, environmental action units, and work space occupancy units are assigned to each sub-object unit, forming a four-dimensional state matrix denoted as M(l, o, a, t), where l is the floor index, ranging from 1 to L, where L is the total number of construction floors in the super high-rise building; o is the construction object index, ranging from 1 to N, where N is the number of construction objects within the same floor; and a is the state attribute. The index, with values ​​from 1 to 6, corresponds to the structural response status, equipment operation status, process execution status, environmental effect status, component installation status, and work space occupancy status, respectively. t is the collection period index, with values ​​from 1 to T, where T is the number of collection periods for construction operation data within the current monitoring time period. The matrix unit M(l, o, a, t) in the four-dimensional state matrix is ​​used to record the corresponding floor, corresponding construction object, corresponding state attribute, and state attribute value under the corresponding collection period. The fields of the matrix unit include state attribute value, state source, collection time, spatial coordinates, associated construction process, and update flag. The update period is consistent with the collection period of the construction operation data. The initial state value is assigned to each unit in the multi-dimensional state matrix according to the construction object type, floor location, and spatial coordinates to form the initial digital twin construction model.

[0031] The temperature, humidity, wind speed, and air pressure parameters in the model's environmental baseline parameters are matched one by one to the state attributes of the corresponding environmental action units in the initial digital twin construction model according to the construction object identifier and spatial coordinates. Allowable ranges are set according to the building structure design code and construction design documents. The assigned state attributes are compared with the allowable ranges. State attributes that deviate from the allowable ranges are marked. The marked state attributes are checked for consistency with the construction foundation information and the spatial location of the construction object. State attributes that deviate from the allowable ranges are corrected to obtain the digital twin construction model.

[0032] It should be noted that the digital twin construction model includes a four-dimensional state matrix, simulation constraint relationships, and object state relationships. Each sub-object unit is coupled through construction process associations and spatial location connections. Constraint relationships exist between the structural response unit and the environmental action unit and the work space occupancy unit. Key parameters include structural displacement, strain, structural vibration, equipment operating position, equipment operating speed, equipment load status, equipment start / stop status, current process, process completion progress, temperature, humidity, wind speed, air pressure, component installation position, component connection status, and work space occupancy status. The digital twin construction model performs discrete state updates according to the construction operation data acquisition cycle. Within the current acquisition cycle, it reads the model simulation state record from the previous acquisition cycle according to the construction object identifier and construction coordinate system, and reads the construction operation data for the current acquisition cycle. When structural response data exists in the current acquisition cycle, the structural response state is refreshed with structural displacement, strain, and structural vibration. When construction equipment operation data exists in the current acquisition period, the equipment operation status is refreshed with equipment operating location, equipment operating speed, equipment load status, and equipment start / stop status. When construction procedure data exists in the current acquisition period, the procedure execution status is refreshed with the current procedure, construction floor, and procedure completion progress. When construction environment data exists in the current acquisition period, the environmental impact status is refreshed with temperature, humidity, wind speed, and air pressure. When component status data exists in the current acquisition period, the component installation status is refreshed with component installation location, component connection status, and component installation completion status. When work space data exists in the current acquisition period, the work space occupancy status is refreshed with personnel distribution, equipment operating range, and material stacking location. The refreshed structural response status, equipment operation status, procedure execution status, environmental impact status, component installation status, and work space occupancy status are written into a four-dimensional status matrix according to the construction object identifier, construction coordinate system, and acquisition period to form the model simulation status record for the current acquisition period.

[0033] S1.2: Divide the construction operation data into multiple object operation data groups according to the construction object identifier, and map the object operation data groups to the construction coordinate system in the digital twin construction model according to the spatial location identifier to generate object coordinate mapping data.

[0034] According to the construction object identifier, each record in the construction operation data is assigned to the corresponding object operation data group. Each object operation data group contains structural response data, construction equipment operation data, construction procedure data, construction environment data, component status data, and work space data of the same construction object. During the assignment process, it is ensured that the timestamps of the same construction object are consistent within the same time period. For example, sensor data and construction log data collected within 1 second are uniformly classified into the same object operation data group. According to the spatial location identifier, each object operation data group is aligned with the construction coordinate system in the digital twin construction model, and the coordinate correspondence of each construction object in three-dimensional space is established to form object coordinate mapping data.

[0035] S1.3: Extract the structural response status, equipment operation status, process execution status, environmental action status, component installation status, and work space occupancy status from the same object's coordinate mapping data, and organize them according to the construction object identifier and construction coordinate system to generate a construction status set.

[0036] Read all object operation data groups in the object coordinate mapping data. In the same object operation data group, read the structural response status, equipment operation status, process execution status, environmental effect status, component installation status, and work space occupancy status to form a construction status record. According to the construction object identifier and construction coordinate system, classify and organize the construction status record according to the floor location and spatial coordinates to generate a construction status set.

[0037] S2: Detect state changes in the construction state set, generate a construction disturbance event set, update the boundary conditions of the digital twin construction model using the construction disturbance event set, generate a disturbance-driven twin model, verify the consistency between the virtual and real states of the model simulation records in the construction state set and the disturbance-driven twin model, and generate a twin credibility evaluation set.

[0038] S2.1: Compare the object state records in the construction state set with the model simulation state records in the digital twin construction model to generate state change data; mark the state change data with disturbance events to generate construction disturbance events, and collect them to form a construction disturbance event set.

[0039] The system reads object status records from the construction status collection point. Based on the construction object identifier, construction coordinate system, and collection time, it reads the model simulation status records for the current collection period from the digital twin construction model. The model simulation status records are the simulation status results generated by the digital twin construction model based on the status attribute values ​​of the previous collection period, the construction operation data of the current collection period, and the simulation constraint relationships. The system pairs the object status records with the same construction object identifier, the same construction coordinate position, and the same collection time with the model simulation status records to form virtual-real comparison data. The virtual-real comparison data includes the structural response status, equipment operation status, process execution status, environmental action status, component installation status, and work space occupancy status actually collected at the construction site, as well as the structural response simulation status, construction equipment operation simulation status, construction process execution simulation status, environmental action simulation status, component installation simulation status, and work space occupancy simulation status generated in the digital twin construction model.

[0040] The data for comparing similar states in the virtual and real comparison data are compared item by item. The structural response state is calculated by comparing the displacement, strain, and vibration amplitude with the structural response simulation state to obtain the displacement difference, strain difference, and vibration amplitude difference, which are then compiled into structural response difference results. The operating position, operating speed, load state, and start / stop state in the equipment operating state are compared item by item with the operating position, operating speed, load state, and start / stop state in the equipment operating simulation state. The operating position offset, operating speed deviation, load difference, and start / stop state inconsistency are recorded to generate equipment operating difference results. The current process, construction floor, and process completion progress in the process execution state are compared item by item with the current process, construction floor, and process completion progress in the process execution simulation state. The process name inconsistency, construction floor deviation, and process completion progress difference are recorded to generate process execution difference results. The temperature, humidity, wind speed, and air pressure in the environmental action state are compared item by item with the temperature, humidity, wind speed, and air pressure in the environmental action simulation state. The temperature difference, humidity difference, wind speed difference, and air pressure difference are recorded to generate environmental action difference results.

[0041] The installation position, connection status, and installation completion status of the component installation state are compared item by item with the installation position, connection status, and installation completion status of the component installation simulation state. The offset of installation position, inconsistency of connection status, and inconsistency of installation completion status are recorded to generate component installation difference results. The personnel distribution, equipment operation range, and material stacking position in the work space occupancy state are compared item by item with the personnel distribution, equipment operation range, and material stacking position in the work space occupancy simulation state. The deviation area of ​​personnel distribution, the overlapping area of ​​equipment operation range, and the offset of material stacking position are recorded to generate work space occupancy difference results. The structural response difference results, equipment operation difference results, process execution difference results, environmental effect difference results, component installation difference results, and work space occupancy difference results are organized according to the construction object identification, construction coordinate system, and collection time to generate state change data.

[0042] State change data is not directly treated as construction disturbance events. Instead, it is first filtered based on the allowable range and state consistency conditions of the corresponding state type. Based on the difference results in the state change data, the structural response difference results, equipment operation difference results, process execution difference results, environmental effect difference results, component installation difference results, and work space occupancy difference results are compared with the corresponding change judgment conditions. The change judgment conditions include numerical deviation conditions and state consistency conditions. The allowable ranges for numerical deviation conditions include displacement difference of 0% to 10% of the corresponding design allowable displacement, strain difference of 0% to 10% of the corresponding design allowable strain, vibration amplitude difference of 0% to 10% of the corresponding design allowable vibration amplitude, and temperature difference of 0%. The temperature difference is between ℃ and 5℃, the humidity difference is between 0%RH and 10%RH, the wind speed difference is between 0% and 10% of the wind speed limited by the construction plan, the air pressure difference is between 0kPa and 3kPa, and the component installation position offset is between 0% and 10% of the design installation allowable deviation. The state consistency condition is used to determine whether the state name, floor position, start / stop state, connection state, and installation completion state are consistent. When the displacement difference, strain difference, or vibration amplitude difference in the structural response difference results exceeds the corresponding allowable range, the state change data is marked as a structural response change event. When the running position offset or running speed in the equipment operation difference results... When deviations or load differences exceed the corresponding allowable ranges, or when start-up and shutdown states are inconsistent, the status change data will be marked as an equipment operation change event. When the process name is inconsistent, the construction floor deviates, or the difference in process completion progress exceeds the corresponding allowable range in the process execution difference results, the status change data will be marked as a construction phase change event. When the temperature difference, humidity difference, wind speed difference, or air pressure difference in the environmental effect difference results exceeds the corresponding allowable range, the status change data will be marked as an environmental effect change event. When the personnel distribution deviates from the area, the equipment operation range overlaps, or the material stacking position offset exceeds the corresponding allowable range in the work space occupancy difference results, the status change data will be marked as an area occupancy change event. When component installation... If the installation position offset in the difference results exceeds the corresponding allowable range, or if the connection status and installation completion status are inconsistent, the status change data will be incorporated into the construction phase change event. When the same status change data meets multiple change judgment conditions at the same time, the event marking priority will be determined according to the order of structural response change event, equipment operation change event, construction phase change event, environmental effect change event, and area occupancy change event. The event type with the highest priority will be taken as the main event type of the construction disturbance event, and the other event types that meet the conditions will be taken as the associated event types of the construction disturbance event. The status change data after the disturbance event marking is completed will be collected according to the construction object identifier, construction coordinate system, and event occurrence time to form a construction disturbance event set.

[0043] For example, taking the construction object of the outer frame beam on the east side of the 34th floor as an example, the simulated displacement in the digital twin construction model is 3.2mm, while the actual displacement collected at the construction site is 6.8mm, with a displacement difference of 3.6mm. The simulated strain is 120με, while the actual strain is 185με, with a strain difference of 65με. The simulated wind speed is 4.0m / s, while the actual wind speed is 7.5m / s, with a wind speed difference of 3.5m / s. Since both the displacement difference and the strain difference exceed the corresponding allowable range, the state change data is marked as a structural response change event. At the same time, since the wind speed difference exceeds the corresponding allowable range, the state change data is marked as an environmental action change event. According to the event marking priority, the structural response change event is taken as the main event type, and the environmental action change event is taken as the associated event type, forming the construction disturbance event for the corresponding construction object.

[0044] S2.2: Determine the event type of the construction disturbance events in the construction disturbance event set. When the construction disturbance event belongs to the structural response change type, perform boundary parameter transformation on the structural response data to generate structural response boundary update data.

[0045] The system reads construction disturbance events from the construction disturbance event set and reads the event markers carried by these events. These event markers are obtained by marking state change data as disturbance events. Event markers include structural response change events, equipment operation change events, construction phase change events, environmental effect change events, and area occupancy change events. The event type of the construction disturbance event is determined based on the event markers. When an event is marked as a structural response change event, its event type is determined to be structural response change; when it is marked as an equipment operation change event, its event type is determined to be equipment operation change; when it is marked as a construction phase change event, its event type is determined to be construction phase change; when it is marked as an environmental effect change event, its event type is determined to be environmental effect change; and when it is marked as an area occupancy change event, its event type is determined to be area occupancy change.

[0046] When a construction disturbance event falls under the category of structural response change, the associated structural response data is read. This data includes structural displacement, strain, and vibration data. The structural displacement data is decomposed into horizontal displacement values, vertical displacement values, and displacement locations according to the construction coordinate system. These values ​​are then converted into displacement boundary correction parameters corresponding to the construction coordinate positions in the digital twin construction model. These parameters are used to update the displacement boundaries in the structural response simulation constraints. Finally, the structural strain data is converted into strain location, strain direction, and strain change parameters according to the construction object identifier and the construction coordinate system. The amplitude, strain location, strain change direction, and strain change amplitude are converted into strain constraint correction parameters. These parameters are used to refresh the strain constraints in the structural response simulation constraint relationship. The structural vibration data are converted into vibration boundary correction parameters according to the vibration amplitude, vibration frequency, and vibration location. These parameters are used to refresh the vibration boundaries in the structural response simulation constraint relationship. The displacement boundary correction parameters, strain constraint correction parameters, and vibration boundary correction parameters are combined according to the construction object identifier, construction coordinate system, and event occurrence time to form a structural response boundary parameter group. The structural response boundary parameter group is associated with the structural response change type to generate updated structural response boundary data.

[0047] S2.3: When the construction disturbance event belongs to the equipment operation change type, the boundary parameters of the construction equipment operation data are transformed to generate equipment operation boundary update data; when the construction disturbance event belongs to the construction stage change type, the boundary parameters of the construction procedure data are transformed to generate construction stage boundary update data; when the construction disturbance event belongs to the environmental action change type, the boundary parameters of the construction environment data are transformed to generate environmental action boundary update data; when the construction disturbance event belongs to the area occupancy change type, the boundary parameters of the work space data are transformed to generate area occupancy boundary update data.

[0048] When a construction disturbance event falls under the category of equipment operation change, the associated construction equipment operation data is read. This data includes equipment operating position, equipment operating speed, equipment load status, equipment start / stop status, and equipment operating range. The equipment operating position is organized into the current position and offset direction according to the construction coordinate system. The equipment operating speed is organized into the speed change amplitude, the equipment load status into the load change amplitude, the equipment start / stop status into start / stop change markers, and the equipment operating range into the operating boundary change range. These are then combined according to the construction object identifier, construction coordinate system, and event occurrence time to generate updated equipment operating boundary data. Specifically, the current position and offset direction are used to refresh the equipment operating position boundary, the speed change amplitude is used to refresh the equipment operating speed boundary, the load change amplitude is used to refresh the equipment load boundary, the start / stop change markers are used to refresh the equipment start / stop status constraints, and the operating boundary change range is used to refresh the equipment operating space boundary.

[0049] Similarly, when a construction disturbance event belongs to the construction stage change type, the construction procedure data associated with the construction disturbance event is read. This construction procedure data includes the current procedure, construction floor, procedure completion progress, and procedure switching status. The current procedure is organized into procedure status parameters, the construction floor into floor stage parameters, the procedure completion progress into progress boundary parameters, and the procedure switching status into stage switching parameters. These are then combined according to the construction object identifier, construction coordinate system, and event occurrence time to generate construction stage boundary update data. Specifically, the procedure status parameters are used to refresh construction procedure status constraints, the floor stage parameters are used to refresh construction floor constraints, the progress boundary parameters are used to refresh procedure completion progress boundaries, and the stage switching parameters are used to refresh construction stage switching constraints. When a construction disturbance event belongs to the environmental effect change type, the construction environment data associated with the construction disturbance event is read. This construction environment data includes temperature, humidity, wind speed, and air pressure. Temperature is organized into temperature effect parameters, humidity into humidity effect parameters, wind speed into wind load effect parameters, and air pressure into air pressure effect parameters. The system combines the construction object identifier, construction coordinate system, and event occurrence time to generate environmental action boundary update data. Temperature action parameters are used to update the temperature action boundary, humidity action parameters to update the humidity action boundary, wind speed is converted to wind load action parameters according to the wind pressure conversion relationship, and wind load action parameters are used to update the wind load boundary. Similarly, when the construction disturbance event belongs to the area occupancy change type, the system reads the work space data associated with the construction disturbance event. Work space data includes personnel distribution, equipment operating range, and material stacking location. Personnel distribution is organized into personnel occupancy boundary parameters, equipment operating range into equipment occupancy boundary parameters, and material stacking location into material occupancy boundary parameters. These are then combined according to the construction object identifier, construction coordinate system, and event occurrence time to generate area occupancy boundary update data. Personnel occupancy boundary parameters are used to update the personnel occupancy area boundary, equipment occupancy boundary parameters are used to update the equipment operation occupancy boundary, and material occupancy boundary parameters are used to update the material stacking occupancy boundary.

[0050] It should be noted that the boundary parameter transformation is performed according to the mapping relationship between event type, data field, boundary object and constraint item. The event type is used to determine the simulation constraint relationship to be refreshed, the data field is used to determine the state variable to be transformed, the boundary object is used to determine the construction object and construction coordinate position to be refreshed in the digital twin construction model, and the constraint item is used to determine the boundary parameter category to be written to the state variable. Numerical state variables are converted into boundary correction parameters, state-type state variables are converted into state constraint parameters, and spatial state variables are converted into spatial boundary parameters. The corresponding boundary update data is written according to the construction object identifier, construction coordinate system and event occurrence time.

[0051] S2.4: Collect the structural response boundary update data, equipment operation boundary update data, construction stage boundary update data, environmental action boundary update data, and area occupancy boundary update data to form a boundary condition update package; based on the boundary condition update package, refresh the simulation constraint relationship and object state relationship in the digital twin construction model to generate a disturbance-driven twin model.

[0052] Boundary update data belonging to the same construction object identifier, construction coordinate system, and event occurrence time are merged into a single boundary update record. Equipment operation boundary update data, construction stage boundary update data, environmental effect boundary update data, and area occupancy boundary update data are written into the equipment operation constraint item, construction stage boundary update data, environmental effect boundary update data, and area occupancy boundary update data, forming a boundary condition update package. The simulation constraint relationships in the digital twin construction model are read through this package. The simulation constraint relationships that need to be updated are located according to the construction object identifier and construction coordinate system in the boundary condition update package. Structural response constraint items are replaced or supplemented in the structural response simulation constraint relationships in the digital twin construction model; equipment operation constraint items are replaced or supplemented in the equipment operation simulation constraint relationships; construction stage constraint items are replaced or supplemented in the construction procedure simulation constraint relationships; environmental effect constraint items are replaced or supplemented in the environmental effect simulation constraint relationships; and area occupancy constraint items are replaced or supplemented in the workspace simulation constraint relationships, resulting in the updated simulation constraint relationships.

[0053] By reading the object state relationships in the digital twin construction model through the updated simulation constraint relationships, the structural response state, equipment operation state, process execution state, environmental effect state, and work space occupancy state are re-associated according to the construction object identifier and construction coordinate system. When the structural response constraint changes, the structural response state is re-associated with the floor position and spatial coordinates of the corresponding construction object. When the equipment operation constraint changes, the equipment operation state is re-associated with the equipment operation range and work space occupancy state. When the construction stage constraint changes, the process execution state is re-associated with the construction floor and construction process plan. When the environmental effect constraint changes, the environmental effect state is re-associated with the structural response state and equipment operation state. When the area occupancy constraint changes, the work space occupancy state is re-associated with the equipment operation state and process execution state, thus obtaining the updated object state relationships.

[0054] The updated simulation constraints and object state relationships are written into the digital twin construction model. The construction object layer, sub-object units, multi-dimensional state matrix, and construction coordinate system in the digital twin construction model are retained. The boundary conditions, constraint connections, and state attributes affected by construction disturbance events in the digital twin construction model are updated to form a disturbance-driven twin model. The disturbance-driven twin model includes the structural response simulation constraints, equipment operation simulation constraints, construction procedure simulation constraints, environmental effect simulation constraints, and work space simulation constraints after being updated by the boundary condition update package.

[0055] It should be noted that the disturbance-driven twin model is formed by locally updating the boundary conditions and state relationships affected by construction disturbance events, based on the unchanged construction object layer, sub-object units, multi-dimensional state matrix, and construction coordinate system of the digital twin construction model. The key parameters in the disturbance-driven twin model include structural displacement, structural strain, structural vibration, equipment position, equipment speed, equipment load, process completion progress, temperature, humidity, wind speed, air pressure, personnel distribution, equipment operating range, and material stacking position. The key parameters are recorded in the state attributes of the multi-dimensional state matrix according to the construction object identifier, construction coordinate system, and event occurrence time.

[0056] S2.5: Extract object state records from the construction state set, and extract model simulation state records from the disturbance-driven twin model that are under the same construction object identifier and the same construction coordinate system as the object state records, forming virtual-real state comparison records; perform state deviation analysis on the virtual-real state comparison records to generate virtual-real deviation data; calculate the credibility evaluation value of the virtual-real state comparison records based on the virtual-real deviation data, and summarize the credibility evaluation values ​​according to the construction object identifier and construction coordinate system to generate a twin credibility evaluation set.

[0057] Based on the construction object identification and construction coordinate system, the model simulation state records under the same construction object identification and construction coordinate position are searched in the disturbance-driven twin model. The structural response state, equipment operation state, process execution state, environmental action state, component installation state, and work space occupancy state in the object state record are paired with the structural response simulation state, equipment operation simulation state, process execution simulation state, environmental action simulation state, component installation simulation state, and work space occupancy simulation state in the model simulation state record, respectively, to form a virtual-real state comparison record. The displacement, strain, and vibration amplitude in the structural response state are read, and the simulated displacement, simulated strain, and simulated vibration amplitude in the structural response simulation state are read. The differences between displacement and simulated displacement, strain and simulated strain, and vibration amplitude and simulated vibration amplitude are calculated. The displacement difference, strain difference, and vibration amplitude difference are aggregated according to the construction coordinate position to form structural response deviation data.

[0058] The system reads the operating position, operating speed, load status, and start / stop status from the equipment's operating status, and reads the simulated operating position, simulated operating speed, simulated load status, and simulated start / stop status from the equipment's operating simulation status. It calculates the offset between the actual operating position and the simulated operating position, the difference between the actual operating speed and the simulated operating speed, and the deviation between the actual load status and the simulated load status. It also determines whether the actual start / stop status is consistent with the simulated start / stop status, generating equipment operating deviation data. The system reads the current process, construction floor, and process completion progress from the process execution status, and reads the simulated current process, simulated construction floor, and simulated process completion progress from the process execution simulation status. It compares the names of the current process and the simulated current process to generate a process name consistency result. It compares the floor positions of the construction floor and the simulated construction floor to generate a floor position deviation result. It calculates the progress difference between the process completion progress and the simulated process completion progress, generating a process progress deviation result. Finally, it aggregates the process name consistency result, floor position deviation result, and process progress deviation result to form process execution deviation data.

[0059] Read the temperature, humidity, wind speed, and air pressure from the environmental action state, and read the simulated temperature, humidity, wind speed, and air pressure from the environmental action simulation state. Calculate the differences between the temperature and simulated temperature, humidity and simulated humidity, wind speed and simulated wind speed, and air pressure and simulated air pressure to form environmental action deviation data. Read the installation position, connection status, and installation completion status from the component installation state, and read the simulated installation position, simulated connection status, and simulated installation completion status from the component installation simulation state. Calculate the offset between the installation position and the simulated installation position, determine whether the connection status is consistent with the simulated connection status, and determine whether the installation completion status is consistent with the simulated installation completion status to form component installation deviation data. Read the work space... The system collects data on personnel distribution, equipment operating range, and material stacking locations in the workspace occupancy state, and reads the simulated personnel distribution, simulated equipment operating range, and simulated material stacking locations in the workspace occupancy simulation state. It calculates the difference between the personnel distribution area and the simulated personnel distribution area to obtain overlap differences, calculates the difference between the equipment operating range and the simulated equipment operating range to obtain boundary differences, and calculates the difference between the material stacking location and the simulated material stacking location to obtain offsets. These are combined to form workspace occupancy deviation data. The system then organizes the structural response deviation data, equipment operation deviation data, process execution deviation data, environmental effect deviation data, component installation deviation data, and workspace occupancy deviation data according to the construction object identification, construction coordinate system, and acquisition time to form virtual-real deviation data.

[0060] The deviation data for structural response, equipment operation, process execution, environmental effects, component installation, and workspace occupancy were compared with their corresponding allowable deviation ranges. These allowable deviation ranges were set according to building structural design codes, construction design documents, construction process plans, and construction equipment operation requirements. Specifically, the structural displacement deviation was 0% to 10% of the corresponding design allowable displacement; the structural strain deviation was 0% to 10% of the corresponding design allowable strain; the structural vibration amplitude deviation was 0% to 10% of the corresponding design allowable vibration amplitude; the equipment operating position offset was 0% to 15% of the allowable deviation for construction equipment layout; and the equipment operating speed deviation was 0% to 10% of the equipment's rated operating speed. The deviation of equipment load is 0% to 10% of the rated load of the equipment; the difference in the progress of the work process is 0% to 5% of the planned progress of the construction process; the temperature difference is 0℃ to 5℃; the humidity difference is 0%RH to 10%RH; the wind speed difference is 0% to 10% of the wind speed limited by the construction plan; the air pressure difference is 0kPa to 3kPa; the offset of component installation position is 0% to 10% of the allowable deviation of the design installation; the deviation area of ​​personnel distribution is 0% to 10% of the area of ​​the designed work area; the overlap area of ​​equipment operation range is 0% to 10% of the area of ​​the equipment operation range; and the offset of material stacking position is 0% to 15% of the allowable deviation of the construction layout. Deviation data within the allowable deviation range are recorded as reliable deviation data, and deviations exceeding the allowable deviation range are recorded as reliable deviation data. Deviations within the specified range are categorized as low-confidence deviations. Confidence and low-confidence deviations are statistically analyzed within the same virtual-to-real state comparison record. The number of confidence deviations is compared to the total number of deviations to obtain the confidence percentage. The deviation exceeding the allowable range in the low-confidence deviations is compared to the upper limit of the allowable range to obtain the low-confidence exceedance ratio. The confidence rating of the virtual-to-real state comparison record is determined based on the confidence percentage and the low-confidence exceedance ratio. Confidence ratings within the same construction object identifier and the same construction coordinate range are categorized to obtain structural response confidence ratings, equipment operation confidence ratings, process execution confidence ratings, environmental effect confidence ratings, and component safety confidence ratings. The reliability evaluation values ​​for installation and work space occupancy are weighted according to the degree of influence of the state attribute type on construction safety. Among them, the reliability evaluation values ​​for structural response, component installation, and work space occupancy have greater weights than those for equipment operation, process execution, and environmental effects. The reliability evaluation values ​​are weighted to obtain a weighted reliability evaluation value. The lowest reliability evaluation value is extracted from each reliability evaluation value. The minimum value between the weighted reliability evaluation value and the lowest reliability evaluation value is taken as the comprehensive reliability evaluation value within the coordinate range of the construction object. The values ​​are then summarized according to the floor location and the construction object identifier to generate a twin reliability evaluation set.

[0061] The expression for the credibility rating is: ; Where Q is the credibility evaluation value of the virtual-real state comparison record, C is the credibility ratio, and R is the low credibility excess ratio. The higher the credibility ratio, the higher the credibility evaluation value; the higher the low credibility excess ratio, the lower the credibility evaluation value.

[0062] The same virtual-real state comparison record includes six types of deviation data: structural response deviation data, equipment operation deviation data, process execution deviation data, environmental effect deviation data, component installation deviation data, and work space occupancy deviation data. Among them, four types of deviation data are within the allowable deviation range, and two types of deviation data exceed the allowable deviation range. The credibility ratio C is 4 / 6, or 0.67. The individual excess ratios of the two low credibility deviation data are 0.2 and 0.3, respectively. After averaging, the low credibility excess ratio R is 0.25. According to the credibility evaluation value expression, Q is 0.54. The credibility evaluation value is used as the credibility evaluation value within the coordinate range of the corresponding construction object.

[0063] S3: Locate the low-confidence state region in the disturbance-driven twin model based on the twin credibility evaluation set, perform state correction on the low-confidence state region, and generate a credible construction twin.

[0064] S3.1: According to the construction object identification and construction coordinate system, the confidence evaluation value that is lower than the preset confidence condition is matched with the model simulation state record in the disturbance-driven twin model, and aggregated to form a low confidence state region.

[0065] The credibility evaluation values ​​are sorted according to the construction object identifier and construction coordinate system. The distribution of credibility evaluation values ​​within the same construction object identifier and the same construction coordinate range is extracted. The credibility evaluation value in the middle position after sorting the credibility evaluation value sequence is taken as the median credibility evaluation value. The absolute difference between each credibility evaluation value in the credibility evaluation value sequence and the median credibility evaluation value is averaged to obtain the credibility fluctuation range. The median credibility evaluation value and the credibility fluctuation range are subtracted to obtain the credibility lower limit. When the credibility evaluation value is lower than the credibility lower limit, the credibility evaluation value is judged as a low credibility evaluation value. The low credibility evaluation value is then searched in the disturbance-driven twin model according to the construction object identifier, construction coordinate system, and collection time. Model simulation state records within the same construction object identifier, the same construction coordinate location, and the same collection time range are searched. A matching relationship between the low credibility evaluation value and the model simulation state record is established. Model simulation state records with adjacent construction coordinate locations, the same construction floor, or a construction process relationship are spatially aggregated to form low credibility state fragments.

[0066] The low-confidence state segments are organized according to floor location, construction object identification, and spatial continuity. If multiple low-confidence state segments are continuously distributed in the construction coordinate system, or if multiple low-confidence state segments have upstream and downstream connections in the construction process, then multiple low-confidence state segments are merged into a low-confidence state region. The low-confidence state region is used to represent the model region in the disturbance-driven twin model that has insufficient confidence and needs state correction.

[0067] S3.2: Perform state deviation correction on the actual state data in the low-confidence state region and the model simulation state record in the disturbance-driven twin model to generate a correction amount; update the model simulation state record of the disturbance-driven twin model with the correction amount to obtain a reliable construction twin.

[0068] The deviation direction and magnitude between the state data in the construction state set and the model simulation state record are read. The state data is used as the correction target, the model simulation state record as the object to be corrected, and the deviation direction as the correction direction and the deviation magnitude as the correction intensity, forming correction quantities. These correction quantities include structural response correction quantities, equipment operation correction quantities, process execution correction quantities, environmental effect correction quantities, component installation correction quantities, and work space occupancy correction quantities. These correction quantities are written into the model simulation state record in the disturbance-driven twin model according to the construction object identifier and construction coordinate system. The structural response state, equipment operation state, process execution state, and environmental effect state in the model simulation state record are then analyzed. The operational status, component installation status, and work space occupancy status are updated separately. After the update is completed, the updated model simulation status record is compared with the actual status data in the low confidence status area to generate corrected deviation data. When the corrected deviation data is within the corresponding allowable deviation range and the newly obtained confidence evaluation value meets the confidence condition, the low confidence status area is marked as the corrected status area, and the corrected disturbance-driven twin model is used as a reliable construction twin. When the corrected deviation data still exceeds the corresponding allowable deviation range, or the newly obtained confidence evaluation value does not meet the confidence condition, the low confidence status area is retained as a status area to be reviewed.

[0069] For example, the simulated displacement of the model corresponding to the low confidence state region of the outer frame beam on the east side of the 34th floor is 3.2 mm, the actual displacement is 6.8 mm, and the displacement deviation is 3.6 mm. According to the state deviation correction, the displacement correction is 3.0 mm. After writing the displacement correction into the disturbance-driven twin model, the updated model simulates a displacement of 6.2 mm. The corrected deviation between the updated model simulated displacement and the actual displacement is 0.6 mm, which is within the corresponding allowable deviation range. The recalculated confidence evaluation value increases from 0.54 to 0.82, and the second lowest confidence state region is marked as the correction passed state region.

[0070] S4: Perform a status risk assessment on the trusted construction twin, identify areas of concentrated risk, improve the local monitoring resolution of areas of concentrated risk, generate twin resolution configuration results, perform local high-precision monitoring on the trusted construction twin based on the twin resolution configuration results, and output monitoring results for super high-rise construction.

[0071] S4.1: Use the number of construction objects, object state complexity, and risk persistence status in the risk concentration area as parameters to improve the local monitoring resolution; configure the model update frequency, object partitioning granularity, state verification frequency, and local accuracy of the risk concentration area according to the local monitoring resolution improvement parameters, and generate twin resolution configuration results.

[0072] The deviations of structural response status, equipment operation status, process execution status, environmental impact status, component installation status, and workspace occupancy status, along with correction records, are used as the basis for status risk assessment. The deviations of each type of deviation are compared to their corresponding allowable deviation ranges to obtain deviation coefficients for each type of status. The values ​​of these deviation coefficients range from 0 to 1. When the deviation does not exceed the corresponding allowable deviation range, the status deviation coefficient is the ratio of the deviation to the allowable deviation range; when the deviation exceeds the allowable deviation range, the status deviation coefficient is 1. The correction amounts in the correction records are also compared to their corresponding allowable deviation ranges to obtain correction influence coefficients. The values ​​of these correction influence coefficients range from 0 to 1. When the correction amount does not exceed the corresponding allowable deviation range… The correction amount influence coefficient is the ratio of the correction amount to the corresponding allowable deviation range. When the correction amount exceeds the corresponding allowable deviation range, the correction amount influence coefficient is 1. The maximum state deviation coefficient among the structural response state, component installation state, and work space occupancy state is taken as the key risk contribution value. The maximum state deviation coefficient among the equipment operation state, process execution state, and environmental action state is taken as the auxiliary risk contribution value. The key risk contribution value, auxiliary risk contribution value, and correction amount influence coefficient are summed to obtain the state risk value. When the state risk value reaches the risk judgment condition, or when any state deviation coefficient among the structural response state, component installation state, and work space occupancy state is 1, the construction coordinate range is marked as a risk candidate area. The risk candidate areas are merged according to the construction floor, construction object identification, and spatial continuity range to determine the risk concentration area.

[0073] The system reads the number of construction objects, their state complexity, and the duration of risk within the risk concentration area. The number of construction objects is determined by the number of construction object identifiers within the risk concentration area. The state complexity is determined by the number of anomalies in structural response status, equipment operation status, process execution status, environmental impact status, component installation status, and workspace occupancy status within the risk concentration area. The duration of risk duration is determined by the length of time the risk concentration area maintains its risk candidate area marker during continuous data collection. These factors are used as parameters to improve local monitoring resolution. When the number of construction objects increases, the state complexity increases, or the duration of risk lengthens, a resolution level table is set based on construction monitoring computing resources, data acquisition frequency, and the calculable granularity of the digital twin construction model. The resolution level table includes six fields: level name, applicable conditions, model update cycle, object division granularity, status verification cycle, and local accuracy. It also includes three levels: standard monitoring, enhanced monitoring, and high-precision monitoring. The standard monitoring level updates every 5 acquisition cycles, is divided by the overall construction object, is verified every 5 acquisition cycles, and uses the initial configuration value for local accuracy. The enhanced monitoring level updates every 2 acquisition cycles, is divided by sub-regions of the construction object, is verified every 2 acquisition cycles, and increases local accuracy to twice the initial configuration value. The high-precision monitoring level updates every 1 acquisition cycle, is divided by component location or work section, is verified every 1 acquisition cycle, and increases local accuracy to the initial configuration value. The initial configuration value of local precision is three times that of the model's local representation precision without local monitoring resolution enhancement. When the number of construction objects does not exceed 3, the object state complexity does not exceed 1 type of anomaly, and the risk persistence state does not exceed 1 collection cycle, the conventional monitoring level is matched. When the number of construction objects exceeds 3, the object state complexity exceeds 1 type of anomaly, or the risk persistence state exceeds 1 collection cycle, the enhanced monitoring level is matched. When the number of construction objects exceeds 5, the object state complexity is not less than 3 types of anomaly, or the risk persistence state is not less than 3 collection cycles, the high-precision monitoring level is matched. The resolution level configuration model update frequency, object partitioning granularity, state verification frequency, and local precision are summarized to generate the twin resolution configuration result.

[0074] S4.2: Based on the twin resolution configuration results, the risk concentration area is divided into local high-precision monitoring areas, and the status of the local high-precision monitoring areas is refreshed to generate local refresh status data.

[0075] Based on the construction object identification and construction coordinate system of the risk-concentrated area, the risk-concentrated area is divided into a local high-precision monitoring area. The local high-precision monitoring area retains the structural response status, equipment operation status, process execution status, environmental effect status, component installation status, and work space occupancy status of the risk-concentrated area. The data refresh cycle of the local high-precision monitoring area is adjusted according to the twin resolution configuration results. According to the adjusted data refresh cycle, the current status of the local high-precision monitoring area is read from the trusted construction twin. At the same time, the latest collected data with the same construction object identification and the same construction coordinate range as the local high-precision monitoring area is read from the construction operation data. The structural response data, construction equipment operation data, construction process data, construction environment data, component status data, and work space data in the latest collected data are updated to the corresponding status of the local high-precision monitoring area to form local refresh status data.

[0076] S4.3: Perform fine-grained object decomposition on the local refresh status data to generate local subdivided status data; perform continuous virtual-real verification on the local subdivided status data to generate local high-precision reliable status data; summarize the local high-precision reliable status data, construction disturbance event set, twin reliability evaluation set, and twin resolution configuration results to output the super high-rise construction monitoring results.

[0077] When the object partitioning granularity is increased, the structural response state in the local refresh state data is split according to component location and stress section; the equipment operation state is split according to equipment operation trajectory and work range; the process execution state is split according to construction floor and process node; the environmental effect state is split according to spatial location and height range; the component installation state is split according to component number and installation location; and the work space occupancy state is split according to personnel area, equipment area, and material stacking area. This yields locally subdivided state data. Based on the state verification frequency in the twin resolution configuration result, the locally subdivided state data is read according to the continuous acquisition time. The model simulation state records with the same construction object identifier, the same construction coordinate position, and the same acquisition time range are read from the trusted construction twin. The structural response state, equipment operation state, process execution state, environmental effect state, component installation state, and work space occupancy state in the locally subdivided state data are compared with the same state in the model simulation state records to obtain continuous verification deviation data.

[0078] When the continuous verification deviation data does not exceed the allowable deviation range of the credibility evaluation value within the continuous acquisition time, the local subdivided state data is marked as credible state data. When the continuous verification deviation data exceeds the allowable deviation range of the corresponding state type at any acquisition time, the local subdivided state data is marked as state data to be reviewed. The construction object identifier, construction coordinate position, and deviation source of the state data to be reviewed are retained. The credible state data and the state data to be reviewed are organized according to the construction object identifier and construction coordinate system to generate local high-precision credible state data.

[0079] The local high-precision reliable status data, construction disturbance event set, twin reliability evaluation set, and twin resolution configuration results are summarized according to the construction object identification, construction coordinate system, and collection time to form the super high-rise construction monitoring results, which include construction disturbance events, reliability evaluation results, local high-precision monitoring status, pending verification status, and resolution configuration status.

[0080] For example, the monitoring results of super high-rise construction include the structural response change event of the outer frame beam on the east side of the 34th floor, the environmental action change event of the tower crane operation area from the 34th to the 35th floor, the comprehensive reliability evaluation value of 0.82 within the coordinate range of the corresponding construction object, the twin resolution configuration results corresponding to the high-precision monitoring level, and local high-precision reliable state data. Among them, the local high-precision reliable state data records a structural displacement of 6.2 mm, a structural strain of 178 με, a wind speed of 7.5 m / s, an area of ​​42 m² occupied by equipment operation, and a material stacking position offset of 0.8 m.

[0081] This embodiment also provides a computer device applicable to the super high-rise construction monitoring method based on digital twins, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the super high-rise construction monitoring method based on digital twins as proposed in the above embodiment.

[0082] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0083] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the digital twin-based super high-rise construction monitoring method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0084] In summary, this invention improves the organization, spatial correlation, and model usability of construction monitoring data by identifying construction objects, mapping construction coordinates, and aggregating object states to generate a construction state set. By refreshing the simulation constraint relationships and object state relationships in the digital twin construction model, a disturbance-driven twin model is generated, improving the reliability of model simulation and the realism of construction state analysis. By performing state correction, a reliable construction twin is generated, enhancing the local realism of the digital twin construction model and the credibility of construction monitoring results. By improving the local monitoring resolution, a twin resolution configuration result is generated, avoiding the waste of computing resources caused by global high-precision monitoring and improving the efficiency of monitoring resource utilization and the response accuracy of risk areas.

[0085] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the construction of super high-rise buildings based on digital twins, characterized in that: include, Acquire construction operation data from the construction site of super high-rise buildings, construct a digital twin construction model, and input the construction operation data into the digital twin construction model to identify construction objects, map construction coordinates, and collect object states, thereby generating a construction state set; The construction state set is subjected to state change detection to generate a construction disturbance event set. The boundary condition disturbance of the digital twin construction model is updated by the construction disturbance event set to generate a disturbance-driven twin model. The consistency between the virtual and real is verified by the model simulation state records in the construction state set and the disturbance-driven twin model to generate a twin credibility evaluation set. Based on the twin credibility evaluation set, the low credibility state region in the disturbance-driven twin model is located, and the state correction of the low credibility state region is performed to generate a credible construction twin. A condition risk assessment is performed on the trusted construction twin to identify areas of concentrated risk. The local monitoring resolution of these areas is then improved to generate twin resolution configuration results. Based on these twin resolution configuration results, local high-precision monitoring is performed on the trusted construction twin to output monitoring results for super high-rise construction.

2. The super high-rise construction monitoring method based on digital twin as described in claim 1, characterized in that: The specific steps for generating the construction status set are as follows: The construction operation data includes structural response data, construction equipment operation data, construction procedure data, construction environment data, component status data, work space data, construction object identification, and spatial location identification; Obtain basic construction information, including super high-rise building design information, construction procedure plan, construction equipment configuration, and model environment benchmark parameters; Based on the design information, construction procedure plan and construction equipment configuration of super high-rise buildings, a construction object and construction coordinate system are established, and the state attributes of the construction object are initialized to form an initial digital twin construction model. The model environment baseline parameters are mapped to the initial digital twin construction model, and correlation verification is performed to form a digital twin construction model. The construction operation data is divided into multiple object operation data groups according to the construction object identifier. The object operation data groups are mapped to the construction coordinate system in the digital twin construction model according to the spatial location identifier to generate object coordinate mapping data. Extract the structural response status, equipment operation status, process execution status, environmental effect status, component installation status, and work space occupancy status from the same object's coordinate mapping data, and organize them according to the construction object identifier and construction coordinate system to generate a construction status set.

3. The super high-rise construction monitoring method based on digital twin as described in claim 2, characterized in that: The specific steps for generating the construction disturbance event set are as follows: The object state records in the construction state set are compared with the model simulation state records in the digital twin construction model to generate state change data. Disturbance events are marked on the state change data to generate construction disturbance events, which are then aggregated into a construction disturbance event set.

4. The super high-rise construction monitoring method based on digital twin as described in claim 3, characterized in that: The specific steps for generating the perturbation-driven twin model are as follows. The event type is determined for construction disturbance events in the construction disturbance event set. When the construction disturbance event belongs to the structural response change type, the boundary parameters of the structural response data are transformed to generate structural response boundary update data. When a construction disturbance event is classified as a change in equipment operation, boundary parameter transformation is performed on the construction equipment operation data to generate updated equipment operation boundary data. When a construction disturbance event belongs to the construction stage change type, the boundary parameters of the construction process data are transformed to generate construction stage boundary update data. When the construction disturbance event belongs to the type of environmental action change, the boundary parameters of the construction environment data are transformed to generate environmental action boundary update data; When a construction disturbance event is classified as a change in area occupancy, boundary parameters are transformed on the work space data to generate updated area occupancy boundary data. The structural response boundary update data, equipment operation boundary update data, construction phase boundary update data, environmental action boundary update data, and area occupancy boundary update data are collected to form a boundary condition update package; The simulation constraint relationships and object state relationships in the digital twin construction model are refreshed based on the boundary condition update package, generating a disturbance-driven twin model.

5. The super high-rise construction monitoring method based on digital twin as described in claim 4, characterized in that: The specific steps for generating the twin credibility evaluation set are as follows: Extract object status records from the construction status set, and extract model simulation status records from the disturbance-driven twin model that are under the same construction object identifier and the same construction coordinate system as the object status records, forming a virtual-real status comparison record; Perform state deviation analysis on the virtual-to-real state comparison records to generate virtual-to-real deviation data; The credibility evaluation value of the virtual-real state comparison record is calculated based on the virtual-real deviation data, and the credibility evaluation value is summarized according to the construction object identification and construction coordinate system to generate a twin credibility evaluation set.

6. The super high-rise construction monitoring method based on digital twin as described in claim 5, characterized in that: The specific steps for generating a trusted construction twin are as follows: According to the construction object identification and construction coordinate system, the confidence evaluation value that is lower than the preset confidence condition is matched with the model simulation state record in the disturbance-driven twin model and aggregated to form a low confidence state region. State deviation correction is performed on the actual state data in the low-confidence state region and the model simulation state record in the perturbation-driven twin model to generate correction amount; A reliable construction twin is obtained by updating the model simulation state record of the disturbance-driven twin model with the correction amount.

7. The super high-rise construction monitoring method based on digital twin as described in claim 6, characterized in that: The specific steps for generating the twin resolution configuration result are as follows: The number of construction objects, the complexity of their status, and the duration of the risk in the high-risk area are used as parameters to improve the resolution of local monitoring. Based on the local monitoring resolution enhancement parameters, configure the model update frequency, object partitioning granularity, status verification frequency, and local accuracy for high-risk areas, and generate twin resolution configuration results.

8. The super high-rise construction monitoring method based on digital twin as described in claim 7, characterized in that: The specific steps for outputting the monitoring results of super high-rise construction are as follows. Based on the twin resolution configuration results, the risk concentration area is divided into local high-precision monitoring areas, and the status of the local high-precision monitoring areas is refreshed to generate local refresh status data. The partial refresh state data is split into fine-grained objects to generate local subdivided state data; Continuous virtual-real verification is performed on local subdivided state data to generate local high-precision reliable state data; The system summarizes local high-precision reliable state data, construction disturbance event set, twin reliability evaluation set, and twin resolution configuration results to output the monitoring results of super high-rise construction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the super high-rise construction monitoring method based on digital twins as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the super high-rise construction monitoring method based on digital twins as described in any one of claims 1 to 8.

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