A method for fire hazard detection and damage assessment in underground engineering projects

Abstract

This invention relates to a method for detecting fire hazards and assessing damage in underground engineering projects, belonging to the field of underground engineering safety monitoring technology. Based on the principle of muon transmission imaging, this method constructs a three-dimensional monitoring network by deploying ground and borehole detection arrays to collect data such as muon flux, energy, and incident angle, inverting the density distribution of coal and rock to generate a density trend map. By analyzing changes in carbon content and combining fault, fracture, and permeability characteristics, a weighted comprehensive discrimination model is established to identify abnormal fire areas. Furthermore, based on density changes before and after combustion, damage variables are defined, dividing the fire area into core damage zone, transitional damage zone, and edge damage zone, achieving quantitative and graded assessment of the degree of damage. This invention features non-invasiveness, deep penetration, and high resolution, making it suitable for dynamic fire monitoring and damage assessment in underground engineering projects such as coal mines and tunnels, providing a reliable basis for fire early warning and post-disaster repair.

Description

Technical Field

[0001] This invention relates to the field of underground engineering safety monitoring technology, and in particular to a method for detecting fire hazards and assessing damage in underground engineering projects. Background Technology

[0002] Underground engineering refers to various engineering facilities constructed, operated, or utilized below the earth's surface, including coal mines, tunnels, underground storage facilities, underground nuclear waste storage facilities, and underground coal gasification facilities. These projects are prone to various high-temperature fire accidents during construction and operation, with underground coal fires being the most typical and dangerous type. Coal seams are inherently flammable and prone to spontaneous combustion, easily ignited under mining disturbances and ventilation / oxygen supply. Once an ignition source forms, the high temperature not only consumes coal resources but also erodes the surrounding rock, causing structural damage or even instability, posing a serious threat to construction safety. Furthermore, in high-temperature operation scenarios such as underground nuclear waste storage and underground coal gasification, the ignition source and its thermal effects also have a continuous impact on the stability of the surrounding rock and the safety of the project.

[0003] Coal fires, as a persistent hazard, are characterized by their insidious nature, difficulty in extinguishing, and wide-ranging impact. They not only lead to massive waste of coal resources and increased mining costs, but also cause long-term environmental damage, including vegetation degradation, soil acidification, and groundwater pollution. Statistics show that more than 200 million tons of coal resources are lost globally each year due to coal fires, while simultaneously releasing large amounts of greenhouse gases such as CO2 and CH4, exacerbating climate change. Although mine fires and coal fires differ in their prevention and control objectives, both require high-precision, real-time fire source detection and damage assessment methods.

[0004] Current fire detection technologies still face multiple bottlenecks: while infrared remote sensing and gas detection methods are suitable for large-scale surveys, they are easily affected by surface cover and have difficulty detecting deep, hidden fire zones; borehole thermography has high accuracy, but its coverage is limited and it is highly destructive; ground-penetrating radar and electromagnetic methods have limited resolution under complex geological conditions, making it difficult to accurately delineate fire zone boundaries; some deep detection methods rely on large equipment, which is costly and difficult to implement continuous monitoring. In addition, these methods mainly focus on fire source identification and lack the ability to quantitatively and hierarchically assess the degree and distribution of thermal damage to the surrounding rock.

[0005] Muon transmission imaging technology utilizes natural muons generated by cosmic rays as the detection medium. Due to their strong penetrating power (capable of penetrating several kilometers of rock layers) and energy attenuation characteristics strongly correlated with material density, it has been used for fault identification (CN115758077A) and geotechnical engineering monitoring (such as CN117388937A), possessing unique advantages in non-invasive, high-penetration, and high-resolution detection. However, existing methods focus on static geological structure analysis, lacking research on fire detection and damage assessment, failing to integrate fire-specific indicators, and thus unable to accurately identify fire anomaly areas and quantitatively assess damage levels. Furthermore, it lacks the capability for coordinated three-dimensional monitoring of both surface and underground areas, making it difficult to accurately delineate the extent of fire damage. Moreover, most systems are only used for post-event analysis and have failed to integrate with real-time risk assessment and zonal damage evaluation.

[0006] Underground engineering fires occur under dynamic, multi-field coupled conditions, with fundamentally different generation mechanisms and early warning indicators. Therefore, existing technologies lack the depth detection accuracy, damage quantification capabilities, and dynamic monitoring methods and systems for underground engineering fires. There is an urgent need for a novel detection and evaluation method that couples muon density inversion with fire characteristic parameters. This method would not only accurately identify abnormal fire areas in complex deep environments but also quantitatively classify the degree of damage to the surrounding rock, thus providing a scientific basis for early warning, prevention, and post-disaster repair of underground engineering fires. Summary of the Invention

[0007] To address the problems of existing technologies, this invention provides a method for detecting fire hazards and assessing damage in underground engineering projects. This method utilizes cosmic ray muons to penetrate coal seams and surrounding rock, and constructs a fire hazard detection and damage assessment model based on changes in carbon content and structural damage characteristics during underground fires. By inverting fire hazard data from muon detection, it achieves accurate identification and quantitative classification of underground fire areas and the extent of damage to surrounding rock. Through comprehensive analysis of underground coal and rock structures, it identifies abnormal fire areas and analyzes the damage caused by these abnormal fire areas to the surrounding coal and rock and overlying strata structure.

[0008] This invention provides a method for fire hazard detection and damage assessment in underground engineering projects. The method is based on a muon transmission imaging detection system, which includes a muon detection network and an information processing and damage assessment subsystem. The muon detection network consists of a ground detection array and a borehole detection array. The information processing and damage assessment subsystem includes a data processing and analysis module and a damage assessment module. The method includes:

[0009] S1. Select the area to be measured, arrange a ground detection array on the surface of the area to cover its horizontal range, and arrange a borehole detection array in the boreholes on both sides of the boundary of the area to cover its vertical range. The ground detection array and the borehole detection array together constitute a three-dimensional muon detection network.

[0010] S2. The ground-based detection array and borehole detection array are used to collect muon data generated by the interaction between muons and underground coal and rock. The collected muon data is then wirelessly transmitted to the data processing and analysis module. The muon data includes the number of muons passing through the underground coal and rock, and the incident zenith angle. azimuth of incidence Muzi flux and muon energy .

[0011] S3. The information processing and damage assessment subsystem inverts the coal and rock density distribution in the area to be tested based on the received muon data, generates an underground coal and rock structure density trend map, infers the carbon content change of underground coal and rock based on the underground coal and rock structure density trend map, and identifies abnormal areas of underground engineering fire based on the carbon content change combined with the coal and rock structural characteristics.

[0012] S4. The information processing and damage assessment subsystem quantitatively calculates the damage variables defined by the density changes before and after coal and rock combustion, divides the fire anomaly area into the damage core area, damage transition area and damage edge area, and, based on the division results of the damage core area, damage transition area and damage edge area, analyzes the damage caused by the fire anomaly area to the surrounding coal and rock and the overlying rock structure in a graded manner, and completes the fire damage assessment of underground engineering.

[0013] Optionally, the ground detection array consists of multiple ground muon detectors, each of which includes a housing. The housing is a hollow cubic structure with protective functions, suitable for underground engineering surface environments.

[0014] A power supply unit, located inside the housing, is used to provide operating power.

[0015] The muon detection unit is located inside the outer shell and includes an upper muon detector layer and a lower muon detector layer arranged in parallel. Both the upper and lower muon detector layers are composed of multiple horizontally spliced ​​triangular prism-shaped plastic scintillators, which are used to detect muon passage information and generate electrical signals.

[0016] The data acquisition unit, located inside the outer casing and electrically connected to the muon detection unit, receives and processes the electrical signals generated by the muon detection unit, generating a data set including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy The muon data.

[0017] The data acquisition unit is located at the bottom inside the outer shell, parallel to the upper and lower muon detector layers, and the two are connected by a transmission line.

[0018] Optionally, the borehole detection array consists of multiple borehole muon detectors, each borehole muon detector including: a housing, which is a pressure-resistant and moisture-proof hollow cylindrical structure suitable for underground engineering drilling environments.

[0019] The muon detection unit, located inside the outer shell, includes a tubular plastic scintillator matrix layer arranged axially at the upper and lower ends of the outer shell, used to detect muon passage information and generate electrical signals.

[0020] The data acquisition unit, located axially at the center of the housing, is connected via data cables to the tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit. It receives and processes the electrical signals to generate data including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy The muon data.

[0021] The power supply unit is located in the gap between the muon detection unit and the data acquisition unit, and supplies power to the muon detection unit and the data acquisition unit through pipelines.

[0022] The tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit are separated axially by the data acquisition unit.

[0023] Optionally, the information processing and damage assessment subsystem inverts the coal and rock density distribution in the area to be tested based on the received muon data to generate an underground coal and rock structure density trend map, including the following steps: acquiring muon data measured by ground muon detectors and borehole muon detectors under the working environment, and maintaining the detector zenith angle With azimuth Without changing the location, isochronous measurements are performed at the same geographic coordinates, aligning with an unobstructed sky area, to obtain the corresponding muon data under the open sky environment.

[0024] Based on muon data from the working environment and the corresponding muon data from the open sky environment, the muon flux was interpolated to obtain the variation in muon quantity in different track directions. The muon transmittance in different track directions was then calculated using the following formula:

[0025] .

[0026] In the formula, Transmittance is the ratio of the number of muons that penetrate the coal and rock in the test area along a specific track direction to the number of muons measured in the same direction under open sky conditions. Its core meaning is to reflect the changing relationship of the number of muons. The muon count is the number of muons measured in the specific track direction under the working environment. For muon counts measured in the same track direction under open sky conditions, Muon flux is the number of muons that pass through a unit solid angle per unit time. The minimum critical energy required for a muon to penetrate the coal and rock medium being tested along this specific ray path.

[0027] For each ray path traversing the region under test, the incident muon energy must exceed the minimum energy required for that path. Only then can it be recorded by the detector. Satisfy the following formula:

[0028] .

[0029] In the formula, the parameters and The constant determined by both the muon energy spectrum and the coal and petrographic composition can be approximated as a constant for conventional rock formations. The opacity along a specific ray path is defined as the product of the path length and the average density of coal and rock along the path, reflecting the medium's total absorption capacity for muons.

[0030] The opacity The specific calculation formula is as follows:

[0031] .

[0032] In the formula, This represents the actual length of the coal and rock sample traversed by the muon along that specific ray path. The average density of coal and rock along that specific ray path. To match the average density of coal and rock The relevant functions reflect the specific impact of density on the muon absorption effect.

[0033] Using the received opacity data of the probe path as input, the inversion equation can be expressed as:

[0034] .

[0035] In the formula, The opacity observations are derived from all ray paths. The matrix representing the path length of the coal and rock traversed by the muon. This represents the density value obtained through inversion.

[0036] Optionally, the method of identifying abnormal fire areas in underground engineering based on the changes in carbon content and coal and rock structural characteristics includes the following steps: dividing the area to be measured into spatial grid units, dynamically adjusting the grid resolution according to the area to be measured and the fire risk level, and making local adjustments for known fault zones or historical fire areas.

[0037] Based on the fire occurrence mechanism, the following fire anomaly sensitivity parameters are selected and assigned weights: the weight for determining the degree to which carbon content deviates from the background value is... The intensity weights for fault and fracture development are: The weight of rock mass permeability grade is .

[0038] For each grid cell region, the normalized deviation factor of the measured carbon content of the grid cell relative to the regional background value is calculated according to a set weight. The fault fracture influence score is determined based on fault density and the number of fractures per m³. And the rock mass permeability rating based on lithology and pore structure. A weighted calculation is performed to obtain the comprehensive discriminant value. .

[0039] Determine the comprehensive discriminant value for each cell grid region. Whether the preset fire anomaly threshold is exceeded, filter comprehensive judgment value. Candidate anomaly grids exceeding a preset fire anomaly threshold, wherein the preset fire anomaly threshold is determined by backtesting and optimization of sample data from known fire areas and non-fire areas.

[0040] Adjacent candidate anomaly grids are merged using four-neighbor or eight-neighbor connectivity analysis methods to form several anomaly regions.

[0041] Calculate the number of grid cells in each anomalous region, and delete areas smaller than the minimum valid anomalous area. Abnormal areas.

[0042] Output the actual spatial location of the remaining abnormal area, which is the identified fire abnormal area.

[0043] Optionally, the quantitative calculation, based on the damage variable defined by the density change of coal and rock before and after combustion, divides the fire anomaly area into a damage core zone, a damage transition zone, and a damage edge zone, including the following steps:

[0044] The damage variable for each cell in the fire anomaly zone is calculated based on the density changes of underground coal and rock before and after combustion and the comprehensive discriminant value. The specific formula is as follows: .

[0045] In the formula, The density of underground coal and rock in the unit grid area before combustion is estimated from historical geological data or adjacent unburned areas. The density of underground coal and rock after combustion in the unit grid area is extracted from the current density trend map. The unit mesh synthesis discrimination value, The function represents the effect of density on the muon absorption cross section, expressed as a linear or exponential relationship, and is a function of the comprehensive discriminant value.

[0046] Based on the damage variables of each cell grid in the fire anomaly zone The numerical range divides the fire anomaly area into core damage zone, transition damage zone and edge damage zone, outputs the spatial distribution map of core damage zone, transition damage zone and edge damage zone, and marks the typical damage characteristics of each zone.

[0047] By adopting the above technical solution, the present invention has at least the following beneficial effects:

[0048] (1) A three-dimensional muon detection network covering the horizontal and vertical directions of the area to be measured was constructed through the coordinated deployment of ground arrays and borehole arrays, realizing comprehensive three-dimensional monitoring of underground coal and rock structures and fire-damaged areas. This network can collect key data such as muon flux and energy attenuation at different incident angles in real time, overcoming the limitations of traditional methods that have monitoring blind spots, and providing a high-precision, blind-spot-free data foundation for fire hazard detection. The detector adopts a weather-resistant design and an adaptive maintenance mechanism to ensure long-term stable operation in complex underground environments, providing reliable data support for the detection of deep, hidden fire areas.

[0049] (2) It breaks through the bottleneck of low resolution and limited coverage of traditional methods such as infrared remote sensing, borehole temperature measurement, and ground-penetrating radar in the detection of deep and hidden fire areas, and realizes non-invasive detection of fire sources and damage within a range of several kilometers. Coupled fire characteristic parameters: For the first time, muon density inversion is combined with the degree of deviation of coal carbon content and the evolution of surrounding rock state, which can effectively distinguish the damage caused by fire from natural geological structures and significantly improve the accuracy of anomaly identification.

[0050] (3) Innovatively, damage variables based on density changes before and after combustion and comprehensive discrimination values ​​are introduced to quantitatively divide the abnormal fire area into core damage zone, transitional damage zone and edge damage zone. By hierarchically analyzing the degree of damage to coal and rock structure, density and carbon content changes in each area, the severity of damage is intuitively reflected, filling the gap in the existing technology for quantitative and hierarchical evaluation of fire damage, and providing accurate scientific basis for the formulation of post-disaster repair and reinforcement plans.

[0051] (4) Efficient acquisition and processing of muon data is achieved through wireless transmission. Combined with calibration data in an open-sky environment and gridded dynamic analysis, the real-time performance and accuracy of the data are ensured. Compared with the traditional post-event analysis mode, this method can dynamically track the fire evolution process and damage development trend, providing timely technical support for early warning and dynamic prevention and control of underground engineering fires.

[0052] (5) This method can achieve long-term, automated monitoring, reduce human intervention, and is suitable for harsh underground environments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a method for detecting fire hazards and assessing damage in underground engineering projects, provided as an embodiment of the present invention.

[0055] Figure 2 This is a diagram showing the layout of the ground detection array and the borehole detection array.

[0056] Figure 3 This is a schematic diagram of the structure of a ground-based muon detector.

[0057] Figure 4 This is a schematic diagram of the borehole muon detector.

[0058] Figure 5 Flowchart for identifying abnormal fire zones.

[0059] Figure 6 The images show the results of merging the fire anomaly area's unit grids. (a) shows the initial scattered candidate anomaly grids merged, and (b) shows the merged result after deleting all grids with a number less than the threshold. The result image after the abnormal area.

[0060] Figure 7 A diagram showing the quantitative division of damage in three zones in an abnormal fire area. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Natural muons are secondary particles generated by the reaction of cosmic rays with atmospheric atomic nuclei. As a detection medium, muons possess extremely strong penetrating power (capable of penetrating several kilometers of rock layers). Their flux and energy attenuation are strongly correlated with material density, providing a revolutionary means for underground structure detection. Compared to traditional technologies, muon technology offers advantages in fire detection such as non-invasiveness, high penetration, and high resolution, effectively improving the accuracy and real-time performance of fire monitoring. It also serves as a technology for real-time monitoring of the actual condition of structures, providing crucial references for the safety, reliability, and durability of large, critical structures.

[0063] CN115758077A discloses a "data processing method for inverting the location of coal mine faults based on muon observation data", which specifically involves processing muon observation data through sliding window to improve the accuracy of coal mine fault location to the 5m level, and verifies the reliability of muon technology in geological structure identification.

[0064] CN117388937A discloses a "method for monitoring the safety of geotechnical engineering based on muon detection technology", which uses a ring-shaped muon detection array to analyze the three-dimensional density field of the soil and rock mass and realize the dynamic inversion of the stress-strain field, providing a paradigm for engineering stability monitoring.

[0065] Although muon imaging technology has been successfully applied to fault identification (CN115758077A) and geotechnical engineering monitoring (CN117388937A), it still has significant gaps in underground engineering fire scenarios: existing methods focus on static geological structure analysis and lack quantitative models for dynamic indicators of the combustion process (such as changes in carbon content and thermal damage gradients), and have not yet covered dynamic fire monitoring and damage classification; traditional density inversion cannot distinguish between the increase in coal and rock porosity caused by fire and the development of natural fractures, making it difficult to accurately delineate the damage range (core damage zone, transitional damage zone, and edge damage zone); existing systems are mostly used for post-event analysis and do not integrate real-time fire risk discrimination algorithms.

[0066] To address the aforementioned issues, this invention proposes a method for detecting fire hazards and assessing damage in underground engineering projects. Its core innovation lies in the coupling of fire dynamic density and carbon content: by inverting the density distribution of coal and rock through an energy decay algorithm and correlating it with changes in carbon content (a core combustion indicator), a system for identifying abnormal fire areas is constructed.

[0067] Quantitative classification of three damage zones: By introducing fire damage variables (combined with grid analysis), the core fire damage zone, transitional fire damage zone, and edge fire damage zone can be accurately classified.

[0068] Real-time monitoring and adaptive calibration: The ground and borehole detectors are networked together, and the fire detection sensitivity is ensured through wireless transmission and dynamic data calibration.

[0069] This invention fills the gap in muon technology for fire detection and quantitative damage assessment by introducing a density inversion framework, fire-specific parameters (carbon content, damage variables), and a dynamic grid densification strategy, providing a scientific basis for post-disaster restoration.

[0070] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0071] like Figure 1 As shown, this invention provides a method for detecting fire hazards and assessing damage in underground engineering projects. This method is based on a muon transmission imaging detection system, which includes a muon detection network and an information processing and damage assessment subsystem. The muon detection network consists of a ground detection array and a borehole detection array. The information processing and damage assessment subsystem includes a data processing and analysis module and a damage assessment module. The method includes:

[0072] 1. System setup.

[0073] In this embodiment, a test area is selected and a ground detection array and a borehole detection array are arranged. Boreholes are drilled downwards at the leftmost and rightmost points of the test area. The drilling depth is determined based on the actual conditions of the test area (let's say it's denoted as...). ), surface detection arrays and borehole detection arrays are deployed in the area to be measured, both on the surface and in underground boreholes. The surface detection arrays are arranged parallel to the surface of the area to be measured and consist of surface muon detectors. The spacing between each surface muon detector is 100m (dynamically adjusted to 50m in high-risk areas), and a total of [number missing] detectors are deployed. 100 points, horizontally covered m, the borehole detection array is vertically arranged in the boreholes on the left and right sides of the area to be measured. The borehole detection array consists of borehole muon detectors, and the leftmost part of the area to be measured is located at a distance of m from the ground surface. Starting from a depth of m, muon detectors are placed downwards every 50m, with the same method applied to the right side. Muon detectors are placed on both sides. There are [number] points, totaling [number] points. Each point, vertically covered m, forming a top-down three-dimensional muon detection network.

[0074] like Figure 2 As shown, the ground detection array has 7 points, and the left and right boreholes each have 3 points.

[0075] like Figure 3 As shown, the ground muon detector includes a housing, a power supply unit, a muon detection unit, and a data acquisition unit. The housing is a hollow cube structure with protective functions, suitable for underground engineering in surface environments.

[0076] The power supply unit, located inside the casing, is used to provide operating power.

[0077] The muon detection unit, located inside the outer casing, includes an upper muon detector layer and a lower muon detector layer arranged in parallel. Both the upper and lower muon detector layers are composed of multiple horizontally spliced ​​triangular prism-shaped plastic scintillators, used to detect muon passage information and generate electrical signals.

[0078] The data acquisition unit, located inside the outer casing, is electrically connected to the muon detection unit. It receives and processes the electrical signals generated by the muon detection unit, generating data including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy The muon data.

[0079] The data acquisition unit is located at the bottom inside the outer shell, parallel to the upper and lower muon detector layers, and the two are connected by a transmission line.

[0080] like Figure 4 As shown, the borehole muon detector includes a housing, a power supply, a muon detection unit, and a data acquisition unit. The housing is a pressure-resistant and moisture-proof hollow cylindrical structure, suitable for underground drilling environments.

[0081] The muon detection unit, located inside the housing, includes a tubular plastic scintillator matrix layer arranged axially at the upper and lower ends of the housing, used to detect muon passage information and generate electrical signals.

[0082] The data acquisition unit, located axially at the center of the casing, is connected to the tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit via data cables. It receives and processes electrical signals to generate data including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy The muon data.

[0083] The power supply unit is located in the gap between the muon detection unit and the data acquisition unit, and supplies power to the muon detection unit and the data acquisition unit through pipelines.

[0084] In this unit, the tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit are separated axially by the data acquisition unit.

[0085] The information processing and damage assessment subsystem is located in the information processing terminal, which is connected to the three-dimensional muon detection network.

[0086] Through tiered hardware maintenance and dynamic data calibration, the muon detector maintains high sensitivity and stable data acquisition during continuous operation. This solution balances preventative maintenance with rapid response capabilities, minimizing downtime risks and providing reliable technical support for fire detection in underground engineering projects. Specifically: Ground-based muon detectors: Regular surface inspections and periodic in-depth inspections are conducted to ensure the plastic scintillator surface is clean and undamaged; specialized cleaning tools are used to maintain the light transmittance of optical components; the tightness of the triangular prism scintillator splicing is verified to prevent misalignment caused by mechanical vibration; and the reliability of the power supply unit connection is continuously monitored.

[0087] Borehole muon detector: Regularly check the housing sealing performance through the maintenance channel to prevent fluid or rock cuttings from seeping in; clean the axial gaps of the detector layer to ensure unobstructed muon penetration path; verify the waterproof and moisture-proof performance of the data acquisition unit (IP68 standard).

[0088] The lifespan of a plastic scintillator is approximately 5 years. Periodic performance tests are required every 2 years, and scintillator modules with significant light output degradation should be replaced promptly.

[0089] Power supply unit maintenance: The ground muon detector uses a solar + lithium battery power supply system. The battery capacity is checked every six months, the battery status is assessed as needed, and units with insufficient capacity are replaced (replace when the capacity is below 80%). The borehole muon detector uses high-temperature resistant lithium batteries, and the battery status is checked once a year. A special maintenance plan is developed based on the working environment.

[0090] Data acquisition unit calibration: Regular online and offline calibrations are performed. Online calibration verifies the detector count rate using a standard muon source to ensure stable sensitivity. Offline calibration uses a laboratory-simulated muon beam to calibrate the detector energy resolution and optimize the data acquisition threshold.

[0091] 2. Data collection.

[0092] In this embodiment, ground-based detection arrays and borehole detection arrays are used to collect muon data generated by the interaction between muons and underground coal and rock. The collected muon data is then wirelessly transmitted to the data processing and analysis module. The muon data includes the number of muons passing through the underground coal and rock, and the incident zenith angle. azimuth of incidence Muzi flux and muon energy .

[0093] 3. Based on the received muon data, the coal and rock density distribution in the area to be measured is inverted to generate an underground coal and rock structure density trend map.

[0094] In this embodiment, the ground-based muon detector and the borehole muon detector employ a dual-layer parallel detector system to collect muon data. Based on the physical assumption that muons move along a straight trajectory, signals that simultaneously trigger both the upper and lower detector layers are defined as valid muon events. All valid events are then projected onto the zenith angle. With azimuth The system constructs a two-dimensional angular space, thereby obtaining the spatial distribution of muons and reconstructing muon paths. During the detection process, muon data are acquired from ground-based muon detectors and borehole muon detectors under the operating environment, while maintaining the detector's zenith angle. With azimuth Without changing the geographic coordinates, isochronous measurements are performed on an unobstructed sky area at the same geographic coordinates to obtain muon data measured by ground muon detectors and borehole muon detectors in an open sky environment.

[0095] Based on muon data from the working environment and the corresponding muon data from the open sky environment, the muon flux was interpolated to obtain the variation in muon quantity in different track directions. The muon transmittance in different track directions was then calculated using the following formula: .

[0096] In the formula, Transmittance is the ratio of the number of muons that penetrate the coal and rock in the test area along a specific track direction to the number of muons measured in the same direction under open sky conditions. Its core meaning is to reflect the changing relationship of the number of muons. The muon count is the number of muons measured in the specific track direction under the working environment. For muon counts measured in the same track direction under open sky conditions, Muon flux is the number of muons that pass through a unit solid angle per unit time. The minimum critical energy required for a muon to penetrate the coal and rock medium being tested along this specific ray path.

[0097] It reflects the degree to which muons are absorbed or scattered after passing through a target object, and is the most fundamental and crucial data source in the density inversion process. and There is a mathematical relationship between them, and the latter is related to the density integral on the path (i.e., "path opacity"), thus establishing a density model, through... From the expression, we can see that the difference between the lower limits of integration of the numerator and denominator is... The value of 0 directly reflects the absorption effect of an object on low-energy muons, therefore, yes The function.

[0098] The measurable muon count ratio ( ) is transformed into physical quantities that cannot be directly observed ( Opacity ,density Transmittance is the mathematical foundation for density inversion. It serves as a bridge connecting observational data with the physical characteristics (density) of underground structures. In muon density inversion, it is the necessary first step in deriving the density distribution from actual detection. Without transmittance, subsequent density inversion models cannot be carried out. Therefore, it plays a fundamental and crucial role in the entire algorithm.

[0099] The algorithm based on muon energy decay calculates opacity according to energy loss theory, infers the density of matter, and determines the minimum energy required for a muon to pass through each detection path. Invert the opacity of each detection path and average density .

[0100] For each ray path traversing the region under test, the incident muon energy must exceed the minimum energy required for that path. Only then can it be recorded by the detector. Satisfy the following formula: .

[0101] In the formula, the parameters and The constant determined by both the muon energy spectrum and the coal and petrographic composition can be approximated as a constant for conventional rock formations. The opacity along a specific ray path is defined as the product of the path length and the average density of coal and rock along the path, reflecting the medium's total absorption capacity for muons.

[0102] The opacity The specific calculation formula is as follows: .

[0103] In the formula, This represents the actual length of the coal and rock sample traversed by the muon along that specific ray path. The average density of coal and rock along that specific ray path. To match the average density of coal and rock The relevant functions reflect the specific impact of density on the muon absorption effect.

[0104] Using the received opacity data of the probe path as input, the inversion equation can be expressed as: .

[0105] In the formula, The opacity observations are derived from all ray paths. The matrix representing the path length of the coal and rock traversed by the muon. This represents the density value obtained through inversion.

[0106] 4. Identification, division, and qualitative analysis of abnormal fire zones in underground engineering projects.

[0107] In this embodiment, the damage assessment module infers the carbon content changes of underground coal and rock based on the density trend map of underground coal and rock structure, identifies abnormal fire areas in underground engineering based on the carbon content changes and coal and rock structural characteristics, divides the abnormal fire area into three damage zones by quantitatively calculating damage variables, and performs hierarchical and qualitative analysis on the damage of the abnormal area to the surrounding coal and rock and overlying strata structure, thus completing the damage assessment of underground engineering fire.

[0108] 4.1 Identification of abnormal fire areas, the process is as follows: Figure 5 As shown.

[0109] The area to be measured is divided into several unit grids: based on the total area of ​​the area to be measured ( Dynamically determine the reference grid resolution. ): ,in, Preset total number of grid cells (default) For high-risk areas such as known fault zones and areas with frequent historical fires, the grid resolution is increased to [specific value]. .

[0110] When underground coal and rock burn, the volatilization of organic matter and the destruction of mineral structure lead to a significant decrease in rock density. At the same time, carbon, as the main component of organic matter, decreases in content as the degree of combustion intensifies. Therefore, density change and carbon content change are strongly correlated. Carbon content can be inferred from density inversion results. A "density-carbon content" calibration model can be established, and a quantitative relationship curve between the two can be fitted using measured data from historical fire areas (such as borehole sampling analysis of carbon content and muon inversion density).

[0111] Weights were assigned to both the carbon content change index and the coal petrographic features to determine the weight of the degree to which the carbon content deviated from the background value. (Reflecting the degree of coal and rock combustion), the weight of fault and fracture development intensity is: (Reflecting oxygen channels and combustion diffusion conditions), the weight of rock mass permeability grade is: (Reflects the conditions of heat accumulation and fluid transport).

[0112] The fuzzy comprehensive evaluation logic is adopted to determine the comprehensive discriminant value. The calculation process is standardized as follows: index quantification ( , , Rating transformation: Transforms the original data into a standardized rating of 0-10 using a membership function.

[0113] Weight determination (calculation) , , The decision matrix is ​​determined by performing pairwise comparisons using AHP and passing a consistency check. and its corresponding eigenvectors.

[0114] Weighted calculation: The fire anomaly is identified by comparing the result with a preset threshold.

[0115] Specifically, the normalized deviation factor of the measured carbon content of the grid cell relative to the regional background value. The fault fracture influence score is determined based on fault density and the number of fractures per m³. And the rock mass permeability rating based on lithology and pore structure. The scoring criteria are shown in Table 1.

[0116] Table 1 Comprehensive Judgment Index Scoring Table

[0117]

[0118] Based on the importance of the indicators in determining fire anomalies, the following 1-5 importance scale is used to compare and evaluate the importance of the indicators (1 = equally important, 5 = important). Quantitative scale 1: Factors and Comparison, equally important; Quantitative scale 3: Factors and Comparison, slightly more important; Quantitative scale 5: Factors and (Comparison, important) Judgment Matrix As shown below:

[0119] .

[0120] Find the matrix Maximum eigenvalue And the corresponding feature vectors, and calculate the weights. , , The calculation process is as follows:

[0121] ;

[0122] ;

[0123] ;

[0124] .

[0125] Calculate the consistency index (3 represents the matrix order); the average random consistency index of a 3rd order matrix. (3rd order matrix); random consistency ratio When the conditions are met, the weighting is effective. If not, the carbon content deviation from the background value needs to be recalculated. Fault and fracture influence score and lithological sealing score Re-evaluate and re-rank importance until the requirements are met.

[0126] Weight validity is determined based on the weight identification results in the matrix: Matrix Maximum eigenvalue 3rd order matrix average random consistency index (3rd order matrix), calculate the consistency index Random consistency ratio Therefore, it can be concluded that the weight assignment was effective. For each unit grid area, the weights are then quantified according to the indicators. , , Rating conversion and weight determination (calculation) , , The weighted comprehensive discrimination value is calculated. .

[0127] The preset fire anomaly threshold is determined based on backtesting and optimization of historical fire case data. It can be set according to actual conditions; here it is set to 0.6, which is the comprehensive discrimination value for judging each unit grid area. Does it exceed the preset fire anomaly threshold of 0.6? Filter the comprehensive judgment value. Candidate anomaly grids with values ​​greater than the preset fire anomaly threshold of 0.6 are stored in a list or matrix, along with their coordinates (such as row and column numbers).

[0128] like Figure 6 As shown in (a), adjacent candidate anomaly grids are merged using a four-neighbor or eight-neighbor connectivity analysis method to form contiguous anomaly regions and the boundaries of the merged regions are recorded. The initial scattered candidate anomaly grids are merged to form three anomaly regions. A unique identifier (such as a number) is assigned to each merged anomaly region to facilitate subsequent statistics.

[0129] like Figure 6 As shown in (b), the number of grid cells in each abnormal region is calculated, and all grid cells with a count less than the threshold are deleted. Abnormal regions, threshold Set to 5 ( Depending on the specific circumstances, two abnormal areas, A and B, were obtained.

[0130] Mark the actual location of the fire anomaly area based on the anomaly area in the unit grid diagram: mark the area of ​​each fire anomaly area (e.g., area A: 8 grids × 100m² / grid = 800m²), and mark the coordinates of the center point of each fire anomaly area.

[0131] 4.2 Delineation of abnormal fire zones.

[0132] In order to accurately and scientifically calculate damage variables and delve into the essence of coal and rock structure and the mechanism of fire occurrence, it is necessary to understand that during the combustion process, the volatilization of organic matter and changes in the mineral structure of coal and rock will cause changes in the density of the surrounding coal and rock. Taking into account all factors, density is selected as a physical quantity as a damage variable to construct a damage variable system that closely matches the actual fire scenario.

[0133] The damage variable for each cell in the fire anomaly zone is calculated based on the density changes of underground coal and rock before and after combustion and the comprehensive discriminant value. The specific formula is as follows: .

[0134] In the formula, The density of underground coal and rock in the unit grid area before combustion is estimated from historical geological data or adjacent unburned areas. The density of underground coal and rock after combustion in the unit grid area is extracted from the current density trend map. The unit mesh synthesis discrimination value, The function representing the effect of density on the muon absorption cross section can usually be expressed as a linear or exponential relationship and is a function of the comprehensive discrimination value.

[0135] Damage variables The magnitude of the damage variable directly represents the degree of damage to the coal and rock during combustion. The larger the value of the damage variable, the more severe the damage to the coal and rock.

[0136] like Figure 7 As shown, based on the damage variables of each cell grid in the fire anomaly area The numerical range divides the two abnormal regions, A and B, into a core damage area (red), a transitional damage area (orange), and a peripheral damage area (yellow). The threshold parameters are shown in Table 2.

[0137] Table 2 Thresholds for Quantitative Division of Three Injury Zones

[0138]

[0139] 4.3 Classification and qualitative analysis of fire anomaly areas.

[0140] Core damage area: The coal and rock in this area are burning most severely, resulting in a significant decrease in density, a large amount of volatilization of organic matter in the coal and rock, and severe damage to the mineral structure. In terms of actual impact, the surrounding coal and rock and the overlying strata may experience obvious crack expansion, rock fragmentation, and even local collapse. This is because the high temperature and gas pressure generated by the combustion have caused severe damage to the surrounding rock structure, resulting in the loss of rock integrity.

[0141] Transitional damage zone: The coal and rock combustion in this area is also quite obvious, with a certain degree of reduction in density and a moderate level of damage variables. In this area, the structure of the surrounding coal and rock and the overlying strata begins to be affected, and some micro-cracks may be generated and expanded. The strength and stability of the rock have decreased, but the overall structure can still remain relatively intact. The transitional damage zone is the part that extends outward from the core damage zone, and its degree of damage gradually weakens as the distance from the combustion center increases.

[0142] Edge damage area: In this area, coal and rock combustion is relatively weak, density changes are small, and damage variables are low. The impact on the surrounding coal and rock and overlying strata is mainly manifested as slight deformation of the rock and the initial formation of micro-cracks. In the edge damage zone, due to the distance from the combustion center and the low degree of combustion, the impact of coal and rock combustion is relatively small, and the damage to the rock structure is in the initial stage and has not yet posed a serious threat to the overall stability.

[0143] The present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for detecting fire hazards and assessing damage in underground engineering projects, characterized in that, This method is implemented based on a muon transmission imaging detection system, which includes a muon detection network and an information processing and damage assessment subsystem. The muon detection network consists of a ground detection array and a borehole detection array. The information processing and damage assessment subsystem includes a data processing and analysis module and a damage assessment module. The method includes: S1. Select the area to be measured, arrange a ground detection array on the surface of the area to be measured to cover its horizontal range, and arrange a borehole detection array in the boreholes on both sides of the boundary of the area to be measured to cover its vertical range. The ground detection array and the borehole detection array together constitute a three-dimensional covering muon detection network. S2. The ground-based detection array and borehole detection array are used to collect muon data generated by the interaction between muons and underground coal and rock. The collected muon data is then wirelessly transmitted to the data processing and analysis module. The muon data includes the number of muons passing through the underground coal and rock, and the incident zenith angle. azimuth of incidence Muzi flux and muon energy ; S3. The information processing and damage assessment subsystem inverts the coal and rock density distribution in the area to be tested based on the received muon data, generates an underground coal and rock structure density trend map, infers the carbon content change of underground coal and rock based on the underground coal and rock structure density trend map, and identifies the abnormal area of ​​underground engineering fire based on the carbon content change combined with the coal and rock structure characteristics. S4. The information processing and damage assessment subsystem quantitatively calculates the damage variables defined by the density changes of coal and rock before and after combustion, divides the fire anomaly area into the damage core area, damage transition area and damage edge area, and, based on the division results of the damage core area, damage transition area and damage edge area, analyzes the damage caused by the fire anomaly area to the surrounding coal and rock and the overlying rock strata structure in a graded manner, and completes the fire damage assessment of underground engineering.

2. The method for fire hazard detection and damage assessment in underground engineering according to claim 1, characterized in that, The ground-based detection array consists of multiple ground-based muon detectors, which include: The outer shell is a hollow cubic structure with protective functions, suitable for underground engineering in surface environments; A power supply unit, located inside the housing, is used to provide operating power; The muon detection unit is located inside the outer shell and includes an upper muon detector layer and a lower muon detector layer arranged in parallel. Both the upper and lower muon detector layers are composed of multiple horizontally spliced ​​triangular prism-shaped plastic scintillators, which are used to detect muon passage information and generate electrical signals. The data acquisition unit, located inside the outer casing and electrically connected to the muon detection unit, receives and processes the electrical signals generated by the muon detection unit to generate a data set including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy Muon data; The data acquisition unit is located at the bottom inside the outer shell, parallel to the upper and lower muon detector layers, and the two are connected by a transmission line.

3. The method for fire hazard detection and damage assessment in underground engineering according to claim 1, characterized in that, The borehole detection array consists of multiple borehole muon detectors, and the borehole muon detectors include: The outer shell is a pressure-resistant and moisture-proof hollow cylindrical structure, suitable for underground engineering drilling environments; The muon detection unit is located inside the outer shell and includes a tubular plastic scintillator matrix layer arranged axially at the upper and lower ends of the outer shell, which is used to detect muon passage information and generate electrical signals. The data acquisition unit, located axially at the center of the housing, is connected via data cables to the tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit. It receives and processes the electrical signals to generate data including the number of muons and the incident zenith angle. azimuth of incidence Muzi flux and muon energy Muon data; A power supply unit is located in the gap between the muon detection unit and the data acquisition unit, and supplies power to the muon detection unit and the data acquisition unit through pipelines. The tubular plastic scintillator matrix layers at the upper and lower ends of the muon detection unit are separated axially by the data acquisition unit.

4. The method for fire hazard detection and damage assessment in underground engineering according to claim 1, characterized in that, The information processing and damage assessment subsystem inverts the coal and rock density distribution in the area to be tested based on the received muon data, and generates an underground coal and rock structure density trend map, including the following steps: Acquire muon data from ground-based muon detectors and borehole muon detectors under operating conditions, while maintaining the detector zenith angle. With azimuth Without changing the geographic coordinates, isochronous measurements are performed on the unobstructed sky area at the same geographic coordinates to obtain the corresponding muon data in the open sky environment; Based on muon data from the working environment and the corresponding muon data from the open sky environment, the muon flux was interpolated to obtain the variation in muon quantity in different track directions. The muon transmittance in different track directions was then calculated using the following formula: ； In the formula, Transmittance is the ratio of the number of muons that penetrate the coal and rock in the test area along a specific track direction to the number of muons measured in the same direction under open sky conditions. Its core meaning is to reflect the changing relationship of the number of muons. The muon count is the number of muons measured in the specific track direction under the working environment. For muon counts measured in the same track direction under open sky conditions, Muon flux is the number of muons that pass through a unit solid angle per unit time. The minimum critical energy required for a muon to penetrate the coal and rock medium under test along a specific ray path; For each ray path traversing the region under test, the incident muon energy must exceed the minimum energy required for that path. Only then can it be recorded by the detector. Satisfy the following formula: ； In the formula, the parameters and The constant determined by both the muon energy spectrum and the coal and petrographic composition can be approximated as a constant for conventional rock formations. The opacity along a specific ray path is defined as the product of the path length and the average density of coal and rock along the path, reflecting the medium's total absorption capacity for muons. The opacity The specific calculation formula is as follows: ； In the formula, This represents the actual length of the coal and rock sample traversed by the muon along that specific ray path. The average density of coal and rock along that specific ray path. To match the average density of coal and rock The relevant functions reflect the specific effects of density on muon absorption; Using the received opacity data of the probe path as input, the inversion equation can be expressed as: ； In the formula, The opacity observations are derived from all ray paths. This represents the path length matrix of the coal and rock that the muon traverses.

5. The method for fire hazard detection and damage assessment in underground engineering according to claim 1, characterized in that, The method for identifying abnormal areas of underground engineering fires based on changes in carbon content combined with coal and rock structural characteristics includes the following steps: The area to be measured is divided into spatial grid units, and the grid resolution is dynamically adjusted according to the area of ​​the area to be measured and the fire risk level. Local adjustments are made for known fault zones or historical fire zones. Based on the fire occurrence mechanism, the following fire anomaly sensitivity parameters are selected and assigned weights: the weight for determining the degree to which carbon content deviates from the background value is... The intensity weights for fault and fracture development are: The weight of rock mass permeability grade is ; For each grid cell region, the normalized deviation factor of the measured carbon content of the grid cell relative to the regional background value is calculated according to a set weight. The fault fracture influence score is determined based on fault density and the number of fractures per m³. And the rock mass permeability rating based on lithology and pore structure. A weighted calculation is performed to obtain the comprehensive discriminant value. ; Determine the comprehensive discriminant value for each cell grid region. Whether the preset fire anomaly threshold is exceeded, filter comprehensive judgment value. Candidate anomaly grids exceeding a preset fire anomaly threshold, wherein the preset fire anomaly threshold is determined by backtesting and optimization of sample data from known fire areas and non-fire areas; Adjacent candidate anomaly grids are merged using four-neighbor or eight-neighbor connectivity analysis methods to form several anomaly regions; Calculate the number of grid cells in each anomalous region, and delete areas smaller than the minimum valid anomalous area. Abnormal areas; Output the actual spatial location of the remaining abnormal area, which is the identified fire abnormal area.

6. The method for fire hazard detection and damage assessment in underground engineering according to claim 1, characterized in that, The quantitative calculation, based on the damage variable defined by the density change before and after coal combustion, divides the abnormal fire area into a damage core zone, a damage transition zone, and a damage edge zone, including the following steps: The damage variable for each cell in the fire anomaly zone is calculated based on the density changes of underground coal and rock before and after combustion and the comprehensive discriminant value. The specific formula is as follows: ； In the formula, The density of underground coal and rock in the unit grid area before combustion is estimated from historical geological data or adjacent unburned areas. The density of underground coal and rock after combustion in the unit grid area is extracted from the current density trend map. The unit mesh synthesis discrimination value, The function represents the effect of density on the muon absorption cross section, expressed as a linear or exponential relationship, and is a function of the comprehensive discriminant value; Based on the damage variables of each cell grid in the fire anomaly zone The numerical range divides the fire anomaly area into core damage zone, transition damage zone and edge damage zone, outputs the spatial distribution map of core damage zone, transition damage zone and edge damage zone, and marks the typical damage characteristics of each zone.

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