Real-time displacement monitoring method and device in slope reinforcing process

Abstract

The invention discloses a real-time displacement monitoring method and device in a side slope reinforcing process, and relates to the field of displacement monitoring, and the method comprises the steps: obtaining the real-time size and real-time direction of the stress of each point position of a side slope, carrying out the summarizing analysis and processing, obtaining the stress distribution of the side slope, and defining each potential risk region in the side slope; the FGB sensors and the MEMS sensors after error correction are integrated, and corresponding adaptive adjustment is carried out; an integrated sensing performance adjustment factor is obtained, and real-time noise is suppressed in the MEMS-FGB integrated sensor; and real-time displacement monitoring results of all the potential risk areas in the slope reinforcing process are obtained. According to the method, the stability and reliability of the whole monitoring system are improved by evaluating the stability of the side slope, adjusting the performance of the sensors in the potential risk area, integrating the FGB sensors and the MEMS sensors, correcting data deviation caused by bonding wire breakage and switching sensor dominance according to needs.

Description

Technical Field

[0001] The invention relates to the field of displacement monitoring, and in particular to a real-time displacement monitoring method and device during slope reinforcement. Background Art

[0002] Modern engineering construction is large-scale and technically complex, and has higher requirements for slope stability. With the rapid development of sensor technology, communication technology and data processing technology, real-time displacement monitoring methods play an irreplaceable role in modern slope reinforcement projects. It not only improves the safety of the project, but also provides a strong guarantee for environmental protection and social and economic development.

[0003] The prior art, such as the invention patent with announcement number: CN118031879B, is a device control method and terminal based on a slope displacement monitoring system. The method includes obtaining a slope displacement safety threshold in a construction area, and setting a maximum upload period and a minimum upload period for controlling a remote communication module to upload data; configuring an initial acquisition period and an initial upload period according to a first comparison result; and continuously acquiring data from the displacement sensor and dynamically configuring the kth acquisition period and the kth upload period when the kth slope displacement data is obtained at the current moment.

[0004] The prior art, such as the invention patent with announcement number: CN115265424B, is a method for monitoring the displacement of geological disaster slopes based on synthetic aperture radar technology, which relates to the technical field of geological disaster monitoring. The synthetic aperture radar receives the original radar echo signal and uploads it to the remote monitoring center; demodulates the reference original data and the observation data to obtain a complex amplitude radar image; moduli the reference image and the observation image, calculates the alignment parameters, and obtains a new image coordinate mapping relationship; coordinates the image through coordinate transformation, cropping, and interpolation to obtain a mutually matching image pair; interferes with the image pair to obtain an intensity field and a phase field, filters and unpacks the phase field, and then converts it to obtain a range displacement; derives the range velocity and range acceleration through the range displacement calculation; the intensity field, range displacement, range velocity, and range acceleration results are input as tensors into the evaluation function to judge the hazard level.

[0005] Based on the above scheme, it can be seen that in the field of slope displacement monitoring, the existing technology often adopts periodic data uploading and then performs data analysis and processing, which has low timeliness. In addition, the existing technology calculates displacement based on the original radar echo signal, but in actual applications, since the displacement detection of the slope will be affected by various external factors, only based on the original detection settings of the device for detection, there may be problems such as the output detection data is not accurate due to the influence of external factors on the device. Therefore, the detection device is calibrated and corrected to ensure the accuracy of the data so that construction personnel can make correct judgments based on the displacement monitoring data of the slope. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a real-time displacement monitoring method and device during slope reinforcement. To achieve the above purpose, the present invention is implemented through the following technical solutions: The real-time displacement monitoring method and device during slope reinforcement includes: During the slope reinforcement process, the stress at each point on the slope is monitored in real time to obtain the real-time magnitude and direction of the stress at each point on the slope. The stress distribution of the slope is obtained through summary analysis and processing to define the potential risk areas in the slope.

[0007] The real-time temperature in the slope environment is monitored and analyzed to obtain the accuracy error coefficient of each FGB sensor, and the error of each FGB sensor is corrected. The error-corrected FGB sensors are integrated with the existing MEMS sensors in each potential risk area, and the integrated MEMS-FGB sensors are adapted and synchronously adjusted accordingly.

[0008] The stability coefficient of each potential risk area is calculated, and the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area is obtained based on the stability coefficient of each potential risk area. At the same time, the real-time noise during the slope reinforcement process is collected and suppressed in the MEMS-FGB integrated sensor.

[0009] The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and the displacement data measured by the MEMS sensor were collected and cross-validated to obtain real-time displacement monitoring results of each potential risk area during the slope reinforcement process.

[0010] As a preferred technical solution, the summary analysis process obtains the stress distribution of the slope and defines the potential risk areas in the slope, specifically including: The stress at each point on the slope is monitored in real time to obtain the real-time magnitude and direction of the stress at each point on the slope, and the real-time stress vector of each point on the slope is obtained after data alignment according to the timestamp.

[0011] Based on the real-time stress vectors at each point on the slope, they are summarized in the data processing terminal, the slope is discretized into grid units of preset size, the maximum principal stress of each unit is calculated, the overall stress distribution of the slope is constructed, and the output is visualized as a schematic diagram of the internal stress distribution of the slope. The grid units whose maximum principal stress exceeds the maximum stress threshold of the slope are marked as stress concentration units.

[0012] Based on the schematic diagram of internal slope stress distribution, the areas where the number of adjacent stress concentration units exceeds the number of risk definition units are defined as potential risk areas and marked.

[0013] As a preferred technical solution, the real-time temperature in the monitoring slope environment is analyzed and processed to obtain the accuracy error coefficient of each FGB sensor, and the error of each FGB sensor is corrected, specifically including: The real-time temperature of each potential risk area in the slope environment is monitored, and the center wavelength drift of each FGB sensor under the real-time temperature is obtained.

[0014] Based on the center wavelength drift of each FGB sensor, a mapping and matching process is performed with the precision error coefficient corresponding to each center wavelength drift interval preset in the data processing terminal to obtain the precision error coefficient of each FGB sensor.

[0015] Based on the precision error coefficient of each FGB sensor, a mapping match is performed with the precision error correction parameters corresponding to each precision error coefficient pre-stored in the data processing terminal to obtain the precision error correction parameters of each FGB sensor, which are imported into the linear compensation model of the FGB sensor to perform error correction on the output results of the FGB sensor.

[0016] As a preferred technical solution, the error-corrected FGB sensors are integrated with the existing MEMS sensors in the potential risk areas, and the integrated MEMS-FGB sensors are adapted and adjusted accordingly, specifically including: The integrated MEMS-FGB sensors are synchronized by hardware and aligned by software.

[0017] The hardware synchronization specifically includes making the MEMS sensor and the FGB sensor share the same clock signal and data line, and realizing data transmission synchronization at the hardware level through a unified data transmission interface protocol.

[0018] The software alignment specifically includes using an external synchronization signal source to generate a synchronization trigger signal, connecting the synchronization signal to the trigger input terminals of the MEMS sensor and the FGB sensor, and adjusting the timing of the synchronization signal so that the MEMS sensor and the FGB sensor start data acquisition at the same time.

[0019] As a preferred technical solution, the stability coefficient of each potential risk area is calculated, and the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area is obtained based on the stability coefficient of each potential risk area, specifically including: The error-corrected FGB sensor is integrated on the MEMS sensor in each potential risk area, which is denoted as a MEMS-FGB integrated sensor.

[0020] Obtain geological data for each potential risk area, including the slope, number of fractures, and average fracture depth of each potential risk area.

[0021] Extract geological safety verification data from the data processing terminal, including verification slope, number of fracture verifications and average depth of fracture verifications.

[0022] The deviation value of the geological data and geological safety verification data of each potential risk area is obtained, and the stability coefficient of each potential risk area is obtained through coupling processing.

[0023] Based on the stability coefficient of each potential risk area, mapping and matching are performed with the integrated sensing performance adjustment factors corresponding to each stability coefficient preset in the data processing terminal, so as to obtain the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor in each potential risk area, and then the integrated sensing performance of each potential risk area is adjusted.

[0024] As a preferred technical solution, the real-time noise collected during the slope reinforcement process and suppressed in the MEMS-FGB integrated sensor specifically includes: During the slope reinforcement process, real-time noise within the defined range is collected, including real-time vibration noise, real-time acoustic noise, real-time electromagnetic interference and real-time geological noise. The real-time noise is compared and corrected with the vibration noise threshold, acoustic noise threshold, electromagnetic interference threshold and geological noise threshold extracted in the data processing terminal to obtain the real-time noise assessment value of the slope reinforcement process.

[0025] Based on the real-time noise assessment value of the slope reinforcement process, the noise suppression scheme corresponding to each real-time noise assessment value interval stored in the data processing terminal is mapped and matched to obtain the noise suppression scheme of the slope reinforcement process, and the MEMS-FGB integrated sensor is subjected to real-time noise suppression.

[0026] As a preferred technical solution, the acquisition of strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and displacement data measured by the MEMS sensor and cross-validation specifically include: The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor is collected and comprehensively analyzed with the pre-collected length data of the attachment structure of the MEMS-FGB integrated sensor to obtain the displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor.

[0027] The displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor is compared with the displacement data measured by the MEMS sensor to obtain the cross-validation value of the MEMS-FGB integrated sensor. If the cross-validation value of a MEMS-FGB integrated sensor is less than or equal to the cross-validation threshold, the data collected by the MEMS-FGB integrated sensor is regarded as valid data and sent to the data processing terminal. If the cross-validation value of the MEMS-FGB integrated sensor is greater than the cross-validation threshold, the MEMS-FGB integrated sensor is marked as an abnormal integrated sensor and the abnormal sensor is commanded to perform abnormal self-check processing.

[0028] As a preferred technical solution, the real-time displacement monitoring results of each potential risk area in the slope reinforcement process are obtained, and the specific processing conditions are: After receiving the valid data, the data processing terminal performs comprehensive processing to obtain the real-time displacement monitoring value of each potential risk area. If the real-time displacement monitoring value of a potential risk area is greater than the real-time displacement threshold, it is determined that there is displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to MEMS sensors to perform anti-data saturation processing. If the real-time displacement monitoring value of a potential risk area is less than or equal to the real-time displacement threshold, it is determined that there is no displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to FGB sensors to perform operations to improve the displacement recognition resolution.

[0029] As a preferred technical solution, the command abnormality sensor performs abnormality self-checking processing, which is characterized by specifically including: A sinusoidal excitation current of a limited current magnitude is sent to the abnormal sensor, and the intensity of each frequency component in the signal is quantified through frequency domain energy distribution to obtain the third harmonic proportion. Based on the third harmonic proportion of the abnormal sensor, a mapping match is performed with each bonding wire fracture area corresponding to the interval of each third harmonic proportion pre-set in the data processing terminal to obtain the bonding wire fracture area of ​​the abnormal sensor, and based on the bonding wire fracture area of ​​the abnormal sensor, the bonding wire fracture detection result of the abnormal sensor is obtained.

[0030] If the bond wire break detection result of the abnormal sensor is normal loss, the bond wire break area of ​​the abnormal sensor is input into the data processing terminal, and the monitoring correction coefficient of the abnormal sensor is obtained by analysis and processing, and the self-test period and sinusoidal excitation current of the abnormal sensor are matched.

[0031] If the bond wire break detection result of the abnormal sensor is abnormal loss, the number and position of the abnormal sensor will be sent to the management terminal for alarm reminder.

[0032] As a preferred technical solution, the device has one or more programs, and the one or more programs are executed by one or more processors to implement the above method.

[0033] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) The present invention provides a real-time displacement monitoring method during the slope reinforcement process. By real-time monitoring of the stress magnitude and direction of the slope, the stability of the slope can be more accurately evaluated, potential risk areas can be discovered in a timely manner, and the results can be output in a visual form to make them more intuitive. In addition, the performance of sensors in potential risk areas can be adjusted so that resources are concentrated in areas with higher risks, which increases the probability of discovering anomalies and reduces resource waste.

[0034] (2) The present invention combines FGB sensors and MEMS sensors, performs temperature compensation on FGB sensors, and dynamically adjusts the noise suppression scheme according to the noise evaluation value, thereby reducing the impact of environmental factors on measurement accuracy, reducing false positives and false negatives, and ensuring the reliability of monitoring results. By cross-verifying the data of MEMS and FGB sensors, the accuracy and effectiveness of the data can be ensured, and the reliability of the monitoring system can be improved.

[0035] (3) The present invention evaluates the bonding wire condition of the abnormal sensor by sending a sinusoidal excitation current, corrects the data deviation caused by the breakage of the bonding wire, and helps to timely discover potential faults of the sensor, ensure the continuous operation of the monitoring system, dynamically adjust the length of the self-test cycle and the size of the sinusoidal excitation current, improve the measurement accuracy and reliability, reduce unnecessary frequent self-tests, and reduce the wear of the sensor.

[0036] (4) The present invention selects the most appropriate sensor based on the real-time displacement monitoring results, which can more accurately reflect the displacement status of the potential risk area. When the displacement is large, the MEMS sensor is used to dominate, which can avoid the signal saturation problem that may occur in the FGB sensor. When the displacement is small, the FGB sensor is used to dominate, and its high-resolution characteristics can be used to obtain more accurate displacement data. By switching the sensor to dominate as needed, the characteristics of the sensor can be more effectively utilized and the sensor life can be extended. The flexible switching mechanism makes the system more adaptable to changes in different environments and conditions, and improves the stability and reliability of the overall monitoring system.

[0037] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0039] Figure 2It is a schematic diagram of the internal stress distribution of the slope involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0042] See also Figure 1 As shown, an embodiment of the present invention provides a real-time displacement monitoring method in a slope reinforcement process, comprising: During the slope reinforcement process, the stress at each point on the slope is monitored in real time to obtain the real-time magnitude and direction of the stress at each point on the slope. The stress distribution of the slope is obtained through summary analysis and processing to define the potential risk areas in the slope.

[0043] The stress at each point of the slope is monitored in real time to obtain the real-time magnitude and real-time direction of the stress at each point of the slope. The specific process is as follows: An electric pressure measuring box is installed at each point on the slope to monitor the real-time stress of each point on the slope, and the real-time stress of each point on the slope is sent to the data processing terminal.

[0044] Stress gauges are installed at various points on the slope to monitor the real-time direction of stress at each point on the slope, and the real-time direction of stress at each point on the slope is sent to the data processing terminal.

[0045] It should be explained that the electric pressure cell is a sensor used to measure the stress in a medium such as soil or rock. The electric pressure cell can monitor the real-time stress at each point of the slope by converting the stress in the medium into an electrical signal.

[0046] A stress gauge is an instrument that can measure the direction and magnitude of stress. It can not only measure the magnitude of stress, but also determine the direction of stress. In slope monitoring, the stress gauge is used to determine the stress direction at each point to help analyze the stability of the slope.

[0047] The stress at each point on the slope is monitored in real time to obtain the real-time magnitude and direction of the stress at each point on the slope, and the real-time stress vector of each point on the slope is obtained after data alignment according to the timestamp.

[0048] In the data processing terminal, the received real-time magnitude and direction of stress at each point on the slope are aligned according to the data timestamp, and after alignment, the real-time stress vector of each point on the slope is obtained through comprehensive analysis.

[0049] The data timestamp is a mark that records the time of data collection. Alignment processing refers to synchronizing the data collected by different sensors at different times according to the timestamp for comprehensive analysis. In the data processing terminal, the data of stress magnitude and stress direction are aligned in chronological order to ensure data consistency during analysis.

[0050] The stress vector is a vector that contains the magnitude and direction of stress. It represents the stress state at a specific point, including the intensity and direction of stress. The stress vector obtained through real-time monitoring can be used to analyze the instantaneous stress state of the slope.

[0051] The summary analysis process obtains the stress distribution of the slope and defines the potential risk areas in the slope, specifically including: Based on the real-time stress vectors at each point on the slope, they are summarized in the data processing terminal, the slope is discretized into grid units of preset size, the maximum principal stress of each unit is calculated, the overall stress distribution of the slope is constructed, and the output is visualized as a schematic diagram of the internal stress distribution of the slope. The grid units whose maximum principal stress exceeds the maximum stress threshold of the slope are marked as stress concentration units.

[0052] Based on the schematic diagram of internal slope stress distribution, the areas where the number of adjacent stress concentration units exceeds the number of risk definition units are defined as potential risk areas and marked, such as Figure 2 Shown is a schematic diagram of the internal stress distribution of the slope involved in the embodiment of the present invention.

[0053] The real-time temperature in the slope environment is monitored and analyzed to obtain the accuracy error coefficient of each FGB sensor, and the error of each FGB sensor is corrected. The error-corrected FGB sensors are integrated with the existing MEMS sensors in each potential risk area, and the integrated MEMS-FGB sensors are adapted and synchronously adjusted accordingly.

[0054] The real-time temperature in the monitoring slope environment is analyzed and processed to obtain the accuracy error coefficient of each FGB sensor, and the error of each FGB sensor is corrected, specifically including: Temperature sensors are deployed in each potential risk area to monitor the real-time temperature of each potential risk area in the slope environment. The temperature-strain response characteristics of the FGB sensor are retrieved from the data processing terminal. Based on the real-time temperature of each potential risk area, the center wavelength drift of each FGB sensor at the real-time temperature is obtained.

[0055] It should be noted that the temperature-strain response characteristics of FGB (fiber grating sensor) are mainly manifested in its sensitivity to temperature changes and mechanical strain changes. FGB sensors can sense changes in the external environment, including temperature and strain, by detecting changes in the wavelength of reflected light in the optical fiber.

[0056] FGB sensors are very sensitive to temperature changes. When the temperature rises, the fiber material expands, causing the grating period to change, which causes the wavelength of the reflected light to drift. This drift is linear, that is, every time the temperature rises by a certain value, the wavelength increases by a fixed value accordingly.

[0057] FGB sensors can also sense mechanical strain. When the fiber is stretched or compressed, the grating period also changes, causing the wavelength of the reflected light to shift. This shift is also linear.

[0058] In practical applications, since both temperature and strain can cause changes in wavelength, steps need to be taken to distinguish between these two effects.

[0059] Based on the center wavelength drift of each FGB sensor, a mapping and matching process is performed with the precision error coefficient corresponding to each center wavelength drift interval preset in the data processing terminal to obtain the precision error coefficient of each FGB sensor.

[0060] Based on the precision error coefficient of each FGB sensor, a mapping match is performed with the precision error correction parameters corresponding to each precision error coefficient pre-stored in the data processing terminal to obtain the precision error correction parameters of each FGB sensor, which are imported into the linear compensation model of the FGB sensor to perform error correction on the output results of the FGB sensor.

[0061] The linear compensation model of the FGB sensor is a linear interpolation model.

[0062] The method of integrating and applying the error-corrected FGB sensors with the existing MEMS sensors in the potential risk areas, and performing corresponding adaptation and synchronization adjustment on the integrated MEMS-FGB sensors, specifically includes: The integrated MEMS-FGB sensors are synchronized by hardware and aligned by software.

[0063] The hardware synchronization specifically includes making the MEMS sensor and the FGB sensor share the same clock signal and data line, and realizing data transmission synchronization at the hardware level through a unified data transmission interface protocol.

[0064] The software alignment specifically includes using an external synchronization signal source to generate a synchronization trigger signal, connecting the synchronization signal to the trigger input terminals of the MEMS sensor and the FGB sensor, and adjusting the timing of the synchronization signal so that the MEMS sensor and the FGB sensor start data acquisition at the same time.

[0065] The stability coefficient of each potential risk area is calculated, and the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area is obtained based on the stability coefficient of each potential risk area. At the same time, the real-time noise during the slope reinforcement process is collected and suppressed in the MEMS-FGB integrated sensor.

[0066] The calculating of the stability coefficient of each potential risk area and obtaining the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area based on the stability coefficient of each potential risk area specifically include: The error-corrected FGB sensor is integrated on the MEMS sensor in each potential risk area, which is denoted as a MEMS-FGB integrated sensor.

[0067] Obtain geological data for each potential risk area, including the slope, number of fractures, and average fracture depth of each potential risk area.

[0068] Extract geological safety verification data from the data processing terminal, including verification slope, number of fracture verifications and average depth of fracture verifications.

[0069] Obtain the deviation value of geological data and geological safety verification data of each potential risk area, and obtain the stability coefficient of each potential risk area through coupling processing, including: ; in, For the The stability coefficient of the potential risk area, For the The slope of the potential risk area For the The number of cracks in the potential risk area, For the The average crack depth of the potential risk area is To check the slope, is the number of crack verifications, is the average depth of crack verification, is the slope influencing factor, is the factor affecting the number of cracks, is the influence factor of the average crack depth, Number the potential risk areas, , is the total number of potential risk areas.

[0070] It should be noted that the slope influence factor, the crack number influence factor and the crack average depth influence factor all have a value range between 0 and 1 and meet the following conditions: The slope influence factor is the influence factor of the slope pre-stored in the data processing terminal, which indicates the degree of influence of the slope on the stability coefficient of each potential risk area; the crack number influence factor is the influence factor of the crack number pre-stored in the data processing terminal, which indicates the degree of influence of the crack number on the stability coefficient of each potential risk area; the crack average depth influence factor is the influence factor of the crack average depth pre-stored in the data processing terminal, which indicates the degree of influence of the crack average depth on the stability coefficient of each potential risk area. When used, it is directly extracted from the data processing terminal, for example, the slope, crack number and crack average depth of each potential risk area are input into the preset mapping set in the data processing terminal to obtain the slope influence factor, crack number influence factor and crack average depth influence factor of each potential risk area, and the corresponding mapping relationship is one-to-one.

[0071] It should also be noted that there is a certain correlation between the parameters of slope, number of cracks and average crack depth in each potential risk area. In geology and engineering, slope, number of cracks and average crack depth are important parameters for assessing slope stability. Generally speaking, the greater the slope, the greater the stress on the surface, which usually leads to more cracks. Therefore, on steep slopes, there are usually more cracks. Areas with larger slopes usually lead to deeper cracks due to stress concentration. Therefore, the greater the slope, the greater the average depth of the cracks. A large number of cracks usually means more stress release, which usually leads to shallower crack depths. Therefore, there is a certain correlation between these parameters.

[0072] Based on the stability coefficient of each potential risk area, mapping and matching are performed with the integrated sensing performance adjustment factors corresponding to each stability coefficient preset in the data processing terminal, so as to obtain the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor in each potential risk area, and then the integrated sensing performance of each potential risk area is adjusted.

[0073] Based on the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor, the sensor sensitivity corresponding to each integrated sensing performance adjustment factor pre-stored in the data processing terminal is mapped and matched to obtain the sensor sensitivity of the MEMS-FGB integrated sensor, and based on the sensor sensitivity of the MEMS-FGB integrated sensor, the amplifier gain increase value of the MEMS-FGB integrated sensor is matched with the amplifier gain value corresponding to each sensitivity pre-stored in the data processing terminal, and the amplifier gain value of the MEMS-FGB integrated sensor is increased. It should be noted that increasing the amplifier gain value can make the signal easier to detect and analyze. At the same time, the increase in gain helps to increase the ratio of the signal to noise, thereby improving the signal-to-noise ratio. By adjusting the sensitivity of the sensor, the adaptability of the system to different measurement objects can be enhanced.

[0074] The real-time noise collected during the slope reinforcement process and suppressed in the MEMS-FGB integrated sensor specifically includes: During the slope reinforcement process, real-time noise within the defined range is collected, including real-time vibration noise, real-time acoustic noise, real-time electromagnetic interference and real-time geological noise. The defined range is a pre-set range in the embodiment of the present invention, with the center point of the slope as the center of the circle and the maximum length of the slope as the diameter, which is circled as the defined range. It should be noted that during the slope reinforcement process, real-time vibration noise refers to the vibration signal of the structure or the ground monitored by the vibration sensor, usually expressed in the form of acceleration, velocity or displacement. Real-time acoustic noise refers to the acoustic signal in the air monitored by the acoustic sensor, and the intensity of the sound is usually measured in decibels (dB). Real-time electromagnetic interference refers to the change of the electromagnetic field monitored by the electromagnetic sensor, and the change of the electromagnetic field will affect the normal operation of the electronic equipment. Real-time geological noise refers to the geophysical signal monitored by the geological sensor. The sensor senses geological activities (such as seismic waves, ground sounds, etc.) and generates corresponding analog signals. The analog signal output by the sensor is amplified by the amplifier and then converted into a digital signal.

[0075] The real-time noise assessment value of the slope reinforcement process is obtained by comparing and correcting the real-time noise with the vibration noise threshold, acoustic noise threshold, electromagnetic interference threshold and geological noise threshold extracted from the data processing terminal, including: ; in, is the real-time noise assessment value of the slope reinforcement process, Real-time vibration and noise of the slope reinforcement process. Real-time acoustic noise of the slope reinforcement process, Real-time electromagnetic interference for slope reinforcement process, Real-time geological noise of the slope reinforcement process, is the vibration noise threshold, is the acoustic noise threshold, is the electromagnetic interference threshold, is the geological noise threshold, is the vibration noise weight factor, is the acoustic noise weight factor, is the electromagnetic interference weight factor, is the geological noise weight factor.

[0076] It should be noted that the vibration noise weight factor, acoustic noise weight factor, electromagnetic interference weight factor and geological noise weight factor all have a value range between 0 and 1 and meet the following conditions: The vibration noise weight factor is the influence factor of the vibration noise pre-stored in the data processing terminal, which indicates the degree of influence of the vibration noise on the real-time noise evaluation value of the slope reinforcement process; the acoustic noise weight factor is the influence factor of the acoustic noise pre-stored in the data processing terminal, which indicates the degree of influence of the acoustic noise on the real-time noise evaluation value of the slope reinforcement process; the electromagnetic interference weight factor is the influence factor of the electromagnetic interference pre-stored in the data processing terminal, which indicates the degree of influence of the electromagnetic interference on the real-time noise evaluation value of the slope reinforcement process; the geological noise weight factor is the influence factor of the geological noise pre-stored in the data processing terminal, which indicates the degree of influence of the geological noise on the real-time noise evaluation value of the slope reinforcement process. When used, it is directly extracted from the data processing terminal. For example, the real-time vibration noise, real-time acoustic noise, real-time electromagnetic interference and real-time geological noise of the slope reinforcement process are input into the preset mapping set in the data processing terminal to obtain the vibration noise weight factor, acoustic noise weight factor, electromagnetic interference weight factor and geological noise weight factor of the slope reinforcement process, and the corresponding mapping relationship is one-to-one.

[0077] It should also be noted that there is a certain correlation between the parameters of real-time vibration noise, real-time acoustic noise, real-time electromagnetic interference and real-time geological noise. Vibration noise is usually generated by the vibration of objects, and these vibrations can be transmitted through the air to become acoustic noise. Therefore, there is a direct connection between vibration noise and acoustic noise. However, vibration noise is more related to the physical vibration of objects, while acoustic noise is related to the propagation characteristics of sound. Electromagnetic interference is usually generated by electrical equipment, and the operation of these equipment is usually accompanied by vibration, so vibration noise and electromagnetic interference usually appear at the same time. Geological noise usually includes vibrations generated by geological activities, which can be detected by vibration sensors. The sound waves generated by geological activities can be captured by acoustic sensors and become part of acoustic noise.

[0078] Based on the real-time noise evaluation value of the slope reinforcement process, the noise suppression scheme corresponding to each real-time noise evaluation value interval stored in the data processing terminal is mapped and matched to obtain the noise suppression scheme of the slope reinforcement process, and the real-time noise of the MEMS-FGB integrated sensor is suppressed.

[0079] Based on the real-time noise evaluation value of the slope reinforcement process, each MEMS-FGB sensor suppresses noise by selecting low-pass filtering, Kalman filtering and digital notch filtering.

[0080] The noise suppression scheme in the slope reinforcement process specifically includes: When the real-time noise evaluation value of the slope reinforcement process is greater than or equal to the first real-time noise threshold, each MEMS-FGB sensor uses digital notch filtering for noise suppression.

[0081] When the real-time noise evaluation value of the slope reinforcement process is greater than or equal to the second real-time noise threshold and less than the first real-time noise threshold, each MEMS-FGB sensor uses Kalman filtering for noise suppression.

[0082] When the real-time noise evaluation value of the slope reinforcement process is less than the second threshold of the real-time noise, each MEMS-FGB sensor uses low-pass filtering to suppress noise.

[0083] It should be noted that the digital notch filter suppresses noise of specific frequencies to reduce the impact of these frequency components. The Kalman filter is an optimization estimation algorithm that can effectively estimate the state of the system from noisy measurement data and is suitable for noise suppression in dynamic systems. The low-pass filter allows signals below a specific cutoff frequency to pass, while signals above this frequency are suppressed, and is suitable for removing high-frequency noise.

[0084] The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and the displacement data measured by the MEMS sensor were collected and cross-validated to obtain real-time displacement monitoring results of each potential risk area during the slope reinforcement process.

[0085] The collecting of strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and displacement data measured by the MEMS sensor and cross-validation specifically includes: The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor is collected and comprehensively analyzed with the pre-collected length data of the attachment structure of the MEMS-FGB integrated sensor to obtain the displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor.

[0086] It should be noted that strain is the ratio of the change in length of a material when subjected to an external force to its original length. Displacement can be calculated by multiplying strain by original length, including: ; in, It is The displacement measured by the FGB sensor, It is The strain measured by the FGB sensor, It is The original length of the FGB sensor attachment structure, is the number of MEMS-FGB integrated sensors in each potential risk area, ,2,3,...,m, where m is the total number of MEMS-FGB integrated sensors in each potential risk area.

[0087] The displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor is compared with the displacement data measured by the MEMS sensor and subtracted to obtain the cross-validation value of the MEMS-FGB integrated sensor. If the cross-validation value of a MEMS-FGB integrated sensor is less than or equal to the cross-validation threshold, the data collected by the MEMS-FGB integrated sensor is regarded as valid data and sent to the data processing terminal. If the cross-validation value of the MEMS-FGB integrated sensor is greater than the cross-validation threshold, the MEMS-FGB integrated sensor is marked as an abnormal integrated sensor and the abnormal sensor is commanded to perform abnormal self-check processing.

[0088] The real-time displacement monitoring results of each potential risk area in the slope reinforcement process are obtained, and the specific processing conditions are: After receiving the valid data, the data processing terminal performs comprehensive processing to obtain the real-time displacement monitoring value of each potential risk area, including extracting the displacement data measured by the FGB sensor in the valid data and coupling the displacement data measured by the MEMS sensor according to the preset weights to obtain the real-time displacement monitoring value of each potential risk area, specifically including: ; in, is the real-time displacement monitoring value of the ith potential risk area, is the effective displacement data measured by the FGB sensor in the yth MEMS-FGB integrated sensor in the ith potential risk area, is the number of MEMS-FGB integrated sensors in the ith potential risk area. The effective displacement data measured by the sensor, is the FGB effective data weight factor, is the MEMS effective data weight factor, i is the potential risk area number, , is the total number of potential risk areas, Valid MEMS-FGB integrated sensor numbers for each potential risk area, , is the total number of effective MEMS-FGB integrated sensors in each potential risk area.

[0089] It should be noted that the FGB effective data weight factor and the MEMS effective data weight factor have a value range between 0 and 1 and satisfy The FGB effective data weight factor is an influence factor of the FGB effective data pre-stored in the data processing terminal, indicating the degree of influence of the FGB effective data on the real-time displacement monitoring value of each potential risk area; the MEMS effective data weight factor is an influence factor of the MEMS effective data pre-stored in the data processing terminal, indicating the degree of influence of the MEMS effective data on the real-time displacement monitoring value of each potential risk area. When used, it is directly extracted from the data processing terminal. For example, the FGB effective data and MEMS effective data of each potential risk area are input into the preset mapping set in the data processing terminal to obtain the FGB effective data weight factor and MEMS effective data weight factor of each potential risk area, and the corresponding mapping relationship is one-to-one.

[0090] If the real-time displacement monitoring value of a potential risk area is greater than the real-time displacement threshold, it is determined that there is displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to MEMS sensors to perform anti-data saturation processing; if the real-time displacement monitoring value of a potential risk area is less than or equal to the real-time displacement threshold, it is determined that there is no displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to FGB sensors to perform operations to improve the displacement recognition resolution.

[0091] It should be noted that MEMS sensors usually have the characteristics of high sensitivity and fast response, and are suitable for monitoring fast changes or large-scale displacements. FGB sensors, namely fiber Bragg grating sensors, have the advantages of high resolution, high temperature resistance, and corrosion resistance, and are suitable for monitoring tiny displacements and high-precision measurements, but may not be sensitive enough in the case of large displacements.

[0092] When the real-time displacement monitoring value is greater than the real-time displacement threshold, it indicates that there may be a large displacement or rapid change. At this time, the use of MEMS sensors can better capture these changes while avoiding data saturation.

[0093] When the real-time displacement monitoring value is less than or equal to the real-time displacement threshold, it indicates that the displacement is small or stable. At this time, using the FGB sensor can improve the resolution of displacement identification and obtain more accurate measurement data.

[0094] The switching method includes that the data processing terminal automatically selects data from different sensors as main input based on the comparison result between the real-time displacement monitoring value and the real-time displacement threshold.

[0095] The present invention selects the most suitable sensor according to the real-time displacement monitoring results, and can more accurately reflect the displacement state of the potential risk area. When the displacement is large, the MEMS sensor is used to dominate, which can avoid the signal saturation problem that may occur in the FGB sensor. When the displacement is small, the FGB sensor is used to dominate, and its high-resolution characteristics can be used to obtain more accurate displacement data. By switching the sensor to dominate as needed, the characteristics of the sensor can be more effectively utilized and the life of the sensor can be extended. The flexible switching mechanism enables the system to better adapt to changes in different environments and conditions, and improves the stability and reliability of the overall monitoring system.

[0096] The command abnormal sensor performs abnormal self-check processing, which is characterized by specifically comprising: A sinusoidal excitation current of a limited current magnitude is sent to the abnormal sensor, and the intensity of each frequency component in the signal is quantified through frequency domain energy distribution to obtain the third harmonic proportion. Based on the third harmonic proportion of the abnormal sensor, a mapping match is performed with each bonding wire fracture area corresponding to the interval of each third harmonic proportion pre-set in the data processing terminal to obtain the bonding wire fracture area of ​​the abnormal sensor, and based on the bonding wire fracture area of ​​the abnormal sensor, the bonding wire fracture detection result of the abnormal sensor is obtained.

[0097] If the bond wire fracture area of ​​the abnormal sensor is greater than or equal to the bond wire fracture critical area, the bond wire fracture detection result of the abnormal sensor is abnormal loss.

[0098] If the bond wire fracture area of ​​the abnormal sensor is smaller than the bond wire fracture critical area, the bond wire fracture detection result of the abnormal sensor is normal loss.

[0099] It should be noted that the sinusoidal excitation current is an alternating current that changes with time according to a sinusoidal function. It is often used in various electronic and electrical tests because the response of many circuits and systems to sinusoidal signals is relatively simple and predictable. The waveform of the sinusoidal excitation current is a smooth periodic fluctuation, and the mathematical expression is ,in is the amplitude, is the angular frequency, It's time. is the phase angle. The frequency of the sine wave determines how fast the current changes. The unit of frequency is Hertz (Hz), which represents the number of cycles completed per second. Amplitude refers to half the difference between the maximum and minimum values ​​of the sine wave. In the case of current, the amplitude represents the maximum range of current change. The phase represents the state of the sine wave at a certain moment, relative to a reference point. In an embodiment of the present invention, the sinusoidal excitation current is used to evaluate the degree of bond wire breakage of a MEMS sensor.

[0100] Harmonics are frequency components that are integer multiples of the fundamental frequency. In signal processing, harmonic analysis is used to evaluate the degree of signal distortion. The third harmonic ratio refers to the ratio of the amplitude of the third harmonic to the amplitude of the fundamental, and is usually used to quantify nonlinear distortion. The time domain signal is converted into a frequency domain signal through Fourier transform (FFT), thereby separating the fundamental and harmonic components and quantifying their strengths.

[0101] The specific steps include: selecting a sinusoidal excitation current, determining the fundamental frequency and amplitude of the excitation current. Using a signal generator to generate a sinusoidal excitation current of a limited current magnitude, and applying the sinusoidal excitation current to the MEMS sensor. Using an oscilloscope to record the output signal of the MEMS sensor. Using a fast Fourier transform (FFT) to convert the time domain signal into a frequency domain signal. Calculating the amplitude spectrum of the frequency domain signal to determine the amplitude of the fundamental wave and each harmonic. In the frequency domain signal, the fundamental frequency is , the third harmonic frequency is . Extract the amplitude values ​​of the fundamental and third harmonic from the amplitude spectrum.

[0102] The third harmonic ratio ( ) is the ratio of the third harmonic amplitude to the fundamental amplitude. Based on the third harmonic proportion, the nonlinear distortion of the signal is determined. If the third harmonic proportion is too high, it indicates that the sensor has a nonlinear effect.

[0103] If the bond wire break detection result of the abnormal sensor is normal loss, the bond wire break area of ​​the abnormal sensor is input into the data processing terminal, and the monitoring correction coefficient of the abnormal sensor is obtained by analysis and processing, and the self-test period and sinusoidal excitation current of the abnormal sensor are matched.

[0104] If the bond wire break detection result of the abnormal sensor is abnormal loss, the number and position of the abnormal sensor will be sent to the management terminal for alarm reminder.

[0105] The analysis and processing to obtain the monitoring correction coefficient of the abnormal sensor specifically includes: After receiving the bond wire fracture area of ​​the abnormal sensor, the data processing terminal maps and matches the bond wire fracture area of ​​the abnormal sensor with the monitoring correction coefficient corresponding to the bond wire fracture area interval pre-stored in the data processing terminal to obtain the monitoring correction coefficient of the abnormal sensor. The monitoring correction coefficient is used to correct the monitoring data of the abnormal sensor. The specific correction process includes: Based on the monitoring correction coefficient of the abnormal sensor, mapping and matching are performed with the signal correction parameters corresponding to the monitoring correction coefficient preset in the data processing terminal to obtain the signal correction parameters of the abnormal sensor, including the filter cutoff frequency, gain coefficient and bias coefficient.

[0106] The gain factor is used to adjust the amplitude of the signal to match the expected output range. The offset factor is used to adjust the horizontal position of the signal to eliminate fixed errors in the sensor output.

[0107] In this embodiment, the present invention provides an apparatus for applying a real-time displacement monitoring method in a slope reinforcement process, including: the apparatus has one or more programs, and the one or more programs are executed by one or more processors to implement the above method.

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0109] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A real-time displacement monitoring method during slope reinforcement, characterized in that: include: During the slope reinforcement process, the stress at each point on the slope is monitored in real time to obtain the real-time magnitude and direction of the stress at each point on the slope. The stress distribution of the slope is obtained through summary analysis and processing to define the potential risk areas in the slope. Monitor the real-time temperature in the slope environment, analyze and process it to obtain the accuracy error coefficient of each FGB sensor, and perform error correction on each FGB sensor. Integrate the error-corrected FGB sensors with the existing MEMS sensors in each potential risk area, and perform corresponding adaptation and synchronization adjustment on the integrated MEMS-FGB sensors. Calculate the stability coefficient of each potential risk area, and obtain the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area based on the stability coefficient of each potential risk area. At the same time, collect the real-time noise during the slope reinforcement process and suppress the real-time noise in the MEMS-FGB integrated sensor. The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and the displacement data measured by the MEMS sensor were collected and cross-validated to obtain real-time displacement monitoring results of each potential risk area during the slope reinforcement process.

2. The real-time displacement monitoring method in the slope reinforcement process according to claim 1 is characterized by: The summary analysis process obtains the stress distribution of the slope and defines the potential risk areas in the slope, specifically including: Monitor the stress at each point on the slope in real time, obtain the real-time magnitude and direction of the stress at each point on the slope, and send it to the data processing terminal. After aligning the data according to the timestamp, obtain the real-time stress vector at each point on the slope. Based on the real-time stress vectors of each point on the slope, the data processing terminal summarizes the stress vectors and discretizes the slope into grid units of preset size. The maximum principal stress of each unit is calculated to construct the overall stress distribution of the slope. The stress distribution diagram of the internal slope is visualized and output. The grid units whose maximum principal stress exceeds the maximum stress threshold of the slope are marked as stress concentration units. Based on the schematic diagram of internal slope stress distribution, the areas where the number of adjacent stress concentration units exceeds the number of risk definition units are defined as potential risk areas and marked.

3. The real-time displacement monitoring method in the slope reinforcement process according to claim 1 is characterized by: The real-time temperature in the monitoring slope environment is analyzed and processed to obtain the accuracy error coefficient of each FGB sensor, and the error of each FGB sensor is corrected, specifically including: Monitor the real-time temperature of each potential risk area in the slope environment and obtain the center wavelength drift of each FGB sensor at the real-time temperature; Based on the center wavelength drift of each FGB sensor, the precision error coefficient corresponding to each center wavelength drift interval preset in the data processing terminal is mapped and matched to obtain the precision error coefficient of each FGB sensor; Based on the precision error coefficient of each FGB sensor, a mapping match is performed with the precision error correction parameters corresponding to each precision error coefficient pre-stored in the data processing terminal to obtain the precision error correction parameters of each FGB sensor, which are imported into the linear compensation model of the FGB sensor to perform error correction on the output results of the FGB sensor.

4. The real-time displacement monitoring method in the slope reinforcement process according to claim 1 is characterized by: The method of integrating and applying the error-corrected FGB sensors with the existing MEMS sensors in the potential risk areas, and performing corresponding adaptation and synchronization adjustment on the integrated MEMS-FGB sensors, specifically includes: Perform hardware synchronization and software alignment on each integrated MEMS-FGB sensor; The hardware synchronization specifically includes making the MEMS sensor and the FGB sensor share the same clock signal and data line, and realizing data transmission synchronization at the hardware level through a unified data transmission interface protocol; The software alignment specifically includes using an external synchronization signal source to generate a synchronization trigger signal, connecting the synchronization signal to the trigger input terminals of the MEMS sensor and the FGB sensor, and adjusting the timing of the synchronization signal so that the MEMS sensor and the FGB sensor start data acquisition at the same time.

5. The real-time displacement monitoring method in the slope reinforcement process according to claim 1 is characterized by: The calculating of the stability coefficient of each potential risk area and obtaining the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor of each potential risk area based on the stability coefficient of each potential risk area specifically include: The error-corrected FGB sensor is integrated on the MEMS sensor in each potential risk area, which is denoted as a MEMS-FGB integrated sensor; Obtain geological data of each potential risk area, including the slope, number of fractures and average fracture depth of each potential risk area; Extracting geological safety verification data from the data processing terminal, including verification slope, number of fracture verifications and average depth of fracture verifications; Obtain the deviation value of geological data and geological safety verification data of each potential risk area, and obtain the stability coefficient of each potential risk area through coupling processing; Based on the stability coefficient of each potential risk area, mapping and matching are performed with the integrated sensing performance adjustment factors corresponding to each stability coefficient preset in the data processing terminal, so as to obtain the integrated sensing performance adjustment factor of the MEMS-FGB integrated sensor in each potential risk area, and then the integrated sensing performance of each potential risk area is adjusted.

6. The real-time displacement monitoring method during slope reinforcement according to claim 1 is characterized by: The real-time noise collected during the slope reinforcement process and suppressed in the MEMS-FGB integrated sensor specifically includes: During the slope reinforcement process, real-time noise within the defined range is collected, including real-time vibration noise, real-time acoustic noise, real-time electromagnetic interference and real-time geological noise. The real-time noise is compared and corrected with the vibration noise threshold, acoustic noise threshold, electromagnetic interference threshold and geological noise threshold extracted from the data processing terminal to obtain the real-time noise assessment value of the slope reinforcement process. Based on the real-time noise assessment value of the slope reinforcement process, the noise suppression scheme corresponding to each real-time noise assessment value interval stored in the data processing terminal is mapped and matched to obtain the noise suppression scheme of the slope reinforcement process, and the MEMS-FGB integrated sensor is subjected to real-time noise suppression.

7. The real-time displacement monitoring method in the slope reinforcement process according to claim 1 is characterized by: The collecting of strain data measured by the FGB sensor in the MEMS-FGB integrated sensor and displacement data measured by the MEMS sensor and cross-validation specifically includes: The strain data measured by the FGB sensor in the MEMS-FGB integrated sensor is collected, and the length data of the attached structure of the MEMS-FGB integrated sensor collected in advance are comprehensively analyzed to obtain the displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor; The displacement data measured by the FGB sensor in the MEMS-FGB integrated sensor is compared with the displacement data measured by the MEMS sensor to obtain the cross-validation value of the MEMS-FGB integrated sensor. If the cross-validation value of a MEMS-FGB integrated sensor is less than or equal to the cross-validation threshold, the data collected by the MEMS-FGB integrated sensor is regarded as valid data and sent to the data processing terminal. If the cross-validation value of the MEMS-FGB integrated sensor is greater than the cross-validation threshold, the MEMS-FGB integrated sensor is marked as an abnormal integrated sensor and the abnormal sensor is commanded to perform abnormal self-check processing.

8. The real-time displacement monitoring method during slope reinforcement according to claim 1 is characterized by: The real-time displacement monitoring results of each potential risk area in the slope reinforcement process are obtained, and the specific processing conditions are: After receiving the valid data, the data processing terminal performs comprehensive processing to obtain the real-time displacement monitoring value of each potential risk area. If the real-time displacement monitoring value of a potential risk area is greater than the real-time displacement threshold, it is determined that there is displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to MEMS sensors to perform anti-data saturation processing. If the real-time displacement monitoring value of a potential risk area is less than or equal to the real-time displacement threshold, it is determined that there is no displacement in the potential risk area, and the MEMS-FGB integrated sensors in the potential risk area are switched to FGB sensors to perform operations to improve the displacement recognition resolution.

9. The real-time displacement monitoring method in the slope reinforcement process according to claim 7 is characterized by: The command abnormal sensor performs abnormal self-check processing, which is characterized by specifically comprising: Send a sinusoidal excitation current of a limited current magnitude to the abnormal sensor, quantify the intensity of each frequency component in the signal through frequency domain energy distribution, and obtain the third harmonic proportion. Based on the third harmonic proportion of the abnormal sensor, the third harmonic proportion is mapped and matched with each bonding wire fracture area corresponding to the interval where each third harmonic proportion is pre-set in the data processing terminal to obtain the bonding wire fracture area of ​​the abnormal sensor, and obtain the bonding wire fracture detection result of the abnormal sensor based on the bonding wire fracture area of ​​the abnormal sensor; If the bond wire fracture detection result of the abnormal sensor is normal loss, the bond wire fracture area of ​​the abnormal sensor is input into the data processing terminal, and the monitoring correction coefficient of the abnormal sensor is obtained through analysis and processing, and the self-test period and the sinusoidal excitation current of the abnormal sensor are matched; If the bond wire break detection result of the abnormal sensor is abnormal loss, the number and position of the abnormal sensor will be sent to the management terminal for alarm reminder.

10. A device for using the real-time displacement monitoring method in the slope reinforcement process as claimed in any one of claims 1 to 9, characterized in that: The device has one or more programs, and the one or more programs are executed by one or more processors to implement the above method.

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