A reservoir dam leakage anomaly identification method based on active heating distributed optical fiber temperature response

Abstract

The application discloses a reservoir dam leakage anomaly identification method based on active heating distributed optical fiber temperature response, comprising the following steps: arranging active heating distributed temperature measurement optical fibers in a reservoir dam anti-seepage structure, and acquiring temperature response data in the whole heating process; preprocessing the data, extracting temperature rise amplitude characteristics, temperature rise response rate characteristics, time scale characteristics and temperature response whole process characteristics, and constructing a temperature response characteristic matrix; standardizing each characteristic parameter, establishing a multi-feature fusion comprehensive evaluation model by using an entropy weight method, and calculating a comprehensive leakage evaluation index; identifying a leakage anomaly area according to the spatial continuous distribution characteristics of the comprehensive leakage evaluation index, and realizing leakage risk grading early warning. The application fully utilizes the whole process information of the active heating distributed optical fiber temperature response, improves the accuracy and stability of the reservoir dam leakage anomaly identification under complex working conditions, and can be applied to the leakage monitoring and safety early warning of geotechnical engineering structures such as pumped storage power stations, reservoir dams and dikes.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for hydraulic structures, specifically to a method for identifying reservoir and dam leakage anomalies based on active heating distributed optical fiber temperature response. Background Technology

[0002] Seepage in reservoirs and dams is a significant factor affecting the long-term safe operation of engineering projects. In its early stages, seepage is typically characterized by its concealment, small flow rate, and indistinct local features. Failure to detect and address it promptly will severely threaten the safe operation of pumped-storage power stations. Therefore, early identification, continuous monitoring, and precise location of reservoir and dam seepage are crucial for ensuring the long-term safe operation of these projects. Currently, seepage monitoring is widely conducted using methods such as piezometers, flow meters, measuring weirs, and manual inspections. While these methods can reflect the seepage status at local locations, they are all point-based monitoring methods with limited monitoring range, making it difficult to obtain continuous spatial seepage information within the reservoir or dam. When the seepage location is unknown or the seepage area is large, relying solely on point monitoring is prone to missed or false diagnoses, failing to meet the long-term continuous monitoring requirements of large hydraulic structures.

[0003] In recent years, distributed fiber optic temperature sensing technology has been widely used in seepage monitoring of projects such as dams, dikes, and canals due to its advantages such as long measurement distance, good spatial continuity, strong resistance to electromagnetic interference, corrosion resistance, and suitability for long-term burial. In particular, Active Heated Distributed Temperature Sensing (AH-DTS) technology applies a stable current to the heating conductor in the optical fiber composite cable, forming a uniform linear heat source along the cable. By utilizing the difference between conduction and convection heat transfer processes under seepage conditions, it can identify the seepage state of the medium, and has higher monitoring sensitivity than traditional passive temperature sensing methods.

[0004] Existing active heating distributed fiber optic leakage monitoring methods primarily rely on temperature rise amplitude or a single temperature response parameter to determine leakage. However, due to the complex composition of materials within reservoirs and dams, variations in thermal conductivity across different areas, and the influence of factors such as ambient temperature, water level changes, boundary conditions, and construction quality, using only a single temperature response characteristic can easily lead to insufficient stability in leakage identification results. When weak leakage, multiple leakage points, or complex heterogeneous media occur, different temperature characteristic parameters may produce different or even contradictory judgments, thereby reducing the accuracy of leakage identification and its engineering applicability.

[0005] Most existing technologies focus only on local information during the temperature response process, failing to fully utilize the multi-dimensional information contained in the entire temperature response curve, such as heat conduction, convection, and diffusion. This results in low utilization of active-heated distributed fiber optic temperature measurement data, hindering its full realization of monitoring advantages. Therefore, how to comprehensively utilize the characteristics of the entire temperature response process and establish a multi-parameter fusion identification method that can comprehensively reflect the heat-flow coupling effect, thereby improving the accuracy and stability of leakage anomaly identification under complex operating conditions, has become a key technical problem urgently needing to be solved in the field of active-heated distributed fiber optic leakage monitoring. Summary of the Invention

[0006] The problem to be solved by this invention is to provide a method for identifying reservoir leakage anomalies based on active heating distributed optical fiber temperature response. This method uses active heating distributed optical fiber to continuously acquire internal temperature response information of the reservoir, extracts multiple feature parameters that can reflect the seepage state from the entire temperature response process, and establishes a comprehensive leakage evaluation model to achieve continuous identification, accurate location, and risk warning of reservoir leakage anomalies under complex working conditions.

[0007] This invention adopts the following technical solution: a method for identifying reservoir dam leakage anomalies based on active heating distributed optical fiber temperature response, comprising the following steps:

[0008] S1. An active heating distributed temperature measurement optical fiber is pre-embedded inside the soil and rock medium of the reservoir dam and seepage prevention structure to be monitored. The active heating distributed temperature measurement optical fiber includes a temperature measurement optical fiber and a heating conductor.

[0009] S2. Actively heat the heating conductor by inputting a set power or set current through a heating control device, so that the temperature measuring optical fiber forms a stable linear heat source along its length, and simultaneously use a distributed optical fiber temperature measurement system to collect temperature data along the optical fiber to obtain a temperature response sequence.

[0010] S3. Preprocess the temperature response sequence to remove outliers and noise interference, determine the heating start time, stable heating stage and cooling stage, and establish temperature response curves for each monitoring location.

[0011] S4. Extract multiple temperature response characteristic parameters based on the temperature response curves of each monitoring location. These temperature response characteristic parameters include temperature rise amplitude characteristic parameters. Temperature rise response rate characteristic parameters and time-scale characteristic parameters ;

[0012] S5. Perform dimensionless processing on each temperature response characteristic parameter, construct a temperature response characteristic matrix, and calculate the weight of each characteristic parameter according to the data dispersion of each temperature response characteristic parameter.

[0013] S6. Based on the weights, multiple temperature response characteristic parameters are fused and calculated to construct a comprehensive leakage evaluation index SI.

[0014] S7. Identify abnormal leakage areas based on the spatial distribution of the comprehensive leakage evaluation index SI along the fiber length direction, and determine the leakage risk level based on the magnitude of the comprehensive leakage evaluation index SI, thereby realizing the identification and location of reservoir and dam leakage anomalies.

[0015] Preferably, the heating conductor in step S1 is a resistance wire that is heated by electricity.

[0016] Preferably, the active heating in step S2 uses a constant heating power to heat for a certain period of time. The heating power is set according to the thermal conductivity of the monitoring medium and the length of the buried optical fiber, so that the optical fiber forms a stable linear heat source.

[0017] Preferably, the temperature rise amplitude characteristic parameter according to Calculate; where T max T0 represents the maximum temperature at the monitoring point during the heating phase, and T0 represents the average temperature during the stable phase before heating begins.

[0018] Preferably, the temperature rise response rate characteristic parameter Extraction is performed based on the temperature response curve during the linear thermal conduction phase, to... As a time variable, ΔT represents the temperature response. The curve is linearly fitted, and the slope of the fitted curve is used as a characteristic parameter of the temperature rise response rate. It is used to characterize the rate of heat transfer.

[0019] Preferably, the time scale feature parameter The time when the logarithmic derivative of the temperature response curve reaches its peak value is obtained by calculating the derivative of the temperature response curve and combining it with a peak search algorithm. It is used to characterize the characteristic time of the transition from heat conduction to convective heat transfer.

[0020] Preferably, the temperature response characteristic parameters further include: statistical characteristics, geometric characteristics, frequency domain characteristics, and thermal response characteristics that reflect the temperature change pattern;

[0021] Preferably, the dimensionless processing in step S5 includes positive index normalization and negative index normalization, so that each temperature response characteristic parameter is converted to a uniform dimension range and a standardized temperature response characteristic matrix is ​​constructed.

[0022] The weights are determined based on the degree of dispersion of information of each temperature response characteristic parameter, and are obtained by any one of the following methods: entropy weight method, CRITIC method, principal component analysis method, and analytic hierarchy process.

[0023] Preferably, the comprehensive leakage evaluation index SI in step S6 is obtained by weighted fusion of multiple temperature response characteristic parameters. A leakage early warning level is established based on the comprehensive leakage evaluation index SI. When SI reaches a preset threshold, it is determined that there is a leakage anomaly at the corresponding location. The preset threshold is determined based on indoor calibration test, on-site monitoring data, or historical operation data.

[0024] Preferably, the method is applicable to leakage monitoring, anomaly identification, and safety early warning for pumped storage power stations, reservoir dams, dikes, canals, tailings dams, underground caverns, diaphragm walls, anti-seepage curtains, and other geotechnical engineering structures.

[0025] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0026] 1. This invention achieves comprehensive utilization of information throughout the entire temperature response process. Existing active heating distributed fiber optic leakage monitoring methods typically only use the maximum temperature rise as a single temperature response parameter for leakage detection, which is insufficient to fully reflect the dynamic changes in the temperature field during the heat-flow coupling process. Based on the complete temperature response curve during active heating, this invention extracts multiple characteristic parameters from the entire temperature response process, such as temperature rise amplitude, temperature rise response rate, and time scale, fully leveraging the heat conduction and convection information contained in the temperature response curve to achieve comprehensive utilization of information throughout the entire temperature response process.

[0027] 2. Improved accuracy in leakage identification under complex working conditions: This invention establishes a multi-feature fusion evaluation model for temperature response, comprehensively analyzing multiple temperature response parameters to avoid misjudgments caused by local anomalies or environmental disturbances in traditional single-parameter methods. Even when projects involve complex geological conditions, cut-fill junctions, material heterogeneity, multiple weak leaks, or environmental temperature changes, this invention can still reliably identify abnormal leakage areas, improving the accuracy of leakage identification under complex engineering conditions.

[0028] 3. Enhanced ability to identify weak and concealed leaks. Since weak leak areas are less affected by a single temperature characteristic, traditional methods are prone to missed detection. This invention comprehensively analyzes multiple temperature response characteristics, amplifying multiple weak anomaly areas, effectively improving the ability to identify weak and early leaks, and enabling early detection of potential leaks. Attached Figure Description

[0029] Figure 1 This is a flowchart of the reservoir dam leakage anomaly identification method of the present invention;

[0030] Figure 2 This is a planar layout diagram of the distributed temperature measurement fiber optic cables in the embodiment;

[0031] Figure 3 This is a diagram showing the seepage identification results for single and multi-parameter flow along line A3 in the embodiment.

[0032] Figure 4 This is a diagram of the test system in the embodiment;

[0033] Figure 5 This is a temperature rise curve at different times under full-section leakage conditions in the embodiment;

[0034] Figure 6 k is the full-section leakage condition in the embodiment. c and t p Curve diagram along the route;

[0035] Figure 7 This is a graph showing the leakage index SI along the path at different times under full-section leakage conditions in the embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the application will be further described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in this invention. All non-innovative embodiments based on these embodiments by other researchers in the art are within the protection scope of this invention. Furthermore, the step numbers in the embodiments of this invention are only set for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0037] like Figure 1 As shown, this invention provides a method for identifying reservoir / dam leakage anomalies based on the temperature response of actively heated distributed optical fibers. The specific steps are as follows:

[0038] S1. Construction of fiber optic monitoring system: Based on the reservoir and dam structure, the layout of the seepage prevention system and key monitoring areas, actively heated distributed temperature measurement fiber optics are laid in the dam body, seepage prevention layer, cut-fill junction, connecting plate, structural joints or other seepage-sensitive parts to form a continuous monitoring network.

[0039] Based on the layout of the reservoir seepage prevention system, active heating distributed temperature measurement optical fibers are buried in the connection plate area, the seepage prevention layer at the bottom of the reservoir, the area where fill and cut meet, and the boundary of the inlet and outlet, forming a continuous monitoring network covering the entire key seepage prevention system.

[0040] S2. Active heating and temperature acquisition: The heating control device applies constant power, constant current or constant voltage to the heating conductor in the optoelectronic composite cable to form a stable linear heat source.

[0041] Before monitoring begins, the stable temperature data before heating is collected as the initial reference temperature.

[0042] Then the constant power heating device is activated to make the actively heated optical fiber form a continuous and stable linear heat source;

[0043] During the heating process, a distributed fiber optic temperature measurement system is used to collect temperature data along the path in real time, obtaining continuous temperature response data for the entire heating and cooling process:

[0044] ;

[0045] Where T is the fiber temperature, z is the fiber spatial position, and t is the heating time.

[0046] S3. Temperature response data preprocessing: After acquiring the temperature response data along the fiber length during the heating and cooling stages, the original temperature response data is preprocessed to eliminate measurement noise, random errors, and the influence of ambient temperature fluctuations on the temperature response curve.

[0047] Preprocessing includes the following steps:

[0048] (1) Based on the heating start time recorded by the heating control system, the temperature data collected by the distributed fiber optic temperature measurement system is synchronized in time;

[0049] (2) Use moving average filtering, median filtering, Savitzky-Golay filtering, wavelet filtering or other digital filtering methods to remove random noise;

[0050] (3) Remove abnormal data points caused by communication anomalies, equipment failures, or partial signal loss;

[0051] (4) Using the average temperature during the steady-state phase before heating as the reference temperature, the temperature response curve is baseline-corrected to obtain the temperature rise response curve. :

[0052] ;

[0053] in: This refers to the temperature of the optical fiber during the heating process. The temperature at the corresponding location is stabilized before heating. After preprocessing, a continuous temperature rise response curve is generated, providing a data basis for subsequent temperature response feature extraction.

[0054] S4. Temperature Response Full-Process Feature Extraction: Extraction includes feature parameters such as temperature rise amplitude. Temperature rise response rate characteristic parameters and time-scale characteristic parameters Multiple feature parameters, including;

[0055] (1) Characteristics of temperature rise amplitude:

[0056] Temperature rise amplitude characteristic parameter ΔT max according to Calculate; where T maxT1 represents the maximum temperature at the monitoring point during the heating phase; T0 represents the average temperature during the stable phase before heating begins.

[0057] (2) Temperature rise rate characteristics:

[0058] According to the theory of linear heat sources, the temperature response satisfies: By performing linear fitting using the ΔT-ln(t) curve, we obtain... , as the temperature rise response rate.

[0059] (3) Time scale characteristics:

[0060] Differentiate the temperature response curve and calculate The time corresponding to when the derivative reaches its maximum value is defined as... .

[0061] S5. Establishment of a multi-feature fusion evaluation model.

[0062] Assuming there are n monitoring points in total, , , The three evaluation indicators correspond to the i-th monitoring point, i=1,2,...,n, and the original data matrix is ​​as follows: :

[0063] ;

[0064] Because the parameters have different dimensions and varying sensitivities to leakage, dimensionless processing and appropriate weighting are required. The normalization formula is as follows:

[0065] ;

[0066] in, Let j be the original value of the j-th indicator at the i-th monitoring point. The normalized index value, and These are the maximum and minimum values ​​of the j-th indicator, respectively. After normalization, the values ​​of each indicator are distributed between 0 and 1, and the larger the value, the higher the leakage risk in the corresponding area.

[0067] S6. Construction of comprehensive evaluation model: Calculate the weight of each indicator.

[0068] Calculate the proportion of the normalized index value of the i-th monitoring point in the sum of the normalized index values ​​of the j-th index across all n monitoring points:

[0069] ;

[0070] Then, the information entropy of each indicator is calculated:

[0071] ;

[0072] Weights are determined based on the difference coefficient;

[0073] .

[0074] Where n is the number of valid monitoring points participating in this evaluation, n>1; m is the number of valid evaluation indicators participating in the fusion, and this embodiment uses ΔT. max , and When there are three indicators, m=3; i=1,2,…,n, j=1,2,…,m; Let i be the proportion of the i-th monitoring point in the j-th indicator, and its denominator is the sum of the normalized index values ​​of the n monitoring points. Let the information entropy of the j-th indicator be , The coefficient of variation, Let the weight of the j-th indicator satisfy... Furthermore, the sum of the weights of each effective indicator is 1.

[0075] Conventions for calculating information entropy corresponding If the sum of the normalized values ​​of a certain indicator is 0, it is removed as an indicator without distinguishing information, and the remaining indicators are re-normalized and weighted; if all indicators have no distinguishing information, no valid SI judgment value is output.

[0076] S7. Leakage Anomaly Judgment: After obtaining the weights of each indicator, calculate the leakage index SI:

[0077] ;

[0078] in, , and These are the normalized temperature rise amplitude characteristic parameters, temperature rise response rate characteristic parameters, and time scale characteristic parameters. , and These are the weights corresponding to each feature parameter.

[0079] Example 1: Based on the method proposed in this invention, the A3 survey line of a pumped storage power station was analyzed. The distributed optical fiber planar layout diagram is as follows. Figure 2 As shown.

[0080] Figure 2In the diagram, A1 represents the monitoring line arranged along the perimeter of the reservoir basin; A2-1, A2-2, A2-3, and A2-4 are four monitoring lines in the reservoir bottom area, with A2-1 and A2-2 arranged laterally and A2-3 and A2-4 arranged longitudinally; A3 is the monitoring line arranged along the cut-fill boundary area; and A4-1 and A4-2 are two monitoring lines in the inlet-outlet boundary area. All these markings are line numbers used to establish the correspondence between the coordinates along the optical fiber and the spatial location of the engineering structure.

[0081] The upper reservoir of this pumped storage power station adopts a full-basin seepage prevention structure, which consists of an asphalt concrete panel around the reservoir and a geomembrane at the bottom of the reservoir to form a seepage prevention system.

[0082] Because the reservoir basin area includes fill areas, cut areas, fill-cut boundary areas, connecting plates, inlet and outlet areas, and areas where different seepage prevention materials overlap, all of these locations are seepage-sensitive areas. Under long-term water storage and release cycles, local seepage channels can easily form, making them key monitoring areas. Based on the layout of the reservoir basin's seepage prevention system, actively heated distributed temperature-measuring optical fibers are buried in the connecting plate areas, the reservoir bottom seepage prevention layer, the fill-cut boundary areas, and the inlet and outlet boundaries to form a continuous monitoring network covering the entire critical seepage prevention system.

[0083] In this embodiment, the A3 monitoring line was selected as a typical monitoring object to carry out leakage identification and analysis. The results are as follows: Figure 3 As shown, the comprehensive leakage evaluation index can accurately identify multiple abnormal areas along the route.

[0084] A distinct anomaly zone was observed approximately 100–200 m along the fiber optic cable. This area corresponds to the boundary between the project's inlet and outlet. Due to frequent water inflow and outflow during long-term operation, local hydraulic conditions have changed significantly, making this a typical leakage-sensitive area. Additionally, a continuous area of ​​anomalous protrusions was also observed approximately 630–660 m along the fiber optic cable. This area corresponds to the cut-fill boundary. Because of differences in material composition, compaction state, and deformation compatibility between the cut and fill areas, areas of discontinuity in local permeability are prone to form during long-term operation.

[0085] The comprehensive evaluation index established in this invention can accurately identify the two typical abnormal areas mentioned above. Compared with judging solely by temperature rise amplitude, the abnormal area boundaries obtained by this invention are clearer, the abnormal peak values ​​are more obvious, and the consistency across the continuous space is better. Furthermore, the comprehensive evaluation index can still maintain high identification sensitivity for localized areas with weak leakage.

[0086] The engineering monitoring results correspond well with the on-site anti-seepage structure layout, weak points of the project, and actual operation, verifying that the temperature response full-process feature fusion leakage identification method proposed in this invention has high engineering applicability and reliability.

[0087] This embodiment verifies the application effect of the reservoir and dam leakage anomaly identification method based on active heating distributed optical fiber temperature response multi-feature fusion in reservoir and dam leakage monitoring. By judging the leakage index SI, it is possible to effectively distinguish between normal and potential leakage areas and achieve qualitative identification of leakage sections. Compared with traditional point pressure monitoring, this method has the advantages of strong continuity and accurate positioning.

[0088] Example 2: This example uses an indoor leakage simulation test system to verify the method proposed in this invention. The system connection diagram is shown below. Figure 4 As shown.

[0089] Figure 4 The labeled equipment includes a head regulating tank, a flow distribution device, a leakage simulation device, a probe thermometer, a temperature data acquisition device, a single-phase voltage regulator, an industrial portable power supply, a water collection device, a flow guide trough, and a support frame. The head regulating tank regulates the water supply head; the flow distribution device distributes the water flow to different test locations; the leakage simulation device contains the test medium and establishes the set leakage conditions; the probe thermometer assists in measuring the medium temperature; the temperature data acquisition device collects and records temperature data; the industrial portable power supply provides power; the single-phase voltage regulator adjusts the heating power supply conditions; the flow guide trough directs the discharged water into the water collection device; and the support frame supports the leakage simulation device.

[0090] The indoor leakage simulation system includes a model tank, actively heated distributed optical fibers, a seepage control system, a constant current heating power supply, a distributed optical fiber temperature measurement host, and a data processing terminal. The model tank is made of stainless steel, with a length of 3.0m, a width of 0.5m, and a height of 0.5m. A uniform drainage sieve is installed at the bottom of the tank to ensure stable drainage of seepage water. The tank is reinforced with ribs around its perimeter to reduce the impact of model deformation on the test results. The model is filled with homogeneous sand, compacted in layers with a thickness of 50 mm each, maintaining consistent compaction to ensure good homogeneity and repeatability of the test medium. Actively heated distributed temperature measurement optical fibers are laid along the length of the model tank, embedded in the middle of the model, approximately 0.20 m from the top. These fibers employ a photoelectric composite cable structure, including Raman temperature measurement fibers and parallel resistance heating conductors. The heating conductors generate a continuous, uniform, linear heat source under constant current power.

[0091] Depend on Figure 5It can be seen that the temperature rise distribution along the optical fiber exhibits significant spatial differences after heating for 1000 s. This region corresponds closely to the locations of leakage valves one through five in the experimental setup, indicating that the seepage water flow has a continuous convective effect on heat removal, significantly suppressing the temperature accumulation process. As the heating time increases from 400 s to 1000 s, the temperature difference between the leakage and non-leakage areas continuously increases, demonstrating that the temperature rise amplitude characteristic parameter can effectively identify abnormal leakage areas.

[0092] The temperature rise response rate characteristic parameter k is further extracted from the temperature response curve. c .like Figure 6 As shown, normal region k c It is mainly distributed between 1.8 and 2.5, while the leakage section k c Significantly reduced. Specifically, k at the 7.40-9.45m position... c It is only 15% to 20% of that in the normal area. This indicates that seepage weakens the soil's ability to accumulate heat, significantly reducing the rate of temperature increase. c The distribution curve along the flow path forms a distinct trough in the leakage area, which corresponds well with the actual leakage location, verifying that the temperature rise response rate parameter is highly sensitive to seepage anomalies.

[0093] Time-scale characteristic parameter t p The analysis results are as follows Figure 6 Normal region t p The time required for temperature to reach the characteristic response state is mainly concentrated between 610 and 680 s, while the leakage zone significantly increases to 556–588 s. This indicates that under seepage conditions, due to continuous convective heat dissipation, the time required is significantly prolonged. (This is related to ΔT...) max and k c In comparison, t p The response pattern of the leakage zone exhibits high-value anomalies, which complement the first two types of parameters.

[0094] In this embodiment, the weighting coefficients for each characteristic parameter are w1=0.43, w2=0.33, and w3=0.24, corresponding to the normalized temperature rise amplitude, temperature rise response rate, and time-scale characteristic parameter, respectively. The comprehensive leakage index SI is calculated according to the aforementioned weights, and the results are shown in the appendix. Figure 7 As shown in the figure, the red dashed line is the leakage identification and warning line, corresponding to a warning threshold SIth = 0.8. When SI ≥ 0.8 at a certain monitoring point, and adjacent monitoring points form a continuous section crossing the line, that section is determined to be a leakage anomaly warning section; points that do not reach this threshold are further analyzed based on the distribution along the line and subsequent monitoring results. This threshold is used for the test conditions in this embodiment; other projects should determine it separately based on indoor calibration tests, on-site monitoring, or historical operating data.

[0095] like Figure 7As shown, the SI value of the leakage section is approximately 0.70–0.78 after heating for 400 seconds, which has not yet reached the warning line. After heating for 500–1000 seconds, a continuous high-value band exceeding 0.8 is formed in this section, with the peak value exceeding 0.9 at 1000 seconds. The SI values ​​of the non-leakage main section are mostly between approximately 0.1 and 0.4, which are significantly lower than the warning line.

[0096] In summary, this invention utilizes active heating distributed fiber optic temperature measurement technology to obtain the internal temperature field response information of reservoirs and dams. By extracting multiple temperature response characteristic parameters, a comprehensive leakage evaluation index is constructed, enabling continuous monitoring, accurate identification, and risk warning of abnormal leakage areas in reservoirs and dams. This method is applicable to leakage safety monitoring of pumped storage power stations, reservoir dams, dikes, canals, and other geotechnical engineering structures.

[0097] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying reservoir / dam leakage anomalies based on active heating distributed optical fiber temperature response, characterized in that, Includes the following steps: S1. An active heating distributed temperature measurement optical fiber is pre-embedded inside the soil and rock medium of the reservoir dam and seepage prevention structure to be monitored. The active heating distributed temperature measurement optical fiber includes a temperature measurement optical fiber and a heating conductor. S2. Actively heat the heating conductor by inputting a set power or set current through a heating control device, so that the temperature measuring optical fiber forms a stable linear heat source along its length, and simultaneously use a distributed optical fiber temperature measurement system to collect temperature data along the optical fiber to obtain a temperature response sequence. S3. Preprocess the temperature response sequence to remove outliers and noise interference, determine the heating start time, stable heating stage and cooling stage, and establish temperature response curves for each monitoring location. S4. Extract multiple temperature response characteristic parameters based on the temperature response curves of each monitoring location. These temperature response characteristic parameters include temperature rise amplitude characteristic parameters. Temperature rise response rate characteristic parameters and time-scale characteristic parameters ; S5. Perform dimensionless processing on each temperature response characteristic parameter, construct a temperature response characteristic matrix, and calculate the weight of each characteristic parameter according to the data dispersion of each temperature response characteristic parameter. S6. Based on the weights, multiple temperature response characteristic parameters are fused and calculated to construct a comprehensive leakage evaluation index SI. S7. Identify abnormal leakage areas based on the spatial distribution of the comprehensive leakage evaluation index SI along the fiber length direction, and determine the leakage risk level based on the magnitude of the comprehensive leakage evaluation index SI, thereby realizing the identification and location of reservoir and dam leakage anomalies.

2. The method according to claim 1, characterized in that: The active heating in step S2 uses a constant heating power to heat for a preset time. The heating power is set according to the thermal conductivity of the monitoring medium and the length of the buried optical fiber, so that the optical fiber forms a stable linear heat source.

3. The method according to claim 1, characterized in that: The characteristic parameter of temperature rise amplitude according to Calculate; where T max T0 represents the maximum temperature at the monitoring point during the heating phase, and T0 represents the average temperature during the stable phase before heating begins.

4. The method according to claim 1, characterized in that: The temperature rise response rate characteristic parameter Extraction is performed based on the linear thermal conduction phase of the temperature response curve, using... Let ΔT be the time variable, representing the temperature response. A linear fit is performed on the ΔT-lnt curve, and the slope of the fit is used as a characteristic parameter of the temperature rise response rate. , used to characterize the rate of heat transfer, is defined as: ； According to the line heat source theory, , where h represents the intercept of the linear fit, in °C.

5. The method according to claim 4, characterized in that: The time scale feature parameters The time when the logarithmic derivative of the temperature response curve reaches its peak value is obtained by calculating the derivative of the temperature response curve and combining it with a peak search algorithm. It is used to characterize the characteristic time of the transition from heat conduction to convection heat transfer, and is defined as follows: 。 6. The method according to claim 1, characterized in that: The temperature response characteristic parameters also include: statistical characteristics, geometric characteristics, frequency domain characteristics, and thermal response characteristics that reflect the temperature change pattern.

7. The method according to claim 1, characterized in that: The dimensionless processing described in step S5 includes positive index normalization and negative index normalization, which transforms each temperature response characteristic parameter to a unified dimension range and constructs a standardized temperature response characteristic matrix. The weights are determined based on the degree of dispersion of information of each temperature response characteristic parameter, and are obtained by any one of the following methods: entropy weighting, CRITIC method, principal component analysis, or analytic hierarchy process.

8. The method according to claim 1, characterized in that: The comprehensive leakage evaluation index SI mentioned in step S6 is obtained by weighted fusion of multiple temperature response characteristic parameters, and its calculation formula is as follows: ； in, , and These are the normalized temperature rise amplitude characteristic parameters, temperature rise response rate characteristic parameters, and time scale characteristic parameters. , and These are the weights corresponding to each feature parameter.

9. The method according to claim 1, characterized in that: A leakage early warning level is established based on the comprehensive leakage evaluation index SI. When SI reaches a preset threshold, it is determined that there is a leakage anomaly at the corresponding location. The preset threshold is determined based on indoor calibration tests, on-site monitoring data, or historical operating data.

10. The method according to claim 1, characterized in that: The method is applicable to leakage monitoring, anomaly identification, and safety early warning for pumped storage power stations, reservoir dams, dikes, canals, tailings dams, underground caverns, diaphragm walls, and anti-seepage curtains.

Images