Coal mine tunnel surrounding rock catastrophe monitoring system and method

Abstract

The invention belongs to the field of coal mine catastrophe monitoring, relates to a data analysis technology, is used for solving the problem that in the prior art, a monitoring means and a protection means of a coal mine tunnel cannot be matched and analyzed by combining historical monitoring data, and particularly relates to a coal mine tunnel surrounding rock catastrophe monitoring system and method. The catastrophe monitoring platform is in communication connection with a historical data statistics module, a historical data processing module, a catastrophe monitoring analysis module and a database; according to the method, the surrounding rock catastrophe historical monitoring data of the coal mine tunnel can be processed, the depth interval is formed after the mining depth value is processed, and then the matching objects of the depth interval are classified according to the monitoring means and the protection means; and finally, according to the monitoring effective value and the catastrophe state, a monitoring matching means and a protection matching means are marked, so that coal mine tunnels with different mining depths can be quickly matched with proper monitoring means and protection means, and the coal mining safety is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of coal mine disaster monitoring, relates to data analysis technology, and specifically is a coal mine roadway surrounding rock disaster monitoring system and method. Background Art

[0002] The stability of the surrounding rock of the underground roadway during the roadway excavation process directly determines the excavation efficiency and safety of the underground roadway. With the continuous increase in the depth of fully mechanized coal mining operations in coal mines, the problems of mine pressure fluctuation and stress concentration faced during the excavation operation of deep roadway are prominent. Especially in areas with low surrounding rock strength and obvious fragmentation, the surrounding rock deformation is serious, posing a serious hidden danger to the safety of underground fully mechanized coal mining operations.

[0003] The invention patent with the publication number of CN116502386B discloses a simulation method and system for the disaster evolution of roadway anchored surrounding rock under static and dynamic loads. This method simulates the bearing condition of the support structure during the actual coal mine mining process by constructing a particle flow numerical model of the roadway surrounding rock under point dynamic loads, and uses the method of applying boundary velocity to the model to constrain the internal particle flow velocity, making the simulation results more restore the actual situation, providing a new idea for preventing the occurrence of surrounding rock disasters in the roadway during the coal mine mining process; however, this method cannot perform matching analysis on the monitoring means and protection means of coal mine roadways by combining historical monitoring data, resulting in the inability of coal mine roadways to adopt the most optimized monitoring means and protection means to avoid disasters, and the safety of coal mine mining is low.

[0004] In view of the above technical problems, the present application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a coal mine roadway surrounding rock disaster monitoring system and method, which is used to solve the problem that the existing technology cannot perform matching analysis on the monitoring means and protection means of coal mine roadways by combining historical monitoring data;

[0006] The technical problem that the present invention needs to solve is: how to provide a coal mine roadway surrounding rock disaster monitoring system and method that can perform matching analysis on the monitoring means and protection means of coal mine roadways by combining historical monitoring data.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A coal mine roadway surrounding rock disaster monitoring system includes a disaster monitoring platform, and the disaster monitoring platform is communicatively connected to a historical data statistics module, a historical data processing module, a disaster monitoring analysis module, and a database;

[0009] The historical data statistics module is used to statistically analyze the historical monitoring data of the surrounding rock disasters in coal mine roadways: mark the roadways that have completed coal mining as statistical objects, and the historical monitoring data of the statistical objects include: mining depth value, monitoring means, protection means, monitoring effectiveness value, and disaster state;

[0010] The historical data processing module is used to process the historical monitoring data of the surrounding rock disasters in coal mine roadways: the maximum and minimum values of the mining depth values of all statistical objects form a depth range, divide the depth range into several depth intervals, mark the statistical objects with mining depth values within the depth intervals as matching objects of the depth intervals, classify the matching objects according to the monitoring means and protection means adopted, and mark the monitoring matching means and protection matching means of the depth intervals;

[0011] The disaster monitoring and analysis module is used to conduct disaster monitoring and analysis on coal mine roadways: mark the coal mine roadways for which disaster monitoring and analysis are to be carried out as analysis objects, obtain the mining depth values of the analysis objects, and retrieve the monitoring matching means and protection matching means corresponding to the depth intervals of the mining depth values and mark them as the monitoring implementation means and protection implementation means of the analysis objects.

[0012] Further, the monitoring means include convergence meter measurement, multi-point displacement meter measurement, acoustic wave monitoring equipment, and vibration meter measurement, and the protection means include support protection, reinforcement protection, stress control protection, and shotcrete protection; the monitoring effectiveness value is the ratio of the number of effective alarm signals to the number of ineffective alarm signals when the statistical object uses the monitoring means for disaster risk monitoring; the disaster state includes the occurrence of a disaster and the avoidance of a disaster. The occurrence of a disaster means that a disaster accident has occurred in the whole process of coal mining for the statistical object, and the avoidance of a disaster means that no disaster accident has occurred in the whole process of coal mining for the statistical object.

[0013] Further, the specific process of marking the monitoring matching means and protection matching means of the depth intervals includes: mark the matching objects using the same monitoring means as monitoring division objects, sum and average the monitoring effectiveness values of all monitoring division objects to obtain the priority data of the monitoring means, and mark the monitoring means with the largest priority data as the monitoring matching means of the depth interval; mark the matching objects using the same protection means as protection division objects, mark the ratio of the number of protection division objects with the disaster state of avoiding a disaster to the total number of protection division objects as protection data, and mark the protection means with the largest protection data as the protection matching means of the depth interval.

[0014] Further, the specific process of monitoring and analyzing the disaster of coal mine roadways includes: arranging the matching objects that adopt the protection matching means and the monitoring matching means within the depth range in descending order of the monitoring effective value to obtain a reference sequence, marking the matching object ranked first in the reference sequence as the layout reference object, comparing the layout reference object with the analysis object to obtain a reference coefficient CK, and extracting the reference data of the layout reference object through the reference coefficient CK.

[0015] Further, the specific process of extracting the reference data of the layout reference object includes: obtaining a reference threshold CKmax through the database, comparing the reference coefficient CK with the reference threshold CKmax: if the reference coefficient CK is less than the reference threshold CKmax, it is determined that the mining environment of the layout reference object is consistent with the analysis object, retrieving the reference data of the layout reference object and sending it to the mobile terminal of the management personnel through the disaster monitoring platform, and the reference data includes the number of monitoring devices, installation locations, and signal transmission methods adopted by the monitoring matching means; if the reference coefficient CK is greater than or equal to the reference threshold CKmax, it is determined that the mining environment of the layout reference object is inconsistent with the analysis object, marking the matching object ranked second in the reference sequence as the new layout reference object, comparing the new layout reference object with the analysis object again, and so on until the extraction of the reference data is completed.

[0016] Further, the specific process of comparing the layout reference object with the analysis object includes: obtaining the stress deviation data YP, temperature deviation data WP, and dip angle deviation data QP between the layout reference object and the analysis object. The stress deviation data YP is the absolute value of the difference between the maximum high ground stress of the layout reference object and the maximum high ground stress of the analysis object. The temperature deviation data YP is the absolute value of the temperature difference between the layout reference object and the analysis object in the underground rock formation. The dip angle deviation data QP is the absolute value of the difference between the coal seam dip angle values of the layout reference object and the analysis object; calculating the reference coefficient CK of the layout reference object relative to the analysis object through numerical calculations of the stress deviation data YP, temperature deviation data WP, and dip angle deviation data QP.

[0017] A method for monitoring the disaster of the surrounding rock of a coal mine roadway includes the following steps:

[0018] Step 1: Statistically analyze the historical monitoring data of the disaster of the surrounding rock of the coal mine roadway: Mark the roadways that have completed coal mining as statistical objects, and the historical monitoring data of the statistical objects includes: mining depth value, monitoring means, protection means, monitoring effective value, and disaster state;

[0019] Step 2: Process the historical monitoring data of the surrounding rock disasters in the coal mine roadway: The depth range is formed by the maximum and minimum values of the mining depth values of all statistical objects. The depth range is divided into several depth intervals. The statistical objects with mining depth values within the depth interval are marked as the matching objects of the depth interval. The monitoring matching means and the protection matching means are marked according to the monitoring effective values and the disaster states of the matching objects;

[0020] Step 3: Conduct disaster monitoring and analysis on the coal mine roadway: Mark the coal mine roadway to be subjected to disaster monitoring and analysis as the analysis object, obtain the mining depth value of the analysis object, and retrieve the monitoring matching means and the protection matching means corresponding to the depth interval of the mining depth value and mark them as the monitoring implementation means and the protection implementation means of the analysis object;

[0021] Step 4: Extract the reference data of the layout reference object: Arrange the matching objects that adopt the protection matching means and the monitoring matching means within the depth interval in descending order of the monitoring effective value to obtain a reference sequence. Screen the layout reference object from the reference sequence, compare the layout reference object with the analysis object, and extract the reference data of the layout reference object.

[0022] The present invention has the following beneficial effects:

[0023] 1. Through the historical data statistics module, the historical monitoring data of the surrounding rock disasters in the coal mine roadway can be statistically analyzed, parameters such as the mining depth value, the monitoring means, and the protection means are recorded, and the recorded results provide data support for the data processing and analysis process;

[0024] 2. Through the historical data processing module, the historical monitoring data of the surrounding rock disasters in the coal mine roadway can be processed. After processing the mining depth value, a depth interval is formed, and then the matching objects in the depth interval are classified according to the monitoring means and the protection means. Finally, the monitoring matching means and the protection matching means are marked according to the monitoring effective value and the disaster state, so that coal mine roadways with different mining depths can quickly match suitable monitoring means and protection means, ensuring the safety of coal mining;

[0025] 3. Through the disaster monitoring and analysis module, the coal mine roadway can be subjected to disaster monitoring and analysis. According to the mining depth value of the analysis object, the monitoring matching means and the protection matching means of the depth interval are retrieved and allocated, and then a reference sequence is generated. The layout reference object and the analysis object are sequentially selected from the reference sequence for comparison, and the reference data of the layout reference object whose mining environment is consistent with the analysis object is extracted. According to the reference data, the monitoring equipment in the roadway of the analysis object is arranged, further improving the effectiveness of the monitoring data. Description of the Drawings

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0027] Figure 1 It is the system block diagram of the first embodiment of the present invention;

[0028] Figure 2 It is the method flow chart of the second embodiment of the present invention. Specific implementation manners

[0029] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0030] Embodiment 1:

[0031] In the prior art, the stability monitoring of the surrounding rock of coal mine roadways mainly relies on real-time data collection and threshold judgment mechanisms; the monitoring equipment collects real-time data such as stress, displacement, and acoustic waves, and compares them with preset thresholds to achieve early warning.

[0032] However, this method has problems such as low utilization rate of historical data, dependence on manual experience for the selection of monitoring means, and lack of multi-dimensional data evaluation mechanisms. For example, in a roadway at a depth of 500 meters in a certain mining area, technicians selected multi-point displacement gauges as the monitoring means based on experience, but did not consider the actual situation that the effective value of the acoustic wave monitoring equipment in the same depth range in historical cases is higher, resulting in a relatively high false alarm rate.

[0033] To solve the above problems, researchers found that establishing a historical data statistical model can improve the scientificity of monitoring decisions. By analyzing the correlation between the monitoring effective values, disaster states, and mining depths of the mined roadways, the optimal selection mechanism of monitoring means and protection means is explored. It is further found that dividing the mining depth into intervals and matching the means can achieve the dynamic optimization of monitoring strategies.

[0034] Therefore, the present application proposes a technical solution including a disaster monitoring platform, such as Figure 1 As shown, a coal mine roadway surrounding rock disaster monitoring system includes a disaster monitoring platform, and the disaster monitoring platform is communicatively connected to a historical data statistical module, a historical data processing module, a disaster monitoring analysis module, and a database; the database is used to store historical monitoring data, surrounding rock geological parameters, and preset standard parameters, and provides a basis for disaster early warning through data comparison and analysis.

[0035] Among them, the disaster monitoring platform refers to the core control system integrating data processing and communication functions, which can be specifically implemented by combining an industrial control computer with an Internet of Things communication module and is responsible for coordinating data interaction among various modules. The historical data statistics module refers to the unit for extracting features from historical monitoring data, which can be specifically implemented by combining a distributed database with statistical analysis algorithms and is used to establish a correlation model between the mining depth and the disaster state. The historical data processing module refers to the arithmetic unit for dividing depth intervals, which can be specifically implemented by using a clustering analysis algorithm and establishes a classification model by classifying statistical objects into different depth intervals. The disaster monitoring and analysis module refers to the decision-making unit for dynamically matching monitoring strategies, which can be specifically implemented by combining a rule engine with a real-time data interface and automatically calls the optimal monitoring and protection plan according to the current roadway depth.

[0036] Specifically, after the data of the completed mined roadways are entered into the system, the extreme values of the mining depth determine the depth range and divide it into multiple intervals. The roadway data within each interval are classified according to the monitoring means, the effective monitoring values and the disaster avoidance rates are statistically analyzed, and the optimal monitoring and protection combination is selected. When a new roadway needs to be monitored, the system automatically matches the optimal means corresponding to the interval according to its mining depth. For example, for a new roadway at a depth of 800 meters, the combination plan of bolt-shotcrete protection and acoustic wave monitoring in this depth interval is directly retrieved, avoiding strategy deviations caused by manual experience.

[0037] Compared with the existing technology, this solution converts experience-driven into data-driven by establishing a quantitative correlation model between the mining depth and the monitoring means. In the traditional method, the selection of monitoring means depends on individual experience, while this solution realizes automatic matching based on the statistical laws of historical data. For example, in the depth interval of 600 - 700 meters, the combination of support protection and multi-point displacement gauges is automatically selected, and this combination shows a disaster avoidance rate of 85% in historical data, which is significantly higher than 72% of manual selection.

[0038] The historical data statistics module is used to statistically analyze the historical monitoring data of surrounding rock disasters in coal mine roadways: the roadways that have completed coal mining are marked as statistical objects, and the historical monitoring data of the statistical objects include: mining depth value, monitoring means, protection means, monitoring effectiveness, and disaster state. The monitoring means include convergence meter measurement, multi-point displacement meter measurement, acoustic wave monitoring equipment, and vibration meter measurement. The protection means include support protection, reinforcement protection, stress control protection, and shotcrete support; the monitoring effectiveness is the ratio of the number of effective alarm signals to the number of ineffective alarm signals when the statistical object uses the monitoring means for disaster risk monitoring; the disaster state includes disaster occurrence and disaster avoidance. Disaster occurrence means that a disaster accident has occurred in the whole process of coal mining of the statistical object, and disaster avoidance means that no disaster accident has occurred in the whole process of coal mining of the statistical object; record parameters such as mining depth value, monitoring means, and protection means, and provide data support for the data processing and analysis process according to the recording results.

[0039] Among them, convergence meter measurement refers to monitoring the deformation of the surrounding rock by measuring the change in the distance between two points on the surface of the surrounding rock of the roadway. Specifically, it can be realized by using a mechanical or electronic convergence meter, and is used to obtain the convergence deformation data of the surrounding rock in real time. Multi-point displacement meter measurement refers to arranging multiple measuring points inside the surrounding rock of the roadway to measure the displacement changes at different depths. Specifically, it can be realized by using an anchored displacement sensor, and is used to analyze the internal deformation gradient of the surrounding rock. Acoustic wave monitoring equipment refers to detecting the development of fractures in the surrounding rock by emitting acoustic wave signals and receiving reflected waves. Specifically, it can be realized by combining an ultrasonic transmitter and a receiver, and is used to evaluate the integrity of the surrounding rock structure. Vibration meter measurement refers to collecting the vibration signals generated by the surrounding rock affected by mining. Specifically, it can be realized by using a three-axis acceleration sensor, and is used to identify the dynamic disturbance characteristics of the surrounding rock. Support protection refers to supporting the surrounding rock by erecting a steel arch or concrete structure. Specifically, it can be realized by using a U-shaped steel support, and is used to limit the deformation of the surrounding rock. Reinforcement protection refers to grouting or bolting the surrounding rock. Specifically, it can be realized by using chemical grouting materials or fully grouted bolts, and is used to improve the bearing capacity of the surrounding rock. Stress control protection refers to adjusting the stress distribution of the surrounding rock by means of pressure relief boreholes or roof cutting measures. Specifically, it can be realized by using high-pressure water jet roof cutting technology, and is used to relieve stress concentration. Shotcrete support refers to combining bolt support and shotcrete to form a combined support system. Specifically, it can be realized by using deformed steel bars and steel fiber concrete, and is used to enhance the surface stability of the surrounding rock. The monitoring effectiveness is used to quantitatively evaluate the reliability of the monitoring means by statistically calculating the ratio of the number of effective alarms to the number of ineffective alarms. The disaster state is used to establish a benchmark for evaluating the effectiveness of the protection means by classifying whether a disaster accident has occurred to the statistical object.

[0040] Specifically, the convergence meter measures and real-time collects the surface displacement data of the surrounding rock through the measuring points installed on the roof and two sides of the roadway, and triggers an alarm when the displacement rate exceeds the preset threshold. The multi-point displacement meter forms a displacement monitoring network by arranging sensors at different depths inside the surrounding rock to identify the characteristics of the deformation depth distribution. The acoustic monitoring device periodically emits ultrasonic waves and analyzes the attenuation degree of the echo signal, and generates an abnormal warning when the crack expansion causes a change in the acoustic wave propagation path. The vibrometer continuously collects the spectral characteristics of the surrounding rock vibration signal, and judges the stability state of the surrounding rock by analyzing the main frequency shift and energy change. The support protection restricts the deformation space of the surrounding rock through a rigid support, and initiates secondary support when the roof subsidence exceeds the safe range. The reinforcement protection uses grouting materials to fill the cracks in the surrounding rock, and improves the compressive strength of the surrounding rock through the composite body formed after the slurry solidifies. The stress control protection adopts directional drilling to release the energy in the high-stress area, and verifies the pressure relief effect by monitoring the deformation of the drill hole. The bolt-shotcrete protection combines the deep reinforcement of the bolt and the surface sealing effect of the shotcrete layer to form a collaborative support system. When calculating the effective monitoring value, the situation where the surrounding rock instability actually occurs after triggering the disaster warning is recorded as an effective alarm, and the situation where no disaster occurs but the warning is triggered is recorded as an invalid alarm, and the monitoring means with a low false alarm rate is selected through the ratio. When classifying the disaster states, the roadway where roof fall, rib spalling or floor heave accidents occur during the mining process is marked as having a disaster, and the roadway without accidents is marked as avoiding disasters, establishing a data basis for evaluating the effectiveness of the protection means.

[0041] The historical data processing module is used to process the historical monitoring data of the surrounding rock disasters in coal mine roadways: the depth range is composed of the maximum and minimum values of the mining depth values of all statistical objects, the depth range is divided into several depth intervals, and the statistical objects with mining depth values within the depth interval are marked as the matching objects of the depth interval. The matching objects are classified according to the monitoring means and protection means adopted. The matching objects using the same monitoring means are marked as the monitoring division objects. The average value of the sum of the effective monitoring values of all monitoring division objects is obtained as the priority data of the monitoring means, and the monitoring means with the largest priority data is marked as the monitoring matching means of the depth interval. The matching objects using the same protection means are marked as the protection division objects. The ratio of the number of protection division objects with the disaster state of avoiding disasters to the total number of protection division objects is marked as the protection data, and the protection means with the largest protection data is marked as the protection matching means of the depth interval. After processing the mining depth values to form depth intervals, then classify the matching objects of the depth intervals according to the monitoring means and protection means, and finally mark the monitoring matching means and protection matching means according to the effective monitoring value and the disaster state, so that coal mine roadways with different mining depths can quickly match suitable monitoring means and protection means, ensuring the safety of coal mine mining.

[0042] The disaster monitoring and analysis module is used to monitor and analyze disasters in coal mine roadways: mark the coal mine roadway to be monitored and analyzed as the analysis object, obtain the mining depth value of the analysis object, retrieve the monitoring matching means and protection matching means corresponding to the depth interval of the mining depth value and mark them as the monitoring implementation means and protection implementation means of the analysis object; arrange the matching objects using the protection matching means and monitoring matching means within the depth interval in descending order of the monitoring effective value to obtain a reference sequence, mark the matching object ranked first in the reference sequence as the layout reference object, compare the layout reference object with the analysis object to obtain a reference coefficient CK; further, the reference coefficient is determined based on the statistical analysis of the historical monitoring data of the surrounding rock of the coal mine roadway and industry standards and stored in the system database.

[0043] Further, during the data collection process of the coal mine roadway, the coal mine roadway enters the database after data collection and serves as the core for storing historical data collection and analysis. At this stage, the discrete state of the collected data of the coal mine roadway will be continuously optimized so that when the data in the database is used as analysis data, the data accuracy of the database can be improved to a greater extent. During the data collection and processing stage, the availability of the data is getting higher and higher, so that the operation of the entire data processing system is smoother, further improving the efficiency of data analysis;

[0044] Obtain the reference threshold CKmax through the database; it should be further explained that the threshold or preset value, preset range, etc. are set for result comparison and analysis to determine whether it is good or bad. Regarding the size value of it, it is set and stored based on the combination of the large model analysis of sample data and manual experience, and can also be appropriately adjusted according to seasonal or regular influencing conditions;

[0045] Compare the reference coefficient CK with the reference threshold CKmax: if the reference coefficient CK is less than the reference threshold CKmax, it is determined that the mining environment of the layout reference object is consistent with the analysis object, retrieve the reference data of the layout reference object and send it to the mobile terminal of the management personnel through the disaster monitoring platform. The reference data includes the number of monitoring devices used by the monitoring matching means, the installation location, and the signal transmission method; if the reference coefficient CK is greater than or equal to the reference threshold CKmax, it is determined that the mining environment of the layout reference object is inconsistent with the analysis object, mark the matching object ranked second in the reference sequence as the new layout reference object, compare the new layout reference object with the analysis object again, and so on until the extraction of the reference data is completed.

[0046] Among them, the monitored division object refers to the set of matching objects using the same monitoring method, which can be specifically implemented through a data classification algorithm. For example, clustering and grouping the matching objects based on the monitoring method type field. The priority data refers to the arithmetic mean of the monitoring effective values of all matching objects under the same monitoring method, which is used to quantify the effectiveness of the monitoring method within a specific depth interval. The protected division object refers to the set of matching objects using the same protection method, and data grouping can be performed through the protection method type field. The protection data is obtained by calculating the proportion of protected division objects that avoid catastrophes, which is used to reflect the catastrophe avoidance ability of the protection method within a specific depth interval.

[0047] Specifically, when determining the monitoring matching method for a depth interval, first group the matching objects according to the monitoring method type to form multiple monitored division object groups. Sum up the monitoring effective values within each group and calculate the average value to generate the corresponding priority data. By comparing the priority data of different monitoring methods, select the monitoring method with the largest value as the recommended solution for this depth interval. When determining the protection matching method, group the matching objects according to the protection method type, count the number of objects with the catastrophe state of avoiding catastrophes in each group, and calculate the ratio of it to the total number as the protection data. Finally, select the protection method with the highest protection data as the recommended solution for this depth interval. For example, among the matching objects in the depth interval of 500 - 600 meters, if the average value of the monitoring effective values measured by the multi-point displacement meter is 0.92, which is higher than other monitoring methods, then mark this monitoring method as the monitoring matching method; if the protection data corresponding to the shotcrete protection is 0.85, which is higher than other protection methods, then mark it as the protection matching method.

[0048] Compared with the prior art, the selection of existing monitoring and protection methods mainly relies on manual experience or single-index judgment, lacking comprehensive analysis of historical data. For example, the prior art may only select based on the alarm times of a single monitoring device or the usage frequency of the protection method, while this solution can perform quantitative analysis by combining multi-dimensional historical data through the average calculation of monitoring effective values and the evaluation of the catastrophe avoidance ratio of protection data, eliminating the decision-making bias caused by subjective experience.

[0049] The specific process of comparing the layout reference object with the analysis object includes: obtaining the stress deviation data YP, temperature deviation data WP, and dip angle deviation data QP between the layout reference object and the analysis object. The stress deviation data YP is the absolute value of the difference between the maximum high ground stress of the layout reference object and the maximum high ground stress of the analysis object. The temperature deviation data YP is the absolute value of the temperature difference between the layout reference object and the analysis object in the underground rock formation. The dip angle deviation data QP is the absolute value of the difference between the coal seam dip angle values of the layout reference object and the analysis object. The reference coefficient CK of the layout reference object relative to the analysis object is obtained through the formula CK = c1×YP + c2×WP + c3×QP, where c1, c2, and c3 are all proportionality coefficients, and c1 > c2 > c3 > 1. According to the mining depth value of the analysis object, the monitoring matching means and protection matching means in the depth interval are retrieved and allocated, and then a reference sequence is generated. The layout reference object and the analysis object are sequentially selected from the reference sequence for comparison. The reference data of the layout reference object whose mining environment matches the analysis object are extracted, and the monitoring equipment in the roadway of the analysis object is arranged according to the reference data, further improving the effectiveness of the monitoring data.

[0050] Among them, the stress deviation data refers to the degree of difference in the maximum high ground stress of the roadway where the layout reference object and the analysis object are located. Specifically, it can be measured by stress sensors arranged inside the surrounding rock of the roadway. The maximum high ground stress can be extracted from the peak value in the continuous monitoring data.

[0051] This data is used to evaluate the geological stress similarity of the mining environment and avoid the failure of protection means due to stress differences. Among them, the temperature deviation data refers to the quantitative index of the temperature difference between the rock formations where the layout reference object and the analysis object are located. Specifically, it can be monitored in real time by a distributed optical fiber temperature measurement system buried in the rock formation.

[0052] This data reflects the difference in the thermal environment of the rock formation and affects the physical properties of the surrounding rock and the adaptability of protection means. Among them, the dip angle deviation data refers to the absolute value of the difference in the coal seam dip angle. Specifically, it can be measured by a laser inclinometer or three-dimensional geological modeling technology. This data is used to judge the similarity of the geometric shape of the mining face and directly affects the stress distribution law and the design of the support structure.

[0053] Among them, the proportionality coefficients are set in decreasing order according to the stress deviation, temperature deviation, and dip angle deviation. Specifically, the influence weights of each parameter on the occurrence of disasters can be determined based on historical data regression analysis. This setting reflects the difference in the contribution degree of different environmental factors to the adaptability of monitoring and protection means, ensuring that key parameters dominate the matching decision.

[0054] Specifically, during the monitoring of roadway surrounding rock disasters, when it is necessary to select an appropriate monitoring and protection plan for the current analysis object, first retrieve the historically optimal layout reference object within the same depth range from the database. Real-time obtain the high ground stress distribution data of the analysis object through the stress monitoring system installed in the roadway, and extract its stress peak value; at the same time, collect the rock formation temperature data using the temperature sensor network, and measure the coal seam dip angle using geological exploration equipment.

[0055] Calculate the difference between the above three measured data and the corresponding data of the layout reference object respectively and take the absolute value to form a quantitative deviation index. Weighted sum according to the pre-set proportional coefficient to generate a comprehensive reference coefficient. The threshold judgment mechanism of this coefficient can effectively identify geological condition differences, ensure that the mining environment of the selected reference object is sufficiently similar to the current analysis object, so as to ensure the reliability of transplanting the historical successful plan.

[0056] Embodiment 2: As Figure 2 shown, a method for monitoring disasters of surrounding rock in coal mine roadways includes the following steps:

[0057] Step 1: Statistically analyze the historical monitoring data of disasters of surrounding rock in coal mine roadways: Mark the roadways that have completed coal mining as statistical objects, and the historical monitoring data of the statistical objects include: mining depth value, monitoring means, protection means, monitoring effective value, and disaster state;

[0058] Step 2: Process the historical monitoring data of disasters of surrounding rock in coal mine roadways: The depth range is composed of the maximum and minimum values of the mining depth values of all statistical objects. Divide the depth range into several depth intervals, and mark the statistical objects whose mining depth values are within the depth intervals as matching objects of the depth intervals. Mark the monitoring matching means and protection matching means according to the monitoring effective value and disaster state of the matching objects;

[0059] Step 3: Conduct disaster monitoring and analysis on coal mine roadways: Mark the coal mine roadway to be subjected to disaster monitoring and analysis as the analysis object, obtain the mining depth value of the analysis object, retrieve the monitoring matching means and protection matching means of the corresponding depth interval of the mining depth value, and mark them as the monitoring implementation means and protection implementation means of the analysis object;

[0060] Step 4: Extract the reference data of the layout reference object: Arrange the matching objects that adopt the protection matching means and monitoring matching means within the depth interval in descending order of the monitoring effective value to obtain a reference sequence. Screen the layout reference object from the reference sequence, compare the layout reference object with the analysis object, and extract the reference data of the layout reference object.

[0061] A coal mine roadway surrounding rock disaster monitoring system and method. During operation, the roadway where coal mining has been completed is marked as the statistical object. The historical monitoring data of the statistical object includes: mining depth value, monitoring means, protection means, monitoring effective value, and disaster state. The depth range is composed of the maximum and minimum values of the mining depth values of all statistical objects. The depth range is divided into several depth intervals. The statistical objects with mining depth values within the depth interval are marked as the matching objects of the depth interval. The monitoring matching means and protection matching means are marked according to the monitoring effective value and disaster state of the matching objects. The coal mine roadway for disaster monitoring analysis is marked as the analysis object. The mining depth value of the analysis object is obtained, and the monitoring matching means and protection matching means corresponding to the depth interval of the mining depth value are retrieved and marked as the monitoring implementation means and protection implementation means of the analysis object. The matching objects within the depth interval that adopt the protection matching means and monitoring matching means are arranged in descending order of the monitoring effective value to obtain a reference sequence. The layout reference objects are screened from the reference sequence, and the layout reference objects are compared with the analysis object and the reference data of the layout reference objects are extracted.

[0062] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0063] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. For example: the formula CK = c1×YP + c2×WP + c3×QP; those skilled in the art collect multiple groups of sample data and set corresponding reference coefficients for each group of sample data; substitute the set reference coefficients and the collected sample data into the formula, and any three formulas form a system of linear equations with three variables. The calculated coefficients are screened and the average value is taken to obtain the values of c1, c2, and c3 as 3.68, 2.81, and 2.32 respectively.

[0064] The magnitude of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the preliminary setting of corresponding reference coefficients for each group of sample data by those skilled in the art; as long as it does not affect the proportional relationship between the parameter and the quantified value, for example, the reference coefficient is proportional to the numerical value of the stress deviation data.

[0065] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0066] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A monitoring system for the disaster transformation of the surrounding rock of a coal mine roadway, characterized in that It includes a disaster monitoring platform, which is communicatively connected to a historical data statistics module, a historical data processing module, a disaster monitoring and analysis module, and a database; the database is used to store data and provide a basis for disaster warning through data comparison and analysis; The historical data statistics module is used to statistically analyze the historical monitoring data of the surrounding rock disasters in coal mine roadways: The historical data processing module is used to process the historical monitoring data of the surrounding rock disasters in coal mine roadways: The disaster monitoring and analysis module is used to monitor and analyze coal mine roadways for disasters.

2. The coal mine roadway surrounding rock disaster monitoring system according to claim 1, wherein Mark the roadways that have completed coal mining as statistical objects. The historical monitoring data of the statistical objects include: mining depth value, monitoring means, protection means, monitoring effectiveness value, and disaster state; the monitoring means include convergence meter measurement, multi-point displacement meter measurement, acoustic monitoring equipment, and vibration meter measurement, and the protection means include support protection, reinforcement protection, stress control protection, and shotcrete protection; the monitoring effectiveness value is the ratio of the number of effective alarm signals to the number of ineffective alarm signals when the statistical object uses the monitoring means for disaster risk monitoring; the disaster state includes disaster occurrence and disaster avoidance. Disaster occurrence means that a disaster accident has occurred in the whole process of coal mining of the statistical object, and disaster avoidance means that no disaster accident has occurred in the whole process of coal mining of the statistical object.

3. The coal mine roadway surrounding rock disaster monitoring system according to claim 2, characterized in that The specific process of marking the monitoring matching means and protection matching means for depth intervals includes: forming a depth range from the maximum and minimum values of the mining depth values of all statistical objects, dividing the depth range into several depth intervals, marking the statistical objects with mining depth values within the depth interval as the matching objects of the depth interval, classifying the matching objects according to the monitoring means and protection means used, and marking the monitoring matching means and protection matching means for the depth interval; marking the matching objects using the same monitoring means as the monitoring classification objects, summing and averaging the monitoring effectiveness values of all monitoring classification objects to obtain the priority data of the monitoring means, and marking the monitoring means with the largest priority data as the monitoring matching means for the depth interval; marking the matching objects using the same protection means as the protection classification objects, marking the ratio of the number of protection classification objects with the disaster state of disaster avoidance to the total number of protection classification objects as the protection data, and marking the protection means with the largest protection data as the protection matching means for the depth interval.

4. The coal mine roadway surrounding rock disaster monitoring system according to claim 3, characterized in that, The specific process of monitoring and analyzing coal mine roadways for disasters includes: marking the coal mine roadway to be monitored and analyzed for disasters as the analysis object, obtaining the mining depth value of the analysis object, retrieving the monitoring matching means and protection matching means for the corresponding depth interval of the mining depth value and marking them as the monitoring implementation means and protection implementation means of the analysis object; arranging the matching objects using the protection matching means and monitoring matching means within the depth interval in descending order of the monitoring effectiveness value to obtain a reference sequence, marking the first matching object in the reference sequence as the layout reference object, comparing the layout reference object with the analysis object to obtain a reference coefficient CK, and extracting the reference data of the layout reference object through the reference coefficient CK.

5. The coal mine roadway surrounding rock disaster monitoring system according to claim 4, characterized in that, The specific process of extracting reference data for the layout reference object includes: obtaining the reference threshold CKmax through the database, and comparing the reference coefficient CK with the reference threshold CKmax. If the reference coefficient CK is less than the reference threshold CKmax, it is determined that the mining environment of the layout reference object conforms to the analysis object, and the reference data of the layout reference object is retrieved and sent to the mobile terminal of the management personnel through the disaster monitoring platform. The reference data includes the number of monitoring devices, installation locations, and signal transmission methods adopted by the monitoring matching means. If the reference coefficient CK is greater than or equal to the reference threshold CKmax, it is determined that the mining environment of the layout reference object does not conform to the analysis object, and the matching object ranked second in the reference sequence is marked as the new layout reference object. The new layout reference object is compared with the analysis object again, and so on until the extraction of the reference data is completed.

6. The coal mine roadway surrounding rock disaster monitoring system according to claim 5, characterized in that, The specific process of comparing the layout reference object with the analysis object includes: obtaining the stress deviation data YP, temperature deviation data WP, and dip angle deviation data QP of the layout reference object and the analysis object. The stress deviation data YP is the absolute value of the difference between the maximum high ground stress of the layout reference object and the maximum high ground stress of the analysis object. The temperature deviation data YP is the absolute value of the temperature difference between the layout reference object and the analysis object in the underground rock formation. The dip angle deviation data QP is the absolute value of the difference between the coal seam dip angle values of the layout reference object and the analysis object. The reference coefficient CK of the layout reference object relative to the analysis object is obtained through the formula CK = c1×YP + c2×WP + c3×QP, where c1, c2, and c3 are all proportionality coefficients, and c1 > c2 > c3 > 1.

7. A method for monitoring the disaster of surrounding rock in a coal mine roadway, characterized in that, It includes the following steps: Step 1: Statistically analyze the historical monitoring data of the surrounding rock disasters in the coal mine roadway: Mark the roadways that have completed coal mining as the statistical objects. The historical monitoring data of the statistical objects includes: mining depth value, monitoring means, protection means, monitoring effective value, and disaster state. Step 2: Process the historical monitoring data of the surrounding rock disasters in the coal mine roadway: The depth range is composed of the maximum and minimum values of the mining depth values of all statistical objects. The depth range is divided into several depth intervals. The statistical objects with mining depth values within the depth interval are marked as the matching objects of the depth interval. The monitoring matching means and protection matching means are marked according to the monitoring effective value and disaster state of the matching objects. Step 3: Conduct disaster monitoring and analysis on the coal mine roadway: Mark the coal mine roadway undergoing disaster monitoring and analysis as the analysis object, obtain the mining depth value of the analysis object, and retrieve the monitoring matching means and protection matching means corresponding to the depth interval of the mining depth value and mark them as the monitoring implementation means and protection implementation means of the analysis object. Step 4: Extract the reference data of the layout reference object: Arrange the matching objects that adopt the protection matching means and monitoring matching means within the depth interval in descending order of the monitoring effective value to obtain the reference sequence. Screen the layout reference object from the reference sequence, compare the layout reference object with the analysis object, and extract the reference data of the layout reference object.

Images