A method and device for dynamically monitoring a three-dimensional temperature field of a leakage point of a soil tank

Abstract

The application provides a kind of soil tank leakage point three-dimensional temperature field dynamic monitoring method and device, the method obtains the original Brillouin gain spectrum data of gain power value of each spatial resolution unit in soil tank along the direction of optical fiber at different optical frequency difference at current acquisition time;Based on the original Brillouin gain spectrum data of each spatial resolution unit, generate smooth lorentz gain spectrum curve, extract the Brillouin frequency shift value corresponding to each curve peak position, and determine the corresponding temperature value;Based on each discrete three-dimensional temperature point, the temperature of the area not laid optical fiber in soil tank is estimated using spatial statistical interpolation method, and the spatial continuous three-dimensional temperature field cloud picture at current acquisition time is generated, and the three-dimensional temperature point is generated by the temperature value of each spatial resolution unit and its three-dimensional interval coordinates. The method effectively suppresses noise interference and improves temperature measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of distributed optical fiber sensing and temperature field reconstruction technology, and more specifically, to a method and device for dynamic monitoring of the three-dimensional temperature field at a leak point in a soil box. Background Technology

[0002] In the field of infrastructure safety monitoring, temperature anomalies in underground pipelines, soil, and other media are often key indicators of potential leaks and seepage. Achieving continuous and high-precision monitoring of temperature distribution is crucial for early leak warning, location, and analysis of spread trends. Distributed fiber optic sensing technology, with its advantages of resistance to electromagnetic interference, corrosion resistance, and the ability to perform long-distance continuous measurements, is gradually becoming the mainstream method for monitoring soil temperature fields.

[0003] Currently, distributed temperature measurement methods based on Brillouin optical time-domain analysis (BOTDA) are widely used. The basic principle is as follows: pump light and probe light are injected into the sensing fiber, and the Brillouin gain spectrum (BGS) of each spatially resolved unit along the fiber is obtained using the stimulated Brillouin scattering effect. By scanning the optical frequency difference between the pump and probe light, the gain power values ​​at different frequency differences are recorded, forming a gain spectrum curve. Traditional data processing methods typically employ a direct peak-finding method, i.e., finding the optical frequency difference corresponding to the maximum power value in the gain spectrum as the Brillouin frequency shift (BFS), and then using a pre-calibrated temperature coefficient to convert the frequency shift into a temperature value, ultimately outputting a one-dimensional temperature curve distributed along the fiber.

[0004] However, the original Brillouin gain spectrum in the above-mentioned prior art is easily affected by factors such as system noise and environmental vibration. Especially in the early stage of temperature change after leakage, the signal is weak and the signal-to-noise ratio is low. The direct peak finding method is significantly affected by noise points, resulting in a large error in the extracted Brillouin frequency shift, which in turn reduces the accuracy of temperature measurement and makes it difficult to capture the weak leakage signal in the early stage. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for dynamic monitoring of the three-dimensional temperature field at the leakage point of a soil box. By fitting the Brillouin gain spectrum of each spatial resolution unit with a Lorentz curve, noise interference is effectively suppressed, and the Brillouin frequency shift can still be accurately extracted in the early stage of leakage with a low signal-to-noise ratio, thereby improving the accuracy of temperature measurement.

[0006] Firstly, a method for dynamic monitoring of the three-dimensional temperature field at the leakage point of a soil box is provided, which may include: Obtain the original Brillouin gain spectrum data, which consists of the gain power values ​​of each spatial resolution unit along the fiber direction in the soil box at different optical frequency differences at the current acquisition time. Based on the original Brillouin gain spectrum data of each spatial resolution cell, a smooth Lorentz gain spectrum curve is generated, the Brillouin frequency shift value corresponding to the peak position of each curve is extracted, and the temperature value of the corresponding spatial resolution cell is determined. Based on each discrete three-dimensional temperature point, the temperature in the area without fiber optic cables within the soil box is estimated using a spatial statistical interpolation method, generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional coordinates.

[0007] In one possible implementation, a smooth Lorentz gain spectrum curve is generated based on the original Brillouin gain spectrum data of each spatial resolution cell, and the Brillouin frequency shift value corresponding to the peak position of each curve is extracted, including: For each spatial resolution cell, the original Brillouin gain spectrum data of the spatial resolution cell is fitted using the Lorentz curve model to obtain the smooth Lorentz gain spectrum curve corresponding to the spatial resolution cell. Extract the optical frequency difference corresponding to the peak position of the smooth Lorentz gain spectrum curve; determine the optical frequency difference as the Brillouin frequency shift value of the spatial resolution unit.

[0008] In one possible implementation, a smooth Lorentz gain spectrum curve is generated based on the original Brillouin gain spectrum data of each spatial resolution cell, and the Brillouin frequency shift value corresponding to the peak position of each curve is extracted, including: For the raw Brillouin gain spectrum data of each spatial resolution cell, calculate its first and second derivatives respectively; The zero point of the first derivative is located as the first frequency shift estimate; the negative peak position of the second derivative is located as the second frequency shift estimate. The weights of the first frequency shift estimate and the second frequency shift estimate are determined based on the signal-to-noise ratio of the original Brillouin gain spectrum data of the spatial resolution cell. The first frequency shift estimate and the second frequency shift estimate are weighted and averaged according to their respective normalized weights, and the weighted average result is used as the Brillouin frequency shift value of the spatial resolution cell.

[0009] In one possible implementation, the weights of the first and second frequency shift estimates are determined based on the signal-to-noise ratio of the original Brillouin gain spectrum data of the spatially resolved cell, including: The signal-to-noise ratio is compared with a preset first threshold and a second threshold, wherein the first threshold is greater than the second threshold; When the signal-to-noise ratio is greater than or equal to the first threshold, the weight of the first frequency shift estimation is set to the first preset value, the weight of the second frequency shift estimation is set to the second preset value, and the first preset value is greater than the second preset value; When the signal-to-noise ratio is less than or equal to the second threshold, the weight of the first frequency shift estimation is set to the third preset value, the weight of the second frequency shift estimation is set to the fourth preset value, and the third preset value is less than the fourth preset value. When the signal-to-noise ratio is between the second threshold and the first threshold, the weights of the first frequency shift estimate and the second frequency shift estimate are determined by linear interpolation based on the relative position of the signal-to-noise ratio between the first threshold and the second threshold, such that the weight of the first frequency shift estimate increases monotonically as the signal-to-noise ratio increases, and the weight of the second frequency shift estimate decreases monotonically as the signal-to-noise ratio increases.

[0010] In one possible implementation, the spatial statistical interpolation method is the Kriging interpolation method, and this Kriging interpolation method employs an anisotropic variogram model; generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time includes: The soil box space is divided into regular three-dimensional grid points, and each three-dimensional grid point represents the position to be interpolated; For any three-dimensional grid point at the current acquisition time, multiple reference points within the neighborhood of the discrete three-dimensional temperature points are searched. Each reference point contains spatial coordinates and its corresponding temperature value. An anisotropic variogram model is established based on the main direction of leakage diffusion within the soil box, wherein the range length along the main direction of leakage diffusion is greater than the range length perpendicular to the main direction of leakage diffusion, and the ratio of range lengths in different directions is determined by the soil thermal diffusion anisotropy coefficient. Using the anisotropic variogram model, the first variogram value between each reference point and the three-dimensional mesh point, and the second variogram value between any two reference points are calculated respectively. Based on the first and second variogram values, the Kriging equations are constructed and solved to obtain the optimal weights for each reference point. The temperature values ​​of each reference point are weighted and summed according to the optimal weights to obtain the temperature estimate of the three-dimensional grid point. All three-dimensional grid points are traversed to generate a continuous three-dimensional temperature field within the entire soil box at the current acquisition time, so as to obtain a continuous three-dimensional temperature field cloud map.

[0011] In one possible implementation, the formula for determining the main direction of leakage diffusion includes: From multiple historical acquisition times prior to the current acquisition time, extract the discrete three-dimensional temperature points corresponding to each historical acquisition time in sequence; For each historical acquisition moment, calculate the spatial centroid position of all discrete three-dimensional temperature points whose temperature values ​​are lower than the preset temperature threshold at that moment, and take the spatial centroid position as the center point of the low temperature zone at that moment. Connect the center points of the low-temperature zone at two adjacent historical data collection times to obtain the low-temperature zone movement vector within that adjacent time interval. Vector superposition and smoothing filtering are performed on the low-temperature zone movement vectors of multiple consecutive adjacent time intervals to obtain the overall movement direction of the low-temperature zone at the current acquisition time, and this overall movement direction is taken as the main direction of leakage diffusion.

[0012] In one possible implementation, after generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition moment, the method further includes: Save spatial continuous three-dimensional temperature field cloud maps corresponding to multiple consecutive acquisition times; Point-by-point difference calculation is performed on the spatial continuous three-dimensional temperature field cloud map of adjacent acquisition time to obtain the temperature change rate cloud map; and the region boundary where the absolute value of the temperature change rate exceeds the preset change threshold is extracted from the temperature change rate cloud map as the thermal front boundary between adjacent time. The thermal front boundaries extracted between adjacent time points are superimposed and displayed in the same three-dimensional spatial coordinate system in chronological order to form a visual display of the thermal front migration trajectory.

[0013] Secondly, a dynamic monitoring device for the three-dimensional temperature field at the leakage point of a soil box is provided, the device including: The acquisition unit is used to acquire the original Brillouin gain spectrum data composed of the gain power values ​​of each spatial resolution unit along the fiber direction in the soil box at different optical frequency differences at the current acquisition time. The generation unit is used to generate a smooth Lorentz gain spectrum curve based on the original Brillouin gain spectrum data of each spatial resolution unit. The extraction unit is used to extract the Brillouin frequency shift value corresponding to the peak position of each curve; The determination unit is used to determine the temperature value of the corresponding spatial resolution unit based on the Brillouin frequency shift value corresponding to the peak position of each curve. The generation unit is used to estimate the temperature of the area in the soil box without fiber optic cables based on each discrete three-dimensional temperature point using a spatial statistical interpolation method, and generate a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional coordinates.

[0014] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0015] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0016] This application provides a method and apparatus for dynamic monitoring of the three-dimensional temperature field at a leak point in a soil box. The method acquires the original Brillouin gain spectrum data, composed of the gain power values ​​of each spatially resolved unit along the optical fiber direction within the soil box at different optical frequency differences at the current acquisition time. Based on the original Brillouin gain spectrum data of each spatially resolved unit, a smooth Lorentz gain spectrum curve is generated. The Brillouin frequency shift value corresponding to the peak position of each curve is extracted, and the temperature value of the corresponding spatially resolved unit is determined. Based on each discrete three-dimensional temperature point, the temperature of the area within the soil box without optical fiber is estimated using a spatial statistical interpolation method, generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by relating the temperature value of each spatially resolved unit to its three-dimensional coordinates. This method effectively suppresses noise interference by fitting the Brillouin gain spectrum of each spatially resolved unit to a Lorentz curve, and can still accurately extract the Brillouin frequency shift in the early stages of leakage when the signal-to-noise ratio is low, thus improving the accuracy of temperature measurement. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for dynamic monitoring of the three-dimensional temperature field at a leak point in a soil box, provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a dynamic monitoring device for the three-dimensional temperature field of a soil box leakage point provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] The hardware system underlying the dynamic monitoring method for the three-dimensional temperature field of a soil box leakage point provided in this application includes: a BOTDA host, a sensing optical fiber, a soil box, and a data processing computer. The sensing optical fiber is laid inside the soil box along a predetermined path, with its two ends connected to the pump light output port and the probe light input port of the BOTDA host, respectively. The BOTDA host generates frequency-adjustable pump light and probe light through a built-in swept-frequency light source, and collects backscattered Brillouin signals at various locations along the optical fiber, converting them into digital signals and transmitting them to the data processing computer. The data processing computer runs the dynamic monitoring method described in this application, processes the original Brillouin gain spectrum data, and finally generates and dynamically displays a three-dimensional temperature field cloud map.

[0021] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0022] Figure 1 This is a flowchart illustrating a method for dynamically monitoring the three-dimensional temperature field at a leak point in a soil box, as provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Obtain the original Brillouin gain spectrum data composed of the gain power values ​​of each spatial resolution unit along the optical fiber direction in the soil box at different optical frequency differences at the current acquisition time.

[0023] At the current acquisition moment, the data processing computer sends an acquisition command to the BOTDA host. The BOTDA host initiates the measurement according to the preset frequency sweep parameters: setting the frequency sweep start frequency. Termination frequency and frequency step size Typically, the Brillouin frequency shift is in the range of 10–13 GHz, therefore it can be set. , , The BOTDA host sequentially outputs pump and probe light at each frequency difference and collects the gain power value of each spatial resolution unit (the length of the spatial resolution unit is usually 0.25m or 0.5m) along the sensing fiber.

[0024] For each spatial resolution cell, the gain power values ​​at all optical frequency differences constitute a raw Brillouin gain spectrum data vector. ,in For the first One optical frequency difference, , This represents the number of sweep points.

[0025] In a preferred embodiment of this example, the frequency sweep range can be not only fixed, but also dynamically adjusted based on the Brillouin frequency shift value extracted from the previous acquisition time. Specifically: The data processing computer reads the Brillouin frequency shift values ​​of all spatial resolution cells from the previous moment and calculates their average value. and range Therefore, the center frequency of the sweep frequency at the current acquisition time is set to... The frequency sweep range is set to ,in Set a minimum sweep frequency range (e.g., 1 GHz). This dynamic adjustment method ensures that the peak value of the Brillouin gain spectrum always falls within the measurement range, while avoiding unnecessary sweep points and shortening the acquisition time.

[0026] Step S120: Based on the original Brillouin gain spectrum data of each spatial resolution unit, generate a smooth Lorentz gain spectrum curve and extract the Brillouin frequency shift value corresponding to the peak position of each curve. For the raw Brillouin gain spectrum data of each spatial resolution cell, this embodiment provides two optional frequency shift extraction methods: Method 1: For each spatial resolution cell, the original Brillouin gain spectrum data of the spatial resolution cell is fitted using the Lorentz curve model to obtain the smooth Lorentz gain spectrum curve corresponding to the spatial resolution cell; the optical frequency difference corresponding to the peak position of the smooth Lorentz gain spectrum curve is extracted; and the optical frequency difference is determined as the Brillouin frequency shift value of the spatial resolution cell.

[0027] In practice, the data processing computer uses the nonlinear least squares method to fit the original gain spectrum data into a Lorentz curve model:

[0028] in, The optical frequency difference between the pump light and the probe light. The optical frequency difference is Gain power at that time Baseline power, Peak amplitude, The Brillouin frequency shift (parameter to be fitted). The gain spectrum is the full width at half maximum (FWHM). The Levenberg-Marquardt algorithm can be used for fitting, and the initial values ​​can be set as follows: Take the optical frequency difference corresponding to the maximum power in the original data. Take 50MHz, The average power at both ends of the spectral line is taken. After the fitting converges, the Brillouin shift value of the spatially resolved cell is obtained. .

[0029] To improve the accuracy of the initial fitting values, in a preferred embodiment, this application can perform peak pre-positioning before fitting, including: calculating the first-order difference sequence of the original gain spectrum. Find the index of the zero point where the difference value changes from positive to negative. ,by as initial values ​​for fitting If multiple zeros exist, select the zero corresponding to the maximum gain power. This prepositioning significantly improves the fitting convergence speed and success rate.

[0030] Method 2: When the signal-to-noise ratio is low, Lorentz fitting may be unstable. This embodiment provides a frequency shift extraction method without iteration: For the original Brillouin gain spectrum data of each spatial resolution cell, calculate its first and second derivatives respectively; locate the zero point of the first derivative as the first frequency shift estimate; locate the negative peak position of the second derivative as the second frequency shift estimate; determine the weights of the first and second frequency shift estimates based on the signal-to-noise ratio of the original Brillouin gain spectrum data of the spatial resolution cell; perform a weighted average of the first and second frequency shift estimates according to their respective normalized weights, and use the weighted average result as the Brillouin frequency shift value of the spatial resolution cell. Specific implementation includes: (1) Calculate the first and second derivatives of the original gain spectrum. Due to discrete sampling, the central difference formula is used:

[0031]

[0032] in, This is the first derivative of the gain power with respect to the optical frequency difference. For the first The optical frequency difference corresponding to each sweep point In the first There are 1 sweep frequency points (i.e., optical frequency difference is 1) The gain power value measured at ( ) In the first There are 1 sweep frequency points (i.e., optical frequency difference is 1) The gain power value measured at ( ) The sweep step size (interval between adjacent optical frequencies) is the frequency sweep step size. This is the second derivative of the gain power with respect to the optical frequency difference.

[0033] Furthermore, to suppress noise amplification, an adaptive window smoothing can be performed on the original gain spectrum before differentiation: a window with a width one-third of the theoretical half-height full width is selected, centered on each data point (e.g., a window width of...). ), calculate the local weighted average of the gain power within the window, where the weighting coefficients follow a Gaussian function. ,in Take one-quarter of the window width. Use the smoothed data instead of the original data to calculate the derivative.

[0034] (2) Locate the zero point of the first derivative Finding satisfaction and index The precise zero point is obtained through linear interpolation:

[0035] in, This is the optical frequency difference (first frequency shift estimate) corresponding to the zero point of the first derivative.

[0036] Locating the negative peak position of the second derivative :turn up The index corresponding to the minimum value Similarly, the precise position can be obtained through parabolic interpolation.

[0037] (3) Calculate the signal-to-noise ratio (SNR) of the spatial resolution unit. This embodiment provides two methods for obtaining the SNR: Method A: Measure the noise floor power of the BOTDA host beforehand when there is no Brillouin gain signal (e.g., the fiber optic connection is disconnected). Then calculate the peak power of the current gain spectrum. The signal-to-noise ratio is .

[0038] Method B: No prior measurement is required; the gain spectrum's inherent characteristics are utilized. The optical frequency difference corresponding to the maximum gain power is recorded as the peak frequency. The average gain power within two sweep step ranges to the left and right of the peak frequency is taken as the signal power:

[0039] in, This is the estimated signal power value. This is the index of the sweep point corresponding to the maximum gain power. For the first Gain power of each sweep frequency point.

[0040] The average gain power in the regions far from the peak at both ends of the gain spectrum is taken as the noise power. For example, the average of the first three and last three sweep points is taken:

[0041] in, This is an estimate of the noise power. This represents the total number of frequency sweep points.

[0042] The signal-to-noise ratio estimate is This method requires no additional measurements and the calculations are simple and reliable.

[0043] (4) Dynamically determine the first frequency shift estimate based on the signal-to-noise ratio. Second frequency shift estimation The weight.

[0044] Preset first threshold (e.g., 20dB) and the second threshold (e.g., 10dB). The weighting rules are as follows: like Then the weight of the first frequency shift estimation is set to the first preset value, i.e. The weights of the second frequency shift estimation are set to the second preset value, i.e. The first preset value is greater than the second preset value.

[0045] like Then the weight of the first frequency shift estimation is set to the third preset value, i.e. The weights of the second frequency shift estimation are set to the fourth preset value, i.e. The third preset value is less than the fourth preset value.

[0046] like At this point, based on the relative position of the signal-to-noise ratio between the first threshold and the second threshold, the weights of the first frequency shift estimation and the second frequency shift estimation are determined by linear interpolation, such that the weight of the first frequency shift estimation increases monotonically with the increase of the signal-to-noise ratio, and the weight of the second frequency shift estimation decreases monotonically with the increase of the signal-to-noise ratio.

[0047] (5) The normalized weights (which already satisfy the condition) Multiply by the corresponding frequency shift estimate and sum: To obtain the Brillouin frequency shift value of the spatial resolution cell.

[0048] in, This is the final Brillouin frequency shift value. It is the zero point of the first derivative (first frequency shift estimate). It is the negative peak value of the second derivative (second frequency shift estimate).

[0049] Step S130: Determine the temperature value of the corresponding spatial resolution cell based on the Brillouin frequency shift value corresponding to each spatial resolution cell.

[0050] For each spatial resolution cell, the data processing computer converts the Brillouin frequency shift value into a temperature value according to a pre-calibrated temperature coefficient. The calibration process is as follows: a standard thermometer is placed inside a soil chamber, the optical fiber is kept in a constant temperature environment, and the reference temperature is recorded. Corresponding reference Brillouin frequency shift Then change the temperature to another known value. Measure the corresponding frequency shift Calculate the temperature coefficient:

[0051] generally Approximately 1 MHz / ℃. The temperature value for each spatial resolution cell is calculated as follows:

[0052] in, This is the temperature coefficient.

[0053] Step S140: Based on each discrete three-dimensional temperature point, the temperature of the area in the soil box without fiber optic cables is estimated using a spatial statistical interpolation method, generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time.

[0054] Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional spatial coordinates.

[0055] After obtaining the temperature values ​​of each spatial resolution unit along the optical fiber, each temperature value corresponds to only a one-dimensional position on the fiber (the length from the BOTDA host port). For use in three-dimensional space, spatial registration is required based on the actual three-dimensional coordinates of the sensing fiber's layout within the soil box. In this embodiment, the sensing fiber can be laid using a three-dimensional spiral path, with the vertical spacing between adjacent ramps equal to the length of the spatial resolution unit (e.g., 0.25m), and the projected spacing of adjacent ramps on the horizontal plane less than half the length of the spatial resolution unit (e.g., 0.1m). This laying method ensures uniform sampling density in three-dimensional space.

[0056] The data processing computer pre-stores a lookup table that records the three-dimensional coordinates corresponding to each spatial resolution unit (identified by its fiber optic location index). The obtained temperature values ​​are associated with the coordinates in the lookup table to generate a discrete three-dimensional temperature point set. Each point contains spatial coordinates and its temperature value.

[0057] Next, the temperature of the area within the soil box without fiber optic cables was estimated using the Kriging interpolation method, generating a spatially continuous three-dimensional temperature field cloud map. The specific steps are as follows: Step 1: Grid Generation. Divide the soil box space into a regular three-dimensional grid. The grid spacing can be set according to the required resolution, such as 0.05m in the x and y directions and 0.05m in the z direction. Each grid point represents a location to be interpolated.

[0058] Step 2: Search for reference points. For the current 3D mesh points to be interpolated... The reference point is searched within the neighborhood of the discrete three-dimensional temperature point set.

[0059] Optionally, during the search for the reference point, an adaptive strategy is adopted for the search radius, including: setting the initial search radius to the current grid point as the center. The search radius is gradually increased (by 0.05m each time) until the number of reference points found reaches a preset minimum threshold, or the search radius reaches a preset maximum limit. In areas with dense fiber optic cables, the search radius is smaller to avoid computational redundancy; near the soil box boundary or in areas with sparse fiber optic cables, the search radius automatically increases to ensure sufficient reference points.

[0060] Step 3: Establish an anisotropic variogram model. This embodiment uses an exponential variogram, whose general form is: ,in, The value is the variogram (the semivariogram of temperature values ​​at two points in space). Let be the vector between two points in space. The magnitude (distance) of the vector. For nugget constant, For the partial sill value, This is a variable range. Because soil thermal diffusion is anisotropic (the leaked low temperature propagates faster along the main direction), the variable range... It is not a scalar, but a function that is related to direction.

[0061] First, determine the main direction of leakage diffusion. ,include: The center point of the low-temperature zone is extracted from discrete three-dimensional temperature points from multiple historical acquisition times (e.g., the previous 10 times) preceding the current acquisition time. Specifically, for each historical acquisition time, all temperature values ​​below a preset temperature threshold are calculated. (For example The spatial centroid position of the discrete three-dimensional temperature point.

[0062] Connecting the centroids of two adjacent historical acquisition moments yields the movement vector.

[0063] Then, the main direction of leak propagation Let the direction of the first principal axis of the anisotropic ellipsoid be defined. Let the direction of the first principal axis be defined along the anisotropic ellipsoid. The range of direction is The horizontal range perpendicular to this direction is Vertical direction range is Usually taken , . and The ratio is pre-calibrated based on the soil bedding direction through thermal response tests. Then, for any direction vector... Equivalent variation It is given by the following formula:

[0064] in, For along direction The equivalent variable process, For spatial vectors, For vectors In the The projected length on the main axis, k takes the values ​​1, 2, and 3; These are unit vectors along the three principal axes.

[0065] Step 4: Calculate the variogram value. For the grid points to be interpolated... and each reference point Calculate vector The first variogram value is calculated based on the anisotropy model described above. For any two reference points and Calculate vector The second variogram value is obtained. .

[0066] Step 5: Construct and solve the Kriging equations. The ordinary Kriging equations are in the following form:

[0067] in, Number of reference points For the first Kriging weights at reference points -- weights to be determined For Lagrange multipliers, For reference point and The variogram values ​​between For reference point The variogram value between the interpolation point and the point to be interpolated. The value obtained in step 4... and Substituting the values ​​into the system of equations and using Gaussian elimination, the optimal weights for each reference point are obtained. .

[0068] Step 6: Estimate the temperature value. The estimated temperature value for the grid points to be interpolated is a weighted sum of the temperature values ​​at each reference point: ,in, Points to be interpolated Temperature estimate For Kriging weights, For the first Temperature values ​​at reference points.

[0069] Step 7: Traverse all grid points. Repeat steps 2 through 6 until temperature estimates are obtained for all grid points. The data processing computer organizes the coordinates of the grid points and their temperature values ​​into a three-dimensional array and calls a visualization library (such as VTK or OpenGL) to generate a spatial continuous three-dimensional temperature field contour map at the current acquisition time. The contour map uses color mapping to represent temperature levels; for example, blue for low-temperature areas and red for high-temperature areas.

[0070] In some embodiments, to visually demonstrate the diffusion process of the leaking cryogenic zone, this embodiment performs the following additional processing after generating the three-dimensional temperature field cloud map at each acquisition time: Step 1: Save historical cloud maps. The data processing computer saves three-dimensional temperature field cloud maps from multiple consecutive acquisition moments (e.g., every 10 seconds) in memory or on the hard drive.

[0071] Step 2: Difference calculation and front extraction. For two adjacent acquisition times... and By performing grid-by-grid point differencing on the three-dimensional temperature field contour maps at two different times, the temperature change rate contour map is obtained:

[0072] In the temperature change rate cloud map, extract the temperature change rate absolute value that exceeds the preset change threshold. (e.g., 0.1℃) region boundary. This embodiment uses the moving cube algorithm to extract the isosurface: setting the isosurface value to... Iterate through all the mesh cubes, and based on the comparison of the temperature change rate at each of the cube's eight vertices with a threshold, calculate the intersection points of the isosurfaces with the cube's edges. Connect these intersection points to form a triangular mesh surface, which serves as the boundary of the thermal front. For In the region, negative threshold isosurfaces are extracted as cooling fronts (representing the expansion front of the low-temperature region); for In the region, the positive threshold isosurface is extracted as the warming front (representing the warming region).

[0073] Step 3: Overlay Display. The extracted thermal front boundaries from adjacent time points are overlaid and displayed in chronological order within the same three-dimensional coordinate system, creating a visualization of the thermal front migration trajectory. For example, different colored surfaces can represent fronts at different times, and time labels can be added, allowing users to visually observe the process of the low-temperature zone spreading outward from the leak point.

[0074] Corresponding to the above method, this application embodiment also provides a dynamic monitoring device for the three-dimensional temperature field of the soil box leakage point, such as... Figure 2 As shown, the device includes: Acquisition unit 210 is used to acquire the original Brillouin gain spectrum data composed of the gain power values ​​of each spatial resolution unit along the optical fiber direction in the soil box at different optical frequency differences at the current acquisition time. The generation unit 220 is used to generate a smooth Lorentz gain spectrum curve based on the original Brillouin gain spectrum data of each spatial resolution unit. Extraction unit 230 is used to extract the Brillouin frequency shift value corresponding to the peak position of each curve; Unit 240 is used to determine the temperature value of the corresponding spatial resolution unit based on the Brillouin frequency shift value corresponding to the peak position of each curve. The generation unit 220 is used to estimate the temperature of the area in the soil box where no optical fiber is laid based on each discrete three-dimensional temperature point using a spatial statistical interpolation method, and generate a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional coordinates.

[0075] The functions of each functional unit of the dynamic monitoring device for the three-dimensional temperature field of the soil box leakage point provided in the above embodiments of this application can be realized through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the dynamic monitoring device for the three-dimensional temperature field of the soil box leakage point provided in the embodiments of this application will not be repeated here.

[0076] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0077] Memory 330 is used to store computer programs; When the processor 310 executes the program stored in the memory 330, it performs the following steps: Obtain the original Brillouin gain spectrum data, which consists of the gain power values ​​of each spatial resolution unit along the fiber direction in the soil box at different optical frequency differences at the current acquisition time. Based on the original Brillouin gain spectrum data of each spatial resolution cell, a smooth Lorentz gain spectrum curve is generated, the Brillouin frequency shift value corresponding to the peak position of each curve is extracted, and the temperature value of the corresponding spatial resolution cell is determined. Based on each discrete three-dimensional temperature point, the temperature in the area without fiber optic cables within the soil box is estimated using a spatial statistical interpolation method, generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional coordinates.

[0078] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0079] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0080] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0081] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0082] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0083] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the dynamic monitoring method for the three-dimensional temperature field of the soil box leakage point as described in any of the above embodiments.

[0084] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the dynamic monitoring method for the three-dimensional temperature field of the soil box leakage point as described in any of the above embodiments.

[0085] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0090] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for dynamic monitoring of the three-dimensional temperature field at a leakage point in a soil box, characterized in that, The method includes: Acquire the original Brillouin gain spectrum data, which consists of the gain power values ​​of each spatial resolution unit under different optical frequency differences along the fiber direction in the soil box at the current acquisition time. Based on the original Brillouin gain spectrum data of each spatial resolution cell, a smooth Lorentz gain spectrum curve is generated, the Brillouin frequency shift value corresponding to the peak position of each curve is extracted, and the corresponding temperature value is determined. Based on each discrete three-dimensional temperature point, the temperature of the area in the soil box without fiber optic cables is estimated using a spatial statistical interpolation method, generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. The discrete three-dimensional temperature points are generated by associating the temperature value of the spatial resolution unit with its three-dimensional coordinates.

2. The method as described in claim 1, characterized in that, Generate smooth Lorentz gain spectrum curves and extract the Brillouin frequency shift values ​​corresponding to the peak positions of each curve, including: For each spatial resolution cell, the original Brillouin gain spectrum data of the spatial resolution cell is fitted using the Lorentz curve model to obtain the smooth Lorentz gain spectrum curve corresponding to the spatial resolution cell. Extract the optical frequency difference corresponding to the peak position of the smooth Lorentz gain spectrum curve; determine the optical frequency difference as the Brillouin frequency shift value of the spatial resolution unit.

3. The method as described in claim 1, characterized in that, Based on the original Brillouin gain spectrum data of each spatial resolution cell, smooth Lorentz gain spectrum curves are generated, and the Brillouin frequency shift values ​​corresponding to the peak positions of each curve are extracted, including: For the raw Brillouin gain spectrum data of each spatial resolution cell, calculate its first and second derivatives respectively; The zero point of the first derivative is located as the first frequency shift estimate; the negative peak position of the second derivative is located as the second frequency shift estimate; the weights of the first and second frequency shift estimates are determined based on the signal-to-noise ratio of the original Brillouin gain spectrum data of the spatial resolution cell. The first frequency shift estimate and the second frequency shift estimate are weighted and averaged according to their respective normalized weights, and the weighted average result is used as the Brillouin frequency shift value of the spatial resolution cell.

4. The method as described in claim 3, characterized in that, Based on the signal-to-noise ratio of the original Brillouin gain spectrum data of the spatial resolution cell, the weights of the first frequency shift estimate and the second frequency shift estimate are determined, including: The signal-to-noise ratio is compared with a preset first threshold and a second threshold, wherein the first threshold is greater than the second threshold; When the signal-to-noise ratio is greater than or equal to the first threshold, the weight of the first frequency shift estimation is set to the first preset value, the weight of the second frequency shift estimation is set to the second preset value, and the first preset value is greater than the second preset value. When the signal-to-noise ratio is less than or equal to the second threshold, the weight of the first frequency shift estimation is set to the third preset value, the weight of the second frequency shift estimation is set to the fourth preset value, and the third preset value is less than the fourth preset value. When the signal-to-noise ratio is between the second threshold and the first threshold, the weights of the first frequency shift estimate and the second frequency shift estimate are determined by linear interpolation based on the relative position of the signal-to-noise ratio between the first threshold and the second threshold, such that the weight of the first frequency shift estimate increases monotonically as the signal-to-noise ratio increases, and the weight of the second frequency shift estimate decreases monotonically as the signal-to-noise ratio increases.

5. The method as described in claim 1, characterized in that, The spatial statistical interpolation method is the Kriging interpolation method, and this Kriging interpolation method uses an anisotropic variogram model; generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition time includes: The soil box space is divided into regular three-dimensional grid points, and each three-dimensional grid point represents the position to be interpolated; For any three-dimensional grid point at the current acquisition time, multiple reference points within the neighborhood of the discrete three-dimensional temperature points are searched. Each reference point contains spatial coordinates and its corresponding temperature value. An anisotropic variogram model is established based on the main direction of leakage diffusion within the soil box, wherein the range length along the main direction of leakage diffusion is greater than the range length perpendicular to the main direction of leakage diffusion, and the ratio of range lengths in different directions is determined by the soil thermal diffusion anisotropy coefficient. Using the anisotropic variogram model, the first variogram value between each reference point and the three-dimensional mesh point, and the second variogram value between any two reference points are calculated respectively. Based on the first and second variogram values, the Kriging equations are constructed and solved to obtain the optimal weights for each reference point. The temperature values ​​of each reference point are weighted and summed according to the optimal weights to obtain the temperature estimate of the three-dimensional grid point. All three-dimensional grid points are traversed to generate a continuous three-dimensional temperature field within the entire soil box at the current acquisition time, so as to obtain a continuous three-dimensional temperature field cloud map.

6. The method as described in claim 5, characterized in that, The formula for determining the main direction of leakage diffusion includes: Extract discrete three-dimensional temperature points corresponding to each historical acquisition moment sequentially from multiple historical acquisition moments prior to the current acquisition moment; For each historical acquisition moment, calculate the spatial centroid position of all discrete three-dimensional temperature points whose temperature values ​​are lower than the preset temperature threshold at that moment, and take the spatial centroid position as the center point of the low temperature zone at that moment. Connect the center points of the low-temperature zone at two adjacent historical data collection times to obtain the low-temperature zone movement vector within that adjacent time interval. Vector superposition and smoothing filtering are performed on the low-temperature zone movement vectors of multiple consecutive adjacent time intervals to obtain the overall movement direction of the low-temperature zone at the current acquisition time, and this overall movement direction is taken as the main direction of leakage diffusion.

7. The method as described in claim 1, characterized in that, After generating a spatially continuous three-dimensional temperature field cloud map at the current acquisition moment, the method further includes: Save spatial continuous three-dimensional temperature field cloud maps corresponding to multiple consecutive acquisition times; Point-by-point difference calculation is performed on the spatial continuous three-dimensional temperature field cloud map of adjacent acquisition time to obtain the temperature change rate cloud map; and the region boundary where the absolute value of the temperature change rate exceeds the preset change threshold is extracted from the temperature change rate cloud map as the thermal front boundary between adjacent time. The thermal front boundaries extracted between adjacent time points are superimposed and displayed in the same three-dimensional spatial coordinate system in chronological order to form a visual display of the thermal front migration trajectory.

8. A dynamic monitoring device for the three-dimensional temperature field at a leakage point in a soil box, characterized in that, The device includes: The acquisition unit is used to acquire the original Brillouin gain spectrum data composed of the gain power values ​​of each spatial resolution unit along the fiber direction in the soil box at different optical frequency differences at the current acquisition time. The generation unit is used to generate a smooth Lorentz gain spectrum curve based on the original Brillouin gain spectrum data of each spatial resolution unit. The extraction unit is used to extract the Brillouin frequency shift value corresponding to the peak position of each curve; The determination unit is used to determine the corresponding temperature value based on the Brillouin frequency shift value corresponding to the peak position of each curve; The generation unit is used to estimate the temperature of the area in the soil box without fiber optic cables based on each discrete three-dimensional temperature point using a spatial statistical interpolation method, and generate a spatially continuous three-dimensional temperature field cloud map at the current acquisition time. Each discrete three-dimensional temperature point is generated by associating the temperature value of each spatial resolution unit with its three-dimensional coordinates.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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