An engineering slope drainage leakage monitoring method and system
By acquiring the static temperature baseline data of the slope and performing cross-difference and thermal hysteresis compensation processing, the problem of accuracy in identifying trace leakage signals in cold-region engineering slopes was solved, and highly reliable leakage monitoring was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG HUAXIN COMM TECH CO LTD
- Filing Date
- 2026-05-09
- Publication Date
- 2026-06-05
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In long-term health monitoring of slopes in cold regions, existing technologies struggle to accurately identify weak thermal anomaly signals caused by minute water leakage. The high false alarm rate, influenced by complex spatial geological heterogeneity and dynamic environmental thermal hysteresis, reduces the accuracy and reliability of drainage leakage monitoring.
By acquiring the static steady-state temperature background data of the slope, the spatial temperature distribution is monitored in real time, and cross-difference, thermal hysteresis compensation and dual constraint conditions are applied to remove geological artifacts and thermal hysteresis interference, and seepage signals are accurately captured.
It significantly improves the initial screening accuracy of monitoring, enhances long-term reliability in dynamic environments, can accurately identify trace leaks, reduces false alarm rates, and ensures the accuracy and reliability of monitoring.
Smart Images
Figure CN122149749A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of slope monitoring technology. More specifically, this invention relates to a method and system for monitoring drainage and seepage in engineering slopes. Background Technology
[0002] In the long-term health monitoring of slopes in cold regions, accurate identification of drainage leakage is crucial to ensuring structural safety. Current mainstream monitoring technologies rely on distributed fiber optic thermometry to obtain temperature profiles along the depth and to determine seepage anomalies by analyzing temperature gradients or absolute temperature thresholds.
[0003] Because the geological structure of slopes is highly non-uniform in space, its inherent thermal conductivity is unevenly distributed. This makes it difficult to distinguish between natural temperature jumps caused by abrupt changes in thermal properties at the rock-soil interface and real abnormal temperature drops caused by seepage when directly comparing temperature gradients at different depths. This results in a high false alarm rate. Furthermore, in dynamic environments, the response of media at different depths to periodic fluctuations in surface temperature, such as diurnal and seasonal changes, exhibits a significant thermal hysteresis effect.
[0004] Based on the above analysis, it can be seen that in a dynamic thermal environment, if the real-time monitoring data is directly compared with the static historical background, the temporal phase misalignment caused by the difference in thermal inertia of the medium is easily misjudged as a continuous temperature difference caused by leakage. Especially during the seasonal transition, this phenomenon will significantly reduce the accuracy and reliability of slope drainage leakage monitoring. Summary of the Invention
[0005] To address the technical problem of identifying weak thermal anomaly signals caused by trace amounts of seepage water from the comprehensive temperature field of complex spatial geological heterogeneity and dynamic environmental thermal hysteresis effects, the present invention provides solutions in the following aspects.
[0006] In a first aspect, the present invention provides a method for monitoring drainage and seepage on engineering slopes, the method comprising: Obtain the static steady-state temperature background data of the engineering slope and monitor in real time to obtain the real-time spatial temperature intrinsic distribution set along the depth;
[0007] The spatial gradient of the real-time spatial temperature intrinsic distribution set is cross-differentiated with the spatial gradient of the static steady-state temperature background data to calculate the residual set stripped of spatial geological artifacts. Based on the pre-obtained distribution of soil thermal diffusivity and surface temperature fluctuation frequency, the depth-dependent thermal hysteresis compensation factor is calculated. The static steady-state temperature background data is reconstructed by time-series stretching using the thermal hysteresis compensation factor to calculate a spatiotemporal isomorphic reference temperature sequence that is in phase and frequency with the real-time environmental thermal excitation. The dynamic residual characteristic quantity is obtained by comparing the real-time spatial temperature intrinsic distribution set with the spatiotemporal isomorphic reference temperature sequence. The second-order partial derivative extrema of the dynamic residual characteristic quantity are calculated, and dual joint constraints are applied to extract the set of seepage target points that meet the conditions, thus completing the monitoring of drainage and seepage on the engineering slope.
[0008] Preferably, the static steady-state temperature background data is obtained by: collecting historical calibration sequences through distributed fiber optic temperature measurement equipment during a static environmental window with no precipitation or ice melting, and then performing time-domain averaging and filtering on the historical calibration sequences.
[0009] Preferably, the real-time spatial temperature intrinsic distribution set is obtained by the following method: during the real-time monitoring stage, continuous high-frequency data is collected along the longitudinal depth of the drainage channel, and adjacent measuring points are divided into upstream temperature sensing nodes and downstream reference sensing nodes.
[0010] Preferably, the specific process of the cross-difference includes: calculating the real-time temperature gradient of adjacent nodes in the real-time spatial temperature intrinsic distribution set and the background gradient of the corresponding position in the static steady-state temperature background data, and then differentiating the two to offset the inherent thermal conduction baseline drift of the geological interface.
[0011] Preferably, the distribution of the soil thermal diffusivity coefficient is obtained and recorded by conducting on-site drilling and core thermal property experiments during the initial stage of project deployment.
[0012] Preferably, the thermal hysteresis compensation factor includes: calculated analytically based on the fluctuation attenuation of one-dimensional unsteady heat conduction, according to the distribution of the surface temperature fluctuation angular frequency and the soil thermal diffusivity coefficient.
[0013] Preferably, the dynamic residual feature quantity includes: obtained by comparing and subtracting the real-time spatial temperature intrinsic distribution set with the spatiotemporal isomorphic reference temperature sequence.
[0014] Preferably, the second-order partial derivative extremum is obtained by calculating the second-order partial derivative of the dynamic residual characteristic with respect to the time dimension and the second-order partial derivative with respect to the spatial dimension, and then weighting and summing them using a spatial smoothing scaling factor.
[0015] Preferably, the dual joint constraint conditions include: the absolute value of the second-order partial derivative extremum is less than or equal to a preset zero-crossing tolerance threshold, and the absolute value of the first-order partial derivative of the dynamic residual feature with respect to time is greater than a preset basic temperature change slope isolation threshold.
[0016] Secondly, the present invention also provides an engineering slope drainage and seepage monitoring system, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned engineering slope drainage and seepage monitoring method is implemented.
[0017] The embodiments of the present invention have at least the following beneficial effects: 1. By collecting static steady-state temperature background data of the inherent geological structure of the slope and cross-differentiating its spatial gradient with the spatial gradient of the real-time monitoring data, the drift of the inherent heat conduction baseline caused by the abrupt change in thermal properties of the rock-soil interface is offset, reducing the main source of false alarms, spatial non-uniformity, and providing a clean background for subsequent weak signal extraction, which significantly improves the initial screening accuracy of monitoring.
[0018] 2. Based on the first principles of thermodynamics, the depth-dependent compensation factor is dynamically calculated using the soil thermal diffusivity and surface temperature fluctuation frequency. The static background is then reconstructed using non-orthogonal temporal stretching, which solves the problem of large-area thermal inertia false alarms caused by seasonal changes or sudden temperature changes. This ensures that the evolution rhythm of historical benchmark data and real-time data remains in sync, thereby ensuring the rationality of benchmark comparison in dynamic environments and greatly enhancing the long-term reliability of monitoring under all-weather and all-season conditions.
[0019] 3. By applying targeted dual joint constraints, the curvature reversal characteristics corresponding to the isothermal plateau period during the water-ice phase transition are accurately captured. This enables robust extraction of signals generated by the release and absorption of latent heat from the real phase transition from complex data that may contain mathematical compensation noise. This effectively distinguishes between real leakage and numerical oscillation, thereby achieving pinpoint identification of trace leakage. Attached Figure Description
[0020] Figure 1 The schematic diagram illustrates the steps of a method for monitoring drainage and seepage on engineering slopes according to the present invention. Detailed Implementation
[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. This application discloses a method for monitoring drainage and seepage on engineering slopes. Figure 1 This includes steps S1-S6.
[0023] S1: Obtain the static steady-state temperature background data of the engineering slope and monitor in real time to obtain the real-time spatial temperature intrinsic distribution set along the depth.
[0024] It should be noted that the geological structure of slopes exhibits strong heterogeneity in both the horizontal and vertical directions, resulting in a non-uniform spatial distribution of their inherent thermal conductivity. In conventional slope monitoring, directly using absolute temperature or simple temperature gradients for discrimination can easily misinterpret natural temperature jumps at the soil-rock interface caused by abrupt changes in thermal properties as temperature drops caused by abnormal seepage, leading to numerous false alarms and severely impacting the reliability of monitoring.
[0025] It is further important to note that, in response to the interference caused by the aforementioned spatial geological heterogeneity, it is essential to separate the inherent static thermal characteristics of the slope from its real-time dynamic thermal response at the data acquisition source. Therefore, by utilizing a specific static environmental window and time-frequency filtering techniques, a reference base is extracted that completely eliminates dynamic moisture interference and purely reflects the steady-state thermal characteristics of the geological structure itself. Simultaneously, real-time temperature distribution capable of characterizing the current transient environmental excitation is captured in parallel.
[0026] Specifically, obtaining the static steady-state temperature background data of the engineering slope includes: Choose a sufficiently long static environmental window during the dry season when there is no precipitation or ice melt, or during a period of stable low temperatures.
[0027] During this period, distributed fiber optic temperature measurement devices pre-embedded in the slope drainage channels or monitoring holes are used to continuously collect temperature data at a first preset sampling interval to form a historical calibration sequence covering the entire monitoring depth. The first preset sampling interval is taken as an empirical value of 30 seconds, which can be adjusted by the implementer according to the specific implementation scenario.
[0028] The historical calibration sequence data corresponding to each depth point is subjected to low-pass filtering to remove high-frequency disturbances such as diurnal variations, resulting in a static temperature profile that reflects only the steady-state thermal characteristics of the slope medium, i.e., the static steady-state temperature background data. The low-pass filtering uses a cutoff frequency of [missing information - likely a frequency range]. The Hz Butterworth filter can be adjusted by the implementer according to the specific implementation scenario.
[0029] The obtained static steady-state temperature background data is denoted as ;parameter The vertical depth of the sensing node above the ground surface, in meters (m); This represents the steady-state temperature background value at this depth, in degrees Celsius (°C).
[0030] During the real-time monitoring phase, distributed fiber optic temperature measurement equipment is used to continuously collect temperature data along the depth of the drainage channel at a second preset sampling frequency, thereby obtaining the real-time spatial temperature intrinsic distribution set.
[0031] To ensure agile response to dynamic leakage events, the second preset sampling frequency must be higher than the sampling frequency during the static environment window. The second preset sampling frequency is set to 1Hz and can be adjusted by the implementer according to the specific implementation scenario. The resulting real-time spatial temperature intrinsic distribution set is denoted as... ;parameter The timestamp for the time of data collection is in seconds (s). For depth At any moment The real-time temperature measurement value is in degrees Celsius (°C).
[0032] It should be noted that the second preset sampling frequency can be adaptively adjusted according to the performance indicators of the actual fiber optic temperature measurement host used, balancing time response and signal-to-noise ratio while ensuring spatial resolution, so as to obtain the optimal set of real-time spatial temperature intrinsic distributions.
[0033] S2: Cross-difference is performed between the spatial gradient of the real-time spatial temperature intrinsic distribution set and the spatial gradient of the static steady-state temperature background data to calculate the residual set stripped of spatial geological artifacts.
[0034] It should be noted that in complex engineering slopes, there are inherent abrupt changes in thermal properties (such as thermal conductivity) at the interfaces of different soil and rock layers, which will cause natural steps or inflection points in the spatial temperature profile along the depth at the interfaces.
[0035] If only the real-time and static absolute temperature amplitudes are compared, the attenuation and drift errors caused by long-distance fiber optic transmission, as well as the inherent static temperature difference characteristics of the rock-soil interface, will severely mask the weak thermal anomaly signals caused by minute leakage.
[0036] It should be further noted that since the relative spatial position of geological structures is fixed, the spatial temperature gradient characteristics they generate appear as a static background over time. Therefore, instead of directly comparing absolute temperatures, we extract the gradient change rate in the spatial domain (depth direction) of real-time and static data respectively, and then cross-subtract the two.
[0037] This differential operation based on spatial derivatives can forcibly cancel out the basement gradient background caused by static geological structures, that is, decouple spatial geological artifacts, thereby extracting the transient gradient changes caused purely by dynamic water seepage.
[0038] Set a spatial sampling resolution step size and calculate the real-time temperature gradient between adjacent depth sensing nodes in the real-time spatial temperature intrinsic distribution set; simultaneously calculate the background temperature gradient at the corresponding depth position in the static steady-state temperature background data; introduce a pre-calibrated spectral attenuation compensation coefficient to calibrate the background temperature gradient to correct the absolute temperature difference demodulation distortion caused by fiber distance accumulation; calculate the residual set containing only the dynamic process temperature gradient change characteristics by subtracting the real-time temperature gradient from the calibrated background temperature gradient.
[0039] The specific values of the spatial sampling resolution step size and the spectral attenuation compensation coefficient can be set according to the spatial resolution capability of the actual fiber optic sensing equipment and the on-site calibration results. The spatial sampling resolution step size is set to 0.5 meters (m), and the spectral attenuation compensation coefficient is set to 1.02, which is a dimensionless gradient calibration coefficient. In other embodiments, the implementers can adjust them according to the accuracy requirements of the engineering deployment.
[0040] The cross-difference is calculated as follows:
[0041] In the formula, The calculated residual set after removing space geological artifacts is given in degrees Celsius (°C). This refers to the spatial sampling resolution step size; Represents real-time temperature monitoring at depth intervals Real-time spatial gradient within; This represents the static steady-state background gradient at the corresponding location; , This is the amplitude attenuation compensation factor that varies with depth. This is a dimensionless spectral attenuation compensation coefficient used to correct fiber path loss; the spectral attenuation compensation coefficient is set to 1.02.
[0042] S3: Calculate the depth-dependent thermal hysteresis compensation factor based on the pre-obtained distribution of soil thermal diffusivity and surface temperature fluctuation frequency.
[0043] It should be noted that in open, cold-region engineering slope environments, surface temperature fluctuates periodically due to heat exchange caused by atmospheric boundary conditions. However, because soil and rock media at different depths have different heat storage and thermal conductivity (i.e., thermal inertia), there is a significant time lag difference in the response of shallow and deep strata to surface thermal fluctuations. This manifests as a phase shifting effect in heat conduction, with shallow soil temperature closely following atmospheric fluctuations, while the response of deep soil and rock is significantly delayed.
[0044] It should be further noted that although the static geological artifacts are removed by spatial cross-difference calculations, if the dynamic mismatch in the time domain mentioned above is ignored, this nonlinear phase misalignment caused by the hysteresis of the medium's thermal response can still be misjudged as a continuous abnormal temperature drop in the deep medium.
[0045] Therefore, based on the one-dimensional unsteady-state heat conduction theory, the surface temperature fluctuation is regarded as a periodic thermal excitation. The physical phase delay of the excitation signal when it propagates to different depths is calculated, and a unique thermal hysteresis compensation factor is assigned to each depth node, thereby completely eliminating false alarm interference caused by thermal inertia in the time domain.
[0046] The basic physical parameters required for the calculation are obtained, including the distribution of soil thermal diffusivity coefficient at different depths of the target engineering slope, and the surface temperature fluctuation angular frequency obtained by fitting on-site meteorological monitoring data. The surface temperature fluctuation angular frequency and the soil thermal diffusivity coefficient distribution are used as input parameters and substituted into the periodic heat conduction theoretical model of a one-dimensional semi-infinite medium to calculate the thermal response time delay generated when the surface thermal excitation is conducted to each target depth node. The time delay is output as the depth-dependent thermal hysteresis compensation factor of the corresponding depth node. The periodic heat conduction theoretical model of a one-dimensional semi-infinite medium is a well-known technical means and will not be described in detail here.
[0047] The distribution of the soil thermal diffusivity coefficient can be pre-determined through geological surveys and core thermophysical property experiments in the early stages of the project; the selection of the components of the surface temperature fluctuation angular frequency can be set based on the dominant influencing factors of the current monitoring environment. The empirical value for the thermal diffusivity coefficient of surface loess is... The value of deep intact rock mass is For diurnal fluctuations, the angular frequency of surface temperature fluctuations is set to... The implementers can adjust the values of the above parameters according to the specific geological structure and meteorological characteristics.
[0048] The formula for calculating the thermal hysteresis compensation factor for deep dependence is as follows:
[0049] In the formula, For depth The thermal hysteresis compensation factor at the node represents the phase delay time, in seconds. ); The vertical depth of the sensing node relative to the ground surface, in meters (m). ); The angular frequency of surface temperature fluctuations, expressed in radians per second. ); This is the thermal diffusivity of the medium at this depth, expressed in square meters per second. ).
[0050] S4: The static steady-state temperature background data is reconstructed by time stretching using the thermal hysteresis compensation factor to calculate the spatiotemporal isomorphic reference temperature sequence that is in phase and frequency with the real-time environmental thermal excitation.
[0051] It should be noted that static steady-state temperature background data reflects the steady-state characteristics of geological structures within a specific historical cycle, and is relatively fixed on the time axis. However, the real-time monitored temperature distribution set is driven by the current diurnal temperature variation at the surface and is in a dynamic periodic evolution. Due to the thermal hysteresis effect in media at different depths, the temperature fluctuations in deep and shallow layers are not synchronized in phase, which leads to a serious dynamic temporal mismatch between the static background and real-time data.
[0052] It should be further noted that if the static background is directly used as a benchmark and compared with the real-time data, the phase difference caused by thermal inertia will be misread as an abnormal temperature difference caused by leakage because the two have different evolution rhythms.
[0053] Therefore, by using the calculated deep-dependent thermal hysteresis compensation factor, the static background data is nonlinearly stretched and reconstructed in time series. By simulating the propagation process of thermal fluctuations in the medium, the original static profile is transformed into a dynamic benchmark that is completely synchronized with the current environment in terms of time evolution phase, thereby ensuring that the subsequent leakage feature extraction is carried out on the same frequency and phase benchmark.
[0054] Specifically, based on the thermal hysteresis compensation factor corresponding to each depth node, a time-series reconstruction operator is constructed to describe the amplitude attenuation and phase shift law when the surface thermal excitation is transmitted to the ground. Using the time-series reconstruction operator, a non-orthogonal time-series stretching transformation is performed on the static steady-state temperature background data to calculate the evolution form of the static profile under the current environmental excitation beat. The data sequence after time-series stretching is output as a spatiotemporally isomorphic reference temperature sequence that is in phase and frequency with the real-time environmental thermal excitation.
[0055] The specific form of the time sequence reconstruction operator is based on the Green's function of the one-dimensional heat conduction equation under periodic boundary conditions, so as to ensure that the reconstructed sequence conforms to the physical nature of heat conservation and heat diffusion.
[0056] In this embodiment, the reconstruction process is achieved through discretized weight matrix operations. The corresponding reconstruction kernel function is calculated using the compensation factor and thermal diffusivity of each node to achieve nonlinear morphological matching.
[0057] The calculation process for reconstructing the spatiotemporally isomorphic reference temperature sequence is as follows:
[0058] In the formula, The calculated spatiotemporal isomorphic reference temperature sequence; This is the static steady-state temperature background data; This is the thermal hysteresis compensation factor for this depth node; An empirical basis function describing the attenuation of temperature fluctuations with depth and the mapping of response; This represents a mapping or convolution operation based on basis functions.
[0059] S5: Compare the real-time spatial temperature intrinsic distribution set with the spatiotemporal isomorphic reference temperature sequence to obtain dynamic residual characteristic quantities.
[0060] It should be noted that although a reference temperature sequence that is in phase and frequency with the real-time environmental thermal excitation has been successfully constructed in the previous steps, this sequence is still a background baseline. In order to extract the thermal anomaly caused by the weak leakage, the current real-time observation status must be accurately compared with this pure dynamic baseline.
[0061] It should be further explained that by comparing the residual data obtained through the operation, the dual effects of static geological heterogeneity and dynamic environmental lag are completely eliminated, resulting in data that contains only the actual physical response of leakage and random measurement noise.
[0062] Specifically, the dynamic residual characteristics are obtained, including: The real-time spatial temperature intrinsic distribution set and the spatiotemporally isomorphic reference temperature sequence are compared and subtracted point by point under the same spatiotemporal coordinates.
[0063] The differenced sequences are output and stored as dynamic residual features, which are then fed into the next level of the feature extraction network.
[0064] The core operational expressions for dynamic residual features are as follows:
[0065] In the formula, The calculated dynamic residual characteristic quantity represents the pure temperature anomaly signal, in degrees Celsius (°C). This is the set of intrinsic temperature distributions in real-time space. It is a spatiotemporally isomorphic reference temperature sequence.
[0066] S6: Calculate the second-order partial derivative extreme values of the dynamic residual characteristic quantity, apply dual joint constraints, extract the set of seepage target points that meet the conditions, and complete the monitoring of drainage and seepage on the engineering slope.
[0067] It should be noted that the temperature change signal caused by leakage is extremely weak, and the preceding time-series stretching and reconstruction operation may nonlinearly modulate and amplify high-frequency components at the signal processing level. If the dynamic residual characteristic output from step five is judged using only the conventional first-order amplitude thresholding method, it is highly susceptible to interference from amplified background noise, leading to false alarms.
[0068] It should be further explained that in the early stages of a small leak, when water undergoes a phase change (such as ice melting or water condensation), it releases or absorbs a large amount of latent heat. Its core manifestation is not a drastic temperature jump, but the formation of a short-term isothermal plateau, i.e., a stationary point, in a specific spatial location.
[0069] At this station, the first-order rate of change of temperature over time approaches zero, while the spatial curvature undergoes a characteristic reversal.
[0070] Specifically, the set of targeted leakage points that meet the conditions is extracted, including: The pre-set high-order differential module is invoked to perform second-order partial derivative operations on the dynamic residual features with respect to the time dimension and spatial depth, and the spatial smoothing reduction ratio coefficient is used for weighted synthesis to calculate the full-field second-order partial derivative extremum distribution.
[0071] The first constraint condition is applied to the distribution of second-order partial derivative extrema across the entire field to filter and extract the spatiotemporal coordinates of the second-order partial derivative extrema whose absolute values are less than or equal to the preset zero-crossing tolerance threshold, so as to identify the isothermal plateau characteristics caused by latent heat release.
[0072] Based on satisfying the first constraint, a second constraint is applied to filter and eliminate points where the absolute value of the first-order partial derivative of the dynamic residual characteristic with respect to time is less than or equal to the preset isolation threshold of the basic temperature change slope, so as to exclude invalid data areas in a long-term thermal equilibrium state.
[0073] Traverse all spatiotemporal coordinates and mark the points that simultaneously and strictly satisfy both the first and second constraints as initial target points.
[0074] Cluster analysis is performed on the initial target points that are continuous in spatial depth and close in time to form connected components. Each independent connected component is determined as a set of leakage target points.
[0075] The leak target set is used as a trigger signal and output to an external early warning relay to trigger an alarm or automated diagnostic process to complete the monitoring.
[0076] Among them, the spatial smoothing conversion ratio coefficient is used to balance the contribution ratio of temporal curvature and spatial curvature; the zero-cross tolerance threshold and the basic temperature change slope isolation threshold can be dynamically adjusted according to the geological sensitivity and noise level of the monitoring area.
[0077] In this embodiment, the empirical value of the spatial smoothing scaling factor is 0.5. The empirical value for the zero-cross tolerance threshold is: The empirical value for the isolation threshold of the basic temperature change slope is: In other embodiments, implementers can set corresponding thresholds based on the disaster prevention level of the specific project.
[0078] The formula for synthesizing the extrema of the second-order partial derivatives is as follows:
[0079] In the formula, The second-order partial derivative extremum of the synthesis represents the joint eigenvalue of the intensity of the temperature inflection point triggered by latent heat release; For dynamic residual characteristic quantities; Let be the second partial derivative of the dynamic residual with respect to time, representing the acceleration of temperature change, expressed in degrees Celsius per square second. ); Let be the second partial derivative of the dynamic residual with respect to depth, and represent the curvature of the temperature distribution along the depth direction, in degrees Celsius per square meter. ); The normalized weighting coefficient with dimensions serves as the spatial smoothing conversion ratio coefficient, with units of square seconds per square meter.
[0080] The decision logic follows the following dual joint constraint criterion:
[0081] In the formula, Zero cross tolerance threshold, dimensions and To indicate consistency, the unit is... ; The isolation threshold is based on the temperature change slope, measured in degrees Celsius per second. ).
[0082] The system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement an engineering slope drainage and seepage monitoring method according to the first aspect of the present invention.
[0083] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.
[0084] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments. Other embodiments are within the scope of this appended application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0085] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0086] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for monitoring drainage and seepage on engineering slopes, characterized in that, Includes the following steps: Step 1, intrinsic differential stripping of spatially heterogeneous thermal distribution: Obtain the static steady-state temperature background data of the engineering slope and monitor in real time to obtain the real-time spatial temperature intrinsic distribution set along the depth; The spatial gradient of the real-time spatial temperature intrinsic distribution set is cross-differentiated with the spatial gradient of the static steady-state temperature background data to calculate the residual set stripped of spatial geological artifacts. Based on the pre-obtained distribution of soil thermal diffusivity and surface temperature fluctuation frequency, the depth-dependent thermal hysteresis compensation factor is calculated. The static steady-state temperature background data is reconstructed by time-series stretching using the thermal hysteresis compensation factor to calculate a spatiotemporal isomorphic reference temperature sequence that is in phase and frequency with the real-time environmental thermal excitation. The dynamic residual characteristic quantity is obtained by comparing the real-time spatial temperature intrinsic distribution set with the spatiotemporal isomorphic reference temperature sequence. The second-order partial derivative extrema of the dynamic residual characteristic quantity are calculated, and dual joint constraints are applied to extract the set of seepage target points that meet the conditions, thus completing the monitoring of drainage and seepage on the engineering slope.
2. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The static steady-state temperature background data is obtained by collecting historical calibration sequences through distributed fiber optic temperature measurement equipment during a static environmental window with no precipitation or ice melting, and then performing time-domain averaging and filtering on the historical calibration sequences.
3. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The real-time spatial temperature intrinsic distribution set is obtained in the following way: during the real-time monitoring phase, continuous high-frequency data is collected along the longitudinal depth of the drainage channel, and adjacent measuring points are divided into upstream temperature sensing nodes and downstream reference sensing nodes.
4. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The specific process of the cross-difference includes: calculating the real-time temperature gradient of adjacent nodes in the real-time spatial temperature intrinsic distribution set and the background gradient of the corresponding position in the static steady-state temperature background data, and then differentiating the two to offset the inherent thermal conduction baseline drift of the geological interface.
5. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The distribution of the soil thermal diffusivity coefficient was obtained and recorded by conducting on-site drilling and core thermophysical property tests during the initial stage of project deployment.
6. The method for monitoring drainage and seepage on engineering slopes according to claim 5, characterized in that, The thermal hysteresis compensation factor includes: calculated analytically based on the fluctuation attenuation of one-dimensional unsteady heat conduction, according to the distribution of the surface temperature fluctuation angular frequency and the soil thermal diffusivity coefficient.
7. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The dynamic residual feature quantity includes: obtained by comparing and subtracting the real-time spatial temperature intrinsic distribution set with the spatiotemporal isomorphic reference temperature sequence.
8. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The second-order partial derivative extremum is obtained by calculating the second-order partial derivative of the dynamic residual characteristic with respect to the time dimension and the second-order partial derivative with respect to the spatial dimension, and then weighting and summing them using a spatial smoothing scaling factor.
9. The method for monitoring drainage and seepage on engineering slopes according to claim 1, characterized in that, The dual joint constraint conditions include: the absolute value of the second-order partial derivative extremum is less than or equal to a preset zero-crossing tolerance threshold, and the absolute value of the first-order partial derivative of the dynamic residual feature with respect to time is greater than a preset basic temperature change slope isolation threshold.
10. A slope drainage and seepage monitoring system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the engineering slope drainage and seepage monitoring method according to any one of claims 1-9.