Complex ore body dynamic monitoring and early warning method and system

Abstract

The application discloses a kind of complex ore body dynamic monitoring early warning method and system.Method includes: in ore body target layer construction directional monitoring drill hole and laying distributed multi-physical quantity sensing optical fiber;Before mining, background monitoring is carried out to determine the normal value of parameter, and maximum principal stress, displacement rate and microseismic frequency are collected in real time during mining;Real-time data and geological static risk base value are substituted into prediction module, and comprehensive disaster risk index is calculated;According to the threshold range where comprehensive disaster risk index is located, risk classification (low, medium, high) is carried out, and corresponding graded response measures (normal production, regional avoidance, production stop and personnel withdrawal) are triggered.System includes data acquisition, calculation and measure formulation module.The application realizes distributed, real-time, multi-parameter, quantitative evaluation and accurate graded early warning to ore body disaster risk, and improves the initiative and scientificity of mine safety management.

Description

Technical Field

[0001] This invention relates to the field of fiber optic monitoring technology, specifically to a method and system for dynamic monitoring and early warning of complex ore bodies. Background Technology

[0002] The production of gently dipping, thin to medium-thick ore bodies results in high phosphate ore loss rates, high dilution rates, and low recovery rates. On the one hand, due to the gentle dip angle of the phosphate ore body, the collapsed ore cannot be fully discharged at the bottom under its own weight, leading to difficulties in placement. On the other hand, the thinness of the ore body prevents the full utilization of efficient self-propelled equipment, resulting in a large amount of mining and cutting work and high production costs. Furthermore, the need to leave numerous permanent pillars to maintain roof safety during the mining of complex phosphate ore bodies leads to significant ore losses. In addition, during underground ore body mining, the stress balance of the original rock is disrupted, causing deformation and movement of the surrounding rock. With increased mining intensity and depth, and the effects of water erosion, weathering, and blasting vibrations, severe mine pressure manifestations such as pillar splitting and roadway surrounding rock deformation occur, potentially triggering large-scale roof collapses in goaf areas, stope collapses, ground subsidence, rock bursts, and other mine dynamic disasters, threatening safe underground phosphate mine production.

[0003] Currently, there are two main types of monitoring and early warning systems for complex phosphate ore body mining: one is geostress monitoring, which uses stress sensors and other equipment to measure the stress of the phosphate ore layer at points or in small areas to determine whether there is stress concentration in local areas of the phosphate ore layer; the other is monitoring of precursory phenomena of disasters, which uses sensors or instruments to measure physical quantities such as stress, deformation, temperature and vibration on the surface of the phosphate ore body during mining to measure the factors that cause mine disasters at points or in areas to determine the likelihood of mine geological disasters occurring during the mining of complex phosphate ore bodies.

[0004] Both of the above monitoring methods have some drawbacks, such as: Point-based or area-based measurements are insufficient to cover the entire range of mine hazards in phosphate rock layers, potentially missing some key monitoring points or areas, leading to untimely or inaccurate early warnings of mine hazards. Traditional sensors or instruments require extensive wiring and installation, increasing system complexity and cost, as well as the failure rate and maintenance difficulty. Traditional sensors or instruments are susceptible to electromagnetic interference, temperature and humidity changes, and mechanical damage, which may reduce the measurement accuracy and stability of the system. Traditional sensors or instruments can only measure single or a few physical quantities, failing to achieve multi-parameter and multi-dimensional monitoring of mine hazards and thus failing to comprehensively reflect the actual situation of mine hazards.

[0005] Therefore, a new monitoring method is needed to achieve fully distributed, real-time, dynamic, multi-parameter, and multi-dimensional monitoring of mine disasters, as well as early warning and location of mine disasters, thereby improving the safety and reliability of the mine disaster monitoring and early warning system. Summary of the Invention

[0006] The purpose of this application is to address the shortcomings of the aforementioned background technology and provide a method and system for dynamic monitoring and early warning of complex ore bodies.

[0007] The technical solution of this application is: a method for dynamic monitoring and early warning of complex ore bodies, comprising the following steps: Directional boreholes were drilled in the target layer of the ore body as monitoring boreholes; Fiber optic cables are laid inside the monitoring borehole and connected to a demodulator located outside the monitoring borehole. Before mining the ore body, a demodulator is activated for background monitoring to determine the normal reference values ​​for maximum principal stress, displacement rate, and microseismic frequency. During mining, the maximum principal stress, displacement rate, and microseismic frequency per unit time of the target layer are collected in real time. A prediction model is constructed by substituting the real-time collected parameters and the normal baseline values ​​into the prediction model to calculate the comprehensive disaster risk index. Based on a calculated comprehensive disaster risk index, graded treatment measures are formulated and implemented.

[0008] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the method for constructing a prediction model includes: constructing a prediction model according to the following formula.

[0009] in: R(t, x) —Comprehensive disaster risk index; R geo —Geophysical static risk baseline; α —Weighting coefficients for the static geological risk baseline; R stress —Increased risk from stress monitoring; β —Weighting coefficient for the incremental risk of stress monitoring; R disp —Increased risk from displacement monitoring; γ —Weighting coefficient for the incremental risk of displacement monitoring; R scis —Increased risk from microseismic monitoring; ε — Weighting coefficient for the incremental risk of microseismic monitoring. α + β + γ + ε =1 .

[0010] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the geological static risk baseline value is calculated according to the following formula.

[0011] in: R geo —Geophysical static risk baseline; F fault —Influence factors of faults or fracture zones; w 1 —Weighting coefficients of the influencing factors of faults or fracture zones; F stress —Ground stress concentration factor; w 2 —Weighting coefficient of the stress concentration factor; F rock —Rock mass strength factor; w 3 —Weighting coefficients for rock mass strength factors; F mining —Influencing factors of mining activities; w 4 —Weighting coefficients of mining-related influencing factors w 1 + w 2 + w 3 + w 4 =1.

[0012] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the stress monitoring risk increment is calculated using the following formula.

[0013] in: R stress —Increased risk from stress monitoring; δ real —Real-time measurement of the maximum principal stress value of the target layer; δ safe —Safety threshold; δ critical — Critical threshold.

[0014] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the displacement monitoring risk increment is calculated according to the following formula.

[0015] in: R disp —Increased risk from displacement monitoring; v —Real-time measured displacement rate of the target layer; v normal —Normal deformation rate; v critical —Critical deformation rate; Δd —Real-time measurement of displacement differences at key points in the target layer; Δd normal —Normal value for displacement difference; Δd critical —Critical value of displacement difference.

[0016] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the microseismic monitoring risk increment is calculated using the following formula.

[0017] in: R scis —Increased risk from microseismic monitoring; N —The frequency of micro-vibrations measured in real time per unit time; N normal —Normal value of micro-vibration frequency per unit time; N critical —The critical value of the frequency of microseismic events per unit time; λ 1 —Weighting coefficients for microseismic frequencies; b —Probability value of high-energy events; λ 2 —Weighting coefficients for the probability values ​​of high-energy events; E —Real-time measured microseismic energy release rate; E normal—Normal value of microseismic energy release rate; E critical —The critical value of microseismic energy release rate; λ 3 —Weighting coefficient for microseismic energy release rate.

[0018] According to the dynamic monitoring and early warning method for complex ore bodies provided in this application, the method for formulating treatment measures based on the calculated comprehensive disaster risk index includes: rating the risk of mine disasters during the ore body mining process, classifying the comprehensive disaster risk index from low to high as low risk, medium risk, and high risk, obtaining the corresponding risk level based on the calculated comprehensive disaster risk index, and determining that no measures may be taken when the risk is low, formulating treatment measures to avoid the risk area when the risk is medium, and formulating treatment measures to stop mining activities when the risk is high.

[0019] According to the method for dynamic monitoring and early warning of complex ore bodies provided in this application, the method of constructing directional boreholes as monitoring boreholes in the target layer of the ore body includes: taking the extension direction of the ore body panel roadway as the longitudinal direction and the horizontal direction perpendicular to the longitudinal direction as the transverse direction, constructing directional boreholes as monitoring boreholes in the panel roadway along the transverse direction, the monitoring boreholes extending transversely to the target layer, and multiple monitoring boreholes on the same side of the panel roadway being distributed longitudinally at intervals.

[0020] According to the method for dynamic monitoring and early warning of complex ore bodies provided in this application, the method of laying optical fibers in the monitoring borehole includes: uniformly laying distributed multi-physical quantity sensing optical fibers in the monitoring borehole using a directional drilling machine; injecting coupling material into the monitoring borehole under pressure to effectively couple the distributed multi-physical quantity sensing optical fibers with the wall of the monitoring borehole; after the coupling material in the monitoring borehole has completely solidified, connecting the distributed multi-physical quantity sensing optical fibers to a demodulator, and connecting the demodulator to a host computer on the ground via a cable.

[0021] This application also relates to a dynamic monitoring and early warning system for complex ore bodies, wherein the early warning system is used to execute the aforementioned dynamic monitoring and early warning method for complex ore bodies, including: The data acquisition module includes an optical fiber installed in the monitoring borehole of the target layer of the ore body and a demodulator connected to the optical fiber for data acquisition of the maximum principal stress value, displacement rate and micro-vibration frequency per unit time of the target layer. The calculation module is used to construct a prediction model and calculate the comprehensive disaster risk index by substituting the collected parameters into the prediction model. The measures formulation module formulates treatment measures based on a calculated comprehensive disaster risk index.

[0022] The advantages of this application are as follows: 1. The dynamic monitoring and early warning method for complex ore bodies in this application constructs a complete technical chain of monitoring, analysis, early warning, and decision-making, reflecting the systematic nature and practicality of the method; it is not only a monitoring method, but also a disaster risk management method, providing an executable operational procedure for mine safety management; this application explicitly uses the three key mechanical and geophysical parameters of "maximum principal stress," "displacement rate," and "microseismic frequency" as model inputs, which directly corresponds to the advantage of "multi-physical quantity detection" in the invention content, providing an accurate data foundation for subsequent comprehensive analysis; the final step of "formulating treatment measures" makes the early warning results have practical value, realizing a leap from perception to control; through the limitation of directional drilling and optical fiber, it has the advantages of distributed, contact-type, and high-precision monitoring; 2. The prediction model constructed in this application creatively integrates the static geological risk baseline value that does not change with mining with the dynamic monitoring risk increment that changes in real time with mining. This takes into account both the inherent geological conditions of the ore body (such as faults and lithology) and the real-time response of mining disturbances (stress redistribution, deformation, and fracturing), making the risk assessment more comprehensive and more in line with the actual situation. The prediction model adopts a linear weighted sum form, which intuitively reflects the superposition effect of different risk factors. By adjusting the weight coefficients, the model can be calibrated for the main disaster control factors in different mining areas, enhancing the universality and flexibility of the method. This prediction model quantifies multi-source heterogeneous data (geological data, stress, displacement, and microseismic data) into a unified comprehensive index, providing a scientific and quantitative calculation basis for accurate graded early warning. 3. This application decomposes traditionally qualitative or empirically judged geological factors into four specific assessable and quantifiable factors for calculating the static geological risk baseline, and integrates them through weighted summation. This makes geological risk assessment more precise than it is vague, which is a key step in building a high-precision transparent geological model and improves the accuracy of transparent geological models for complex mines. The four selected factors (structure, stress field, rock mass quality, and mining layout) are the most core static factors affecting the stability of mine rock masses, and their comprehensive coverage ensures the scientific nature and representativeness of the static risk baseline. 4. This application also defines a formula for calculating the risk increment of stress monitoring. When the measured stress is below the safety threshold, the risk increment is 0; when it is between the safety and critical thresholds, the risk increases linearly; when it exceeds the critical value, the risk is greater than 1 (after weighting). This is directly based on the criterion of "stress exceeding the limit leads to failure" in rock mechanics, realizing the risk transformation of key mechanical parameters; it quantifies the transition process from safety to criticality, cleverly mapping continuous stress measurements to continuous risk levels, avoiding binary judgments of either / or, and can more sensitively reflect the changing trend of risk. 5. This application also defines a formula for calculating the displacement monitoring risk increment, which considers both displacement rate and displacement difference as indicators. Monitoring both rate and difference simultaneously assesses deformation risk from both kinematic and geometric dimensions. High rate may indicate accelerated instability, while large displacement difference may indicate structural surface slippage or delamination. Combining the two can more comprehensively capture the precursor information of rock mass deformation and instability. The square root form of the sum of squares (similar to two-dimensional spatial distance) has the advantage that when either the rate or the difference increases abnormally, it will significantly increase the value of the displacement monitoring risk increment. This calculation method is more sensitive to abnormal signals and helps to provide early warning. 6. This application defines a formula for calculating the incremental risk of microseismic monitoring. This formula delves into the multidimensional information of microseismic data: frequency (activity frequency), b-value (proportion of large and small events, with a decrease in b-value indicating an increased risk of large earthquakes), and energy release rate (activity intensity). This comprehensive analysis far surpasses the traditional method of simply counting events, and can more profoundly reveal the evolution of ruptures and instability tendencies within rock masses. The introduction of the classic seismological parameter "b-value" for mine disaster early warning reflects the scientific depth of the method and is a typical manifestation of the mechanical (geophysical) criterion for rock mass instability, significantly improving the scientific nature and foresight of the early warning. 7. This application, by mapping the comprehensive risk index to three risk levels—low, medium, and high—and specifying concrete countermeasures for each level (no measures, avoidance, or cessation of operations), transforms the calculated abstract index into concrete and actionable measures, enabling the technological achievement to directly serve safety production command. This is crucial for the practicality of the early warning system. It embodies the advanced concept of graded response and precise prevention and control, adopting measures of varying intensity according to different risk levels (from observation to intervention and then to emergency braking), avoiding the economic losses caused by a "one-size-fits-all" shutdown and preventing safety risks arising from insufficient response. It achieves precise graded early warning of mine disasters, providing important support for precise disaster prevention and control. 8. This application describes a network method for constructing directional boreholes laterally in the mine roadway and arranging them at intervals longitudinally. By "extending laterally to the target layer" and "distributing longitudinally at intervals," a two-dimensional monitoring network covering the target layer of the ore body is constructed, which can capture the spatial distribution and evolution of risks, greatly reducing monitoring blind spots. It is a concrete realization of the advantages of distributed monitoring in spatial layout. The scheme closely fits the mine roadway layout and utilizes existing engineering conditions (mine roadway) for borehole construction, making it highly practical. The directional drilling technology ensures that optical fibers can be accurately laid to the target layer that needs to be monitored. 9. This application utilizes a distributed multi-physical quantity sensing fiber and employs a process of injecting coupling material to effectively couple it with the borehole wall. The pressurized injection of coupling material eliminates the air gap between the fiber and the rock mass, ensuring that signals such as strain and vibration are transmitted to the fiber efficiently and without distortion. This solves the key engineering challenge of poor coupling in contact monitoring, thus helping to improve the signal attenuation and low accuracy problems of traditional non-contact detection methods. The application clarifies the use of a distributed multi-physical quantity sensing fiber and a demodulator, ultimately connecting to a ground-based host, outlining a complete hardware system architecture and supporting the physical feasibility of the method. 10. This application also relates to an early warning system that maps abstract methods and steps into specific functional modules (data acquisition module, calculation module, and measure formulation module), clarifies the components of the system and their logical relationships, and embodies a complete solution combining software and hardware.

[0023] The dynamic monitoring and early warning method for complex ore bodies proposed in this application can accurately predict the mining risks of complex ore bodies, with comprehensive monitoring coverage. It solves the problems of signal attenuation and low accuracy of conventional monitoring, and realizes accurate graded early warning of mine disasters, which has great promotional value. Attached Figure Description

[0024] Figure 1 : Schematic diagram of the fiber optic arrangement structure of this application (side view); Figure 2 : Schematic diagram of the optical fiber arrangement structure of this application (top view); Wherein: 1—panel roadway; 2—monitoring borehole; 3—optical fiber; 4—demodulator. Detailed Implementation

[0025] The embodiments of this application are described in detail below, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] In the description of this application, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0028] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0029] This application relates to a dynamic monitoring and early warning method for complex ore bodies. The method involves drilling holes within the ore body to deploy optical fibers, which are then used to collect data from the target layer. The collected data is processed using a constructed prediction model to obtain a comprehensive disaster risk index. Based on this index, the risk of the ore body is analyzed to determine the risk level of the target layer, and corresponding risk mitigation measures are formulated. This early warning method accurately identifies risks during ore body mining, provides timely and accurate warnings, and offers comprehensive monitoring coverage. It solves the problems of signal attenuation and low accuracy associated with conventional monitoring methods, achieving precise graded early warning of mine disasters. Specifically, this application provides a basic method for dynamic monitoring and early warning of complex ore bodies; the method includes the following core steps: S1: In the target layer of the ore body, specific directional boreholes are drilled as monitoring boreholes; S2: Lay a dedicated optical fiber inside the aforementioned monitoring borehole and connect the optical fiber to a demodulator located outside the monitoring borehole to construct a sensor network; S3: Before mining the ore body, the demodulator is turned on to conduct background monitoring to determine the normal reference values ​​of various monitoring parameters; during the mining process, dynamic parameters such as the maximum principal stress value, displacement rate, and micro-vibration frequency per unit time of the target layer are collected in real time. S4: Construct a comprehensive prediction model by substituting the parameters collected in real time in step S3 and the static geological data into the model to calculate a quantitative comprehensive disaster risk index. S5: Based on the calculated comprehensive disaster risk index, formulate and implement corresponding risk management measures.

[0030] This application constitutes the logical framework of the entire early warning method; its principle lies in establishing a closed loop of perception, analysis, and decision-making. First, continuous, contact-based perception of a large area of ​​rock mass is achieved through a fiber optic sensor network within the borehole, acquiring multi-dimensional physical signals that truly reflect the mechanical state of the rock mass. Second, these discrete monitoring signals of different properties are integrated with static geological knowledge, and normalized and comprehensively analyzed through a structured mathematical model, transforming the complex rock mass state into an intuitive risk index. Finally, differentiated management measures are formulated based on this index, realizing the transformation from data to control instructions and guiding on-site safe production.

[0031] In the target area underground of the mine, technicians first determine the "target layers" (such as the ore layer to be mined or the key rock layers above it) that require key monitoring based on geological exploration data. Then, using a directional drilling rig, boreholes are drilled from the panel roadway towards the target layer to form monitoring holes. Reinforced distributed sensing optical fibers are laid inside the holes, and the boreholes are filled with special coupling material to ensure coupling between the optical fibers and the rock mass. The fiber optic leads are connected to a demodulator installed in the panel roadway chamber. After system initialization, the demodulator begins collecting data and transmitting it to the ground data center. The data center runs built-in early warning software (containing a predictive model) to automatically calculate the real-time risk index. Based on the risk level report output by the software, the mine dispatch center issues instructions according to the predetermined emergency plan. The early warning method proposed in this application seamlessly integrates advanced sensing technology, information processing technology and safety management practices. It not only proposes a monitoring technology, but also provides a complete solution from data acquisition to risk control. Step S3 clearly collects three key parameters: stress, displacement and microseismic activity, laying a data foundation for comprehensive analysis of multiple physical quantities. Steps S4 and S5 directly realize graded early warning based on mechanical criteria, enabling the early warning results to be directly used to guide production decisions, forming an effective risk control closed loop.

[0032] In some embodiments of this application, the prediction model involved in step S4 above has been optimized. Specifically, the prediction model is constructed according to the following formula:

[0033] in: R(t, x) —Comprehensive disaster risk index; R geo —Geophysical static risk baseline; α —Weighting coefficients for the geological static risk baseline, set values; R stress —Increased risk from stress monitoring; β—Weighting coefficient for stress monitoring risk increment, set value; R disp —Increased risk from displacement monitoring; γ —Weighting coefficient for displacement monitoring risk increment, set value; R scis —Increased risk from microseismic monitoring; ε—Weighting coefficient for incremental risk in microseismic monitoring, set value. α + β + γ + ε =1。

[0034] Calculate the geological static risk baseline value using the following formula.

[0035] in: R geo —Geophysical static risk baseline; F fault —Influence factor of fault or fracture zone, with a value range of 0~1, calculated based on fault distance, scale, etc. in geological structure model; w 1 —Weighting coefficients of the influencing factors of faults or fracture zones; F stress —The stress concentration factor, with a value range of 0 to 1, is determined based on the ratio of the maximum principal stress to the rock mass compressive strength in the stress model. w 2 —Weighting coefficient of the stress concentration factor; F rock —Rock mass strength factor, with a value range of 0 to 1, obtained based on the lithology model; w 3 —Weighting coefficients for rock mass strength factors; F mining —Mining-induced impact factor, with a value range of 0 to 1, taking into account the geometric stress concentration effects such as goaf distance and roadway intersections; w 4 —Weighting coefficients of mining-related influencing factors w 1 + w 2 + w 3 + w 4=1 .

[0036] w 1 、w 2 、 w 3 and w 4 The corresponding weighting coefficients can be determined through grey relational analysis or AHP method.

[0037] The stress monitoring risk increment is calculated using the following formula.

[0038] in: R stress —Increased risk from stress monitoring; δ real —The maximum principal stress value of the target layer is measured in real time, and the maximum principal stress value is measured in real time by the fiber optic sensor; δ safe —Safety threshold, a set value, such as 50% of the rock mass compressive strength; δ critical — Critical threshold, a set value, such as 80% of the rock mass compressive strength.

[0039] The displacement monitoring risk increment is calculated using the following formula.

[0040] in: R disp —Increased risk from displacement monitoring; v —Real-time measured displacement rate of the target layer, measured value, unit is mm / d; v normal —Normal deformation rate is obtained by monitoring the deformation rate in areas without disaster risk and calculating its average value; v critical —Critical deformation rate, a critical value determined based on historical abrupt events; Δd —Real-time measurement of displacement differences at key points in the target layer; Δd normal —The normal value of displacement difference is obtained by monitoring the displacement difference in areas without disaster risk and calculating its average value; Δd critical— Displacement difference critical value, a critical value determined based on historical abrupt events.

[0041] The microseismic monitoring risk increment is calculated using the following formula.

[0042] in: R scis —Increased risk from microseismic monitoring; N —The frequency of micro-seismic events per unit time measured in real time; N normal —The normal value of microseismic frequency per unit time is obtained by monitoring the microseismic frequency per unit time in areas without disaster risk and calculating its average value; N critical —The critical value of microseismic frequency per unit time, determined based on historical abrupt events; λ 1 —Weighting coefficient for microseismic frequencies, set value; b —The probability value of high-energy events is the b value in the Gutenberg-Richter law. A decrease in the b value indicates an increase in the probability of high-energy events. λ 2 —Weighting coefficients for the probability values ​​of high-energy events, set values; E —Real-time measured microseismic energy release rate, measured value; E normal —The normal value of the microseismic energy release rate is obtained by monitoring the microseismic energy release rate in areas without disaster risk and calculating its average value; E critical —The critical value of microseismic energy release rate, a critical value calibrated based on historical abrupt events; λ 3 —Weighting coefficient for microseismic energy release rate, set value. λ 1 + λ 2 + λ 3 =1 .

[0043] The weighting coefficients (α, β, γ, and ε); w 1 、w 2、 w 3 and w 4 ; λ 1 、λ 2 and λ 3 The initial weights were determined using the Analytic Hierarchy Process (AHP) combined with historical disaster cases in the mining area. The specific steps were as follows: Geological, mining, and safety experts were invited to conduct pairwise comparisons and scoring of various risk factors to construct a judgment matrix and calculate the initial weights for each factor; then, based on data from several successful and unsuccessful early warning cases of rock micro-fractures and deformation exceeding limits that occurred in the past year, the initial weights were inverted and optimized to finally determine a set of weight coefficients suitable for the mining area.

[0044] The security threshold ( δ safe ), critical threshold ( δ critical ), normal value ( v normal ,N normal (etc.) are determined in the following ways: In the first mining area of ​​the mine or in areas with similar geological conditions, a pre-mining background monitoring period of no less than 1 month is carried out, and the average value of the monitoring data is taken as the normal value by a certain percentage (such as 20%); Based on the rock mass compressive strength obtained from laboratory rock sample mechanical tests, 50% of it is taken as the safety threshold and 80% is taken as the critical threshold.

[0045] The early warning model in this embodiment is based on the risk composition theory, which decouples mine disaster risk into inherent static geological background risk and dynamic risk induced by mining disturbance. Static risk is the foundation, and dynamic risk is the increment. Through weighted summation, the model realizes the fusion and quantification of multi-source heterogeneous data. The calculation formula of each dynamic component is designed based on the corresponding rock mass instability mechanics or geophysical precursor criteria, such as: stress over-limit criterion, deformation acceleration and delamination criterion, and microseismic activity parameter (b-value, energy) anomaly criterion.

[0046] Pre-set model parameters in the early warning system software: 1) Weight coefficients α, β, γ, ε, w 1 ~w 4 , λ 1 ~λ 3 The thresholds can be determined through expert scoring or reverse analysis of historical cases; 2) various thresholds ( δ safe , δ critical ,v normal(etc.), based on rock mechanics tests, standards, and historical monitoring data of this mining area; during system operation, the software automatically reads static geological factor scores and real-time monitoring data streams from the database, calculates each component according to the above formula sequence, and finally synthesizes them. R(t, x) Value; this calculation can be performed on a timed basis (e.g., every minute) or triggered.

[0047] The greatest advantage of the early warning model in this embodiment lies in its ability to achieve refined, dynamic, and quantitative risk assessment. The model comprehensively considers four major categories of information: geology, stress, deformation, and rupture, completely overcoming the limitations and randomness of early warning based on a single physical quantity, and significantly improving the accuracy of early warnings. Each calculation formula has a clear mechanical or statistical basis, for example... R stress Based on strength theory, R scis Introduction b This classic seismological parameter elevates early warning from empirical judgment to theory-driven; the weighting coefficients and thresholds can be adjusted according to different mines and different mining stages, giving the model good adaptability and generalizability; finally, a normalized comprehensive index is output, providing accurate and objective numerical basis for risk level classification.

[0048] In a further embodiment of this application, step S5 above has been optimized, and the specific execution strategy is as follows: Based on the calculated comprehensive disaster risk index R(t, x) The risks of mine disasters are divided into three levels, and corresponding handling measures are formulated: Low risk level ( R < R low ,for example R low (If the value is 0.3): The system displays a blue warning, indicating that the current mining activity is within a safe and controllable range, and is in normal production without the need for special measures, but continuous monitoring is still required; Medium risk level ( R low ≤ R < R high ,for example R high (If the value is 0.6): The system displays a yellow warning, indicating a significant increase in risk and potential hidden dangers. The risk avoidance plan needs to be activated immediately, including adjusting the mining sequence, suspending entry into high-risk areas, strengthening manual inspections in the area, and implementing engineering interventions such as pressure relief drilling. High risk level ( R ≥ R high The system displays a red alert and issues an audible and visual alarm; if the possibility of a disaster is determined to be extremely high, an order to immediately cease mining activities is issued, all personnel in the risk area are forcibly evacuated, and the highest level of emergency response procedures are activated.

[0049] The principle of this embodiment is graded response and precise prevention and control; different risk levels correspond to different rock mass stability states and safety margins; low risk corresponds to a stable state; medium risk corresponds to instability precursors or local stress concentration states, with the rock mass system before the critical point; high risk corresponds to instability criticality or local damage states; according to the risk and response matching principle, measures with progressively increasing intensity are adopted, aiming to achieve the greatest safety benefits with the least production disruption cost.

[0050] In the early warning software platform, risk level thresholds are set in advance. R low and R high (For example, they can be set to 0.3 and 0.6 respectively. In practical applications, these values ​​are not limited to and can be adjusted according to needs); the software performs real-time calculations. R(t, x) After the value is calculated, it is automatically compared with the threshold to determine the current risk level. The platform interface color changes accordingly, and a text report containing the risk level, main contributing factors, and recommended measures is automatically generated and pushed to the dispatch center and relevant production terminals. For medium and high risks, the system can set up pop-up reminders that require dispatcher confirmation to ensure that instructions are issued.

[0051] This embodiment achieves precise alignment and dynamic optimization of early warning and control; it changes the past extensive management model of stopping production at the first sign of an anomaly, and adopts differentiated measures based on quantified risk levels, avoiding production losses caused by overreaction; the automated level determination and alarm push greatly shorten the time from risk identification to decision response, winning valuable time for disaster relief and avoidance; the measures are specific and clear (from observation to avoidance to cessation), forming a standardized emergency procedure that is easy for on-site personnel to understand and implement, truly transforming early warning information into safety assurance capabilities.

[0052] In other embodiments of this application, the engineering layout and fiber optic laying process of the monitoring network described above have been optimized, specifically, as follows: Figures 1 - 2 As shown, the arrangement of monitoring boreholes can be carried out in the following way: take the extension direction of the ore body panel roadway as the longitudinal direction (Y-axis); in the panel roadway 1, construct directional boreholes as monitoring boreholes 2 along the transverse direction (X-axis) perpendicular to the panel roadway 1; the monitoring boreholes 2 extend horizontally or inclined upwards to the interior or boundary of the target layer to be monitored; on one or both sides of the same panel roadway 1, construct multiple such monitoring boreholes 2 at certain intervals (such as 20-50 meters) along the longitudinal direction, thereby forming a monitoring network that penetrates laterally and covers longitudinally in the target layer.

[0053] The laying and coupling of optical fiber 3 can be carried out as follows: Select a distributed multi-physical quantity sensing optical fiber that integrates temperature, strain, and vibration sensing functions; slowly and evenly send the optical fiber with protective armor into the bottom of the monitoring borehole 2 through the drill rod of the directional drilling rig or a special pushing device; then, inject a special low-shrinkage, high-adhesion polymer coupling material (such as resin-based grout) into the monitoring borehole 2 under high pressure through the grouting pipe until the entire monitoring borehole 2 is filled; after the coupling material has completely solidified, connect the pigtail of optical fiber 3 led out from the monitoring borehole 2 to the demodulator 4 installed in the waterproof protective box; the demodulator 4 is connected to the host of the ground monitoring center through an industrial ring network or optical fiber link.

[0054] The gridded arrangement of directional boreholes in this embodiment ensures comprehensive coverage of the monitoring space and eliminates blind spots. The high-pressure injection coupling material process eliminates any gaps or weak interlayers between the fiber optic sensor and the rock mass, achieving rigid coupling. Micro-strain and weak vibrations (micro-vibrations) generated by the rock mass can be transmitted to the fiber optic sensing unit with almost no loss, ensuring that the acquired signals accurately reflect the mechanical behavior of the rock mass.

[0055] Before construction, the monitoring network was simulated and optimized using geological models and mining design drawings. During construction, measurement while drilling (MWD) technology was used to ensure that the borehole trajectory accurately reached the target layer. When laying optical fibers, the pushing speed needed to be controlled to prevent the fibers from knotting or bending excessively. Grouting coupling is a critical process, and the grouting pressure and grout volume need to be calculated to ensure that the annulus is full and dense. The borehole opening needs to be protected during the curing period. After all boreholes were completed, the entire network channel was tested and initially calibrated to establish the mapping relationship between the spatial coordinates of each sensing fiber and the monitoring data.

[0056] This embodiment solves the key engineering and technical challenges of high-precision monitoring implementation; the grid-based directional borehole network truly achieves transparent and continuous monitoring of the target area in three-dimensional space, perfectly overcoming the blind spot problem of point monitoring; the high-pressure injection coupling material process is the key to ensuring the performance of distributed fiber optic sensing technology; it changes the monitoring method from non-contact to full contact, fundamentally solving the inherent defects of traditional remote sensing and geophysical exploration methods such as large signal attenuation, weak anti-interference ability, and low accuracy, thereby significantly improving the detection accuracy; the reasonable layout and robust encapsulation protection ensure that the monitoring system can work stably for a long time in complex downhole environments.

[0057] The specific implementation method of the dynamic monitoring and early warning method for complex ore bodies in this application is as follows: Step 1: Geological Analysis and Monitoring Design Collect geological exploration, geostress measurement, rock mechanics test and mining design data of the mining area; based on this, determine the target layer for disaster early warning and use numerical simulation to analyze potential risk areas; based on the simulation results and roadway conditions, design the spatial layout (borehole azimuth, dip angle, spacing and depth) of the above-mentioned directional borehole monitoring network. Step 2: Construction and Installation of the Monitoring System According to the design, a series of directional boreholes were constructed in the tunnels of the panel area; In each borehole, distributed multi-physical quantity sensing optical fibers are laid, and special coupling materials are injected using a high-pressure grouting process to achieve rigid, full-section coupling between the optical fibers and the borehole wall rock mass. All the optical fibers brought out from the boreholes are connected to the demodulator acquisition station integrated in the tunnel, and the acquisition station is connected to the ground monitoring center through the communication network. Step 3: System Initialization and Threshold Calibration Before mining the ore body, the system will be activated to monitor background values ​​for no less than one month; based on the data during this period, statistical analysis will be conducted to determine the normal values ​​of various dynamic parameters. v normal ,N normal (etc.); combining rock mechanics test results, theoretical calculations, and industry standards, comprehensively determine various safety thresholds ( δ safe ) and critical threshold ( δ critical Organize geological, mining, and safety experts to conduct geological static risk factor analysis of the target area. F fault (etc.) scoring; using historical data or analogy, preliminarily determine the weight coefficients of various categories in the prediction model; Step 4: Real-time monitoring and risk fusion calculation Once the mining phase begins, the system operates 24 / 7 without interruption. Data acquisition: The demodulator acquires strain (converted to stress), temperature (for auxiliary correction), and vibration (for identifying micro-vibrations) signals distributed along the optical fiber in real time; Signal processing: A dedicated algorithm extracts micro-vibration events from vibration signals and calculates the frequency per unit time. N, b Value and energy release rate E ; calculate the displacement difference of key points from the strain signal Δd and displacement rate v and maximum principal stress δ real ; Model Calculation: The early warning software calls the comprehensive prediction model constructed in Example 2; first, it retrieves the pre-stored static risk baseline value from the database. R geo Then, the results obtained from real-time processing will be...δ real , v, Δd, N, b, E Substitute the parameters into the corresponding... R stress ,R disp ,R scis The formula is used for calculation; finally, the results are calculated according to the preset weights. α, β, γ, ε Weighted synthesis of current time and different spatial locations ( x Comprehensive disaster risk index R(t, x) ; Step 5: Risk Classification Visualization and Early Warning Issuance The software will calculate R(t, x) Value and preset level threshold ( R low ,R high The system compares data to automatically determine the risk level (low, medium, or high) of each monitored area. Using a 3D mine model as the base map, the system displays the spatial distribution of risks in real-time and dynamically through the monitoring center's large screen and remote terminals, using different colors (blue, yellow, and red). Once the risk level of an area changes to "yellow" or "red," the system immediately triggers the corresponding level of early warning information according to the aforementioned regulations and pushes a report containing the main causes of the risk and recommended measures to the relevant personnel. Step 6: Closed-loop response and model optimization Based on the received warning levels and reports, on-site production commanders strictly implement the corresponding handling measures in the contingency plan (normal operation, avoidance of intervention, or production stoppage and evacuation). At the same time, the response process for each warning and subsequent changes in the rock mass state (whether instability occurs) are recorded as cases in the database. The accumulated case data is used regularly (such as every quarter or after each mining area is completed) to back-analyze and optimize the weight coefficients and thresholds in the prediction model, so that the model becomes more and more in line with the actual situation of the mining area, forming a continuous improvement closed loop of monitoring, early warning, feedback, and optimization.

[0058] The dynamic monitoring and early warning method for complex ore bodies proposed in this application integrates three core advantages: networked high-precision monitoring using distributed optical fiber sensing, risk quantification assessment through the fusion of dynamic and static multi-source information, and precise intelligent response based on hierarchical classification. It is not only a monitoring method, but also an intelligent risk management system that deeply integrates mine geology, rock mechanics, information technology, and safety management, which can significantly improve the safe mining level and resource recovery efficiency of complex and difficult-to-mine ore bodies.

[0059] In addition, this application also relates to a dynamic monitoring and early warning system for complex ore bodies. Specifically, the early warning system of this application includes: The data acquisition module includes an optical fiber installed in the monitoring borehole of the target layer of the ore body and a demodulator connected to the optical fiber for data acquisition of the maximum principal stress value, displacement rate and micro-vibration frequency per unit time of the target layer. The calculation module is used to construct a prediction model and calculate the comprehensive disaster risk index by substituting the collected parameters into the prediction model. The measures formulation module formulates treatment measures based on a calculated comprehensive disaster risk index.

[0060] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic monitoring and early warning of complex ore bodies, characterized in that, Includes the following steps: Directional boreholes were drilled in the target layer of the ore body as monitoring boreholes; Fiber optic cables are laid inside the monitoring borehole and connected to a demodulator located outside the monitoring borehole. Before mining the ore body, the demodulator is turned on to conduct background monitoring in order to determine the normal reference values ​​of the maximum principal stress, displacement rate and micro-vibration frequency. During the mining process, the maximum principal stress value, displacement rate, and micro-vibration frequency per unit time of the target layer are collected in real time. A prediction model is constructed by substituting the real-time collected parameters and the normal baseline values ​​into the prediction model to calculate the comprehensive disaster risk index. Based on a calculated comprehensive disaster risk index, graded treatment measures are formulated and implemented.

2. The method for dynamic monitoring and early warning of complex ore bodies according to claim 1, characterized in that, The method for constructing the prediction model includes: constructing the prediction model according to the following formula. in: R(t,x) —Comprehensive disaster risk index; R geo —Geophysical static risk baseline; α —Weighting coefficients for the static geological risk baseline; R stress —Increased risk from stress monitoring; β —Weighting coefficient for the incremental risk of stress monitoring; R disp —Increased risk from displacement monitoring; γ —Weighting coefficient for the incremental risk of displacement monitoring; R scis —Increased risk from microseismic monitoring; ε — Weighting coefficient for the incremental risk of microseismic monitoring. α+β+γ+ε=1 .

3. The method for dynamic monitoring and early warning of complex ore bodies according to claim 2, characterized in that, Calculate the geological static risk baseline value using the following formula. in: R geo —Geophysical static risk baseline; F fault —Influence factors of faults or fracture zones; w 1 —Weighting coefficients of the influencing factors of faults or fracture zones; F stress —Ground stress concentration factor; w 2 —Weighting coefficient of the stress concentration factor; F rock —Rock mass strength factor; w 3 —Weighting coefficients of rock mass strength factor; F mining —Influencing factors of mining activities; w 4 —Weighting coefficients of mining-related influencing factors w 1 + w 2 + w 3 + w 4 =1.

4. The method for dynamic monitoring and early warning of complex ore bodies according to claim 2, characterized in that, The stress monitoring risk increment is calculated using the following formula. in: R stress —Increased risk from stress monitoring; δ real —Real-time measurement of the maximum principal stress value of the target layer; δ safe —Safety threshold; δ critical — Critical threshold.

5. The method for dynamic monitoring and early warning of complex ore bodies according to claim 2, characterized in that, The displacement monitoring risk increment is calculated using the following formula. in: R disp —Increased risk from displacement monitoring; v —Real-time measured displacement rate of the target layer; v normal —Normal deformation rate; v critical —Critical deformation rate; Δd —Real-time measurement of displacement differences at key points in the target layer; Δd normal —Normal value for displacement difference; Δd critical —Critical value of displacement difference.

6. The method for dynamic monitoring and early warning of complex ore bodies according to claim 2, characterized in that, The microseismic monitoring risk increment is calculated using the following formula. in: R scis —Increased risk from microseismic monitoring; N —The frequency of micro-vibrations measured in real time per unit time; N normal —Normal value of micro-vibration frequency per unit time; N critical —The critical value of the frequency of microseismic events per unit time; λ 1 —Weighting coefficients for microseismic frequencies; b —Probability value of high-energy events; λ 2 —Weighting coefficients for the probability values ​​of high-energy events; E —Real-time measured microseismic energy release rate; E normal —Normal value of microseismic energy release rate; E critical —The critical value of microseismic energy release rate; λ 3 —Weighting coefficient for microseismic energy release rate λ 1 +λ 2 +λ 3 =1 .

7. The method for dynamic monitoring and early warning of complex ore bodies according to claim 1, characterized in that, The method for formulating handling measures based on the calculated comprehensive disaster risk index includes: rating the risk of mine disasters during the mining process; classifying the comprehensive disaster risk index from low to high as low risk, medium risk, and high risk; obtaining the corresponding risk level based on the calculated comprehensive disaster risk index; not taking any measures when the risk is low; formulating handling measures to avoid the risk area when the risk is medium; and formulating handling measures to stop mining activities when the risk is high.

8. The method for dynamic monitoring and early warning of complex ore bodies according to claim 1, characterized in that, The method of constructing directional boreholes as monitoring boreholes in the target layer of the ore body includes: taking the extension direction of the ore body panel roadway as the longitudinal direction and the horizontal direction perpendicular to the longitudinal direction as the transverse direction, constructing directional boreholes as monitoring boreholes along the transverse direction in the panel roadway, the monitoring boreholes extending transversely to the target layer, and multiple monitoring boreholes on the same side of the panel roadway being distributed longitudinally at intervals.

9. A method for dynamic monitoring and early warning of complex ore bodies according to claim 8, characterized in that, The method for laying optical fibers in the monitoring borehole includes: uniformly laying distributed multi-physical quantity sensing optical fibers in the monitoring borehole using a directional drilling machine; injecting coupling material into the monitoring borehole under pressure to effectively couple the distributed multi-physical quantity sensing optical fibers with the wall of the monitoring borehole; after the coupling material in the monitoring borehole has completely solidified, connecting the distributed multi-physical quantity sensing optical fibers to a demodulator, and connecting the demodulator to a host computer on the ground via a cable.

10. A dynamic monitoring and early warning system for complex ore bodies, characterized in that, The early warning system is used to execute a dynamic monitoring and early warning method for complex ore bodies as described in any one of claims 1 to 9, including: The data acquisition module includes an optical fiber installed in the monitoring borehole of the target layer of the ore body and a demodulator connected to the optical fiber data, used to acquire the maximum principal stress value, displacement rate and micro-vibration frequency per unit time of the target layer. The calculation module is used to construct a prediction model and calculate the comprehensive disaster risk index by substituting the collected parameters into the prediction model. The measures formulation module formulates treatment measures based on a calculated comprehensive disaster risk index.

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