Slope detection device and method based on distributed optical fiber sensing

Abstract

This application discloses a slope detection device and method based on distributed optical fiber sensing, comprising: an optical fiber measurement unit deployed inside the slope to measure the acceleration time series of multiple state parameters of the slope; a demodulator connected to the optical fiber measurement unit to receive the acceleration time series measured by the slope; and a terminal device connected to the demodulator to perform multi-domain coupled analysis on the acceleration time series, generate a feature cloud map of the slope, and perform sub-item detection of slope stability based on the feature cloud map and preset multi-domain feature parameter indicators to obtain the sub-item detection results of the slope state, thereby generating detection results of the slope's dynamic characteristics and deformation characteristics. This allows for a comprehensive, accurate, and efficient identification of the slope's health status by integrating the multi-domain analysis results, improving the reliability of slope stability health detection and diagnosis, as well as the accuracy and efficiency of distributed optical fiber measurement.

Description

Technical Field

[0001] This application relates to the field of geotechnical engineering vibration measurement technology, and in particular to a slope detection device and method based on distributed optical fiber sensing. Background Technology

[0002] Slope deformation is one of the major natural disasters globally. Under external triggering factors, slopes become unstable and slide, making landslides a serious geological hazard threatening people's lives and property. Under the long-term influence of internal and external forces such as earthquakes, heavy rainfall, excavation unloading, natural weathering, freeze-thaw cycles, and fissure development, many slopes in nature have suffered damage and deformation to varying degrees, weakening their stability and making them prone to landslides under triggering factors. In areas adjacent to human activity zones, landslide prevention and control has become one of the important engineering measures to reduce the threat of geological disasters. Slope health monitoring and diagnosis can quickly identify the stability status and deformation characteristics of slopes, such as the location of damage, becoming an important prerequisite for landslide prevention and control. In particular, the obvious discontinuities in internal structural planes, joints, and weak interlayers within slopes make slope stability assessment very complex. Therefore, it is necessary to conduct health monitoring and diagnosis of slopes in areas with human activity to assess their deformation characteristics and stability status.

[0003] Distributed fiber acoustic sensing (DAS) technology is a novel fiber optic sensing technology that uses optical fibers as sensors and acquires vibration signals based on Rayleigh scattering of light. It offers advantages such as low cost, high measurement accuracy, resistance to electromagnetic interference, and ease of installation. Compared to conventional single-point and quasi-distributed sensors, DAS is more suitable for applications requiring long distances or high temporal and spatial resolution, and is widely used in oil exploration, pipeline leak monitoring, and border security monitoring. However, the application of DAS technology in geotechnical engineering is still in its developmental stage. Slopes are subjected to varying degrees of damage due to internal and external dynamic forces such as geological structures, seismic loads, and periodic heavy rainfall, resulting in extremely complex catastrophic processes. Therefore, conducting stability health monitoring and diagnosis of complex slopes is particularly necessary. Currently, most methods use sensors such as acceleration and displacement sensors to monitor slopes, or elastic wave methods to perform microseismic detection. By analyzing the characteristics of changes in acceleration and displacement data, the deformation trend and stability state of the slope are identified. The assessment of slope stability based solely on time-domain parameters such as acceleration and displacement often carries significant uncertainties. Slope vibration characteristics encompass three factors: time, frequency, and amplitude, necessitating a combination of time-domain, frequency-domain, and time-frequency-domain analyses for stability assessment. Slopes under multi-load coupling and complex geological conditions exhibit characteristic patterns in the time, frequency, and time-frequency domains. Accurate detection and identification of damage states in different slope regions are prerequisites for precise, time- and zone-based slope reinforcement. However, current research on the systematic analysis of slope stability using multi-domain parameters in the time, frequency, and time-frequency domains is limited, particularly regarding multi-domain, multi-parameter slope stability health detection and diagnosis methods based on DAS technology; this area remains largely unexplored.

[0004] The axial sensitivity of optical fibers leads to different response mechanisms for different types of seismic waves. Studying the influence of seismic wave incident angle, velocity characteristics, and medium properties on the axial strain of optical fibers, starting from the propagation characteristics of seismic waves, helps to better understand the role of distributed optical fiber (DAS) in seismic data acquisition. Since P-waves and S-waves are coupled together, conventional geotechnical engineering testing acquires vibration signals that are orthogonal in the X, Y, and Z directions. Information on the P-waves and S-waves needs to be indirectly obtained by processing these three components. Currently, distributed optical fiber measurement technology using three-component seismic motion and three-component strain has not yet been applied in slope engineering, leaving this field largely unexplored.

[0005] In summary, there is a lack of efficient and accurate scientific detection and analysis methods and technologies for time- and zone-based precise targeted reinforcement and treatment of existing complex slopes, which urgently need to be addressed. Summary of the Invention

[0006] This application provides a slope detection device and method based on distributed optical fiber sensing to solve the problem that related technologies lack efficient and high-precision scientific detection and analysis methods for time-sharing and zone-based precise targeted reinforcement and treatment.

[0007] The first aspect of this application provides a slope detection device based on distributed optical fiber sensing, comprising: an optical fiber measurement unit deployed inside the slope to measure the acceleration time series of multiple state parameters of the slope; a demodulator connected to the optical fiber measurement unit to receive the acceleration time series measured by the slope; and a terminal device connected to the demodulator to perform multi-domain coupling analysis on the acceleration time series, generate a feature cloud map of the slope, and perform sub-item detection of the slope stability state based on the feature cloud map and preset multi-domain feature parameter indicators to obtain sub-item detection results of the slope state, and generate detection results of the dynamic characteristics and deformation characteristics of the slope based on the sub-item detection results.

[0008] Optionally, in one embodiment of this application, the state parameters of the slope include one or more of the following: slope vibration, temperature, pressure, strain, and moisture content.

[0009] Optionally, in one embodiment of this application, the fiber optic measurement unit is arranged in an S-shape around the slope surface at a preset interval, and the fiber optic measurement unit is connected in series around the borehole inside the slope by drilling at fixed points.

[0010] Optionally, in one embodiment of this application, generating a feature cloud map using multi-domain coupling analysis of the acceleration time series includes: performing time-domain transformation analysis on the acceleration time series to obtain peak accelerations at different locations on the slope; generating a first distribution cloud map of peak accelerations at different cross- and longitudinal profiles of the slope based on the peak accelerations; performing frequency-domain transformation analysis on the acceleration time series to generate a second distribution cloud map of peak spectral values ​​at different cross- and longitudinal profiles of the slope by analyzing the spectrum and peak variation characteristics; performing time-frequency transformation analysis on the acceleration time series to generate a third distribution cloud map of peak energy spectrum values ​​at different cross- and longitudinal profiles of the slope by analyzing the energy spectrum characteristics and peak variation; and generating a feature cloud map of the slope based on the first distribution cloud map, the second distribution cloud map, and the third distribution cloud map.

[0011] Optionally, in one embodiment of this application, the multi-domain characteristic parameter index includes time-domain parameter index, frequency-domain parameter index, and time-frequency domain parameter index, wherein the time-domain parameter index includes one or more of peak acceleration, PGA, amplification factor, peak displacement, RD, and PEC; the frequency-domain parameter index includes one or more of natural frequency, relative displacement, and spectral peak value; and the time-frequency domain parameter index includes one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

[0012] A second aspect of this application provides a slope detection method based on distributed optical fiber sensing, comprising the following steps: acquiring acceleration time series of multiple state parameters of the slope; performing multi-domain coupling analysis on the acceleration time series to generate a feature cloud map of the slope; performing sub-item detection of the slope stability state based on the feature cloud map and preset multi-domain feature parameter indices to obtain sub-item detection results of the slope state, and generating detection results of the dynamic characteristics and deformation characteristics of the slope based on the sub-item detection results.

[0013] Optionally, in one embodiment of this application, generating a feature cloud map using multi-domain coupling analysis of the acceleration time series includes: performing time-domain transformation analysis on the acceleration time series to obtain peak accelerations at different locations on the slope; generating a first distribution cloud map of peak accelerations at different cross- and longitudinal profiles of the slope based on the peak accelerations; performing frequency-domain transformation analysis on the acceleration time series to generate a second distribution cloud map of peak spectral values ​​at different cross- and longitudinal profiles of the slope by analyzing the spectrum and peak variation characteristics; performing time-frequency transformation analysis on the acceleration time series to generate a third distribution cloud map of peak energy spectrum values ​​at different cross- and longitudinal profiles of the slope by analyzing the energy spectrum characteristics and peak variation; and generating a feature cloud map of the slope based on the first distribution cloud map, the second distribution cloud map, and the third distribution cloud map.

[0014] Optionally, in one embodiment of this application, the multi-domain characteristic parameter index includes time-domain parameter index, frequency-domain parameter index, and time-frequency domain parameter index, wherein the time-domain parameter index includes one or more of peak acceleration, PGA, amplification factor, peak displacement, RD, and PEC; the frequency-domain parameter index includes one or more of natural frequency, relative displacement, and spectral peak value; and the time-frequency domain parameter index includes one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

[0015] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the slope detection method based on distributed optical fiber sensing as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the slope detection method based on distributed optical fiber sensing as described in the above embodiments.

[0017] Therefore, the embodiments of this application have the following beneficial effects:

[0018] The embodiments of this application utilize fiber optic measurement units deployed within the slope to measure the acceleration time series of multiple state parameters of the slope. A demodulator connected to the fiber optic measurement unit receives the acceleration time series measured by the slope. Furthermore, a terminal device connected to the demodulator performs multi-domain coupled analysis on the acceleration time series, generating a feature cloud map of the slope. Based on the feature cloud map and preset multi-domain feature parameter indicators, the slope stability state is detected in stages, yielding the stage detection results. Based on these stage detection results, the dynamic characteristics and deformation characteristics of the slope are generated. This comprehensive, accurate, and efficient multi-domain analysis allows for a comprehensive assessment of the slope's health state, improving the reliability of slope stability health detection and diagnosis, as well as the accuracy and efficiency of distributed fiber optic measurement. This solves the problem of the lack of efficient and high-precision scientific detection and analysis methods for time- and zone-based precise targeted reinforcement and treatment in related technologies.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is an example diagram of a slope detection device based on distributed optical fiber sensing according to an embodiment of this application;

[0022] Figure 2 This is a slope profile and top view based on distributed optical fiber sensing according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of a three-component fiber optic sensor improved by using a fiber optic loop method according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a three-component fiber optic sensor improved by adding fiber cores according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of the execution logic of a slope detection device based on distributed optical fiber sensing according to an embodiment of this application;

[0026] Figure 6 This is a flowchart of a slope detection method based on distributed optical fiber sensing provided according to an embodiment of this application;

[0027] Figure 7 A schematic diagram of the structure of the electronic device provided in the application embodiment.

[0028] Explanation of reference numerals in the attached diagram: Fiber optic measurement unit-1, demodulator-2, terminal equipment-3, slope-4, vertical drilling and layout within the slope body-5, metal pipe-6, fiber optic core-7, grating signal-8, coating-9, memory-701, processor-702, communication interface-703. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] The following describes an embodiment of the slope detection device and method based on distributed optical fiber sensing, with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides a slope detection device based on distributed optical fiber sensing. Embodiments of this application use optical fiber measurement units deployed inside the slope to measure the acceleration time series of multiple state parameters of the slope. A demodulator connected to the optical fiber measurement unit receives the acceleration time series measured by the slope. Further, a terminal device connected to the demodulator performs multi-domain coupling analysis on the acceleration time series, generating a feature cloud map of the slope. Based on the feature cloud map and preset multi-domain feature parameter indicators, the slope stability state is detected in sub-items, obtaining the sub-item detection results of the slope state. Based on the sub-item detection results, detection results of the slope's dynamic characteristics and deformation characteristics are generated. Thus, by comprehensively analyzing the multi-domain results, the slope health state is comprehensively, accurately, and efficiently identified, improving the reliability of slope stability health detection and diagnosis, as well as the accuracy and efficiency of distributed optical fiber measurement. This solves the problem of the lack of efficient and high-precision scientific detection and analysis methods for time-division and zone-based precise targeted reinforcement and treatment in related technologies.

[0031] Specifically, Figure 1This is a block diagram of a slope detection device based on distributed optical fiber sensing according to an embodiment of this application.

[0032] like Figure 1 As shown, the slope detection device 10 based on distributed optical fiber sensing includes: an optical fiber measurement unit 1, a demodulator 2, and a terminal device 3.

[0033] The fiber optic measurement unit 1, deployed inside the slope, measures the acceleration time series of multiple state parameters of the slope; the demodulator 2 is connected to the fiber optic measurement unit 1 and receives the acceleration time series measured by the slope; the terminal device 3 is connected to the demodulator 2, performs multi-domain coupling analysis on the acceleration time series, generates a feature cloud map of the slope, and performs sub-item detection of slope stability based on the feature cloud map and preset multi-domain feature parameter indicators to obtain the sub-item detection results of the slope state, and generates the detection results of the dynamic characteristics and deformation characteristics of the slope based on the sub-item detection results.

[0034] It should be noted that the embodiments of this application improve upon the conventional fiber optic sensors used in existing DAS technology by integrating the slope state parameters into a single system to form a three-component seismic motion three-component fiber optic sensor, which includes a fiber optic measurement unit.

[0035] Optionally, in one embodiment of this application, the slope state parameters include one or more of the following: slope vibration, temperature, pressure, strain, and moisture content.

[0036] It is understood that, in the embodiments of this application, the aforementioned fiber optic sensor is used to measure the acceleration time series of multiple state parameters of the slope to obtain real-time data such as vibration, temperature, pressure, strain and moisture content, thereby providing a real-time and reliable data source for the sub-item identification results of slope stability detection and the accurate and efficient identification of slope health status.

[0037] Optionally, in one embodiment of this application, the fiber optic measurement unit 1 is arranged in an S-shape around the slope surface at a preset interval, and the fiber optic measurement unit 1 is connected in series around the borehole inside the slope by drilling at fixed points.

[0038] like Figure 2 As shown, in the embodiments of this application, a certain length of distributed optical fiber sensors are deployed on the surface / inside of the slope. The optical fiber sensors are arranged in an S-shape around the slope surface and are wrapped with tubing made of materials such as metal. The optical fibers are wound along a spirally wound armored optical cable, and the optical fibers are wound with a certain spacing, such as... Figure 3 As shown, this allows for a wider distribution of optical fibers on the slope surface. Inside the slope, fixed-point drilling is used to connect the optical fibers in series around the borehole.

[0039] In addition, such as Figure 4 As shown, in the embodiments of this application, in addition to using optical fiber wrapping, the optical fiber measurement unit 1 also adopts the method of increasing the number of fiber cores. By improving the conventional optical fiber, the number of fiber cores 7 in the original optical fiber is increased to obtain the required grating signal 8. The fiber cores are built into the coating 9 to achieve the purpose of obtaining a three-component seismic three-component strain optical fiber sensor.

[0040] It should be noted that, in the embodiments of this application, the fiber optic cable can be laid by trenching or quick-drying cement bonding. The fiber optic cable is wrapped with metal or other materials and coupled with butyl strong adhesive and then implanted into the soil and rock. The coupling is reinforced by backfilling with sand. The fiber optic cable is laid by drilling in the longitudinal profile of the slope where the focus is on. The fiber optic sensor is embedded in the soil and rock, thereby realizing the coupling between the fiber optic sensor and the stratum.

[0041] Therefore, the embodiments of this application integrate parameters such as temperature, strain, moisture content, and acceleration into a single system, realizing the identification of slope stability detection results by sub-items, and comprehensively, accurately, and efficiently identifying the health status of slopes by integrating multi-domain analysis results, thereby improving the accuracy and efficiency of existing distributed optical fiber measurement methods.

[0042] Subsequently, in the embodiments of this application, the demodulator 2 connected to the fiber optic measurement unit 1 can receive the acceleration time series of the slope measurement, convert the above data series into corresponding waveform signals, and digitize the signals.

[0043] Furthermore, embodiments of this application can utilize a terminal device 3 connected to the demodulator 2 to perform multi-domain coupling analysis on the acceleration time series. The terminal device 3 can be a computer decision analysis terminal.

[0044] Specifically, embodiments of this application can receive digital signals from the ground demodulator 2 and perform data processing. Data processing includes automated and manual processing. Automated processing includes filtering and parameter selection, while manual processing includes noise reduction, accuracy extraction based on the data interface, and displaying the vibration location, magnitude, and source parameters when changes in vibration, temperature, and pressure occur within the slope via a graphical interface. A computer decision analysis terminal is used to predict the internal damage state of the slope based on the processed data and images, and to perform analysis and prediction of the internal slope damage.

[0045] Optionally, in one embodiment of this application, multi-domain coupled analysis of acceleration time series is used to generate feature cloud maps, including: performing time-domain transformation analysis on the acceleration time series to obtain peak accelerations at different locations on the slope, and generating a first distribution cloud map of peak accelerations at different cross and longitudinal profiles of the slope based on the peak accelerations; performing frequency-domain transformation analysis on the acceleration time series, and generating a second distribution cloud map of peak spectral values ​​at different cross and longitudinal profiles of the slope by analyzing the spectrum and peak variation characteristics; performing time-frequency transformation analysis on the acceleration time series, and generating a third distribution cloud map of peak energy spectrum values ​​at different cross and longitudinal profiles of the slope by analyzing the energy spectrum characteristics and peak variation; and generating a feature cloud map of the slope based on the first, second, and third distribution cloud maps.

[0046] It should be noted that the aforementioned computer decision analysis terminal employs a multi-domain coupled analysis method that comprehensively considers the time domain, frequency domain, and time-frequency domain to obtain the time domain, frequency domain, and time-frequency domain data processing program of the measured data, and further generates feature cloud maps. The specific steps are as follows:

[0047] Step 1: The time domain data processing program in the computer decision analysis terminal 3 can obtain the peak value of the acceleration time series. By analyzing the characteristics of peak value variation, a distribution cloud map of peak acceleration in different cross and longitudinal profiles of the slope is drawn.

[0048] Step 2: The frequency domain data processing program in the computer decision analysis terminal 3 can perform frequency domain transformation analysis on the measured acceleration time series. By analyzing the spectrum and its peak variation characteristics, it can draw the distribution cloud map of the spectrum peaks of different cross and longitudinal profiles of the slope.

[0049] Step 3: The time-frequency domain data processing program in the computer decision analysis terminal 3 can perform time-frequency domain transformation analysis on the measured acceleration time series. By analyzing its energy spectrum characteristics and peak value changes, it can draw the distribution cloud map of the energy spectrum peak values ​​of different cross and longitudinal profiles of the slope.

[0050] Furthermore, in the embodiments of this application, the multi-domain waveform characteristics of the vibration signal are obtained by performing time-domain, frequency-domain, and time-frequency-domain analysis on the acceleration signal inside the slope. The multi-domain and multi-parameter distribution characteristics of the slope inside and on the surface are processed by imaging using software such as Surfer, and then cloud map is drawn by interpolation method to obtain a multi-domain and multi-parameter cloud map of the slope.

[0051] Therefore, this application embodiment utilizes a multi-domain coupling analysis method to integrate time domain, frequency domain, and time-frequency domain data into a single system, and obtains the dynamic characteristics and deformation characteristics of the slope based on the characteristic parameters of the multiple domains.

[0052] Optionally, in one embodiment of this application, the multi-domain characteristic parameter index includes time-domain parameter index, frequency-domain parameter index, and time-frequency-domain parameter index. The time-domain parameter index includes one or more of peak ground acceleration (PGA), amplification factor, peak displacement, residual displacement (RD), and plastic effect coefficient (PEC). The frequency-domain parameter index includes one or more of natural frequency, relative displacement, and spectral peak value. The time-frequency-domain parameter index includes one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

[0053] Specifically, in this application, the multi-domain, multi-parameter architecture of slope dynamic characteristics includes time domain, frequency domain, and key analysis parameters in the time and frequency domains.

[0054] The time-domain parameters mainly include PGA, PGV (Peak Ground Velocity), PGD (Peak Ground Displacement), RD, and PEC. The frequency-domain parameters mainly include the spectrum, frequency f, and Fourier peak spectral PFSA. The time-frequency domain parameters mainly include Ia (Arias Intensity), seismic Hilbert energy peak spectral PHSA, and marginal peak spectral PFSA. Their specific characterization meanings are as follows:

[0055] In the time domain parameters, PGA represents the maximum inertial force borne by the slope under dynamic load; MPGA represents the ratio of the maximum inertial force borne at any point on the slope to the inertial force at the toe of the slope; PGD represents the maximum absolute value of the displacement of a point on the slope under dynamic load; PEC represents the ratio of plastic deformation to peak displacement at any moment, indicating the degree of irreversible damage to the slope.

[0056] In the frequency domain parameters, the frequency f is determined by the inherent characteristics of the slope, such as its hardness, mass, and dimensions; the relative displacement U is the degree of relative deformation of a point compared to the toe of the slope under a certain natural frequency vibration; PFSA characterizes the regions in the Fourier spectrum where energy is highly concentrated in different natural frequency bands, and can reveal the relationship between different frequency components and slope deformation characteristics.

[0057] In the time-frequency domain parameters, Ia reflects the energy released by ground vibration at a certain point on the slope recorded by the instrument, characterizing the amount of total seismic energy absorbed by the ground and the degree of damage, as well as the local intensity; PHSA characterizes the propagation characteristics of the total seismic energy of the original signal within the slope, and is used to analyze the global deformation response characteristics of the slope; PMSA reflects the energy propagation characteristics of a certain high-resolution sub-signal of the original signal, and is used to reflect the local deformation response characteristics of the slope.

[0058] It is understood that the embodiments of this application utilize acceleration time series for multi-domain coupled analysis to generate feature cloud maps, thereby predicting the stability and deformation trend of slopes by analyzing the parameter distribution characteristics in the time domain, frequency domain, and time-frequency domain, and achieving a comprehensive assessment of the stability and health status of slopes.

[0059] The following will describe the slope detection device based on distributed optical fiber sensing of this application through a specific embodiment.

[0060] Figure 5 This is a schematic diagram of the execution logic of a slope detection process based on distributed fiber optic sensing, as shown below. Figure 5 As shown, the specific steps for slope detection in this embodiment of the application are as follows:

[0061] Step 1: Based on the improved three-component DAS technology, determine the on-site fiber optic deployment scheme according to the actual site conditions, and carry out dense fiber optic deployment on typical slope profiles and areas. Fiber optic cables are implanted into the soil and rock through drilling to acquire imaging cloud maps of multiple longitudinal and transverse profiles and areas of the slope. Data such as temperature, strain, moisture content, and acceleration are collected.

[0062] Step 2: Establish a multi-domain, multi-parameter analysis framework for slope dynamic characteristics. Time-domain parameters mainly include PGA, PGV, PGD, RD, and PEC; frequency-domain parameters mainly include the spectrum, f, and PFSA; time-frequency domain parameters mainly include Ia, PHSA, and PMSA.

[0063] Step 3: Using a ground-based modem or computer with built-in signal processing and data imaging software, visualize the aforementioned multi-domain, multi-parameter cloud maps. By analyzing the distribution characteristics of the visualized cloud maps of different parameters in each domain and the physical meaning of their representations, the stability state of the slope is assessed and diagnosed in detail, and conclusions are drawn from these assessments.

[0064] Step 4: Based on the multi-domain and multi-parameter sub-response results of the integrated slope dynamic response, an accurate and efficient comprehensive assessment of the current and future slope health monitoring and diagnosis is made, and the results are output in Word / image format to provide engineers with a reference for on-site engineering decisions.

[0065] According to the slope detection device based on distributed optical fiber sensing proposed in the embodiments of this application, the embodiments of this application measure the acceleration time series of multiple state parameters of the slope through optical fiber measurement units deployed inside the slope. A demodulator is connected to the optical fiber measurement unit to receive the acceleration time series measured by the slope. Furthermore, a terminal device is connected to the demodulator to perform multi-domain coupling analysis on the acceleration time series, generating a feature cloud map of the slope. Based on the feature cloud map and preset multi-domain feature parameter indicators, the slope stability state is detected separately to obtain the sub-item detection results of the slope state. Based on the sub-item detection results, the detection results of the slope's dynamic characteristics and deformation characteristics are generated. Thus, the comprehensive multi-domain analysis results are used to comprehensively, accurately and efficiently identify the slope health state, improving the reliability of slope stability health detection and diagnosis, as well as the accuracy and efficiency of distributed optical fiber measurement.

[0066] Next, referring to the accompanying drawings, a slope detection method based on distributed optical fiber sensing proposed according to an embodiment of this application is described.

[0067] Figure 6 This is a flowchart illustrating a slope detection method based on distributed optical fiber sensing, provided as an embodiment of this application.

[0068] like Figure 6 As shown, the slope detection method based on distributed optical fiber sensing includes the following steps:

[0069] In step S601, the acceleration time series of multiple state parameters of the slope are obtained.

[0070] In step S602, a multi-domain coupling analysis is performed on the acceleration time series to generate a characteristic cloud map of the slope.

[0071] Optionally, in one embodiment of this application, multi-domain coupled analysis of acceleration time series is used to generate feature cloud maps, including: performing time-domain transformation analysis on the acceleration time series to obtain peak accelerations at different locations on the slope, and generating a first distribution cloud map of peak accelerations at different cross and longitudinal profiles of the slope based on the peak accelerations; performing frequency-domain transformation analysis on the acceleration time series, and generating a second distribution cloud map of peak spectral values ​​at different cross and longitudinal profiles of the slope by analyzing the spectrum and peak variation characteristics; performing time-frequency transformation analysis on the acceleration time series, and generating a third distribution cloud map of peak energy spectrum values ​​at different cross and longitudinal profiles of the slope by analyzing the energy spectrum characteristics and peak variation; and generating a feature cloud map of the slope based on the first, second, and third distribution cloud maps.

[0072] In step S603, the slope stability status is detected by sub-item detection based on the feature cloud map and preset multi-domain feature parameter indexes, and the sub-item detection results of the slope status are obtained. Based on the sub-item detection results, the detection results of the dynamic characteristics and deformation characteristics of the slope are generated.

[0073] Optionally, in one embodiment of this application, the multi-domain characteristic parameter index includes time-domain parameter index, frequency-domain parameter index, and time-frequency domain parameter index, wherein the time-domain parameter index includes one or more of peak acceleration, PGA, amplification factor, peak displacement, RD, and PEC; the frequency-domain parameter index includes one or more of natural frequency, relative displacement, and spectral peak value; and the time-frequency domain parameter index includes one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

[0074] It should be noted that the foregoing explanation of the slope detection device based on distributed optical fiber sensing also applies to the slope detection method based on distributed optical fiber sensing in this embodiment, and will not be repeated here.

[0075] The slope detection method based on distributed optical fiber sensing proposed in this application acquires the acceleration time series of multiple state parameters of the slope, performs multi-domain coupling analysis on the acceleration time series to generate a feature cloud map of the slope, and then performs sub-item detection of slope stability state based on the feature cloud map and preset multi-domain feature parameter indices to obtain the sub-item detection results of the slope state. Based on the sub-item detection results, the detection results of the slope's dynamic characteristics and deformation characteristics are generated, thereby identifying the internal stability and health status of the slope. The comprehensive multi-domain analysis results provide a comprehensive, accurate, and efficient identification of the slope's health status, improving the reliability of slope stability and health detection and diagnosis. Furthermore, integrating parameters such as temperature, strain, water content, and acceleration into a single system enhances the accuracy and efficiency of existing distributed optical fiber measurement methods. In addition, using time-domain, frequency-domain, and time-frequency-domain multi-parameter sub-item imaging of the measured vibration signal makes slope stability and health detection and diagnosis more convenient and intuitive, improving the ability to identify local slope deformation.

[0076] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0077] The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0078] When the processor 702 executes the program, it implements the slope detection method based on distributed optical fiber sensing provided in the above embodiments.

[0079] Furthermore, electronic devices also include:

[0080] Communication interface 703 is used for communication between memory 701 and processor 702.

[0081] The memory 701 is used to store computer programs that can run on the processor 702.

[0082] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0083] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0084] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0085] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0086] This embodiment also provides a computer-readable storage medium storing a computer program, characterized in that the program, when executed by a processor, implements the above-described slope detection method based on distributed optical fiber sensing.

[0087] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0088] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0090] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0091] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A slope detection device based on distributed optical fiber sensing, characterized in that, include: The fiber optic measurement unit deployed inside the slope measures the acceleration time series of multiple state parameters of the slope. A demodulator, which is connected to the fiber optic measurement unit, receives the acceleration time series of the slope measurement; The terminal device is connected to the demodulator and performs multi-domain coupling analysis on the acceleration time series to generate a feature cloud map of the slope. Based on the feature cloud map and preset multi-domain feature parameter indicators, it performs sub-item detection of the slope stability state to obtain sub-item detection results of the slope state. Based on the sub-item detection results, it generates detection results of the dynamic characteristics and deformation characteristics of the slope.

2. The apparatus according to claim 1, characterized in that, The slope state parameters include one or more of the following: slope vibration, temperature, pressure, strain, and moisture content.

3. The apparatus according to claim 1, characterized in that, The fiber optic measurement unit is deployed in an S-shape around the slope surface at a preset interval, and the fiber optic measurement unit is connected in series around the borehole inside the slope by drilling at fixed points.

4. The apparatus according to claim 1, characterized in that, Multi-domain coupling analysis using the acceleration time series to generate feature cloud maps includes: The acceleration time series is subjected to time domain transformation analysis to obtain the peak acceleration at different locations of the slope, and the first distribution cloud map of the peak acceleration of different cross and longitudinal profiles of the slope is generated based on the peak acceleration. The acceleration time series is subjected to frequency domain transformation analysis. By analyzing the spectrum and peak value variation characteristics, a second distribution cloud map of the spectrum peak values ​​of different transverse and longitudinal profiles of the slope is generated. The acceleration time series is subjected to time-frequency domain transformation analysis. By analyzing the energy spectrum characteristics and peak value changes, a third distribution cloud map of the energy spectrum peak values ​​of different transverse and longitudinal profiles of the slope is generated. The feature cloud map of the slope is generated based on the first distribution cloud map, the second distribution cloud map, and the third distribution cloud map.

5. The apparatus according to claim 1 or 4, characterized in that, The multi-domain characteristic parameter indexes include time-domain parameter indexes, frequency-domain parameter indexes, and time-frequency domain parameter indexes. The time-domain parameter indexes include one or more of peak acceleration, PGA, amplification factor, peak displacement, RD, and PEC. The frequency-domain parameter indexes include one or more of natural frequency, relative displacement, and spectral peak value. The time-frequency domain parameter indexes include one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

6. A slope detection method based on distributed optical fiber sensing, characterized in that, The slope detection device based on distributed optical fiber sensing according to any one of claims 1-5, the method includes the following steps: Obtain the acceleration time series of multiple state parameters of the slope; Multi-domain coupling analysis is performed on the acceleration time series to generate a feature cloud map of the slope; The slope stability state is detected by sub-item detection based on the feature cloud map and preset multi-domain feature parameter indexes, and the sub-item detection results of the slope state are obtained. The dynamic characteristics and deformation characteristics of the slope are then generated based on the sub-item detection results.

7. The method according to claim 6, characterized in that, Multi-domain coupling analysis using the acceleration time series to generate feature cloud maps includes: The acceleration time series is subjected to time domain transformation analysis to obtain the peak acceleration at different locations of the slope, and the first distribution cloud map of the peak acceleration of different cross and longitudinal profiles of the slope is generated based on the peak acceleration. The acceleration time series is subjected to frequency domain transformation analysis. By analyzing the spectrum and peak value variation characteristics, a second distribution cloud map of the spectrum peak values ​​of different transverse and longitudinal profiles of the slope is generated. The acceleration time series is subjected to time-frequency domain transformation analysis. By analyzing the energy spectrum characteristics and peak value changes, a third distribution cloud map of the energy spectrum peak values ​​of different transverse and longitudinal profiles of the slope is generated. The feature cloud map of the slope is generated based on the first distribution cloud map, the second distribution cloud map, and the third distribution cloud map.

8. The method according to claim 6 or 7, characterized in that, The multi-domain characteristic parameter indexes include time-domain parameter indexes, frequency-domain parameter indexes, and time-frequency domain parameter indexes. The time-domain parameter indexes include one or more of peak acceleration, PGA, amplification factor, peak displacement, RD, and PEC. The frequency-domain parameter indexes include one or more of natural frequency, relative displacement, and spectral peak value. The time-frequency domain parameter indexes include one or more of Arias intensity, seismic Hilbert energy spectrum peak value, and marginal spectral peak value.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the slope detection method based on distributed optical fiber sensing as described in any one of claims 6-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the slope detection method based on distributed optical fiber sensing as described in any one of claims 6-8.

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