Multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning method and system

Abstract

The invention discloses a multi-parameter space-time correlation coal mine dynamic disaster dynamic assessment early warning method and system, and relates to the technical field of coal mine disaster early warning. The method comprises the following steps: acquiring multi-parameter monitoring data based on an optical fiber acceleration sensor and a mining pressure sensor which are arranged in a coal mining area; inputting the multi-parameter monitoring data into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation data set; constructing a dynamic disaster risk judgment model based on the disaster influence assessment data set, performing dynamic weight distribution on the dynamic disaster risk judgment model, and outputting a disaster risk assessment result; and matching the disaster risk assessment result with a preset risk level threshold, determining a risk level, and executing an early warning response according to the risk level. The technical problems of early warning response lag and insufficient accuracy caused by lack of multi-parameter comprehensive analysis of coal mine dynamic disaster assessment early warning in the prior art are solved, and the technical effect of improving the accuracy and timeliness of disaster early warning is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine disaster early warning, in particular to a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning method and system. BACKGROUND

[0002] With the continuous extension of mining to the deep part, the mine environment presents complex characteristics such as high ground stress, high ground temperature, low permeability and strong disturbance, and the frequency of coal mine dynamic disasters increases significantly, and the impact of pressure, coal and gas outburst and combined disasters are more likely to be induced, which seriously threatens the safety of the mine. The existing dynamic disaster detection methods include drill cuttings method, stress monitoring method, electromagnetic radiation method and ground sound and microseismic monitoring method, etc. Although they have been applied in most mines, each system operates independently, lacks multi-parameter data fusion, and forms an "information island". The drill cuttings method relies on manual observation and is difficult to realize continuous monitoring; the stress monitoring can only reflect the stress of local points, and the equipment is easily damaged by the underground environment; the electromagnetic radiation monitoring is easily disturbed and the signal distortion is serious; the ground sound and microseismic monitoring have limited identification accuracy under the influence of noise and uncertainty of wave velocity model. The above problems result in the deficiencies of the existing technology in timeliness, accuracy and reliability, which is difficult to meet the actual needs of coal mine dynamic disaster prevention and control under deep mining conditions. SUMMARY

[0003] The present application provides a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning method and system, which solves the technical problem that the existing coal mine dynamic disaster evaluation and early warning lacks multi-parameter comprehensive analysis, resulting in delayed response and insufficient accuracy of the early warning.

[0004] In a first aspect, the present application provides a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning method, which comprises: Based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data is obtained; the multi-parameter monitoring data is input into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation data set, wherein the disaster influence evaluation data set includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient and a microseismic proportion coefficient; a dynamic disaster risk judgment model is constructed based on the disaster influence evaluation data set, and dynamic weight distribution is performed on the dynamic disaster risk judgment model to output a disaster risk evaluation result; the disaster risk evaluation result is matched with a preset risk level threshold to determine a risk level, and a warning response is performed according to the risk level.

[0005] In a second aspect, the present application provides a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning system, which comprises: The data acquisition module: based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data are acquired; the calculation module: the multi-parameter monitoring data are input into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation dataset, wherein the disaster influence evaluation dataset includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient and a microseismic proportion coefficient; the risk assessment module: a dynamic disaster risk judgment model is constructed based on the disaster influence evaluation dataset, dynamic weight distribution is performed on the dynamic disaster risk judgment model, and a disaster risk evaluation result is output; the early warning module: the disaster risk evaluation result is matched with a preset risk level threshold, a risk level is determined, and early warning response is performed according to the risk level.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: Firstly, based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data are acquired. Then, the multi-parameter monitoring data are input into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation dataset, wherein the disaster influence evaluation dataset includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient and a microseismic proportion coefficient. Then, a dynamic disaster risk judgment model is constructed based on the disaster influence evaluation dataset, dynamic weight distribution is performed on the dynamic disaster risk judgment model, and a disaster risk evaluation result is output. Finally, the disaster risk evaluation result is matched with a preset risk level threshold, a risk level is determined, and early warning response is performed according to the risk level. The technical problem that the coal mine dynamic disaster evaluation and early warning in the prior art lack multi-parameter comprehensive analysis, resulting in lagging early warning response and insufficient accuracy is solved, and the technical effects of improving the accuracy and timeliness of disaster early warning are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0008] Figure 1 A flowchart of a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning method provided by the embodiments of the present application is shown in the figure. Figure 2 A structure diagram of a multi-parameter space-time correlation coal mine dynamic disaster dynamic evaluation and early warning system provided by the embodiments of the present application is shown in the figure.

[0009] Explanation of reference signs: data acquisition module 11, calculation module 12, risk assessment module 13, early warning module 14. DETAILED DESCRIPTION

[0010] The present application provides a multi-parameter space-time correlation coal mine dynamic disaster evaluation and early warning method and system, which solves the technical problems of lack of multi-parameter comprehensive analysis in coal mine dynamic disaster evaluation and early warning, leading to lagging response and insufficient accuracy of early warning.

[0011] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0012] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units need not be limited to only those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment one, as shown in the present application provides a multi-parameter space-time correlation coal mine dynamic disaster evaluation and early warning method, wherein the method comprises: Figure 1 Based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data is obtained. Based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data is obtained.

[0014] Based on the optical fiber acceleration sensor and the mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data is obtained, including shear wave velocity, main frequency, amplitude, duration and stress change data. Specifically, the optical fiber acceleration sensor is reasonably arranged according to the distribution law of roadway, working face and fault in the coal mining area, for real-time acquisition of vibration waveform signals generated by the coal and rock mass in the mining disturbance process. The optical fiber acceleration sensor has the characteristics of strong anti-electromagnetic interference ability and adaptation to complex underground environment, and can continuously output microseismic waveform parameters. Through the signal processing module, the collected vibration waveform signals are subjected to frequency spectrum analysis, and characteristic parameters such as shear wave velocity, main frequency, amplitude and duration are extracted, which are used to represent the energy release and fracture state of the coal and rock mass. At the same time, the mine pressure sensor is arranged in the key stress area of the coal and rock mass, for real-time monitoring of the stress change process of the coal and rock mass under mining disturbance, and obtaining stress change data.

[0015] The multi-parameter monitoring data is input into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation data set, wherein the disaster influence evaluation data set includes energy accumulation coefficient, maximum principal stress concentration coefficient, support efficiency coefficient and microseismic proportion coefficient.

[0016] The multi-parameter monitoring data is input into the disaster influence evaluation platform for calculation to obtain a disaster influence evaluation data set, which is used to represent the energy evolution characteristics, stress distribution characteristics, support stability and microseismic activity level of the coal rock mass in the coal mining process, and specifically includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient and a microseismic proportion coefficient.

[0017] Further, before the multi-parameter monitoring data is input into the disaster influence evaluation platform for calculation to obtain the disaster influence evaluation data set, the method comprises: A plurality of calculation frameworks for respectively obtaining the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportion coefficient are obtained; and the plurality of calculation frameworks are integrated to constitute the disaster influence evaluation platform.

[0018] Firstly, a plurality of calculation frameworks for respectively calculating the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportion coefficient are established. The energy accumulation coefficient calculation framework is constructed based on the ratio of the microseismic elastic energy cumulative value to the critical impact energy threshold value; the maximum principal stress concentration coefficient calculation framework is constructed based on the spatial gradient modulus of the monitored stress field and the maximum principal stress value; the support efficiency coefficient calculation framework is constructed by weighting and fusing the strength parameters of different support structures and combining the surrounding rock pressure and the load partial coefficient; and the microseismic proportion coefficient calculation framework is constructed based on the relationship curve of the magnitude and frequency, and the fitting algorithm is used to extract the coefficient. Subsequently, the plurality of calculation frameworks are modularly packaged and integrated to constitute a unified disaster influence evaluation platform.

[0019] Further, before the multi-parameter monitoring data is input into the disaster influence evaluation platform for calculation to obtain the disaster influence evaluation data set, the method comprises: The energy accumulation coefficient is determined according to the ratio of the microseismic elastic energy cumulative value to the critical impact energy threshold value; the microseismic elastic energy cumulative value is obtained based on the multi-parameter monitoring data, wherein the microseismic elastic energy cumulative value is: wherein, represents the density of the coal seam, represents the shear wave velocity, represents the duration interval, represents the main frequency, represents the amplitude; and the critical impact energy threshold value is: wherein, represents the uniaxial compressive strength of the coal body, , represents the fitting statistical value of the coal body sample in the experiment process.

[0020] According to the ratio of the microseismic elastic energy accumulation value and the critical impact energy threshold value, an energy accumulation coefficient is determined to represent the energy accumulation degree of the coal rock mass under the mining disturbance condition. Specifically, based on the vibration signals collected by the optical fiber acceleration sensor, the transverse wave velocity, the main frequency, the amplitude and the duration interval are extracted, and the microseismic elastic energy accumulation value is calculated in combination with the coal seam density.

[0021] Preferably, the coal body is cored from the mine, and a uniaxial compression experiment is performed in a laboratory to measure the uniaxial compressive strength of the coal body.

[0022] Further, the multi-parameter monitoring data is input into a disaster influence evaluation platform for calculation to obtain a disaster influence evaluation data set, and the method comprises: Based on the multi-parameter monitoring data, a stress gradient modulus is calculated, wherein the stress gradient modulus: , represents the stress gradient modulus, represents the gradient vector of the stress field, , , respectively represent the partial derivatives of the stress in the x, y and z directions; according to the stress gradient modulus and the sensor arrangement reference distance, a maximum principal stress concentration coefficient is calculated; the maximum principal stress concentration coefficient: wherein, represents the maximum principal stress value of the coal rock mass, represents the uniaxial compressive strength of the coal body, represents the sensor arrangement reference distance.

[0023] Based on the multi-parameter monitoring data, a stress field distribution model of the coal rock mass is constructed, and partial derivative operations are performed on the stress field in the three directions of spatial coordinates x, y and z to obtain a stress gradient vector . On this basis, the stress gradient modulus is calculated, wherein, represents the gradient vector of the stress field, , , respectively represent the change rates of the stress in the x, y and z directions, which are used to reflect the local stress concentration change characteristics of the coal rock mass.

[0024] After obtaining the stress gradient modulus, the maximum principal stress concentration coefficient is calculated in combination with the maximum principal stress value of the coal rock mass and the sensor arrangement reference distance (such as 5 m). Wherein, represents the uniaxial compressive strength of the coal body, which reflects the compression limit of the coal rock mass under the experimental condition.

[0025] Further, the multi-parameter monitoring data is input into a disaster impact assessment platform for calculation to obtain a disaster impact assessment data set, and the method comprises: The anchor rod support strength, anchor cable support strength, steel shed support strength, and sprayed layer support strength are weighted and calculated to obtain a combined support strength; and a support efficiency coefficient is calculated based on the combined support strength, load partial coefficient, and surrounding rock pressure: , The combined support strength is represented by: The load partial coefficient is represented by: The surrounding rock pressure is represented by: wherein, The average rock stratum bulk density is represented by: The falling arch height is represented by:

[0026] Preferably, the mine working face adopts an anchor net cable spraying + 40U steel shed combined support mode, mine intrinsic safety type pressure sensors are arranged in the roadway and mining area to monitor the force bearing condition of the support components in real time, and the anchor rod support strength, anchor cable support strength, steel shed support strength, and sprayed layer support strength are obtained by calculation.

[0027] The anchor rod support strength is represented by: wherein, n is the number of anchor rods, the single anchor rod design tensile force is greater than or equal to 50KN, the anchor rod inclination angle is S is the support area.

[0028] The anchor cable support strength is represented by: wherein, d is the anchor cable diameter, the anchor cable tensile strength is k is the group anchor coefficient (0.8-0.9), the prestress efficiency is greater than or equal to 0.7.

[0029] The steel shed support strength is represented by: wherein, N is the number of steel sheds, the steel yield strength is 40U steel ≥ 350MPa, A is the steel shed cross-sectional area, and L is the shed distance.

[0030] The sprayed layer support strength is represented by: , the sprayed layer material compressive strength is t is the sprayed layer thickness, B is the sprayed layer width, and K is the correction coefficient.

[0031] The anchor rod support strength, anchor cable support strength, steel shed support strength, and sprayed layer support strength are weighted and calculated by a coordination coefficient (0.85-0.95), to obtain a combined support strength, which is used to represent the overall bearing capacity of various support methods.

[0032] On this basis, the load sub-coefficient and the surrounding rock pressure are introduced to calculate the support efficiency coefficient, which is used to reflect the effective resistance of the support system to the surrounding rock pressure, and the greater the value, the more significant the support effect.

[0033] Further, the multi-parameter monitoring data is input into a disaster impact assessment platform for calculation to obtain a disaster impact assessment data set, and the method comprises: The vibration data of the coal mining area is collected in real time based on a mine microseismic monitoring system to construct a G-R relationship: wherein, represents the microseismic frequency, represents the magnitude, is a regional microseismic activity level constant, is a microseismic proportion coefficient; the least square method is used to fit the vibration data after the magnitude is graded to obtain the microseismic proportion coefficient, wherein the least square method calculation formula is: ; wherein, m represents the total number of magnitude grading, represents the i-th magnitude grading, represents the microseismic frequency of the i-th magnitude grading.

[0034] The vibration data of the coal mining area is collected in real time based on a mine microseismic monitoring system, and the collected magnitude and frequency information is statistically analyzed to construct a G-R relationship, wherein N represents the frequency of the observed microseismic event within a certain time window, M represents the corresponding magnitude, c is a constant reflecting the regional microseismic activity level, and d is a microseismic proportion coefficient, which is used to represent the relative proportion and energy release characteristics of different magnitude events.

[0035] The monitored magnitude data is grouped according to a preset grading interval to obtain a plurality of data sets after the magnitude is graded; then the least square method is used to fit the relationship between the magnitude and the corresponding frequency to obtain the microseismic proportion coefficient.

[0036] Further, before fitting the vibration data after the magnitude is graded using the least square method, the method comprises: The magnitude grading adopts an adaptive grading strategy, wherein, represents the median of the i-th group, represents the lower limit of the minimum effective magnitude, represents the magnitude grading interval; The adaptive grading strategy is: .

[0037] The magnitude grading adopts an adaptive grading strategy, and the median calculation formula is: wherein, characterizing a median value of the i-th group (a midpoint value of the magnitude group), characterizing a minimum effective magnitude lower limit, characterizing a magnitude bin interval.

[0038] The adaptive binning strategy dynamically adjusts the bin interval according to different magnitude ranges, specifically: In this way, when the magnitude is in the low value interval, a smaller bin interval is used to improve the resolution of weak events; when the magnitude is in the medium interval, a medium interval is used to ensure the balance of the data; when the magnitude is in the high value interval, a larger bin interval is used to enhance the statistical stability of large earthquake events. The adaptive binning strategy can automatically adjust the binning precision according to the distribution characteristics of the magnitude data, avoiding overfitting or data sparsity problems caused by fixed binning methods, thereby improving the fitting reliability and parameter estimation accuracy of the microseismic proportion coefficient.

[0039] Based on the disaster impact assessment data set, a dynamic disaster risk judgment model is constructed, and the dynamic disaster risk judgment model is dynamically weighted to output a disaster risk assessment result.

[0040] Specifically, the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportion coefficient are taken as input parameters to establish a dynamic disaster risk judgment model. The model obtains the quantitative expression of the dynamic disaster risk level of the coal rock mass by constructing a risk index formula to weight and superimpose each parameter. Secondly, in terms of weight setting, a basic weight is set for each parameter in the initial stage to reflect its importance to the disaster incubation process under normal working conditions; then, combined with actual mining conditions, including working face type, fault distribution, coal seam thickness and support conditions, the basic weight is dynamically adjusted, and normalized processing is used to ensure that the weight sum is 1, thereby obtaining a dynamic weight coefficient set. Finally, the dynamic weight coefficient set is used to weight the risk judgment model for calculation, and the disaster risk assessment result is output. The disaster risk assessment result can reflect the dynamic disaster risk level of the coal mining area in real time.

[0041] Further, based on the disaster impact assessment data set, a dynamic disaster risk judgment model is constructed, the dynamic disaster risk judgment model is dynamically weighted, and a disaster risk assessment result is output, the method comprising: The dynamic disaster risk judgment model: wherein, ; set the basic weight of the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportion coefficient; dynamically adjust the basic weight according to the working face type, the fault distribution, the coal seam thickness, the surrounding rock condition and the support condition, and perform normalization processing to obtain a dynamic weight coefficient set; perform weighted calculation on the dynamic disaster risk judgment model based on the dynamic weight coefficient set, and output a dynamic risk assessment result.

[0042] A dynamic disaster risk judgment model is established, and the energy accumulation coefficient, the maximum principal stress concentration coefficient, the microseismic proportion coefficient and the support efficiency coefficient are taken as input parameters to construct a risk index formula: , wherein, , , , are weight parameters of the energy accumulation coefficient, the maximum principal stress concentration coefficient, the microseismic proportion coefficient and the support efficiency coefficient respectively, and satisfy the constraint condition .

[0043] Optionally, the basic weights of the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportion coefficient are set according to a large number of mine engineering documents and historical disaster case experiences, and are 0.25, 0.35, 0.15 and 0.25 respectively.

[0044] The initial weights are dynamically corrected in combination with the mine geological condition and the mining condition: , wherein, is a geological condition adjustment item, is a dynamic parameter adjustment item, denotes normalization processing, and ensures that the constraint condition always holds.

[0045] .

[0046] The geological condition adjustment item includes: when the monitored working face is a mining working face, the weight of is increased; when the monitored working face is a tunneling working face, the weight of is reduced; when the region is located in a fault zone, the weight of is increased; when the region is located in a thick coal seam area, the weight of is reduced; and when the region is located in a floor heave area, the weight of is increased.

[0047] The dynamic parameter adjustment item includes: the weight of is adjusted according to the microseismic proportion coefficient d value and the change rate dd / dt of the microseismic proportion coefficient d value: when d is less than 1.0, ; and when less than -0.1, ; otherwise, .

[0048] The disaster risk assessment result is matched with a preset risk level threshold to determine a risk level, and a warning response is performed according to the risk level.

[0049] Further, the disaster risk assessment result is matched with a preset risk level threshold to determine a risk level, and a warning response is performed according to the risk level, and the method comprises: The dynamic risk assessment result is matched with a preset risk level threshold to determine a risk level; when the dynamic risk assessment result is less than 0.6, a low risk level is determined, and a prompt warning is output; when the dynamic risk assessment result is greater than or equal to 0.6 and less than 0.8, a medium risk level is determined, and a general warning is output; when the dynamic risk assessment result is greater than or equal to 0.8 and less than 1.0, a high risk level is determined, and a strong warning is output; and when the dynamic risk assessment result is greater than or equal to 1.0, an extremely high risk level is determined, and an emergency warning is output.

[0050] The calculated dynamic risk assessment result is compared with a disaster risk level threshold to determine the risk level currently assumed by the mine. Specifically, when the dynamic risk assessment result is less than 0.6, a low risk level is determined, and only a prompt warning information is output, which is used to remind the management personnel to maintain routine monitoring; when the dynamic risk assessment result is greater than or equal to 0.6 and less than 0.8, a medium risk level is determined, and a general warning is output, prompting to strengthen on-site patrol and to increase monitoring frequency; when the dynamic risk assessment result is greater than or equal to 0.8 and less than 1.0, a high risk level is determined, and a strong warning is output, requiring to take targeted control measures, such as increasing support and reducing mining intensity; and when the dynamic risk assessment result is greater than or equal to 1.0, an extremely high risk level is determined, and an emergency warning is output, linking with the mine safety control system to automatically trigger a stop-mining instruction or a personnel evacuation program.

[0051] In summary, the embodiments of the present application have at least the following technical effects: Firstly, based on the optical fiber acceleration sensor and mine pressure sensor arranged in the coal mining area, multi-parameter monitoring data is obtained. Secondly, the multi-parameter monitoring data is input into the disaster influence evaluation platform for calculation to obtain a disaster influence evaluation dataset, wherein the disaster influence evaluation dataset includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient, and a microseismic proportion coefficient. Then, a dynamic disaster risk judgment model is constructed based on the disaster influence evaluation dataset, and dynamic weight distribution is performed on the dynamic disaster risk judgment model to output a disaster risk evaluation result. Finally, the disaster risk evaluation result is matched with a preset risk level threshold to determine a risk level, and a warning response is executed according to the risk level. The technical problem that the coal mine dynamic disaster evaluation and warning in the prior art lack multi-parameter comprehensive analysis, resulting in lagging warning response and insufficient accuracy is solved, and the technical effects of improving the accuracy and timeliness of disaster warning are achieved.

[0052] In the second embodiment, based on the same inventive concept as the coal mine dynamic disaster dynamic evaluation and warning method of multi-parameter space-time correlation in the foregoing embodiments, as shown in the following table, the present application provides a coal mine dynamic disaster dynamic evaluation and warning system of multi-parameter space-time correlation, wherein the system comprises: Figure 2 The data acquisition module 11 acquires multi-parameter monitoring data based on the optical fiber acceleration sensor and mine pressure sensor arranged in the coal mining area; the calculation module 12 inputs the multi-parameter monitoring data into the disaster influence evaluation platform for calculation to obtain a disaster influence evaluation dataset, wherein the disaster influence evaluation dataset includes an energy accumulation coefficient, a maximum principal stress concentration coefficient, a support efficiency coefficient, and a microseismic proportion coefficient; the risk assessment module 13 constructs a dynamic disaster risk judgment model based on the disaster influence evaluation dataset, and performs dynamic weight distribution on the dynamic disaster risk judgment model to output a disaster risk evaluation result; and the warning module 14 matches the disaster risk evaluation result with a preset risk level threshold to determine a risk level, and executes a warning response according to the risk level.

[0053] Further, the calculation module 12 is configured to perform the following method: A plurality of calculation frameworks of the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient, and the microseismic proportion coefficient are respectively acquired; and the plurality of calculation frameworks are integrated to constitute a disaster influence evaluation platform.

[0054] Further, the calculation module 12 is configured to perform the following method: The energy accumulation coefficient is determined according to the ratio of the microseismic elastic energy cumulative value to the critical impact energy threshold; the microseismic elastic energy cumulative value is acquired based on the multi-parameter monitoring data, wherein the microseismic elastic energy cumulative value is: wherein, represents the density of the coal seam.​ characterize the shear wave velocity, characterize the duration interval, characterize the dominant frequency, characterize the amplitude; the critical impact energy threshold: wherein, characterize the coal body uniaxial compressive strength, , characterize the fitting statistical value of the coal body sample in the experiment process.

[0055] Further, the calculation module 12 is configured to execute the following method: based on the multi-parameter monitoring data, calculate the stress gradient modulus, wherein the stress gradient modulus: , characterize the stress gradient modulus, characterize the gradient vector of the stress field, , , respectively characterize the partial derivative of the stress in the x, y, z three directions; according to the stress gradient modulus and the sensor arrangement reference distance, calculate the maximum principal stress concentration coefficient; the maximum principal stress concentration coefficient: wherein, characterize the maximum principal stress value of the coal rock mass, characterize the coal body uniaxial compressive strength, characterize the sensor arrangement reference distance.

[0056] Further, the calculation module 12 is configured to execute the following method: obtain the joint support strength by weighting calculation of the anchor rod support strength, the anchor cable support strength, the steel shed support strength and the shotcrete layer support strength; calculate the support efficiency coefficient based on the joint support strength, the load subcoefficient and the surrounding rock pressure: , characterize the joint support strength, characterize the load subcoefficient, characterize the surrounding rock pressure; wherein the surrounding rock pressure calculation formula: wherein, characterize the average unit weight of the rock stratum, is the height of the caving arch.

[0057] Further, the calculation module 12 is configured to execute the following method: based on the real-time collection of the vibration data of the coal mining area by the mine microseismic monitoring system, to construct the G-R relationship: wherein, characterize the microseismic frequency, characterize the magnitude, is the regional microseismic activity level constant, is a microseismic proportionality coefficient; the least square method is used to fit the vibration data after magnitude grading to obtain the microseismic proportionality coefficient, wherein the least square method calculation formula is: ; wherein m represents the total number of magnitude grading, represents the i-th magnitude, represents the microseismic frequency of the i-th magnitude.

[0058] Further, the calculation module 12 is configured to execute the following method: The magnitude grading adopts an adaptive grading strategy, wherein, represents the group median of the i-th group, represents the lower limit of the minimum effective magnitude, represents the magnitude grading interval; the adaptive grading strategy is: .

[0059] Further, the risk assessment module 13 is configured to execute the following method: The dynamic disaster risk determination model is: wherein, ; the basic weights of the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support efficiency coefficient and the microseismic proportionality coefficient are set; the basic weights are dynamically adjusted according to the working face type, the fault distribution, the coal seam thickness, the surrounding rock condition and the support condition, and normalized to obtain a dynamic weight coefficient set; the dynamic disaster risk determination model is weighted calculated based on the dynamic weight coefficient set, and a dynamic risk assessment result is output.

[0060] Further, the early warning module 14 is configured to execute the following method: The dynamic risk assessment result is matched with a preset risk level threshold to determine the risk level; when the dynamic risk assessment result is less than 0.6, it is determined as a low risk level, and a prompt early warning is output; when the dynamic risk assessment result is greater than or equal to 0.6 and less than 0.8, it is determined as a medium risk level, and a general early warning is output; when the dynamic risk assessment result is greater than or equal to 0.8 and less than 1.0, it is determined as a high risk level, and a strong early warning is output; when the dynamic risk assessment result is greater than or equal to 1.0, it is determined as an extremely high risk level, and an emergency early warning is output.

[0061] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0062] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0063] The specification and drawings are only exemplary and illustrative of the present application and are considered to cover any and all modifications, variations, combinations or equivalents that are within the scope of the present application. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application cover the modifications and changes as they come within the scope of the application, and that the scope of the application be defined not by the description of the application contained herein but by the claims, including all equivalents thereof.

Claims

1. A dynamic assessment and early warning method for coal mine dynamic disasters based on multi-parameter spatiotemporal correlation, characterized in that, The method includes: Multi-parameter monitoring data are acquired based on fiber optic accelerometers and mine pressure sensors deployed in coal mining areas. The multi-parameter monitoring data is input into the disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset, wherein the disaster impact assessment dataset includes energy accumulation coefficient, maximum principal stress concentration coefficient, support effectiveness coefficient, and microseismic ratio coefficient. A dynamic disaster risk assessment model is constructed based on the disaster impact assessment dataset, and dynamic weights are dynamically assigned to the dynamic disaster risk assessment model to output disaster risk assessment results. The disaster risk assessment results are matched with preset risk level thresholds to determine the risk level, and an early warning response is executed based on the risk level.

2. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 1, characterized in that, Before inputting the multi-parameter monitoring data into the disaster impact assessment platform for calculation to obtain the disaster impact assessment dataset, the method includes: Multiple calculation frameworks were used to obtain the energy accumulation coefficient, maximum principal stress concentration coefficient, support effectiveness coefficient, and microseismic proportionality coefficient, respectively. The multiple computing frameworks are integrated to form a disaster impact assessment platform.

3. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 2, characterized in that, The method involves inputting the multi-parameter monitoring data into a disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset. The energy accumulation coefficient is determined based on the ratio of the cumulative value of microseismic elastic energy to the critical impact energy threshold. Based on the multi-parameter monitoring data, the cumulative value of microseismic elastic energy is obtained, wherein the cumulative value of microseismic elastic energy is: ,in, Characterizing coal seam density, Characterizing transverse wave velocity, Characterizing the duration time interval, Characterizes the main frequency, Characterizes amplitude; The critical impact energy threshold: ,in, Characterizing the uniaxial compressive strength of coal, , Fitting statistics of coal samples during the characterization experiment.

4. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 2, characterized in that, The method involves inputting the multi-parameter monitoring data into a disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset. Based on the multi-parameter monitoring data, the stress gradient modulus is calculated, wherein the stress gradient modulus is: , Characterizing the stress gradient modulus, The gradient vector characterizing the stress field. , , These characterize the partial derivatives of stress in the x, y, and z directions, respectively. The maximum principal stress concentration factor is calculated based on the stress gradient modulus and the sensor placement reference distance. The maximum principal stress concentration factor: ,in, Characterizing the maximum principal stress value of coal and rock mass, Characterizing the uniaxial compressive strength of coal, Characterizes the reference distance for sensor placement.

5. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 2, characterized in that, The method involves inputting the multi-parameter monitoring data into a disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset. The combined support strength is obtained by weighting the strengths of anchor bolt support, anchor cable support, steel canopy support, and shotcrete support. The support effectiveness coefficient is calculated based on the combined support strength, load partial factor, and surrounding rock pressure. , Characterizing the combined support strength, Characterizing the load partial factor, Characterizes the pressure on the surrounding rock; The formula for calculating the surrounding rock pressure is as follows: ,in, Characterizes the average unit weight of rock strata. To prevent the arch from collapsing.

6. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 2, characterized in that, The method involves inputting the multi-parameter monitoring data into a disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset. Based on the real-time acquisition of vibration data in the coal mine mining area using a mine microseismic monitoring system, a ground motion (GR) relationship is constructed: ,in, Characterizing microseismic frequency, Characterizing the magnitude, This represents the regional microseismic activity level constant. This is the microseismic proportionality coefficient; The least squares method is used to fit the seismic data after magnitude gradation to obtain the microseismic proportionality coefficient. The calculation formula for the least squares method is as follows: Where m represents the total number of magnitude categories, Characterizing the magnitude of the i-th earthquake, Microseismic frequencies characterizing the i-th magnitude.

7. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 6, characterized in that, Before fitting the magnitude-graded seismic data using the least squares method, the method includes: The earthquake magnitude classification adopts an adaptive classification strategy. ,in, The midpoint of the i-th group is represented by the midpoint of the i-th group. Characterizing the lower limit of the minimum effective magnitude Characterizing the intervals between earthquake magnitude classifications; The adaptive grading strategy: .

8. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 1, characterized in that, A dynamic disaster risk assessment model is constructed based on the disaster impact assessment dataset. Dynamic weights are dynamically assigned to the dynamic disaster risk assessment model, and disaster risk assessment results are output. The method includes: Dynamic disaster risk assessment model: ,in, ; Set the basic weights for the energy accumulation coefficient, the maximum principal stress concentration coefficient, the support effectiveness coefficient, and the microseismic proportion coefficient; The basic weights are dynamically adjusted based on the working face type, fault distribution, coal seam thickness, surrounding rock conditions, and support conditions, and then normalized to obtain a dynamic weight coefficient set. The dynamic disaster risk assessment model is weighted based on the dynamic weight coefficient set to output dynamic risk assessment results.

9. The method for dynamic assessment and early warning of coal mine dynamic disasters based on multi-parameter spatiotemporal correlation as described in claim 8, characterized in that, The method involves matching the disaster risk assessment results with preset risk level thresholds to determine the risk level, and then executing an early warning response based on the risk level. The dynamic risk assessment results are matched with preset risk level thresholds to determine the risk level; When the dynamic risk assessment result is less than 0.6, it is determined to be at a low risk level, and a warning is issued. When the dynamic risk assessment result is greater than or equal to 0.6 and less than 0.8, it is determined to be at a medium risk level, and a general warning is output. When the dynamic risk assessment result is greater than or equal to 0.8 and less than 1.0, it is judged as a high-risk level, and a strong warning is output. When the dynamic risk assessment result is greater than or equal to 1.0, it is determined to be at an extremely high risk level, and an emergency warning is output.

10. A dynamic assessment and early warning system for coal mine dynamic disasters based on multi-parameter spatiotemporal correlation, characterized in that, The system is used to implement the multi-parameter spatiotemporal correlation dynamic assessment and early warning method for coal mine dynamic disasters according to any one of claims 1-9, the system comprising: Data acquisition module: Based on fiber optic accelerometers and mine pressure sensors deployed in the coal mining area, it acquires multi-parameter monitoring data; Calculation module: Inputs the multi-parameter monitoring data into the disaster impact assessment platform for calculation to obtain a disaster impact assessment dataset, wherein the disaster impact assessment dataset includes energy accumulation coefficient, maximum principal stress concentration coefficient, support effectiveness coefficient, and microseismic ratio coefficient; Risk assessment module: Constructs a dynamic disaster risk determination model based on the disaster impact assessment dataset, dynamically assigns weights to the dynamic disaster risk determination model, and outputs disaster risk assessment results; Early warning module: Matches the disaster risk assessment results with preset risk level thresholds to determine the risk level, and executes an early warning response based on the risk level.

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