Dynamic change detection and real-time early warning method for roadway transient electromagnetic anomalous body

Abstract

The invention discloses a roadway transient electromagnetic anomalous body dynamic change detection and real-time early warning method, which comprises the following steps of: firstly, dividing a detection area into a plurality of detection positions, and adjusting a detection angle stepping rotation strategy by using a mechanical arm at each detection position, so as to realize omnibearing, multi-layer and unattended efficient monitoring in a tunneling roadway; a plurality of detection time points are set, data acquisition is carried out on each detection position at each detection time point, then a dynamic weight fusion algorithm is adopted and reference data are determined, so that tiny change of the anomalous body can be highlighted, and therefore, fusion correction is carried out on resistivity data at different time points; obtaining the dynamic correction resistivity of each time point, and dividing different regions in combination with a division standard so as to form an anomalous body dynamic change diagram of the whole detection region; and finally, according to the dynamic change diagram and in combination with threshold warning conditions, the dynamic change condition of the abnormal body is judged, early warning information is sent out according to the condition, and the underground engineering safety guarantee level is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of underground space exploration, and in particular to a method for detecting dynamic changes of roadway transient electromagnetic anomaly bodies and real-time early warning. BACKGROUND

[0002] With the continuous expansion of underground engineering construction scale in China, the safety monitoring demand of underground space such as coal mine tunneling roadway, subway tunnel, water conservancy project and the like is growing. As a key link of underground engineering construction, the stability and safety of tunneling roadway is directly related to the construction progress and personnel life and property safety of the whole project. The complexity of underground rock stratum and potential abnormal bodies (such as water-conducting fissure, fracture zone, cavity and the like) seriously affect the stability of roadway, and the traditional manual inspection and simple physical detection method cannot meet the efficient, accurate and safe monitoring demand.

[0003] With the advantages of large penetration depth, high detection sensitivity and fast data acquisition efficiency, the transient electromagnetic method has become one of the important means for detecting underground space abnormal bodies. In recent years, the transient electromagnetic detection technology has been widely applied in the fields of mine safety, tunnel construction and underground water resource evaluation. However, the current traditional transient electromagnetic detection mostly relies on manual carrying of receiving and transmitting coils for on-site measurement, and the staff needs to approach the dangerous area, which has great safety hidden danger. Especially in the complex environment of narrow tunneling roadway, poor ventilation and easy collapse, the risk of manual operation increases significantly. In addition, the current on-site transient electromagnetic receiving and transmitting coils mostly adopt manual adjustment mode, and the angle adjustment between the coil and the rock wall is difficult to accurately control, resulting in unstable air coupling gap between the coil and the rock wall, noise and signal distortion, which affects the accuracy and reproducibility of the detection data. The most critical problem is that: the traditional transient electromagnetic detection method usually adopts static data analysis in the data post-processing process, that is, relying on the data obtained by one-time measurement for interpretation, and repeatedly correcting and detecting the abnormal body which is assumed to be in a stable state. However, in reality, due to the complexity of underground space environment, the physical properties and position of abnormal body will change dynamically over time, and the resistivity of abnormal body is also likely to change significantly due to external factors (such as water flow, geological changes and the like). The traditional data processing method cannot timely capture these changes, which leads to the inability to effectively monitor and warn in real time, and further cannot quickly respond to the dynamic evolution of abnormal body based on real-time data.

[0004] In view of the problems existing in the prior art, how to provide a new detection method which can realize omnibearing and multi-angle high-precision transient electromagnetic detection in the tunneling roadway under the premise of ensuring high mobility, and realize real-time dynamic analysis of the collected data through efficient data processing algorithm, and realize dynamic change monitoring and real-time early warning of abnormal body, has become a technical problem to be solved. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a roadway transient electromagnetic anomaly body dynamic change detection and real-time early warning method, which can effectively solve the above technical problems.

[0006] In order to achieve the above object, the technical scheme adopted by the present application is as follows: a roadway transient electromagnetic anomaly body dynamic change detection and real-time early warning method, comprising the following steps: Step one, monitoring preparation and partition layout: a three-dimensional space model of the tunneling roadway is constructed by using laser radar scanning or a tunnel design drawing; a plurality of detection positions are set along the tunneling direction according to the size of the roadway and engineering requirements, to ensure that all key monitoring areas, i.e. safety risk areas, are covered, which are high-risk hidden danger areas defined in combination with engineering and geological background and are also the key objects of attention for anomaly body dynamic monitoring and early warning.

[0007] Step two, equipment positioning and mechanical arm rotation scanning: the tunneling machine alternately cycles between the tunneling period and the intermittent period during the tunneling process in the roadway, and during each intermittent period, the convenient transient unmanned track vehicle is controlled to travel to each preset detection position, and at the initial time of each detection position, the mechanical arm controls the side of the transient electromagnetic coil facing the front to be the side of the tunneling face, at this time, the transient electromagnetic detection is performed on the side of the tunneling face, after completion, the transient electromagnetic coil is rotated by 15° with the direction perpendicular to the roof and floor of the roadway as the axis to perform transient electromagnetic detection again, and the above process is repeated, and the detection is performed once every 15° of rotation, until the coil front facing direction is rotated by 180°, and the coil front facing direction is from one side of the roadway to the other side of the roadway through the front of the tunneling face, and the multi-angle data acquisition of each detection position is completed.

[0008] Step three, multi-time period and multi-angle data acquisition: the conventional roadway blasting generally adopts the drill-and-blast method, and the whole intermittent period is about 24 hours, and in the roadway with poor geological conditions, the time may be longer, and at this time point, disasters are most likely to be incubated and occur, therefore, a plurality of detection time points are set, step two is repeated at each time point, the transient electromagnetic data of each detection position at different time points are obtained, and a time series data set is formed; the coil angle and position are adjusted in real time according to the real-time feedback of the mechanical arm and the track vehicle, to ensure that the coil side edge is as close to the rock wall as possible, to reduce the air coupling error and to improve the data stability.

[0009] Step four, data dynamic response matrix construction: for the resistivity value obtained at each time point for each detection position, the resistivity change amount (i.e. the resistivity difference value of adjacent time points) of the data collected at adjacent time points is calculated.

[0010] Step five, calculation of fused and corrected resistivity value: the resistivity obtained by the first detection is taken as the reference data, an initial weight is given, the dynamic weight corresponding to the resistivity change amount of the data collected at different time points is calculated, and the reference data is corrected through the dynamic weight, to obtain the dynamic correction resistivity of each detection position at different time points.

[0011] Step six, anomaly body determination: first select a detection position, and divide the dynamic correction resistivity of each time point according to the division standard, so as to obtain the dynamic change of the anomaly body in the area around the detection position; after completion, repeat the step for each detection position, so as to form the dynamic change graph of the anomaly body in the whole detection area.

[0012] Step seven, setting early warning mechanism and early warning information output: set threshold warning condition, if the dynamic change of the anomaly body reaches the threshold warning condition, the portable transient unmanned tracked vehicle sends early warning information, and uploads the real-time monitoring result and early warning information to the ground data processing center, completes the detection and real-time early warning work.

[0013] Further, the detection positions in step one are numbered in order according to the detection sequence, which are P1, P2, …, PM.

[0014] Further, the specific calculation process of the resistivity change amount in step four is: For each detection position Pi, the resistivity change amount between the adjacent two time points is calculated: Wherein, R j is the resistivity change amount between time point j and time point j+1; R(t j+1 ) is the resistivity at time point j+1; R(t j ) is the resistivity at time point j.

[0015] Further, the specific formula of the dynamic correction resistivity in step five is: In the formula, is the fusion correction resistivity at time point j, is the weight of the reference data, is the resistivity obtained by the first detection, is the dynamic weight at time point j, is the resistivity change amount between time point j and time point j+1; wherein the dynamic weight at time point j is distributed with the resistivity change amount of each time point normalized, reflecting the intensity of the anomaly body activity in the time point, and the specific formula is: Wherein, is the dynamic weight at time point j, is the absolute value of the resistivity change amount between time point j and time point j+1; is the absolute value of the resistivity change amount of each time point in the whole collection period.

[0016] Further, the division standard in the step six is specifically: when the dynamic correction resistivity of a detection position at a time point is reduced by no less than 25% compared with the reference data, it is determined that there is an abnormal body around the detection position at the current time point, that is, a dangerous area; when the dynamic correction resistivity of a detection position at a time point is reduced within the range of 20% to 25% compared with the reference data, it is determined that the detection position at the current time point is a suspicious area; when the dynamic correction resistivity of a detection position at a time point is reduced by no more than 20% compared with the reference data, it is determined that the detection position at the current time point is a safe area.

[0017] Further, the threshold warning condition in the step seven is a quantitative early warning standard based on the dynamic change trend of the abnormal body, and specifically includes the following conditions: a space condition: more than three continuous detection positions are determined as dangerous areas; a time condition: the same detection position is upgraded from a suspicious area to a dangerous area within two continuous time points; and a comprehensive condition: the distribution area of the dangerous areas exceeds a preset threshold. When the dynamic change of the abnormal body meets any of the above threshold warning conditions, early warning is performed.

[0018] Compared with the prior art, the present application has the following advantages: 1. The present application first divides the detection area along the roadway trend into a plurality of detection positions, and adopts a mechanical arm to adjust the detection angle step-by-step rotation strategy at each detection position, thereby realizing full-range, multi-level and unattended efficient monitoring in the excavated roadway.

[0019] 2. In order to obtain the change of the abnormal body, the present application sets a plurality of detection time points, each detection position collects data at each detection time point, then adopts a dynamic weight fusion algorithm and determines the reference data, the algorithm adjusts the weight adaptively, so that the small change of the abnormal body can be highlighted, thereby fusing and correcting the resistivity data at different time points, obtaining the dynamic correction resistivity at each time point, combining the division standard, dividing different areas, and forming an abnormal body dynamic change graph of the entire detection area; finally, according to the dynamic change graph and the threshold warning condition, the dynamic change of the abnormal body is determined and early warning information is sent according to the situation, which is convenient for subsequent processing measures; this method can capture the evolution trend of the abnormal body in real time, early warn potential risks, and improve the safety guarantee level of underground engineering.

[0020] 3. The portable transient unmanned tracked vehicle adopted by the present application can flexibly adjust the adhesion state of the transient electromagnetic coil and the rock wall, effectively eliminate the air coupling error, and thereby improve the accuracy of the detection data. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a layout schematic diagram during detection of the present application.

[0022] Figure 2 is the grade division schematic diagram of the dynamic correction resistivity acquired at the second time point.

[0023] Figure 3 is the grade division schematic diagram of the dynamic correction resistivity acquired at the third time point.

[0024] Figure 4 is the grade division schematic diagram of the dynamic correction resistivity acquired at the fourth time point.

[0025] Figure 5 is the grade division schematic diagram of the dynamic correction resistivity acquired at the fifth time point.

[0026] In the figure: 1-transient electromagnetic main machine, 2-wireless communication module, 3-alarm, 4-mechanical arm, I-dangerous area, II-suspect area; III-safe area. DETAILED DESCRIPTION

[0027] The application will be further described below.

[0028] As Figure 1 shown, the application comprises the following steps: Step one, monitoring preparation and partition layout: using laser radar scanning or roadway design drawing, a three-dimensional space model of the tunneling roadway is constructed; according to the roadway size and engineering requirements, a plurality of detection positions are set along the tunneling direction, the detection positions are numbered in order according to the detection sequence, and are respectively P1, P2, …, PM. Ensure that all key monitoring areas, i.e. safety risk areas, are covered, which are high-risk hidden danger areas divided in combination with engineering and geological background, and are also the key attention objects of anomaly body dynamic monitoring and early warning.

[0029] Step two, equipment positioning and mechanical arm rotating scanning: the tunneling machine is composed of tunneling period and intermittent period in the tunneling process of the roadway, as Figure 1 shown, during each intermittent period, the portable transient unmanned track vehicle is controlled to travel to each preset detection position, at the initial time of each detection position, the mechanical arm 4 controls the side of the tunneling machine to face the front of the transient electromagnetic coil, at this time, the side of the tunneling machine is detected once by the transient electromagnetic detection, after completion, the front of the transient electromagnetic coil is rotated 15° with the direction perpendicular to the roof and floor of the roadway as the axis to complete another transient electromagnetic detection, so as to repeat, detect once every 15° rotation, until the front of the coil is rotated 180°, then make the front of the coil pass through the front of the tunneling machine from one side of the roadway to the other side of the roadway, complete the multi-angle data acquisition of each detection position.

[0030] Step three, multi-time period and multi-angle data acquisition: Traditional roadway blasting generally adopts the drilling and blasting method, and the entire intermittent period is about 24 hours. In the roadway with poor geological conditions, the time may be longer. At this time point, disasters are extremely likely to be incubated and occur. Therefore, five detection time points are set, each time point repeats step two, and the transient electromagnetic data of each detection position at five different time points are obtained to form a time series data set. The mechanical arm 4 and the tracked vehicle fine-tune the coil angle and position according to the real-time feedback to ensure that the side of the coil is as close to the rock wall as possible, reduce the air coupling error, and improve the data stability.

[0031] Step four, data dynamic response matrix construction: for the resistivity value of each detection position Pi (i = 1, …, M) at each time point tj (j = 1, 2, …, 5), the resistivity change amount (i.e., the resistivity difference between adjacent time points) of the data collected at adjacent time points is calculated. The specific calculation process is as follows: For each detection position Pi, the resistivity change amount between the adjacent two time points is calculated: Wherein, R j is the resistivity change amount between time point j and time point j+1; R(t j+1 ) is the resistivity at time point j+1; R(t j ) is the resistivity at time point j.

[0032] Step five, calculation of fusion corrected resistivity value: the resistivity obtained by the first detection (i.e., the resistivity of each detection position at the first time point) is taken as the reference data, and an initial weight is given. The initial weight should be set to a high value. In this embodiment, it is set to 0.8. The corresponding dynamic weight is calculated according to the resistivity change amount of the data collected at the subsequent different time points. The sum of the dynamic weights of all time points is 1. The reference data is corrected through the dynamic weight to obtain the dynamic correction resistivity of each detection position at different time points. The specific formula of the dynamic correction resistivity is as follows: In the formula, is the fusion corrected resistivity at time point j, is the weight of the reference data, is the resistivity obtained by the first detection, is the dynamic weight at time point j, is the resistivity change amount between time point j and time point j+1; wherein the dynamic weight at time point j is distributed by normalizing the resistivity change amount at each time point to reflect the intensity of the abnormal body activity at this time point. The specific formula is as follows: Wherein, is the dynamic weight at time point j, is the absolute value of the change in resistivity between time point j and time point j+1; is the absolute value of the change in resistivity between time point j and time point j+1;

[0033] Step six, anomaly body determination: first select a detection position, and divide the dynamic correction resistivity of the four time points except the first time point into regions according to the division standard, so as to obtain the dynamic change of the anomaly body in the region around the detection position; the division standard is specifically as follows: when the dynamic correction resistivity of the detection position at a certain time point decreases by no less than 25% compared with the reference data (i.e. ΔR - ≥ 0.25 ), it is determined that there is an anomaly body around the detection position at the current time point, that is, a dangerous area I; when the dynamic correction resistivity of the detection position at a certain time point decreases within the range of 20% to 25% compared with the reference data (i.e. 0.2 < ΔR - < 0.25 ), it is determined that the detection position at the current time point is a suspicious area II; when the dynamic correction resistivity of the detection position at a certain time point decreases by no more than 20% compared with the reference data (i.e. ΔR - ≤ 0.2 ), it is determined that the detection position at the current time point is a safe area III. After completion, the other detection positions are repeated, so as to form an anomaly body dynamic change graph of the entire detection area, as shown in Figures 2 to 4 .

[0034] Step seven, setting a warning mechanism and outputting warning information: setting a threshold warning condition, if the anomaly body dynamic change meets the threshold warning condition, the portable transient unmanned tracked vehicle sends a warning information, and uploads the real-time monitoring result and the warning information to the ground data processing center, so as to complete the detection and real-time warning work, which is specifically as follows: the threshold warning condition is a quantitative warning standard based on the anomaly body dynamic change trend, which includes the following conditions: spatial condition: more than 3 continuous detection positions are determined as the dangerous area I; time condition: the same detection position is upgraded from the suspicious area II to the dangerous area I within two continuous time points; comprehensive condition: the distribution area of the dangerous area I exceeds the preset threshold, that is, the total area of the detection area is greater than or equal to 15%.

[0035] When the anomaly body dynamic change meets any of the above threshold warning conditions, the system performs the following operations: in-situ warning: the alarm 3 of the portable transient unmanned tracked vehicle starts the sound and light alarm to prompt the on-site personnel to evacuate; information uploading: the formed anomaly body dynamic change graph is uploaded to the ground data processing center in real time through the wireless communication module 2Figures 2 to 5 The detection position number and the abnormal level triggering the early warning can be further determined according to the resistivity change amount ΔR and the dynamic weight calculation result. The subsequent ground data processing center combines the three-dimensional apparent resistivity map and the dynamic evolution track to assist engineering decision and risk control; and adjusts the detection frequency and coverage range in combination with the early warning result, so as to realize closed-loop optimization of dynamic monitoring.

[0036] The portable transient unmanned tracked vehicle is an existing device and can be obtained by market purchase. The portable transient unmanned tracked vehicle mainly comprises a vehicle body, a transient electromagnetic main machine 1, a control center, a wireless communication module 2, an alarm 3, a mechanical arm 4 and a transient electromagnetic coil. The control center is used for controlling the vehicle body to travel, the transient electromagnetic main machine 1 to open and close, the mechanical arm 4 to act and the alarm 3 to open and close, and is connected with the ground control center through the wireless communication module 2. The transient electromagnetic coil is connected with the transient electromagnetic main machine 1 and is used for exciting and receiving transient electromagnetic data. The detection direction of the transient electromagnetic coil is controlled through the mechanical arm 4.

[0037] The above only describes the preferred embodiments of the present application. It should be noted that those skilled in the art can make some improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for detecting and providing real-time early warning of dynamic changes in transient electromagnetic anomalies in roadways, characterized in that, Includes the following steps: Step 1: Monitoring Preparation and Zoning Deployment: Construct a three-dimensional spatial model of the tunnel using lidar scanning or tunnel design drawings; set multiple detection positions along the tunneling direction according to the tunnel dimensions and engineering requirements; Step 2, Equipment Positioning and Robotic Arm Rotation Scanning: The tunneling machine consists of alternating tunneling and intermittent periods during tunnel excavation. During each intermittent period, the portable transient unmanned tracked vehicle is controlled to move to each preset detection position. At the beginning of each detection position, the robotic arm controls the transient electromagnetic coil to face one side of the wall. At this time, a transient electromagnetic detection is performed on that side of the wall. After completion, the transient electromagnetic coil is rotated 15° with the face of the coil facing the direction perpendicular to the top and bottom plates of the tunnel as the axis, and another transient electromagnetic detection is performed. This process is repeated, with a detection performed every 15° rotation, until the coil faces the wall and rotates 180°. Then, the coil faces the wall from one side of the tunnel, passes in front of the tunnel excavation face, and reaches the other side of the tunnel, completing the multi-angle data acquisition of each detection position. Step 3: Multi-time period and multi-angle data acquisition: Set multiple detection time points, and repeat step 2 at each time point to acquire transient electromagnetic data of each detection location at different time points, forming a time series dataset; Step 4: Construction of dynamic response matrix for data: For the resistivity value obtained at each detection location at each time point, calculate the resistivity change of the data collected at adjacent time points; Step 5: Calculate the fused corrected resistivity value: Use the resistivity obtained from the first detection as the reference data and assign it an initial weight. Calculate the corresponding dynamic weight based on the resistivity change of the data collected at different time points in the subsequent period. Then, correct the reference data using the dynamic weight to obtain the dynamic corrected resistivity at different time points for each detection location. Step 6, Anomaly Identification: First, select a detection location and divide it into regions according to the dynamic corrected resistivity at each time point, thereby obtaining the dynamic changes of anomalies in the area surrounding the detection location; after completion, repeat this step for each other detection location to form a dynamic change map of anomalies in the entire detection area. Step 7: Set up an early warning mechanism and output early warning information: Set threshold warning conditions. If the dynamic changes of the abnormal body reach the threshold warning conditions, the portable transient unmanned tracked vehicle will issue an early warning information and upload the real-time monitoring results and early warning information to the ground data processing center to complete the detection and real-time early warning work.

2. The method for dynamic change detection and real-time early warning of transient electromagnetic anomalies in roadways according to claim 1, characterized in that, In step one, the detection locations are numbered sequentially according to the order of detection, namely P1, P2, ..., PM.

3. The method for dynamic change detection and real-time early warning of transient electromagnetic anomalies in roadways according to claim 1, characterized in that, The specific calculation process for the resistivity change in step four is as follows: For each detection location Pi, calculate the change in resistivity between two adjacent time points: Among them, R j R(t) represents the change in resistivity between time point j and time point j+1. j+1 R(t) represents the resistivity at time point j+1; j (j) represents the resistivity at time point j.

4. The method for dynamic change detection and real-time early warning of transient electromagnetic anomalies in roadways according to claim 3, characterized in that, The specific formula for dynamically correcting resistivity in step five is as follows: In the formula, For the fusion-corrected resistivity at time point j, As the weight of the benchmark data, The resistivity obtained from the first detection. The dynamic weight at time point j, This represents the change in resistivity between time point j and time point j+1. The dynamic weight at time point j is assigned by normalizing the resistivity change at each time point, reflecting the intensity of the anomalous body activity at that time point. The specific formula is as follows: in, The dynamic weight at time point j, This represents the absolute value of the change in resistivity between time point j and time point j+1. It is the sum of the absolute values ​​of the resistivity changes at each time point during the entire data collection period.

5. The method for dynamic change detection and real-time early warning of transient electromagnetic anomalies in roadways according to claim 1, characterized in that, The specific criteria for division in step six are as follows: When the dynamic corrected resistivity at a certain time point of the detection location decreases by no less than 25% compared to the baseline data, it is determined that there is an anomaly around the detection location at the current time point, i.e., a dangerous area. When the dynamic corrected resistivity at a certain detection location at a certain time point decreases by 20% to 25% compared to the baseline data, the area around the detection location at the current time point is determined to be a suspicious area. If the dynamic corrected resistivity at a certain time point of the detection location decreases by no more than 20% compared to the baseline data, then the area around the detection location at the current time point is determined to be a safe zone.

6. The method for dynamic change detection and real-time early warning of transient electromagnetic anomalies in roadways according to claim 5, characterized in that, The threshold warning conditions in step seven are quantitative early warning standards based on the dynamic change trend of anomalies, specifically including the following conditions: Spatial condition: more than three consecutive detection locations are identified as dangerous areas; Temporal condition: the same detection location is upgraded from a suspicious area to a dangerous area within two consecutive time points; Comprehensive condition: the area of ​​the dangerous areas exceeds a preset threshold. An early warning is issued when the dynamic change of the anomaly meets any of the above threshold warning conditions.

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