A modular, segmented, and grouped monitoring method for early warning of roof collapse in roadways.

Abstract

This invention provides a modular segmented and grouped monitoring method for early warning of roof fall in roadways, comprising the following steps: S1, obtaining the initial and maximum preload of anchor bolts used in coal mine production sites, and determining the anchor bolt safety factor; S2, modularly segmenting and grouping the roadway, subdividing the complete roadway into regional subdivisions, forming a modular monitoring layout of roadway-segment, segment-group, and group-monitoring point, and obtaining the roof stability coefficient within the group; S3, based on the modular segmented and grouped method, continuously monitoring and collecting the anchor bolt preload of the monitoring points, and transmitting it to the integrated data processing platform via a data transmission module; S4, the integrated data processing platform receives the monitoring data, obtains the anchor bolt safety factor of the monitoring point, the roof stability coefficient within the group, and the rate of change of the average anchor bolt preload, and captures abnormal data for early warning and storage. The roof fall early warning method provided by this invention can realize intelligent, timely, and accurate early warning of potential roof fall hazards in roadways, promoting the development of smart mine construction.

Description

Technical Field

[0001] This invention relates to the field of coal mine roof detection technology, and in particular to a modular segmented and grouped monitoring method for early warning of roof collapse in roadways. Background Technology

[0002] In coal mine production, the stability of roadways directly determines the safety of the underground working environment. Due to the continuous advancement of coal mining technology in my country, requirements for transportation and ventilation have also increased, leading to a trend towards larger cross-sections and multiple roadway layouts. In coal mine production sites, stress concentration can occur in localized areas of roadways under the influence of regional stress fields and mining disturbances, easily inducing roof collapses and seriously threatening the safety of mine workers and production equipment. Currently, rock bolt support technology is widely used in mines both domestically and internationally. Applying pre-tightening force to the ends of the rock bolts anchors them to the surrounding roof rock, providing active support to pre-constrain and control the roadway roof rock. This effectively improves the stability of the roadway surrounding rock, reduces the accident rate, and contributes to increased safety and efficiency in coal mine production.

[0003] Currently, early warning of roof falls in coal mine roadways mainly relies on monitoring and analyzing the amount of roof strata delamination and mine pressure. Since roof delamination displacement information can only be identified after delamination has occurred, the feedback of early warning information is relatively delayed, and preventative measures are often taken too late. Furthermore, to save resources, comprehensive monitoring of the entire roadway is not conducted during roof delamination and mine pressure monitoring; instead, measurements are often taken in typical local areas. This means that current technologies cannot provide comprehensive, timely, and intelligent monitoring and early warning of the roadway roof. Moreover, manual monitoring suffers from low accuracy and strong subjectivity. In addition, with the rapid development of smart mining technology, various specialized monitoring anchor bolts equipped with sensors have been gradually and effectively applied in the field. However, these specialized anchor bolts are expensive, and the sensors driven into the rock strata are easily damaged under mine pressure, which is detrimental to the stability of the surrounding rock in the roadway. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a modular segmented and grouped monitoring method for early warning of roof collapse in roadways. The roadway is modularly segmented and grouped, and the anchor bolt preload is monitored and collected at each group. The data is then intelligently analyzed and processed by a comprehensive data processing platform, and early warning of roof collapse is achieved based on multiple characteristic quantities of the monitoring data.

[0005] To achieve the above objectives, the technical solution adopted by the present invention includes the following steps:

[0006] S1, obtain the initial preload and maximum preload of the anchor bolts used in the coal mine production site, and determine the safety factor α of the anchor bolts;

[0007] S2, modular segmentation and grouping of the roadway, subdividing the complete roadway into regional subdivisions to form a modular monitoring layout of roadway-segment, segment-group, and group-monitoring point, and obtaining the roof stability coefficient within the group;

[0008] S3, based on the modular segmented grouping method, continuously monitors and collects the anchor bolt preload at the monitoring points, and transmits it to the integrated data processing platform via the data transmission module;

[0009] S4, the integrated data processing platform receives the anchor bolt preload information and obtains the anchor bolt safety factor α, the top plate stability coefficient β within the group, and the average change rate ΔF of the anchor bolt preload. i It captures abnormal data for early warning and storage.

[0010] Furthermore, the initial preload of the anchor bolt mentioned in S1 is the preload applied during anchor bolt installation on site, while the maximum preload is the preload obtained from a laboratory pull-out test when the anchor bolt is on the verge of breaking. An early warning sensitivity is set for the obtained initial and maximum preload of the anchor bolt, and an anchor bolt safety factor α is proposed to assess the health status of a single anchor bolt.

[0011] The safety factor α of the anchor bolt is expressed by the following formula:

[0012] aF c <α<aF max (1)

[0013] In the formula: α is the safety factor of the anchor bolt, F c F represents the initial preload of the anchor bolt, in MPa. max denoted as the maximum preload of the anchor bolt, in MPa; 'a' represents the early warning sensitivity.

[0014] Furthermore, the modular segmentation and grouping described in S2 involves finely dividing the roadway into several sections, each section into several groups, and each group into several monitoring points. The health status of the anchor bolts at the monitoring points within each group is analyzed to propose a roof stability coefficient β. Based on the anchor bolt preload monitoring results, the average anchor bolt preload F of any group within the section is calculated. i The data is then compared and analyzed in real time. This modular segmentation and grouping method is directly integrated into the comprehensive information processing platform, enabling intelligent analysis and processing of monitoring data to achieve early warning of roof collapse in multi-parameter roadways.

[0015] The stability coefficient of the top plate is calculated by the following formula:

[0016]

[0017] In the formula: β is the stability coefficient of the top plate, n i Let N be the number of healthy anchors in group i. i This represents the number of monitoring points within group i.

[0018] Furthermore, the anchor bolt preload continuous monitoring and acquisition device described in S3 uses a force sensor, which is fixed to the anchor bolt tray nut with a nut. An automatic acquisition frequency is set to achieve continuous monitoring and acquisition. In addition, the data transmission module is electrically connected to the monitoring device and transmits the monitoring data to the integrated data processing platform wirelessly at preset time intervals.

[0019] Furthermore, the integrated data processing platform described in S4 includes a data transceiver module, a data processing module, a data visualization module, an early warning module, and a data storage module, all of which are electrically connected. Due to the special nature of underground coal mine work, all of the above-mentioned device modules are explosion-proof. The safety factor α of the anchor bolts at the monitoring points and the roof stability coefficient β within the group are calculated, and the rate of change ΔF of the average preload of the anchor bolts in each group is obtained. i The data results are then visualized as curves. The anchor bolt safety factor α, roof stability coefficient β, and the rate of change of the mean anchor bolt preload ΔF are captured. i Abnormal data enables intelligent, timely, and accurate early warning of roof collapse in roadways.

[0020] The mean change rate ΔF of the anchor bolt preload i Calculated using the following formula:

[0021]

[0022] In the formula: ΔF i F represents the rate of change of the mean preload of the anchor bolt. i(t) Let F be the average preload data at time t for group i. i(t-1) The mean value of the preload force data at time t-1 in group i.

[0023] By adopting the above technical solution, the present invention has the following beneficial effects:

[0024] 1. The early warning method proposed in this invention modularly segments and groups the entire roadway, subdividing the complete roadway into regional areas to form a modular monitoring layout of roadway-segment, segment-group, and group-monitoring point. The monitoring area is refined and the coverage is wide, which significantly improves the efficiency of roof fall early warning, ensures safe production in coal mines, and promotes the development of smart mine construction.

[0025] 2. The early warning method proposed in this invention uses a monitoring device that belongs to non-destructive testing technology. It will not affect the original quality of the surrounding rock support of the roadway. The device can automatically monitor and collect information, avoiding the shortcomings of low accuracy and strong subjectivity of manual monitoring.

[0026] 3. The early warning method proposed in this invention uses a comprehensive data processing platform to intelligently analyze and process monitoring data, combining the anchor bolt safety factor α, the roof stability coefficient β, and the average change rate ΔF of the anchor bolt preload.i Three characteristic quantities are used to provide early warning of potential roof collapse in roadways, and the early warning results are highly reliable.

[0027] 4. The early warning method proposed in this invention stores abnormal data and establishes an early warning case library, which can provide a reference for subsequent early warning of roof collapse in roadways and the design of support schemes. Attached Figure Description

[0028] Figure 1 The flowchart illustrates the implementation of a modular segmented group monitoring method for early warning of roof collapse in roadways, as provided by this invention.

[0029] Figure 2 A block diagram of a modular segmented grouping structure for a roadway, provided as an embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the monitoring device arrangement for an embodiment of the present invention.

[0031] Figure 4 The above is a block diagram of the overall device structure of an embodiment of the present invention.

[0032] Figure 5 A flowchart of the early warning method provided in the embodiments of the present invention.

[0033] Explanation of reference numerals in the attached figures:

[0034] 1-Roof strata, 2-Anchor bolt, 3-Force sensor, 4-Wireless transmission module, 5-Anchor bolt tray nut, 6-Force sensor fixing nut, 7-Integrated data processing platform. Detailed Implementation

[0035] The present invention will now be described in further detail with reference to the accompanying drawings.

[0036] like Figure 1 As shown, this embodiment of the invention provides a modular segmented group monitoring method for early warning of roof collapse in roadways, comprising the following steps:

[0037] S1, obtain the initial preload and maximum preload of the anchor bolts used in the coal mine production site, and determine the safety factor α of the anchor bolts.

[0038] Pull-out force tests were conducted on anchor bolts used in coal mine production sites. An anchor bolt preload monitoring instrument and force sensor were used to measure the preload, obtaining the maximum preload when the anchor bolt was on the verge of breakage. An early warning sensitivity was set based on the initial and maximum preload values ​​of the anchor bolt, and the health status of individual anchor bolts was assessed using the anchor bolt safety factor α.

[0039] In this invention, to achieve flexibility and convenience, a simple force sensor is used as the monitoring device, resulting in some measurement error. By comparing the results with those of an anchor bolt preload monitoring instrument, the error range is determined. An early warning sensitivity is introduced to reduce the impact of the error, ultimately yielding an anchor bolt safety factor α to evaluate the quality of the anchor bolt support. The anchor bolt safety factor α is expressed by the following formula:

[0040] aF c <α<aF max (1)

[0041] In the formula: α is the safety factor of the anchor bolt, F c F represents the initial preload of the anchor bolt, in MPa. max is the maximum preload of the anchor bolt, in MPa; 'a' is the early warning sensitivity, typically taken as 0.8.

[0042] S2 involves modularly segmenting and grouping the roadway, subdividing the complete roadway into regional areas, and forming a modular monitoring layout of roadway-segment, segment-group, and group-monitoring point to obtain the roof stability coefficient within the group.

[0043] like Figure 2 As shown, to perform refined monitoring of the tunnel, the entire tunnel is divided into segment 1, segment 2, and so on up to segment N (N>2), and each segment is further divided into more refined groups. In this embodiment, segment 2 is taken as an example, and it is divided into group 1, group 2, and so on up to group n (n>2). Taking group 2 as an example, monitoring points are set for local areas within the group, and these areas are divided into monitoring point 1, monitoring point 2, and so on up to monitoring point n (n>2). To ensure the accuracy of the early warning results, the number of monitoring points should be more than 30% higher than the number of anchor bolts within the group.

[0044] Based on a modular segmented grouping method, the health status of anchor bolts at monitoring points within a group is assessed using the anchor bolt safety factor α, and the roof stability coefficient β within that group is obtained. Based on the anchor bolt preload monitoring results, the rate of change ΔF of the average anchor bolt preload in any group within the segment is calculated. i And perform real-time comparison and analysis.

[0045] The stability coefficient β of the top plate is calculated by the following formula:

[0046]

[0047] In the formula: β is the stability coefficient of the top plate, n i Let N be the number of healthy anchors in group i. i This represents the number of monitoring points within group i.

[0048] The modular segmentation and grouping of roadways takes into account multiple factors such as mine geological conditions, current support status, and historical roof falls. When dividing the roadways into segments, the segments can be relatively sparse, while when dividing them into segments, the segments can be relatively dense, and the division is not fixed.

[0049] S3, based on a modular segmented grouping method, continuously monitors and collects the preload of anchor bolts at monitoring points, and transmits the data to the integrated data processing platform via a data transmission module.

[0050] like Figure 3 As shown, the monitoring device uses a force sensor 3, which is fixed to the exposed end of the anchor rod 2 by the force sensor fixing nut 6 and tightly connected to the anchor rod tray nut 5. The device automatically monitors and collects the preload of the anchor rod 2 according to the set acquisition frequency. In addition, the wireless transmission module 4 is electrically connected to the force sensor 3 and sends the collected preload information to the integrated data processing platform 7 at a preset time interval (1 min).

[0051] S4, the integrated data processing platform receives the anchor bolt preload information and obtains the anchor bolt safety factor α, the top plate stability coefficient β within the group, and the average change rate ΔF of the anchor bolt preload. i It captures abnormal data for early warning and storage.

[0052] like Figure 4 As shown, the integrated data processing platform includes a data transceiver module, a data visualization module, a data processing module, an early warning module, and a data storage module, all of which are electrically connected. Due to the special nature of underground coal mining operations, all of the above-mentioned modules are explosion-proof. The data receiving module receives monitoring data; the data processing module intelligently analyzes and processes the monitoring data, calculating the anchor bolt safety factor α, the roof stability coefficient β within the group, and the rate of change ΔF of the average anchor bolt preload. i Simultaneously, it compares historical data in real time; the data visualization module displays the data results in the form of curves; the early warning module captures abnormal data and provides audible and visual warnings for areas with potential roof fall hazards, promptly reminding workers to investigate and handle the hazards; the data storage module stores abnormal data and establishes an early warning case library to provide comparative analysis for subsequent roadway roof fall monitoring, early warning, and support effects.

[0053] The mean change rate ΔF of the anchor bolt preload i Calculated using the following formula:

[0054]

[0055] In the formula: F i(t) Let F be the average preload data at time t for group i. i(t-1) The mean value of the preload force data at time t-1 in group i.

[0056] like Figure 5 As shown, the roof fall early warning method in this embodiment is based on the roof stability coefficient β and the average change rate ΔF of the anchor bolt preload. i Both systems provide independent early warnings, resulting in highly accurate and reliable warning results.

[0057] (1) Early warning based on the roof stability coefficient β. The number of healthy anchor bolts is assessed by the safety factor α of the anchor bolts at the monitoring points, and then the roof stability coefficient β is obtained. The level of roof fall warning is determined according to the specific value of β. When β is greater than 0.95, the area is safe; when β is between 0.90 and 0.95, there is a risk of roof fall, but it is not easy to fall, so a Level I warning is issued; when β is between 0.80 and 0.90, it is easy to fall, so a Level II warning is issued; when β is less than 0.80, it is extremely easy to fall, so a Level III warning is issued.

[0058] (2) Based on the rate of change of the average anchor bolt preload ΔF i Early warning was issued. The rate of change ΔF of the average preload of each anchor bolt group within the section was calculated. i When ΔF i When ΔF is greater than 2 or less than 0.5, roof collapse is highly likely, triggering a Level III warning; when ΔF i When the value is between 1.5 and 2 or between 0.5 and 0.7, roof collapse is likely to occur, triggering a Level II warning; when ΔF i When the value is between 1.1 and 1.5 or between 0.7 and 0.9, there is a risk of roof collapse, but it is not likely to occur, so a Level I warning is issued.

[0059] The solutions in this embodiment are not intended to limit the scope of patent protection of this invention. All equivalent implementations or modifications that do not depart from the scope of this invention are included in the patent scope of this case.

Claims

1. A modular, segmented, and grouped monitoring method for early warning of roof collapse in roadways, characterized in that, Includes the following steps: S1, obtain the initial preload and maximum preload of the anchor bolts used in the coal mine production site, and determine the safety factor α of the anchor bolts; S2, modular segmentation and grouping of the roadway, subdividing the complete roadway into regional subdivisions, forming a modular monitoring layout of roadway-segment, segment-group, and group-monitoring point, and obtaining the roof stability coefficient β within the group; S3, based on the modular segmented grouping method, continuously monitors and collects the anchor bolt preload at the monitoring points, and transmits it to the integrated data processing platform via the data transmission module; S4, the integrated data processing platform receives the anchor bolt preload information and obtains the anchor bolt safety factor α, the roof stability coefficient β within the group, and the rate of change of the average anchor bolt preload. Capture abnormal data for early warning and storage; The specific steps of step S1 are as follows: setting an early warning sensitivity for the initial preload and maximum preload of the anchor bolt obtained from the experiment, adjusting the threshold of the anchor bolt safety factor α according to the early warning sensitivity, evaluating the health status of a single anchor bolt, and calculating the anchor bolt safety factor α for the preload monitored in real time on site. The safety factor α of the anchor bolt is expressed by the following formula: ； In the formula: α is the safety factor of the anchor bolt. The initial preload of the anchor bolt is expressed in MPa. 'a' represents the maximum preload of the anchor bolt, in MPa; 'a' represents the early warning sensitivity. Specifically, step S2 involves: assessing the health of the anchor bolts based on the safety factor of the anchor bolts at the monitoring points within the group, and further calculating the stability coefficient β of the roof within the group. The stability coefficient β of the top plate is calculated by the following formula: ； In the formula: β is the stability coefficient of the top plate. The number of healthy anchor bolts in group i. This represents the number of monitoring points within group i. Step S4 specifically involves: the integrated data processing platform including a data receiving and transmitting module, a data processing module, a data visualization module, an early warning module, and a data storage module; the data receiving module receiving monitoring data; and the data processing module intelligently analyzing and processing the monitoring data to obtain the anchor bolt safety factor α, the top plate stability coefficient β within the group, and the average change rate of the anchor bolt preload. Simultaneously, it compares with historical data; the data visualization module visualizes the data results; the early warning module captures abnormal data and provides audible and visual warnings for areas with potential roof collapse hazards; the data storage module stores abnormal data and establishes an early warning case library to provide comparative analysis for subsequent roadway roof collapse monitoring, early warning, and support effects.

2. The modular segmented group monitoring method for early warning of roof collapse in roadways according to claim 1, characterized in that: The anchor bolt preload continuous monitoring and acquisition device uses a force sensor, which is fixed to the anchor bolt tray nut with a nut. An automatic acquisition frequency is set to achieve continuous monitoring and acquisition. The data transmission module is electrically connected to the monitoring device and transmits the monitoring data to the integrated data processing platform wirelessly at preset time intervals.

3. The modular segmented group monitoring method for early warning of roof collapse in roadways according to claim 1, characterized in that: The mean change rate of the anchor bolt preload Calculated using the following formula: ； In the formula: The mean change rate of anchor bolt preload. Let be the average preload data at time t for group i. The mean value of the preload force data at time t-1 in group i.

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