Methods and apparatus for control of response to and prevention of underground accidents

Abstract

A control method and a control device for responding to and preventing underground accidents are disclosed. The control method may include the steps of: collecting relevant data related to underground accidents from a measuring device deployed at the site; generating virtual space data that reproduces the actual geospatial space of a target area based on the collected relevant data; predicting the risk of underground accidents for a target area based on at least one of the relevant data or the virtual space data; and visually applying at least some of the predicted risk and relevant data to the virtual space data.

Description

Technology Field

[0001] The following disclosure relates to a control method and device for responding to and preventing underground accidents. Background Technology

[0002] Recently, the frequency of underground accidents has been increasing, and cases of human and material losses resulting from them are being reported. During the period from 2017 to June 2021, a total of 1,176 cases of ground subsidence occurred in urban areas due to damage to water and sewage pipes, poor soil compaction, etc. Due to the increase in underground accidents, there is a need to identify and manage related risk factors in advance. In order to respond to the recent rapid increase in underground accidents, the Groundwater Act (2002) and the Underground Safety Act (2018) were enacted.

[0003] The integrated underground space map, aimed at strengthening underground safety management, is static data. In contrast, the production data associated with generating the integrated underground space map can be utilized as dynamic data regarding vulnerable regions in South Korea. Currently, when an underground accident occurs, forming an investigation committee and conducting the investigation take a long time, depending on the specific case.

[0004] A control method for responding to and preventing underground accidents may include: collecting relevant data related to underground accidents from a measuring device deployed at the site; generating virtual space data that reproduces the actual geographic space of a target area based on the collected relevant data; predicting the risk of underground accidents for the target area based on at least one of the relevant data or the virtual space data; and visually applying the predicted risk and at least some of the relevant data to the virtual space data.

[0005] The step of predicting the risk level may include: a step of comparing the relevant data corresponding to the target area based on the correlation between the relevant data in the case where an actual underground accident occurred and the underground accident that occurred; and a step of predicting the risk level of the underground accident for the target area based on the result of the comparison.

[0006] The step of generating the virtual space data may include: a step of obtaining infrastructure identification data from the relevant data using a detection model that takes the relevant data as input; and a step of generating the virtual space data based on the obtained infrastructure identification data and the relevant data.

[0007] The above detection model can generate infrastructure identification data that identifies infrastructure including at least one of underground pipes, manholes, transverse pipes, or longitudinal pipes from Ground Penetrating Radar (GPR) exploration information among the above related data.

[0008] A control method according to one embodiment may further include the step of generating at least one of underground accident simulation data related to the occurrence of an underground accident or countermeasure simulation data related to countermeasures for an underground accident based on the virtual space data.

[0009] A control method according to one embodiment further includes the step of obtaining causal relationship inference data for the occurred underground accident by using an inference model that takes relevant data as input when an underground accident actually occurs, wherein the causal relationship inference data for the occurred underground accident includes information inferring the cause of the occurrence of the underground accident, and the inference model can generate the causal relationship inference data including information inferring the cause of the occurrence of the underground accident based on the input relevant data.

[0010] A control method according to one embodiment may further include the step of generating connection relationship data representing the connection relationship between data included in the relevant data; and the step of generating decision data related to a response plan for an underground accident based on the generated connection relationship data.

[0011] The above-mentioned relevant data may include at least one of image data in which at least a portion of the target area is captured, stratigraphic information, bedrock depth, ground strength, ground change characteristic information, ground deformation modulus, topographic survey information, ground survey information, building survey information, ground subsidence internal survey information, rescuer location information, rescuer status information, stratigraphic structure information, or ground weak zone information.

[0012] A control device for responding to and preventing underground accidents according to one embodiment includes a memory and a processor, wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor may enable the control device to collect relevant data related to underground accidents from a measuring device deployed at the site, generate virtual space data that reproduces the actual geographic space of a target area based on the collected relevant data, predict the risk of underground accidents for the target area based on at least one of the relevant data or the virtual space data, and visually apply the predicted risk and at least some of the relevant data to the virtual space data. Brief explanation of the drawing

[0013] FIG. 1 is a schematic diagram illustrating an overview of a control system according to one embodiment. FIG. 2 is a flowchart illustrating a control method according to one embodiment. FIG. 3 is a diagram illustrating a series of processes for providing a control method according to one embodiment. FIG. 4 is a drawing illustrating an example of virtual space data according to one embodiment. FIG. 5 is a drawing for explaining a control system according to one embodiment. FIG. 6 is a drawing illustrating the configuration of a control device according to one embodiment. Specific details for implementing the invention

[0014] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, actual implementations are not limited to the specific embodiments disclosed, and the scope of this specification includes modifications, equivalents, or substitutions included in the technical concept described by the embodiments.

[0015] Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0016] When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or coupled with that other component, or that there may be other components in between.

[0017] Singular expressions include plural expressions unless the context clearly indicates otherwise. In this document, phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B, or C” may each include any one of the items listed together with the corresponding phrase, or all possible combinations thereof. In this specification, terms such as “comprising” or “having” are intended to designate the existence of the described feature, number, step, action, component, part, or combination thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0018] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification.

[0019] As used herein, the term "module" may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0020] As used in this document, the term "part" refers to a software or hardware component, such as an FPGA or ASIC, that performs certain roles. However, "part" is not limited to software or hardware. "Part" may be configured to reside in an addressable storage medium or configured to operate one or more processors. For example, "part" may include components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." Furthermore, components and "parts" may be implemented to operate one or more CPUs within a device or secure multimedia card. Additionally, '~part' may include one or more processors.

[0021] Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are given the same reference numeral regardless of the drawing number, and redundant descriptions thereof will be omitted.

[0022] FIG. 1 is a schematic diagram illustrating an overview of a control system according to one embodiment.

[0023] The control system described in this specification can provide a control method and a control device that integrate digital twin technology and augmented reality technology to realize three-dimensional visualization or visualization of the site where an underground accident has occurred, thereby enabling the real-time assessment of the situation of the underground accident and the taking of immediate response measures. By centrally accessing and analyzing various data from the site in real time, the control system can support rapid decision-making in emergency situations. By integrating and analyzing data, the control system can understand the situation at the site in detail when a disaster occurs and, through data-based risk prediction functions, can support preemptive judgments compared to existing methods focused on post-accident response. The control system can establish a foundation for managing and utilizing various data sources within a single integrated system. The control system can contribute to rapidly integrating and analyzing Geographic Information System (GIS) data, sensor data, image data, etc., necessary for responding to underground safety accidents, and establishing and operating response strategies at the site of the underground accident based on the analysis results. Based on data and technical achievements obtained through empirical research, the control system can suggest planning directions for related studies and establish future R&D strategies, thereby promoting the continuous improvement of the response system for underground accidents. Through convergence analysis technology for underground accident response, the control system can analyze real-time data and develop prediction and response scenarios. Through this, the control system can provide control methods capable of prevention and rapid response in disaster management regarding underground accidents. Furthermore, the control system can simulate disaster situations through simulation and situational control technologies, enabling the implementation of corresponding response training.The control system can support the strengthening of the disaster management team's response capabilities and enable appropriate resource allocation and efficient disaster response through real-time situation monitoring in the event of a disaster. In this specification, the control system may be described in more detail through control methods and control devices. The control system described in this specification may be named Geo-Ambulance. The term Geo-Ambulance is a compound word of Geo (Geospatial) and Ambulance. Geo-Ambulance may refer to a safety management platform utilizing digital twin and extended reality (XR) technologies to respond to disasters and emergencies in national land space, such as underground accidents. The control system may also provide cyber security and connectivity functions to ensure the operation of the Geo-Ambulance is not compromised. Geo-Ambulance is a physical artificial intelligence device and may include Geo-Ambulance exploration vehicles, drone corps, and robot dog corps; the Geo-Ambulance exploration vehicles, drone corps, and robot dog corps may correspond to the measurement devices described in this specification. Here, surveying can be a general term for investigation and exploration.

[0024] The control device can monitor a target area in real time using composite sensors mounted on a measuring device, analyze or model relevant data, and manage information on the surface and underground in an integrated manner in two or three dimensions. The control device can perform data artificial intelligence refinement in real time based on relevant data and predict real-time changes in relevant data. Underground accidents referred to in this specification may include, for example, ground subsidence, flooding, ground collapse (sinkhole), damage to underground facilities (water supply, sewage, electricity, gas, telecommunications, heating), and damage to underground structures (subway, underpass, underground walkway, underground shopping mall, underground parking lot, utility tunnel), but are not limited to those described in this specification and may further include all accidents related to underground infrastructure.

[0025] Referring to FIG. 1, a control device (120) can collect relevant data (110) related to underground accidents from a measuring device (150) deployed at the site. Based on the collected relevant data (110), the control device (120) can generate three-dimensional virtual space data (140) that reproduces the actual geographic space of the target area. The virtual space data (140) may be data that reproduces the actual geographic space of the site, and various data may be visualized or displayed in the virtual space data (140). The control device (120) can predict the risk of underground accidents for the target area based on at least one of the relevant data (110) or the virtual space data (140). The control device (120) can visually apply at least some of the predicted risk and relevant data (110) to the virtual space data (140).

[0026] FIG. 2 is a flowchart illustrating a control method according to one embodiment.

[0027] A control method according to one embodiment may be performed by a control device for responding to and preventing underground accidents as described herein. Referring to FIG. 2, in step (210), the control device may collect relevant data related to underground accidents from a measuring device deployed at the site. The measuring device may include at least one of a physical exploration device, an exploration vehicle, an exploration robot, a device equipped with a Mobile Mapping System (MMS), a drone, a satellite, or an Artificial Intelligence of Things (AIoT) that is communicationally connected to the control device. Here, the exploration robot may have an appearance that mimics, for example, an animal, particularly a dog, but is not limited to the description in this specification. The site may refer to, for example, at least one of a target area for predicting the risk of underground accidents or a place where an underground accident has occurred. The measuring device may measure at least some of the relevant data by performing drilling surveys and physical explorations and may transmit at least some of the measured relevant data to the control device. The control device may collect relevant data by receiving relevant data from the measuring device. Relevant data may include, for example, at least one of image data in which at least a portion of the target area is captured, stratigraphic information, bedrock depth, ground strength, ground deformation characteristic information, ground deformation modulus, topographic survey information, ground survey information, building survey information, ground subsidence internal survey information, rescuer location information, rescuer status information, GPR survey information, stratigraphic structure information, or ground weak zone information, but is not limited to those described in this specification.

[0028] For example, a measuring device may measure at least one of the relevant data, such as stratigraphic information, bedrock depth, ground strength, or ground deformation modulus, through drilling surveys. Additionally, a measuring device equipped with an exploration robot or MMS may measure at least one of, for example, topographic survey information, ground survey information, building survey information, ground subsidence internal survey information, rescuer location information, or rescuer status information, but is not limited to the descriptions in this specification. Furthermore, a physical exploration device among the measuring devices may measure at least one of, for example, two-dimensional underground facility information, three-dimensional underground facility information, stratigraphic structure information based on geophysical exploration (GPR survey, electrical resistivity survey, seismic exploration, etc.), or ground weak zone information, but is not limited to the descriptions in this specification. Ground weak zone information may be measured, for example, through electrical resistivity surveys. Among the measuring devices, drones or AIoT can measure topographic survey information that precisely surveys the terrain around an area where the ground has subsided, can precisely monitor changes in terrain periodically, and can detect precise changes inside and outside buildings, terrain, and ground subsidence. At least some of the information collected by the measuring device may be included as at least some of the relevant data.

[0029] In step (220), the control device can generate virtual space data that visually reproduces the actual geographic space of the target area based on the collected relevant data. At least some of the relevant data collected by the measurement device may be visually implemented in the virtual space data. The virtual space data may be generated based on a Digital Twin (DT) standard database. The control device can generate virtual space data using augmented reality technology and can visually implement at least some of the relevant data in the virtual space data.

[0030] Here, the target region may refer to the region to which the control method described herein is applied. Additionally, virtual space data may include data that reproduces the actual geographic space of the target region in two or three dimensions. The control device may acquire infrastructure identification data from relevant data by using a detection model that takes relevant data as input. The control device may generate virtual space data based on the acquired infrastructure identification data and relevant data. The detection model may generate infrastructure identification data that identifies infrastructure including at least one of manholes, transverse pipes, or longitudinal pipes from GPR exploration information among the relevant data, but is not limited to the description in this specification. The detection model may generate infrastructure identification data that identifies at least one of various infrastructures other than manholes, transverse pipes, and longitudinal pipes from the relevant data. The infrastructure identification data may include information regarding the type, state, or location of the identified infrastructure. At least some of the infrastructure identification data may be included in the relevant data as at least some of the relevant data.

[0031] The detection model may be trained to identify at least one of various infrastructures, including manholes, transverse pipes, and intermediate pipes, from relevant data, and to generate infrastructure identification data based on the identification results. In one embodiment, the detection model may be trained according to a machine learning process. A machine learning process is a process that enables the detection model to recognize patterns in input data and make predictions on its own using given data. Through the learning process, the detection model can learn patterns from the given data and acquire the ability to make predictions regarding new data.

[0032] The control device can visualize three-dimensional markers in virtual space data based on at least some of the relevant data or infrastructure identification data. The control device can also visualize geological layer information among the relevant data in virtual space data. The control device can visualize geological layer information in virtual space data by distinguishing the material of each layer based on the geological layer information. The control device can parse GPR exploration information entered in JSON (JavaScript Object Notation) format and use the parsed GPR exploration information to create an object in Unity virtual space data in which the GPR exploration information is visualized. When interacting with an object created based on GPR exploration information, a panel providing detailed GPR exploration information at that location may be displayed. When the control device receives user button input for a pipeline toggle button in a layer group, it can visualize the underground pipeline as an underground pipeline object in virtual space data. In object movement mode, the control device can facilitate interaction between the user and the underground pipeline object created in virtual space data, and the height of the underground pipeline object can be manipulated by the user's operation.

[0033] The control device can visualize underground pipelines in virtual space data when it receives user button input for a groundwater toggle button in a layer group. When user selection input for display location markers for groundwater generated in virtual space data is received, the control device can visualize information related to groundwater as a graph.

[0034] The control device may visualize at least some of the relevant data using graphs or charts. The control device may visualize at least some of the relevant data as high-dimensional data, as spatiotemporal data, or as heterogeneous data.

[0035] In step (230), the control device can predict the risk of an underground accident for a target area based on at least one of relevant data or virtual space data. The control device can divide the target area into predetermined zones and predict the risk of an underground accident corresponding to each zone. The control device can compare relevant data corresponding to the target area based on the correlation between relevant data in the case of an actual underground accident and the underground accident that occurred. Here, the correlation between relevant data in the case of an actual underground accident and the underground accident that occurred may be generated in advance or may be generated through analysis by the control device. The correlation between relevant data in the case of an actual underground accident and the underground accident that occurred may include information on the influence that at least some of the relevant data had on the underground accident that actually occurred. The correlation between relevant data in the case of an actual underground accident and the underground accident that occurred may include, for example, the correlation between ground strength in the case of an actual underground accident and the underground accident that actually occurred. More specifically, the correlation between relevant data in the event of an actual underground accident and the actual underground accident may include information regarding the influence of the ground strength in the event of the actual underground accident on the actual underground accident. The control device can predict the risk of an underground accident for a target area based on the results of the comparison. Specific embodiments of the control device predicting the risk of an underground accident for a target area are not limited to those described in this specification and may be performed in various ways.

[0036] In step (240), the control device may visually apply at least some of the predicted risk and related data to the virtual space data. In one embodiment, a specific color or specific shade may be assigned to each range of risk values. The control device may determine the range to which the risk value in a specific area belongs and display the specific area on the virtual space data with a color or shade corresponding to that range. An embodiment of displaying risk based on color or shade may be described in more detail in FIG. 4.

[0037] In one embodiment, the control device may generate at least one of underground accident simulation data or countermeasure simulation data. More specifically, the control device may generate at least one of underground accident simulation data related to the occurrence of an underground accident or countermeasure simulation data related to countermeasures for an underground accident based on virtual space data. The control device may also visually output the generated underground accident simulation data or countermeasure simulation data. Underground accident simulation data related to the occurrence of an underground accident may include simulation data that virtually reproduces an underground accident that actually occurred. Additionally, underground accident simulation data related to the occurrence of an underground accident may further include simulation data that predicts changes in terrain, IoT (Internet of Things), buildings, or subsidence areas after the occurrence of an underground accident.

[0038] Simulation data for response measures related to underground accidents may include simulation data that virtually demonstrates response measures for underground accidents. This simulation data allows users to preview various response measures for dealing with underground accidents and evaluate their effectiveness, thereby supporting the application of appropriate response measures at the actual site. The response measures for underground accidents may include at least one of various measures related to containing the underground accident, performing rescue operations related to the underground accident, preventing additional accidents, and restoring the site where the underground accident occurred.

[0039] In one embodiment, when an underground accident actually occurs, the control device may obtain causal relationship inference data regarding the occurred underground accident by using an inference model that takes relevant data as input. The causal relationship inference data regarding the occurred underground accident may include information inferring the cause of the underground accident. For example, the causal relationship inference data may include information inferring the cause of the underground accident as ground weakening or excessive building expansion, but is not limited to the description in this specification and may further include information inferring various causes.

[0040] The inference model can generate causal inference data that includes information inferring the cause of the underground accident based on input relevant data. The inference model may be trained to generate causal inference data that includes information inferring the cause of the underground accident based on input relevant data. In one embodiment, the inference model may be trained according to a machine learning process. A machine learning process is a process that enables the inference model to independently recognize patterns in input data and make predictions using given data. Through the learning process, the inference model can learn patterns from the given data and acquire the ability to predict new data.

[0041] In one embodiment, the control device may generate decision data related to response measures for underground accidents. The control device may generate connection relationship data indicating the connection relationships between data included in related data. Here, the connection relationship data may include data indicating at least one of the interaction or association between the data included in the related data. For example, the connection relationship data may include data indicating that at least some of the data included in the related data have a proportional relationship with each other, or data indicating that at least some of the data included in the related data have an inversely proportional relationship with each other, but may further include data indicating various associations, not limited to the description in this specification. The control device may generate decision data related to response measures for underground accidents based on the generated connection relationship data. The decision data may include, for example, selection information regarding applying at least one of the response measures to a target area. For example, among the related data, geological information may affect bedrock depth, and geological information and bedrock depth may affect ground strength. Based on this, the control device may generate connection relationship data between geological information, bedrock depth, and ground strength among the related data of the target area. Based on the generated connectivity data, the control device can determine what countermeasures to apply to the site, which is the target area where the underground accident occurred. Based on the generated connectivity data, the control device can generate decision data suitable for geological layer information, bedrock depth, and ground strength. Based on the determined decision data, the control device may also transmit request signals related to the application of countermeasures to linked agencies.

[0042] The control device can continuously accumulate at least some of the relevant data collected while performing the control method, virtual space data generated based on the relevant data, risk level, infrastructure identification data, underground accident simulation data, countermeasure simulation data, causal relationship inference data, connection relationship data, or decision data. The control device can predict the risk of an underground accident for a target area, generate decision data, and perform the control method based on at least some of the accumulated relevant data, virtual space data, risk level, infrastructure identification data, underground accident simulation data, countermeasure simulation data, causal relationship inference data, connection relationship data, or decision data.

[0043] FIG. 3 is a diagram illustrating a series of processes for providing a control method according to one embodiment.

[0044] Referring to FIG. 3, the control device can collect relevant data in step (310), and can store and manage the relevant data by linking them together. The control device can store and manage a large volume of relevant data collected from a measuring device, which is a moving object, in the form of time-series data. The control device may perform data preprocessing and refinement on the relevant data. The relevant data used to perform the control method described in this specification may refer to a state in which data preprocessing and refinement have been performed. The control device can link newly collected relevant data, newly generated virtual space data, decision data, infrastructure identification data, etc., with existing relevant data, existing virtual space data, decision data, infrastructure identification data, etc., that were stored in the control device. While performing the control method, the control device can classify the collected or generated data into dynamic data and static data, and can also classify underground accident data related to underground accidents and store them in a fusion database.

[0045] The control device can perform analysis on relevant data in step (320). The control device can perform analysis on image data in which at least a portion of the target area among the relevant data is captured, based on geospatial AI (GeoAI) technology. Additionally, the control device can analyze events occurring in the target area in real time based on the relevant data, and can detect and analyze changes in the relevant data by converting the relevant data into the form of time-series data. The control device can align relevant data related to the surface, relevant data related to the underground, static data, and dynamic data. The control device can generate a scenario in which an underground accident occurred by analyzing spatial and temporal changes. By performing analysis of the relevant data, the control device can generate fusion data necessary to perform the control method.

[0046] In step (330), the control device can generate various simulation data based on relevant data. The simulation data may include, for example, at least one of underground accident simulation data related to the occurrence of an underground accident or countermeasure simulation data related to countermeasures for an underground accident, but is not limited to the description in this specification. Underground accident simulation data related to the occurrence of an underground accident may also be generated based on the scenario in which the underground accident occurred. The control device can perform multi-faceted analysis and various predictions regarding the underground accident that occurred. The control device can generate countermeasure simulation data related to countermeasures for an underground accident. Countermeasure simulation data related to countermeasures for an underground accident may include simulation data for responding to the underground accident and simulation data for training on responding. The control device may generate simulation data for a virtual underground accident and may also generate countermeasure simulation data related to countermeasures for responding to a virtual underground accident. Based on the countermeasure simulation data related to countermeasures for responding to a virtual underground accident, the control device can virtually execute and verify various countermeasures. The control device can predict the risk of underground accidents for a target area based on at least one of relevant data or virtual space data. The control device can detect on-site conditions related to an underground accident that has occurred. The control device can evaluate or estimate vulnerability to the occurrence of underground accidents by region based on at least one of relevant data or virtual space data.

[0047] In step (340), the control device can provide integrated control functions for a target area where an underground accident has occurred or a target area where an underground accident is likely to occur, based on twin-bus technology. The control device can provide a platform for controlling situations related to underground accidents. The control device can transmit various data related to control methods to user terminals at the site. Additionally, the control device can receive various data from user terminals and respond to the data to enable control of the underground accident site by the user terminals. The control device can cooperate with related agencies regarding underground accidents to control the situation of underground accidents across government departments or to conduct joint training to prevent and respond to underground accidents. The control device can provide diversified visual data in real time and can visualize various analysis results and simulation data.

[0048] FIG. 4 is a drawing illustrating an example of virtual space data according to one embodiment.

[0049] Referring to FIG. 4, virtual space data may be data that virtually reproduces a site or target area related to an underground accident. A control device may generate visualized virtual space data such as FIG. 4. Virtual space data may be implemented, for example, in two dimensions or three dimensions. At least some of the relevant data described herein may be visually implemented in the virtual space data. Based on the value of the risk level of the underground accident, the control device may display a specific color or a specific shade for each corresponding location in the virtual space data.

[0050] For example, if the value of the risk of an underground accident corresponding to one region is determined to be low based on a predetermined range, the control device determines that the region is safe from the risk of an underground accident and can display the corresponding portion of the virtual space data in green. In another example, if the value of the risk of an underground accident corresponding to another region is determined to be medium based on a predetermined range, the control device determines that the other region is slightly dangerous from the risk of an underground accident and can display the corresponding portion of the virtual space data in yellow. In yet another example, if the value of the risk of an underground accident corresponding to another region is determined to be high based on a predetermined range, the control device determines that the other region is dangerous from the risk of an underground accident and can display the corresponding portion of the virtual space data in red.

[0051] FIG. 5 is a drawing for explaining a control system according to one embodiment.

[0052] The control system described in this specification can be controlled by a geo-ambulance center (505). The geo-ambulance center (505) can perform tasks such as collecting relevant data at the site, processing relevant data, estimating the risk of an underground accident, generating virtual space data based on relevant data and risk, controlling the collection of relevant data and response in the event that the likelihood of an underground accident is determined to be high based on predetermined criteria or that an underground accident occurs, requesting cooperation from linked agencies, and sharing data with linked systems. The geo-ambulance center (505) can perform the role of an underground accident integrated control system that comprehensively controls matters related to underground accidents.

[0053] FIG. 5 illustrates an example of virtual space data for a target area. Referring to FIG. 5, the virtual space data may reflect simulation data that reproduces an underground accident and may visualize various measuring devices. Additionally, at least some of the relevant data may be visualized as cross-sectional views in the virtual space data. Infrastructures based on infrastructure identification data may be visualized in the virtual space data.

[0054] Various physical geo-ambulances may be deployed at the site. Physical geo-ambulances may include a drilling geo-ambulance (501) that collects at least some of the relevant data related to drilling and ground geological exploration, and a geophysical exploration geo-ambulance (502) that measures at least some of the relevant data by investigating underground facilities and evaluating geological anomalies. A platform geo-ambulance (504) may perform at least some of the control methods at the site. The platform geo-ambulance (504) may receive relevant data collected by at least one of the drilling geo-ambulance (501), the geophysical exploration geo-ambulance (502), and the digital twin geo-ambulance (503) from at least one of the drilling geo-ambulance (501), the geophysical exploration geo-ambulance (502), and the digital twin geo-ambulance (503). The platform geo-ambulance (504) can perform predictions and optimizations related to underground accidents based on an artificial intelligence metaverse. At least some of the operations of the control device described in this specification that generate virtual space data can be performed by the digital twin geo-ambulance (503). The digital twin geo-ambulance (503) can generate virtual space data based on digital twin technology. The physical geo-ambulance may be a vehicle or a robot having the appearance of an animal that is easy to move, but the description in this specification is merely an example and is not limited to the description in this specification. The drilling geo-ambulance (501), the geophysical exploration geo-ambulance (502), the digital twin geo-ambulance (503), and the platform geo-ambulance (504) may be included in the underground accident site system (506). In the underground accident site system (506), at least some of the operations of the control device described in this specification may be performed. The geo-ambulance center (505) can receive at least some of the relevant data or virtual space data from the underground accident site system (506).The Geo-Ambulance Center (505) can control the security of the Geo-Ambulance based on at least some of the relevant data or virtual space data, and can perform spatiotemporal processing, management, prediction, estimation, etc. of the Geo-Ambulance. The Geo-Ambulance Center (505) can share data related to underground accidents with at least one of the linked systems or linked agencies and request cooperation from at least one of the linked systems or linked agencies regarding the response to underground accidents. Here, the linked agencies may also be referred to as external linked agencies and may include the government, local governments, police departments, fire departments, etc., but are not limited to those described in this specification. The linked systems may include, for example, an underground safety information system, a facility integrated information management system, a building administration system, and a disaster safety data sharing platform, but are not limited to those described in this specification.

[0055] FIG. 6 is a drawing illustrating the configuration of a control device according to one embodiment.

[0056] Referring to FIG. 6, the control device (600) may include a processor (610), memory (620), and communication circuit (630). According to an embodiment, the control device (600) may further include a database (640). The control device (600) may correspond to the control device described herein and may perform at least some of the operations performed by the geo-ambulance system described herein.

[0057] The memory (620) is connected to the processor (610) and can store instructions executable by the processor (610), data to be computed by the processor (610), or data processed by the processor (610). The memory (620) may include a non-transient computer-readable medium, such as high-speed random access memory and / or a non-volatile computer-readable storage medium (e.g., one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0058] The communication circuit (630) may provide an interface for communicating with an external device (e.g., a measuring device, a linked system, a server of a linked organization, etc.). The communication circuit (630) may communicate with the external device via a wired or wireless network.

[0059] The database (640) may store data that is needed or generated while the control device (600) performs a control method. For example, the database (640) may store at least one of relevant data, virtual space data, risk of underground accidents for a target area, virtual space data, infrastructure identification data, underground accident simulation data related to the occurrence of an underground accident, countermeasure simulation data related to countermeasures for underground accidents, causal relationship inference data, connection relationship data, or decision data.

[0060] The processor (610) may perform one or more operations related to the operation of the control device described herein. For example, the processor (610) may enable the control device (600) to collect relevant data related to underground accidents from a measuring device deployed at the site and, based on the collected relevant data, generate virtual space data that reproduces the actual geospatial area of ​​the target region. The processor (610) may enable the control device (600) to obtain infrastructure identification data from the relevant data using a detection model that takes the relevant data as input, and to generate virtual space data based on the obtained infrastructure identification data and relevant data.

[0061] The processor (610) can enable the control device (600) to predict the risk of an underground accident in a target area based on at least one of relevant data or virtual space data. The processor (610) can enable the control device (600) to compare relevant data corresponding to the target area based on the correlation between relevant data in the case where an actual underground accident occurs and the underground accident that occurred, and to predict the risk of an underground accident in the target area based on the result of the comparison. The processor (610) can enable the control device (600) to visually apply at least some of the predicted risk and relevant data to virtual space data.

[0062] The processor (610) can enable the control device (600) to generate at least one of underground accident simulation data related to the occurrence of an underground accident or countermeasure simulation data related to countermeasures for an underground accident based on virtual space data, and, when an underground accident actually occurs, to obtain causal relationship inference data for the occurred underground accident by using an inference model that takes relevant data as input.

[0063] The processor (610) may also cause the control device (600) to generate connection relationship data indicating the connection relationship between data included in the relevant data. The processor (610) may cause the control device (600) to generate decision data related to response measures for underground accidents based on the generated connection relationship data.

[0064] The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0065] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based thereon. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0066] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below. Explanation of the symbols

[0067] 110: Relevant data 120, 600: Control device 140: 3D virtual space data 150: Measuring device 610: Processor 620: Memory 630: Communication circuit 640: Database

Claims

Claim 1 A control method for responding to and preventing underground accidents performed by a control device, comprising: a step of collecting relevant data related to underground accidents from a measuring device deployed at the site; a step of generating virtual space data that reproduces the actual geographic space of a target area based on the collected relevant data; a step of predicting the risk of underground accidents for the target area based on at least one of the relevant data or the virtual space data; and a step of visually applying at least some of the predicted risk and relevant data to the virtual space data, wherein the step of collecting relevant data includes a step of measuring ground weak zone information through electrical resistivity surveying, the step of generating virtual space data includes a step of visualizing the geological information in the virtual space data by distinguishing the material of each layer based on the geological information among the relevant data, and the step of applying includes a step of visualizing at least some of the relevant data as at least one of a graph or a chart, and visualizing the risk in the virtual space data by assigning a predetermined color or a predetermined shade corresponding to a range of values ​​of the risk for each location on the virtual space data. Claim 2 A control method according to claim 1, wherein the step of predicting the risk level comprises: a step of comparing relevant data corresponding to the target area based on the correlation between relevant data in the case where an actual underground accident occurs and the underground accident that occurred; and a step of predicting the risk level of the underground accident for the target area based on the result of the comparison. Claim 3 A control method according to claim 1, wherein the step of generating the virtual space data comprises: a step of obtaining infrastructure identification data from the relevant data using a detection model that takes the relevant data as input; and a step of generating the virtual space data based on the obtained infrastructure identification data and the relevant data. Claim 4 In paragraph 3, the detection model generates infrastructure identification data that identifies infrastructure including at least one of a manhole, a transverse pipe, or a longitudinal pipe from GPR exploration information among the relevant data. Claim 5 A control method according to claim 1, further comprising the step of generating at least one of underground accident simulation data related to the occurrence of an underground accident or countermeasure simulation data related to countermeasures for an underground accident based on the virtual space data. Claim 6 A control method according to claim 1, wherein, when an underground accident actually occurs, the method further comprises the step of obtaining causal relationship inference data for the occurred underground accident using an inference model that takes relevant data as input, wherein the causal relationship inference data for the occurred underground accident includes information inferring the cause of the occurrence of the underground accident, and the inference model generates the causal relationship inference data including information inferring the cause of the occurrence of the underground accident based on the input relevant data. Claim 7 A control method according to claim 1, further comprising: a step of generating connection relationship data indicating the connection relationship between data included in the relevant data; and a step of generating decision data related to a response plan for an underground accident based on the generated connection relationship data. Claim 8 A control method according to claim 1, wherein the relevant data comprises at least one of image data in which at least a portion of the target area is captured, stratigraphic information, bedrock depth, ground strength, ground change characteristic information, ground deformation modulus, topographic survey information, ground survey information, building survey information, ground subsidence internal survey information, rescuer location information, rescuer status information, stratigraphic structure information, or ground weak zone information. Claim 9 A control device for responding to and preventing underground accidents comprises a memory and a processor, wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor enables the control device to collect relevant data related to underground accidents from a measuring device deployed at the site, generate virtual spatial data that reproduces the actual geospatial space of a target area based on the collected relevant data, predict the risk of underground accidents for the target area based on at least one of the relevant data or the virtual spatial data, and visually apply the predicted risk and at least some of the relevant data to the virtual spatial data, wherein the processor enables the control device to measure ground weak zone information through electrical resistivity survey, visualize the geospatial information in the virtual spatial data by distinguishing the material of each layer based on the geospatial information among the relevant data, visualize at least some of the relevant data as at least one of a graph or a chart, and assign a predetermined color or a predetermined shade corresponding to a range of the risk value at each location on the virtual spatial data. A control device that visualizes the above risk level in the above virtual space data by means of. Claim 10 In claim 9, the processor is a control device that enables the control device to compare relevant data corresponding to the target area based on the correlation between relevant data in the event of an actual underground accident and the said underground accident, and to predict the risk of an underground accident for the said target area based on the result of the comparison.

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