Fabricated anti-slide pile service safety digital twinborn early warning system

Abstract

The invention relates to the technical field of geotechnical engineering and intelligent monitoring, and discloses an assembly type anti-slide pile service safety digital twinborn early warning system which comprises a sensing module used for collecting physical parameters of an anti-slide pile in real time and comprising a sensor array and a distributed optical fiber temperature measuring device; the transmission module is connected with the sensing module, is used for receiving and preprocessing the sensor data, and comprises a wireless communication unit and an edge computing unit; the digital twin module is connected with the transmission module, is used for constructing a pile-soil coupling dynamic model and updating the pile-soil coupling dynamic model in real time, and comprises a cloud numerical simulation engine and a three-dimensional visualization unit; and the early warning decision module is connected with the digital twinning module, is used for outputting and triggering a grading response mechanism according to the model, and comprises a risk judgment unit and a processing strategy library. According to the invention, the real-time monitoring and intelligent early warning of the health state of the slide-resistant pile in the whole life cycle are realized by combining the Internet of Things sensor, edge calculation, three-dimensional modeling and big data analysis technologies.

Description

Technical Field

[0001] The present invention relates to the fields of geotechnical engineering and intelligent monitoring technology, and in particular to a digital twin early warning system for the service safety of assembled anti-slide piles. Background Art

[0002] Existing anti-slide pile monitoring systems generally utilize distributed sensor networks (strain gauges / inclinometers / earth pressure cells) and wired transmission architectures, achieving safety warnings through manually set displacement thresholds. The accompanying analysis software uses traditional finite element methods to construct offline data value models, requiring manual intervention to update boundary conditions. The typical data update cycle is ≥7 days.

[0003] Although this technical system can obtain basic parameters such as pile displacement and stress, it has significant limitations: the monitoring data is fragmented into a single physical quantity (such as displacement measurement only) and lacks multi-field coupling analysis capabilities; the update of the numerical model seriously lags behind the actual working conditions, and it is difficult to track the structural response in real time during the acceleration stage of the landslide.

[0004] There are certain key bottlenecks in existing technologies: There are defects in the fusion of multi-source heterogeneous data. The single physical quantity detection system has not established the key model of temperature-stress-deformation, resulting in an increased false alarm rate; the model time change is delayed, and traditional offline simulation requires manual resetting of boundary conditions, and the warning delay in the landslide acceleration stage exceeds 2 hours. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a digital twin early warning system for the service safety of assembled anti-slip piles to solve the above technical problems.

[0006] A digital twin early warning system for the service safety of assembled anti-slide piles, comprising: A sensing module, used to collect the physical parameters of the anti-slip pile in real time, including a sensor array and an optical fiber temperature measurement device; a transmission module, connected to the perception module, for receiving and preprocessing sensor data, including a wireless communication unit and an edge computing unit; A digital twin module, connected to the transmission module, is used to construct a pile-soil coupled dynamic model and update it in real time, including a cloud-based numerical simulation engine and a three-dimensional visualization unit; An early warning decision module, connected to the digital twin module, is used to trigger a hierarchical response mechanism based on the model output, including a risk determination unit and a processing strategy library; Among them, the perception module, transmission module, digital twin module and early warning decision module are connected in series through data flow to form a closed-loop monitoring and early warning architecture. The closed-loop monitoring and early warning architecture drives model updates through real-time data feedback and adjusts the monitoring strategy according to the early warning results.

[0007] Furthermore, the sensor array is distributed at the mortise and tenon joints of the anti-slip pile at a preset interval, and the optical fiber temperature measuring device is arranged along the axial extension of the prestressed structure of the pile body.

[0008] Furthermore, the edge computing unit integrates a data cleaning algorithm to filter abnormal data and compress the transmission volume, and exchanges data with the cloud data simulation engine in real time through a high-speed communication network.

[0009] Furthermore, the cloud-based numerical simulation engine couples the BIM model with geological radar data through a dynamic data interface to generate a three-dimensional pile-soil model.

[0010] Furthermore, the three-dimensional visualization unit dynamically displays the stress distribution cloud diagram of the anti-slide pile and the simulation results of the soil rupture process through a three-dimensional rendering engine.

[0011] Furthermore, the risk determination unit includes a neural network prediction submodule and a fuzzy logic analysis submodule, which are used to output risk level signals and trigger corresponding level alarm processing.

[0012] Furthermore, the processing strategy library stores response strategies for a variety of typical working conditions and continuously optimizes model prediction logic based on historical monitoring data.

[0013] Furthermore, the transmission module supports a multi-protocol communication interface.

[0014] Furthermore, the digital twin module optimizes the dynamic response of the pile-soil model through parameter inversion technology based on real-time sensor data, and combines with the three-dimensional visualization unit to realize real-time interactive display of the health status of the pile.

[0015] The invention adopting the above technical solution has the following advantages: The present invention combines Internet of Things sensors, edge computing, three-dimensional modeling and big data analysis technologies to achieve real-time monitoring and intelligent early warning of the health status of anti-slip piles throughout their life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the specific embodiments. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0017] Figure 1 This is a structural diagram of a digital twin early warning system for the service safety of assembled anti-slide piles according to the present invention; Figure 2 This is a structural diagram of the risk judgment unit in the digital twin early warning system for the service safety of assembled anti-sliding piles of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0020] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0021] like Figures 1 and 2 As shown, the present invention provides a digital twin early warning system for the service safety of assembled anti-sliding piles, comprising: A sensing module, used to collect the physical parameters of the anti-slip pile in real time, including a sensor array and an optical fiber temperature measurement device; The transmission module is connected to the perception module and is used to receive and pre-process sensor data, including a wireless communication unit and an edge computing unit; The digital twin module, connected to the transmission module, is used to construct the pile-soil coupled dynamic model and update it in real time. It includes a cloud-based numerical simulation engine and a 3D visualization unit. The early warning decision module is connected to the digital twin module and is used to trigger a hierarchical response mechanism based on the model output, including a risk determination unit and a processing strategy library; Among them, the perception module, transmission module, digital twin module and early warning decision module are connected in series through data flow to form a closed-loop monitoring and early warning architecture. The closed-loop monitoring and early warning architecture drives model updates through real-time data feedback and adjusts the monitoring strategy according to the early warning results.

[0022] Specifically, the present invention combines Internet of Things sensors, edge computing, three-dimensional modeling and big data analysis technology to achieve real-time monitoring and intelligent early warning of the health status of anti-slip piles throughout their life cycle.

[0023] In this embodiment, the sensor array is distributed at the mortise and tenon joints of the anti-slip pile at a preset interval, and the optical fiber temperature measuring device is arranged along the axial extension of the prestressed structure of the pile body.

[0024] Specifically, a micro sensor array is embedded and installed at the mortise and tenon joints of the anti-slip piles at a spacing of 1m to collect displacement, strain and inclination data in real time.

[0025] For the displacement and strain data collected by the sensor array, an adaptive Kalman filter algorithm is used to eliminate environmental noise.

[0026] Equation of state: in, is the state vector (displacement, strain), is the state transition matrix, is the process noise.

[0027] Observation equation: in, represents the observation vector, represents the observation matrix, represents the observation noise.

[0028] An optical fiber temperature measurement system is arranged along the axial direction of the prestressed structure of the pile body, with a spatial resolution of 0.5m.

[0029] Wavelet transform compression algorithm: Use wavelet basis to perform multi-scale decomposition on temperature data, retain low-frequency coefficients and discard high-frequency noise components, with a compression rate of >80%, retaining effective low-frequency components.

[0030] In this embodiment, the edge computing unit integrates a data cleaning algorithm to filter abnormal data and compress the transmission volume, and exchanges data with the cloud data simulation engine in real time through a high-speed communication network.

[0031] Specifically, a low-power LoRa gateway (transmission distance 3km, power consumption <1W) is deployed. After receiving sensor data, an integrated Gaussian filtering algorithm is used to eliminate outliers (elimination rate >95%), and Huffman coding is used to compress the data volume to reduce transmission bandwidth occupancy.

[0032] The pre-processed data is uploaded to the cloud through the 5G edge computing terminal, which supports multi-protocol interfaces and is compatible with new sensor expansions.

[0033] In this embodiment, the cloud-based numerical simulation engine couples the BIM model with geological radar data through a dynamic data interface to generate a three-dimensional pile-soil model.

[0034] Specifically, the cloud-based numerical simulation engine imports the BIM model through a dynamic data interface, integrates the soil layer parameters (density, moisture content) detected by geological radar, and inverts the pile-soil parameters in real time based on the particle swarm optimization (PSO) algorithm.

[0035] Sensor data drives adaptive correction of model parameters, such as inverting the pile stiffness coefficient based on strain data and adjusting soil permeability parameters based on groundwater level changes.

[0036] In this embodiment, the three-dimensional visualization unit dynamically displays the stress distribution cloud diagram of the anti-slide pile and the simulation results of the soil rupture process through a three-dimensional rendering engine.

[0037] Specifically, the Unity3D rendering engine dynamically displays pile stress cloud maps (color grading accuracy ±10kPa) and crack extension simulations (crack resolution 0.1mm).

[0038] In this embodiment, the risk determination unit includes a neural network prediction submodule and a fuzzy logic analysis submodule, which are used to output risk level signals and trigger alarm processing of corresponding levels.

[0039] In this embodiment, the processing strategy library stores coping strategies for a variety of typical working conditions and continuously optimizes the model prediction logic based on historical monitoring data.

[0040] In this embodiment, the transmission module supports a multi-protocol communication interface. The transmission module supports Modbus and OPC UA protocols and is compatible with data access from newly added sensors (such as radars and drones).

[0041] Specifically, the LSTM neural network of the risk assessment unit predicts the displacement trend in the next 30 minutes (error < 2%), and the fuzzy logic algorithm integrates temperature, displacement rate, and soil stress data to output the risk level (grades I-IV).

[0042] The early warning decision module dynamically adjusts the sensor sampling frequency according to the risk level: Risk level I-II: the sampling frequency remains at 1 Hz; Risk level III: increased to 5Hz; Risk Level IV: Increase to 10 Hz and activate backup sensor nodes.

[0043] Trigger the corresponding level of alarm mechanism (such as Level IV risk through sound and light alarm, SMS push and APP pop-up window synchronization alarm), and call the preset plan in the processing strategy library (such as "pile foundation grouting reinforcement" or "emergency evacuation").

[0044] The warning results are fed back to the perception module in real time, and the sensor sampling frequency is dynamically adjusted (for example, from 1Hz to 10Hz when the risk is high).

[0045] In this embodiment, the digital twin module optimizes the dynamic response of the pile-soil model through parameter inversion technology based on real-time sensor data, and combines it with a three-dimensional visualization unit to realize real-time interactive display of the pile health status.

[0046] Specifically, the visual interface implements The 3D pile-soil model is rendered using the Unity 3D engine, dynamically displaying pile stress contours (color grading accuracy ±10kPa) and soil fracture processes (crack resolution 0.1mm). Engineers can view pile deformation trends via PC or mobile devices.

[0047] The multi-source data fusion of the present invention reduces the false alarm rate to below 5%, and the displacement monitoring resolution reaches 0.01mm; Real-time model updates enable landslide acceleration phase warnings to be ≥4 hours in advance, and major emergency response times to be shortened to within 10 minutes. Wireless ad hoc networks reduce wiring costs by 70%, and edge computing reduces cloud traffic consumption; 3D visualization enables engineers to locate hidden dangers more efficiently and match treatment plans more accurately; The accumulated data training model can improve the prediction accuracy year by year and extend the service life of the pile by 3-5 years.

[0048] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0049] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0051] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0052] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0053] When the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application (or its contribution to the prior art or all / part of the solution) can essentially be embodied as a computer software product. The software product is stored in a memory and contains a number of operating guides (or "manual instructions") for guiding users to operate computer equipment (such as personal computers, servers, network equipment, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The memory includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard drives, magnetic disks, optical disks, and other media that can store program code.

[0054] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A digital twin early warning system for the service safety of assembled anti-slide piles, characterized by: include: A sensing module, used to collect the physical parameters of the anti-slip pile in real time, including a sensor array and an optical fiber temperature measurement device; a transmission module, connected to the perception module, for receiving and preprocessing sensor data, including a wireless communication unit and an edge computing unit; A digital twin module, connected to the transmission module, is used to construct a pile-soil coupled dynamic model and update it in real time, including a cloud-based numerical simulation engine and a three-dimensional visualization unit; An early warning decision module, connected to the digital twin module, is used to trigger a hierarchical response mechanism based on the model output, including a risk determination unit and a processing strategy library; Among them, the perception module, transmission module, digital twin module and early warning decision module are connected in series through data flow to form a closed-loop monitoring and early warning architecture. The closed-loop monitoring and early warning architecture drives model updates through real-time data feedback and adjusts the monitoring strategy according to the early warning results.

2. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The sensor array is distributed at the mortise and tenon joints of the anti-slip pile at a preset interval, and the optical fiber temperature measuring device is arranged along the axial extension of the prestressed structure of the pile body.

3. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The edge computing unit integrates a data cleaning algorithm to filter abnormal data and compress the transmission volume, and exchanges data with the cloud data simulation engine in real time through a high-speed communication network.

4. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The cloud-based numerical simulation engine couples the BIM model with geological radar data through a dynamic data interface to generate a three-dimensional pile-soil model.

5. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 4 is characterized in that: The three-dimensional visualization unit dynamically displays the stress distribution cloud diagram of the anti-slide pile and the simulation results of the soil rupture process through a three-dimensional rendering engine.

6. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The risk determination unit includes a neural network prediction submodule and a fuzzy logic analysis submodule, which are used to output risk level signals and trigger alarm processing of corresponding levels.

7. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The processing strategy library stores response strategies for a variety of typical working conditions and continuously optimizes model prediction logic based on historical monitoring data.

8. The digital twin early warning system for service safety of assembled anti-slide piles according to claim 1 is characterized in that: The transmission module supports a multi-protocol communication interface.

9. A digital twin early warning system for service safety of assembled anti-slide piles according to any one of claims 1 to 8, characterized in that: The digital twin module optimizes the dynamic response of the pile-soil model through parameter inversion technology based on real-time sensor data, and combines with the three-dimensional visualization unit to realize real-time interactive display of the health status of the pile.

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