Method and System for Evaluating the Deformation Characteristics of Stope Rock Strata Based on Optical Fiber Monitoring Technology

Abstract

The present invention discloses a method and system for evaluating the deformation characteristics of rock formations in a mining area based on optical fiber monitoring technology, which relates to the field of rock mass monitoring technology. The system includes a data monitoring module, an edge computing module, a core processor, an early warning response module and an optimization management module. The data monitoring module creates an optical fiber layout model and collects optical fiber sensing data. The edge computing module then preliminarily analyzes the optical fiber sensing data to evaluate the degree of deformation of the mining area rock formation, thereby accurately capturing local deformation. Subsequently, a deep analysis model is constructed through the core processor to generate and output a mining area environment management plan and early warning prompt signals. The early warning response module then performs response prompt operations, and the optimization management module performs model optimization operations, thereby achieving timely and accurate response prompts to the deformation status of the mining area rock formation, providing strong guarantees for the safe and efficient operation of mining projects.

Description

Technical Field

[0001] The present invention relates to the field of rock mass monitoring technology, and in particular to a method and system for evaluating rock deformation characteristics in a stope based on optical fiber monitoring technology. Background Art

[0002] In coal mining, mining activities can cause overlying rock layers to collapse, sink, and crack, creating a direct threat to mine safety and personnel life, potentially leading to catastrophic accidents. Therefore, characterizing the degree of rock deformation in a stope not only helps engineers understand the impact of mining activities on the overlying rock layers, but also provides technical support for safe coal mining, ensuring mine safety and mining stability.

[0003] Existing methods for assessing rock deformation in mining sites based on fiber-optic monitoring technology suffer from insufficient refinement of feature monitoring and poor environmental adaptability. Because fiber-optic monitoring provides global deformation information, it struggles to capture localized deformations, such as at convergence points. This results in inadequately targeted rock deformation responses and inefficient management, hindering geological hazard monitoring and early warning during mining operations. Fiber-optic monitoring systems are sensitive to external environmental changes, such as temperature and humidity, which can affect measurement accuracy. Extreme weather conditions can degrade fiber performance, leading to poor environmental adaptability and insufficient sensing accuracy.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the defects of the existing technology in insufficient feature monitoring refinement and poor environmental adaptability, and to realize a real-time and accurate assessment method of rock formation deformation in the mining area based on optical fiber monitoring technology, thereby providing a strong guarantee for the safe and efficient operation of coal mines.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The method for evaluating rock deformation characteristics in a stope based on optical fiber monitoring technology includes the following steps:

[0008] Step 1: Create a fiber optic layout model and collect fiber optic sensor data: Based on the characteristics of the rock formation structure in the stope, arrange drill holes and bury sensor fibers. Then, use 3D physical modeling technology to create a fiber optic layout model, set a data acquisition cycle Tc, and regularly collect fiber optic sensor data. The fiber optic sensor data includes rock formation deformation parameters and fiber optic environmental parameters. The rock formation deformation parameters include the initial deformation data set and the mining deformation data set. The fiber optic environmental parameters include the temperature and humidity values of the rock formation.

[0009] Step 2: Construct a rock formation deformation analysis model and preliminarily analyze the fiber optic sensing data: Use the rock formation deformation analysis model to pre-process the rock formation deformation parameters, thereby obtaining the deformation characteristic data of the stope rock formation and evaluating the deformation state of the stope rock formation;

[0010] Step 3: Build a deep analysis model to analyze the stope rock deformation characteristic data and optical fiber environmental parameters: Through the deep analysis model, establish the influence function between the stope rock deformation characteristic data and optical fiber environmental parameters, and generate and output the stope environmental management plan and corresponding early warning prompt signals;

[0011] Step 4: Receive the warning prompt signal and perform corresponding response prompt operation: the response prompt operation is to edit the signal prompt text and perform a warning response to the abnormal rock formation environment;

[0012] Step 5: Receive the stope environment management plan and perform corresponding model optimization operations: The stope environment management plan is to finely regulate and manage the environmental status of the stope rock formation and optimize the design of the optical fiber layout model.

[0013] Furthermore, the specific process of constructing the rock deformation analysis model is as follows:

[0014] Divide the rock formation in the stope into N1 characteristic areas, mark any characteristic area as Z, and obtain the rock formation deformation parameters of the characteristic area Z. The rock formation deformation parameters include the initial deformation number set and the mining deformation number set;

[0015] The initial deformation data set includes the initial fiber frequency shift fa, Brillouin frequency shift BFSa and stress change of the sensing fiber before the mining work in the stope rock formation. ;

[0016] Set and mark the Brillouin constant as φ, and calculate the initial Brillouin frequency shift BFSa by combining the initial fiber frequency shift fa with the Brillouin constant φ; set and mark the variation coefficient of Brillouin frequency shift and stress as ω through the sensing fiber material, and obtain the initial stress change of the sensing fiber by combining the initial Brillouin frequency shift BFSa with the variation coefficient ω. ;

[0017] The mining deformation data set includes the fiber frequency shift fb, Brillouin frequency shift BFSb and stress variation periodically collected by the sensing fiber during the mining process of the working face. ;

[0018] Set n1 data acquisition cycles for timed acquisition. By combining the fiber frequency shift fb of any data acquisition cycle Tc with the Brillouin constant φ, the Brillouin frequency shift BFSb of that cycle is measured and obtained. Then, the Brillouin frequency shift BFSb of that cycle is combined with the variation coefficient ω to obtain the stress change of the sensing fiber in that cycle. ;

[0019] The rock formation deformation parameters are preprocessed through the rock formation deformation analysis model to obtain the deformation characteristic data of the stope rock formation and evaluate the deformation degree of the stope rock formation.

[0020] Furthermore, the specific process of preprocessing the rock formation deformation parameters through the rock formation deformation analysis model is as follows:

[0021] The rock deformation data matrix Vz of the characteristic area Z is constructed by using the rock deformation parameters of any characteristic area Z of the stope rock:

[0022] ;

[0023] Extract the row vector of stress variation of the rock formation deformation data matrix Vz and mark it as Vzh3;

[0024] By analyzing the row vector Vzh3 of the stress variation, the optical fiber stress coefficient Gxsl of the characteristic area Z is obtained;

[0025] ;

[0026] Any group of row vectors of the rock formation deformation data matrix Vz is marked as Vzhm, where m is the group number of the row vector, and any vector data of the row vector Vzhm is marked as Em. The change trend coefficient Bhqs of the row vector group Vzhm is obtained by the difference between two adjacent vector data of the row vector Vzhm;

[0027] By performing trend analysis on the row vectors of the rock deformation data matrix Vz, the trend coefficients of fiber frequency shift, Brillouin frequency shift and stress variation are obtained and marked as ρ1, ρ2 and ρ3 respectively;

[0028] The optical fiber stress coefficient Gxsl of the characteristic region Z is combined with the change trend coefficient ρ1 of the optical fiber frequency shift, the change trend coefficient ρ2 of the Brillouin frequency shift, and the change trend coefficient ρ3 of the stress variation to comprehensively obtain the deformation degree evaluation index Zbx of the characteristic region Z. Then, an evaluation interval of the deformation degree evaluation index Zbx is set, and the deformation degree of the characteristic region Z is evaluated by comparing the intervals.

[0029] The rock formation deformation data matrix Vz and deformation degree evaluation index Zbx of N1 characteristic areas Z are integrated and marked as rock formation deformation characteristic data.

[0030] Furthermore, the specific process of building a deep analysis model is as follows:

[0031] The optical fiber layout model includes N0 segments of sensing optical fibers. The sensing optical fibers in the characteristic area Z are marked as J. The optical fiber environment parameters of the characteristic area Z are obtained.

[0032] Extract n0 characteristic points of the sensing optical fiber J, mark any characteristic point of the sensing optical fiber J as i, obtain the optical fiber environmental parameters of the characteristic point i, and mark the temperature value and humidity value of the characteristic point i as Wi and Di respectively;

[0033] By measuring the temperature values Wi and humidity values Di of n0 characteristic points i of the sensing optical fiber J, the abnormal characteristic points of the sensing optical fiber are judged respectively, thereby obtaining the temperature evaluation coefficient Xw and humidity evaluation coefficient Xd of the characteristic area Z;

[0034] Constructing the influence function F0 between the deformation degree evaluation index Zbx of the feature area Z and the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd;

[0035] Substitute the fiber environment parameters of the characteristic area into the influence function F0 to obtain the deformation degree evaluation index Zbx of the characteristic area Z. Then, the overall deformation degree index ZT of the stope rock formation is obtained comprehensively through the deformation degree evaluation index Zbx of N1 characteristic areas.

[0036] The evaluation interval of the overall deformation index ZT of the stope rock strata is set to [y1, y2], and the stope environmental management plan and corresponding early warning signals are generated through interval comparison.

[0037] Furthermore, the specific process of determining abnormal feature points of the sensing optical fiber is as follows:

[0038] Set a standard interval Qw for the temperature value Wi. When the temperature value Wi is higher than the standard interval Qw, the feature point i is judged to be too high in temperature, and the number of feature points with too high temperature is obtained and marked as Nf1. When the temperature value Wi is lower than the standard interval Qw, the feature point i is judged to be too low in temperature, and the number of feature points with too low temperature is obtained and marked as Nf2.

[0039] Set the standard interval Qd of the humidity value Di. When the humidity value Di is higher than the standard interval Qd, the feature point i is judged to have too high humidity, and the number of feature points with too high humidity is obtained and marked as Nf3; when the humidity value Di is lower than the standard interval Qd, the feature point i is judged to have too low humidity, and the number of feature points with too low humidity is obtained and marked as Nf4.

[0040] Furthermore, the specific generation process of the early warning signal is as follows:

[0041] When the overall deformation index ZT of the rock formation in the stope is less than y1, a No. 1 warning signal is generated;

[0042] When the overall deformation index ZT of the stope rock strata is in the evaluation interval [y1, y2], refined early warning analysis and targeted processing are carried out: the risk threshold of the deformation evaluation index Zbx of the characteristic area Z is set to Y0. When the deformation evaluation index Zbx of the characteristic area Z is higher than the risk threshold Y0, the second early warning signal of the characteristic area Z is generated;

[0043] When the overall deformation index ZT of the rock formation in the mining area is greater than y2, a No. 3 early warning signal is generated.

[0044] Furthermore, the refined control management operation is to control abnormal temperature and humidity, and set the risk thresholds of temperature assessment coefficient Xw and humidity assessment coefficient Xd respectively and mark them as U1 and U2 respectively;

[0045] When the temperature assessment coefficient Xw is higher than the risk threshold U1, the temperature of the characteristic area is regulated and managed; when the humidity assessment coefficient Xd is higher than the risk threshold U2, the humidity of the characteristic area is regulated and managed.

[0046] A stope rock formation deformation characteristic assessment system based on optical fiber monitoring technology includes a data monitoring module, an edge computing module, a core processor, an early warning response module, and an optimization management module. The data monitoring module includes an optical fiber monitoring submodule and an environmental monitoring submodule. The data monitoring module, the edge computing module, the core processor, the early warning response module, and the optimization management module are communicatively connected to each other. The system applies the above-mentioned stope rock formation deformation characteristic assessment method based on optical fiber monitoring technology.

[0047] The data monitoring module is used to collect optical fiber sensing data: the optical fiber sensing data includes rock formation deformation parameters and optical fiber environmental parameters, which are obtained through the optical fiber monitoring submodule and the environmental monitoring submodule respectively;

[0048] The edge computing module is used to preliminarily analyze the fiber optic sensing data. It pre-processes the rock deformation parameters by building a rock deformation analysis model, thereby obtaining the deformation characteristic data of the stope rock formation and evaluating the deformation degree of the stope rock formation.

[0049] The core processor is used to build a depth analysis model: the depth analysis model analyzes the influence relationship between the stope rock deformation characteristic data and the optical fiber environmental parameters, and generates and outputs the stope environmental management plan and corresponding early warning prompt signals;

[0050] The early warning response module is used to receive early warning prompt signals and perform corresponding response prompt operations;

[0051] The optimization management module is used to receive the stope environment management plan and perform corresponding model optimization operations.

[0052] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0053] The present invention uses a data monitoring module to create a fiber optic layout model and collect fiber optic sensor data. The edge computing module then preliminarily analyzes the fiber optic sensor data to evaluate the deformation state of the stope rock strata. The core processor constructs a depth analysis model, establishes an influence function between the stope rock strata deformation characteristic data and the fiber optic environmental parameters, generates and outputs a stope environmental management plan and an early warning prompt signal. The early warning prompt signal is received and a response prompt operation is performed through the early warning response module, and the stope environmental management plan is received and a model optimization operation is performed through the optimization management module. This realizes real-time and accurate evaluation of the stope rock strata deformation state based on fiber optic monitoring technology, providing a strong guarantee for the safe and efficient operation of the mining project.

[0054] Among them, the present invention constructs a rock formation deformation analysis model to preliminarily analyze the fiber optic sensing data, evaluate the deformation state of the rock formation in the mining area, obtains the corresponding deformation degree evaluation index of each characteristic area of the rock formation in the mining area by calculation, and finely evaluates the deformation degree of the area to achieve accurate capture of local deformation; by editing the signal prompt text and responding to rock formation environmental anomalies, timely and accurate response prompts are achieved; through the mining area environmental management plan, the environmental state of the rock formation in the mining area is finely regulated and managed, and the optical fiber layout model is optimized and designed to improve the system's anti-interference ability to environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram showing the steps of the overall process of the present invention is shown;

[0056] Figure 2 A connection diagram of the system modules of the present invention is shown. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] Example 1:

[0059] like Figure 1-Figure 2 As shown in FIG, the method for evaluating deformation characteristics of stope rock formations based on optical fiber monitoring technology includes the following steps:

[0060] S1, creating a fiber optic layout model and collecting fiber optic sensor data: Drill holes are arranged and sensor fibers are buried based on the rock structure characteristics of the stope. 3D physical modeling technology is then used to create a fiber optic layout model, and a data collection cycle Tc is set to periodically collect fiber optic sensor data.

[0061] The optical fiber sensing data includes rock formation deformation parameters and optical fiber environmental parameters: the rock formation deformation parameters include the initial deformation data set and the mining deformation data set; the optical fiber environmental parameters include the temperature and humidity values of the rock formation;

[0062] An optical fiber temperature sensor and an optical fiber humidity sensor are sheathed on the outside of the sensing optical fiber, and the temperature value and humidity value of the rock layer where the sensing optical fiber is located are respectively collected by the optical fiber temperature sensor and the optical fiber humidity sensor;

[0063] S2, constructing a rock formation deformation analysis model to preliminarily analyze the fiber optic sensing data: pre-processing the rock formation deformation parameters through the rock formation deformation analysis model to obtain the deformation characteristic data of the stope rock formation and evaluate the deformation degree of the stope rock formation;

[0064] The specific process of constructing the rock deformation analysis model is as follows:

[0065] S2-1, dividing the stope rock layer into N1 characteristic areas, marking any characteristic area as Z, and obtaining the rock layer deformation parameters of characteristic area Z, which include an initial deformation number set and a mining deformation number set;

[0066] Brillouin frequency shift refers to the frequency shift phenomenon caused by the interaction between light signals and acoustic waves transmitted in optical fibers. Using Brillouin optical frequency domain analysis (BOFDA) technology, we can collect the initial fiber frequency shift of the sensing fiber along the length of the fiber, and measure the distribution of the Brillouin frequency shift and stress changes, thereby preliminarily analyzing and obtaining rock formation deformation parameters.

[0067] The initial deformation data set includes the initial fiber frequency shift fa, Brillouin frequency shift BFSa and stress change of the sensing fiber before the mining work in the stope rock formation. ;

[0068] Set and mark the Brillouin constant as φ, and calculate the initial Brillouin frequency shift BFSa by combining the initial fiber frequency shift fa with the Brillouin constant φ: ; Set and mark the Brillouin frequency shift and stress variation coefficient as ω by sensing optical fiber material, and obtain the initial stress variation of sensing optical fiber by combining the initial Brillouin frequency shift BFSa with the variation coefficient ω : ;

[0069] The mining deformation data set includes the fiber frequency shift fb, Brillouin frequency shift BFSb and stress variation periodically collected by the sensing fiber during the mining process of the rock formation in the mining field. ;

[0070] Set n1 data acquisition cycles for timed acquisition. By combining the fiber frequency shift fb of any data acquisition cycle Tc with the Brillouin constant φ, the Brillouin frequency shift BFSb of that cycle is calculated: Then, the stress variation of the sensing fiber in this cycle is obtained by combining the Brillouin frequency shift BFSb of this cycle with the variation coefficient ω. : ;

[0071] S2-2, pre-process the rock formation deformation parameters through the rock formation deformation analysis model to obtain the deformation characteristic data of the stope rock formation and evaluate the deformation degree of the stope rock formation. The specific process is as follows:

[0072] The rock deformation data matrix Vz of the characteristic area Z is constructed by using the rock deformation parameters of any characteristic area Z of the stope rock:

[0073] ;

[0074] Extract the row vector of stress variation from the rock deformation data matrix Vz and label it as Vzh3: ;

[0075] By analyzing the row vector Vzh3 of the stress variation, the fiber stress coefficient Gxsl in the characteristic area Z is obtained:

[0076] , where u is the serial number of the data collection cycle, is the stress variation of the data acquisition period corresponding to the current time node; when the stress variation The higher the stress variation that is collected periodically The change in stress from the initial The higher the difference between them, the higher the optical fiber stress coefficient Gxsl. The higher the real-time stress level of the assessment feature area Z and the larger the stress variation amplitude in the historical period, indicating that the regional rock formation deformation is high. The optical fiber stress coefficient obtained by the optical fiber indicates the development of rock formation cracks.

[0077] ;

[0078] Any group of row vectors of the rock deformation data matrix Vz is marked as Vzhm, where m is the group number of the row vector, and any vector data of the row vector Vzhm is marked as Em. The change trend coefficient Bhqs of the row vector group Vzhm is obtained by the difference between two adjacent vector data of the row vector Vzhm:

[0079] ; Wherein, w is the sequence number of the vector data Em; when the change trend coefficient Bhqs is positive and the higher it is, the higher the overall growth trend of the evaluated row vector Vzhm is; when the change trend coefficient Bhqs is negative and the lower it is, the higher the overall downward trend of the evaluated row vector Vzhm is; when the change trend coefficient Bhqs approaches 0, the higher the stability of the evaluated row vector Vzhm is;

[0080] By performing trend analysis on the three groups of row vectors of the rock deformation data matrix Vz, the trend coefficients of fiber frequency shift, Brillouin frequency shift and stress variation are obtained and marked as ρ1, ρ2 and ρ3 respectively;

[0081] Among them, the change trend coefficient ρ1 of the optical fiber frequency shift is obtained by measuring the first group of row vectors of the rock deformation data matrix Vz; the change trend coefficient ρ2 of the Brillouin frequency shift is obtained by measuring the second group of row vectors of the rock deformation data matrix Vz; and the change trend coefficient ρ3 of the stress change is obtained by measuring the third group of row vectors of the rock deformation data matrix Vz.

[0082] S2-3, by combining the fiber stress coefficient Gxsl of the characteristic region Z with the fiber frequency shift trend coefficient ρ1, the Brillouin frequency shift trend coefficient ρ2, and the stress variation trend coefficient ρ3, the deformation degree evaluation index Zbx of the characteristic region Z is comprehensively obtained:

[0083] ;

[0084] Among them, α0, α1, α2, and α3 are weight coefficients of the optical fiber stress coefficient Gxsl, the change trend coefficient of the optical fiber frequency shift ρ1, the change trend coefficient of the Brillouin frequency shift ρ2, and the change trend coefficient of the stress variation ρ3, respectively, and α0, α1, α2, and α3 are all greater than 0. The weight coefficients are preset and obtained after measuring a large amount of experimental data. When the optical fiber stress coefficient Gxsl, the change trend coefficient of the optical fiber frequency shift ρ1, the change trend coefficient of the Brillouin frequency shift ρ2, and the change trend coefficient of the stress variation ρ3 are higher, the deformation degree evaluation index Zbx is higher, and the deformation degree of the evaluation characteristic area Z is higher. The deformation degree of the characteristic area Z is comprehensively evaluated based on the stress condition of the optical fiber and the change trend of the parameters to ensure the comprehensiveness of data processing and deformation evaluation;

[0085] S2-4, setting an evaluation interval for the deformation degree evaluation index Zbx, and evaluating the deformation degree of the feature area Z by comparing the intervals;

[0086] The rock formation deformation data matrix Vz and its deformation degree evaluation index Zbx of N1 characteristic areas Z are integrated and marked as rock formation deformation characteristic data;

[0087] By calculating and obtaining the corresponding deformation degree assessment index Zbx of N1 characteristic areas of the stope rock formation, the deformation degree of the area can be refined and the local deformation can be accurately captured;

[0088] S3, building a deep analysis model to analyze the stope rock deformation characteristic data and optical fiber environmental parameters: The deep analysis model is used to build an influence function between the stope rock deformation characteristic data and the optical fiber environmental parameters, thereby generating and outputting a stope environmental management plan and corresponding early warning prompt signals;

[0089] The specific process of building a deep analysis model is as follows:

[0090] S3-1, the optical fiber layout model includes N0 segments of sensing optical fibers, the sensing optical fibers in characteristic area Z are marked as J, and the optical fiber environment parameters of characteristic area Z are obtained;

[0091] Extract n0 characteristic points of the sensing optical fiber J, mark any characteristic point of the sensing optical fiber J as i, obtain the optical fiber environmental parameters of the characteristic point i, and mark the temperature value and humidity value of the characteristic point i as Wi and Di respectively;

[0092] By sensing the temperature values Wi and humidity values Di of n0 characteristic points i of the optical fiber J, the temperature evaluation coefficient Xw and humidity evaluation coefficient Xd of the characteristic area Z are obtained;

[0093] S3-101, set a standard interval Qw for the temperature value Wi. When the temperature value Wi is higher than the standard interval Qw, the feature point i is determined to be too high in temperature, and the number of feature points with too high temperatures is obtained and marked as Nf1. When the temperature value Wi is lower than the standard interval Qw, the feature point i is determined to be too low in temperature, and the number of feature points with too low temperatures is obtained and marked as Nf2.

[0094] Then the temperature evaluation coefficient Xw is: , where μ1 is the conversion coefficient of the temperature value and μ1 is in the interval (0, 1). When the total number of characteristic points of temperature anomaly The closer it is to 0, the closer the temperature evaluation coefficient Xw is to the average value of the temperature values Wi of n0 feature points i; when the total number of feature points with abnormal temperature is The higher it is, the lower the temperature assessment coefficient Xw is, and the higher the assessed temperature risk level is;

[0095] S3-102, set a standard interval Qd for the humidity value Di. When the humidity value Di is higher than the standard interval Qd, the feature point i is determined to have too high humidity. The number of feature points with too high humidity is obtained and marked as Nf3. When the humidity value Di is lower than the standard interval Qd, the feature point i is determined to have too low humidity. The number of feature points with too low humidity is obtained and marked as Nf4.

[0096] Then the humidity assessment coefficient Xd: , where μ2 is the conversion coefficient of humidity value and μ2 is in the interval (0, 1). When the total number of characteristic points of humidity anomaly is The closer it is to 0, the closer the humidity assessment coefficient Xd is to the average humidity value Di of n0 feature points i; when the total number of feature points with abnormal humidity is The higher it is, the lower the humidity assessment coefficient Xd is, and the higher the degree of humidity risk is;

[0097] S3-2, constructing the influence function F0 between the deformation degree evaluation index Zbx of the characteristic region Z and the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd:

[0098] ;

[0099] Among them, β1 and β2 are the logarithmic bases of the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd, and the preset β1 and β2 are both greater than 1; r1 and r2 are the adjustment coefficients of the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd, and the adjustment coefficients r1 and r2 are to ensure and A preset constant value that is always greater than 1; when the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd are higher, the deformation degree evaluation index Zbx indicates that the optical fiber environmental parameters have a greater impact on the deformation degree of the characteristic region Z;

[0100] S3-3, the optical fiber environment parameters of the characteristic area are substituted into the influence function F0 to obtain the deformation degree evaluation index Zbx of the characteristic area Z. Then, the overall deformation degree index ZT of the stope rock formation is obtained comprehensively through the corresponding deformation degree evaluation index Zbx of the N1 characteristic areas: ;

[0101] in, is the weight factor of the deformation degree evaluation index Zbx of the characteristic area Z. When the deformation degree evaluation index Zbx of the N1 characteristic areas Z is higher, the overall deformation degree index ZT of the stope rock formation is higher, and the overall deformation degree of the stope rock formation is evaluated to be higher.

[0102] S3-4, setting the evaluation interval of the overall deformation index ZT of the stope rock strata to [y1, y2], and generating a stope environmental management plan and corresponding early warning signals through interval comparison;

[0103] When the overall deformation index ZT of the stope rock stratum is less than y1, a No. 1 warning signal is generated, indicating that the overall deformation index of the stope rock stratum is low and no treatment is performed at this time;

[0104] When the overall deformation index ZT of the stope rock stratum is in the evaluation interval [y1, y2], refined early warning analysis and targeted processing are carried out: the risk threshold of the deformation evaluation index Zbx of the characteristic area Z is set to Y0. When the deformation evaluation index Zbx of the characteristic area Z is higher than the risk threshold Y0, the second early warning prompt signal of the characteristic area Z is generated, and the characteristic area Z needs to be processed in a targeted manner; on the contrary, when the deformation evaluation index Zbx of the characteristic area Z is not higher than the risk threshold Y0, the characteristic area Z does not need to be processed;

[0105] When the overall deformation index ZT of the stope rock stratum is greater than y2, a No. 3 warning signal is generated, indicating that the overall deformation of the stope rock stratum is too high and comprehensive treatment of the stope rock stratum is required.

[0106] S4, receiving the early warning prompt signal and performing corresponding response prompt operation: the response prompt operation includes editing the signal prompt text and performing an early warning response to the abnormal rock formation environment;

[0107] When receiving the No. 1 warning signal, edit the text "the overall deformation degree of the rock formation in the stope is low" and do nothing at this time;

[0108] When a No. 2 warning signal is received, the text "The risk level of characteristic area Z is relatively high" is edited to prompt management personnel to take targeted measures for characteristic area Z, including suspending rock mining in the area;

[0109] When a No. 3 warning signal is received, the text "the overall deformation degree of the stope rock stratum is too high" is edited to prompt management personnel to carry out comprehensive treatment of the stope rock stratum, including completely suspending mining work in the stope rock stratum;

[0110] S5, receiving the stope environment management plan and performing corresponding model optimization operations: The stope environment management plan includes fine-grained control and management of the environmental status of the stope rock formation and optimized design of the optical fiber layout model;

[0111] The refined control and management operation is mainly to control abnormal temperature and humidity, setting the risk thresholds of temperature assessment coefficient Xw and humidity assessment coefficient Xd respectively and marking them as U1 and U2 respectively;

[0112] When the temperature assessment coefficient Xw is higher than the risk threshold U1, the temperature of the characteristic area is regulated and managed; when the humidity assessment coefficient Xd is higher than the risk threshold U2, the humidity of the characteristic area is regulated and managed. Specific regulation and management operations are designed based on actual conditions, for example: strengthening ventilation and heat dissipation in the mine tunnels, or installing air conditioning equipment to control temperature and humidity;

[0113] Optimize the design of the optical fiber layout model. For example, choose high-performance optical fiber materials that are insensitive to temperature and humidity changes. These optical fibers usually have better thermal stability and humidity stability. Or provide appropriate protection for the optical fiber, such as using optical fiber protective sleeves, to avoid abnormal effects of environmental factors on subsequent data monitoring of the sensing fiber.

[0114] A stope rock formation deformation characteristic assessment system based on optical fiber monitoring technology includes a data monitoring module, an edge computing module, a core processor, an early warning response module, and an optimization management module. The data monitoring module includes an optical fiber monitoring submodule and an environmental monitoring submodule. The data monitoring module, the edge computing module, the core processor, the early warning response module, and the optimization management module are communicatively connected to each other. The system applies the above-mentioned stope rock formation deformation characteristic assessment method based on optical fiber monitoring technology.

[0115] The data monitoring module is used to collect fiber optic sensing data: Fiber optic sensing data includes rock formation deformation parameters and fiber optic environmental parameters, which are obtained through the fiber optic monitoring submodule and the environmental monitoring submodule respectively. The fiber optic monitoring submodule includes a sensor cable, a demodulation device, and a cloud server. The sensor cable is laid out in two ways: vertically or horizontally. The sensor cable is connected to the demodulation device, and the demodulation device is connected to the cloud server via wireless or wired devices.

[0116] The edge computing module is used to preliminarily analyze the fiber optic sensing data. It pre-processes the rock deformation parameters by building a rock deformation analysis model, thereby obtaining the deformation characteristic data of the stope rock formation and evaluating the deformation degree of the stope rock formation.

[0117] The core processor is used to build a deep analysis model: the deep analysis model analyzes the influence relationship between the stope rock deformation characteristic data and the optical fiber environmental parameters, thereby generating and outputting the stope environmental management plan and corresponding early warning prompt signals;

[0118] The early warning response module is used to receive early warning prompt signals and perform corresponding response prompt operations;

[0119] The optimization management module is used to receive the stope environment management plan and perform corresponding model optimization operations.

[0120] The specific operations for collecting optical fiber sensing data are as follows:

[0121] First, based on the deformation characteristics of the mining-induced rock formation, drill holes are arranged on the ground above the mining face or in the roof of the intake and return airways of the mining face. Then, sensing optical fibers are buried in these drill holes and sealed from the inside out with concrete slurry to ensure the stability and accuracy of the sensing fibers.

[0122] Next, the reserved sensing optical fibers in each borehole are connected in series and brought to the monitoring room via transmission cables. Before mining, an initial value test is conducted on the rock formation and rock mass to obtain initial deformation data.

[0123] As the mining face advances or retreats, the optical fiber can accurately sense the deformation of the rock formation during this process. This is because when the rock formation deforms, it will produce tensile strain on the strain sensing cable, and parameters such as the frequency shift of the cable will also change accordingly.

[0124] Distributed optical fiber (weak grating) is used for the sensing cables, which are laid out both vertically and horizontally. Optical cables are laid every 20 to 50 cm vertically and every 10 to 50 cm horizontally. A real-time optical fiber monitoring system is then established, with all sensing cables integrated into the communication cables. These cables are then connected to a demodulation device via jumpers. The signals received by the optical fibers are collected by the demodulation device, which is then connected to the cloud server.

[0125] The fiber optic monitoring submodule can obtain fiber optic sensing data such as strain and temperature of the rock formation, thereby calculating the settlement of the rock formation and determining the water inrush coefficient of the rock formation. The specific actual analysis process is as follows:

[0126] By integrating the obtained axial strain of the rock formation, the full-section settlement value of the rock formation caused by coal mining is obtained, and the calculation formula is: , S is the settlement value of the calculated depth of the rock layer; and are the distances between the top of the coal seam and the i-th layer and the i+1-th layer respectively; is the strain value;

[0127] The strain distribution of the optical fiber can be used to analyze the subsidence of the soil layer. The axial strain at the center of the optical fiber can be expressed by the formula Calculate, where L is the length of the fiber unit after deformation, is the length of the optical fiber unit before deformation. Through this formula, combined with the strain data of the optical fiber, the subsidence angle of the soil layer can be determined, and then the surface movement angle uphill can be determined by the strain data. and coal seam inclination , then we can use the formula Calculate the collapse angle ;

[0128] The temperature data obtained by optical fiber can determine the distribution of aquifers, according to the formula , T0 is the water inrush coefficient, M0 is the thickness of the aquiclude, obtained from the optical fiber monitoring data, and p is the water pressure of the aquifer, so as to predict the water inrush of the coal seam;

[0129] Finally, through the analysis and processing of monitoring data, the strain distribution map of rock deformation can be obtained, thereby determining key information such as the height of rock deformation and damage caused by mining and the stress condition, which is of great significance for evaluating the stability of rock formations in the mining area, predicting potential geological disasters, and formulating effective prevention and control measures.

[0130] In summary, the present invention creates an optical fiber layout model and collects optical fiber sensor data through a data monitoring module, and then preliminarily analyzes the optical fiber sensor data through an edge computing module to evaluate the deformation degree of the stope rock strata. Subsequently, a deep analysis model is constructed through a core processor to build an influence function between the stope rock strata deformation characteristic data and the optical fiber environment parameters, thereby generating and outputting a stope environment management plan and a corresponding early warning prompt signal. The early warning prompt signal is then received and a response prompt operation is performed through the early warning response module, and the stope environment management plan is received and a model optimization operation is performed through the optimization management module. Finally, real-time and accurate evaluation of the stope rock strata deformation is achieved based on optical fiber monitoring technology, providing a strong guarantee for the safe and efficient conduct of mining projects.

[0131] Among them, the present invention constructs a rock formation deformation analysis model to preliminarily analyze the fiber optic sensing data, evaluate the deformation degree of the rock formation in the mining area, and obtains the corresponding deformation degree evaluation index of each characteristic area of the rock formation in the mining area by calculation, so as to refine the evaluation of the regional deformation degree and realize the accurate capture of local deformation; by editing the signal prompt text and responding to the abnormal rock formation environment, timely and accurate response prompts are realized, and the environmental status of the rock formation in the mining area is finely regulated and managed through the mining area environment management plan, and the optical fiber layout model is optimized and designed to improve the system's anti-interference ability to environmental changes.

[0132] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0133] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by those skilled in the art according to actual conditions.

[0134] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for evaluating rock deformation characteristics in a stope based on optical fiber monitoring technology, characterized by: The following steps are involved: Step 1: Create a fiber optic layout model and collect fiber optic sensor data: Based on the characteristics of the rock formation structure in the stope, arrange drill holes and bury sensor fibers. Then, use 3D physical modeling technology to create a fiber optic layout model, set a data acquisition cycle Tc, and regularly collect fiber optic sensor data. The fiber optic sensor data includes rock formation deformation parameters and fiber optic environmental parameters. The rock formation deformation parameters include the initial deformation data set and the mining deformation data set. The fiber optic environmental parameters include the temperature and humidity values of the rock formation. Step 2: Construct a rock formation deformation analysis model and preliminarily analyze the fiber optic sensing data: Use the rock formation deformation analysis model to pre-process the rock formation deformation parameters, thereby obtaining the deformation characteristic data of the stope rock formation and evaluating the deformation state of the stope rock formation; Step 3: Build a deep analysis model to analyze the stope rock deformation characteristic data and fiber environment parameters: Through the deep analysis model, establish the influence function between the stope rock deformation characteristic data and fiber environment parameters, generate and output the stope environment management plan and corresponding early warning prompt signals. The specific process is as follows: S3-1, the optical fiber layout model includes N0 segments of sensing optical fibers, the sensing optical fibers in characteristic area Z are marked as J, and the optical fiber environment parameters of characteristic area Z are obtained; S3-2, constructing an influence function F0 between the deformation degree evaluation index Zbx of the characteristic region Z and the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd; S3-3, substituting the optical fiber environmental parameters of the characteristic area into the influence function F0 to obtain the deformation degree evaluation index Zbx of the characteristic area Z, and then comprehensively obtaining the overall deformation degree index ZT of the stope rock formation through the deformation degree evaluation index Zbx of the N1 characteristic areas; S3-4, setting the evaluation interval of the overall deformation index ZT of the stope rock strata to [y1, y2], and generating a stope environmental management plan and corresponding early warning signals through interval comparison; Step 4: Receive the warning prompt signal and perform corresponding response prompt operation: the response prompt operation is to edit the signal prompt text and perform a warning response to the abnormal rock formation environment; Step 5: Receive the stope environment management plan and perform corresponding model optimization operations: The stope environment management plan is to finely regulate and manage the environmental status of the stope rock formation and optimize the design of the optical fiber layout model.

2. The method for evaluating rock formation deformation characteristics in a stope based on optical fiber monitoring technology according to claim 1, characterized in that: The specific process of constructing the rock deformation analysis model is as follows: Divide the rock formation in the stope into N1 characteristic areas, mark any characteristic area as Z, and obtain the rock formation deformation parameters of the characteristic area Z. The rock formation deformation parameters include the initial deformation number set and the mining deformation number set; The initial deformation data set includes the initial fiber frequency shift fa, Brillouin frequency shift BFSa and stress change of the sensing fiber before the mining work in the stope rock formation. ; Set and mark the Brillouin constant as φ, and calculate and obtain the initial Brillouin frequency shift BFSa by combining the initial fiber frequency shift fa with the Brillouin constant φ; The Brillouin frequency shift and stress variation coefficient are set and marked as ω by the sensing fiber material. The initial stress variation of the sensing fiber is obtained by combining the initial Brillouin frequency shift BFSa with the variation coefficient ω. ; The mining deformation data set includes the fiber frequency shift fb, Brillouin frequency shift BFSb and stress variation periodically collected by the sensing fiber during the mining process of the working face. ; Set n1 data acquisition cycles for timed acquisition. By combining the fiber frequency shift fb of any data acquisition cycle Tc with the Brillouin constant φ, the Brillouin frequency shift BFSb of that cycle is measured and obtained. Then, the Brillouin frequency shift BFSb of that cycle is combined with the variation coefficient ω to obtain the stress change of the sensing fiber in that cycle. ; The rock formation deformation parameters are preprocessed through the rock formation deformation analysis model to obtain the deformation characteristic data of the stope rock formation and evaluate the deformation degree of the stope rock formation.

3. The method for evaluating rock formation deformation characteristics in a stope based on optical fiber monitoring technology according to claim 2, characterized in that: The specific process of preprocessing rock deformation parameters through the rock deformation analysis model is as follows: The rock deformation data matrix Vz of the characteristic area Z is constructed by using the rock deformation parameters of any characteristic area Z of the stope rock: ； Extract the row vector of stress variation of the rock formation deformation data matrix Vz and mark it as Vzh3; By analyzing the row vector Vzh3 of the stress variation, the optical fiber stress coefficient Gxsl of the characteristic area Z is obtained; ； Any group of row vectors of the rock formation deformation data matrix Vz is marked as Vzhm, where m is the group number of the row vector, and any vector data of the row vector Vzhm is marked as Em. The change trend coefficient Bhqs of the row vector group Vzhm is obtained by the difference between two adjacent vector data of the row vector Vzhm; By performing trend analysis on the row vectors of the rock deformation data matrix Vz, the trend coefficients of fiber frequency shift, Brillouin frequency shift and stress variation are obtained and marked as ρ1, ρ2 and ρ3 respectively; The optical fiber stress coefficient Gxsl of the characteristic region Z is combined with the change trend coefficient ρ1 of the optical fiber frequency shift, the change trend coefficient ρ2 of the Brillouin frequency shift, and the change trend coefficient ρ3 of the stress variation to comprehensively obtain the deformation degree evaluation index Zbx of the characteristic region Z. Then, an evaluation interval of the deformation degree evaluation index Zbx is set, and the deformation degree of the characteristic region Z is evaluated by comparing the intervals. The rock formation deformation data matrix Vz and deformation degree evaluation index Zbx of N1 characteristic areas Z are integrated and marked as rock formation deformation characteristic data.

4. The method for evaluating rock formation deformation characteristics in a stope based on optical fiber monitoring technology according to claim 3, characterized in that: The specific process of building a deep analysis model is as follows: S3-1, the optical fiber layout model includes N0 segments of sensing optical fibers, the sensing optical fibers in characteristic area Z are marked as J, and the optical fiber environment parameters of characteristic area Z are obtained; Extract n0 characteristic points of the sensing optical fiber J, mark any characteristic point of the sensing optical fiber J as i, obtain the optical fiber environmental parameters of the characteristic point i, and mark the temperature value and humidity value of the characteristic point i as Wi and Di respectively; By measuring the temperature values Wi and humidity values Di of n0 characteristic points i of the sensing optical fiber J, the abnormal characteristic points of the sensing optical fiber are judged respectively, thereby obtaining the temperature evaluation coefficient Xw and humidity evaluation coefficient Xd of the characteristic area Z; S3-101, set a standard interval Qw for the temperature value Wi. When the temperature value Wi is higher than the standard interval Qw, the feature point i is determined to be too high in temperature, and the number of feature points with too high temperatures is obtained and marked as Nf1. When the temperature value Wi is lower than the standard interval Qw, the feature point i is determined to be too low in temperature, and the number of feature points with too low temperatures is obtained and marked as Nf2. Then the temperature evaluation coefficient Xw is: , where μ1 is the conversion coefficient of the temperature value and μ1 is in the interval (0, 1); S3-102, set a standard interval Qd for the humidity value Di. When the humidity value Di is higher than the standard interval Qd, the feature point i is determined to have too high humidity. The number of feature points with too high humidity is obtained and marked as Nf3. When the humidity value Di is lower than the standard interval Qd, the feature point i is determined to have too low humidity. The number of feature points with too low humidity is obtained and marked as Nf4. Then the humidity assessment coefficient Xd: , where μ2 is the conversion coefficient of humidity value and μ2 is in the interval (0, 1); S3-2, constructing the influence function F0 between the deformation degree evaluation index Zbx of the characteristic region Z and the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd: ； Wherein, β1 and β2 are the logarithmic bases of the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd, respectively, and β1 and β2 are both preset to be greater than 1; r1 and r2 are the adjustment coefficients of the temperature evaluation coefficient Xw and the humidity evaluation coefficient Xd, respectively; S3-3, substitute the fiber environment parameters of the characteristic area into the influence function F0 to obtain the deformation degree evaluation index Zbx of the characteristic area Z, and then comprehensively obtain the overall deformation degree index ZT of the stope rock formation through the deformation degree evaluation index Zbx of N1 characteristic areas: ; in, is the weight factor of the deformation evaluation index Zbx of the feature area Z; S3-4, setting the evaluation interval of the overall deformation index ZT of the stope rock strata to [y1, y2], and generating a stope environmental management plan and corresponding early warning signals through interval comparison; When the overall deformation index ZT of the rock formation in the stope is less than y1, a No. 1 warning signal is generated; When the overall deformation index ZT of the stope rock strata is in the evaluation interval [y1, y2], refined early warning analysis and targeted processing are carried out: the risk threshold of the deformation evaluation index Zbx of the characteristic area Z is set to Y0. When the deformation evaluation index Zbx of the characteristic area Z is higher than the risk threshold Y0, the second early warning prompt signal of the characteristic area Z is generated. Otherwise, the characteristic area Z is not processed. When the overall deformation index ZT of the rock formation in the mining area is greater than y2, a No. 3 early warning signal is generated.

5. The method for evaluating rock formation deformation characteristics in a stope based on optical fiber monitoring technology according to claim 4, characterized in that: The refined control management operation is to control abnormal temperature and humidity, setting risk thresholds of temperature assessment coefficient Xw and humidity assessment coefficient Xd respectively and marking them as U1 and U2 respectively; When the temperature assessment coefficient Xw is higher than the risk threshold U1, the temperature of the characteristic area is regulated and managed; when the humidity assessment coefficient Xd is higher than the risk threshold U2, the humidity of the characteristic area is regulated and managed.

6. The stope rock deformation characteristic assessment system based on optical fiber monitoring technology is characterized by: The system comprises a data monitoring module, an edge computing module, a core processor, an early warning response module, and an optimization management module, wherein the data monitoring module comprises an optical fiber monitoring submodule and an environmental monitoring submodule; the data monitoring module, the edge computing module, the core processor, the early warning response module, and the optimization management module are communicatively connected to each other, and the system applies the method for evaluating rock formation deformation characteristics in a stope based on optical fiber monitoring technology as described in any one of claims 1 to 5 above; The data monitoring module is used to collect optical fiber sensing data: the optical fiber sensing data includes rock formation deformation parameters and optical fiber environmental parameters, which are obtained through the optical fiber monitoring submodule and the environmental monitoring submodule respectively; The edge computing module is used to preliminarily analyze the fiber optic sensing data. It pre-processes the rock deformation parameters by building a rock deformation analysis model, thereby obtaining the deformation characteristic data of the stope rock formation and evaluating the deformation degree of the stope rock formation. The core processor is used to build a depth analysis model: the depth analysis model analyzes the influence relationship between the stope rock deformation characteristic data and the optical fiber environmental parameters, and generates and outputs the stope environmental management plan and corresponding early warning prompt signals; The early warning response module is used to receive early warning prompt signals and perform corresponding response prompt operations; The optimization management module is used to receive the stope environment management plan and perform corresponding model optimization operations.

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