CN116562656B - Tunnel construction geological disaster early warning and prevention and control intelligent decision method and auxiliary platform - Google Patents
Tunnel construction geological disaster early warning and prevention and control intelligent decision method and auxiliary platform Download PDFInfo
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Abstract
The application relates to an intelligent decision method and an auxiliary platform for tunnel construction geological disaster early warning and prevention and control, and belongs to a data processing system or method for supervision or prediction purposes. The decision method comprises the steps of collecting tunnel data, identifying bad geology, three-dimensional geological modeling, disaster monitoring and early warning and disaster prevention and control decision, and the auxiliary platform comprises an application layer, a data layer, an auxiliary layer and a display layer. The data layer comprises a data access module, a data storage module and a data management module; the auxiliary layer comprises a user management module, a security management module, a log management module and a help feedback module. According to the tunnel construction geological disaster early warning and prevention and control intelligent decision method and the auxiliary platform, the possible or potential tunnel construction geological disasters are early warned by combining the big data with the data acquired in the actual scene, so that prevention and control countermeasures can be provided timely, accurately and intelligently.
Description
Technical Field
The application relates to the technical field of geological disaster early warning and prevention and control, in particular to an intelligent decision method and an auxiliary platform for tunnel construction geological disaster early warning and prevention and control.
Background
The occurrence of tunnel construction geological disasters can cause mechanical facility damage and casualties of operation personnel, so that the engineering progress is seriously influenced, and the construction cost is increased. In order to reduce the influence of construction geological disasters, effective disaster early warning and prevention and control are required.
In engineering application, construction measures of special rock and poor geological tunnels are standardized by the railway tunnel design Specification (TB 10003-2016), the high-speed railway tunnel engineering construction technical Specification (Q/CR 9406-2015) and the railway tunnel engineering construction safety technical Specification (TB 10304-2020), and guidance is provided for construction of single type of poor geological high risk areas. Based on a large number of engineering cases, students and technical specialists develop a large number of researches on cause analysis, risk assessment and prevention and control measures of disasters such as water burst, mud burst, collapse and the like in tunnels, and form important insights. In the aspect of intelligent decision of disaster prevention and control, xie Xiaokun and Huang Yahua are respectively researched aiming at construction treatment of sudden water disaster and collapse disaster.
However, under complex geological conditions, tunnel construction geological disasters are subjected to the coupling action of various disaster sources, the catastrophe evolution mechanism is extremely complex, and elements such as principles, measures, opportunities, materials, equipment and procedures related to disaster prevention and control countermeasures are closely related to engineering geology, hydrogeological conditions, disaster types, positions, scales, forms, properties and other characteristics of the disasters, so that prevention and control decisions based on past experience or standard specifications have larger uncertainty and blindness.
Disclosure of Invention
The application provides an intelligent decision method and an auxiliary platform for early warning and prevention and control of tunnel construction geological disasters, which are used for early warning possible or potential tunnel construction geological disasters by combining big data with data acquired in an actual scene so as to provide prevention and control countermeasures timely, accurately and intelligently.
The above object of the present application is achieved by the following technical solutions:
in a first aspect, the application provides an intelligent decision method for early warning, prevention and control of geological disasters in tunnel construction, which is characterized by comprising the following steps:
collecting tunnel data, including tunnel profile, geological survey data and advanced forecast data;
identifying bad geology, comprehensively identifying bad geological environment of a tunnel through a multi-source information fusion technology based on different scale or precision detection results provided by geological exploration and advanced forecast, and determining the type, position, scale and property of a disaster source;
three-dimensional geologic modeling, namely constructing a refined three-dimensional geologic model by applying a multi-scale geologic model theory and a multi-scale topological reconstruction technology based on tunnel engineering geology, hydrogeology conditions and poor geologic distribution conditions;
disaster monitoring and early warning are carried out, real-time disaster monitoring is carried out based on disaster source distribution conditions of important areas of poor geology obtained in the previous step, and timely early warning for disaster is realized through disaster diagnosis, risk assessment and early warning release;
and (3) disaster prevention and control decision, establishing a disaster prevention and control intelligent decision knowledge graph, forming a disaster prevention and control intelligent cognitive system, measuring the association degree of disaster prevention countermeasures and specific disasters based on the distance similarity, and intelligently recommending various disaster prevention countermeasures according to disaster characteristics.
In one possible implementation manner of the present application, the method further includes:
aiming at the important sections of poor geology, carrying out informatization construction design based on a three-dimensional geological model and disaster prevention and control intelligent decision;
the three-dimensional geological model provides a geological environment where the tunnel is located in a visual mode, and disaster prevention and control intelligent decision provides prevention and control countermeasures which are needed to be adopted for specific characteristic disasters;
and (3) integrating the three-dimensional geological model and the prevention and control countermeasures, and automatically generating a tunnel construction geological disaster prevention and control construction scheme.
In one possible implementation manner of the present application, the disaster monitoring and early warning further includes:
based on mass data obtained by long-time monitoring of disasters, after noise and false information are removed by data preprocessing, a catastrophe feature diagnosis technology is applied, abnormal responses reflected by a time-varying rule of monitoring variables are mined, internal relations between multiple types of disasters and information responses are constructed, and association relations between disaster evolution states and precursor rules are analyzed;
constructing an effective information judgment mining and uncertainty information space-time deduction model, building a tunnel construction geological disaster risk assessment index system, and identifying possible disaster features in tunnel construction;
the disaster characteristics comprise the type, the position, the scale, the shape, the property, the evolution stage, the occurrence probability and the risk level of tunnel construction geological disasters.
In one possible implementation manner of the present application, the method further includes:
based on the existing expert knowledge and a large number of disaster prevention and control cases, constructing a disaster prevention and control knowledge system and a term dictionary, establishing a disaster prevention and control intelligent decision knowledge graph model through the steps of knowledge modeling, storage, extraction, fusion, calculation and the like, forming a disaster prevention and control intelligent cognition system, and mining potential association relations between prevention and control countermeasures and disaster characteristics;
updating and perfecting an intelligent cognitive system through knowledge graph model dynamic learning;
based on the distance similarity and other variables, the association degree of disaster prevention countermeasures and specific disasters is measured, and various prevention and control countermeasures are intelligently recommended according to the priority sequence according to the characteristics of disaster types, positions, scales, forms, properties, evolution stages and the like.
In a second aspect, the application provides an intelligent decision-making auxiliary platform for early warning, prevention and control of geological disasters in tunnel construction, which comprises the following components:
the application layer comprises an advanced forecast data interpretation module, a bad geological intelligent identification module, a three-dimensional geological fine modeling module, a control construction intelligent design module, a catastrophe diagnosis module, an early warning response module, an intelligent analysis module and an intelligent decision module;
the data layer comprises a data access module, a data storage module and a data management module;
the auxiliary layer comprises a user management module, a security management module, a log management module and a help feedback module;
the display layer comprises a tunnel position plane display module, a tunnel basic information list display module, an advanced forecast original data display module, a monitoring data real-time display module, a three-dimensional geological model module and a disaster prevention effect dynamic display module.
In one possible implementation of the present application, the sources of relevant data of the data access module include survey design, advanced forecast, construction monitoring and prevention and control construction, and the data types include tunnel profile, geological survey, advanced forecast, construction monitoring and disaster prevention and control;
the tunnel profile provides basic information and design conditions of tunnel engineering, and the data format is represented as a structural table or report text;
geological survey provides macroscopic engineering geology and hydrogeological conditions along the tunnel, and the data format is represented by original survey data, result data, report text and picture images;
the advanced forecast adopts methods of geological investigation, geophysical prospecting, drilling, leveling, and the like to acquire detailed engineering geology and hydrogeology conditions of key sections of the tunnel, and the data format is represented by original forecast data, result interpretation data, conclusion report text and field picture images;
the construction monitoring obtains dynamic change information of surrounding rock and structure of the tunnel through various sensors, and the data format is represented by original data, catastrophe diagnosis data, conclusion report text and field picture images;
disaster prevention and control provides construction measures, working procedure parameters and prevention and control effects which are adopted for dealing with different types and nature disasters, and the data format is represented by a prevention and control scheme report, an effect evaluation report and a field picture image;
for data processing, the method comprises the following steps:
establishing a standardized unified data interface, performing necessary data verification, format conversion, data management and authority control, performing targeted pretreatment on different types of data from different sources, and storing and warehousing;
setting two types of data access modes, including offline loading and real-time transmission;
the data storage comprises a multi-mode data unified mapping data structure which can store binary data, time sequences, structured data tables, documents, images and videos;
storing structured data by using a relational database, storing unstructured data files by using a distributed object storage service, and storing a prevention and control decision knowledge graph model comprising entities, attributes and relations by using a graph database;
the data of different sources related to the same tunnel work area are accessed into the system in batches, and unified data management is needed;
establishing a high-efficiency semantic index model, providing semantic-oriented rapid sharing interaction and high-efficiency cross-modal query, and realizing data integration fusion, dynamic association and space-time retrieval standardized management;
and setting different data authorities according to different user types, and performing necessary addition, deletion, correction, editing and export.
In one possible implementation of the present application, the bad geological intelligent judgement module includes:
based on the survey design data along the tunnel and combining the exposure conditions of the tunnel construction site, applying various technologies to the heavy-point section to develop advanced geological forecast of the tunnel, providing detection results with different scales or precision for the geological conditions in front of the working face of the tunnel by different advanced forecast technologies, and further defining basic information of disaster sources by intelligent judgment and identification of multi-source information fusion, wherein the basic information comprises types, positions and scales.
In one possible implementation of the present application, the three-dimensional geologic refinement modeling module includes:
based on more comprehensive engineering geology, hydrogeology conditions and poor geological distribution conditions in front of a tunnel working face obtained by geological investigation and advanced geological forecast, a refined three-dimensional geological model is constructed by applying technologies such as a multi-scale geological model theory, a multi-scale topology reconstruction technology and the like, and three-dimensional visualization of tunnel geological environment is provided.
In one possible implementation of the application, the catastrophe diagnostic module includes:
based on mass data obtained by long-time monitoring of disasters, after noise and false information are removed by data preprocessing, a catastrophe feature diagnosis technology is applied, abnormal responses reflected by a time-varying rule of monitoring variables are mined, internal relations between multiple types of disasters and information responses are constructed, and association relations between disaster evolution states and precursor rules are analyzed;
the method comprises the steps of integrating space distribution of bad geological disaster sources and catastrophe time-varying characteristics, providing a multi-source information fusion analysis method for advanced prediction and monitoring data, constructing an effective information judgment mining and uncertainty information space-time deduction model, establishing a tunnel construction geological disaster risk assessment index system, identifying characteristics such as geological disaster types, scales and the like which possibly appear in tunnel construction, and realizing effective prediction of the evolution state and occurrence probability of important geological disasters;
based on the catastrophe diagnosis and the risk assessment conclusion, the potential danger of the tunnel construction geological disaster is discovered, early warning information such as disaster characteristics, risk grades, evolution states, disaster occurrence probability and the like is issued aiming at the high-risk area, and decision basis of prevention and control measures and treatment opportunities is provided for disaster prevention and control.
In one possible implementation of the present application, the intelligent decision module includes:
constructing a disaster prevention and control knowledge system and a term dictionary, establishing a disaster prevention and control intelligent decision knowledge graph model through the steps of knowledge modeling, storage, extraction, fusion, calculation and the like, forming a disaster prevention and control intelligent cognitive system, and mining potential association relations between prevention and control countermeasures and disaster characteristics; along with the accumulation of the case samples, the intelligent cognitive system is updated and perfected continuously through the dynamic learning of the knowledge graph model;
based on the distance similarity and other variables, the association degree of disaster prevention countermeasures and specific disasters is measured, and various prevention and control countermeasures are intelligently recommended according to the priority sequence according to the characteristics of disaster types, positions, scales, forms, properties, evolution stages and the like.
Drawings
Fig. 1 is a schematic diagram of a processing procedure of a tunnel construction geological disaster early warning and prevention and control intelligent decision method.
Fig. 2 is a schematic block diagram of a tunnel construction geological disaster early warning and prevention and control intelligent decision-making auxiliary platform.
Detailed Description
The ultra-long deep buried tunnel is often built in extremely complex geological environments with complex structures and changeable lithology, the construction faces high-energy environments such as high water pressure, high ground temperature, high ground stress and the like, bad geological problems such as disaster-causing water body, fracture, karst, alteration zone, high ground temperature, harmful gas and the like are prominent, disasters such as water burst, rock burst, large deformation, collapse, high temperature heat damage, harmful gas protrusion and the like are easy to occur, and tunnel construction safety is threatened. Under the coupling effect of multiple disaster sources, the catastrophe evolution mechanism is extremely complicated, the catastrophe information is difficult to accurately identify, the disaster prevention and control decision is blindly and inefficiently realized, the requirements on 'timely, accurate, active and intelligent' prevention and control of tunnel construction geological disasters are difficult to be met, large construction accidents are easy to cause, and the construction progress and the engineering quality are seriously influenced.
Aiming at the defects of poor stability, poor pertinence, untimely and the like of a typical construction geological disaster prevention and control decision depending on personnel experience in a tunneling process, the patent provides an intelligent decision method for preventing and controlling the geological disaster of tunnel construction, develops an intelligent decision auxiliary platform for disaster early warning and prevention and control, and provides prevention and control countermeasures timely, accurately and intelligently based on characteristics such as geology, engineering environment, disaster type, nature and the like of the tunnel.
The technical scheme in the application is further described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the application discloses an intelligent decision method for early warning, prevention and control of geological disasters in tunnel construction, which comprises the following steps:
s101, collecting tunnel data, including tunnel overview, geological survey data and advanced forecast data;
s102, identifying bad geology, comprehensively identifying bad geological environment where a tunnel is located through a multi-source information fusion technology based on different scale or precision detection results provided by geological exploration and advanced prediction, and determining the type, position, scale and property of a disaster source;
s103, three-dimensional geologic modeling, namely constructing a refined three-dimensional geologic model by applying a multi-scale geologic model theory and a multi-scale topological reconstruction technology based on tunnel engineering geology, hydrogeology conditions and poor geologic distribution conditions;
s104, disaster monitoring and early warning are carried out, disaster real-time monitoring is carried out on the basis of disaster source distribution conditions of important areas of poor geology obtained in the previous step, and disaster early warning is realized through disaster diagnosis, risk assessment and early warning release;
s105, disaster prevention and control decisions are made, a disaster prevention and control intelligent decision knowledge graph is established, a disaster prevention and control intelligent cognitive system is formed, the degree of association between disaster prevention countermeasures and specific disasters is measured based on the distance similarity, and various disaster prevention countermeasures are intelligently recommended according to disaster characteristics.
In some examples, the relevant content of the informationized construction design is also added, specifically as follows:
aiming at the important sections of poor geology, carrying out informatization construction design based on a three-dimensional geological model and disaster prevention and control intelligent decision;
the three-dimensional geological model provides a geological environment where the tunnel is located in a visual mode, and disaster prevention and control intelligent decision provides prevention and control countermeasures which are needed to be adopted for specific characteristic disasters;
and (3) integrating the three-dimensional geological model and the prevention and control countermeasures, and automatically generating a tunnel construction geological disaster prevention and control construction scheme.
In some examples, the disaster monitoring pre-warning further includes the following:
based on mass data obtained by long-time monitoring of disasters, after noise and false information are removed by data preprocessing, a catastrophe feature diagnosis technology is applied, abnormal responses reflected by a time-varying rule of monitoring variables are mined, internal relations between multiple types of disasters and information responses are constructed, and association relations between disaster evolution states and precursor rules are analyzed;
constructing an effective information judgment mining and uncertainty information space-time deduction model, building a tunnel construction geological disaster risk assessment index system, and identifying possible disaster features in tunnel construction;
the disaster characteristics comprise the type, the position, the scale, the shape, the property, the evolution stage, the occurrence probability and the risk level of tunnel construction geological disasters.
In some examples, the method also comprises the steps of constructing a disaster prevention and control knowledge system and a term dictionary based on the existing expert knowledge and a large number of disaster prevention and control cases, establishing a disaster prevention and control intelligent decision knowledge graph model through the steps of knowledge modeling, storage, extraction, fusion, calculation and the like, forming a disaster prevention and control intelligent cognition system, and mining potential association relations between prevention and control countermeasures and disaster characteristics;
updating and perfecting an intelligent cognitive system through knowledge graph model dynamic learning;
based on the distance similarity and other variables, the association degree of disaster prevention countermeasures and specific disasters is measured, and various prevention and control countermeasures are intelligently recommended according to the priority sequence according to the characteristics of disaster types, positions, scales, forms, properties, evolution stages and the like.
Referring to fig. 2, the application also discloses an intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention and control, which can be divided into an application layer, a data layer, an auxiliary layer and a display layer.
The application layer comprises an advanced forecast data interpretation module, a bad geological intelligent identification module, a three-dimensional geological fine modeling module, a control construction intelligent design module, a catastrophe diagnosis module, an early warning response module, an intelligent analysis module and an intelligent decision module;
the data layer comprises a data access module, a data storage module and a data management module;
the auxiliary layer comprises a user management module, a security management module, a log management module and a help feedback module;
the display layer comprises a disaster prevention effect dynamic display module, a three-dimensional geological model module, a monitoring data real-time display module, an advanced forecast original data display module, a tunnel basic information list display module and a tunnel position plane display module.
Tunnel data management: the method is oriented to early warning and prevention and control of tunnel construction geological disasters, and data related to tunnel engineering are preconditions of early warning and prevention and control.
Data profile: sources of relevant data include survey design, advanced forecasting, construction monitoring, prevention and control construction and the like, and data types include tunnel profile, geological survey, advanced forecasting, construction monitoring and disaster prevention and control.
Wherein, the tunnel profile provides basic information and design conditions of tunnel engineering, and the data format is represented as a structured table or report text; geological survey provides macroscopic engineering geology and hydrogeological conditions along the tunnel, and the data format is represented by original survey data, result data, report text and picture images; the advanced forecast adopts various methods such as geological investigation, geophysical prospecting, drilling, leveling and the like to acquire detailed engineering geology and hydrogeology conditions of key sections of the tunnel, and the data format is represented by original forecast data, result interpretation data, conclusion report text and field picture images; the construction monitoring obtains dynamic change information of surrounding rock and structure of the tunnel through various sensors, and the data format is represented by original data, catastrophe diagnosis data, conclusion report text and field picture images; disaster prevention and control provides construction measures, process parameters, prevention and control effects and the like which are adopted for different types and nature disasters, and data formats are represented as prevention and control scheme reports, effect evaluation reports, field picture images and the like. The data sources are different, the formats are different, and the data are expressed as multi-source heterogeneous data.
And (3) data access: the multi-source heterogeneous data has the pain points of data island phenomenon, uneven format, poor safety, high use cost and the like. Therefore, a standardized unified data interface is established, necessary data verification, format conversion, data management and authority control are carried out, targeted pretreatment is carried out on different types of data from different sources, and then the data are stored and warehoused. Two types of data access modes, namely offline loading and real-time transmission, are set in consideration of the timeliness requirements and acquisition and transmission conditions of different data.
And (3) data storage: in order to effectively store and manage multi-source heterogeneous data, a multi-mode data unified mapping data structure is designed, and can store binary data, time sequences, structured data tables, documents, images, videos and other multi-mode data. By establishing a standardized storage architecture, structured data is stored by utilizing a relational database, unstructured data files are stored by utilizing a distributed object storage service, and contents such as entities, attributes, relations and the like related to a prevention and control decision knowledge graph model are stored by utilizing a graph database.
And (3) data management: and the data of different sources related to the same tunnel work area are accessed into the system in batches, so that unified data management is required. An efficient semantic index model is constructed, semantic-oriented rapid sharing interaction and efficient cross-modal query are provided, and data integration fusion, dynamic association and space-time retrieval standardized management are realized. And setting different data authorities according to different user types, and performing necessary addition, deletion, correction, editing and export to realize efficient utilization on the premise of ensuring the data safety.
Information display: the method comprises the steps of counting and displaying the quantity of various source data, the quantity of monitoring index variables, the occurrence times of various disasters and early warning information through a visual map to display the space position, the excavation state and the basic information of the tunnel, dynamically displaying the basic information and the field picture of geological disasters of tunnel construction, and associating and displaying common data files.
Poor geological identification: based on the survey design data along the tunnel, combining the exposure condition of the tunnel construction site, applying various technologies to the heavy section to develop advanced geological prediction of the tunnel. In general, geological investigation and prediction acquire basic properties of tunnel surrounding rock and development characteristics of a structural surface through tunnel face sketch and tunnel body sketch; the advanced geophysical prospecting is based on disaster-causing structures such as earthquake wave method, electromagnetic wave method, electric method detection fracture, karst, and alteration zone and disaster-causing water bodies; drilling advanced prediction is used for detecting rock mass characteristics, ground stress, sub-meter level structure, water body and the like through technologies such as drilling disclosure, in-hole geophysical prospecting and the like; the method is characterized by detecting hidden disaster sources such as a surrounding rock structural surface of the tunnel, a disaster-causing water body, high ground stress, harmful gas, high ground temperature and the like by means of flat-guiding advanced prediction. Different advanced prediction technologies provide detection results with different scales or precision for geological conditions in front of tunnel working faces, and basic information such as the type, the position, the scale and the like of disaster sources are further defined through intelligent judgment of multi-source information fusion.
Three-dimensional geologic modeling: based on the more comprehensive engineering geology (geological structure, stratum lithology, surrounding rock property and the like) in front of the tunnel working face, hydrogeological conditions (water source distribution, water supply type, water supply capacity, water guide channel and the like) and poor geological distribution conditions obtained by geological investigation and advanced geological forecast, a refined three-dimensional geological model is constructed by applying technologies such as multi-scale geological model theory, multi-scale topological reconstruction technology and the like, and three-dimensional visualization of tunnel geological environment is provided.
Disaster monitoring and early warning: based on disaster source distribution conditions of important areas of poor geology obtained in the previous step, real-time monitoring of disasters is carried out, and timely early warning of the disasters is realized through disaster diagnosis.
Disaster monitoring: and selecting proper monitoring technology and equipment in a targeted manner according to the characteristics of disaster source types, positions and the like, and designing a monitoring scheme. For example, for high-pressure water burst and mud surge disasters caused by faults, karst and cracks, the variables such as water seepage pressure, drilling water quantity, water burst profile, rock microseismic and rock temperature are monitored; for large-scale collapse disasters caused by weak fracture zones, fault water enrichment, block joints and the like, indexes such as vault sinking, clearance convergence, arch foot displacement, surrounding rock pressure and the like are monitored; for high-temperature heat damage caused by high-temperature heat and high-temperature water, indexes such as ambient temperature, water temperature and the like are monitored; for toxic and harmful gases, indexes such as gas type, concentration, gas permeation point, gas permeation pressure, overflow amount and the like are monitored in a key way.
Catastrophe diagnosis: based on mass data obtained by long-term monitoring of disasters, after noise and false information are removed through data preprocessing, disaster characteristic diagnosis technology is applied, abnormal responses reflected by time-varying rules of monitored variables are mined, internal relations between multiple types of disasters and information responses are constructed, association relations between disaster evolution states and precursor rules are analyzed, and multiple disaster identification problems such as high-pressure water burst, mud burst, large-volume collapse and the like are solved.
Risk assessment: the method is characterized by combining the spatial distribution of poor geological disaster sources and the time-varying characteristics of catastrophes, providing a multi-source information fusion analysis method for advanced prediction and monitoring data, constructing an effective information judgment mining and uncertainty information space-time deduction model, establishing a tunnel construction geological disaster risk assessment index system, identifying the characteristics of possible occurrence of geological disasters, such as types, scales and the like in tunnel construction, and realizing effective prediction of the evolution state and occurrence probability of important geological disasters.
Disaster early warning: based on the catastrophe diagnosis and the risk assessment conclusion, the potential danger of the tunnel construction geological disaster is discovered, early warning information such as disaster characteristics, risk grades, evolution states, disaster occurrence probability and the like is issued aiming at the high-risk area, and decision basis of prevention and control measures and treatment opportunities is provided for disaster prevention and control.
Disaster prevention and control decision: aiming at the defects of poor stability, poor pertinence, untimely and the like of the conventional disaster prevention and control decision depending on personnel experience, the patent provides a disaster prevention and control intelligent decision method based on a knowledge graph technology. On the basis of fully researching and summarizing the existing expert knowledge and a large number of disaster prevention and control cases, a disaster prevention and control knowledge system and a term dictionary are constructed, and a disaster prevention and control intelligent decision knowledge map model is established through the steps of knowledge modeling, storage, extraction, fusion, calculation and the like, so that a disaster prevention and control intelligent cognitive system is formed, and potential association relations between prevention and control countermeasures and disaster characteristics are mined. Along with the accumulation of the case samples, the intelligent cognitive system is updated and perfected continuously through the knowledge graph model dynamic learning. Furthermore, based on the degree of association between disaster prevention countermeasures and specific disasters and based on variables such as distance similarity, various prevention and control countermeasures are intelligently recommended according to the priority order according to the characteristics of disaster types, positions, scales, forms, properties, evolution stages and the like, and prevention and control principles, measures, opportunities, procedures, materials, equipment and construction parameters are provided as detailed as possible for technicians to select.
Informationized construction design: furthermore, aiming at the important sections of poor geology, the informatization construction design is developed based on the three-dimensional geological model and the disaster prevention and control intelligent decision, and the site construction is directly guided. The three-dimensional geological model provides a geological environment where the tunnel is located in a visual mode, and disaster prevention and control intelligent decision provides prevention and control countermeasures which are needed to be adopted for specific characteristic disasters. And integrating the three-dimensional geological model and the prevention and control countermeasures, automatically generating a tunnel construction geological disaster prevention and control construction scheme, simulating prevention and control construction dynamics through numerical simulation, and verifying disaster prevention and control effects.
In the whole, the intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention and control has the following advantages:
the method fully considers the influence of geological environment, disaster causes and expression forms where tunnel construction geological disasters occur on the selection of prevention and control countermeasures, relies on multi-azimuth perception of tunnel engineering geology, hydrogeology, disaster behaviors and the like by various means such as geological exploration, advanced forecast, construction monitoring, and the like, determines basic information such as the type, the position, the scale and the like of disaster sources through poor geological comprehensive judgment and identification of multi-source information fusion, and further evaluates disaster evolution states, risk grades and occurrence probability through space-time deduction disaster feature mining, and provides decision basis for disaster prevention and control.
The application constructs a domain knowledge graph model for intelligent decision-making of geological disaster prevention and control in tunnel construction, forms an intelligent disaster prevention and control cognitive system, and digs potential association relation between prevention and control countermeasures and disaster characteristics. Based on the relation degree of the disaster prevention countermeasures and specific disasters and the like, various prevention and control countermeasures are intelligently recommended according to the priority sequence according to the characteristics of the disaster types, positions, scales, forms, properties, evolution stages and the like, and prevention and control principles, measures, opportunities, procedures, materials, equipment and construction parameters are provided as detailed as possible for technicians to select.
On the basis of multi-source heterogeneous data comprehensive management, the method realizes comprehensive identification of bad geology and effective mining of catastrophe characteristics, provides three-dimensional visualization of a geological model, supports disaster pre-warning response and disaster prevention and control intelligent decision, and provides prevention and control construction scheme and prevention and control effect simulation through informatization construction design. In a word, the tunnel construction geological disaster early warning and prevention and control intelligent decision-making auxiliary platform is convenient for management personnel to manage projects, provides an auxiliary scheme for designers, and guides constructors to operate on site.
The embodiments of the present application are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in this way, therefore: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (8)
1. The intelligent decision method for tunnel construction geological disaster early warning and prevention and control is characterized by comprising the following steps:
collecting tunnel data, wherein the data are multi-source heterogeneous data, and comprise tunnel profile, geological survey data and advanced forecast data; wherein, the tunnel profile provides basic information and design conditions of tunnel engineering, and the data format is represented as a structured table or report text; geological survey provides macroscopic engineering geology and hydrogeological conditions along the tunnel, and the data format is represented by original survey data, result data, report text and picture images; the advanced forecast adopts geological investigation, geophysical prospecting, drilling and leveling method to obtain detailed engineering geology and hydrogeology conditions of key sections of the tunnel, and the data format is represented by original forecast data, result interpretation data, conclusion report text and field picture images;
identifying bad geology, comprehensively identifying bad geological environment of a tunnel through a multi-source information fusion technology based on different scale or precision detection results provided by geological exploration and advanced forecast, and determining the type, position, scale and property of a disaster source;
three-dimensional geologic modeling, namely constructing a refined three-dimensional geologic model by applying a multi-scale geologic model theory and a multi-scale topological reconstruction technology based on tunnel engineering geology, hydrogeology conditions and poor geologic distribution conditions;
disaster monitoring and early warning are carried out, real-time disaster monitoring is carried out based on disaster source distribution conditions of important areas of poor geology obtained in the previous step, and timely early warning for disaster is realized through disaster diagnosis, risk assessment and early warning release;
the disaster diagnosis comprises the steps of based on mass data obtained by long-term monitoring of disasters, applying disaster characteristic diagnosis technology after noise and false information are removed by data preprocessing, mining abnormal responses reflected by a time-varying rule of a monitored variable, constructing internal relations between multiple types of disasters and information responses, and analyzing association relations between disaster evolution states and precursor rules;
the risk assessment comprises the steps of constructing an effective information judgment mining and uncertainty information space-time deduction model, constructing a tunnel construction geological disaster risk assessment index system, and identifying possible disaster characteristics in tunnel construction;
disaster prevention and control decision, constructing a disaster prevention and control knowledge system and a term dictionary based on existing expert knowledge and a large number of disaster prevention and control cases, establishing a disaster prevention and control intelligent decision knowledge graph model through knowledge modeling, storage, extraction, fusion and calculation steps to form a disaster prevention and control intelligent cognitive system, and mining potential association relations between prevention and control countermeasures and disaster characteristics; updating and perfecting an intelligent cognitive system through knowledge graph model dynamic learning; based on the relation degree of the disaster prevention countermeasures and specific disasters, intelligently recommending a plurality of prevention and control countermeasures according to the disaster type, position, scale, shape, property, evolution stage, occurrence probability and risk level and the priority order;
aiming at the important sections of poor geology, carrying out informatization construction design based on a three-dimensional geological model and disaster prevention and control intelligent decision; the three-dimensional geological model provides a geological environment where the tunnel is located in a visual mode, and disaster prevention and control intelligent decision provides prevention and control countermeasures which are needed to be adopted for specific characteristic disasters; and integrating the three-dimensional geological model and the prevention and control countermeasures, automatically generating a tunnel construction geological disaster prevention and control construction scheme, simulating prevention and control construction dynamics through numerical simulation, and verifying disaster prevention and control effects.
2. The intelligent decision method for pre-warning and controlling geological disasters in tunnel construction according to claim 1, wherein the disaster monitoring pre-warning further comprises:
the disaster characteristics comprise the type, the position, the scale, the shape, the property, the evolution stage, the occurrence probability and the risk level of tunnel construction geological disasters.
3. The intelligent decision-making auxiliary platform for early warning and prevention and control of geological disasters in tunnel construction based on the method of any one of claims 1-2, which is characterized by comprising:
the application layer comprises an advanced forecast data interpretation module, a bad geological intelligent identification module, a three-dimensional geological fine modeling module, a control construction intelligent design module, a catastrophe diagnosis module, an early warning response module, an intelligent analysis module and an intelligent decision module;
the data layer comprises a data access module, a data storage module and a data management module;
the auxiliary layer comprises a user management module, a security management module, a log management module and a help feedback module;
the display layer comprises a tunnel position plane display module, a tunnel basic information list display module, an advanced forecast original data display module, a monitoring data real-time display module, a three-dimensional geological model module and a disaster prevention effect dynamic display module.
4. The intelligent decision-making auxiliary platform for early warning and controlling geological disasters in tunnel construction according to claim 3, wherein the sources of relevant data of the data access module comprise investigation design, advanced forecast, construction monitoring and control construction, and the data types comprise tunnel overview, geological investigation, advanced forecast, construction monitoring and disaster control;
the tunnel profile provides basic information and design conditions of tunnel engineering, and the data format is represented as a structural table or report text;
geological survey provides macroscopic engineering geology and hydrogeological conditions along the tunnel, and the data format is represented by original survey data, result data, report text and picture images;
the advanced forecast adopts geological investigation, geophysical prospecting, drilling and leveling method to obtain detailed engineering geology and hydrogeology conditions of key sections of the tunnel, and the data format is represented by original forecast data, result interpretation data, conclusion report text and field picture images;
the construction monitoring obtains dynamic change information of surrounding rock and structure of the tunnel through various sensors, and the data format is represented by original data, catastrophe diagnosis data, conclusion report text and field picture images;
disaster prevention and control provides construction measures, working procedure parameters and prevention and control effects which are adopted for dealing with different types and nature disasters, and the data format is represented by a prevention and control scheme report, an effect evaluation report and a field picture image;
for data processing, the method comprises the following steps:
establishing a standardized unified data interface, performing necessary data verification, format conversion, data management and authority control, performing targeted pretreatment on different types of data from different sources, and storing and warehousing;
setting two types of data access modes, including offline loading and real-time transmission;
the data storage comprises a multi-mode data unified mapping data structure which can store binary data, time sequences, structured data tables, documents, images and videos;
storing structured data by using a relational database, storing unstructured data files by using a distributed object storage service, and storing a prevention and control decision knowledge graph model comprising entities, attributes and relations by using a graph database;
the data of different sources related to the same tunnel work area are accessed into the system in batches, and unified data management is needed;
establishing a high-efficiency semantic index model, providing semantic-oriented rapid sharing interaction and high-efficiency cross-modal query, and realizing data integration fusion, dynamic association and space-time retrieval standardized management;
and setting different data authorities according to different user types, and performing necessary addition, deletion, correction, editing and export.
5. The intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention control according to claim 3, wherein the poor geological intelligent decision-making module comprises:
based on the survey design data along the tunnel and combining the exposure condition of the tunnel construction site, applying various technologies to the heavy-point section to develop advanced geological forecast of the tunnel, providing detection results with different scales or precision for the geological condition in front of the working face of the tunnel by different advanced forecast technologies, and determining the basic information of disaster sources through intelligent judgment and identification of multi-source information fusion, wherein the basic information comprises types, positions and scales.
6. The intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention control according to claim 3, wherein the three-dimensional geological fine modeling module comprises:
based on more comprehensive engineering geology, hydrogeology conditions and poor geology distribution conditions in front of a tunnel working face obtained by geological investigation and advanced geological forecast, a fine three-dimensional geological model is constructed by applying a multi-scale geological model theory and a multi-scale topology reconstruction technology, and three-dimensional visualization of tunnel geological environment is provided.
7. The intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention control according to claim 3, wherein the catastrophe diagnosis module comprises:
based on mass data obtained by long-time monitoring of disasters, after noise and false information are removed by data preprocessing, a catastrophe feature diagnosis technology is applied, abnormal responses reflected by a time-varying rule of monitoring variables are mined, internal relations between multiple types of disasters and information responses are constructed, and association relations between disaster evolution states and precursor rules are analyzed;
the method comprises the steps of integrating space distribution of bad geological disaster sources and catastrophe time-varying characteristics, providing a multi-source information fusion analysis method for advanced prediction and monitoring data, constructing an effective information judgment mining and uncertainty information space-time deduction model, establishing a tunnel construction geological disaster risk assessment index system, identifying the type and scale of geological disasters possibly occurring in tunnel construction, and realizing effective prediction of the evolution state and occurrence probability of important geological disasters;
based on the catastrophe diagnosis and the risk assessment conclusion, the potential dangers of tunnel construction geological disasters are discovered, disaster characteristics, risk grades, evolution states and disaster occurrence probability early warning information are issued for high-risk areas, and decision basis of prevention and control measures and treatment opportunities is provided for disaster prevention and control.
8. The intelligent decision-making auxiliary platform for tunnel construction geological disaster early warning and prevention control according to claim 3, wherein the intelligent decision-making module comprises:
constructing a disaster prevention and control knowledge system and a term dictionary, establishing a disaster prevention and control intelligent decision knowledge graph model through knowledge modeling, storage, extraction, fusion and calculation steps to form a disaster prevention and control intelligent cognitive system, and mining potential association relations between prevention and control countermeasures and disaster characteristics; along with the accumulation of the case samples, the intelligent cognitive system is updated and perfected continuously through the dynamic learning of the knowledge graph model;
and (3) measuring the association degree of disaster prevention countermeasures and specific disasters based on the distance similarity variable, and intelligently recommending various prevention and control countermeasures according to the type, position, scale, shape, property and evolution stage of the disasters and the priority order.
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