CN-121979056-A - Intelligent roadway support system and method based on multi-module cooperation

CN121979056ACN 121979056 ACN121979056 ACN 121979056ACN-121979056-A

Abstract

The invention relates to the technical field of mine tunnel construction intellectualization and discloses an intelligent tunnel supporting system and method based on multi-module cooperation. The system comprises an intelligent advanced geological detection module, a surrounding rock quality dynamic evaluation module, a self-adaptive support parameter optimization module, a roadway construction equipment monitoring module, a roadway environment monitoring module, a safety risk early warning module, a construction data decision support module, a personnel positioning safety management module and an integrated control module. The advanced prediction of the geological conditions of the roadway, the real-time evaluation of surrounding rock quality, the dynamic optimization of a supporting scheme and the intelligent control of the whole construction process are realized through the cooperative work of the modules, the problems that the traditional roadway support depends on manual experience, has delayed response and is insufficient in safety are solved, and the safety, efficiency and intelligent level of roadway construction under complex geological conditions are remarkably improved.

Inventors

JIAO YANG
JING QINGHE
GUO JIE
YANG YU
DI JUNZHEN
XIN CHANGHAO

Assignees

扎赉诺尔煤业有限责任公司
辽宁工程技术大学

Dates

Publication Date: 20260505
Application Date: 20260202

Claims (9)

1. The intelligent roadway support system based on multi-module cooperation is characterized by comprising an intelligent advanced geological detection module, a surrounding rock quality dynamic evaluation module, a self-adaptive support parameter optimization module, a roadway construction equipment monitoring module, a roadway environment monitoring module, a safety risk early warning module, a construction data decision support module, a personnel positioning safety management module and an integrated control module; The intelligent advanced geological detection module is used for acquiring geological data of a preset range in front of a tunnel face, analyzing the geological data by using a deep learning network and predicting geological change probability and risk level; The surrounding rock quality dynamic evaluation module is used for collecting multi-source data of surrounding rock around a roadway, calculating a surrounding rock quality index based on a data fusion algorithm, and dividing the surrounding rock quality grade according to the surrounding rock quality index; The self-adaptive support parameter optimization module is used for receiving the surrounding rock quality index and geological change prediction result, dynamically adjusting support parameters and recommending an optimal support scheme according to the roadway engineering requirements; the roadway construction equipment monitoring module is used for collecting operation parameters of roadway construction equipment in real time, triggering alarm prompt when the operation parameters exceed a preset threshold value, and predicting potential faults of the equipment; the roadway environment monitoring module is used for monitoring the environment parameters in the roadway and starting an alarm mechanism and environment regulation measures when the environment parameters exceed a safety threshold; the safety risk early warning module is used for integrating the operation parameters and the environment parameters to form safety monitoring parameters, identifying potential safety risks and carrying out hierarchical early warning based on a historical case database and a pattern identification method; The construction data decision support module is used for collecting monitoring data and construction progress, material consumption and personnel activity data of each module, predicting construction trend by combining historical construction data, and providing decision support; The personnel positioning safety management module is used for acquiring real-time position information of constructors in the roadway, sending an alarm when the constructors approach a dangerous area or exceed a safety range, and guiding the constructors to evacuate in emergency; The integrated control module is used for coordinating information sharing and cooperative work among the modules, and realizing data integration, intelligent decision and quick response.
2. The intelligent roadway support system based on multi-module collaboration of claim 1, wherein the intelligent advanced geological detection module is specifically configured to: The method comprises the steps of collecting geological data in a certain range in front of a tunnel face through a geophysical detection device, carrying out denoising, outlier rejection and standardized pretreatment on the geological data, extracting geological parameter characteristics through a convolutional neural network, inputting the geological parameter characteristics into a trained deep learning network model, and outputting geological change probability and risk grades, wherein the geological change comprises rock fracture, collapse, rock burst, fault activity and gas burst, and the risk grades are classified into low risk, medium risk, high risk and extremely high risk.
3. The intelligent roadway support system based on multi-module cooperation according to claim 1, wherein the surrounding rock quality dynamic evaluation module is specifically configured to: Collecting data of density, humidity, compressive strength, fracture density and ground stress of surrounding rock around the roadway through a geological radar, a seismic wave velocity measuring instrument and a stress sensor, carrying out fusion processing on the collected data by adopting a fuzzy C-means clustering algorithm and a self-adaptive neural network, calculating the quality index of the surrounding rock, and grading.
4. The intelligent roadway support system based on multi-module collaboration according to claim 1, wherein the adaptive support parameter optimization module is specifically configured to: The method comprises the steps of receiving the surrounding rock quality index and the geological change prediction result in real time, storing a reference supporting scheme corresponding to different surrounding rock quality grades by a built-in supporting parameter database, comprehensively evaluating parameters such as the surrounding rock quality index, the ground water level, the ground stress and the gas content by a fuzzy logic controller, optimizing the supporting scheme by a genetic algorithm, and realizing cost minimization on the premise of meeting the safety requirement.
5. The intelligent roadway support system based on multi-module collaboration of claim 1, wherein the roadway construction equipment monitoring module is specifically configured to: The method comprises the steps of acquiring core operation parameters of construction equipment through an Internet of things sensor, setting a first preset threshold value, immediately alarming when the operation parameters exceed the first preset threshold value, and recording abnormal data, wherein the core operation parameters comprise hydraulic system pressure, flow, engine temperature, pressure, mechanical transmission system vibration and electric system current and voltage.
6. The intelligent roadway support system based on multi-module collaboration of claim 1, wherein the roadway environment monitoring module is specifically configured to: The method comprises the steps of collecting environmental parameters including temperature, relative humidity and harmful gas concentration of carbon monoxide, methane, gas and the like through sensor nodes distributed in a roadway, setting a second preset threshold, starting audible and visual alarm when the temperature exceeds 40 ℃, the relative humidity is lower than 30% or the harmful gas concentration exceeds a safety standard, automatically starting environmental regulation measures such as ventilation, dehumidification or gas dilution, recording environmental parameter historical data, carrying out trend analysis, and providing a basis for adjustment of a construction plan.
7. The intelligent roadway support system based on multi-module collaboration according to claim 1, wherein the personnel location security management module is specifically configured to: The method comprises the steps of providing construction personnel with RFID positioning tags, sending position signals at a certain frequency, installing signal readers in a roadway according to grid layout, receiving the positioning signals, transmitting the positioning signals to a central monitoring platform, determining the positions of the personnel through a positioning algorithm, displaying the positions of the personnel on a three-dimensional simplified map of the roadway in real time, sending an alarm to a terminal carried by the personnel when the personnel approaches a dangerous area or exceeds a preset safety range, notifying the personnel to a manager, planning an optimal evacuation route according to personnel position information in an emergency, guiding personnel evacuation, and recording time for the personnel to enter and exit the roadway and working time.
8. The intelligent roadway support system based on multi-module collaboration of claim 1, wherein the integrated control module is specifically configured to: The method comprises the steps of collecting data flows of all modules in real time through a central data bus, integrating heterogeneous data by adopting a data fusion technology to form a unified construction data view, carrying out deep analysis on integrated data by adopting an optimization algorithm and an artificial intelligence technology to generate an optimal decision scheme, including support parameter adjustment, risk emergency response and resource configuration optimization, rapidly implementing decision results by an automatic executing mechanism, adjusting emergency resources, guiding personnel evacuation and adjusting the running state of construction equipment in emergency, providing a graphical user interaction interface, displaying construction states, monitoring data and decision results, and facilitating monitoring and operation of management personnel.
9. An intelligent roadway support method based on multi-module cooperation is characterized by comprising the following steps: S1, acquiring geological data in front of a tunnel face in real time by utilizing an intelligent advanced geological detection module, predicting geological change probability and risk level by a deep learning network, and updating forecast information once every fixed time such as 5 seconds; s2, acquiring surrounding rock multi-source data of the periphery of the roadway by using a surrounding rock quality dynamic evaluation module, calculating a surrounding rock quality index, dividing quality grades, and updating an evaluation result every fixed time such as 10 seconds; S3, receiving surrounding rock quality indexes and geological change prediction results by using a self-adaptive support parameter optimization module, dynamically adjusting support parameters and recommending an optimal support scheme, and updating design parameters once every fixed time such as 10 seconds; S4, acquiring core operation parameters of the construction equipment by using a roadway construction equipment monitoring module, monitoring the state of the equipment and predicting potential faults, carrying out state evaluation once every fixed time such as 15 seconds, and alarming when the state exceeds a threshold value; S5, acquiring environmental parameters such as temperature, humidity, harmful gas concentration and the like in a roadway by utilizing a roadway environment monitoring module, updating data once every fixed time such as 5 seconds, and starting alarm and environment regulation measures when the data exceeds a safety threshold; s6, integrating equipment operation parameters and environment parameters by utilizing a safety risk early warning module, identifying potential safety risks based on a historical case database, analyzing once per minute and carrying out grading early warning; S7, collecting data of each module, construction progress, material consumption and personnel activity data by using a construction data decision support module, predicting construction trend, and providing a decision support report every fixed time such as 30 minutes; s8, positioning the position of a constructor in real time by using a personnel positioning safety management module, updating position information once every fixed time such as 10 seconds, and sending an alarm when the constructor approaches a dangerous area; and S9, coordinating the modules to cooperatively work by utilizing an integrated control module, realizing data integration, intelligent decision and quick response, and ensuring intelligent management and control of the whole roadway construction process.

Description

Intelligent roadway support system and method based on multi-module cooperation Technical Field The invention relates to the technical field of mine roadway construction intellectualization, in particular to an intelligent roadway support system and method based on multi-module cooperation. The method aims at improving the safety, efficiency and intelligentization level in the mine tunnel construction process. Background The tunnel is used as an important channel for mine exploitation, and the construction safety and stability of the tunnel directly influence the mine production efficiency and personnel safety. The traditional roadway support method mainly depends on field experience and manual detection of engineers, and has the problems that firstly, rock burst, collapse, rock burst, gas surge and other geological risks under complex geological conditions are difficult to accurately predict, secondly, the support scheme is mainly of fixed design and cannot be dynamically adjusted according to real-time surrounding rock quality change, adaptability is poor, thirdly, information such as construction equipment states, roadway environment parameters and personnel positions are scattered and managed, unified cooperation is lacking, safety early warning hysteresis is caused, thirdly, the traditional intelligent support technology is used for referring to tunnel scenes and comprises redundant modules such as digital twinning and material supply chain management, adaptability is poor, deployment cost is high, and the core requirements of roadway construction such as narrow space, variable geology, concentrated risk and high efficiency priority are difficult to meet. The existing intelligent support technology is mostly in a local application stage, a multi-module collaborative comprehensive solution adapted to a roadway scene is not formed yet, the phenomenon of data island among systems is serious, and intelligent management and control of the whole roadway construction flow are difficult to realize. Therefore, development of an intelligent roadway support system focusing on core requirements is needed to improve the safety, efficiency and intelligent level of roadway construction. Disclosure of Invention The invention aims to provide an intelligent roadway support system and method based on multi-module cooperation, which can comprehensively monitor and dynamically optimize roadway construction through various intelligent technologies, and effectively improve the safety, efficiency and intelligent level in the construction process. In order to achieve the above object, the present invention provides the following technical solutions: An intelligent roadway support system based on multi-module cooperation comprises an intelligent advanced geological detection module, a surrounding rock quality dynamic evaluation module, a self-adaptive support parameter optimization module, a roadway construction equipment monitoring module, a roadway environment monitoring module, a safety risk early warning module, a construction data decision support module, a personnel positioning safety management module and an integrated control module; The intelligent advanced geological detection module is used for acquiring geological data in front of a tunnel face, analyzing the geological data by utilizing a deep learning network and predicting the probability and risk level of geological change; The surrounding rock quality dynamic evaluation module is used for collecting multisource data of surrounding rocks around a roadway, calculating a surrounding rock quality index and dividing surrounding rock quality grades according to the index; the self-adaptive support parameter optimization module is used for dynamically adjusting support parameters according to the surrounding rock quality index and the geological change prediction result and recommending an optimal support scheme; the roadway construction equipment monitoring module is used for collecting operation parameters of construction equipment in real time, triggering an alarm when the operation parameters exceed a preset threshold value, and predicting potential faults; The roadway environment monitoring module is used for monitoring environmental parameters (such as temperature, humidity, harmful gas concentration and the like) in a roadway in real time, and starting alarming and regulating measures when the environmental parameters exceed a safety threshold value; Integrating equipment operation parameters and environment monitoring data, identifying potential risks through a historical case library and carrying out hierarchical early warning; The construction data decision support module is used for collecting monitoring data and construction progress data of each module and providing construction trend prediction and decision support: The personnel positioning safety management module is used for positioning the position of a constructor in real time, giving an alarm when