AU2021104472A4 - Method for establishing tunnel digital twin scenario and computer device - Google Patents
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Abstract
OF THE DISCLOSURE
A method for establishing a tunnel digital twin (DT) scenario and a computer device are
provided, relating to the technical field of transportation informatization, and configured to analyze
complex problems in tunnel traffic operation and provide corresponding solutions. The method for
establishing the tunnel DT scenario includes: model initialization, panoramic monitoring, data
aggregation, data analysis, physical parameter update, behavioral parameter update, geometric
model update, review and verification, simulation and analysis, prediction and deduction, auxiliary
decision making, feedback control and iterative optimization. The computer device is configured to
execute the method for establishing the tunnel DT scenario described above.
ABSTRACT DRAWING - Fig 2
17905215_1 (GHMatters) P116824.AU
2/2
The computer device 100 establishes an initial geometric model, an initial physical model, sIoo
an initial behavioral model and a DT database according to an actual tunnel scenario to
form an initial tunnel DT scenario
The computer device 100 performs real-time three-dimensional fusion on multichannel
monitoring videos in the actual tunnel scenario and the initial tunnel DT scenario S200
according to multichannel monitoring video data in the actual tunnel scenario to form a
panoramic tunnel DT scenario, and performs structural analysis and semantic annotation
on a static scenario in the multichannel monitoring videos
The computer device 100 performs association registration, data access and dynamic
annotation on a virtual device in the panoramic tunnel DT scenario according to an IoT 300
sensing device and a traffic affiliated facility in the actual tunnel scenario
The computer device 100 performs real-time recognition, semantic annotation, abnormality
detection and structural output on a dynamic object in the multichannel monitoring videos S400
according to the multichannel monitoring video data, data of the IoT sensing device and
meteorological data
The computer device 100 updates corresponding models and/or related parameters in the S500
panoramic tunnel DT scenario according to a change and an analysis result of the data of
the IoT sensing device
The computer device 100 reviews and verifies a geometric model, a physical model, a
behavioral model and the related parameters in the panoramic tunnel DT scenario S 600
according to historical data or detection data
The computer device 100 respectively simulates and analyzes a corresponding service
requirement or problem in the actual tunnel scenario according to the panoramic tunnel DT S700
scenario
The computer device 100 predicts and deduces a change of the actual tunnel scenario S800
according to the geometric model, the physical model and the behavioral model in the
panoramic tunnel DT scenario, to alarm an abnormal event in the actual tunnel scenario
The computer device 100 acquires event responses and handling solutions in different
conditions according to the simulated and analyzed result as well as the predicted and s900
deduced result, to form a three-dimensional emergency plan and an auxiliary decision
making suggestion for the actual tunnel scenario
The computer device 100 controls, according to the auxiliary decision-making suggestion, S1000
the IoT sensing device to perform a reverse physical control on the actual tunnel scenario,
and updates the corresponding models of the panoramic tunnel DT scenario
The computer device 100 updates, according to the user requirement, the DT database, S100
and iterates the panoramic tunnel DT scenario
FIG. 2
Description
2/2
The computer device 100 establishes an initial geometric model, an initial physical model, sIoo an initial behavioral model and a DT database according to an actual tunnel scenario to form an initial tunnel DT scenario
The computer device 100 performs real-time three-dimensional fusion on multichannel monitoring videos in the actual tunnel scenario and the initial tunnel DT scenario S200 according to multichannel monitoring video data in the actual tunnel scenario to form a panoramic tunnel DT scenario, and performs structural analysis and semantic annotation on a static scenario in the multichannel monitoring videos
The computer device 100 performs association registration, data access and dynamic annotation on a virtual device in the panoramic tunnel DT scenario according to an IoT 300 sensing device and a traffic affiliated facility in the actual tunnel scenario
The computer device 100 performs real-time recognition, semantic annotation, abnormality detection and structural output on a dynamic object in the multichannel monitoring videos S400 according to the multichannel monitoring video data, data of the IoT sensing device and meteorological data
The computer device 100 updates corresponding models and/or related parameters in the S500 panoramic tunnel DT scenario according to a change and an analysis result of the data of the IoT sensing device
The computer device 100 reviews and verifies a geometric model, a physical model, a behavioral model and the related parameters in the panoramic tunnel DT scenario S 600 according to historical data or detection data
The computer device 100 respectively simulates and analyzes a corresponding service requirement or problem in the actual tunnel scenario according to the panoramic tunnel DT S700 scenario
The computer device 100 predicts and deduces a change of the actual tunnel scenario S800 according to the geometric model, the physical model and the behavioral model in the panoramic tunnel DT scenario, to alarm an abnormal event in the actual tunnel scenario The computer device 100 acquires event responses and handling solutions in different conditions according to the simulated and analyzed result as well as the predicted and s900 deduced result, to form a three-dimensional emergency plan and an auxiliary decision making suggestion for the actual tunnel scenario
The computer device 100 controls, according to the auxiliary decision-making suggestion, S1000 the IoT sensing device to perform a reverse physical control on the actual tunnel scenario, and updates the corresponding models of the panoramic tunnel DT scenario
The computer device 100 updates, according to the user requirement, the DT database, S100 and iterates the panoramic tunnel DT scenario
FIG. 2
[01] The present disclosure relates to the technical field of transportation informatization, and in particular, to a method for establishing a tunnel digital twin (DT) scenario and a computer device.
[02] At present, tunnel traffic has typical features, such as linear engineering, semi-closed scenarios, and bottleneck traffic. Meanwhile, the tunnel traffic also has explicit holographic perception requirements, higher requirements on safe operations and large pressure on traffic-keeping.
[03] The DT is a virtual model of a physical entity established digitally, simulates behaviors of the physical entity in real environments by virtue of data, and increases or expands new capabilities of the physical entity by means of virtual reality (VR) interaction feedback, data fusion analysis, decision-making iterative optimization and so on. However, existing DT technologies are more applied to factory workshops and industrial manufacturing and the like, but rarely applied to traffic scenarios.
[04] The present disclosure intends to provide a method for establishing a tunnel DT scenario and a computer device, to analyze complex problems in tunnel traffic operation and provide corresponding solutions.
[05] According to a first aspect, the present disclosure provides a method for establishing a tunnel DT scenario, including the following steps:
[06] establishing an initial geometric model, an initial physical model, an initial behavioral model and a DT database according to an actual tunnel scenario to form an initial tunnel DT scenario; performing real-time three-dimensional fusion on multichannel monitoring videos in the actual tunnel scenario and the initial tunnel DT scenario according to multichannel monitoring video data in the actual tunnel scenario to form a panoramic tunnel DT scenario, and performing structural analysis and semantic annotation on a static scenario in the multichannel monitoring videos; performing association registration, data access and dynamic annotation on a virtual device in the panoramic tunnel DT scenario according to an Internet-of-Things (oT) sensing device and a traffic affiliated facility in the
17905215_1 (GHMatters) P116824.AU I actual tunnel scenario; performing real-time recognition, semantic annotation, abnormality detection and structural output on a dynamic object in the multichannel monitoring videos according to the multichannel monitoring video data, data of the IoT sensing device and meteorological data; updating corresponding models and/or related parameters in the panoramic tunnel DT scenario according to a change and an analysis result of the data of the IoT sensing device; reviewing and verifying a geometric model, a physical model, a behavioral model and the related parameters in the panoramic tunnel DT scenario according to historical data or detection data; respectively simulating and analyzing a corresponding service requirement or problem in the actual tunnel scenario according to the panoramic tunnel DT scenario; predicting and deducing a change of the actual tunnel scenario according to the geometric model, the physical model and the behavioral model in the panoramic tunnel DT scenario, to alarm an abnormal event in the actual tunnel scenario; acquiring event responses and handling solutions in different conditions according to the simulated and analyzed result as well as the predicted and deduced result, to form a three-dimensional emergency plan and an auxiliary decision-making suggestion for the actual tunnel scenario; and controlling, according to the auxiliary decision-making suggestion, the IoT sensing device to perform a reverse physical control on the actual tunnel scenario, and updating the corresponding models of the panoramic tunnel DT scenario.
[07] Compared with the conventional art, the method for establishing the tunnel DT scenario provided by the present disclosure has the following beneficial effects.
[08] (1) The panoramic tunnel DT scenario is implemented, in which multichannel videos are fused in real time. A spatial association between discrete videos and a monitoring scenario is established. A global perspective is provided by utilizing video fragments to restore a three-dimension scenario, so that a global monitoring scenario of the tunnel may be dynamically displayed with "a picture". Continuous and intuitive monitoring of multiple regions is provided to facilitate a monitoring manager to overview an overall situation.
[09] (2) Tunnel DT holographic perception integrated with each professional subsystem is formed. Data barriers between the professional subsystems in the tunnel is broken down, multi-source sensing data, such as video data, detection data, monitoring data and online IoT data, are uniformly integrated and displayed with augmented reality (AR), thereby implementing three-dimensional digital assets management fused with the actual scenario.
[10] (3) The alarm and timely response for the abnormal event of the tunnel are supported based on the DT. The abnormal event may be discovered timely in a tunnel by utilizing the panoramic tunnel DT scenario. A three-dimensional emergency response plan for the abnormal event is generated according to the geometric model, the physical model
17905215_1 (GHMatters) P116824.AU 2 and the behavioral model in the panoramic tunnel DT scenario, and a solution is deduced.
[11] (4) Trial-and-error cost is reduced. Matters and phenomenon that cannot be observed and controlled in a real world due to a time and space limitation, processes that change too fast or too slow, and experiments that are dangerous, destructive, and harmful to environment are implemented in the panoramic tunnel DT scenario, and a reproduction scenario and a solution test environment for a complex problem during the tunnel traffic operation are provided to reduce the trial-and-error cost.
[12] According to a second aspect, the present disclosure further provides a computer device, including an IoT sensing device, a processor, a memory and computer executable instructions stored in the memory and configured to be executed by the processor; and when executing the computer executable instructions, the processor implements the method for establishing the tunnel DT scenario in the first aspect.
[13] Compared with the conventional art, the beneficial effects of the computer device provided by the present disclosure are the same as those of the method for establishing the tunnel DT scenario in the above solutions, and will not be repeated herein.
[14] The accompanying drawings described herein are provided for a further understanding of the present disclosure, and constitute a part of the present disclosure. The exemplary embodiments and illustrations of the present disclosure are intended to explain the present disclosure, but do not constitute inappropriate limitations to the present disclosure. In the accompanying drawings:
[15] FIG. 1 is a schematic structure diagram of a computer device provided by an embodiment of the present disclosure.
[16] FIG. 2 is a flow chart of a method for establishing a tunnel DT scenario provided by an embodiment of the present disclosure.
[17] For the ease of clearly describing the technical solutions in the embodiments of the present disclosure, words "first", "second" and the like are used in the embodiments of the present disclosure to distinguish same or similar items that are basically the same in functions and effects. For example, the first threshold and the second threshold are merely for the purpose of distinguishing different thresholds, rather than limiting a sequential order. It may be understood by those skilled in the art that the words "first", "second" and the like are not intended to limit the number and execution sequence, and the words "first", "second" 17905215_1 (GHMatters) P116824.AU 3 and the like are also unnecessarily different.
[181 It should be noted that the words "exemplary" or "for example" or the like represents serving as an example, instance or illustration in the present disclosure. Any embodiment or design solution described herein as "exemplary" or "for example" should not be construed as being more preferred or advantageous over other embodiments or design solutions. Exactly, the words "exemplary" or "for example" or the like is intended to present related concepts specifically.
[19] In the present disclosure, the term "at least one" refers to one or more items, and the term "multiple" refers to two or more items. The term "and/or" is an association relationship for describing associated objects, and represents that three relationships may exist, for example, A and/or B may represent that: A exists alone, A and B exist at the same time, and B exists alone. Wherein, A and B may be singular or plural. The character "/" generally indicates that the associated objects are in an "or" relationship. The term "at least one of the followings ()" or similar expression refers to any combination of these items, including any combination of single item or plural items. For example, at least one of a, b or c may be expressed as: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c or a combination of a, b and c; wherein the a, b and c may be the single item, and may also be the plural items.
[20] The embodiments of the present disclosure intend to provide a system for establishing a tunnel traffic DT scenario, to solve problems, such as video fragmentation, subsystem separation from each other, late event alarm and response, and high trial-and-error cost during tunnel traffic management. The system for establishing the tunnel traffic DT scenario establishes a tunnel DT scenario basically consistent with a real tunnel traffic operation scenario, provides more timely, efficient and intelligent tunnel wisdom management and service, and supports accurate perception and fine management for infrastructures and traffic operation of the tunnel.
[21] During actual applications, the system for establishing the tunnel traffic DT scenario may include a computer device 100, a data acquisition device, an IoT sensing device and a communication device. The computer device 100 is respectively and communicatively connected to the data acquisition device and the IoT sensing device through the communication device. The data acquisition device may be a monitoring camera.
[22] FIG. 1 illustrates a schematic structure diagram of the computer device. Referring to FIG. 1, the computer device 100 may include a processor 101, a communication interface 102, a memory 103 and a display interface. The processor 101 is configured to execute computer instructions, and process data in computer software, to execute a method for 17905215_1 (GHMatters) P116824.AU 4 establishing a tunnel DT scenario. The display interface may be configured to display the tunnel DT scenario and an actual tunnel scenario, such that a manager knows a condition in real time. The specific type of the computer device 100 may be selected according to an actual condition.
[23] The method for establishing the tunnel DT scenario provided by an embodiment of the present disclosure respectively involves 4 types of objects, namely "human, vehicle, road and environment" in the actual tunnel traffic operation scenario, and each type of objects respectively involves 3 types of attribute modeling, namely geometric modeling, physical modeling and behavioral modeling.
[24] Human: referring to traffic participants in the tunnel traffic scenario, including car drivers, truck drivers, dangerous goods vehicle drivers, bus drivers, bus passengers, passenger drivers, non-motor vehicle drivers, pedestrians, etc.
[25] Vehicle: referring to traffic objects in the tunnel traffic scenario, including cars, trucks, dangerous goods vehicles, buses, passenger vehicles, non-motor vehicles, pedestrians, etc.
[26] Road: referring to infrastructures and devices in the tunnel traffic scenario, including tunnel trunks, linings, portals, communication passages, roads, concealed works, affiliated facilities, etc.
[27] Environment: referring to environment-related objects in the tunnel traffic scenario, including weather, visibility, illuminance, particulate content, ponding event, fire event, etc.
[28] FIG. 2 illustrates a flow chart of the method for establishing the tunnel DT scenario provided by an embodiment of the present disclosure. Referring to FIG. 2, the method for establishing the tunnel DT scenario may include the following steps:
[29] Step S100: The computer device 100 establishes an initial geometric model, an initial physical model, an initial behavioral model and a DT database according to the actual tunnel scenario to form an initial tunnel DT scenario.
[30] The establishment of the initial geometric model may include: a geometric model of a tunnel infrastructure and a virtual environment model of a tunnel scenario are initialized to establish the initial geometric model. The establishment of the initial geometric model may mainly include the following steps:
[31] Step SI11: The computer device 100 establishes a Building Information Modeling (BIM) of the tunnel infrastructure according to a tunnel as-built drawing. The tunnel infrastructure may include the tunnel trunks, linings, portals, concealed works and affiliated facilities, but is not limited thereto.
[32] Step S112: The computer device 100 establishes, according to three-dimensional 17905215_1 (GHMatters) P116824.AU point cloud scanning performed on the infrastructure in the actual tunnel scenario, a present three-dimensional model of the tunnel infrastructure that is based on point cloud data.
[33] Step S113: The computer device 100 updates related parameters of the BIM model according to a difference between the BIM model and the present three-dimensional model, such that an approximate rate between the BIM model and the present three-dimensional model is more than a preset value. The preset value may be set according to an actual condition.
[34] Step S114: The computer device 100 sets environmental parameters such as a global and local illumination, a wind field, a drainage facility and a traffic safety facility in the BIM model according to environmental parameters of the actual tunnel scenario such as an illumination, a wind field, a drainage facility and a traffic safety facility. At this time, the BIM model in which the environmental parameters are set is the initial geometric model.
[35] During the actual applications, the illumination may include a lighting device and a signal lamp, but is not limited thereto. The wind field may include a ventilator, but is not limited thereto. The traffic safety facility may include an electronic message board, but is not limited thereto.
[36] The establishment of the initial physical model may include: a stress model of a tunnel structure and an attenuation model of tunnel pavement performance are initialized to establish the initial physical model. The establishment of the initial physical model may mainly include the following steps:
[37] Step S121: The computer device 100 determines the stress model of the tunnel structure such as a pressure, sedimentation, inclination, displacement, an internal force, seepage and a crack according to the tunnel as-built drawing and design data.
[38] Step S122: The computer device 100 sets initial parameters of the stress model of the tunnel structure according to an actual tunnel design condition and historical monitoring data. The initial parameters of the stress model of the tunnel structure may include: tunnel clearance convergence, surface subsidence, soil displacement, arch pressure, lining internal force, seepage pressure, seepage quantity, temperature and humidity, but are not limited thereto.
[39] Step S123: The computer device 100 determines the attenuation model of the tunnel pavement performance according to the tunnel as-built drawing and the design data. The attenuation model of the tunnel pavement performance mainly includes: evaluation indexes such as a pavement condition index (PCI), a riding quality index (RQI), a rutting depth index (RDI), a pavement bumping index (PBI), a pavement wear index (PWI), a skidding resistance index (SRI) and a pavement structure strength index (PSSI).
17905215_1 (GHMatters) P116824.AU 6
[40] Step S124: The computer device 100 sets initial parameters of the attenuation model of the tunnel pavement performance according to the actual tunnel design condition and the historical monitoring data. The parameters involved by the attenuation model of the tunnel pavement performance mainly include: pavement strength, pavement damage, pavement flatness, a skidding resistance coefficient of the pavement, a structural bearing capability of the pavement, a cost-benefit ratio, etc.
[41] The establishment of the initial behavioral model may include: a traffic operation simulated road network model is initialized, and behavioral parameters are set, to establish the initial behavioral model. The establishment of the initial behavioral model may mainly include the following steps:
[42] Step S131: The computer device 100 establishes a traffic simulated road network and a cell according to real tunnel design data and the initial geometric model.
[43] Step S132: The computer device 100 sets lane attribute parameters such as a lane speed limit, a height limit, no overtaking and a bus priority according to design data of an actual tunnel and a real condition of the tunnel.
[44] Step S133: The computer device 100 sets initial parameters of a traffic signal according to the design data of the actual tunnel and the real condition of the tunnel.
[45] Step S134: The computer device 100 sets initial parameters such as a tunnel traffic origin destination (OD) and a proportion for different types of vehicles according to historical monitoring data of the actual tunnel. The tunnel traffic OD is the origin destination of the tunnel traffic.
[46] Step S135: The computer device 100 sets initial parameters related to simulated operation such as a simulation duration, a detector position and an output data format.
[47] During the actual applications, the initial tunnel DT scenario may further include initialization of other geometric, physical and behavioral models that are involved possibly, which is not specifically limited in the embodiment of the present disclosure.
[48] Step S200: The computer device 100 performs real-time three-dimensional fusion on multichannel monitoring videos in the actual tunnel scenario and the initial tunnel DT scenario according to multichannel monitoring video data in the actual tunnel scenario to form a panoramic tunnel DT scenario, and performs structural analysis and semantic annotation on a static scenario in the multichannel monitoring videos.
[49] During the actual applications, Step S200 may include the following steps.
[50] Step S201: The computer device 100 calibrates a position, a height and an orientation of a monitoring camera in the actual tunnel scenario to obtain a calibration result.
[51] Step S202: The computer device 100 analyzes a topological relation of a monitoring 17905215_1 (GHMatters) P116824.AU 7 video picture according to the calibration result and the multichannel monitoring video data.
[52] Step S203: The computer device 100 performs real-time VR fusion on the multichannel monitoring videos and the initial geometric model in the initial tunnel DT scenario according to the topological relation, to form the panoramic tunnel DT scenario in which a three-dimensional real scenario is fused.
[53] Step S204: The computer device 100 performs the structural analysis on the static scenario in the multichannel monitoring videos according to the panoramic tunnel DT scenario, establishes a corresponding relation between a two-dimensional video and a three-dimensional scenario monitoring region, and performs the semantic annotation on a corresponding region of the panoramic tunnel DT scenario.
[54] During the actual applications, the structural analysis is performed on the static scenario in the multichannel monitoring videos in combination with a VR fusion result, to recognize static objects such as the lining, the pavement and electromechanical devices in the multichannel monitoring videos. The corresponding relation between the two-dimensional video and the three-dimensional scenario monitoring region is established, and the semantic annotation is performed on an important region in the panoramic tunnel DT scenario. The important region may be determined according to an actual condition, which is not specifically limited in the embodiment of the present disclosure.
[55] Step S300: The computer device 100 performs association registration, data access and dynamic annotation on a virtual device in the panoramic tunnel DT scenario according to an IoT sensing device and a traffic affiliated facility in the actual tunnel scenario.
[56] During the actual applications, the IoT sensing device and the traffic affiliated facility in the actual tunnel scenario are respectively subjected to the association registration, the data access and the dynamic annotation with the virtual device in the panoramic tunnel DT scenario. The IoT sensing device and the traffic affiliated facility may include: a traffic monitoring facility, a structural deformation monitoring facility, an environmental monitoring facility, a video monitoring facility, a lighting facility, a power facility, a firefighting device, a ventilator, a water supply and drainage facility, a traffic signal facility, an emergency phone facility, a variable message board, a broadcasting facility, etc. The traffic monitoring facility may include: an ultra-high detection system and a coil detection system.
[57] The traffic monitoring facility is mainly configured to monitor traffic behavioral indicators such as a height of a traffic vehicle, a traffic flow, an average speed and a lane occupancy ratio in the tunnel. The structural deformation monitoring facility is mainly configured to monitor structure indicators such as a cross-sectional convergence, a crack 17905215_1 (GHMatters) P116824.AU 8 width, relative displacement of a contact passage, longitudinal settlement and a joint opening of the tunnel structure. The environmental monitoring facility is mainly configured to monitor environmental indicators such as temperature, humidity, PM2.5, CO, illuminance and brightness in the tunnel scenario. Step S300 may specifically include the following steps:
[58] Step S301: The computer device 100 establishes a mapping relation between the IoT sensing device in the actual tunnel scenario and an IoT sensing device in the panoramic tunnel DT scenario, and establishes a mapping relation between the traffic affiliated facility in the actual tunnel scenario and a traffic affiliated facility in the panoramic tunnel DT scenario.
[59] Step S302: The computer device 100 establishes a data encoding specification for the IoT sensing device and the traffic affiliated facility, and sets a unique digital identifier and message format for each of the IoT sensing device and the traffic affiliated facility.
[60] Step S303: The computer device 100 associates static attribute information of the IoT sensing device and the traffic affiliated facility in the actual tunnel scenario to a corresponding object in the panoramic tunnel DT scenario, and supports timed or untimed update operation.
[61] Step S304: The computer device 100 accesses state and dynamic data of the IoT sensing device and the traffic affiliated facility in the actual tunnel scenario to the panoramic tunnel DT scenario in real time according to an agreed message interface protocol.
[62] Step S305: The computer device 100 denoises, matches and verifies data of multiple IoT sensing devices accessed to the panoramic tunnel DT scenario, to ensure accuracy of data accessed to the panoramic tunnel DT scenario.
[63] Step S306: The computer device 100 adds three-dimensional state annotations at corresponding positions of the IoT sensing device and the traffic affiliated facility in the panoramic tunnel DT scenario, and performs AR real-time display on data of a key IoT sensing device or a state of a key traffic affiliated facility.
[64] During the actual applications, objects of data aggregation may further include: online meteorological data, daily inspection data, periodic tunnel detection data, aperiodic tunnel detection data, daily service data, etc. The association registration, the data access and the dynamic annotation may be performed on the online meteorological data, the daily inspection data, the periodic tunnel detection data, the aperiodic tunnel detection data or the daily service data in the actual tunnel scenario with online meteorological data, daily inspection data, periodic tunnel detection data, aperiodic tunnel detection data or daily service data in the panoramic tunnel DT scenario according to user requirements. 17905215_1 (GHMatters) P116824.AU 9
[65] Step S400: The computer device 100 performs real-time recognition, semantic annotation, abnormality detection and structural output on a dynamic object in the multichannel monitoring videos according to the multichannel monitoring video data, data of the IoT sensing device and meteorological data.
[66] During the actual applications, Step S400 may include the following steps:
[67] Step S401: The computer device 100 performs the real-time recognition, the semantic annotation, the abnormality detection and the structural output on the dynamic object in the multichannel monitoring video data to structurally process unstructured video data.
[68] Step S402: The computer device 100 analyzes the data of the IoT sensing device to determine a relation between the data of the IoT sensing device and a set threshold. Under a condition that the data of the IoT sensing device is greater than the set threshold, alarm display is performed in the panoramic tunnel DT scenario.
[69] Step S403: The computer device 100 verifies associated parameters according to the structured video data and the data of the IoT sensing device, to establish an association and restriction relation between the structured video data and the data of the IoT sensing device.
[70] Step S404: The computer device 100 analyzes a relation between parameters of geometric models, parameters of physical models and parameters of behavioral models for the actual tunnel scenario and the panoramic tunnel DT scenario according to the structured video data, the unstructured video data and the data of the IoT sensing device.
[71] Step S500: The computer device 100 updates corresponding models and/or related parameters in the panoramic tunnel DT scenario according to a change and an analysis result of the data of the IoT sensing device.
[72] During the actual applications, related parameters of such physical models as a tunnel structural deformation physical model, a pavement dynamic evolution physical model and a material attenuation physical model in the panoramic tunnel DT scenario are updated according to the change and the analysis result of the data of the IoT sensing device, which may specifically include the following steps:
[73] Step S511: The computer device 100 analyzes historical and real-time monitoring data of tunnel structure-related indicators such as the cross-sectional convergence, the crack width, the relative displacement of the contact passage, the longitudinal settlement and the joint opening, and converts the monitoring data into related parameters of the stress model of the tunnel structure.
[74] Step S512: The computer device 100 updates the related parameters of the stress model of the tunnel structure according to the real-time monitoring data, and analyzes and
17905215_1 (GHMatters) P116824.AU 10 alarms an abnormal fluctuation or over-standard phenomenon.
[75] Step S513: The computer device 100 analyzes historical and present detection data related to an attenuation of the tunnel pavement performance such as a pavement anti-slip pendulum value, a deflection value, a type of pavement damage, an area of pavement damage, an extent of pavement damage and a planeness of the pavement, and converts the detection data into related parameters of the attenuation model of the tunnel pavement performance.
[76] Step S514: The computer device 100 updates the related parameters of the attenuation model of the pavement performance according to the historical and present detection data, and analyzes and alarms an abnormal change.
[77] During the actual applications, parameters related to such behavioral models as a pedestrian behavioral model, a non-motor vehicle behavioral model and a vehicle behavioral model in the panoramic tunnel DT scenario are updated according to the change and the analysis result of the data of the IoT sensing device, which may specifically include the following steps:
[78] Step S521: The computer device 100 hourly updates a tunnel traffic simulated OD parameters of a traffic simulation model in the panoramic tunnel DT scenario according to statistics and analysis on structured data of actual tunnel monitoring videos and traffic monitoring data.
[79] Step S522: The computer device 100 hourly updates a vehicle type ratio parameter of the traffic simulation model in the panoramic tunnel DT scenario according to the statistics and analysis on structured data of the multichannel monitoring videos and the traffic monitoring data.
[80] Step S523: The computer device 100 hourly forms a tunnel traffic simulated OD and a vehicle type ratio database, to fit change rules of the tunnel traffic simulated OD and the vehicle type ratio every day.
[81] Step S524: The computer device 100 fits the change rules of the tunnel traffic simulated OD and the vehicle type ratio at the same time period of every week, month and year, and updates recommended parameter values for the tunnel traffic simulated OD and the vehicle type ratio at different time periods of different occasions such as a workday, a weekend, a holiday and festival, and an important activity day.
[82] During the actual applications, geometric models related to the environment such as a structural deformation geometric model, a pavement disease geometric model, a fire smoke diffusion geometric model, a tunnel ponding geometric model, a tunnel seepage geometric model, a different visibility geometric model, and a geometric model for start-stop 17905215_1 (GHMatters) P116824.AU I1 of facilities and devices in the panoramic tunnel DT scenario, and scenario drawing results are updated according to the change and the analysis result of the data of the IoT sensing device, which may specifically include the following steps:
[83] Step S531: The computer device 100 updates a tunnel structural parameterization model driven by a structural deformation parameter.
[84] Step S532: The computer device 100 updates a pavement disease parameterization model driven by pavement detection data.
[85] Step S533: The computer device 100 updates a rendering parameter of homogeneous participating medium driven by environmental detection data.
[86] Step S534: The computer device 100 updates a global illumination parameter driven by the online meteorological data.
[87] Step S535: The computer device 100 updates a local illumination parameter of the tunnel scenario driven by monitoring data for the lighting, the signal lamp and a message sign.
[88] Step S536: The computer device 100 updates a fire smoke inhomogeneous participating medium diffusion model driven by fire control event monitoring data.
[89] Step S537: The computer device 100 updates a three-dimensional wind field model and parameters driven by ventilation monitoring data.
[90] Step S538: The computer device 100 updates a fluid dynamic model driven by tunnel ponding monitoring data.
[91] Step S539: The computer device 100 updates a fluid diffusion model driven by tunnel seepage monitoring data.
[92] It should be understood that other geometric models, visual perception-related models and/or parameters may further be updated during the actual applications, which is not specifically limited in the embodiment of the present disclosure.
[93] Step S600: The computer device 100 reviews and verifies the geometric model, the physical model, the behavioral model and the related parameters in the panoramic tunnel DT scenario according to historical data or detection data.
[94] During the actual applications, the geometric model, the physical model, the behavioral model and the parameters in the panoramic tunnel DT scenario are reviewed by utilizing the historical data, and feasibility of the panoramic tunnel DT scenario and the parameters are verified, to have an insight into an operation rule of the actual tunnel traffic scenario, and improve consistency between the panoramic tunnel DT scenario and the real scenario. Step S600 may specifically include the following steps:
[95] Step 601: The computer device 100 defines an evaluation function of a tunnel DT 17905215_1 (GHMatters) P116824.AU 12 model, and determines a difference between the tunnel DT scenario and actual tunnel scenario according to the evaluation function of the tunnel DT model.
[96] Step 602: The computer device 100 reviews the panoramic tunnel DT scenario according to the historical data as well as the corresponding geometric model, physical model or behavioral model in the panoramic tunnel DT scenario; or, reviews the panoramic tunnel DT scenario according to the detection data as well as the corresponding geometric model, physical model or behavioral model in the panoramic tunnel DT scenario.
[97] Step 603: The computer device 100 evaluates the reviewed tunnel DT scenario according to the evaluation function of the tunnel DT model, and minimizes a difference between the reviewed tunnel DT scenario and the actual tunnel scenario to solve an optimization parameter of the model.
[98] Step 604: The computer device 100 executes Step S602 and Step S603 repeatedly, and verifies feasibility of the corresponding models and corresponding parameters when a result converges or reaches the maximum number of iterations.
[99] During the actual applications, a certain geometric, physical or behavioral model in the panoramic tunnel DT scenario may be selected, the tunnel DT scenario is reviewed with the historical monitoring data or detection data, then the reviewed tunnel DT scenario is evaluated with the evaluation function of the tunnel DT model, and the difference between the reviewed tunnel DT scenario and the actual tunnel scenario is minimized to solve the optimization parameter of the model.
[100] Step S602 and Step S603 are repeated in the above Step S604, till the result converges or reaches the maximum number of iterations, thereby completing the reviewed and verification of one model. Another model in the panoramic tunnel DT scenario is selected, and Step S602 to Step S604 are repeated till the result converges or the traversal is completed.
[101] Step S700: The computer device 100 respectively simulates and analyzes a corresponding service requirement or problem in the actual tunnel scenario according to the panoramic tunnel DT scenario.
[102] During the actual applications, Step S700 may include the following steps.
[103] Step S701: The computer device 100 analyzes, for a certain requirement or problem of a user in actual tunnel traffic management, a panoramic tunnel DT scenario involved by the requirement or problem.
[104] Step S702: The computer device 100 reproduces a scenario related to the requirement or problem in the panoramic tunnel DT scenario.
[105] Step S703: The computer device 100 adjusts a parameter affecting the requirement 17905215_1 (GHMatters) P116824.AU 13 or problem in the panoramic tunnel DT scenario, and analyzes evolution results in conditions of different parameter values.
[106] Step S704: The computer device 100 compares the evolution results under the different parameter values, and performs repeatable simulation and analysis on the requirement or problem, to have an insight into a rule therein, thereby meeting an application requirement and solving the problem in the actual traffic management.
[107] Step S800: The computer device 100 predicts and deduces a change of the actual tunnel scenario according to the geometric model, the physical model and the behavioral model in the panoramic tunnel DT scenario, to alarm an abnormal event in the actual tunnel scenario.
[108] During the actual applications, Step S800 may include the following steps.
[109] Step S801: The computer device 100 performs, in the panoramic tunnel DT scenario, deduction and VR perception test on changes of human perception-related elements such as driving visibility, driving safety, pavement flatness, pavement skidding resistance, driving comfort, and bump at bridge-head based on the historical and present detection data by utilizing the geometric model, the physical model and the behavioral model as well as the related parameters, finds a problem timely and alarms the problem.
[110] Step S802: The computer device 100 predicts and deduces, in the panoramic tunnel DT scenario, changes of vehicle-related elements such as a vehicle flow change, traffic signal adjustment, a traffic event response, a vehicle number-limiting test, traffic restriction of a truck, and traffic congestion based on the historical and present detection data by utilizing the geometric model, the physical model and the behavioral model as well as the parameters, finds a problem timely and alarms the problem.
[111] Step S803: The computer device 100 predicts and deduces, in the panoramic tunnel DT scenario, changes of infrastructure-related elements such as the tunnel structural deformation, a tunnel seepage change, pavement disease evolution, and a fault of an electromechanical device based on the historical and present detection data by utilizing the geometric model, the physical model and the behavioral model as well as the parameters, finds a problem timely and alarms the problem.
[112] Step S804: The computer device 100 predicts and deduces, in the panoramic tunnel DT scenario, changes of environment-related elements such as fire, ponding, fog, abnormality in tunnel particulate, abnormality in temperature and humidity, and illuminance based on the historical and present detection data by utilizing the geometric model, the physical model and the behavioral model as well as the parameters, finds a problem timely and alarms the problem.
17905215_1 (GHMatters) P116824.AU 14
[113] Step S900: The computer device 100 acquires event responses and handling solutions in different conditions according to the simulated and analyzed result as well as the predicted and deduced result, to form a three-dimensional emergency plan and an auxiliary decision-making suggestion for the actual tunnel scenario.
[114] During the actual applications, Step S900 may include the following steps.
[115] Step S901: The computer device 100 timely responds to and tracks abnormality of the traffic monitoring data, and deduces and alarms the abnormality in the panoramic tunnel DT scenario.
[116] Step S902: The computer device 100 provides the auxiliary decision-making suggestion in the panoramic tunnel DT scenario by supporting three-dimensional deduction and comparative analysis of different handling solutions for the same event.
[117] Step S903: The computer device 100 generates corresponding three-dimensional emergency handling plans for different types and extents of traffic emergency events in the panoramic tunnel DT scenario, to form a three-dimensional plan database for the emergency events of the actual tunnel traffic.
[118] Step S1000: The computer device 100 controls, according to the auxiliary decision-making suggestion, the IoT sensing device to perform a reverse physical control on the actual tunnel scenario, and updates the corresponding models of the panoramic tunnel DT scenario.
[119] During the actual applications, the auxiliary decision-making suggestion is fed back to the manager, a decision-making operation confirmed by the manager is configured to realize the reverse physical control on the actual tunnel scenario through the IoT sensing device and the corresponding DT scenario is updated. Step S1000 may specifically include the following steps:
[120] Step S10: The computer device 100 performs, according to the auxiliary decision-making suggestion, an associated control on the lighting facility, the power facility, the firefighting device, the ventilator, the water supply and drainage facility and the like in the actual tunnel scenario in the panoramic tunnel DT scenario.
[121] Step S1002: The computer device 100 notifies or broadcasts, according to the auxiliary decision-making suggestion, a person in the actual tunnel scenario through the emergency phone facility and the broadcasting facility in the panoramic tunnel DT scenario.
[122] Step S1003: The computer device 100 publishes, according to the auxiliary decision-making suggestion, information such as traffic guidance, a violation and a warning in the actual tunnel scenario through the variable message sign in the panoramic tunnel DT scenario.
17905215_1 (GHMatters) P116824.AU
[123] Step S1004: The computer device 100 controls, according to the auxiliary decision-making suggestion, vehicle access permission of the actual tunnel through a traffic signal control in the panoramic tunnel DT scenario.
[124] Step S1005: The computer device 100 updates the panoramic tunnel DT scenario according to a feedback control operation and a result in the actual tunnel scenario. At this time, the establishment of one entire tunnel DT scenario is completed.
[125] Step S1100: The computer device 100 updates, according to the user requirement, the DT database, and iterates the panoramic tunnel DT scenario.
[126] During the actual applications, Step S1100 may include the following steps:
[127] Step S1101: The computer device 100 updates and responds to a user management requirement, and repeats Step S200 to Step S1000, till a system service ends.
[128] Step S1102: The computer device 100 stores data during scenario monitoring and handling, and updates the DT database.
[129] Step S1103: The computer device 100 optimizes the geometric model, the physical model and the behavioral model as well as the parameters in the panoramic tunnel DT scenario.
[130] Compared with the conventional art, the method for establishing the tunnel DT scenario provided by the present disclosure has the following beneficial effects.
[131] (1) The panoramic tunnel DT scenario is implemented, in which multichannel videos are fused in real time. A spatial association between discrete videos and a monitoring scenario is established. A global perspective is provided by utilizing video fragments to restore a three-dimension scenario, so that a global monitoring scenario of the tunnel may be dynamically displayed with "a picture". Continuous and intuitive monitoring of multiple regions is provided to facilitate a monitoring manager to overview an overall situation.
[132] (2) Tunnel DT holographic perception integrated with each professional subsystem is formed. Data barriers between the professional subsystems in the tunnel is broken down, multi-source sensing data, such as video data, detection data, monitoring data and online IoT data, are uniformly integrated and displayed with augmented reality (AR), thereby implementing three-dimensional digital assets management fused with the actual scenario.
[133] (3) The alarm and timely response for the abnormal event of the tunnel are supported based on the DT. The abnormal event may be discovered timely in a tunnel by utilizing the panoramic tunnel DT scenario. A three-dimensional emergency response plan for the abnormal event is generated according to the geometric model, the physical model and the behavioral model in the panoramic tunnel DT scenario, and a solution is deduced.
[134] (4) Trial-and-error cost is reduced. Matters and phenomenon that cannot be observed 17905215_1 (GHMatters) P116824.AU 16 and controlled in a real world due to a time and space limitation, processes that change too fast or too slow, and experiments that are dangerous, destructive, and harmful to environment are implemented in the panoramic tunnel DT scenario, and a reproduction scenario and a solution test environment for a complex problem during the tunnel traffic operation are provided to reduce the trial-and-error cost.
[135] The embodiments of the present disclosure further provide a computer storage medium, including a processor 101. The computer storage medium stores instructions. When executed by the processor 101, the instructions implement the method for establishing the tunnel DT scenario described above.
[136] Compared with the conventional art, the beneficial effects of the computer storage medium provided by the embodiments of the present disclosure are the same as those of the method for establishing the tunnel DT scenario in the above solutions, and will not be repeated herein.
[137] Although the present disclosure has been described in combination with the embodiments, those skilled in the art may understand and implement other changes of the embodiments of the present disclosure by checking the accompanying drawings, disclosures and appended claims during implementation of the present disclosure. In the claims, the word "comprising" does not exclude other components or steps, and the word "a" or"an" does not exclude a plural case. A single processor 101 or other units may implement a plurality of functions listed in the claims. Some measures are recorded in dependent claims that are different from one another. However, it does not mean that these measures cannot be combined together to achieve a desirable effect.
[138] Although the present disclosure has been described in combination with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made without departing from the spirit and scope of the present disclosure. Correspondingly, the specification and accompanying drawings are merely exemplary descriptions of the present disclosure that are defined by the appended claims, and are deemed as covering any and all of the modifications, changes, combinations or equivalents within the scope of the present disclosure. Apparently, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. In this way, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and equivalent technologies thereof, the present disclosure is further intended to include these modifications and variations.
[139] It is to be understood that, if any prior art publication is referred to herein, such 17905215_1 (GHMatters) P116824.AU 17 reference does not constitute an admission that the publication forms a part of the common general knowledge in the art, in Australia or any other country.
[140] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" is used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.
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Claims (5)
1. A method for establishing a tunnel digital twin (DT) scenario, comprising: establishing an initial geometric model, an initial physical model, an initial behavioral model and a DT database according to an actual tunnel scenario to form an initial tunnel DT scenario; performing real-time three-dimensional fusion on multichannel monitoring videos in the actual tunnel scenario and the initial tunnel DT scenario according to multichannel monitoring video data in the actual tunnel scenario to form a panoramic tunnel DT scenario, and performing structural analysis and semantic annotation on a static scenario in the multichannel monitoring videos; performing association registration, data access and dynamic annotation on a virtual device in the panoramic tunnel DT scenario according to an Internet-of-Things (IoT) sensing device and a traffic affiliated facility in the actual tunnel scenario; performing real-time recognition, semantic annotation, abnormality detection and structural output on a dynamic object in the multichannel monitoring videos according to the multichannel monitoring video data, data of the IoT sensing device and meteorological data; updating corresponding models and/or related parameters in the panoramic tunnel DT scenario according to a change and an analysis result of the data of the IoT sensing device; reviewing and verifying a geometric model, a physical model, a behavioral model and the related parameters in the panoramic tunnel DT scenario according to historical data or detection data; respectively simulating and analyzing a corresponding service requirement or problem in the actual tunnel scenario according to the panoramic tunnel DT scenario; predicting and deducing a change of the actual tunnel scenario according to the geometric model, the physical model and the behavioral model in the panoramic tunnel DT scenario, to alarm an abnormal event in the actual tunnel scenario; acquiring event responses and handling solutions in different conditions according to the simulated and analyzed result as well as the predicted and deduced result, to form a three-dimensional emergency plan and an auxiliary decision-making suggestion for the actual tunnel scenario; and controlling, according to the auxiliary decision-making suggestion, the IoT sensing device to perform a reverse physical control on the actual tunnel scenario, and updating the corresponding models of the panoramic tunnel DT scenario.
17905215_1 (GHMatters) P116824.AU 19
2. The method for establishing the tunnel DT scenario according to claim 1, wherein the performing the real-time three-dimensional fusion on the multichannel monitoring videos in the actual tunnel scenario and the tunnel twin scenario according to the multichannel monitoring video data in the actual tunnel scenario, and performing the structural analysis and the semantic annotation on the static scenario in the panoramic tunnel DT scenario comprises: calibrating a position, a height and an orientation of a monitoring camera in the actual tunnel scenario to obtain a calibration result; analyzing a topological relation of a monitoring video picture according to the calibration result and the multichannel monitoring video data; performing real-time virtual reality (VR) fusion on the multichannel monitoring videos and the initial geometric model in the initial tunnel DT scenario according to the topological relation, to form the panoramic tunnel DT scenario in which a three-dimensional real scenario is fused; and performing the structural analysis on the static scenario in the multichannel monitoring videos according to the panoramic tunnel DT scenario, establishing a corresponding relation between a two-dimensional video and a three-dimensional scenario monitoring region, and performing the semantic annotation on a corresponding region of the panoramic tunnel DT scenario.
3. The method for establishing the tunnel DT scenario according to claim 1, wherein the performing the real-time recognition, the semantic annotation, the abnormality detection and the structural output on the dynamic object in the multichannel monitoring videos according to the multichannel monitoring video data, the data of the IoT sensing device and the meteorological data comprises: performing the real-time recognition, the semantic annotation, the abnormality detection and the structural output on the dynamic object in the multichannel monitoring video data, to structurally process unstructured video data; analyzing the data of the IoT sensing device to determine a relation between the data of the IoT sensing device and a set threshold; verifying associated parameters according to the structured video data and the data of the IoT sensing device, to establish an association and restriction relation between the structured video data and the data of the IoT sensing device; and analyzing a relation between parameters of geometric models, parameters of physical models and parameters of behavioral models for the panoramic tunnel DT scenario and the
17905215_1 (GHMatters) P116824.AU 20 actual tunnel scenario according to the structured video data, the unstructured video data and the data of the IoT sensing device.
4. The method for establishing the tunnel DT scenario according to claim 1, wherein the reviewing and verifying the geometric model, the physical model, the behavioral model and the related parameters in the panoramic tunnel DT scenario according to the historical data or the detection data comprises: determining a difference between the tunnel DT scenario and the actual tunnel scenario according to an evaluation function of a tunnel DT model; reviewing the panoramic tunnel DT scenario according to the historical data as well as the corresponding geometric model, physical model or behavioral model in the panoramic tunnel DT scenario; or, reviewing the panoramic tunnel DT scenario according to the detection data as well as the corresponding geometric model, physical model or behavioral model in the panoramic tunnel DT scenario; evaluating the reviewed tunnel DT scenario according to the evaluation function of the tunnel DT model to determine optimization parameters of the corresponding models; and verifying feasibility of the corresponding models and corresponding parameters when a result converges or reaches a maximum number of iterations.
5. A computer device, comprising an Internet-of-Things sensing device, a processor, a memory and computer executable instructions stored in the memory and configured to be executed by the processor; and when executing the computer executable instructions, the processor implements the method for establishing the tunnel digital twin scenario according to any one of claims I to 4.
17905215_1 (GHMatters) P116824.AU 21
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| CN115775092B (en) * | 2022-11-11 | 2023-12-22 | 中电建铁路建设投资集团有限公司 | Construction process safety risk management and control system based on digital twin technology |
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