A construction risk early warning system and method based on dynamic simulation

Abstract

The application relates to a construction risk early warning system and method based on dynamic simulation. The early warning system at least comprises a data acquisition platform, a digital twin platform and a central control platform. The data acquisition platform acquires feature data in a construction site in real time through information collection equipment arranged at the construction site and / or remotely, and transmits the feature data to the digital twin platform and the central control platform. The digital twin platform establishes a corresponding three-dimensional model based on original data of the construction site, updates the three-dimensional model by using the feature data, and displays the established three-dimensional model through a configured display unit. The central control platform analyzes the feature data, and when the analysis result is that there is a risk, the analysis result is transmitted to the digital twin platform. The digital twin platform generates a danger prompt model corresponding to the analysis result, and transmits the danger prompt model to the display unit to visually display the impending danger.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, and in particular to a construction risk early warning system and method based on dynamic simulation. Background Technology

[0002] Currently, urbanization has entered a new era, meaning that when new civil engineering projects are carried out, there are often existing buildings around the construction site that cannot be demolished. During construction, it is necessary to ensure the safety of the new project while also ensuring that it does not compromise the structural safety of the existing buildings. For example, when laying underground pipelines or building subway stations, it is necessary to avoid damaging surface buildings, roads, or existing underground municipal pipelines (sewage pipes, natural gas pipelines).

[0003] Existing technologies, such as BIM, can transform two-dimensional construction design drawings into three-dimensional images before actual construction, providing a more intuitive display and facilitating on-site construction training. While existing BIM models can mark potentially risky areas, they lack the ability to predict risk areas during construction. In underground construction, the primary impact on safety lies in the properties of the soil layers. Current risk assessment methods mainly rely on finite element numerical simulations before formal construction, using experimental data for soil parameters that fail to accurately reflect the complex conditions of the construction site and the actual changes in soil parameters during underground construction.

[0004] Chinese Patent Publication No. CN105416343A discloses a comprehensive early warning method and system for railway construction. The method includes: collecting basic data from each individual business subsystem in the construction monitoring and management system; using engineering visualization technology and Geographic Information System (GIS) to fuse the basic data, generating early warning information for different business subsystems, and dynamically displaying the early warning information on an electronic map; monitoring the early warning information of the different business subsystems and obtaining the early warning values ​​corresponding to the different business subsystems; comparing the early warning values ​​corresponding to the different business subsystems with corresponding preset thresholds, and issuing a corresponding early warning signal if the early warning value corresponding to the different business subsystem is greater than the corresponding preset threshold.

[0005] The existing technology for risk warning by comparing warning values ​​with preset thresholds has at least the following shortcomings:

[0006] Existing technologies for monitoring soil parameters often include vibration monitoring, meaning that early warning values ​​include the vibration values ​​of the soil. However, since existing underground engineering projects often need to pass through the soil beneath buildings such as high-rises and roads, taking roads as an example, when construction is being carried out on the soil beneath the road, the road still needs to remain open. This can lead to the monitored soil vibration values ​​exceeding the preset threshold due to vibrations caused by vehicles, resulting in misjudgments.

[0007] To address the shortcomings of existing technologies, this invention provides a construction risk early warning and method based on dynamic simulation. This invention uses information collection devices installed at and / or remotely at the construction site to collect feature data of various entities in real time, and transmits this feature data to a digital twin platform and a central control platform. The digital twin platform builds corresponding 3D models based on the collected raw data of each entity at the construction site, and updates the 3D models according to the feature data, thereby achieving dynamic simulation of the construction process. The central control platform uses a big data analysis model to analyze the feature data. When the analysis result indicates the presence of risk, it sends a risk warning to the relevant departments for risk management, and transmits the analysis results to the digital twin platform for visualization of the impending danger. The central control platform has at least a first risk analysis mode and a second risk analysis mode. The first risk analysis mode involves the central control platform comparing feature data with a preset threshold, and the second risk analysis mode involves the central control platform determining whether entities corresponding to feature data exceeding the preset threshold pose a risk.

[0008] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0009] In view of the shortcomings of existing technologies, this invention aims to associate the surrounding environment of the project with the project structure, and to dynamically simulate the changes of the project structure and the surrounding environment during the construction process through a three-dimensional model. At the same time, this invention can also realize an early warning function based on the changes of the project structure and the surrounding environment.

[0010] A construction risk early warning system based on dynamic simulation includes at least a data acquisition platform, a digital twin platform, and a central control platform. The data acquisition platform collects feature data of various entities at the construction site in real time using information collection devices located at and / or remotely, and transmits this feature data to the digital twin platform and the central control platform. The digital twin platform builds corresponding 3D models based on the collected raw data of each entity at the construction site, updates the 3D models according to the feature data, and displays the built 3D models through a configured display unit. The central control platform analyzes the feature data using a big data analysis model. When the analysis result indicates the existence of a risk, it sends a risk warning to the relevant department for risk management and transmits the analysis result to the digital twin platform. In response to the receipt of the analysis result, the digital twin platform extracts a partial model corresponding to the analysis result from the continuously updated 3D model to generate a hazard warning model, and transmits the hazard warning model to the display unit to visualize the impending hazard.

[0011] According to a preferred embodiment, the data acquisition platform, the digital twin platform, and the central control platform share a data storage platform. The data storage platform pre-stores the original data of each entity at the construction site and can store feature data collected by the data acquisition platform, 3D model data established by the digital twin platform, and analysis results from the central control platform throughout the entire construction process.

[0012] According to a preferred embodiment, the data acquisition platform includes at least a first information collection device and a second information collection device with a different collection method than the first information collection device, as well as a first processing unit. Preferably, the first information collection device and the second information collection device simultaneously collect the feature data of the same entity at the construction site. The first processing unit performs cross-verification on the feature data collected by the first information collection device and the feature data collected by the second information collection device to determine the accuracy of the feature data.

[0013] According to a preferred embodiment, the central control platform is equipped with at least a first risk analysis mode and a second risk analysis mode. Preferably, the first risk analysis mode refers to the central control platform comparing the feature data sent by the data acquisition platform with a preset threshold to determine whether the entity corresponding to the feature data poses a risk. Preferably, the second risk analysis mode refers to the central control platform summarizing the ratio of the feature data at different times to its corresponding preset threshold, and determining whether the entity corresponding to the feature data poses a risk by analyzing the trend of the ratio's change.

[0014] According to a preferred embodiment, the digital twin platform is further configured with a modeling unit and a second processing unit. The second processing unit is equipped with at least a first modeling instruction, a second modeling instruction, and a third modeling instruction capable of switching the modeling basis of the modeling unit. In response to the initialization of the digital twin platform, the second processing unit sends the first modeling instruction to the modeling unit, causing the modeling unit to acquire the original data of each entity at the construction site from the data storage platform to establish a corresponding 3D model. In response to the completion of the 3D model, the second processing unit sends the second modeling instruction to the modeling unit, causing the modeling unit to continuously acquire the feature data collected by the data acquisition platform to update the 3D model. In response to the receipt of the analysis result, the second processing unit sends the third modeling instruction to the modeling unit, causing the modeling unit to extract a local model corresponding to the analysis result from the continuously updated 3D model to generate a hazard warning model.

[0015] According to a preferred embodiment, the central control platform includes at least a third processing unit and a risk analysis unit. The third processing unit sends instructions to the risk analysis unit to perform analysis using either a first risk analysis mode or a second risk analysis mode. If the risk analysis unit determines that a risk exists, the third processing unit transmits the analysis result to the digital twin platform.

[0016] According to a preferred embodiment, the display unit is equipped with an interactive module. The user can select a portion of the 3D model for observation via the interactive module. When observing a portion of the model, the user can adjust the depth of field through the interactive module. Preferably, when the user views the graphics and their attributes within the specified depth of field of the portion of the model, the image is clear. When the user reaches the edge of the portion of the model, the graphics within the edge are of high definition, while the graphics outside the edge have reduced clarity. This prompts the user that the viewed content exceeds the portion of the model, allowing the user to re-plan the viewed portion of the model and avoid viewing without a clear target.

[0017] According to a preferred embodiment, the first processing unit is also capable of preprocessing the received feature data. Preferably, the preprocessing operation refers to removing duplicate data and integrating discrepancies in the feature data.

[0018] This invention also provides a construction risk early warning method based on dynamic simulation. The method includes at least:

[0019] Collect raw data of each entity at the construction site to create corresponding 3D models;

[0020] Real-time acquisition of characteristic data of various entities at the construction site through information collection equipment set up at the construction site and / or remotely;

[0021] The 3D model is updated based on the feature data, and the established 3D model is displayed through the display unit;

[0022] The feature data is analyzed using a big data analysis model. When the analysis results indicate the presence of risk, the risk is addressed, and a hazard warning model is generated based on the analysis results, which can visualize the impending danger through a display unit.

[0023] According to a preferred embodiment, the method further includes:

[0024] A first information collection device and a second information collection device with a different collection method are set up for the same entity at the construction site. The feature data collected by the first information collection device and the feature data collected by the second information collection device are cross-verified to determine the accuracy of the feature data. Attached Figure Description

[0025] Figure 1 This is a simplified schematic diagram of the module connection relationship of an early warning system according to a preferred embodiment of the present invention;

[0026] Figure 2 This is a simplified schematic diagram of the layout of a data acquisition platform according to a preferred embodiment of the present invention;

[0027] Figure 3 This is a simplified schematic diagram of the module connection relationship of a digital twin platform according to a preferred embodiment of the present invention;

[0028] Figure 4 This is a simplified schematic diagram of the module connection relationship of a central control platform according to a preferred embodiment of the present invention.

[0029] List of reference numerals

[0030] 100: Early warning system; 101: High-risk building; 110: Data acquisition platform; 111: First processing unit; 112: Monitoring drone; 113: Laser sensor; 120: Digital twin platform; 121: Second processing unit; 122: Modeling unit; 123: Display unit; 130: Central control platform; 131: Third processing unit; 132: Risk analysis unit; 140: Data storage platform. Detailed Implementation

[0031] The following is a detailed description with reference to the accompanying drawings. The technical solution of this invention provides a construction risk early warning system based on dynamic simulation. The early warning system establishes three-dimensional models of various entities at the construction site through a digital twin platform, and then collects feature data of each entity at the construction site through a data acquisition platform equipped with at least two data acquisition methods. The data acquisition platform can cross-verify the feature data collected through different data acquisition methods to determine the accuracy of the feature data. The data acquisition platform transmits the feature data to both the digital twin platform and the central control platform. The digital twin platform updates the three-dimensional model based on the feature data, thereby realizing dynamic simulation of the construction process. The central control platform analyzes the feature data using a big data analysis model. When the analysis result indicates the existence of a risk, the analysis result is transmitted to the digital twin platform to visualize the impending danger.

[0032] Example 1

[0033] This embodiment relates to a construction risk early warning system 100 based on dynamic simulation. Preferably, this embodiment can be applied to subway station construction projects. Preferably, the safety risk management platform 100 establishes the relationship between the surrounding environment and the engineering structure of the project, and builds a complete three-dimensional model based on relevant drawings and other technical data. During the actual construction process, the data acquisition platform 110 collects the feature data of each entity on the construction site in real time, updates the three-dimensional model, thereby realizing dynamic tracking of on-site construction and enabling early warning functions based on risk conditions.

[0034] See Figure 1 Preferably, the early warning system 100 includes at least a data acquisition platform 110, a digital twin platform 120, a central control platform 130, and a data storage platform 140. The data acquisition platform 110 collects feature data of various entities at the construction site in real time using information collection devices installed at and / or remotely, and transmits the feature data to the digital twin platform 120 and the central control platform 130 respectively. The digital twin platform 120 establishes corresponding three-dimensional models based on the original data of each entity at the construction site, updates the three-dimensional models according to the feature data, and displays the established three-dimensional models through a configured display unit 123. The central control platform 130 analyzes the feature data using a big data analysis model, and when the analysis result indicates the existence of a risk, sends a risk warning to the relevant departments for risk management, and transmits the analysis results to the digital twin platform 120. In response to the receipt of the analysis results, the digital twin platform 120 extracts a local model corresponding to the analysis results from the continuously updated 3D model to generate a hazard warning model, and transmits the hazard warning model to the display unit 123 to visualize the impending hazard.

[0035] Preferably, the data acquisition platform 110, the digital twin platform 120, and the central control platform 130 share the data storage platform 140. The data storage platform 140 pre-stores the original data of each entity at the construction site and can store the feature data collected by the data acquisition platform 110, the three-dimensional model data established by the digital twin platform 120, and the analysis results of the central control platform 130 throughout the construction process.

[0036] Preferably, the data acquisition platform 110 includes at least a first information collection device and a second information collection device with a different collection method than the first information collection device, as well as a first processing unit 111. Preferably, the first information collection device and the second information collection device simultaneously collect feature data of the same entity at the construction site. The first processing unit 111 cross-verifies the feature data collected by the first information collection device and the feature data collected by the second information collection device to determine the accuracy of the feature data.

[0037] Preferably, the first processing unit 111 is also capable of preprocessing the received feature data. Preferably, the preprocessing operation refers to removing duplicate data and integrating discrepancies in the feature data.

[0038] Preferably, in the construction of subway stations, this embodiment monitors the settlement data of surface buildings through the data acquisition platform 110, and uses the settlement data of surface buildings as its feature data for updating the three-dimensional model and risk analysis.

[0039] See Figure 2 Preferably, the first processing unit 111 of the data acquisition platform 110 collects feature data of the entities at the construction site using two types of information collection devices. Preferably, the first processing unit 111 uses the information collection devices to collect settlement data of the foundation of the risky building 101 affected by the construction of the subway station. Preferably, the first information collection device can be a laser sensor 112 installed around the risky building 101. Preferably, the second information collection device can be a monitoring drone 113 that collects settlement data of the foundation of the risky building 101 from the air using image recognition technology.

[0040] According to a preferred embodiment, the data acquisition platform 110 uses the data collected by the laser sensor 112 as real-time settlement data of the foundation of the risky building 101, and the data acquisition platform 110 uses the settlement data of the foundation of the risky building 101 collected by the monitoring drone 113 as verification data. Preferably, the laser sensor 112 is set around the risky building 101 to collect settlement data at multiple locations on the foundation of the risky building 101, and establishes a data connection with the first processing unit 111 via wired or wireless means. Preferably, the laser sensor 112 sends the collected settlement data to the first processing unit 111. After verifying the correctness of the settlement data, the first processing unit 111 sends it to the digital twin platform 120 and the central control platform 130 for updating the three-dimensional model and risk warning.

[0041] During underground construction projects such as subway station construction, the soil and existing buildings above the construction area are prone to settlement. When the settlement value is within a safe range, it will not pose a danger to the entities (existing buildings, engineering structures, etc.) in the construction area. However, when abnormal settlement occurs, the existing buildings and engineering structures in the construction area will be at risk.

[0042] Taking a building as an example, when there is an underground construction area beneath a building, the building is considered a risk building (Building 101). The foundation of Risk Building 101 will settle as construction progresses. When uneven settlement occurs in the foundation of Risk Building 101, that is, when the settlement value of one part of the foundation exceeds that of other parts, shear stress will be generated within the building structure, causing vertical relative displacement of the local structural system and subjecting the structure to unexpected stress. When the building structure cannot withstand the stress, shear failure will occur, commonly manifested as shear cracks in the walls. Wall cracks caused by uneven settlement are structural damage cracks. The hazards they cause range from minor impacts on the building's aesthetics to severe damage such as water seepage and air intrusion, affecting the building's functionality and causing psychological anxiety for the residents; in severe cases, it can lead to wall collapse, roof collapse, injuries, and property damage. Furthermore, even if the settlement of the foundation of the risky building 101 is uniform, if the settlement value exceeds the safe range, it may cause the road surface connected to the foundation to crack and form potholes, or even damage the structure of the underground construction space, leading to accidents such as collapse and ground subsidence.

[0043] Since collapse, subsidence, or building cracks are all processes of energy accumulation, this embodiment analyzes the settlement data of the foundation of the risky building 101 to predict whether the foundation will experience uneven settlement that causes the internal stress of the building to exceed the structural bearing capacity, and whether the uneven settlement of the foundation of the risky building 101 will cause dangers such as collapse or subsidence.

[0044] Preferably, the monitoring drone 113 periodically or non-periodically collects data on the foundation of the risky building 101. Preferably, the monitoring drone 113 flies over the risky building 101 to collect settlement data of the foundation of the risky building 101 within a certain time period, and then sends the collected data as verification data to the first processing unit 111.

[0045] The first processing unit 111 filters settlement data within the same time period from the settlement data collected by the laser sensor 112 based on the acquisition time of the verification data. By comparing the two sets of settlement data, the correctness of the settlement data collected by the laser sensor 112 is determined. Preferably, when the first processing unit 111 determines that the settlement data collected by the laser sensor 112 is correct, the first processing unit 111 will send it to the digital twin platform 120 and the central control platform 130 for updating the three-dimensional model and risk warning.

[0046] Preferably, when the first processing unit 111 determines that the settlement data collected by the laser sensor 112 is correct, the first processing unit 111 sends the settlement data collected by the laser sensor 112 to the digital twin platform 120 and the central control platform 130 for updating the three-dimensional model and risk warning.

[0047] Preferably, when the first processing unit 111 determines that the settlement data collected by the laser sensor 112 is incorrect, the first processing unit 111 stops transmitting the settlement data collected by the laser sensor 112 to the digital twin platform 120 and the central control platform 130, and dispatches maintenance personnel to troubleshoot the laser sensor 112.

[0048] Preferably, when the first processing unit 111 determines that the settlement data collected by the laser sensor 112 is incorrect, the first processing unit 111 may also choose to send the settlement data of the foundation collected by the monitoring drone 113 when it flies over the risky building 101 to the digital twin platform 120 and the central control platform 130 for updating the three-dimensional model and risk warning when dispatching maintenance personnel to troubleshoot the laser sensor 112.

[0049] Preferably, while maintenance personnel are troubleshooting the laser sensor 112, the monitoring drone 113 continues to collect data on the foundation settlement of the building at risk 101 and sends the collected settlement data to the first processing unit 111.

[0050] Preferably, the monitoring drone 113 is inspected before each patrol, thus the probability of malfunction is lower than that of the laser sensor 112, and the maintenance of the monitoring drone 113 is more convenient than that of the laser sensor 112. Typically, the maintenance of the monitoring drone 113 only needs to be performed after takeoff and landing, allowing maintenance personnel to perform maintenance only at the takeoff and landing locations; whereas the maintenance of the laser sensor 112 requires maintenance personnel to enter the construction site and reach the location of the laser sensor 112 for inspection and maintenance. Due to the complex environment of the construction site, personnel entering the construction site inevitably pose certain risks. Preferably, setting two or more data acquisition methods can avoid data dependency in the early warning system 100, thereby preventing the early warning system 100 from failing due to sensor data errors.

[0051] Preferably, in this embodiment, the real-time settlement data of the foundation of the risky building 101 is collected by the laser sensor 112, and the verification data is collected periodically or non-periodically by the monitoring drone 113. While increasing the accuracy of the data and eliminating data dependence, the frequency of inspection personnel entering the construction site can be reduced, thereby reducing the risk of safety accidents and also reducing the obstruction of the normal construction process caused by the entry of inspection personnel into the construction site.

[0052] See Figure 3 Preferably, the digital twin platform 120 is further configured with a second processing unit 121, a modeling unit 122, and a display unit 123. Preferably, the second processing unit 121 is provided with at least a first modeling instruction, a second modeling instruction, and a third modeling instruction capable of switching the modeling basis of the modeling unit 122.

[0053] In response to the initialization of the digital twin platform 120, the second processing unit 121 sends a first modeling instruction to the modeling unit 122, causing the modeling unit 122 to acquire the original data of each entity at the construction site from the data storage platform 140 to establish the corresponding three-dimensional model. In response to the completion of the three-dimensional model, the second processing unit 121 sends a second modeling instruction to the modeling unit 122, causing the modeling unit 122 to continuously acquire feature data collected by the data acquisition platform 110 to update the three-dimensional model. In response to the receipt of the analysis results, the second processing unit 121 sends a third modeling instruction to the modeling unit 122, causing the modeling unit 122 to extract a local model corresponding to the analysis results from the continuously updated three-dimensional model to generate a hazard warning model.

[0054] Preferably, when the analysis result indicates the presence of risk, the modeling unit 122 marks the portion corresponding to the risk entity from the continuously updated 3D model with text or other information. For example, when the settlement data of the foundation of the risky building 101 is determined to be risky, the modeling unit 122 extracts a portion of the 3D model containing the risky building 101 from the entire project and adds corresponding warning information (such as abnormal settlement, settlement exceeding limits, etc.) to generate a hazard warning model.

[0055] Preferably, after the modeling unit 122 completes the creation of the 3D model, the display unit 123 displays the completed 3D model. Preferably, after the modeling unit 122 completes the update of the 3D model, the display unit 123 displays the updated 3D model. Preferably, after the modeling unit 122 generates the hazard warning model, the display unit 123 interrupts the display of the 3D model and switches the displayed content to the hazard warning model.

[0056] Preferably, the display unit 123 is equipped with an interactive module. Users can select a portion of the 3D model for observation through the interactive module. When observing a portion of the model, users can adjust the depth of field through the interactive module. Preferably, when a user views the graphics and their attributes within a specified depth of field, the image is clear. When the user reaches the edge of the local model area, the graphics within the edge are in high definition, while the graphics outside the edge have reduced clarity. This prompts the user that the viewed content exceeds the local model area, allowing the operator to re-plan the viewed local model area and avoid viewing without a clear target.

[0057] Preferably, when the display unit 123 interrupts the display of the 3D model and switches the displayed content to the danger warning model, the user closes the danger warning model through the interaction module, and the display unit 123 resumes the display of the 3D model.

[0058] Preferably, when observing details of a 3D model, the user can adjust the depth of field through the interactive module. The display unit 123 selects a local model corresponding to the user's observation target from the 3D model based on the user's adjusted depth of field. The internal area of ​​the local model has complete model parameters, thus ensuring that the image is complete and clear when the user views the internal area of ​​the local model. Preferably, to provide the user with a good sensory experience, the display unit 123 blurs the area outside the local model when selecting a local model, and sets the area outside the local model as a two-dimensional graphic at the edge of the local model. This alerts the user to the edge of the local model without disrupting the continuity of the user's observation, i.e., while maintaining a consistent image style, allowing the user to reselect the local model to be observed. In this embodiment, the depth of field range refers to the display range that can form a clear image on the display when acquiring a view at a limited distance.

[0059] See Figure 4 Preferably, the central control platform 130 includes at least a third processing unit 131 and a risk analysis unit 132. The third processing unit 131 sends instructions to the risk analysis unit 132 to perform analysis using a first risk analysis mode or a second risk analysis mode. If the analysis result of the risk analysis unit 132 indicates the existence of a risk, the third processing unit 131 transmits the analysis result to the digital twin platform 120.

[0060] Preferably, the central control platform 130 is equipped with at least a first risk analysis mode and a second risk analysis mode. Preferably, the first risk analysis mode refers to the central control platform 130 comparing the feature data sent by the data acquisition platform 110 with a preset threshold to determine whether the entity corresponding to the feature data poses a risk. Preferably, the second risk analysis mode refers to the central control platform 130 summarizing the ratios of feature data at different times to their corresponding preset thresholds, and determining whether the entity corresponding to the feature data poses a risk by analyzing the trend of the ratio changes.

[0061] Preferably, the risk analysis unit 132 performs risk analysis using a big data analysis model. Preferably, the risk analysis unit 132 acquires historical project data packages (including historical project structural data and corresponding historical environmental and risk data) through the data storage platform 140. The risk analysis unit 132 uses the historical project data packages to establish a big data analysis model. Preferably, the risk analysis unit 132 acquires the raw data of each entity at the construction site through the data storage platform 140 and inputs it into the big data analysis model to obtain preset thresholds for each entity at different construction stages. Due to differences in soil composition, soil depth, etc., the preset thresholds also differ at different construction stages.

[0062] Preferably, the risk analysis unit 132 obtains the original data of the risk building 101 (geometric parameters, building materials, and related soil layer parameters of the risk building 101) through the data storage platform 140, and uses a big data analysis model to obtain preset thresholds for the settlement data of the foundation of the risk building 101 at different stages of construction.

[0063] Preferably, the third processing unit 131 sends an instruction to the risk analysis unit 132 to perform risk analysis in the first risk analysis mode, either periodically or non-periodically, based on the risk analysis performed in the first risk analysis mode. Preferably, the first risk analysis mode allows the risk analysis unit 132 to perform point-to-point one-dimensional comparisons, comparing settlement data at a single moment with its preset threshold to determine whether a risk has occurred. This mode consumes minimal logical computation resources and enables real-time analysis. The second risk analysis mode allows the risk analysis unit 132 to perform line-segment two-dimensional analysis. In the second risk analysis mode, the risk analysis unit 132 needs to summarize and analyze settlement data over a period of time with its corresponding preset thresholds. This not only consumes more logical computation resources but also requires a certain sample size (settlement data and its preset thresholds) to function. By performing risk analysis in the first risk analysis mode periodically or non-periodically, the risk analysis unit 132 can enhance the accuracy of the analysis structure while achieving real-time analysis.

[0064] Preferably, the risk analysis unit 132 performs continuous risk analysis in a first risk analysis mode. Preferably, when the risk analysis unit 132's previous analysis result in the first risk analysis mode indicates the existence of risk, and the subsequent analysis result indicates the absence of risk, the risk analysis unit 132 switches to a second risk analysis mode. Preferably, the risk analysis unit 132 only generates one analysis result indicating the existence of risk in the first risk analysis mode, and it cannot be determined whether this result is due to accidental factors (such as a sudden increase in foundation settlement data caused by construction vehicles passing by the risky building). In this case, the risk analysis unit 132 actively switches to the second risk analysis mode for analysis, thereby avoiding misjudgment.

[0065] When the risk analysis unit 132 determines that there is a risk in the second risk analysis mode, the central control platform 130 sends a risk warning to the handling department to handle the risk, and transmits the analysis results to the digital twin platform 120.

[0066] Preferably, when the risk analysis unit 132 obtains two consecutive analysis results indicating the existence of risk under the first risk analysis mode, the central control platform 130 sends a risk warning to the disposal department to handle the risk, and transmits the analysis results to the digital twin platform 120. Preferably, when the risk analysis unit 132 obtains two consecutive analysis results indicating the existence of risk under the first risk analysis mode, it can be confirmed that the trend of settlement data change exceeds the preset threshold, and the risk analysis unit 132 does not need to perform analysis under the second risk analysis mode, thereby saving warning response time.

[0067] Preferably, the analysis results of risk analysis unit 132 in the second risk analysis mode have higher priority than the analysis results of risk analysis unit 132 in the first risk analysis mode. When the analysis result of risk analysis unit 132 in the first risk analysis mode is that there is no risk, but the analysis result of risk analysis unit 132 in the second risk analysis mode is that there is a risk, the central control platform 130 sends a risk warning to the handling department to handle the risk, and transmits the analysis results to the digital twin platform 120. In other words, even if the feature data at each moment does not exceed the preset threshold within a certain period of time, if the trend of the change of the ratio of the feature data to the corresponding preset threshold within that period of time gradually increases, the analysis result of risk analysis unit 132 is that there is a risk.

[0068] Preferably, when the monitoring drone 113 is conducting inspections, the drone operator can request the central control platform 130 to perform risk analysis in the second risk analysis mode on the feature data of the abnormality observed by the operator based on the anomalies subjectively observed from the images transmitted by the monitoring drone 113.

[0069] Preferably, when workers at the construction site subjectively perceive an anomaly, they can send the perceived anomaly in the form of a text description to the central control platform 130 through their smart terminals or other devices, and request the central control platform 130 to analyze the anomaly.

[0070] Preferably, the central control platform 130 obtains the location and time of the anomaly from the text description sent by the staff. Then, the central control platform 130 obtains the characteristic data of the location of the anomaly within a certain period before the time of the anomaly from the data storage platform 140. The central control platform 130 performs risk analysis on the characteristic data through the risk analysis unit 132 in the second risk analysis mode, thereby confirming whether the anomaly will bring risks and thus improving the early warning capability of the early warning system.

[0071] Preferably, on-site workers or drone operators can request the central control platform 130 to analyze risk data at the location of the anomaly based on their subjectively perceived anomalies to confirm whether the anomaly poses a risk. Preferably, the anomalies subjectively perceived by drone operators are mainly generated through observation of images transmitted by the monitoring drone 113, primarily anomalies that can be visually detected, such as building tilting or the appearance of water flow. Preferably, the anomalies subjectively perceived by on-site workers, in addition to visually detectable anomalies, also include anomalies perceived through other bodily senses, such as vibration, wind, humidity, or even chest tightness that may be caused by poor air circulation.

[0072] Preferably, if the central control platform 130 fails to identify any risk, workers at the construction site or drone operators may request the central control platform 130 to analyze the characteristic data of the location of the anomaly based on their subjective perception of the anomaly, and send their perceived anomaly to the central control platform 130 via text or other means. Preferably, for risk analysis requests from workers at the construction site or drone operators, the central control platform 130 uses a second risk analysis mode to analyze the characteristic data of the location of the anomaly in the period preceding the anomaly's occurrence to confirm whether the anomaly poses a risk, thereby reducing the likelihood of missed detections by the early warning system.

[0073] Accidents caused by changes in ground pressure, such as abnormal settlement and tunnel collapses, involve a slow and continuous process of ground pressure accumulation and release. Therefore, compared to the threshold comparison analysis at a single moment in the second risk analysis mode, the second risk analysis mode, which analyzes the risk by analyzing the changing trend of the ratio of characteristic data to its corresponding preset threshold over a period of time, has higher reliability and can eliminate the interference of abnormal data. For example, when monitoring the settlement of the foundation of risky building 101, if accidental factors such as vehicles passing by cause the settlement data of the foundation of risky building 101 to exceed the preset threshold at a certain moment, the analysis result of risk analysis unit 132 in the first risk analysis mode will indicate that there is a risk. If the second risk analysis mode is not switched, the central control platform 130 will output the analysis result indicating that there is a risk, thus causing misjudgment. However, in this embodiment, the analysis unit 132 can switch to the second risk analysis mode, and by analyzing the changing trend of the ratio of the settlement data of the foundation of risky building 101 to its corresponding preset threshold over a period of time, the interference of abnormal data can be eliminated, avoiding misjudgment.

[0074] For example, risk analysis unit 132 conducted four analyses over a period of time. In these four analyses, the unit values ​​of the settlement data of the foundation of risky building 101 were 1, 3, 1, and 7, respectively; the unit values ​​of the preset thresholds corresponding to the settlement data of the foundation of risky building 101 were 5, 10, 2, and 8, respectively. Although the settlement data of the foundation of risky building 101 did not exceed the corresponding preset threshold in each analysis, the ratio of the settlement data of the foundation of risky building 101 to its corresponding preset threshold has continuously increased from 1 / 5 to 7 / 8. Therefore, the analysis result of analysis unit 132 is that there is a risk.

[0075] Example 2

[0076] This embodiment is a further improvement on embodiment 1, and repeated content will not be described again.

[0077] This embodiment also provides a construction risk early warning method based on dynamic simulation. The early warning method includes at least:

[0078] Collect raw data of each entity at the construction site to create corresponding 3D models;

[0079] Real-time acquisition of characteristic data of various entities at the construction site through information collection equipment set up at the construction site and / or remotely;

[0080] The 3D model is updated based on the feature data, and the established 3D model is displayed through the display unit 123;

[0081] The feature data is analyzed using a big data analysis model. When the analysis results indicate the presence of risk, the risk is addressed. Based on the analysis results, a hazard warning model is generated that can be visualized through display unit 123 to indicate an impending hazard.

[0082] Preferably, the early warning method further includes: setting up a first information collection device and a second information collection device with a different collection method for the same entity at the construction site, and cross-verifying the feature data collected by the first information collection device and the feature data collected by the second information collection device to determine the accuracy of the feature data.

[0083] Preferably, the early warning method uses the original data of each entity at the construction site pre-stored in the data storage platform 140, and can store the collected feature data, the established three-dimensional model data and the analysis results throughout the construction process.

[0084] Preferably, this early warning method includes at least a first risk analysis mode and a second risk analysis mode. Preferably, the first risk analysis mode compares feature data with a preset threshold to determine whether the entity corresponding to the feature data poses a risk; the second risk analysis mode summarizes the ratios of feature data at different times to their corresponding preset thresholds and analyzes the trend of these ratios to determine whether the entity corresponding to the feature data poses a risk.

[0085] Preferably, this early warning method includes a first modeling instruction, a second modeling instruction, and a third modeling instruction capable of switching the modeling basis. Preferably, the first modeling instruction allows the modeling unit 122 to obtain the original data of each entity at the construction site from the data storage platform 140 to establish a corresponding three-dimensional model. Preferably, the second modeling instruction allows the modeling unit 122 to continuously acquire feature data collected in real time by the information collection device to update the three-dimensional model. Preferably, the third modeling instruction allows the modeling unit 122 to extract a local model corresponding to the analysis results from the continuously updated three-dimensional model to generate a hazard warning model.

[0086] Preferably, the display unit 123 used in this warning method is equipped with an interactive module. Preferably, the user can select a portion of the 3D model for observation through the interactive module. When observing a portion of the model, the user can adjust the depth of field through the interactive module. Preferably, when the user views the graphics and their attributes within the portion of the model area based on a specified depth of field, the image is clear. When the user reaches the edge of the portion of the model area, the graphics within the edge are in high definition, while the graphics outside the edge have reduced clarity, thus prompting the operator that the viewed content exceeds the portion of the model area, allowing the operator to re-plan the viewed portion of the model area and avoid viewing without a clear target.

[0087] Preferably, this early warning method can also preprocess the received feature data. Preferably, the preprocessing operation refers to removing duplicate data and integrating discrepancies in the feature data.

[0088] Preferably, by setting two or more data acquisition methods, this early warning method can avoid data dependency, thereby preventing the early warning method from failing due to sensor data errors.

[0089] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of this invention is defined by the claims and their equivalents. Throughout the text, features introduced by "preferred" are merely optional and should not be construed as mandatory. Therefore, the applicant reserves the right to abandon or delete relevant preferred features at any time. This specification contains multiple inventive concepts. Phrases such as "preferred," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A construction risk early warning system based on dynamic simulation, characterized in that, This includes a data acquisition platform, a digital twin platform, and a central control platform; A data acquisition platform for monitoring ground settlement data of buildings collects characteristic data of various entities at the construction site in real time through information collection devices set up at the construction site and / or remotely, and transmits the characteristic data to a digital twin platform and a central control platform respectively. The data acquisition platform includes a first information collection device and a second information collection device with a different collection method than the first information collection device, as well as a first processing unit. The first and second information collection devices simultaneously collect characteristic data of the same entity at the construction site. The first processing unit verifies the characteristic data collected by the first and second information collection devices to determine the accuracy of the characteristic data. The first information collection device is a laser sensor set up around the risky building to collect foundation settlement data in real time, and the second information collection device is a monitoring drone that periodically or non-periodically collects foundation settlement data of the risky building from the air. The digital twin platform builds corresponding 3D models based on the original data of each entity at the construction site, updates the 3D models according to the feature data, and displays the 3D models through the configured display units. The settlement data of the surface buildings is used as feature data for updating the 3D models and risk analysis. The central control platform uses big data analysis models to analyze characteristic data. When the analysis results indicate that there is a risk, it sends a risk warning to the relevant departments to handle the risk and transmits the analysis results to the digital twin platform. In response to the receipt of the analysis results, the digital twin platform extracts a local model corresponding to the analysis results from the continuously updated 3D model to generate a hazard warning model, and transmits the hazard warning model to the display unit to visualize the impending hazard. The central control platform is equipped with a first risk analysis mode and a second risk analysis mode. The first risk analysis mode refers to the central control platform comparing the feature data sent by the data collection platform with a preset threshold to determine whether the entity corresponding to the feature data is at risk. The second risk analysis mode refers to the central control platform summarizing the ratio of characteristic data at different times to their corresponding preset thresholds, and judging whether there is a risk to the entity corresponding to the characteristic data by analyzing the trend of the ratio. The analysis results under the second risk analysis mode have higher priority than the analysis results under the first risk analysis mode.

2. The construction risk early warning system based on dynamic simulation according to claim 1, characterized in that, The data acquisition platform, the digital twin platform, and the central control platform share a data storage platform. The data storage platform pre-stores the original data of each entity at the construction site, and can store the feature data collected by the data acquisition platform, the three-dimensional model data established by the digital twin platform, and the analysis results of the central control platform throughout the construction process.

3. The construction risk early warning system based on dynamic simulation according to claim 2, characterized in that, The digital twin platform is also equipped with a modeling unit and a second processing unit. The second processing unit is equipped with a first modeling instruction, a second modeling instruction, and a third modeling instruction to switch the modeling basis of the modeling unit. In response to the initialization of the digital twin platform, the second processing unit sends the first modeling instruction to the modeling unit, enabling the modeling unit to obtain the original data of each entity at the construction site from the data storage platform to build the corresponding three-dimensional model. In response to the completion of the three-dimensional model, the second processing unit sends the second modeling instruction to the modeling unit, enabling the modeling unit to continuously obtain feature data collected by the data acquisition platform to update the three-dimensional model. In response to the receipt of analysis results, the second processing unit sends the third modeling instruction to the modeling unit, enabling the modeling unit to extract the local model corresponding to the analysis results from the continuously updated three-dimensional model to generate a hazard warning model.

4. The construction risk early warning system based on dynamic simulation according to claim 3, characterized in that, The central control platform includes at least a third processing unit and a risk analysis unit; The third processing unit sends an instruction to cause the risk analysis unit to perform analysis in either the first risk analysis mode or the second risk analysis mode; If the risk analysis unit determines that a risk exists, the third processing unit transmits the analysis result to the digital twin platform.

5. The construction risk early warning system based on dynamic simulation according to claim 1, characterized in that, The display unit is equipped with an interactive module; Users can select a part of the 3D model for observation through the interactive module; When observing a local model, users can adjust the depth of field through the interactive module. When the user views the graphics and their attributes within the local model area based on a specified depth of field, the image is clear. When the user reaches the edge of the local model area, the graphics within the edge are in high definition, while the graphics outside the edge have reduced clarity. This prompts the user that the content being viewed exceeds the local model area, allowing the user to re-plan the local model area being viewed and avoid viewing without a clear target.

6. The construction risk early warning system based on dynamic simulation according to claim 1, characterized in that, The first processing unit can also preprocess the received feature data, wherein the preprocessing operation refers to removing duplicate data and integrating differential data in the feature data.

7. A method for using the construction risk early warning system based on dynamic simulation as described in any one of claims 1 to 6, characterized in that, The method includes at least: Collect raw data of each entity at the construction site to create corresponding 3D models; Real-time acquisition of characteristic data of various entities at the construction site through information collection equipment set up at the construction site and / or remotely; The 3D model is updated based on the feature data, and the established 3D model is displayed through the display unit; The feature data is analyzed using a big data analysis model. When the analysis results indicate the presence of risk, the risk is addressed, and a hazard warning model is generated based on the analysis results, which can visualize the impending danger through a display unit. Set up a first risk analysis mode and a second risk analysis mode, where... The first risk analysis mode refers to comparing the feature data sent by the data acquisition platform with a preset threshold to determine whether the entity corresponding to the feature data is at risk; The second risk analysis mode refers to summarizing the ratio of the feature data at different times to its corresponding preset threshold, and judging whether the entity corresponding to the feature data has a risk by analyzing the changing trend of the ratio; The analysis results under the second risk analysis model have higher priority than the analysis results under the first risk analysis model. A first information collection device and a second information collection device with a different collection method are set up for the same entity at the construction site. The feature data collected by the first information collection device and the feature data collected by the second information collection device are cross-verified to determine the accuracy of the feature data.

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