CN115130356A - Collapse monitoring system and method based on digital twin technology - Google Patents
Collapse monitoring system and method based on digital twin technology Download PDFInfo
- Publication number
- CN115130356A CN115130356A CN202211065374.2A CN202211065374A CN115130356A CN 115130356 A CN115130356 A CN 115130356A CN 202211065374 A CN202211065374 A CN 202211065374A CN 115130356 A CN115130356 A CN 115130356A
- Authority
- CN
- China
- Prior art keywords
- collapse
- model
- module
- monitored
- digital twin
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 127
- 238000005516 engineering process Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000006378 damage Effects 0.000 claims abstract description 91
- 230000007246 mechanism Effects 0.000 claims abstract description 77
- 238000004364 calculation method Methods 0.000 claims abstract description 58
- 230000003993 interaction Effects 0.000 claims abstract description 41
- 238000006073 displacement reaction Methods 0.000 claims abstract description 30
- 230000002093 peripheral effect Effects 0.000 claims abstract description 4
- 238000004088 simulation Methods 0.000 claims description 32
- 230000005540 biological transmission Effects 0.000 claims description 29
- 238000007670 refining Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 238000012800 visualization Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 7
- 238000005094 computer simulation Methods 0.000 claims description 4
- 238000013480 data collection Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 abstract description 2
- 230000008447 perception Effects 0.000 abstract description 2
- 239000011435 rock Substances 0.000 description 16
- 230000006870 function Effects 0.000 description 13
- 238000012360 testing method Methods 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 238000009826 distribution Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000011148 porous material Substances 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000010076 replication Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 230000002618 waking effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Computer Graphics (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
A collapse monitoring system and method based on a digital twin technology belong to the technical field of collapse monitoring, the collapse type, lithology and peripheral earth surface conditions of collapse to be monitored are collected and recorded, a collapse damage model is selected, a field monitoring scheme is formulated, and a collapse digital twin organism is established; interaction between the collapse digital twin bodies and the entity is carried out by utilizing a position state display sub-module, an advance prediction sub-module and a virtual-real interaction module, and real-time intelligent perception of the collapse state along the traffic line in a special period, which is possible to occur to the collapse, is realized through displacement advance prediction and safety coefficient calculation of the collapse to be monitored; the twin awakening mechanism is adopted, so that the computing power can be saved to the greatest extent, the manpower and material resources are optimized, and the twin awakening mechanism can be popularized and copied along the whole traffic line.
Description
Technical Field
The invention belongs to the technical field of collapse monitoring, and particularly relates to a collapse monitoring system based on a digital twin technology and a construction method thereof.
Background
The collapse is one of the main types of geological disasters at present, and important building facilities such as roads, railways and houses facing slopes can be threatened by the collapse disaster. Based on the consideration of economic cost performance, the necessary collapse monitoring investment is carried out on the premise of not considering old demolition and new demolition, and the method is a basic method in the industry. The purpose of monitoring collapse is to pursue the advance of the maximum degree of collapse, ensure that the first time after the collapse occurs is to invest in manual intervention strength, reduce loss to the maximum degree, and ensure the safety of lives and properties.
There are many monitoring systems for the current market collapse, and the following problems can be summarized: firstly, the pertinence of the disaster-oriented object is insufficient, other collapse damage mechanisms except for collapse are not fully considered, and the monitoring means and the monitoring method are generally carried out according to the landslide monitoring, so that the collapse monitoring accuracy is influenced to a certain extent; secondly, the means and the idea are deviated from the tradition, most of the means and the idea depend on the traditional mode of 'curve + early warning threshold', and certain visualization display is lacked; thirdly, the accuracy of monitoring hardware is excessively pursued, the energizing effect of software and a data layer is neglected, the calculation and prediction capability of the current finite element software is not fully utilized, and the intelligence and the practicability are insufficient. The problems in the three aspects are common short boards of the current urban collapse monitoring system.
It is known through research that the digital twin technology has become a hot spot of industrial development at present, and develops rapidly in the industrial manufacturing field. The digital twin is to create a physical virtual entity in a digital way, and simulate, verify, predict and interact with the whole life cycle process of the physical entity by means of historical data, real-time data and an algorithm model. The technical characteristics are interoperability, expansibility, real-time property, fidelity and closed-loop property, and the method is widely applied in the industrial field at present. The development of the artificial intelligent human body is divided into five stages, namely a three-dimensional geometric model, a three-dimensional simulation model, an enhanced body simulation model, a dynamic twin body and an autonomous twin body model, wherein the first three stages belong to the category of the traditional simulation model, and the last two stages cover the artificial intelligent technology. The digital twin technology maps the objects in the physical world into the digital space in the form of data, and the digital twin technology is not a simple clone of the physical objects by collecting dynamic data, but a set of digital systems independent of the physical objects. The basic function of the method is to continuously monitor real entities, react at the first time when an abnormality is found, and perform advanced prediction and trial and error on various possible conditions.
In recent years, a collapse monitoring real-time data acquisition sensing technology is continuously developed, a collapse monitoring cloud numerical simulation technology is continuously mature, various physical and mechanical models are continuously researched, algorithm models are evolved and fused, a parallel computing and edge cloud cooperation technology is broken through, a digital twin technology and a digital twin concept are not greatly obstructed in the application field of collapse monitoring, the application of the digital twin technology and the digital twin concept can reach a digital twin third stage (an enhanced body simulation model) and even a fourth stage (a dynamic twin body), and the digital twin technology and the digital twin concept are utilized, so that the three-aspect problems and the dilemma of the traditional collapse monitoring system can be effectively solved. However, in the industry, geological disaster digital twin technology and concept are hardly used for monitoring disaster bodies, and no digital twin-based collapse monitoring system exists in the market.
Disclosure of Invention
The invention aims to provide a digital twin-based collapse monitoring system, which combines a collapse scene along a traffic line, establishes a targeted model according to the damage type of the collapse, establishes a reasonable monitoring and control scheme, introduces a digital twin technology and concept into the collapse monitoring system under the scene, combines various real-time physical field data in a collapse environment by means of visualized model display, awakens cloud real-time simulation under specific conditions, realizes real-time intelligent perception of the collapse state along the traffic line in a special period possibly occurring in the collapse, supplements the performance shortboards of traditional collapse monitoring software and data layers, and really enables the safety management work of related geological disasters along the traffic line. It is particularly emphasized that, because the monitoring system adopts a twin wakeup mechanism, the monitoring system can save the calculation power to the maximum extent, optimize the manpower and material resources and can be popularized and copied along the whole traffic line.
The invention provides a collapse monitoring system based on a digital twin technology, which comprises a collapse basic data module, a collapse damage model base module, a collapse digital twin body module and a digital twin body and entity interaction module, wherein the collapse basic data module is used for acquiring and recording collapse types and lithology to be monitored and collapse peripheral earth surface conditions; the collapse damage model library module is used for storing an ideal collapse damage model and a collapse damage refining model formed by improving and refining the ideal model according to the actual collapse; the collapse digital twin organism module comprises a monitoring data acquisition and transmission module and a collapse digital twin organism; the monitoring data acquisition and transmission module is used for acquiring and transmitting the on-site monitoring data aiming at the collapse to be monitored on site; the collapse digital twin body is a digital twin body digital model and comprises an entity display model for visualization display and a calculation model for finite element calculation of the digital twin body; the digital twin and entity interaction module comprises: the bit state display sub-module, the advanced prediction sub-module and the virtual-real interaction module; the bit state display sub-module is used for synchronizing the on-site monitoring data obtained from the monitoring data acquisition and transmission module to the entity display model of the collapsing digital twin body and macroscopically displaying the real-time deformation state of the collapse to be monitored; the advanced prediction submodule is used for predicting the displacement of the collapse to be monitored in advance; and the virtual-real interaction module is used for calculating the safety coefficient of the collapse to be monitored.
Further, the collapsing digital twin is connected to the cloud.
And the wake-up mechanism module is used for starting the monitoring data acquisition and transmission module to perform high-frequency monitoring according to a preset wake-up mechanism, waking up the digital twin body and entity interaction module, and starting a simulation interaction function. The awakening mechanism module is provided with three awakening modules according to the occurrence mechanisms of three types of collapse: the device comprises a cause awakening mechanism module, an instrument abnormal awakening mechanism module and a big data awakening mechanism module; the reason arousing mechanism module is used for setting an arousing mechanism according to possible reasons for collapse; the instrument abnormity awakening mechanism module is used for setting an awakening mechanism according to abnormity of instrument acquisition data; and the big data awakening mechanism module is used for setting an awakening mechanism according to the big data feedback along the traffic to be monitored.
Furthermore, the monitoring data acquisition and transmission module comprises a sensor, a field centralized controller and a data transmission device; the type, the number and the arrangement scheme of the sensors are determined according to the damage model of the collapse to be monitored, wherein the damage model of the collapse to be monitored is matched according to the collapse damage model library module.
Further, the calculation model in the collapse digital twin body module comprises a block model for stability calculation of the collapse to be monitored and a collapse body finite element physical field simulation calculation model.
Further, the advanced prediction submodule uses the block model and the field monitoring data collected in the monitoring data collecting and transmitting module to predict the displacement of the collapse to be monitored in advance; and the virtual-real interaction module utilizes the finite element physical field simulation calculation model of the collapse body to calculate the safety coefficient of the collapse to be monitored.
Further, when the displacement advance prediction of the collapse to be monitored or the safety coefficient exceeds a specified threshold value, the digital twin body and entity interaction module starts an alarm.
The invention provides a collapse monitoring method based on a digital twin technology, which mainly comprises the following steps:
s1, collecting information of the collapse to be monitored and site environment, including the type and lithology of the collapse to be monitored and the ground surface condition around the collapse;
step S2, matching the collapse damage model to be monitored from the existing collapse damage model, wherein the existing collapse damage model comprises a collapse damage ideal model and a collapse damage refining model; preferentially matching a collapse damage refinement model when a damage model of the collapse to be monitored is matched;
step S3, arranging sensors and data transmission devices on the collapse site to be monitored; determining the type, the number and the arrangement scheme of the sensors according to the damage model of the collapse to be monitored, which is matched in the step S2;
step S4, establishing an entity display model for visualization display and a calculation model for digital twin finite element calculation according to the information of collapse to be monitored and site environment acquired in the step S1; the calculation model comprises a block model used for calculating the stability of the collapse to be monitored and a finite element physical field simulation calculation model of the collapse body;
step S5, according to the entity display model and the calculation model established in the step S4, the interaction between the digital twin and the entity is carried out; the method comprises the following steps:
s501, synchronizing the field monitoring data change obtained by the sensor in the step S3 to the entity display model, and performing macroscopic display on the real-time state of the collapse to be monitored;
step S502, adopting an advanced prediction algorithm, and performing displacement prediction on the collapse to be monitored by using the block model in the step S4 and the monitoring data acquired in the step S3;
step S503, establishing a balance equation based on the collapse monitoring damage model obtained in the step S2, and calculating a safety coefficient of collapse to be monitored by using the collapse body finite element physical field simulation calculation model in the step S4;
step S6, when the displacement prediction or the safety factor in step S5 exceeds a prescribed threshold, an alarm is activated.
Further, the method can also comprise the following steps:
and step S7, determining a wake-up mechanism of the interaction between the digital twin and the entity in the step S5, and when a wake-up condition is met, starting the sensor to perform high-frequency monitoring and starting the interaction function between the digital twin and the entity. The awakening mechanism can comprise meteorological inducement awakening, instrument abnormal awakening and/or large data feedback awakening along the traffic line within the collapse influence range to be monitored.
By adopting the invention, the following technical effects can be realized:
(1) based on an ideal model, analyzing a failure mechanism presented by historical collapse occurrence individual case monitoring data by taking the data individual case of collapse which has occurred in monitoring as an analysis object, inputting the failure mechanism into a model database, and continuously enriching and accumulating according to the monitored historical collapse data, so that the problem of insufficient pertinence of the conventional collapse monitoring system can be gradually solved;
(2) the monitoring object is displayed visually, the monitoring data is synchronized to the three-dimensional entity display, and the deformation condition of the collapsed body can be displayed visually and vividly by means of the BIM technology;
(3) digital twin technology, standard and concept are introduced, so that the data are used effectively in real time to the maximum extent, calculation and prediction capabilities of current finite element software are fully utilized, a targeted finite element calculation model is established to realize cloud calculation by combining the environmental characteristics and the damage model of each collapse body, short plates in the aspects of current collapse monitoring system software and data layer efficiency are supplemented, and the intellectualization and the practicability of the monitoring system are improved;
(4) the monitoring system has a wake-up mechanism, can greatly save calculation power, can report possible results in advance by alarming, can monitor and popularize and copy the whole traffic collapse along the line, really enables traffic safety management work, and has better practicability.
Drawings
For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which:
fig. 1 is a schematic structural diagram of a digital twin technology-based collapse monitoring system according to the present invention;
fig. 2 is a schematic diagram of a collapse x sensor arrangement;
FIG. 3 is a schematic top view of a simulated wind speed distribution cloud.
Detailed Description
For the purpose of illustrating the invention, its technical details and its practical application to thereby enable one of ordinary skill in the art to understand and practice the invention, reference will now be made in detail to the embodiments of the present invention with reference to the accompanying drawings. It is to be understood that the embodiments described herein are merely illustrative and explanatory of the invention and are not restrictive thereof.
The traffic along-line collapse monitoring method is a typical scene for monitoring the collapse, and has strong representativeness; the collapse monitoring system and method provided by the invention can be applied to the collapse monitoring along the traffic line and can also be applied to the collapse scenes at other positions.
The invention provides a collapse monitoring system based on a digital twin technology, which comprises a collapse basic data module 400, a collapse digital twin body module 300, a collapse damage model library module 900, a wake-up mechanism module 700 and a twin body and entity interaction mechanism module 100, wherein the collapse basic data module, the collapse digital twin body module 300, the collapse damage model library module 900, the wake-up mechanism module 700 and the twin body and entity interaction mechanism module 100 are shown in the attached drawing 1 of the specification.
The collapse basic data module 400 is used for collecting and recording collapse types, lithology and peripheral earth surface conditions of collapse to be monitored.
Preferably, the collapse type and lithology are researched and judged through field and field investigation; obtaining the ground surface situation and Digital Elevation Model (DEM) data within the range of 2km multiplied by 2km of the ground surface around the collapse through the unmanned aerial vehicle orthographic mapping; acquiring mechanical properties of the collapsed rock and modulus of the rock mass through field collection of collapsed fragment samples and an indoor point load test; calculating the elastic modulus of the rock through a rock acoustic velocity test; the density of the rock was obtained by a room bulk density test.
And the collapse damage model library module 900 is used for storing an ideal collapse damage model and a collapse damage refining model formed by improving and refining the ideal model according to the actual collapse.
Specifically, according to three basic forms (slumping, dumping and falling) of collapse and damage, basic criteria of three ideal models are respectively established, and a model library of the three ideal models of collapse and damage is established; based on the ideal model, with the data of the individual case of collapse which has occurred in the monitoring as an analysis object, in the framework of the three types of collapse damage ideal models, analyzing the damage mechanism presented by the individual case data, improving and refining the collapse damage ideal model to obtain a collapse damage refining model, and storing the collapse damage refining model into the collapse damage model library module 900.
Preferably, the collapse damage model library module 900 should be dynamically updated, and once the actual occurrence of the collapse individual case is monitored, the collapse damage refinement model presented by the collapse damage model library module 900 is updated into the collapse damage model library module 900.
The collapsing digital twin body module 300 comprises a monitoring data acquisition and transmission module 301 and a collapsing digital twin body 302.
The monitoring data acquisition and transmission module 301 includes a sensor, a field centralized controller, and a data transmission device.
According to the collapse type of the collapse to be monitored obtained in the collapse basic data module 400, a more accurate collapse damage model is matched in the collapse damage model library module 900, basic data required for creating the digital twin is determined according to the determined collapse damage model, and then the type, the number and the arrangement scheme of the sensors are determined according to the required basic data. And transmitting the data acquired by the sensors to the collapsing digital twin body 302 through a data transmission device.
The collapsing digital twin 302 is a digital twin digital model, which includes an entity display model for visual display and a calculation model for digital twin finite element calculation.
The entity display model can adjust the position state of the entity display model in real time according to the displacement deformation data collected by the monitoring data collection and transmission module 301, and can be checked in a three-dimensional rotating mode. The entity display model is established in the following way: establishing a terrain model of a collapse area according to DEM data in the collapse basic data module 400; determining an entity display model of collapse according to the three-dimensional laser scanner data in the collapse basic data module 400 and the data of the collapsed rock mass range surveyed on site; according to the orthophoto map of the unmanned aerial vehicle in the collapse basic data module 400, the entity display model is enriched by using BIM and AE skin pasting technologies.
The calculation model comprises a block model for calculating the stability of the collapsed body and a finite element physical field simulation calculation model of the collapsed body. The finite element physical field simulation calculation model of the collapse body is established according to a collapse damage model of the collapse to be monitored, and is determined according to the collapse damage model library module 900.
Further, the collapsing digital twin 302 is connected to the cloud.
The wake-up mechanism module 700 is configured to start the monitoring data acquisition and transmission module 301 for high-frequency monitoring according to a preset wake-up mechanism, and wake up a cloud digital twin simulation interaction function. The module mainly aims to guarantee the effectiveness of monitoring and early warning, simultaneously furthest save attention and calculation power, and provide a basis for the greatest popularization and replication of the monitoring system along the traffic line.
The wake-up mechanism module 700 sets three wake-up modules according to the occurrence mechanisms of the three types of collapse: a cause wake mechanism module 701, an instrument exception wake mechanism module 702, and a big data wake mechanism module 703.
The incentive wakeup mechanism module 701 is configured to set a wakeup mechanism according to a possible incentive of collapse; and (4) accumulating the inducement quantity to a certain degree, starting high-frequency monitoring, and awakening the cloud digital twin simulation interaction function. The inducement mainly refers to meteorological factors, and specifically comprises rainfall, snowfall, wind load, air temperature factors and the like.
For example, rainfall or wind factors in the weather factors, the incentive wake-up mechanism module 701 obtains weather data forecasts in three days in the future, when the rainfall or wind level of the weather forecasts reaches a certain degree, the incentive wake-up mechanism module 701 pushes relevant information and instructions to the site set controller of the monitoring data acquisition and transmission module 301, and meanwhile, cloud digital twin finite element calculation is started. After receiving the instruction of the incentive wakeup mechanism module 701, the site set controller of the monitoring data acquisition and transmission module 301 controls the sensor of the monitoring data acquisition and transmission module 301 to change from low-frequency monitoring to high-frequency monitoring; and if the meteorological predicted data do not accord with the local monitoring or the inducement is finished, the field integrated controller controls the field sensor to change from high frequency to low frequency. The mechanism can ensure the power supply and data acquisition work of the monitoring instrument to the maximum extent, prolong the service life of the instrument and save the calculation power of the system.
The device exception wakeup mechanism module 702 is configured to set a wakeup mechanism according to an exception of device acquisition data; if the monitoring data in the monitoring data acquisition and transmission module 301 is abnormal, high-frequency monitoring is started to wake up the cloud digital twin simulation interaction function. Wherein the instrument data anomaly mainly refers to a sudden increase (a threshold value may be set, e.g. 50%).
For example, when the monitoring of a certain displacement crack is suddenly increased by 50%, the acceleration of a curve is suddenly increased, the displacement sensor sends an instruction to the field centralized controller, the field centralized controller controls all sensors of the collapsed body to increase the monitoring frequency and feeds back information to the instrument abnormity awakening mechanism module 702, and the instrument abnormity awakening mechanism module 702 starts the cloud digital twin body simulation interaction function. If each subsequent item of data tends to be stable, no risk is confirmed through the group policy group preventive measures, the risk is relieved by the instrument abnormal wake-up mechanism module 702, and the sensor recovers to normal working frequency.
The big data wake-up mechanism module 703 is configured to set up a wake-up mechanism according to the big data feedback along the traffic line.
For example, when a train of a certain shift is expected to reach a collapse point at the time a, information of the train of the shift enters traffic big data, the big data awakening mechanism module 703 captures the data and the information, and starts awakening a digital twin simulation interaction function at the time a-30min when the train is about to reach the point, and improves the monitoring frequency of an instrument. The train passes by and the awakening mechanism is released.
Preferably, the three types of wake-up mechanisms can be used in a superposed manner, and a combined wake-up mechanism for a collapse point can be set according to specific situations of a collapse entity and by combining historical data. For example, a certain area is rainy for three days (day 1-day 3) in the future, the influence range of a certain train entering the collapse point at a certain time A and a certain shift is known through big data, and the awakening condition can be set to (day 1-day3 and A-30 min) through the system.
The twin and entity interaction mechanism module 100 specifically includes: a bit state display sub-module 101, a look-ahead sub-module 102, and a virtual-real interaction module 103.
The bit state display submodule 101 is configured to synchronize changes of field monitoring data obtained in the monitoring data acquisition and transmission module 301 to the entity display model of the collapsing digital twin body 302, and perform macroscopic display on the real-time deformation state of the to-be-monitored collapse. The difference between the display and the video monitoring system is that the video monitoring system needs to be artificially identified and is limited by the angle of a lens, so that the purpose of visualization display cannot be really achieved, and the entity display model established by the system can be adjusted in accurate position according to monitoring data and can be dragged and displayed in a three-dimensional manner, so that the visualization target can be realized.
The advanced prediction submodule 102 is configured to predict a collapse displacement in advance based on a BP neural network algorithm on the basis of determining a collapse type according to the collapse damage model library module 900.
The specific mode of the advanced prediction is as follows: normalizing collapse displacement data obtained by monitoring; determining the number of nodes of an input layer and an output layer by adopting a rolling modeling method through a metabolism theory in grey system modeling; further determining the number of hidden layers and the number of nodes of the hidden layers; further determining a transfer function of the BP network; and further determining the learning efficiency eta by adopting an iterative calculation method, and performing iterative calculation by taking 0.01 as a step length within the range of 0.01-1 of an empirical value. And constructing a collapse prediction BP network model for displacement prediction. When the displacement prediction exceeds a set threshold, an alarm is initiated.
The virtual-real interaction module 103 is used for correcting a basic balance equation of the collapse model and calculating a safety coefficient of the collapse to be monitored based on the basic balance equation of the collapse damage model and a physical field finite element method calculation result of the environment; and when the safety coefficient exceeds a specified threshold value, starting an alarm.
Preferably, short message broadcasting equipment is arranged outside the 2km part of the upstream traffic line of the collapse body. When the alarm is started, namely the function of broadcasting the message by the short message on site is realized, and the alarm is sent to the trains in the past shift and possible harm objects around the trains.
Preferably, the alarm is accessed to a big data push service.
The monitoring system mainly aims at the application scene of monitoring the collapse along the traffic line, establishes a collapse damage database integrating rich ideal and statistical correction, and establishes a digital twin collapse monitoring system on the basis of acquiring field data by one hand; and when the external conditions accord with the awakening conditions, an awakening mechanism is started, the cloud digital twin computing function is started, the visual bit state display and displacement advanced prediction of the collapse disaster hidden danger points are carried out, the virtual and real interaction between the digital twin and the external entity is carried out when the conditions are met, and information warning is sent to the loss objects possibly existing in the surrounding environment. Due to the adoption of a digital twin awakening mechanism, the computing power is better saved, and the system can be monitored, popularized and copied along the whole traffic line, thereby really enabling traffic safety management work.
The construction and operation of the digital twin technology-based collapse monitoring system according to the present invention will be specifically described below by taking a certain practical engineering as an example.
The general investigation of field geological disasters in a western province discovers that a collapse x is arranged at one position along a railway, the damage of the collapse x is large through preliminary evaluation, and the essential monitoring of the collapse x is carried out through analysis to accord with the economic cost performance principle, so that a collapse monitoring system based on a digital twin technology is established according to the following steps:
step 1, collecting field data and storing the field data into a collapse basic data module 400.
Performing on-site survey, determining topographic data of a collapse area through field survey, determining that the type of collapse x is collapse, determining the length L, the height H and the width B of the collapse x through on-site survey, and outlining the rock mass range of the collapse x; acquiring DEM elevation data in a 2km multiplied by 2km range of a collapse area through an orthographic projection image of the unmanned aerial vehicle, and comparing and checking the DEM elevation data with the elevation data obtained by manual surveying and mapping; acquiring vertical face stereogram data of the front side face of the collapse area through a three-dimensional laser scanner; obtaining 20 groups of samples for indoor rock testing by field proofing; the density of the collapsed rock mass was determined by indoor rock testing. The data is entered into the collapse profile module 400.
And 2, selecting a collapse damage model from the existing collapse damage model library module 900.
And (3) selecting a basic large class of collapse models from the existing collapse damage model library module 900, and selecting existing more detailed damage models established by the collapse monitoring from the collapse damage model library module 900 according to the field data obtained in the step 1. If no more detailed model is found, selecting an ideal collapse model; in the actual engineering, a detailed model is found in the existing collapse damage models in the collapse damage model base module 900, the model is established based on the collapse data of a certain place of a monitored nearby area in history, the collapse data of the certain place shows that the collapse direction has a lateral trend along with a larger wind level when the collapse occurs, namely the detailed model is the collapse damage model influenced by the wind direction, and therefore the collapse damage model influenced by the wind direction is selected as the collapse damage model of collapse x.
And 3, establishing a digital twin body of collapse x.
Step 1, determining collapse x as collapse, and step 2, determining collapse x as a collapse damage model influenced by wind direction; and determining the data types to be monitored according to the collapse damage model influenced by the wind direction as follows: rainfall, wind direction and wind level, transverse crack displacement at the rear side of collapse, longitudinal crack water level at the rear side of collapse, displacement of the front side of collapse and the sudden condition of collapse of internal rock mass.
The sensor arrangement in the monitoring data acquisition and transmission module 301 is established according to the type of the monitoring data:
referring to the attached figure 2 of the specification, 1 set of stay cord displacement meters 1 are arranged at two ends of the rear side of the collapse x, so that the rear side transverse crack displacement is accurately monitored; setting up a total station base on a firm and stable field outside the distance of 5H of collapse x (H is the height of collapse x obtained in step 1), and fixing an automatic total station 3 on the base; installing a plurality of prisms on the stable block body for checking coordinates of the total station, setting a coordinate origin of the total station on the stable block body in front of the collapsed body, and setting a plurality of prisms at different positions on the collapsed body as measuring targets so as to master the deformation condition of the collapse; installing 1 set of bidirectional inclinometer 5 on two sides of the top of the collapsed x rock block for acquiring inclination angle change; the above-mentioned combination is deformation monitoring of collapse x, and the obtained deformation data is used in the step 5 bit state display sub-module 101. Installing 1 set of acoustic generation (AE) sensors 4 behind the collapsing x, and monitoring possible sudden rock mass damage inside the collapsing x rock mass; arranging 1 set of pore water pressure gauges 2 in the longitudinal crack at the rear side of the collapse x for monitoring the pore water pressure in the rear side; and a set of wind direction test sensor 5 is arranged at the top of the collapse x and used for monitoring the wind direction.
Preferably, the monitoring video equipment 6 can be further installed on a structure (such as a telegraph pole or a tunnel portal) in the 45-degree direction of the collapse side.
A collapsing digital twin 302 is established, including an entity presentation model and a computational model.
The entity display model is established as follows: establishing a terrain model of a collapse area according to DEM data in the collapse basic data module 400 in the step 1; according to the three-dimensional laser scanner data in the collapse basic data module 400 and the rock mass range data of the collapse x which is surveyed on site, an entity display model x-1 of the collapse x is determined; and enriching an entity display model x-1 by using BIM and AE skin pasting technologies according to the orthophoto map of the unmanned aerial vehicle in the collapse basic data module 400. The entity display model can adjust the position state of the entity display model x-1 in real time according to the displacement deformation data generated by the monitoring data acquisition and transmission module 301, and can be viewed in a three-dimensional rotating mode.
The calculation model comprises a collapsed body stability calculation landmass model x-2, and ICEM CFD 15.0 software is selected to complete geometric model construction, calculation domain setting and grid model division to form a wind load CFD simulation grid division model x-3.
Further, the collapsing digital twin 302 is connected to the cloud.
And 4, selecting a wake-up mechanism.
For example, through historical statistical data, the provincial collapse often occurs in rainy seasons, a train of a certain shift enters a collapse x area every morning at 4, a cloud system of a certain day detects that there is heavy rainfall in the area in three days in the future, a module "(cause wakeup mechanism module 701 and big data wakeup mechanism module 703) or (instrument abnormal wakeup mechanism module 702)" can be selected as a wakeup mechanism of the collapse at the area, and the system establishes that the final wakeup effect is as follows: starting and awakening within three days in the future within 30 minutes to 4 minutes at 3 days each day, and if the monitoring data of collapse x is abnormal, starting and awakening; and if the monitoring data of collapse x is abnormal, starting and awakening.
And 5, building a digital twin body and entity interaction mechanism.
(1) Establishing a bit state display submodule 101: and synchronizing the change of the field monitoring data to the entity display model of the collapsing digital twin body 302, and performing macroscopic display on the real-time deformation state of the collapsing x. The difference between the display and the video monitoring system is that the video monitoring system needs to be identified artificially and is limited by the angle of a lens, so that the aim of visual display cannot be really fulfilled; and the entity display model of the collapsing digital twin body 302 can be adjusted in a precise position according to the monitoring data, can be displayed in a three-dimensional dragging mode, and can realize an visualized target.
(2) Advance prediction: and according to the algorithm of the advance prediction submodule 102, displacement prediction is carried out by combining a displacement characteristic curve of collapse x, and an alarm is given if a predicted value exceeds a set threshold value.
(3) And (3) virtual-real interaction: the ideal model for analyzing collapse x collapse damage is as follows:
physical field simulation: the actual physical field comprises wind, snow and seismic waves, the collapse x collapse damage model is selected according to specific conditions, wind load participates in the collapse x collapse damage model, and wind pressure is calculated and established by using the existing real-time finite element simulation technology CFD under an awakening condition according to the monitored wind data by combining the wind load CFD simulation grid subdivision model x-3 established in the step 3.
When more CFD finite element calculation software exists, ANSYS Fluent 15.0 software is used in the embodiment, according to the wind load CFD simulation grid subdivision model x-3 established in the step 3, the boundary condition of a calculation domain is set, a turbulence model is set, and simulation parameters are set to solve the flow process; selecting Tecplot 360 software to generate a wind speed distribution cloud picture, referring to the attached figure 3 of the specification; and synchronizing the calculation result to a cloud server in real time, generating a three-dimensional wind speed cloud picture by combining the entity display model, and checking the three-dimensional wind speed cloud picture in real time.
Further establishing wind pressure load according to the obtained wind speed distribution cloud chartM wind ：
In the formula (I), the compound is shown in the specification,yis a infinitesimal areadAThe arm of force of the overturning moment to the center of gravity omega is an integral surface selected according to actual conditions,w p is a infinitesimal areadAThe wind pressure is calculated as follows:
in the formula (I), the compound is shown in the specification,r 0 the air density can be taken as the gravity of the wind fluid;vis a infinitesimal areadAThe wind speed of (2) can be obtained from the obtained wind speed distribution cloud chart.
Preferably, the selection of the calculation surface Ω and the integration processing are implemented by a written software program.
Preferably, the update iteration time of the calculation result can be set according to the calculation force of the system, for example, 1 min/time.
Preferably, in view of the hysteresis of the calculation, the calculation result may be multiplied by a certain expansion coefficient, and considered in a disadvantageous case.
And (3) integrating the calculated wind pressure result into a balance equation of collapse x, and calculating the safety coefficient of the collapse x by combining the displacement data and the pore water pressure data monitored in real time, wherein the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,Fin order to calculate the resulting safety factor,Gto weight of collapsed mass x (G = can be calculated from volume and weight established in step 1)r × H × B × L），rTo the severity of the collapsed mass (from the rock test in step 1),dto collapse the moment arm length of the block x (investigated in step 1),r w in the case of the heavy weight of the water,hthe water level of the rear side crack (measured),M wind the overturning moment generated by the wind load (integrated by the result of the wind pressure simulation).
X safety factor when collapsingFAnd when the set threshold value is exceeded, starting an alarm, and carrying out short message broadcasting and big data pushing by using the virtual-real interaction module 103.
Based on a basic balance equation of a collapse damage model and a physical field finite element volume method calculation result of an environment, correcting the basic balance equation of the damage model, calculating a safety coefficient, starting a field short message broadcasting message function to give an alarm to a passing train in a shift and possible harm objects around when the safety coefficient exceeds a specified threshold value, and starting a big data push service; when the displacement prediction exceeds the set ground threshold, the alarm service is started.
The invention provides a collapse monitoring method based on a digital twin technology, which mainly comprises the following steps:
and step S1, collecting information of the collapse to be monitored and site environment, including the type and lithology of the collapse to be monitored and the ground surface condition around the collapse.
Step S2, matching the damage model of the collapse to be monitored from the existing collapse damage models, wherein the existing collapse damage models comprise an ideal collapse damage model and a collapse damage refining model, and when the damage model of the collapse to be monitored is matched, the collapse damage refining model is preferentially matched.
Step S3, arranging a sensor, a field centralized controller and a data transmission device on the collapse field to be monitored; and determining the type, the number and the arrangement scheme of the sensors according to the damage model of the collapse to be monitored, which is matched in the step S2.
S4, establishing an entity display model for visualization display and a calculation model for finite element calculation of a digital twin body according to the information of the collapse to be monitored and the site environment acquired in the step S1; and the calculation model comprises a block model for calculating the stability of the collapse to be monitored and a finite element physical field simulation calculation model of the collapse body.
Step S5, according to the entity display model and the calculation model established in the step S4, the interaction between the digital twin and the entity is carried out; the method comprises the following steps:
s501, synchronizing the field monitoring data change obtained by the sensor in the step S3 to the entity display model, and performing macroscopic display on the real-time state of the collapse to be monitored;
step S502, adopting an advanced prediction algorithm, and performing displacement prediction on the collapse to be monitored by using the block model in the step S4 and the monitoring data acquired in the step S3;
step S503, establishing a balance equation based on the collapse monitoring damage model obtained in the step S2, and calculating a safety coefficient of collapse to be monitored by using the collapse body finite element physical field simulation calculation model in the step S4;
step S6, when the displacement prediction or the safety factor in step S5 exceeds a prescribed threshold, an alarm is activated.
Further, the method can also comprise the following steps:
and step S7, determining a wake-up mechanism of the interaction between the digital twin and the entity in the step S5, and when a wake-up condition is met, starting the sensor to perform high-frequency monitoring and starting the interaction function between the digital twin and the entity.
The awakening mechanism can comprise meteorological inducement awakening, instrument abnormal awakening and/or large data feedback awakening along the traffic line within the collapse influence range to be monitored.
Claims (11)
1. A collapse monitoring system based on digital twin technology comprises a collapse basic data module, a collapse damage model base module, a collapse digital twin body module and a digital twin body and entity interaction module, wherein,
the collapse basic data module is used for acquiring and recording collapse types, lithology and collapse peripheral earth surface conditions to be monitored;
the collapse damage model library module is used for storing an ideal collapse damage model and a collapse damage refining model formed by improving and refining the ideal model according to the actual collapse;
the collapse digital twin organism module comprises a monitoring data acquisition and transmission module and a collapse digital twin organism; the monitoring data acquisition and transmission module is used for acquiring and transmitting the on-site monitoring data aiming at the collapse to be monitored on site; the collapse digital twin body is a digital twin body digital model and comprises an entity display model for visualization display and a calculation model for finite element calculation of the digital twin body;
the digital twin and entity interaction module comprises: the state display module, the advance prediction module and the virtual-real interaction module are connected in series; the bit state display sub-module is used for synchronizing the on-site monitoring data obtained from the monitoring data acquisition and transmission module to the entity display model of the collapsing digital twin body and macroscopically displaying the real-time deformation state of the collapse to be monitored; the advanced prediction submodule is used for predicting the displacement of the collapse to be monitored in advance; and the virtual-real interaction module is used for calculating the safety coefficient of the collapse to be monitored.
2. The system of claim 1, wherein the collapsing digital twin accesses a cloud.
3. The system of claim 1, further comprising a wake-up mechanism module, configured to start the monitoring data acquisition and transmission module to perform high-frequency monitoring according to a preset wake-up mechanism, and wake up the digital twin and entity interaction module to start a simulation interaction function.
4. The system of claim 3, wherein the wake-up mechanism module sets up three wake-up modules according to the occurrence mechanism of three types of collapse: the device comprises a cause awakening mechanism module, an instrument abnormal awakening mechanism module and a big data awakening mechanism module; the reason arousing mechanism module is used for setting an arousing mechanism according to possible reasons for collapse; the instrument abnormity wake-up mechanism module is used for setting a wake-up mechanism according to the abnormity of instrument acquisition data; and the big data awakening mechanism module is used for setting an awakening mechanism according to the big data feedback along the traffic to be monitored.
5. The system of claim 1, wherein the monitoring data acquisition and transmission module comprises a sensor, a field centralized controller and a data transmission device; the type, the number and the arrangement scheme of the sensors are determined according to the damage model of the collapse to be monitored, wherein the damage model of the collapse to be monitored is matched according to the collapse damage model library module.
6. The system according to claim 1, wherein the computational models in the collapsing digital twin body module comprise a mass model for stability calculation of the collapse to be monitored, and a collapsing body finite element physical field simulation computational model.
7. The system of claim 6, wherein the look-ahead sub-module uses the block model and the field monitoring data collected in the monitoring data collection and transmission module to look ahead for the displacement of the collapse to be monitored; and the virtual-real interaction module utilizes the finite element physical field simulation calculation model of the collapse body to calculate the safety coefficient of the collapse to be monitored.
8. The system according to claim 1, wherein the digital twin and entity interaction module initiates an alarm when the displacement of the monitored collapse is predicted ahead of time or the safety factor exceeds a prescribed threshold.
9. A collapse monitoring method based on a digital twin technology mainly comprises the following steps:
s1, collecting information of the collapse to be monitored and site environment, including the type and lithology of the collapse to be monitored and the ground surface condition around the collapse;
step S2, matching the collapse damage model to be monitored from the existing collapse damage model, wherein the existing collapse damage model comprises a collapse damage ideal model and a collapse damage refining model; preferentially matching a collapse damage refinement model when a damage model of the collapse to be monitored is matched;
step S3, arranging sensors and data transmission devices on the collapse site to be monitored; determining the type, the number and the arrangement scheme of the sensors according to the damage model of the collapse to be monitored, which is matched in the step S2;
s4, establishing an entity display model for visualization display and a calculation model for finite element calculation of a digital twin body according to the information of the collapse to be monitored and the site environment acquired in the step S1; the calculation model comprises a block model used for calculating the stability of the collapse to be monitored and a finite element physical field simulation calculation model of the collapse body;
step S5, according to the entity display model and the calculation model established in the step S4, the interaction between the digital twin and the entity is carried out; the method comprises the following steps:
s501, synchronizing the field monitoring data change obtained by the sensor in the step S3 to the entity display model, and performing macroscopic display on the real-time state of the collapse to be monitored;
step S502, adopting an advanced prediction algorithm, and performing displacement prediction on the collapse to be monitored by using the block model in the step S4 and the monitoring data acquired in the step S3;
step S503, establishing a balance equation based on the collapse monitoring damage model obtained in the step S2, and calculating a safety coefficient of collapse to be monitored by using the collapse body finite element physical field simulation calculation model in the step S4;
step S6, when the displacement prediction or the safety factor in step S5 exceeds a prescribed threshold, an alarm is activated.
10. The method of claim 9, further comprising the steps of:
and step S7, determining the awakening mechanism of the interaction between the digital twin and the entity in the step S5, starting the sensor to perform high-frequency monitoring when the awakening condition is met, and starting the interaction function between the digital twin and the entity.
11. The method according to claim 10, wherein the wake-up mechanism comprises a weather-induced wake-up, an instrumental wake-up anomaly, and/or a big data feedback wake-up along the traffic within the impact range of the monitored collapse.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211065374.2A CN115130356B (en) | 2022-09-01 | 2022-09-01 | Collapse monitoring system and method based on digital twin technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211065374.2A CN115130356B (en) | 2022-09-01 | 2022-09-01 | Collapse monitoring system and method based on digital twin technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115130356A true CN115130356A (en) | 2022-09-30 |
| CN115130356B CN115130356B (en) | 2022-11-25 |
Family
ID=83388025
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211065374.2A Active CN115130356B (en) | 2022-09-01 | 2022-09-01 | Collapse monitoring system and method based on digital twin technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115130356B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116977122A (en) * | 2023-07-06 | 2023-10-31 | 双龙软创(深圳)科技有限公司 | Remote automatic monitoring method for dangerous rooms based on digital twin technology |
| CN117332664A (en) * | 2023-12-01 | 2024-01-02 | 北京云庐科技有限公司 | Geological disaster digital twin live-action display method based on physical calculation result |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009007914A (en) * | 2007-06-28 | 2009-01-15 | Dai Mineyama | Method and device for predicting slope face collapse and landslide |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN111256753A (en) * | 2020-01-09 | 2020-06-09 | 成都理工大学 | Intelligent collapse monitoring device and monitoring method thereof |
| CN112364802A (en) * | 2020-11-19 | 2021-02-12 | 中国地质调查局水文地质环境地质调查中心 | Deformation monitoring method for collapse landslide disaster body |
| CN114241720A (en) * | 2021-12-24 | 2022-03-25 | 北京市市政工程研究院 | Tunnel construction intelligent forecasting and early warning system and method based on digital twins |
-
2022
- 2022-09-01 CN CN202211065374.2A patent/CN115130356B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009007914A (en) * | 2007-06-28 | 2009-01-15 | Dai Mineyama | Method and device for predicting slope face collapse and landslide |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN111256753A (en) * | 2020-01-09 | 2020-06-09 | 成都理工大学 | Intelligent collapse monitoring device and monitoring method thereof |
| CN112364802A (en) * | 2020-11-19 | 2021-02-12 | 中国地质调查局水文地质环境地质调查中心 | Deformation monitoring method for collapse landslide disaster body |
| CN114241720A (en) * | 2021-12-24 | 2022-03-25 | 北京市市政工程研究院 | Tunnel construction intelligent forecasting and early warning system and method based on digital twins |
Non-Patent Citations (1)
| Title |
|---|
| 尚浩等: "基于数字孪生理论的济南四维地质环境信息系统研发", 《地质学刊》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116977122A (en) * | 2023-07-06 | 2023-10-31 | 双龙软创(深圳)科技有限公司 | Remote automatic monitoring method for dangerous rooms based on digital twin technology |
| CN116977122B (en) * | 2023-07-06 | 2024-04-19 | 双龙软创(深圳)科技有限公司 | Remote automatic monitoring method for dangerous rooms based on digital twin technology |
| CN117332664A (en) * | 2023-12-01 | 2024-01-02 | 北京云庐科技有限公司 | Geological disaster digital twin live-action display method based on physical calculation result |
| CN117332664B (en) * | 2023-12-01 | 2024-02-09 | 北京云庐科技有限公司 | Geological disaster digital twin live-action display method based on physical calculation result |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115130356B (en) | 2022-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115130356B (en) | Collapse monitoring system and method based on digital twin technology | |
| CN115063963B (en) | Landslide monitoring system and method based on digital twin technology | |
| CN111191880B (en) | Slope full life cycle safety management method based on digital mapping | |
| CN115688227B (en) | Digital twin hydraulic engineering operation safety monitoring system and operation method | |
| CN113610301B (en) | Prediction algorithm for representing ground settlement time sequence evolution | |
| CN109165406A (en) | The quick dynamic classification prediction technique of country rock during a kind of constructing tunnel | |
| CN115422766B (en) | Debris flow monitoring method and system based on digital twinning technology | |
| CN111815117B (en) | Grasshopper platform-based urban waterlogging susceptibility simulation evaluation method | |
| CN115391474A (en) | Digital twin-based watershed management method and system | |
| CN116663762A (en) | Urban planning underground space investigation and mapping method and system | |
| CN114943155A (en) | Flood emergency rescue method and virtual command system based on digital twin technology | |
| CN113111411A (en) | System and method for reinforcing deep silt foundation | |
| CN105427189A (en) | Power grid vulnerability evaluation method under landslide hazard | |
| CN109471205A (en) | A kind of monitoring and pre-alarming method based on gridding meteorological data in grid operation | |
| CN206021512U (en) | A kind of detection forecasting and warning device of heavy rain type diluted debris flow | |
| JP2006317460A (en) | Method of creating contour diagram of physical quantity, and method of estimating physical quantity | |
| CN111881566A (en) | Landslide displacement detection method and device based on live-action simulation | |
| CN116029217A (en) | Hydraulic engineering digital twin modeling early warning system combined with geological data | |
| CN116089406A (en) | Barrier visualization processing system for ocean mapping | |
| JP4043779B2 (en) | Basic wind speed map creation method by wind direction and basic wind speed estimation method by wind direction | |
| CN106897517A (en) | Line of high-speed railway gale monitoring optimizes automatic search method of arranging net | |
| CN111765870A (en) | Earth volume calculation method based on oblique photography technology and regional accumulation | |
| Constantinescu et al. | TR-626: Optimization of Snow Drifting Mitigation and Control Methods for Iowa Conditions | |
| LU501229B1 (en) | Integrated control system and method of geological disaster prevention information service based on big data | |
| CN110263369B (en) | Building surface wind resistance grade design method based on climate analysis and numerical simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A collapse monitoring system and method based on digital twin technology Granted publication date: 20221125 Pledgee: Shijiazhuang Luquan Rural Commercial Bank Co.,Ltd. Pledgor: BEIJING YUNLU TECHNOLOGY CO.,LTD. Registration number: Y2024980015341 |