A method and system for detecting and monitoring integrated early warning of rock burst disaster

Abstract

This invention discloses an integrated early warning method and system for detecting and monitoring rockburst disasters, relating to the field of monitoring and early warning technology for dynamic disasters in mine safety and geotechnical engineering. The early warning method includes the following steps: actively acquiring three-dimensional distribution data of resistivity and wave velocity reflecting the rock mass structural field; performing spatial coupling analysis on resistivity anomaly areas and wave velocity anomaly areas to generate a static hazard zoning map; passively receiving acoustic emission, electromagnetic radiation, microseismic, and stress signals generated by rock mass fracturing, and simultaneously acquiring dynamic data reflecting the evolution of the stress field; spatiotemporally fusing the acquired actively detected structural field data with the passively monitored stress field data, and generating a spatiotemporal feature cloud map containing a comprehensive risk index through joint inversion; establishing a risk assessment model based on the dynamic evolution of the spatiotemporal feature cloud map, using multi-parameter spatiotemporal evolution characteristics as early warning criteria, comparing the acquired real-time dynamic evolution parameters with the early warning criteria, and performing graded early warning.

Description

Technical Field

[0001] This invention relates to the field of mine safety and geotechnical engineering dynamic disaster monitoring and early warning technology, and in particular to an integrated method and system for detecting and monitoring rockburst disasters. Background Technology

[0002] Rockbursts, as the most destructive dynamic disaster in deep resource extraction, have always posed a global challenge in early warning and prevention. With the continuous increase in mining depth and intensity, the manifestation mechanism of rockbursts has become increasingly complex, the disaster-causing range has become wider, and the destructive power has become stronger, exhibiting new characteristics of large scale and complexity, which poses a severe challenge to existing early warning technologies. Current rockburst monitoring and early warning technologies largely focus on monitoring localized, isolated dynamic signals. Static geological exploration methods such as electrical resistivity tomography (EDT) and seismic CT, which assess the "structural field," can identify tectonic anomalies, but their results are delayed and cannot capture the dynamic evolution of mining-induced stress fields, and are prone to multiple interpretations. Dynamic monitoring methods, such as microseismic and acoustic emission monitoring, can acquire real-time precursor signals, but their sensor deployment often relies on experience and lacks targeted coverage of hazardous areas revealed by static detection, leading to inaccurate measurements. A deeper problem lies in the disconnect between static detection and dynamic monitoring, creating "data silos." Their data analysis is often independent, and static structural anomalies and dynamic precursor information fail to achieve effective fusion and joint inversion in the spatiotemporal dimensions, making it difficult to form a systematic understanding of the disaster's gestation process. In summary, existing large-scale rockburst early warning technologies face the core dilemma of "static detection lacking dynamism, dynamic monitoring lacking focus, and the two types of data being disconnected." There is an urgent need for an innovative method that can break down data barriers and achieve integrated fusion of the entire process from "structural field" to "stress field" and from "static assessment" to "dynamic early warning," in order to achieve accurate spatiotemporal identification and forward-looking early warning of rockburst disaster risks.

[0003] A literature search of existing technologies revealed the following: Patent application CN120447032A proposes a mine microseismic monitoring and early warning method, which dynamically identifies target hazardous areas and implements graded treatment measures by combining rockburst risk prediction with drill cuttings data for dual verification; Patent application CN119825469A proposes an intelligent monitoring and early warning method for coal mine rockbursts, which achieves precise source location by deploying a microseismic sensor network and using a redundant time-difference set hyperboloid interpolation positioning method, thereby establishing an early warning model to achieve accurate dynamic early warning of rockbursts; Patent CN119778032A proposes a coal mine rockburst monitoring and early warning scheme, which dynamically assesses the degree of rockburst danger and achieves accurate early warning by analyzing the correlation index between ground sound frequency and ground sound energy data; Patent application CN119493154A proposes a rockburst early warning method based on microseismic data, which constructs a rockburst danger assessment model by calculating the surrounding rock damage classification dimension of a gridded area and combining it with crack geometric characteristic parameters, thereby achieving accurate and advanced early warning. However, the aforementioned existing technologies still have the following limitations: their monitoring methods mostly rely on single or a few types of dynamic monitoring data, lacking active and precise detection of rock mass structural fields, resulting in insufficient foresight of potential hazardous areas; they fail to achieve spatiotemporal fusion and joint inversion of static structural field and dynamic stress field data, making it difficult to form a systematic understanding of the disaster gestation process; existing early warning models mostly rely on empirical thresholds or simple trend judgments of single parameters, lacking the ability to identify multi-parameter, nonlinear, and spatiotemporally evolving disaster precursors. Summary of the Invention

[0004] This solution addresses the problems and needs raised above by proposing an integrated early warning method and system for detecting and monitoring rockburst disasters. The above technical objectives are achieved by adopting the following technical features, and several other technical benefits are also brought about.

[0005] One objective of this invention is to provide an integrated early warning method for the detection and monitoring of rockburst disasters, comprising the following steps: S10: The target area is periodically or triggered by parallel electrical resistivity and seismic wave CT to achieve joint detection, obtain active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis on resistivity anomaly area and wave velocity anomaly area to generate static hazard zoning map. S20: Based on the delineated static hazard zoning map of rockburst, optimize the spatial location of various dynamic monitoring sensors, prioritize and concentrate their placement within and at the boundaries of high and medium hazard zones, so that the spatial layout of various dynamic monitoring sensors matches the static hazard zoning map revealed by static detection, passively receive signals generated by rock mass fracturing, and simultaneously acquire dynamic data reflecting the evolution of dynamic stress field to form passive monitoring dynamic stress field data. S30: The acquired active detection static structural field data and passive monitoring dynamic stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. S40: Based on the dynamic evolution of the spatiotemporal feature cloud map, establish a risk assessment model, and make early warning criteria based on multi-parameter spatiotemporal evolution characteristics. Compare the obtained real-time comprehensive risk index with the early warning criteria. When the risk index exceeds the threshold or the evolution trend meets the disaster criteria, trigger a graded early warning.

[0006] Furthermore, the integrated early warning method for detecting and monitoring rockburst disasters according to the present invention may also have the following technical features: In one example of the present invention, step S10 specifically includes the following steps: S11: Spatial coupling analysis based on three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT; S12: Based on the abnormal resistivity and wave velocity state of the impact area, scores are assigned to them respectively. The resistivity is assigned 2, 4, and 6 points according to the low, medium, and high anomaly levels, respectively, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; S13: The overlap between resistivity anomaly regions and wave velocity anomaly regions is determined in three-dimensional space, and the overlapping area is delineated with a hazard index K=K. d +K b The theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

[0007] In one example of the present invention, in step S20, based on the delineated static hazard zone map, the spatial locations of various dynamic monitoring sensors are optimized, specifically including: Within and along the boundaries of the designated hazardous area, acoustic emission sensors, electromagnetic radiation sensors, microseismic sensors, and stress sensors are deployed to obtain dynamic data within the hazardous area, forming a dynamic precursor monitoring network.

[0008] In one example of the present invention, in step S30, the acquired actively detected structural field data and passively monitored stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion. Specifically, the steps include the following: S31: The target area is meshed in three dimensions, and the active detection structural field data and passive monitoring stress field data are unified into the same spatiotemporal coordinate system; S32: The comprehensive risk index R is calculated using a hierarchical weighted fusion model. The comprehensive risk index R includes: static structural risk index, dynamic precursor risk index and comprehensive index. S33: Perform three-dimensional visualization rendering of the comprehensive risk index R value of all grid units in the entire monitoring area, and finally generate a spatiotemporal feature cloud map.

[0009] In one example of the present invention, in step S32, the expressions for the static structural risk index, the dynamic precursor risk index, and the comprehensive risk index are as follows: Static structural risk index The expression is: In the formula, K is the hazard risk index defined in static hazard zoning; Dynamic precursor risk index The expression is as follows: In the formula, , and These are the weighting coefficients for acoustic-electrical, micro-seismic, and stress monitoring, respectively, and they satisfy... + + = 1; The acoustic-electric activity index; The microseismic intensity index; It is the stress concentration index; The expression for the comprehensive risk index R is as follows: In the formula, ω is a static risk weighting factor, where 0 < ω < 1.

[0010] In one example of the invention, the acoustic-electric activity index The microseismic intensity index is and stress concentration index is The expression is as follows: Acoustic activity index The expression is: In the formula, N is the count of acoustic emission events within the monitoring unit; The cumulative energy released by acoustic emission; This refers to the real-time or average intensity of electromagnetic radiation. , , These are the threshold values ​​for each parameter preset based on historical data and on-site conditions; α, β, and γ are the acoustic emission event count weighting coefficient, acoustic emission cumulative energy weighting coefficient, and electromagnetic radiation intensity weighting coefficient, respectively, and satisfy α + β + γ = 1, used to adjust the contribution of each parameter; Microseismic Intensity Index The expression is as follows: In the formula, D represents the spatial density of microseismic events within the monitoring unit; To monitor the cumulative energy of microseismic events within the monitoring unit; , The preset thresholds for the spatial density and cumulative energy of microseismic events within the monitoring unit are defined; λ and μ are the weighting coefficients for the spatial density and cumulative energy of microseismic events, respectively, and satisfy λ + μ = 1. Stress Concentration Index The expression is as follows: In the formula, To monitor the absolute change in stress within the unit; To monitor the stress gradient within the unit; , The preset thresholds for monitoring the absolute change in stress and stress gradient within the unit are: η and ξ are the weighting coefficients for the absolute change and stress gradient, respectively, and satisfy η + ξ = 1.

[0011] In one example of the present invention, step S40 specifically includes the following steps: S41: Implement tiered early warning based on the real-time level of the comprehensive risk index R; when 0.6 ≤ R < 0.7, a yellow warning is triggered, prompting enhanced on-site monitoring and observation; when 0.7 ≤ R < 0.85, an orange warning is triggered, prompting the implementation of local pressure relief and control measures; when R ≥ 0.85, a red warning is triggered, prompting immediate cessation of operations and evacuation of personnel. S42: Graded early warning based on the real-time trend of the comprehensive risk index R: During the risk zone expansion phase, an orange alert will be triggered when the following conditions are met; In the formula, This is the sum of the volumes of all grid cells with a comprehensive risk index R of not less than 0.6 at the current time t; for The total volume of high-risk areas under the same risk threshold before a time window; For the observation time span; This is the threshold for volume expansion rate; During the phase of accelerated risk increase, for any sequence with a continuous risk index { , ..., , , A local area of ​​} will trigger an orange alert when the following conditions are met; In the formula, This represents the average comprehensive risk index of the region at the current time t. , , These are the average comprehensive risk indices for the first three consecutive time windows; During the multi-parameter precursor coordinated anomaly phase, within the same monitoring sub-region, the instantaneous increments of three dynamic sub-indices within the time window t are defined as: the change in the acoustic-electric activity index. Changes in microseismic intensity index and stress concentration index change Establish the normal fluctuation range for each parameter, and denot the standard deviation over historical normal periods as follows: , , Abnormal fluctuations are defined using the 2σ principle from mathematical statistics; If the exponential change of a parameter exceeds twice its own historical normal fluctuation range, the change is considered an abnormal fluctuation. When it occurs simultaneously in the same time window and the same spatial region, the number of parameters that simultaneously meet its own abnormal conditions is recorded. If this count is 2, that is, two parameters have simultaneously experienced abnormal fluctuations, an orange warning is issued. If this count is 3, that is, three parameters have simultaneously experienced abnormal fluctuations, a red warning is issued.

[0012] Another objective of this invention is to provide an integrated early warning system for the detection and monitoring of rockburst disasters, comprising: The active detection module is configured to use parallel electrical resistivity and seismic wave CT to perform periodic or triggered scanning of the target area to achieve joint detection, acquire active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis of resistivity anomaly areas and wave velocity anomaly areas to generate static hazard zoning maps. The passive monitoring module is configured to optimize the spatial location of various dynamic monitoring sensors based on the delineated static hazard zoning map of rockburst. The sensors are preferentially and centrally located within and at the boundaries of high and medium hazard zones, so that the spatial layout of the various dynamic monitoring sensors matches the static hazard zoning map revealed by the static detection. The module passively receives signals generated by rock mass fracturing and simultaneously acquires dynamic data reflecting the evolution of the dynamic stress field, forming passive monitoring dynamic stress field data. The data fusion module is configured to perform spatiotemporal fusion of the acquired active detection static structural field data and passive monitoring dynamic stress field data, and generate a spatiotemporal feature cloud map containing a comprehensive risk index through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. The graded early warning module is configured to establish a risk assessment model based on the dynamic evolution of the spatiotemporal feature cloud map, and to make early warning criteria based on the multi-parameter spatiotemporal evolution characteristics. The module compares the acquired real-time comprehensive risk index with the early warning criteria, and triggers a graded early warning when the risk index exceeds the threshold or the evolution trend meets the disaster criteria.

[0013] In one example of the present invention, the active detection module includes: The coupling analysis unit is configured to perform spatial coupling analysis based on the three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT. The scoring unit is configured to assign scores based on the abnormal resistivity and wave velocity state of the impact region. The resistivity is assigned scores of 2, 4, and 6 points respectively, according to the low, medium, and high anomaly levels, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; The hazard delineation unit is configured as a resistivity anomaly zone and a wave velocity anomaly zone, and the overlap degree of these zones is determined in three-dimensional space. The overlapping areas are then delineated using the hazard index K=K. d +K b The theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

[0014] In one example of the present invention, the data fusion module includes: The data coordinate unit is configured to mesh the target area in three dimensions and unify the active detection structural field data and the passive monitoring stress field data into the same spatiotemporal coordinate system. The risk index unit is configured to use a hierarchical weighted fusion model to calculate the comprehensive risk index R, which includes: static structural risk index, dynamic precursor risk index and comprehensive index; The three-dimensional visualization unit is configured to perform three-dimensional visualization rendering of the comprehensive risk index R value of all grid units in the entire monitoring area, and finally generate a spatiotemporal feature cloud map.

[0015] Compared with the prior art, the present invention has the following beneficial effects: At the detection level, this invention innovatively employs parallel electrical resistivity and seismic wave CT to perform periodic or triggered scanning of the target area. Through spatial coupling analysis of resistivity anomaly zones and wave velocity anomaly zones, it achieves for the first time a three-dimensional visualization of the large-scale rock mass structure field, generating a static hazard zoning map and providing a scientific basis for the optimized deployment of the monitoring network.

[0016] This invention combines actively detected structural field data with multi-source dynamic monitoring data such as acoustic emission, electromagnetic radiation, microseismic activity, and stress for joint inversion. It pioneers a comprehensive risk index calculation method based on a hierarchical weighted fusion model, generating a dynamic risk cloud map with spatiotemporal continuity. This forms an integrated technical system that combines surface and point data with dynamic and static data, overcoming the drawbacks of the disconnect between the two in traditional methods.

[0017] At the early warning decision-making level, this invention provides early warning based on the spatiotemporal evolution trend of multiple parameters, breaking through the limitations of traditional single threshold criteria. It constructs a dual early warning mechanism that includes both horizontal and trend criteria, realizing the transformation from local, static, and passive monitoring to global, dynamic, and proactive intelligent early warning. This makes the early warning more scientific and forward-looking, and significantly improves the accuracy and reliability of early warning for large-scale rockbursts under deep and complex conditions.

[0018] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0020] Figure 1 This is a flowchart of an integrated early warning method for large-scale rockburst disaster detection, monitoring, and control, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the detection and monitoring device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the spatiotemporal feature cloud mapping process according to an embodiment of the present invention; Figure 4 This is a structural diagram of the integrated detection and monitoring early warning criteria according to an embodiment of the present invention.

[0021] List of reference numerals in the attached diagram: Electrical resistivity detector 1; Stress sensor 2; Acoustic and electrical monitoring instrument 3; Micro-vibration sensor 4; Vibration wave CT sensor 5; Data acquisition instrument 6; Inoue Server 7. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0024] According to a first aspect of the present invention, an integrated early warning method for detecting and monitoring rockburst disasters is provided, such as... Figure 1 As shown, it includes the following steps: S10: The target area is periodically or triggered by parallel electrical resistivity and seismic wave CT to achieve joint detection, obtain active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis on resistivity anomaly area and wave velocity anomaly area to generate static hazard zoning map. S20: Based on the delineated static hazard zoning map of rockburst, optimize the spatial location of various dynamic monitoring sensors, prioritizing and centrally configuring them within and at the boundaries of high and medium hazard zones. This ensures that the spatial layout of the various dynamic monitoring sensors matches the static hazard zoning map revealed by static detection. The sensors passively receive ring counts, fractal b-values, wave velocity energy, frequency and intensity, seismic source, and stress signals generated by rock mass fracturing, and simultaneously acquire dynamic data reflecting the evolution of the dynamic stress field, forming passive monitoring dynamic stress field data. S30: The acquired active detection static structural field data and passive monitoring dynamic stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. S40: Based on the dynamic evolution of the spatiotemporal feature cloud map, establish a risk assessment model, and make early warning criteria based on multi-parameter spatiotemporal evolution characteristics. Compare the obtained real-time comprehensive risk index with the early warning criteria. When the risk index exceeds the threshold or the evolution trend meets the disaster criteria, trigger a graded early warning.

[0025] At the detection level, this early warning method innovatively employs parallel electrical resistivity and seismic wave CT to perform periodic or triggered scanning of the target area. Through spatial coupling analysis of resistivity anomaly zones and wave velocity anomaly zones, it achieves for the first time a three-dimensional visualization of the large-scale rock mass structure field, generating a static hazard zoning map and providing a scientific basis for the optimized deployment of the monitoring network.

[0026] This early warning method combines actively detected structural field data with multi-source dynamic monitoring data such as acoustic emission, electromagnetic radiation, microseismic activity, and stress for joint inversion. It pioneered a comprehensive risk index calculation method based on a hierarchical weighted fusion model, generating a dynamic risk cloud map with spatiotemporal continuity. This forms an integrated technical system that is "from surface to point, combining dynamic and static aspects," overcoming the drawbacks of the disconnect between the two in traditional methods.

[0027] At the early warning decision-making level, this method provides early warning based on the spatiotemporal evolution trend of multiple parameters, breaking through the limitations of traditional single threshold criteria. It constructs a dual early warning mechanism that includes both horizontal and trend criteria, realizing the transformation from local, static, and passive monitoring to global, dynamic, and proactive intelligent early warning. This makes the early warning more scientific and forward-looking, and significantly improves the accuracy and reliability of early warning for large-scale rockbursts under deep and complex conditions.

[0028] In one example of the present invention, step S10 specifically includes the following steps: S11: Spatial coupling analysis based on three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT; S12: Based on the abnormal resistivity and wave velocity state of the impact area, scores are assigned to them respectively. The resistivity is assigned 2, 4, and 6 points according to the low, medium, and high anomaly levels, respectively, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; S13: The overlap between resistivity anomaly regions and wave velocity anomaly regions is determined in three-dimensional space, and the overlapping area is delineated with a hazard index K=K. d +K bThe theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

[0029] In one example of the present invention, in step S10, as Figure 2 The diagram shows the arrangement of the electrical resistivity detector 1 and the seismic CT sensor 5 used for periodic or triggered scanning of the target area using parallel electrical resistivity and seismic CT, as well as the arrangement of various dynamic monitoring sensors including: acoustic-electrical monitor 3, micro-vibration sensor 4 and stress sensor 2.

[0030] The electrical resistivity detector 1 mainly consists of the main instrument unit, distributed acquisition base stations, dual-mode electrodes, connecting cables, ABN converters, and infinity cables. The dual-mode electrodes are arranged at 5-meter intervals, and the acquired data is transmitted to the main instrument unit through the base stations. To improve electrode power supply and reception, each borehole is drilled approximately 30cm into the coal seam using a handheld drill. After the electrodes are inserted, the boreholes are sealed with yellow mud mixed with NaCl solution to ensure sufficient coupling with the coal and rock floor. Multiple retests are performed during on-site data acquisition to guarantee the accuracy of the test results. The seismic wave CT sensor 5 mainly consists of a high-precision digital seismograph, a seismic gun, and a three-component geophone array. A borehole-earth joint observation system is employed, with geophones deployed at 5-10 meter intervals, using quick-setting plaster to ensure coupling. The seismic gun is used to excite the seismograph at the designed source points, and synchronous triggering with the seismograph is achieved through a source controller. Multiple excitations are performed at each point to improve the signal-to-noise ratio. Data quality is monitored in real time during acquisition, and any substandard data is immediately supplemented. The acoustic and electrical monitoring instrument 3 mainly includes the GDD12 online acoustic and electrical monitoring instrument, a general-purpose substation, an intrinsically safe power supply, and the KJ796 software system. The GDD12 can be equipped with both electromagnetic radiation and acoustic emission probes. A measuring point is arranged every 20m to 30m in the working face and roadway. The distance between the electromagnetic radiation antenna and the coal and rock wall of the measured area is most suitable at 0.3 to 1.0m. For acoustic emission, a special clamp is used to fix the sensor to the anchor bolt. Microseismic sensors 4 are installed at different rock strata and heights to obtain high-quality waveform data through direct contact between the sensors and the coal and rock mass, forming a three-dimensional microseismic network covering the entire spatial area of ​​roof dynamic hazards. After the sensors are deployed, they are connected to a mine-use DAQlink-4 microseismic acquisition instrument via signal transmission cables. Time synchronization of waveforms acquired from different channels is achieved through a time synchronizer. The monitoring signals are transmitted to the ground monitoring host for data processing and display through the underground monitoring ring network. Stress sensor 2 uses a borehole stress gauge or fiber optic grating sensor. It is installed through drilling and pre-tightened to ensure coupling with the surrounding rock, forming a real-time sensing network for the evolution of stress field in the hazardous area.

[0031] The data acquisition unit 6 is communicatively connected to the electrical resistivity detector 1, the seismic wave CT sensor 5, the acoustic-electrical monitoring instrument 3, the micro-vibration sensor 4, and the stress sensor 2. The data acquisition unit 6 mainly consists of a multi-channel data acquisition module, a signal conditioning module, a clock synchronization module, and a data transmission module, realizing the synchronous acquisition, analog-to-digital conversion, filtering, amplification, and preliminary storage of multi-source monitoring signals. The acquired data, after unified time calibration and formatting, is transmitted in real-time to the well-ground server 7.

[0032] The server 7 communicates with the data acquisition instrument 6 and is mainly responsible for the centralized storage, fusion processing, anomaly identification and result display of monitoring data. It can comprehensively analyze multiple types of data such as electrical resistivity, seismic wave CT, acoustic and electrical, microseismic and stress data to form dynamic monitoring results of dangerous areas, providing data support for on-site early warning and pressure relief treatment.

[0033] In one example of the present invention, in step S20, based on the delineated static hazard zone map, the spatial locations of various dynamic monitoring sensors are optimized, specifically including: Within and along the boundaries of the designated hazardous area, acoustic emission sensors, electromagnetic radiation sensors, microseismic sensors, and stress sensors are deployed to obtain dynamic data within the hazardous area, forming a dynamic precursor monitoring network.

[0034] Specifically, the acoustic emission sensor, based on the fracture development and stress state characteristics of the surrounding rock in the danger zone, processes and calculates the real-time intensity, cumulative intensity, and pulse timing characteristic parameters of electromagnetic radiation in real time, and obtains acoustic emission ringing count, sound wave intensity, fractal b-value, spatial location, and released energy. Based on the acoustic and electrical index set and monitoring data, it quantitatively reflects the frequency and intensity of fracture evolution of coal and rock mass, and then dynamically assesses the stability level and energy evolution trend of the surrounding rock in the danger zone.

[0035] Microseismic sensors, based on fractal analysis of raw waveform signals within the hazard zone, extract time-frequency data of daily frequency, energy-frequency ratio, dominant frequency, and amplitude of microseismic events. Through source location and moment tensor inversion, spatial density contour maps, rupture scale, tension-shear type, focal mechanism, and released energy of the events are obtained. A multi-parameter spatiotemporal index system for microseismic events is established and fused and optimized to quantitatively characterize the source characteristics and rock strata rupture activity within the hazard zone, thereby assessing and predicting the risk of subsequent large-energy dynamic load disturbances.

[0036] Stress sensors, based on data from various stress sensors within the hazardous area, calculate real-time stress values, increments, gradients, and rates of change, constructing a spatiotemporal evolution cloud map. By analyzing stress concentration coefficients and energy densities, stress anomaly zones and high static load-bearing structures are identified. A dynamic evaluation index system for the static load stress field is established to quantitatively characterize the degree and distribution of stress concentration within the hazardous area, thereby assessing the risk of rock mass failure and instability dominated by high static loads.

[0037] In one example of the present invention, in step S30, as Figure 3 As shown, the acquired actively detected structural field data and passively monitored stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion. The specific steps include the following: S31: The target area is meshed in three dimensions, and the active detection structural field data and passive monitoring stress field data are unified into the same spatiotemporal coordinate system; S32: The comprehensive risk index R is calculated using a hierarchical weighted fusion model. The comprehensive risk index R includes: static structural risk index, dynamic precursor risk index and comprehensive index. S33: The comprehensive risk index R value of all grid units within the entire monitoring area is rendered in three dimensions and mapped using a color spectrum from blue (R≈0, low risk) to red (R≈1, extremely high risk) to generate a spatiotemporal feature cloud map. This cloud map is dynamically updated and intuitively displays the spatiotemporal distribution and evolution of rockburst risk.

[0038] In other words, the entire target area is divided into tens of thousands of regular three-dimensional cubic grids; all discrete data from different sources are allocated to each grid node; all monitoring devices use a unified time server to ensure data time consistency and accuracy; the continuous time stream is divided into fixed time windows, and the above operations are repeated for each time window; a standardized spatiotemporal data sequence is obtained, where each element is a three-dimensional data cube, completely recording the spatial distribution of all physical parameters within a single time window; the comprehensive risk index (R) is calculated using a hierarchical weighted fusion model, and the R values ​​of all grid units in the entire monitoring area are rendered in three dimensions using a color spectrum from blue (R≈0, low risk) to red (R≈1, extremely high risk) for mapping, ultimately generating a spatiotemporal feature cloud map. This cloud map is dynamically updated, intuitively displaying the spatiotemporal distribution and evolution of rockburst risk.

[0039] In one example of the present invention, in step S32, the expressions for the static structural risk index, the dynamic precursor risk index, and the comprehensive risk index are as follows: Static structural risk index The expression is: In the formula, K is the hazard risk index defined in static hazard zoning (K = K d + K b The theoretical threshold is [0, 12]. Through this calculation, the static risk is normalized to a dimensionless index in the interval [0, 1]. This index reflects the background risk determined by static factors such as geological structure and the physical and mechanical properties of coal and rock masses.

[0040] Dynamic precursor risk index The expression is as follows: In the formula, , and These are the weighting coefficients for acoustic-electrical, micro-seismic, and stress monitoring, respectively, and they satisfy... + + = 1; The acoustic-electric activity index; The microseismic intensity index; This is the stress concentration index. These weights can be adjusted specifically according to the specific disaster formation pattern of the mine (such as seismic-dominated or stress concentration-dominated).

[0041] The expression for the comprehensive risk index R is as follows: The comprehensive risk index R is obtained by integrating static structural risk and dynamic precursor risk, and the calculation formula is as follows: In the formula, Let ω be the static risk weighting factor, where 0 < ω < 1. This formula ensures the robustness of the method: in the high static risk zone, even if the dynamic signal is temporarily stable, the overall risk will not be underestimated; in the low static risk zone, drastic changes in the dynamic precursor signal can trigger effective risk warnings.

[0042] In one example of the invention, the acoustic-electric activity index The microseismic intensity index is and stress concentration index is The expression is as follows: Acoustic activity index The expression is: In the formula, N is the count of acoustic emission events (ringing count) within the monitoring unit. The cumulative energy released by acoustic emission; This refers to the real-time or average intensity of electromagnetic radiation. , , These are the threshold values ​​for each parameter preset based on historical data and on-site conditions; α, β, and γ are the acoustic emission event count weighting coefficient, acoustic emission cumulative energy weighting coefficient, and electromagnetic radiation intensity weighting coefficient, respectively, and satisfy α + β + γ = 1, used to adjust the contribution of each parameter; Microseismic Intensity Index The expression is as follows: In the formula, D represents the spatial density of microseismic events within the monitoring unit; To monitor the cumulative energy of microseismic events within the monitoring unit; , The preset thresholds for the spatial density and cumulative energy of microseismic events within the monitoring unit are defined; λ and μ are the weighting coefficients for the spatial density and cumulative energy of microseismic events, respectively, and satisfy λ + μ = 1. Stress Concentration Index The expression is as follows: In the formula, To monitor the absolute change in stress within the unit; To monitor the stress gradient within the unit; , The preset thresholds for monitoring the absolute change in stress and stress gradient within the unit are: η and ξ are the weighting coefficients for the absolute change and stress gradient, respectively, and satisfy η + ξ = 1.

[0043] In one example of the present invention, in step S40, as Figure 4 As shown, step S40 specifically includes the following steps: S41: Implement tiered early warning based on the real-time level of the comprehensive risk index R; when 0.6 ≤ R < 0.7, a yellow warning is triggered, prompting enhanced on-site monitoring and observation; when 0.7 ≤ R < 0.85, an orange warning is triggered, prompting the implementation of control measures such as local pressure relief; when R ≥ 0.85, a red warning is triggered, prompting immediate cessation of operations and evacuation of personnel. S42: Graded early warning based on the real-time trend of the comprehensive risk index R: During the risk zone expansion phase, an orange alert will be triggered when the following conditions are met; In the formula, Let t be the sum of the volumes of all grid cells with a comprehensive risk index R of not less than 0.6 at the current time t. for The total volume of high-risk areas under the same risk threshold before a time window; This represents the observation time span; a value of 2 or 3 data update cycles is recommended. The volume expansion rate threshold is a dimensionless constant. An orange alert is issued when the above conditions are met. During the phase of accelerated risk increase, for any sequence with a continuous risk index { , ..., , , A local area of ​​} will trigger an orange alert when the following conditions are met; In the formula, This represents the average comprehensive risk index of the region at the current time t. , , These are the average comprehensive risk indices for the first three consecutive time windows; During the multi-parameter precursor coordinated anomaly phase, within the same monitoring sub-region, the instantaneous increments of three dynamic sub-indices within the time window t are defined as: the change in the acoustic-electric activity index. Changes in microseismic intensity index and stress concentration index change ; Changes in the acoustic-electric activity index: Change in microseismic intensity index: Change in stress concentration index: In the formula, , , These are the acoustic-electric activity index, microseismic intensity index, and stress concentration index at the current time window t, respectively. , , The acoustic-electric activity index, microseismic intensity index, and stress concentration index are for the previous time window t-1. Establish the normal fluctuation range for each parameter, and denot the standard deviation over historical normal periods as . , , We use the 2σ principle from mathematical statistics to define abnormal fluctuations: Acoustic and electrical anomalies: Micro-seismic anomalies: Stress anomalies: If the change of a parameter If the change exceeds twice its own historical normal fluctuation range, it is considered an abnormal change. When it occurs simultaneously in the same time window and the same spatial region, the number of parameters that simultaneously meet its own abnormal conditions is recorded. If this count is 2, that is, two parameters have simultaneously experienced abnormal changes, an orange warning is issued. If this count is 3, that is, three parameters have simultaneously experienced abnormal changes, a red warning is issued.

[0044] According to a second aspect of the present invention, an integrated early warning system for detecting and monitoring rockburst disasters includes: The active detection module is configured to use parallel electrical resistivity and seismic wave CT to perform periodic or triggered scanning of the target area to achieve joint detection, acquire active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis of resistivity anomaly areas and wave velocity anomaly areas to generate static hazard zoning maps. The passive monitoring module is configured to optimize the spatial location of various dynamic monitoring sensors based on the delineated static hazard zoning map of rockburst. The sensors are preferentially and centrally located within and at the boundaries of high and medium hazard zones, so that the spatial layout of the various dynamic monitoring sensors matches the static hazard zoning map revealed by the static detection. The module passively receives ring counts, fractal b-values, wave velocity energy, frequency and intensity, seismic source, and stress signals generated by rock mass fracturing. It simultaneously acquires dynamic data reflecting the evolution of the dynamic stress field, forming passive monitoring dynamic stress field data. The data fusion module is configured to perform spatiotemporal fusion of the acquired active detection static structural field data and passive monitoring dynamic stress field data, and generate a spatiotemporal feature cloud map containing a comprehensive risk index through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. The graded early warning module is configured to establish a risk assessment model based on the dynamic evolution of the spatiotemporal feature cloud map, and to make early warning criteria based on the multi-parameter spatiotemporal evolution characteristics. The module compares the acquired real-time comprehensive risk index with the early warning criteria, and triggers a graded early warning when the risk index exceeds the threshold or the evolution trend meets the disaster criteria.

[0045] At the detection level, this early warning system innovatively employs parallel electrical resistivity and seismic wave CT to perform periodic or triggered scans of the target area. Through spatial coupling analysis of resistivity anomaly zones and wave velocity anomaly zones, it achieves for the first time a three-dimensional visualization of the large-scale rock mass structure field, generating a static hazard zoning map and providing a scientific basis for the optimized deployment of the monitoring network.

[0046] This early warning system combines actively detected structural field data with multi-source dynamic monitoring data such as acoustic emission, electromagnetic radiation, microseismic activity, and stress for joint inversion. It pioneered a comprehensive risk index calculation method based on a hierarchical weighted fusion model, generating a dynamic risk cloud map with spatiotemporal continuity. This forms an integrated technical system that is "from surface to point, combining dynamic and static aspects," overcoming the drawbacks of the disconnect between the two in traditional methods.

[0047] At the early warning decision-making level, this early warning system conducts early warnings based on the spatiotemporal evolution trends of multiple parameters, breaking through the limitations of traditional single threshold criteria. It constructs a dual early warning mechanism that includes both horizontal and trend criteria, realizing the transformation from local, static, and passive monitoring to global, dynamic, and proactive intelligent early warning. This makes the early warning more scientific and forward-looking, and significantly improves the accuracy and reliability of early warnings for large-scale rockbursts under deep and complex conditions.

[0048] In one example of the present invention, the active detection module includes: The coupling analysis unit is configured to perform spatial coupling analysis based on the three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT. The scoring unit is configured to assign scores based on the abnormal resistivity and wave velocity state of the impact region. The resistivity is assigned scores of 2, 4, and 6 points respectively, according to the low, medium, and high anomaly levels, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; The hazard delineation unit is configured as a resistivity anomaly zone and a wave velocity anomaly zone, and the overlap degree of these zones is determined in three-dimensional space. The overlapping areas are then delineated using the hazard index K=K. d +K bThe theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

[0049] In one example of the present invention, the data fusion module includes: The data coordinate unit is configured to mesh the target area in three dimensions and unify the active detection structural field data and the passive monitoring stress field data into the same spatiotemporal coordinate system. The risk index unit is configured to use a hierarchical weighted fusion model to calculate the comprehensive risk index R, which includes: static structural risk index, dynamic precursor risk index and comprehensive index; The three-dimensional visualization unit is configured to perform three-dimensional visualization rendering of the comprehensive risk index R value of all grid units in the entire monitoring area, using a color spectrum from blue (R≈0, low risk) to red (R≈1, extremely high risk) for mapping, and finally generating a spatiotemporal feature cloud map.

[0050] It should be noted that the integrated early warning system for detecting and monitoring rockburst disasters of the present invention can also perform any of the processing described in the previously described integrated early warning method for detecting and monitoring rockburst disasters, and the specific details are not repeated here.

[0051] The foregoing description, with reference to preferred embodiments, details an exemplary implementation of the integrated early warning method and system for detecting and monitoring rockburst disasters proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention, without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. An integrated early warning method for detecting and monitoring rockburst disasters, characterized in that, Includes the following steps: S10: The target area is periodically or triggered by parallel electrical resistivity and seismic wave CT to achieve joint detection, obtain active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis on resistivity anomaly area and wave velocity anomaly area to generate static hazard zoning map. S20: Based on the delineated static hazard zoning map of rockburst, optimize the spatial location of various dynamic monitoring sensors, prioritize and concentrate their placement within and at the boundaries of high and medium hazard zones, so that the spatial layout of various dynamic monitoring sensors matches the static hazard zoning map revealed by static detection, passively receive signals generated by rock mass fracturing, and simultaneously acquire dynamic data reflecting the evolution of dynamic stress field to form passive monitoring dynamic stress field data. S30: The acquired active detection static structural field data and passive monitoring dynamic stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. S40: Based on the dynamic evolution of the spatiotemporal feature cloud map, establish a risk assessment model, conduct early warning criteria based on multi-parameter spatiotemporal evolution characteristics, compare the obtained real-time comprehensive risk index with the early warning criteria, and trigger a graded early warning when the risk index exceeds the threshold or the evolution trend meets the disaster criteria.

2. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 1, characterized in that, Step S10 specifically includes the following steps: S11: Spatial coupling analysis based on three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT; S12: Based on the abnormal resistivity and wave velocity state of the impact area, scores are assigned to them respectively. The resistivity is assigned 2, 4, and 6 points according to the low, medium, and high anomaly levels, respectively, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; S13: The overlap between resistivity anomaly regions and wave velocity anomaly regions is determined in three-dimensional space, and the overlapping area is delineated with a hazard index K=K. d +K b The theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

3. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 1, characterized in that, In step S20, based on the defined static hazard zone map, the spatial locations of various dynamic monitoring sensors are optimized, specifically including: Within and along the boundaries of the designated hazardous area, acoustic emission sensors, electromagnetic radiation sensors, microseismic sensors, and stress sensors are deployed to obtain dynamic data within the hazardous area, forming a dynamic precursor monitoring network.

4. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 1, characterized in that, In step S30, the acquired actively detected structural field data and passively monitored stress field data are spatiotemporally fused, and a spatiotemporal feature cloud map containing a comprehensive risk index is generated through joint inversion. The specific steps include the following: S31: The target area is meshed in three dimensions, and the active detection structural field data and passive monitoring stress field data are unified into the same spatiotemporal coordinate system; S32: The comprehensive risk index R is calculated using a hierarchical weighted fusion model. The comprehensive risk index R includes: static structural risk index, dynamic precursor risk index and comprehensive index. S33: Perform three-dimensional visualization rendering of the comprehensive risk index R value of all grid units in the entire monitoring area, and finally generate a spatiotemporal feature cloud map.

5. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 4, characterized in that, In step S32, the expressions for the static structural risk index, the dynamic precursor risk index, and the comprehensive risk index are as follows: Static structural risk index The expression is: In the formula, K is the hazard risk index defined in static hazard zoning; Dynamic precursor risk index The expression is as follows: In the formula, , and These are the weighting coefficients for acoustic-electrical, micro-seismic, and stress monitoring, respectively, and they satisfy... + + = 1; The acoustic-electric activity index; The microseismic intensity index; It is the stress concentration index; The expression for the comprehensive risk index R is as follows: In the formula, ω is a static risk weighting factor, where 0 < ω < 1.

6. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 5, characterized in that, Acoustic activity index The microseismic intensity index is and stress concentration index is The expression is as follows: Acoustic activity index The expression is: In the formula, N is the count of acoustic emission events within the monitoring unit; The cumulative energy released by acoustic emission; This refers to the real-time or average intensity of electromagnetic radiation. , , These are the threshold values ​​for each parameter preset based on historical data and on-site conditions; α, β, and γ are the acoustic emission event count weighting coefficient, the acoustic emission cumulative energy weighting coefficient, and the electromagnetic radiation intensity weighting coefficient, respectively, and satisfy α + β + γ = 1; Microseismic Intensity Index The expression is as follows: In the formula, D represents the spatial density of microseismic events within the monitoring unit; To monitor the cumulative energy of microseismic events within the monitoring unit; , The preset thresholds for the spatial density and cumulative energy of microseismic events within the monitoring unit are defined; λ and μ are the weighting coefficients for the spatial density and cumulative energy of microseismic events, respectively, and satisfy λ + μ = 1. Stress Concentration Index The expression is as follows: In the formula, To monitor the absolute change in stress within the unit; To monitor the stress gradient within the unit; , The preset thresholds for monitoring the absolute change in stress and stress gradient within the unit are: η and ξ are the weighting coefficients for the absolute change and stress gradient, respectively, and satisfy η + ξ = 1.

7. The integrated early warning method for detection and monitoring of rockburst disasters according to claim 1, characterized in that, Step S40 specifically includes the following steps: S41: Implement tiered early warning based on the real-time level of the comprehensive risk index R; when 0.6 ≤ R < 0.7, a yellow warning is triggered, prompting enhanced on-site monitoring and observation; when 0.7 ≤ R < 0.85, an orange warning is triggered, prompting the implementation of local pressure relief and control measures; when R ≥ 0.85, a red warning is triggered, prompting immediate cessation of operations and evacuation of personnel. S42: Graded early warning based on the real-time trend of the comprehensive risk index R: During the risk zone expansion phase, an orange alert will be triggered when the following conditions are met; In the formula, This is the sum of the volumes of all grid cells with a comprehensive risk index R of not less than 0.6 at the current time t; for The total volume of high-risk areas under the same risk threshold before a time window; For the observation time span; This is the threshold for volume expansion rate; During the phase of accelerated risk increase, for any sequence with a continuous risk index { , ..., , , A local area of ​​} will trigger an orange alert when the following conditions are met; In the formula, This represents the average comprehensive risk index of the region at the current time t. , , These are the average comprehensive risk indices for the first three consecutive time windows; During the multi-parameter precursor coordinated anomaly phase, within the same monitoring sub-region, the instantaneous increments of three dynamic sub-indices within the time window t are defined as: the change in the acoustic-electric activity index. Changes in microseismic intensity index and stress concentration index change Establish the normal fluctuation range for each parameter, and denot the standard deviation over historical normal periods as follows: , , Abnormal fluctuations are defined using the 2σ principle from mathematical statistics; If the exponential change of a parameter exceeds twice its own historical normal fluctuation range, the change is considered an abnormal fluctuation. When it occurs simultaneously in the same time window and the same spatial region, the number of parameters that simultaneously meet its own abnormal conditions is recorded. If this count is 2, that is, two parameters have simultaneously experienced abnormal fluctuations, an orange warning is issued. If this count is 3, that is, three parameters have simultaneously experienced abnormal fluctuations, a red warning is issued.

8. An integrated early warning system for detecting and monitoring rockburst disasters, characterized in that, include: The active detection module is configured to use parallel electrical resistivity and seismic wave CT to perform periodic or triggered scanning of the target area to achieve joint detection, acquire active detection static structural field data reflecting rock resistivity and wave velocity, and perform spatial coupling analysis of resistivity anomaly areas and wave velocity anomaly areas to generate static hazard zoning maps. The passive monitoring module is configured to optimize the spatial location of various dynamic monitoring sensors based on the delineated static hazard zoning map of rockburst. The sensors are preferentially and centrally located within and at the boundaries of high and medium hazard zones, so that the spatial layout of the various dynamic monitoring sensors matches the static hazard zoning map revealed by the static detection. The module passively receives signals generated by rock mass fracturing and simultaneously acquires dynamic data reflecting the evolution of the dynamic stress field, forming passive monitoring dynamic stress field data. The data fusion module is configured to perform spatiotemporal fusion of the acquired active detection static structural field data and passive monitoring dynamic stress field data, and generate a spatiotemporal feature cloud map containing a comprehensive risk index through joint inversion, so as to realize the spatiotemporal identification of the risk of rockburst disaster. The graded early warning module is configured to establish a risk assessment model based on the dynamic evolution of the spatiotemporal feature cloud map, perform early warning judgment based on multi-parameter spatiotemporal evolution characteristics, compare the acquired real-time comprehensive risk index with the early warning judgment, and trigger graded early warning when the risk index exceeds the threshold or the evolution trend meets the disaster judgment.

9. The integrated early warning system for detection and monitoring of rockburst disasters according to claim 8, characterized in that, The active detection module includes: The coupling analysis unit is configured to perform spatial coupling analysis based on the three-dimensional distribution data of resistivity and wave velocity obtained by parallel electrical methods and seismic wave CT. The scoring unit is configured to assign scores based on the abnormal resistivity and wave velocity state of the impact region. The resistivity is assigned scores of 2, 4, and 6 points respectively, according to the low, medium, and high anomaly levels, denoted as K. d Wave velocity was assigned scores of 2, 4, and 6 respectively based on low, medium, and high anomaly levels, denoted as K. b ; The hazard delineation unit is configured as a resistivity anomaly zone and a wave velocity anomaly zone, and the overlap degree of these zones is determined in three-dimensional space. The overlapping areas are then delineated using the hazard index K=K. d +K b The theoretical threshold is [4, 12]. Low, medium and high risk areas are divided as follows: When 4≤K≤6, it is a low risk area with a weak risk of rock bursts. The stability of the surrounding rock should be checked regularly, normal operations should be maintained and attention should be paid to abnormal signals. When 6<K≤9, it is a medium risk area with a moderate risk of rock bursts. The monitoring frequency should be increased and a prevention and control plan should be formulated. When 9<K≤12, it is a high risk area. The monitoring data should be monitored in real time, and personnel should be evacuated immediately if necessary.

10. The integrated early warning system for detection and monitoring of rockburst disasters according to claim 8, characterized in that, The data fusion module includes: The data coordinate unit is configured to mesh the target area in three dimensions and unify the active detection structural field data and the passive monitoring stress field data into the same spatiotemporal coordinate system. The risk index unit is configured to use a hierarchical weighted fusion model to calculate the comprehensive risk index R, which includes: static structural risk index, dynamic precursor risk index and comprehensive index; The three-dimensional visualization unit is configured to perform three-dimensional visualization rendering of the comprehensive risk index R value of all grid units in the entire monitoring area, and finally generate a spatiotemporal feature cloud map.

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