High-strength lightweight steel arch roadway support effect real-time wireless monitoring system and method

Abstract

The application discloses a high-strength light-weight steel arch roadway supporting effect real-time wireless monitoring system and method, relates to the technical field of wireless monitoring, fuses three types of key indexes of strain imbalance degree, stress wave attenuation rate and main frequency offset rate, and constructs a multi-source information judgment model. By monitoring the strain difference value of the two sides of the node and the attenuation rate of the bolt piezoelectric pulse signal, dynamic threshold correction is combined with vibration main frequency offset, which significantly improves the state judgment accuracy under complex working conditions. The method introduces change slope analysis, realizes early identification of the stiffness degradation trend and high-risk failure state, and can output safety margin evaluation and graded maintenance response. The overall scheme has the advantages of accurate identification, timely response and convenient deployment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless monitoring, in particular to a high-strength lightweight steel arch roadway support effect real-time wireless monitoring system and method. BACKGROUND

[0002] In underground engineering such as coal mines, subways and tunnels, with the continuous increase of mining depth and ground stress level, the stability of surrounding rock of the roadway is increasingly prominent. In order to ensure the safety of operation and the reliability of support, high-strength lightweight steel arches are widely used in various roadway support scenes as common support structures due to their high bearing capacity, strong anti-deformation ability, convenient installation and other advantages. The steel arch is usually connected by node bolts to form an overall support system, and the safety of the support system depends largely on the stability of the node connection state.

[0003] However, during long-term service, the node bolt connection part is easily affected by periodic dynamic load disturbance, surrounding rock pressure stress fluctuation and other factors, resulting in connection loosening or local stiffness degradation. If it cannot be identified in time, it is easy to cause overall instability of the support system.

[0004] Most of the existing methods rely on static signal threshold set for node state identification, and do not consider the interference caused by environmental vibration factors in actual operation to the collected signals. This fixed threshold strategy without adaptability is easy to produce false alarm or miss report in the interference enhancement stage, resulting in the early degradation trend being covered up, and the accurate identification of the potential failure of the support structure cannot be realized. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a high-strength lightweight steel arch roadway support effect real-time wireless monitoring system and method.

[0006] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0007] The high-strength lightweight steel arch roadway support effect real-time wireless monitoring method comprises the following steps:

[0008] Obtain the strain data of both sides of the steel arch node, and calculate the strain imbalance degree based on the proportional relationship between the difference of the strain data of both sides and the initial calibration value;

[0009] Obtain the pulse signal at the node bolt of the steel arch, and calculate the stress wave attenuation rate based on the attenuation ratio of the amplitude of the current pulse signal to the amplitude of the initial pulse signal, the pulse signal being periodically generated by the piezoelectric ceramic piece preinstalled inside the node bolt;

[0010] The vibration signal at the steel arch node is acquired in real time, a frequency deviation rate is calculated based on a deviation ratio of a main frequency of the vibration signal and an initial reference main frequency, and preset strain imbalance threshold values and strain attenuation threshold values are positively corrected based on the frequency deviation rate;

[0011] When the strain imbalance degree is greater than the strain imbalance threshold value and the stress wave attenuation rate is greater than the strain attenuation threshold value, a change slope of the strain imbalance degree within a preset time window is extracted;

[0012] It is judged whether the change slope is greater than a preset slope threshold value;

[0013] If the judgment result is yes, it is determined that the node is in a high-risk failure state;

[0014] If the judgment result is no, it is determined that the node is in a stiffness degradation state;

[0015] According to the node being in a high-risk failure state or a stiffness degradation state, a safety margin evaluation result containing a node position identifier is generated, and a corresponding level of maintenance instruction is triggered.

[0016] The high-strength lightweight steel arch roadway support effect real-time wireless monitoring system comprises:

[0017] A strain imbalance degree determination module is configured to acquire strain data on both sides of a steel arch node and calculate a strain imbalance degree based on a proportional relationship between the difference between the strain data on both sides and an initial calibration value;

[0018] A stress wave attenuation rate determination module is configured to acquire a pulse signal at a bolt of the steel arch node and calculate a stress wave attenuation rate based on an attenuation ratio of an amplitude of the current pulse signal to an amplitude of an initial pulse signal, the pulse signal being periodically generated by a piezoelectric ceramic sheet preinstalled in the node bolt;

[0019] A threshold correction module is configured to acquire a vibration signal at the steel arch node in real time, calculate a frequency deviation rate based on a deviation ratio of a main frequency of the vibration signal and an initial reference main frequency, and positively correct preset strain imbalance threshold values and strain attenuation threshold values based on the frequency deviation rate;

[0020] A change slope determination module is configured to extract a change slope of the strain imbalance degree within a preset time window when the strain imbalance degree is greater than the strain imbalance threshold value and the stress wave attenuation rate is greater than the strain attenuation threshold value;

[0021] A state judgment module is configured to perform the following steps:

[0022] It is judged whether the change slope is greater than a preset slope threshold value;

[0023] If the judgment result is yes, it is determined that the node is in a high-risk failure state;

[0024] If the judgment result is no, it is determined that the node is in a stiffness degradation state;

[0025] The evaluation result generation module is configured to generate a safety margin evaluation result containing a node position identifier according to the node being in a high-risk failure state or a stiffness degradation state, and trigger a maintenance instruction of a corresponding level.

[0026] Compared with the prior art, the present application has the following beneficial effects:

[0027] 1. By introducing the main frequency offset rate of the node vibration signal as a dynamic adjustment factor, a correction mode based on environmental vibration adjustment threshold is constructed to adapt to environmental vibration. When the structure is subjected to non-structural disturbances such as vibration enhancement and stress fluctuation, the reference threshold for strain imbalance and stress wave judgment can be actively adjusted, thereby avoiding false positives caused by unreasonable fixed threshold setting;

[0028] 2. The strain imbalance degree and stress wave attenuation rate are used as the judgment standard, which can reflect the performance changes of the steel arch node at the stress structure level and the connection interface level. The former can reflect the symmetry of the stress on both sides of the node and capture the trend of stiffness change, and the latter can judge the internal contact quality of the bolt. The two work together to reduce the risk of misjudgment and missed judgment;

[0029] 3. The piezoelectric components or sensors used for monitoring can transmit data wirelessly without relying on large wiring or fixed power supply, which can be deployed in harsh environments such as high humidity and high dust underground, significantly reducing operation and maintenance costs and deployment complexity, and expanding the practical application scenarios of the scheme in intelligent mines, automated tunnel monitoring and other fields. BRIEF DESCRIPTION OF DRAWINGS

[0030] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0031] Figure 1 The step flowchart of the present application;

[0032] Figure 2 The data flowchart of the present application;

[0033] Figure 3 The system module diagram of the present application. DETAILED DESCRIPTION

[0034] It is easy to understand that according to the technical solutions of the present application, those skilled in the art can propose various structures and implementation modes that can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solutions of the present application, and should not be regarded as the whole or as the limitation or restriction of the technical solutions of the present application.

[0035] Summary of the application

[0036] In the traditional existing steel arch node state monitoring method, the static threshold strategy cannot adapt to the dynamic drift of signal characteristics caused by environmental vibration interference. Since the stress fluctuation of surrounding rock in underground engineering and the vibration disturbance caused by mechanical operation have time-varying characteristics, the fixed threshold value cannot distinguish between the real connection state degradation and the signal fluctuation caused by external disturbance, resulting in the deviation of the judgment basis of strain imbalance degree and stress wave attenuation rate from the actual working condition. The signal acquisition system is easy to trigger false judgment in the vibration interference enhancement stage, which covers up the early trend of node stiffness degradation, and the high-frequency disturbance may cause false negatives under the fixed threshold, affecting the timeliness and accuracy of the generation of maintenance instructions.

[0037] For example, in the scene of deep coal mine roadway support, the steel arch node bolt connection part is disturbed by mining machinery vibration and surrounding rock stress release, and the strain data on both sides of the node presents periodic fluctuation due to vibration interference. At this time, the fixed strain imbalance threshold based on the initial calibration value cannot be self-adaptively corrected, resulting in that the system misjudges the instantaneous strain difference caused by vibration as connection failure. At the same time, the pulse signal amplitude generated by the piezoelectric ceramic sheet inside the node bolt is affected by vibration coupling, and the calculated value of stress wave attenuation rate deviates from the real physical attenuation state. When the vibration main frequency deviates and accumulates continuously, the judgment logic under the fixed threshold cannot identify the correlation between the main frequency deviation and the connection stiffness degradation, causing false alarm or delayed identification of high-risk failure state.

[0038] If the above problems are not solved, the misjudgment of the node connection state will lead to the mismatch of maintenance resources, and cannot trigger early warning in the early stage of stiffness degradation. Long-term accumulation of unidentified connection failure may cause a sudden drop in the bearing capacity of local nodes, and then induce a chain instability through stress redistribution of the overall structure of the steel arch. In addition, the redundant maintenance instructions caused by false alarm will increase the operation and maintenance cost, reduce the credibility of the support system state evaluation, and ultimately threaten the safe operation efficiency of underground engineering.

[0039] In the face of the above problems, the application first realizes that the traditional static threshold strategy cannot effectively distinguish between real connection state degradation and signal fluctuation caused by external vibration interference. In this regard, the application attempts to introduce a dynamic threshold correction mechanism, taking the main frequency shift rate of the vibration signal as a real-time correction parameter, so that the strain imbalance threshold and the strain attenuation threshold can be dynamically adjusted according to the actual vibration environment. Further, the application finds that relying only on threshold correction still has the problem of insufficient distinction between node failure modes, so it proposes to combine the time window change slope of the strain imbalance degree, and identify high-risk failure and stiffness degradation in two different failure stages through the slope difference, thereby establishing a multi-dimensional state judgment logic.

[0040] As shown in Figure 1 , the application proposes a real-time wireless monitoring method for the supporting effect of high-strength lightweight steel arch roadway, comprising the following steps:

[0041] Obtain strain data on both sides of the steel arch node, and calculate the strain imbalance degree based on the proportional relationship between the difference of the strain data on both sides and the initial calibration value;

[0042] The process of obtaining the strain imbalance degree is:

[0043] Symmetrically arrange strain sensors on both sides of the steel arch connection node to real-time collect strain value data on both sides of the node and , and obtain the initial calibration strain difference ;

[0044] The strain imbalance degree is:

[0045] .

[0046] The strain imbalance degree is the proportional relationship between the difference of the strain data on both sides of the steel arch node and the initial calibration value. Specifically, it can be realized by real-time collecting the strain data on both sides using strain sensors and calculating the difference ratio through a microcontroller, which is used to reflect the change in the symmetry of the node stress and solve the problem that traditional static monitoring cannot dynamically identify the imbalance of the node stress.

[0047] Obtain the pulse signal at the bolt of the steel arch node, and calculate the stress wave attenuation rate based on the attenuation ratio of the amplitude of the current pulse signal to the amplitude of the initial pulse signal, the pulse signal being periodically generated by a piezoelectric ceramic sheet preinstalled inside the node bolt;

[0048] The determination process of the stress wave attenuation rate is:

[0049] Periodically emit stress waves and receive pulse signals through the piezoelectric ceramic sheet preinstalled in the node bolt to obtain the current signal amplitude and the initial amplitude ;

[0050] stress wave attenuation rate is:

[0051] =1- .

[0052] stress wave attenuation rate refers to the attenuation ratio of the pulse signal amplitude at the node bolt, which can be specifically realized by periodically generating a pulse signal by a piezoelectric ceramic sheet, measuring the amplitude change through a signal receiver, and calculating the attenuation ratio combined with the initial amplitude, and is used for detecting the stress wave energy loss caused by bolt loosening and solving the problem of early identification of bolt loosening.

[0053] The vibration signal at the node of the steel arch is acquired in real time, the frequency deviation rate is calculated based on the frequency deviation ratio of the vibration signal and the initial reference frequency, and the preset strain imbalance threshold and strain attenuation threshold are positively corrected based on the frequency deviation rate.

[0054] The calculation process of the frequency deviation rate is:

[0055] The vibration signal is collected in real time, and the frequency of the vibration signal is extracted to determine the initial frequency .

[0056] The frequency deviation rate is:

[0057] .

[0058] The frequency deviation rate refers to the frequency deviation ratio of the vibration signal relative to the initial reference frequency, which can be specifically realized by collecting the vibration signal by an acceleration sensor, extracting the frequency parameter by fast Fourier transform, and calculating the deviation ratio, and is used for quantifying the interference degree of environmental vibration on the stability of the node and solving the problem that the fixed threshold cannot adapt to dynamic interference. The positive correction refers to dynamically adjusting the strain imbalance threshold and the strain attenuation threshold based on the frequency deviation rate, which can be specifically realized by mapping the frequency deviation rate to a threshold adjustment coefficient by using a linear or nonlinear function, and updating the threshold in real time by using an embedded algorithm, and is used for enhancing the adaptability of the threshold when the vibration interference is enhanced and reducing the false alarm rate.

[0059] When the strain imbalance degree is greater than the strain imbalance threshold and the stress wave attenuation rate is greater than the strain attenuation threshold, the change slope of the strain imbalance degree in the preset time window is extracted; the change slope refers to the change rate of the strain imbalance degree in the preset time window, which can be specifically realized by fitting a linear regression model by using time series data and calculating the slope parameter, and is used for distinguishing the suddenness and gradualness of node failure and solving the problem that the traditional method cannot distinguish high-risk failure and stiffness degradation.

[0060] Determine whether the slope of change is greater than a preset slope threshold;

[0061] If the judgment result is yes, then the node is determined to be in a high-risk failure state. A high-risk failure state refers to a node being in a risky state of imminent breakage or complete detachment. Specifically, it can be achieved by triggering an alarm command when the change in slope exceeds a preset threshold. This is used to guide emergency maintenance measures and prevent the overall instability of the support system.

[0062] If the judgment result is negative, then the node is determined to be in a stiffness degradation state. The stiffness degradation state refers to the state in which the stiffness of the node connection gradually decreases but does not reach the critical failure state. Specifically, it can be achieved by triggering an early warning command when the change slope does not exceed the threshold but remains at a high level. This is used to formulate preventive maintenance plans and extend the life of the support system.

[0063] Based on whether a node is in a high-risk failure state or a stiffness degradation state, a safety margin assessment result containing the node's location identifier is generated, and a corresponding level of maintenance instruction is triggered. The safety margin assessment result refers to a quantitative indicator that includes the node's location identifier and maintenance level. Specifically, a weighted algorithm is used to combine strain imbalance and stress wave attenuation rate to generate an assessment value, which is then transmitted to the monitoring center via a wireless communication module. This is used to accurately locate risky nodes and match maintenance resources, thereby improving maintenance efficiency.

[0064] The core innovation of this application lies in the fact that by integrating multi-dimensional monitoring data such as strain imbalance, stress wave attenuation rate and dominant frequency offset rate, and combining dynamic threshold correction mechanism and slope analysis, it can accurately distinguish between high-risk failure and stiffness degradation of steel arch frame nodes, and solve the problems of high misjudgment rate and difficulty in early degradation identification of traditional fixed threshold method under vibration interference.

[0065] like Figure 2 The diagram shown is a data flow chart of this application; the specific implementation of the solution in this application is as follows:

[0066] In a deep coal mine roadway support system, strain sensors are installed on both sides of the steel arch frame nodes, piezoelectric ceramic plates are pre-embedded inside the node bolts, and vibration sensors are installed at the nodes. The system collects strain data and pulse signals every 5 minutes and vibration signals every 1 minute, and calculates the strain imbalance. Stress wave attenuation rate , frequency offset.

[0067] Obtain preset strain imbalance thresholds and strain decay thresholds, and supervise the process based on the dominant frequency offset rate. Based on the main frequency offset The dynamic threshold correction mechanism is activated to obtain the corrected strain imbalance threshold and strain decay threshold.

[0068] When the strain imbalance degree is greater than the corrected strain imbalance threshold and the stress wave attenuation rate is greater than the corrected strain attenuation threshold, the change slope of the strain imbalance degree in the last 30 minutes is extracted. The preset slope threshold is 0.01 / minute. If the change slope is greater than 0.01 / minute, it is determined that the node is in a high-risk failure state; otherwise, it is determined to be in a stiffness degradation state.

[0069] According to the node state, a safety margin evaluation result is generated. At the same time, the high-risk failure state triggers an emergency maintenance instruction, and the stiffness degradation state triggers a regular maintenance instruction.

[0070] Through the above scheme, the application can realize real-time wireless monitoring of the supporting effect of the steel arch roadway. By introducing a dynamic threshold correction mechanism and multi-dimensional state judgment logic, the problem that the traditional static threshold strategy cannot distinguish between real connection state degradation and external vibration interference is effectively solved. The system can dynamically adjust the judgment threshold according to the actual vibration environment, and identify different failure stages in combination with the time window change slope of the strain imbalance degree, thereby improving the accuracy and timeliness of node failure identification. This improvement makes the support system state evaluation more reliable, can trigger an early warning at the early stage of stiffness degradation, avoids the risk of chain instability caused by sudden reduction of local node bearing capacity, reduces redundant maintenance instructions caused by false alarms, reduces operation and maintenance costs, and improves the safety operation efficiency of underground engineering.

[0071] The application further proposes a correction logic for the strain imbalance threshold based on the main frequency offset rate, which is:

[0072] Obtain the main frequency offset rate in the set time window and the preset strain imbalance threshold;

[0073] Calculate the average change of the main frequency offset rate in the set time window;

[0074] When the average change of the main frequency offset rate exceeds a first preset threshold, and the current main frequency offset rate exceeds a second preset threshold, it is determined that the current node is in a vibration disturbance enhancement state. The determination of the vibration disturbance enhancement state needs to meet the double conditions of the average change threshold and the instantaneous offset threshold to avoid false triggering caused by occasional interference.

[0075] In the vibration disturbance enhancement state, an adjustment factor is constructed according to the main frequency offset rate, and the adjustment factor is combined with the preset strain imbalance threshold to generate a corrected strain imbalance threshold. The adjustment factor monotonically increases with the increase of the main frequency offset rate.

[0076] The length of the time window can be set to five minutes to thirty minutes, and the first preset threshold is determined according to the fluctuation range of the frequency deviation rate in the stable state in the historical data. The adjustment factor adopts a linear or nonlinear function form, for example, is generated by multiplying the ratio of the frequency deviation rate to the second preset threshold by a weight coefficient. The combination operation mode includes addition superposition or multiplication amplification, for example, the adjustment factor is added to the original threshold to form a new threshold after dynamic adjustment.

[0077] As a preferred embodiment, the scheme of the application is implemented as follows:

[0078] For example, the first preset threshold is set to 0.5% / min, and the second preset threshold is set to 5%. If the average change of the frequency deviation rate is greater than 0.5% / min, and the current frequency deviation rate is greater than 5%, it is determined that the node is in a vibration disturbance enhanced state.

[0079] The adjustment factor can be set to , and then the adjustment factor is multiplied by the preset strain imbalance threshold to obtain a corrected strain imbalance threshold. For example, the original threshold is 5%, and the corrected threshold is raised to 5.3%. The dynamic adjustment mechanism makes the strain imbalance threshold adaptively rise with the vibration intensity, avoiding misjudgment of the strain imbalance degree as a failure state due to environmental interference.

[0080] Through the above technical scheme, the application realizes dynamic adjustment of the strain imbalance threshold. Thus, in the case of enhanced vibration disturbance, the false alarm situation caused by environmental vibration is reduced by increasing the strain imbalance threshold. At the same time, the adjustment factor monotonically increases with the increase of the frequency deviation rate, ensuring the rationality and stability of the threshold adjustment. This dynamic adjustment mechanism improves the accuracy and reliability of node state recognition, effectively avoiding the false alarm or missed alarm problem that may be caused by the fixed threshold strategy in the interference enhancement stage.

[0081] The application further proposes a correction logic for the strain decay threshold based on the frequency deviation rate as follows:

[0082] The frequency deviation rate is obtained, and the variance thereof in a continuous time period is calculated;

[0083] When the variance of the frequency deviation rate exceeds a preset disturbance stability threshold, an uncertainty enhancement state flag bit is activated, and an over-limit duration is recorded;

[0084] The variance of the frequency deviation rate is used to quantify the fluctuation degree of the vibration signal frequency, and the preset disturbance stability threshold is used as a reference for judging whether the frequency deviation is in a stable range; the uncertainty enhancement state flag bit is used to identify whether the current node is in an abnormal disturbance environment, and the over-limit duration is used to measure the persistence of the abnormal disturbance.

[0085] When the over-limit duration exceeds a preset over-limit time threshold, it is determined that the current node is in an uncertainty enhancement state;

[0086] In the uncertainty enhancement state, the value threshold of the strain decay threshold is increased by a set proportion, and the sampling period of the stress wave decay rate is also extended;

[0087] When the variance of the main frequency offset rate is less than a preset disturbance stability threshold for three consecutive times, the original sampling period is restored.

[0088] The adjustment of the strain decay threshold and the extension of the sampling period are used to reduce the influence of noise interference on the judgment result; the variance of the main frequency offset rate being less than the threshold for three consecutive times is used as a condition for restoring the original monitoring period, to ensure that the system restores normal monitoring in a timely manner after the disturbance is weakened.

[0089] Specifically, when the variance of the main frequency offset rate exceeds a preset disturbance stability threshold, the system activates an uncertainty enhancement state flag bit and starts recording the over-limit duration. If the over-limit duration exceeds a preset over-limit time threshold, it is determined that the node is in an uncertainty enhancement state. At this time, the strain decay threshold is increased by a set proportion, for example, the threshold is increased to 1.2 times the original value, and the sampling period is extended from the initial 10 seconds to 15 seconds. The extension of the sampling period can reduce the influence of high-frequency noise on the measurement of the pulse signal amplitude and improve the stability of the stress wave decay rate calculation. When the variance of the main frequency offset rate is less than the preset disturbance stability threshold for three consecutive times, it indicates that the environmental disturbance has tended to be stable, and the original sampling period is automatically restored, for example, the sampling period is adjusted from 15 seconds back to 10 seconds. By dynamically adjusting the threshold and the sampling period, the real stress wave decay trend and the transient interference signal can be effectively distinguished, and false judgments caused by environmental disturbance can be avoided.

[0090] As a preferred embodiment, the scheme of the application is implemented as follows:

[0091] Select 10 minutes as a continuous time period, and collect the main frequency offset rate data every 30 seconds to calculate the variance of the 20 data points.

[0092] Set the disturbance stability threshold to 0.05, and when the calculated variance is greater than 0.05, set the uncertainty enhancement state flag bit to 1 and start recording the over-limit duration.

[0093] Set the over-limit time threshold to 30 minutes, and if the over-limit duration reaches or exceeds 30 minutes, it is determined that the node enters an uncertainty enhancement state.

[0094] In the uncertainty enhancement state, the value threshold of the strain decay threshold is increased by a set proportion, and the sampling period of the stress wave decay rate is also extended; specifically, the strain decay threshold can be increased by 20%, and the original sampling period of 5 minutes can be extended to 10 minutes.

[0095] And when the variance of the three consecutive calculations is less than 0.05, the sampling period is readjusted to 5 minutes.

[0096] Through the above technical solution, the strain decay threshold and the sampling period can be dynamically adjusted according to the change of the main frequency offset rate, effectively dealing with the increase of the uncertainty of the node vibration state. When the main frequency offset rate is detected to have abnormal fluctuations, by increasing the strain decay threshold and prolonging the sampling period, the misjudgment caused by short-term vibration disturbance is reduced, and the anti-interference ability and stability of the monitoring system are improved. At the same time, the strategy of restoring the original sampling period only when the value is less than the threshold for three times in a row avoids the instability of the system that may be caused by frequent switching of the sampling period, ensuring the continuity and reliability of the monitoring data. This adaptive adjustment mechanism enables the monitoring system to better adapt to the complex and variable tunnel environment, improving the identification accuracy of the state change of the steel arch node.

[0097] The application further proposes prolonging the sampling period of the stress wave decay rate, which includes:

[0098] Based on the ratio of the variance of the main frequency offset rate to the preset disturbance stability threshold, a sampling period extension coefficient is determined; the sampling period extension coefficient is quantitatively calculated by the deviation of the variance of the main frequency offset rate from the threshold, which is specifically that the greater the variance exceeds the threshold, the greater the extension coefficient increases.

[0099] Based on the ratio of the over-limit duration to the preset over-limit time threshold, a time-varying factor is determined; the time-varying factor is nonlinearly mapped by the ratio of the over-limit duration to the threshold, realizing the weakening processing of the time accumulation effect.

[0100] The sampling period extension coefficient, the time-varying factor and the original sampling period are multiplied to obtain the extended sampling period. The extended sampling period reflects the influence of disturbance intensity and duration through the product operation.

[0101] After multiplying the two dynamic parameters with the original sampling period, it is ensured that the sampling period is moderately extended with the increase of the disturbance intensity, and the overextension is avoided through the weakening processing of the time accumulation factor. The dynamic adjustment mechanism realizes the precise matching of the sampling period and the field working condition through the linkage of quantitative parameters, effectively balancing the contradiction between monitoring accuracy and real-time performance.

[0102] For example, when the variance of the main frequency offset rate is 0.05 and the preset disturbance stability threshold is 0.03, the ratio is 1.67.

[0103] When the over-limit duration is 30 minutes and the preset over-limit time threshold is 60 minutes, the ratio is 0.5.

[0104] Assuming the original sampling period is 5 minutes, and the calculated sampling period extension coefficient is 1.3 and the time-varying factor is 1.1, then the extended sampling period is 5 * 1.3 * 1.1 = 7.15 minutes.

[0105] Through the above technical solution, this application achieves dynamic adjustment of the sampling period, improving the system's adaptability to environmental changes. Therefore, under conditions of increased uncertainty, the system can adaptively extend the sampling period based on the fluctuation of the main frequency offset rate and the duration of the out-of-limit condition, avoiding noise interference that may be introduced by frequent sampling, while ensuring the validity and representativeness of the data. Furthermore, by introducing a time-varying factor, the system can gradually increase the sampling period as the duration of the out-of-limit condition increases, reflecting a cautious handling strategy for long-term abnormal states. This dynamic adjustment mechanism effectively balances system response speed and data reliability, improving the accuracy and stability of support effect monitoring.

[0106] This application further proposes a sampling period extension factor. The calculation formula is:

[0107] , of which The ratio of the variance of the main frequency offset to the preset disturbance stability threshold; sampling period extension coefficient. The base value is set to 1.2. When the variance of the main frequency offset exceeds the preset disturbance stability threshold, the coefficient increases by 0.1 for every 1 times the threshold is exceeded, so as to achieve a linear relationship between the disturbance intensity and the period extension.

[0108] The time-varying factor The calculation formula is:

[0109] ,in The ratio of the over-limit duration to the preset over-limit time threshold; time-varying factor. The factor is calculated by raising the ratio of the over-limit duration to the preset over-limit time threshold to a power of one-third, so that the factor increases slowly and non-linearly with the duration, avoiding overly aggressive periodic adjustments due to excessively rapid linear growth.

[0110] The extended sampling period The calculation formula is:

[0111] ;

[0112] And when hour:

[0113] ;in, This is the original sampling period.

[0114] The prolonged sampling period is obtained by multiplying the original period by a coefficient and a factor, and the product is set to be no more than 2.5 times the original period to prevent excessive prolongation from affecting the timeliness of monitoring.

[0115] For example, when the ratio of the variance of the main frequency offset rate to the preset disturbance stability threshold is 3, the sampling period prolongation coefficient is calculated as 1.2+0.1*(3-1)=1.4.

[0116] The ratio of the over-limit duration to the preset over-limit time threshold is 8, and one-third of the ratio is 2. The time-varying factor is 1+0.052=1.1.

[0117] At this time, the prolonged sampling period is 1.4*1.1=1.54 times the original period.

[0118] If the product exceeds 2.5, for example, the coefficient is 2.0 and the factor is 1.3, then the period is limited to 2.5 times the original period.

[0119] This calculation method not only considers the dynamic changes of disturbance intensity, but also balances the data acquisition frequency and stability through nonlinear factors and upper limit constraints, ensuring effective monitoring under complex working conditions.

[0120] Through the above technical solutions, the application realizes dynamic adjustment of the sampling period. By introducing the sampling period prolongation coefficient and the time-varying factor, the system can flexibly adjust the sampling period according to the variance of the main frequency offset rate and the change of the over-limit duration. This dynamic adjustment mechanism effectively avoids the information loss or resource waste problem caused by fixed sampling period. At the same time, by setting the upper limit value 2.5, the sampling period is prevented from being excessively prolonged, ensuring the timeliness and effectiveness of data acquisition. This adaptive sampling strategy improves the response capability of the system to environmental changes and enhances the reliability and representativeness of the monitoring data.

[0121] The application further proposes that the stress wave attenuation rate includes temperature compensation before being compared with the strain attenuation threshold, and the temperature compensation process is:

[0122] The current temperature value of the target steel arch node and its change rate in a preset time period are obtained;

[0123] It is judged whether the current temperature value is in a preset temperature sensitive interval or the change rate exceeds a temperature stability threshold;

[0124] If the judgment result is true, a temperature correction factor is constructed according to the temperature value and the change rate;

[0125] The original stress wave attenuation rate is adjusted using the temperature correction factor to form a temperature-compensated stress wave attenuation rate, which is used as the basis for comparison with the strain attenuation threshold.

[0126] The temperature sensitive interval is determined by experimental data, covering the temperature range in which the thermal expansion coefficient of the covering material changes significantly. The temperature stability threshold is configured as a critical value of temperature change exceeding 1℃ per minute, which can be set to 0.5-1.5℃ per second, for identifying rapid temperature fluctuation scenarios. The temperature correction factor consists of a linear term corresponding to the temperature offset and a nonlinear term corresponding to the influence of temperature change rate.

[0127] Through the above technical solutions, the application can effectively eliminate the influence of temperature change on stress wave attenuation rate measurement, improve the accuracy and reliability of stress wave attenuation rate data. Thus, the connection state of the steel arch support node can be accurately evaluated under different temperature environments, avoiding misjudgment caused by temperature fluctuations, and enhancing the adaptability and stability of support effect monitoring. Further, by introducing the temperature change rate as a basis for judgment, the case of rapid temperature fluctuation can be captured in time, and the influence of temperature factors is more comprehensively considered, making the compensation effect more accurate.

[0128] The application further proposes a calculation formula for adjusting the original stress wave attenuation rate using the temperature correction factor:

[0129] ; wherein, is the stress wave attenuation rate after temperature compensation, is the original stress wave attenuation rate, is the current temperature value relative to the reference temperature, which is configured by the staff according to historical experience, is the temperature change rate, , are the adjustment coefficients set according to historical experience, respectively.

[0130] The temperature correction factor is realized by , linear combination compensation, wherein is used to compensate for the influence of temperature absolute offset on the output amplitude of the piezoelectric ceramic sheet, is used to suppress the instantaneous error caused by rapid temperature fluctuation. This compensation formula can eliminate the piezoelectric ceramic signal amplitude attenuation error caused by temperature, so that the stress wave attenuation rate truly reflects the bolt pre-tightening force state. The reference temperature is set according to the annual average temperature of the installation environment, adjusted to 10℃ in cold regions and 30℃ in tropical regions. Through dynamic temperature compensation, the effectiveness of the strain attenuation threshold is improved, avoiding false alarms caused by high temperature or rapid temperature change.

[0131] For example:

[0132] When the node temperature is at -10℃, the piezoelectric ceramic sheet causes the pulse signal amplitude to decrease due to shrinkage, at this time the offset of the current temperature value relative to the reference temperature 20℃ is -30℃, Set to 0.003 / ℃, the temperature correction factor will increase the stress wave attenuation rate by 0.003*30=9% compensation. When the temperature rises by 8℃ in 5 minutes, the temperature change rate reaches 1.6℃ / min, exceeding the temperature stability threshold of 1℃ / min, Set to 0.005 / (℃ / min), the temperature correction factor increases the stress wave attenuation rate by 0.005*1.6=0.8% compensation. By superimposing the two compensation amounts, the original stress wave attenuation rate is corrected by 9.8%, effectively eliminating the measurement error caused by temperature changes, ensuring that the comparison result with the strain attenuation threshold accurately reflects the bolt connection state. The reference temperature, , Through laboratory calibration, for example, at a reference temperature of 20℃, the linear coefficient of the piezoelectric ceramic sheet output amplitude with temperature change is measured to be 0.3% / ℃, and accordingly the is configured to 0.003.

[0133] Therefore, by introducing a temperature correction factor to adjust the original stress wave attenuation rate, the influence of temperature changes on the stress wave attenuation rate measurement result can be eliminated, and the accuracy of the stress wave attenuation rate data can be improved.

[0134] Through the above technical solutions, the present application can effectively eliminate the influence of temperature changes on the stress wave attenuation rate measurement result, improve the accuracy and reliability of the stress wave attenuation rate data. Further, by introducing a temperature correction factor, the present application can adapt to stress wave attenuation rate measurement in different temperature environments, enhancing adaptability and robustness. Therefore, the present application can more accurately evaluate the state of the steel arch node, improve the precision of support effect monitoring, and provide reliable data support for timely discovery of potential support structure failure risks.

[0135] The present application further proposes that the strain imbalance degree includes a sliding average filtering process before it is compared with the strain imbalance threshold, and the specific process includes:

[0136] Determine the filtering time window;

[0137] Calculate the arithmetic mean of the strain imbalance degree in the filtering time window, and take the arithmetic mean as the value of the strain imbalance degree after sliding average filtering.

[0138] The length of the filtering time window is set according to the actual working condition, for example, 10 seconds to 30 seconds, to cover at least three vibration periods. The calculation of the arithmetic mean value is achieved by continuously collecting the strain imbalance degree values of all sampling points in the time window, and then dividing the total number of sampling points. This processing method can effectively suppress the influence of high-frequency noise or occasional interference signals on the imbalance degree values and retain the trend change characteristics. The moving average filtering process eliminates abnormal peaks or valleys caused by transient interference through data smoothing mechanism within the time window.

[0139] For example, when the strain imbalance degree suddenly jumps due to sudden vibration at a certain time, the filtering process performs average operation on the values at multiple time points before and after it, so that the output value is closer to the real state. The strain imbalance degree curve after filtering presents a more gentle change trend, avoiding false comparison with the threshold value caused by single-point data mutation. This processing process maintains real-time while significantly improving the stability of state judgment and reducing the false triggering probability of maintenance instructions.

[0140] Through the above technical solutions, the application effectively suppresses abnormal fluctuations in the original strain imbalance degree data caused by transient mechanical impact or electromagnetic interference, improves the stability of the strain imbalance degree parameter through data smoothing processing, avoids false triggering problems caused by single-point noise interference, and enhances the reliability of node state judgment.

[0141] The application further proposes to generate a safety margin evaluation result containing a node position identifier, including:

[0142] Calculate the safety margin according to the stiffness degradation state :

[0143] ; wherein, is the strain imbalance degree, is the stress wave attenuation rate, , are preset coefficients, respectively.

[0144] wherein, , The strain imbalance degree and the stress wave attenuation rate are determined through correlation analysis of the strain imbalance degree and the stress wave attenuation rate in the historical data and the node failure state, and their value ranges are 0.2-0.5 and 0.1-0.3, respectively. The strain imbalance degree reflects the degree of asymmetry of stress distribution on both sides of the node, and the stress wave attenuation rate characterizes the energy dissipation characteristics of the bolted joint interface. Both are integrated into the safety margin calculation through linear combination, and the coefficient weight is dynamically configured according to the load difference of the node position.

[0145] For example, when is set to 0.35, is set to 0.15, the node measures the strain imbalance degree is 0.6, the stress wave attenuation rate is 0.4, the safety margin is calculated as The value is compared with the preset safety level threshold through the node position identifier, and when it is lower than 0.7, a secondary maintenance instruction is triggered, and when it is in the interval of 0.7-0.8, a primary maintenance instruction is triggered. Through linear superposition of quantitative parameters, the sensitivity of strain imbalance degree to structural symmetry is retained, and the characterization ability of stress wave attenuation to interface damage is integrated, so that the safety margin evaluation result can comprehensively reflect the multi-dimensional degradation characteristics of the node.

[0146] The evaluation result is packaged into a JSON format message together with the node number and timestamp data, and is transmitted to the ground monitoring center through LoRa wireless transmission to trigger a secondary maintenance instruction requiring a technician to recheck within 72 hours.

[0147] Through the above technical solutions, the application effectively solves the problem of inaccurate safety evaluation of the traditional static threshold method in a vibration interference environment. By establishing a linear weighted calculation model, the dynamic change characteristics of multi-source monitoring data are quantified into a unified safety margin index, so that the system can accurately distinguish between slight stiffness degradation and serious failure risk according to the real-time mechanical state of the node, significantly improving the spatio-temporal resolution of the early warning signal and providing a quantifiable basis for maintenance decisions.

[0148] As shown in Figure 3 , it is a system module diagram of the application; the application further proposes a high-strength lightweight steel arch roadway support effect real-time wireless monitoring system, which includes a strain imbalance degree determination module, a stress wave attenuation rate determination module, a threshold correction module, a change slope determination module, a state judgment module, and an evaluation result generation module.

[0149] The strain imbalance degree determination module calculates the imbalance degree through the proportional relationship between the strain data difference on both sides of the steel arch node and the initial calibration value. The imbalance degree can be realized by using the ratio of the absolute value of the strain difference on both sides to the initial calibration value.

[0150] The stress wave attenuation rate determination module calculates the attenuation rate through the amplitude attenuation ratio of the pulse signal generated by the piezoelectric ceramic sheet at the node bolt. Specifically, the attenuation percentage of the current amplitude and the initial amplitude is obtained.

[0151] The threshold correction module dynamically adjusts the preset threshold based on the frequency offset rate of the vibration signal. For example, when the frequency offset rate exceeds the reference value of 5%, the strain imbalance threshold is increased by 10%.

[0152] The change slope determination module extracts the change trend of the imbalance degree within a preset time window when the double thresholds are exceeded, and calculates the slope value using a linear regression algorithm.

[0153] The state judgment module divides the high-risk failure and the stiffness degradation two states through the slope threshold value, and the slope threshold value can be set to 0.15% / min.

[0154] The evaluation result generation module generates a safety margin evaluation report containing position coding according to the state type, and sends a maintenance instruction through the wireless transmission module.

[0155] As a preferred embodiment, the scheme of the application is implemented as follows:

[0156] The monitoring system is composed of five functional modules. The strain imbalance degree determination module installs double strain sensors on both sides of the steel arch support node. The strain data on both sides are uploaded to the data processing unit in real time through the wireless transmission unit. The difference value of the strain data on both sides is calculated and proportionally operated with the initial calibration value to output the strain imbalance degree parameter.

[0157] The stress wave attenuation rate determination module embeds a piezoelectric ceramic sheet in the node bolt. The piezoelectric ceramic sheet generates a pulse signal at a period of 0.5 seconds. The signal amplitude is collected by an ultrasonic receiver installed on the surface of the bolt. The attenuation ratio of the current amplitude and the initial amplitude is converted into the stress wave attenuation rate through logarithmic operation.

[0158] The threshold correction module installs a vibration sensor on the surface of the node. The vibration signal is extracted by fast Fourier transform to extract the main frequency feature. The main frequency shift rate is calculated by the difference percentage of the real-time main frequency and the initial reference main frequency. The shift rate is input to the dynamic threshold adjustment algorithm. When the strain imbalance degree and the stress wave attenuation rate exceed the dynamically adjusted threshold value at the same time, the change slope determination module starts the time window screening mechanism. The least square method is used to calculate the linear change slope of the strain imbalance degree within a 10-second time window.

[0159] The state judgment module compares the slope with the preset 0.15 / s threshold value. When the slope exceeds the threshold value, the high-risk failure state flag bit is activated. Otherwise, the stiffness degradation state is marked.

[0160] The evaluation result generation module generates a safety margin evaluation report containing the node number according to the state flag, and sends a maintenance instruction code of the corresponding level to the maintenance terminal through the wireless communication module.

[0161] Through the above technical scheme, the application effectively solves the problem that the static threshold strategy in the prior art cannot adapt to environmental vibration interference. Through multi-parameter collaborative monitoring and dynamic threshold correction mechanism, the accuracy of node state recognition is significantly improved. The system can automatically adjust the judgment standard when the vibration disturbance is enhanced, avoid misjudgment caused by signal interference, and distinguish between sudden failure and gradual degradation through slope analysis, so as to ensure that the triggering time of the maintenance instruction and the failure mode are accurately matched, and provide real-time protection for the safe operation of the roadway support system.

[0162] The technical scope of the present application is not limited to the above-described embodiments, and various modifications and changes can be made to the above-described embodiments without departing from the technical idea of the present application, and these modifications and changes should be included in the scope of the present application.

Claims

1. A method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels, characterized by: Includes the following steps: Obtain strain data on both sides of the steel arch frame node, and calculate the strain imbalance based on the ratio of the difference between the strain data on both sides and the initial calibration value; The pulse signal at the node bolt of the steel arch frame is acquired, and the stress wave attenuation rate is calculated based on the attenuation ratio of the amplitude of the current pulse signal to the amplitude of the initial pulse signal. The pulse signal is periodically generated by a piezoelectric ceramic plate pre-placed inside the node bolt. The vibration signal at the node of the steel arch frame is acquired in real time. The main frequency offset rate is calculated based on the offset ratio between the main frequency of the vibration signal and the initial reference main frequency. The preset strain imbalance threshold and strain attenuation threshold are positively corrected based on the main frequency offset rate. When the strain imbalance degree is greater than the strain imbalance threshold and the stress wave attenuation rate is greater than the strain attenuation threshold, the slope of the change of the strain imbalance degree within a preset time window is extracted. Determine whether the slope of change is greater than a preset slope threshold; If the judgment result is yes, then the node is determined to be in a high-risk failure state; If the judgment result is negative, then the node is determined to be in a state of stiffness degradation. Based on whether a node is in a high-risk failure state or a stiffness degradation state, a safety margin assessment result containing the node location identifier is generated, and maintenance instructions of the corresponding level are triggered.

2. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 1, characterized in that: The correction logic for the strain imbalance threshold based on the dominant frequency offset rate is as follows: Obtain the main frequency offset rate and the preset strain imbalance threshold within the set time window; Calculate the average change in the main frequency offset within a set time window; When the average change in the main frequency offset exceeds the first preset threshold and the current main frequency offset exceeds the second preset threshold, the current node is determined to be in a state of enhanced vibration disturbance. Under enhanced vibration disturbance, an adjustment factor is constructed based on the dominant frequency offset rate, and this adjustment factor is combined with a preset strain imbalance threshold to generate a corrected strain imbalance threshold. The adjustment factor increases monotonically with the increase of the dominant frequency offset rate.

3. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 1, characterized in that: The correction logic for the strain attenuation threshold based on the dominant frequency offset is as follows: Obtain the main frequency offset and calculate its variance over a continuous time period; When the variance of the main frequency offset exceeds the preset disturbance stability threshold, the uncertainty enhancement state flag is activated and the duration of the over-limit is recorded. When the duration of the over-limit exceeds a preset over-limit time threshold, it is determined that the current node is in a state of increased uncertainty. Under conditions of increased uncertainty, the numerical threshold of strain attenuation is increased by a set ratio, while the sampling period of stress wave attenuation rate is extended. When the variance of the main frequency offset rate is lower than the preset disturbance stability threshold three times in a row, the original sampling period is restored.

4. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 3, characterized in that: The sampling period for the extended stress wave attenuation rate includes: The sampling period extension coefficient is determined based on the ratio of the variance of the main frequency offset rate to the preset disturbance stability threshold. The time-varying factor is determined based on the ratio of the over-limit duration to the preset over-limit time threshold. The extended sampling period is obtained by multiplying the sampling period extension coefficient and the time-varying factor by the original sampling period.

5. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 4, characterized in that: The sampling period extension coefficient The calculation formula is: , of which The ratio of the variance of the main frequency offset to the preset disturbance stability threshold; The time-varying factor The calculation formula is: ,in It is the ratio of the duration of the over-limit to the preset over-limit time threshold; The extended sampling period The calculation formula is: ； And when hour: ;in, This is the original sampling period.

6. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 1, characterized in that: The stress wave attenuation rate is further compensated for temperature before being compared with the strain attenuation threshold. The temperature compensation process is as follows: Obtain the current temperature value of the target steel arch node and its rate of change within a preset time period; Determine whether the current temperature value is within a preset temperature sensitive range, or whether the rate of change exceeds a temperature stability threshold; If the judgment result is true, construct a temperature correction factor based on the temperature value and the rate of change. The original stress wave attenuation rate is adjusted using the temperature correction factor to form a temperature-compensated stress wave attenuation rate, which is then used as a basis for comparison with the strain attenuation threshold.

7. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 6, characterized in that: The formula for adjusting the original stress wave attenuation rate using the temperature correction factor is as follows: ;in, The stress wave attenuation rate after temperature compensation. This represents the original stress wave attenuation rate. Current temperature value The offset relative to the reference temperature, which is configured by staff based on historical experience. For the rate of temperature change, , These are adjustment coefficients set based on historical experience.

8. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 1, characterized in that: Before comparing the strain imbalance degree with the strain imbalance threshold, it also undergoes a moving average filtering process, the specific process of which includes: Determine the filtering time window; Calculate the arithmetic mean of strain imbalance within the filtering time window, and use the arithmetic mean as the value of strain imbalance after moving average filtering.

9. The method for real-time wireless monitoring of the support effect of high-strength lightweight steel arch tunnels according to claim 1, characterized in that: The generated security margin assessment results, which include node location identifiers, include: Calculate the safety margin based on the stiffness degradation state. : ;in, For strain imbalance, The stress wave attenuation rate, , These are preset coefficients.

10. A real-time wireless monitoring system for the support effect of high-strength lightweight steel arch tunnels, characterized in that: include: The strain imbalance determination module is used to acquire strain data on both sides of the steel arch frame node and calculate the strain imbalance based on the ratio of the difference between the strain data on both sides and the initial calibration value. The stress wave attenuation rate determination module is used to acquire the pulse signal at the node bolt of the steel arch frame, and calculate the stress wave attenuation rate based on the attenuation ratio of the amplitude of the current pulse signal to the amplitude of the initial pulse signal. The pulse signal is periodically generated by a piezoelectric ceramic sheet pre-placed inside the node bolt. The threshold correction module is used to acquire the vibration signal at the node of the steel arch frame in real time, calculate the main frequency offset rate based on the offset ratio between the main frequency of the vibration signal and the initial reference main frequency, and positively correct the preset strain imbalance threshold and strain attenuation threshold based on the main frequency offset rate. The slope determination module is used to extract the slope of the strain imbalance within a preset time window when the strain imbalance is greater than the strain imbalance threshold and the stress wave attenuation rate is greater than the strain attenuation threshold. The status determination module is used to perform the following steps: Determine whether the slope of change is greater than a preset slope threshold; If the judgment result is yes, then the node is determined to be in a high-risk failure state; If the judgment result is negative, then the node is determined to be in a state of stiffness degradation. The assessment result generation module is used to generate a safety margin assessment result containing node location identifiers based on whether the node is in a high-risk failure state or a stiffness degradation state, and to trigger maintenance instructions of the corresponding level.

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