A buried CO2 pipeline leakage source positioning method and device

Abstract

The application provides a buried CO2 pipeline leakage source positioning method and device. The method obtains temperature change time sequences of each spatial node in a three-dimensional temperature measurement grid around a buried pipeline; for each spatial node, based on the corresponding temperature change time sequence, a wave front arrival time when a temperature drop rate first exceeds a preset rate threshold is detected; at least four spatial nodes that do not share a surface are selected, and based on a time difference between the wave front arrival times of the selected spatial nodes, a walk time equation that is previously constructed based on a soil heat diffusion model is combined to obtain an isochrone difference hyperboloid from the leakage source to each spatial node; and based on spatial intersection points of all the isochrone difference hyperboloids, three-dimensional coordinates of the leakage source are determined. The method improves positioning accuracy and multi-source resolution capability.

Description

Technical Field

[0001] This application relates to the field of buried pipeline leakage monitoring technology, and more specifically, to a method and device for locating leakage sources in buried CO2 pipelines. Background Technology

[0002] With the large-scale adoption of carbon capture, utilization, and storage (CCUS) technologies, long-distance buried pipeline transportation of supercritical or gaseous CO2 has become a crucial infrastructure for achieving carbon neutrality. However, buried pipelines are subject to significant leakage risks due to long-term effects such as soil corrosion, geological subsidence, and third-party construction. CO2 leaks not only cause greenhouse gas emissions and economic losses but can also lead to rapid localized soil cooling due to the Joule-Thomson effect, potentially triggering secondary disasters such as frost heave and subsidence. Therefore, developing high-precision, fast-response leak source location technologies is of great significance for ensuring the safe operation of pipelines.

[0003] Among existing buried pipeline leak detection technologies, distributed fiber optic temperature sensing (DTS) is a relatively advanced method. DTS deploys optical fibers along the pipeline axis and identifies areas of temperature anomalies by measuring the temperature distribution at various points along the fiber. When a CO2 leak causes a localized temperature drop, the system determines the axial coordinates of the leak point based on the location of the sudden temperature drop on the optical fiber. In addition, some studies have attempted to bury multiple single-point temperature sensors around the pipeline and roughly estimate the leak location by comparing the temperature drop of each sensor.

[0004] However, existing temperature-based DTS methods can only obtain a one-dimensional temperature curve along the pipe axis and cannot sense the radial temperature gradient perpendicular to the pipe direction. This makes it impossible to determine the circumferential angle of the leak point on the pipe wall and the radial distance from the pipe. The positioning result is only the axial coordinate, and the actual error can reach several meters. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for locating leak sources in buried CO2 pipelines. By introducing the arrival time difference of temperature disturbance wavefronts and constructing a hyperboloid intersection location model, the location of leak sources is extended from one-dimensional axis to three-dimensional space, which significantly improves the location accuracy and multi-source resolution.

[0006] Firstly, a method for locating leak sources in buried CO2 pipelines is provided, which may include: Obtain the time sequence of temperature changes at each spatial node in the three-dimensional temperature measurement grid around the buried pipeline; For each spatial node, based on the corresponding temperature change time sequence, the arrival time of the wavefront when the temperature drop rate first exceeds the preset rate threshold is detected; At least four spatially non-coplanar nodes are selected. Based on the time difference between the wavefront arrival times of the selected spatial nodes and the travel time equations pre-built based on the soil heat diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are solved. The three-dimensional coordinates of the leakage source are determined based on the spatial intersections of all isochronous hyperboloids.

[0007] In one possible implementation, the three-dimensional temperature measurement grid is constructed in the following manner: At least four temperature measurement sections are arranged at equal intervals along the axial direction of the buried pipeline. At least four radial temperature measurement arms are arranged at equal angles around the circumference of the pipeline in each temperature measurement section. At least four temperature sensing nodes are arranged at equal intervals on each radial temperature measurement arm extending outward from the outer surface of the pipe wall.

[0008] In one possible implementation, for each spatial node, the wavefront arrival time at which its temperature decrease rate first exceeds a preset threshold is detected, including: For the temperature change time series of the corresponding spatial nodes, a sliding median filter is performed with a preset window length to obtain the filtered time series temperature value sequence. Calculate the temperature difference between the current sampling time and the previous sampling time in the filtered sequence, divide the temperature difference by the sampling time interval, and obtain the instantaneous temperature drop rate at the current sampling time. When the instantaneous temperature drop rate of three consecutive sampling times exceeds the preset rate threshold, and the current filtered temperature value is lower than the historical normal operating temperature average minus twice the standard deviation, the first sampling time among the three consecutive sampling times is marked as the wavefront arrival time of the spatial node.

[0009] In one possible implementation, the preset rate threshold can be determined in the following ways: The standard deviation of temperature fluctuation is calculated based on the historical temperature change sequence of each spatial node within a preset time period before the leak occurs. The larger of three times the standard deviation of the temperature fluctuation and the configured minimum sensitive cooling rate is determined as the preset rate threshold; wherein, the minimum sensitive cooling rate is pre-calibrated based on the initial pressure difference of CO2 leakage and the range of soil thermophysical properties.

[0010] In one possible implementation, the travel-time equations pre-constructed based on the soil thermal diffusion model are obtained in the following way: At the site of the buried pipeline to be monitored, select a typical soil area free from pipeline interference, and bury a thermal pulse generator and at least four temperature sensors at different distances to form an in-situ thermal diffusivity calibration device. A thermal pulse of a preset duration is released by a thermal pulse generator. The arrival time of the temperature peak detected by each temperature sensor is recorded. Based on the relationship between the distance from the thermal pulse generation point to each sensor and the square of the peak arrival time, the soil thermal diffusivity is calculated. By approximating the leakage source as an instantaneous point cold source, a functional relationship between the propagation distance from the leakage source to the spatial node and the arrival time of the temperature disturbance wavefront is established based on the heat conduction theory. Substituting the soil thermal diffusivity obtained from the inverse equation into the functional relationship, a complete travel time equation is constructed.

[0011] In one possible implementation, based on the travel-time equations pre-built from the soil heat diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are solved. From the spatial nodes with marked wavefront arrival times, select any two nodes as a pair of focal nodes and calculate the absolute value of the time difference between the wavefront arrival times of these two nodes. Based on the soil thermal diffusivity pre-calibrated on site, the absolute value of the time difference is converted into an equivalent thermal wave propagation distance difference through the travel time equation. This distance difference represents the difference in spatial distance from the leakage source to the two focal nodes. Using the three-dimensional spatial coordinates of these two focal nodes as the two foci of the hyperboloid, and using the constant distance difference as the real axis length parameter of the hyperboloid, an isochronous hyperboloid is constructed in three-dimensional space. Repeat the above process until all spatial node pairs generate corresponding isochronous hyperboloids.

[0012] In one possible implementation, the three-dimensional coordinates of the leakage source are determined based on the spatial intersections of all isochronous hyperboloids, including: Based on the isochronous hyperboloids constructed for each pair of nodes corresponding to at least four spatially non-coplanar nodes, the spatial point with the smallest total deviation is searched within the spatial range covered by the three-dimensional temperature measurement grid as the initial candidate point. Centered on the initial candidate point, its spatial coordinates are iteratively adjusted using the least squares optimization method to minimize the sum of squared distance deviations from the point to all hyperboloids, thus obtaining a candidate intersection point; From all spatial nodes with marked wavefront arrival times, randomly select multiple different non-coplanar node subsets and repeat the above process to obtain multiple candidate intersection points. Calculate the average spatial distance between each candidate intersection point and other candidate intersection points, and remove candidate intersection points whose average distance is significantly greater than that of the other intersection points as outliers. For the retained candidate intersection points, weights are assigned according to the sum of squared distance deviations corresponding to each intersection point, and a weighted average is performed to obtain the three-dimensional coordinates of the leakage source.

[0013] Secondly, a device for locating leak sources in buried CO2 pipelines is provided, the device including: The acquisition unit is used to acquire the time sequence of temperature changes of each spatial node in the three-dimensional temperature measurement grid around the buried pipeline. The detection unit is used to detect the arrival time of the wavefront when the temperature drop rate first exceeds a preset rate threshold for each spatial node, based on the corresponding temperature change time sequence. The acquisition unit is also used to select at least four spatially non-coplanar spatial nodes, and based on the time difference between the wavefront arrival times of the selected spatial nodes, combined with the travel time equations pre-built based on the soil heat diffusion model, to solve for the isochronous hyperboloids from the leakage source to each spatial node. The determination element is used to determine the three-dimensional coordinates of the leakage source based on the spatial intersections of all isochronous hyperboloids.

[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 locating leak sources in buried CO2 pipelines. The method acquires the time series of temperature changes at each spatial node in a three-dimensional temperature measurement grid surrounding the buried pipeline. For each spatial node, based on the corresponding temperature change time series, it detects the wavefront arrival time at which the temperature drop rate first exceeds a preset threshold. It selects at least four spatially non-coplanar nodes and, based on the time difference between the wavefront arrival times of the selected spatial nodes and a travel time equation pre-constructed based on a soil thermal diffusion model, solves for the isochronous hyperboloids from the leak source to each spatial node. Based on the spatial intersections of all isochronous hyperboloids, it determines the three-dimensional coordinates of the leak source. This method, by introducing the time difference of wavefront arrival due to temperature disturbances and constructing a hyperboloid intersection location model, extends leak source location from one-dimensional axis to three-dimensional space, significantly improving location accuracy and multi-source resolution. 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 locating a leak source in a buried CO2 pipeline, provided as an embodiment of this application; Figure 2 A schematic diagram of a device for locating leak sources in buried CO2 pipelines provided in this application embodiment; 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 of ordinary skill 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 terms "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 terms "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 does not exclude other elements or objects. The terms "connection," "coupled," or "linked," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0020] The method for locating leak sources in buried CO2 pipelines provided in this application relies on a buried CO2 pipeline leak source location system that includes the following structural components: The three-dimensional temperature measurement grid assembly consists of multiple temperature sensing nodes arranged in a specific spatial layout. At least four temperature measurement sections are evenly spaced along the axial direction of the buried pipeline. Within each temperature measurement section, at least four radial temperature sensing arms are arranged at equal angles around the circumference of the pipeline. Each radial temperature sensing arm has at least four temperature sensing nodes evenly spaced extending outwards from the outer surface of the pipe wall. All temperature sensing nodes are connected to the data acquisition unit via corrosion-resistant shielded cables or communicate with the data acquisition unit via a wireless transmission module.

[0021] The data acquisition unit comprises a synchronization clock module, a high-frequency sampling module, and a data buffer module. The synchronization clock module calibrates the sampling clocks of all temperature sensing nodes via a wired synchronization signal line or a wireless time synchronization protocol (such as the IEEE 1588 Precision Time Protocol or GPS time synchronization), ensuring that the temperature acquisition times of all nodes remain synchronized. The high-frequency sampling module continuously acquires the temperature values ​​of each node at a sampling frequency of at least 10 Hz. The data buffer module allocates independent buffer space for each node to store at least the most recent 60 seconds of time-series temperature data.

[0022] Data processing and positioning calculation unit: Typically an industrial control computer or edge computing server deployed in the monitoring room, it receives temperature time-series data uploaded by the data acquisition unit via a data bus or network. This unit incorporates a travel time equation parameter library, a soil thermal diffusivity calibration module, a wavefront arrival time detection module, a hyperboloid construction module, an intersection point solution module, and a positioning result output module. Data is transferred between modules via a communication interface.

[0023] Auxiliary calibration device: Used for on-site determination of soil thermal diffusivity, it includes a burialable thermal pulse generator and at least four temperature sensors at different distances, forming an in-situ thermal diffusivity calibration device. This device is only used during the initial system deployment or when soil conditions change significantly, and is not used for routine leak monitoring.

[0024] The overall process of the buried CO2 pipeline leakage source localization method based on time-series temperature wavefront propagation provided in this application is as follows: First, time-series temperature change data for each spatial node is continuously acquired using a three-dimensional temperature measurement grid. The time-series data for each node consists of multiple temperature samples arranged in chronological order. All nodes employ synchronous high-frequency sampling to ensure the accuracy of subsequent wavefront arrival time difference calculations.

[0025] Secondly, for each spatial node, the moment when the rate of temperature decrease first exceeds a preset threshold is detected based on its temperature change time series, and this moment is marked as the wavefront arrival time of that node. This step is the data foundation for the subsequent construction of the isochronous hyperboloid.

[0026] Then, at least four spatially non-coplanar nodes are selected. Based on the time difference between the wavefront arrival times of the selected nodes and the travel time equations pre-constructed based on the soil heat diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are solved. Each hyperboloid corresponds to the wavefront arrival time difference of a pair of nodes.

[0027] Finally, the spatial intersections of all isochronous hyperboloids are calculated, and outlier removal and weighted averaging are performed to obtain the three-dimensional coordinates of the leak source. These three-dimensional coordinates can be further projected onto the pipeline model to obtain the actual location of the leak point on the pipe wall (axial mileage station, circumferential angle, and radial penetration depth), and output to the monitoring terminal in a visualized form.

[0028] 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.

[0029] Figure 1 This is a schematic flowchart illustrating a method for locating leak sources in buried CO2 pipelines, provided as an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Obtain the time sequence of temperature changes of each spatial node in the three-dimensional temperature measurement grid around the buried pipeline.

[0030] First, a three-dimensional temperature measurement grid is constructed. Specifically, around the buried CO2 pipeline, a temperature measurement section is installed every 10 to 50 meters along the pipeline's axial direction (determined based on pipeline length and monitoring accuracy requirements), for a total of at least four temperature measurement sections. Within each temperature measurement section, at least four radial temperature measurement arms are installed around the pipeline at equal angles (e.g., every 90 or 60 degrees). Each radial temperature measurement arm starts from the outer surface of the pipe wall and extends radially outward with at least four temperature sensing nodes at equal intervals, for example, one temperature sensing node at distances of 0.1 meters, 0.3 meters, and 0.6 meters from the outer surface of the pipe wall. All temperature sensing nodes use high-precision negative temperature coefficient thermistors or thermocouples, with a temperature measurement accuracy of no less than ±0.05 degrees Celsius.

[0031] Then, the data acquisition unit performs synchronous high-frequency sampling. Specifically, the sampling clocks of all temperature sensing nodes are calibrated via a wired synchronization signal line (such as an RS-485 synchronization bus) or a wireless timing protocol (such as the IEEE 1588PTP protocol) to ensure that the sampling time deviation between each temperature sensing node is less than 1 millisecond. The sampling frequency is set to no less than 10 Hz; for example, 20 Hz means that the temperature value of each node is collected every 0.05 seconds. Each temperature sensing node is allocated an independent circular buffer with a capacity of at least the most recent 60 seconds of time-series data, i.e., storing at least 60 × 20 = 1200 temperature sampling values. The buffer is updated using a first-in, first-out (FIFO) method, always retaining the latest 60 seconds of data.

[0032] The temperature change sequence of each spatial node (i.e., the deployed temperature sensing node) consists of multiple temperature values ​​arranged in chronological order, denoted as: Where i represents the node number, This represents the k-th sampling time.

[0033] Step S120: For each spatial node, based on the corresponding temperature change time series, detect the wavefront arrival time when the temperature decrease rate first exceeds a preset rate threshold. This step may include the following sub-steps: S21. Sliding Median Filtering: First, perform sliding median filtering on the temperature change time series of each spatial node. Specifically, set the filtering window length to an odd value and no less than 5 sampling periods, such as a window length of 9 sampling points. For the current sampling time... Take the temperature values ​​of a total of 9 sampling points before and after it (including (The sample itself and its four preceding and four following sampling points) are used to sort these nine temperature values ​​by magnitude, and the median value is taken as the median. The temperature value after time-series filtering. For cases where the window length is insufficient at the start and end boundaries of the time series, symmetrical filling or window reduction is used. Sliding median filtering effectively eliminates sensor random noise and instantaneous spikes caused by soil micro-disturbances, resulting in a smooth temperature change curve.

[0034] S22. Calculate the instantaneous temperature drop rate: Calculate the temperature difference between the current sampling time and the previous sampling time in the filtered sequence, divide the difference by the sampling time interval, and obtain the instantaneous temperature drop rate at the current sampling time.

[0035] Let the filtered temperature sequence be If the sampling time interval is Δt (e.g., 0.05 seconds), then the instantaneous temperature decrease rate is... The calculation formula is: ;in: For node i in The instantaneous rate of temperature decrease at any given moment; a positive value indicates cooling and a negative value indicates heating. For node i at the current sampling time The filtered temperature value; Δt is the sampling time interval.

[0036] It should be noted that this value is negative when the temperature rises and positive when the temperature falls. When a leak occurs, due to the Joule-Thomson expansion cooling effect of CO2, the soil temperature around the leak point will drop rapidly, so the instantaneous temperature drop rate will show a positive peak.

[0037] S23, (1) Determination of wavefront arrival time: First, determine the historical average normal operating temperature. During the normal operating period when the pipeline is confirmed to be leak-free (such as the first 300 seconds after the system is initially put into operation), collect the filtered temperature data of each spatial node and calculate its arithmetic mean as the historical average normal operating temperature of that node. This value represents the baseline temperature level for that node under leak-free conditions. It drifts slowly with seasonal changes and therefore can be updated periodically (e.g., recalculated weekly). The standard deviation of this historical data segment is also calculated. This is used for subsequent threshold comparisons.

[0038] Then, the wavefront arrival time determination logic is executed: when the instantaneous temperature drop rate at three consecutive sampling times (i.e., three consecutive sampling points, covering a time length of 3Δt) exceeds a preset rate threshold. And the filtered temperature value at the third sampling time. Below 2 When the first of these three consecutive sampling times is marked as the wavefront arrival time of the spatial node, the time of sampling is recorded.

[0039] The "three consecutive sampling times" setting in the above judgment conditions is to avoid false triggering caused by a single noise spike, thus improving the reliability of the detection. Absolute temperature condition (below...) 2 This ensures that the detected cooling is not caused by normal ambient temperature fluctuations, but rather by a genuine significant temperature anomaly.

[0040] (2) Preset rate threshold Determination: Based on the historical temperature change sequence of each spatial node within a preset time period (e.g., the first 60 seconds) before the leak occurs, the standard deviation of temperature fluctuation is calculated. Let the nodes within this time period... The standard deviation of the filtered temperature sequence is Then three times the standard deviation of temperature fluctuation is At the same time, the system is configured with a minimum sensitive cooling rate. This value is pre-calibrated based on the initial pressure differential of the CO2 leak and the range of soil thermophysical properties (e.g., 0.05 degrees Celsius per second). Ultimately, a preset rate threshold is established. Take the larger of the two: ; in: The preset rate threshold; For nodes Standard deviation of the filtered temperature sequence during the leak-free period; This is the minimum sensitive cooling rate, a pre-calibration constant.

[0041] Furthermore, the pre-calibration method for the minimum sensitive cooling rate is as follows: Before system deployment, based on the operating pressure range of the pipeline to be monitored (e.g., 8~15MPa) and the soil type (e.g., sandy soil, clay, etc.), the maximum cooling rate at different distances from the leak outlet is calculated using finite element numerical simulation under different leakage pressure differentials and different soil thermal diffusivity. Statistical results show that, under typical operating conditions, the maximum cooling rate within 1.0 meter of the leak outlet is usually not less than 0.05 degrees Celsius per second. Considering the sensor noise level and the requirements for combating false alarms, the minimum sensitive cooling rate is pre-calibrated to 0.05 degrees Celsius per second. After on-site installation, correction and verification can be performed by actively injecting a small amount of cold source.

[0042] Step S130: Select at least four spatial nodes that are not coplanar in spatial location. Based on the time difference between the wavefront arrival times of the selected spatial nodes and the travel time equation pre-built based on the soil heat diffusion model, solve for the isochronous hyperboloid from the leakage source to each spatial node.

[0043] (1) Construction of travel time equations In the initial stage of system deployment or when soil conditions change, it is necessary to pre-construct travel-time equations. The specific steps are as follows: S31: In-situ thermal diffusivity calibration. At the site of the buried pipeline to be monitored, select a typical soil area with no pipeline interference and soil structure consistent with the actual buried pipeline area, and bury a thermal pulse generator (such as a miniature electric heating resistor) and at least four temperature sensors at different distances (e.g., 0.2 meters, 0.5 meters and 1.0 meters respectively) to form an in-situ thermal diffusivity calibration device.

[0044] S32: Inversion of soil thermal diffusivity. A transient thermal pulse (with known heat value) is released by a thermal pulse generator for a duration of 1 second. Simultaneously, the time when each temperature sensor detects the occurrence of a temperature peak is recorded. (i.e., peak arrival time). Based on the theory of heat conduction in a semi-infinite medium, the distance from the point where the heat pulse originates to the sensor... With peak arrival time The relationship is: ,Right now,

[0045] in: From the point where the heat pulse occurs to the... The distance between the temperature sensors; The soil thermal diffusivity; For the first The arrival time of the temperature peak was measured by a single sensor; It is a dimensionless constant that depends on the geometry of the heat source and the boundary conditions. For a point heat source in a semi-infinite medium, the theoretical value is... .

[0046] Through at least four different distances The data points were fitted using linear regression (passing through the origin) to obtain the slope. From this, the field-measured value of the soil thermal diffusivity was derived. .

[0047] S33: Establish theoretical functional relationships. Approximate the leakage source as an instantaneous point cold source, and establish the propagation distance from the leakage source to the space node based on heat conduction theory. Time of arrival of temperature disturbance wavefront The functional relationship between them is: the square of the propagation distance is proportional to the product of the wavefront arrival time and the soil thermal diffusivity, that is:

[0048] in: This represents the propagation distance from the leak source to the space node. The soil thermal diffusivity; The arrival time of the temperature disturbance wavefront; It is a dimensionless proportionality constant.

[0049] S34: Construct the complete travel-time equation. Calculate the soil thermal diffusivity obtained in step S32. Substituting the above functional relationship, and comparing the scale constant through finite element numerical simulation. Calibration is performed. The calibration method is as follows: A leakage source at a known location is set in the simulation model, the theoretical wavefront arrival time of each node is calculated, and then the result is calculated by reverse calculation. The optimal value for is obtained. The final complete travel time equation is: ; Among them, the preset coefficient for: Or decomposed into: ; In the formula: Preset coefficients for the travel time equation; , is a geometric factor, determined through finite element numerical simulation calibration; This is the soil thermal diffusivity measured on-site.

[0050] (2) Constructing an isochronous hyperboloid After constructing the travel time equations, an isochronous hyperboloid can be constructed using the wavefront arrival times of each node. The specific steps are as follows: S41: Select a focal node pair. Select any two nodes from the spatial nodes whose wavefront arrival times have been marked. and As a pair of focal nodes, calculate the absolute value of the time difference between the wavefront arrival times of these two nodes: ;in: For nodes With nodes The absolute value of the wavefront arrival time difference; They are nodes and nodes The wavefront arrival time.

[0051] S42: Converted to distance difference. Based on the pre-calibrated soil thermal diffusivity. The absolute value of the time difference is converted into an equivalent difference in the heat wave propagation distance using the travel time equation. Specifically, consider the leakage source to the node. and nodes The distances are respectively and From the travel time equation We can obtain:

[0052] because Given constants (from) and (Confirmed), therefore It can be calculated directly.

[0053] S43: Construct a hyperboloid. Using nodes... and nodes The three-dimensional spatial coordinates are used as the two foci of the hyperboloid, and the constant distance difference is calculated in step S42. Using the real axis length parameter of the hyperboloid (i.e., the difference in distance from any point on the hyperboloid to the two foci equals this constant value), construct a hyperboloid of revolution in three-dimensional space. The mathematical definition of this hyperboloid is: satisfying... All points A set of.

[0054] S44: Repeat construction. Repeat steps S41 to S43 to construct the corresponding isochronous hyperboloid for all selected spatial node pairs (at least all possible combinations of node pairs need to be generated from no fewer than four nodes; for example, four nodes can generate six node pairs). Each hyperboloid is uniquely determined by the spatial coordinates of the two focal nodes and the constant distance difference between the leakage source and these two nodes.

[0055] Step S140: Determine the three-dimensional coordinates of the leakage source based on the spatial intersections of all isochronous hyperboloids.

[0056] This step includes the following sub-steps: S51: Obtain a set of hyperboloids. Based on the selected at least four spatially non-coplanar nodes, use the hyperboloids corresponding to each pair of nodes to obtain a set of hyperboloids (e.g., four nodes can generate six pairs of nodes).

[0057] S52: Initial Mesh Search. Within the three-dimensional space covered by the 3D temperature measurement mesh (e.g., with the pipe axis as the center, the radial range is the distance from the farthest spatial node to the pipe axis plus 1 meter, and the axial range is the distance between the first and last temperature measurement sections plus one section spacing), a uniform mesh search is performed using a preset initial step size (e.g., 0.1 meters) to generate discrete mesh points. For each mesh point... Calculate the distance deviation from the grid point to each isochronous hyperboloid. For nodes... The distance deviation of the constructed hyperboloid is defined as: grid points to the two foci of the hyperboloid and The absolute value of the distance difference, compared with the constant distance difference of the hyperboloid. The absolute value of the difference between them, i.e.: ; in: For grid points For hyperboloids Distance deviation; For point To focus The Euclidean distance; For point To focus The Euclidean distance; The constant distance difference between the hyperboloids.

[0058] S53: Find the initial candidate points. For each grid point... The total deviation value of the grid point is obtained by summing the absolute values ​​of the distance deviations of all hyperboloids corresponding to that grid point.

[0059] Then, iterate through all grid points and find the grid point with the smallest total deviation value, which will be used as the initial candidate point. .

[0060] S54: Local optimization yields candidate intersection points. Using initial candidate points... Centered on a point, the search step size is reduced (e.g., to 0.01 meters) and the local search range is limited (e.g., a cube with a side length of 0.5 meters). The spatial coordinates of the candidate points are iteratively adjusted using a least-squares optimization method. Specifically, the objective function is defined as follows:

[0061] That is, the sum of squared distance deviations of all hyperboloids. This can be solved iteratively using the Levenberg-Marquardt algorithm or the Gauss-Newton method. Minimized Value. During the iteration process, the current value is calculated each time. The objective function value and gradient at the point are updated along the descent direction. until the change in the objective function is less than a set threshold (e.g., ...). (or reaching the maximum number of iterations.) The spatial point after optimization convergence is used as a candidate intersection point. .

[0062] S55: Generate multiple candidate intersection points using random subsets. From all spatial nodes with labeled wavefront arrival times, randomly select four different non-coplanar node subsets multiple times (e.g., randomly select 100 times). For each subset, repeat steps S51 to S54 (i.e., replace all the original nodes with nodes from this subset, construct a hyperboloid, and solve for the optimized intersection points) to obtain multiple candidate intersection points.

[0063] S56: Outlier Removal. Calculate the average Euclidean distance of each candidate intersection point to all other candidate intersection points, denoted as . Calculate the median of the average distances between all candidate intersection points:

[0064] Average distance Exceed Candidate intersection points are identified as outliers and removed. These outliers are usually caused by abnormal wavefront detection or node failure.

[0065] S57: The final coordinates are obtained by weighted averaging. For the retained candidate intersection points, the sum of squared distance deviations corresponding to each intersection point is used as the averaging factor. The reciprocal of the factorial is used as the weight (i.e., the weight is inversely proportional to the fitting error; the smaller the error, the larger the weight). Calculate the weighted average coordinates: ,Will Output the three-dimensional coordinates of the leakage source. To prevent the denominator from being zero, the unit is the same as the constant value. same.

[0066] In some embodiments, after determining the three-dimensional coordinates of the leakage source, the method further includes: Substitute the coordinates into the distance deviation formula for each isochronous hyperboloid to calculate the residual (i.e., the absolute value of the distance deviation) for each hyperboloid. If any residual exceeds a preset tolerance threshold (e.g., 0.2 meters), the positioning result is unreliable, possibly due to an anomaly in wavefront detection at a certain node. In this case, an automatic relocation mode is triggered: among the spatial nodes with marked wavefront arrival times, the hyperboloid corresponding to the node with the largest residual is identified, and this node is excluded (i.e., removed from the node set). Steps S130 and S140 are then re-executed. If the residuals after relocation are all below the threshold, the positioning result is accepted; otherwise, the node with the largest residual is excluded until the condition is met or the number of nodes is less than four, at which point a positioning failure is reported.

[0067] It should be noted that the CO2 transported by the buried CO2 pipeline addressed in this application is in a supercritical or dense phase state. When a leak occurs, high-pressure CO2 is ejected from the leak outlet and expands rapidly. Due to the Joule-Thomson effect, the soil temperature around the leak outlet drops sharply, and the temperature drop is positively correlated with the pressure difference before and after the leak. The wavefront arrival time used in this method is independent of the initial temperature disturbance intensity of the leak source (i.e., the temperature drop), and depends only on the thermal diffusion path length and the soil thermal diffusivity.

[0068] In some embodiments, after obtaining the three-dimensional coordinates of the leak source, these coordinates are projected onto a pre-stored three-dimensional centerline model of the pipeline along a direction perpendicular to the pipeline axis. The pipeline centerline model is obtained from design drawings or actual surveys and stored in the form of a parametric curve. Through projection calculations, the axial mileage station (e.g., K10+235) of the leak point on the pipe wall, the circumferential angle (e.g., 65 degrees clockwise from the top of the pipeline), and the radial penetration depth (i.e., whether the leak point is on the upper wall, side wall, or bottom of the pipeline) can be obtained. Simultaneously, on the human-machine interface of the monitoring terminal, a three-dimensional temperature measurement grid, the wavefront arrival time of each spatial node (using color depth to indicate early or late time), and the location reliability are displayed in a three-dimensional graphical manner, facilitating an intuitive understanding of the leak situation by maintenance personnel.

[0069] Furthermore, when the number of selected spatial nodes is greater than three, this method also provides a multi-subset voting strategy as an optional enhanced localization method. Specifically, four non-coplanar nodes are randomly selected from all nodes with labeled wavefront arrival times to form a localization subset, generating multiple independent subsets (e.g., 100 to 500). For each subset, candidate coordinates of the leakage source are calculated according to steps S130 and S140, resulting in a set of candidate coordinates. Then, DBSCAN clustering is performed on the candidate coordinates output from all subsets, and the cluster center containing the most candidate points is selected as the final leakage source coordinates. Simultaneously, the voting consistency rate is output, which is the ratio of the number of candidate points in the largest cluster to the total number of subsets. This ratio can be used as a localization reliability indicator (e.g., a voting consistency rate of 85% indicates a high degree of localization reliability). When the voting consistency rate is below 50%, the system issues an alarm indicating the possible presence of multiple leakage sources or complex interference.

[0070] Corresponding to the above method, this application also provides a device for locating leak sources in buried CO2 pipelines, such as... Figure 2 As shown, the device includes: Acquisition unit 210 is used to acquire the time sequence of temperature changes of each spatial node in the three-dimensional temperature measurement grid around the buried pipeline; The detection unit 220 is used to detect the arrival time of the wavefront when the temperature drop rate first exceeds a preset rate threshold for each spatial node, based on the corresponding temperature change time sequence. The acquisition unit 210 is also used to select at least four spatial nodes that are not coplanar in spatial location, and to solve for the isochronous hyperboloid from the leakage source to each spatial node based on the time difference between the arrival times of the wavefronts of the selected spatial nodes and the travel time equations pre-built based on the soil heat diffusion model. Unit 230 is used to determine the three-dimensional coordinates of the leakage source based on the spatial intersections of all isochronous hyperboloids.

[0071] The functions of each functional unit of the buried CO2 pipeline leakage source locating device provided in the above embodiments of this application can be realized through the above-described methods and steps. Therefore, the specific working process and beneficial effects of each unit in the buried CO2 pipeline leakage source locating device provided in the embodiments of this application will not be repeated here.

[0072] 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.

[0073] 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 time sequence of temperature changes at each spatial node in the three-dimensional temperature measurement grid around the buried pipeline; For each spatial node, based on the corresponding temperature change time sequence, the arrival time of the wavefront when the temperature drop rate first exceeds the preset rate threshold is detected; At least four spatially non-coplanar nodes are selected. Based on the time difference between the wavefront arrival times of the selected spatial nodes and the travel time equations pre-built based on the soil heat diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are solved. The three-dimensional coordinates of the leakage source are determined based on the spatial intersections of all isochronous hyperboloids.

[0074] 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.

[0075] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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 any of the above-described methods for locating leak sources in buried CO2 pipelines.

[0080] 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 any of the above embodiments of the method for locating leak sources in buried CO2 pipelines.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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 locating leakage sources in buried CO2 pipelines, characterized in that, The method includes: Obtain the time sequence of temperature changes at each spatial node in the three-dimensional temperature measurement grid around the buried pipeline; For each spatial node, based on the corresponding temperature change time sequence, the arrival time of the wavefront when the temperature drop rate first exceeds the preset rate threshold is detected; At least four spatially non-coplanar nodes are selected. Based on the time difference between the wavefront arrival times of the selected spatial nodes and the travel time equations pre-built based on the soil heat diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are solved. The three-dimensional coordinates of the leakage source are determined based on the spatial intersections of all isochronous hyperboloids.

2. The method as described in claim 1, characterized in that, The three-dimensional temperature measurement grid is constructed in the following way: At least four temperature measurement sections are arranged at equal intervals along the axial direction of the buried pipeline. At least four radial temperature measurement arms are arranged at equal angles around the circumference of the pipeline in each temperature measurement section. At least four temperature sensing nodes are arranged at equal intervals on each radial temperature measurement arm extending outward from the outer surface of the pipe wall.

3. The method as described in claim 1, characterized in that, For each spatial node, the arrival time of the wavefront at which its temperature decrease rate first exceeds a preset threshold is detected, including: For the temperature change time series of the corresponding spatial nodes, a sliding median filter is performed with a preset window length to obtain the filtered time series temperature value sequence. Calculate the temperature difference between the current sampling time and the previous sampling time in the filtered sequence, divide the temperature difference by the sampling time interval, and obtain the instantaneous temperature drop rate at the current sampling time. When the instantaneous temperature drop rate of three consecutive sampling times exceeds the preset rate threshold, and the current filtered temperature value is lower than the historical normal operating temperature average minus twice the standard deviation, the first sampling time among the three consecutive sampling times is marked as the wavefront arrival time of the spatial node.

4. The method as described in claim 1, characterized in that, The methods for determining the preset rate threshold include: The standard deviation of temperature fluctuation is calculated based on the historical temperature change sequence of each spatial node within a preset time period before the leak occurs. The larger of three times the standard deviation of the temperature fluctuation and the configured minimum sensitive cooling rate is determined as the preset rate threshold; wherein, the minimum sensitive cooling rate is pre-calibrated based on the initial pressure difference of CO2 leakage and the range of soil thermophysical properties.

5. The method as described in claim 1, characterized in that, The travel-time equations pre-constructed based on the soil thermal diffusion model are obtained in the following way: At the site of the buried pipeline to be monitored, select a typical soil area free from pipeline interference, and bury a thermal pulse generator and at least four temperature sensors at different distances to form an in-situ thermal diffusivity calibration device. A thermal pulse of a preset duration is released by a thermal pulse generator. The arrival time of the temperature peak detected by each temperature sensor is recorded. Based on the relationship between the distance from the thermal pulse generation point to each sensor and the square of the peak arrival time, the soil thermal diffusivity is calculated. By approximating the leakage source as an instantaneous point cold source, a functional relationship between the propagation distance from the leakage source to the spatial node and the arrival time of the temperature disturbance wavefront is established based on the heat conduction theory. Substituting the soil thermal diffusivity obtained from the inverse equation into the functional relationship, a complete travel time equation is constructed.

6. The method as described in claim 1 or 5, characterized in that, Based on the travel-time equations pre-constructed using the soil thermal diffusion model, the isochronous hyperboloids from the leakage source to each spatial node are obtained by solving. From the spatial nodes with marked wavefront arrival times, select any two nodes as a pair of focal nodes and calculate the absolute value of the time difference between the wavefront arrival times of these two nodes. Based on the soil thermal diffusivity pre-calibrated on site, the absolute value of the time difference is converted into an equivalent thermal wave propagation distance difference through the travel time equation. This distance difference represents the difference in spatial distance from the leakage source to the two focal nodes. Using the three-dimensional spatial coordinates of these two focal nodes as the two foci of the hyperboloid, and using the constant distance difference as the real axis length parameter of the hyperboloid, an isochronous hyperboloid is constructed in three-dimensional space. Repeat the above process until all spatial node pairs generate corresponding isochronous hyperboloids.

7. The method as described in claim 1, characterized in that, Based on the spatial intersections of all isochronous hyperboloids, the three-dimensional coordinates of the leakage source are determined, including: Based on the isochronous hyperboloids constructed for each pair of nodes corresponding to at least four spatially non-coplanar nodes, the spatial point with the smallest total deviation is searched within the spatial range covered by the three-dimensional temperature measurement grid as the initial candidate point. Centered on the initial candidate point, its spatial coordinates are iteratively adjusted using the least squares optimization method to minimize the sum of squared distance deviations from the point to all hyperboloids, thus obtaining a candidate intersection point; From all spatial nodes with marked wavefront arrival times, randomly select multiple different non-coplanar node subsets and repeat the above process to obtain multiple candidate intersection points. Calculate the average spatial distance between each candidate intersection point and other candidate intersection points, and remove candidate intersection points whose average distance is significantly greater than that of the other intersection points as outliers. For the retained candidate intersection points, weights are assigned according to the sum of squared distance deviations corresponding to each intersection point, and a weighted average is performed to obtain the three-dimensional coordinates of the leakage source.

8. A device for locating leak sources in buried CO2 pipelines, characterized in that, The device includes: The acquisition unit is used to acquire the time sequence of temperature changes of each spatial node in the three-dimensional temperature measurement grid around the buried pipeline. The detection unit is used to detect the arrival time of the wavefront when the temperature drop rate first exceeds a preset rate threshold for each spatial node, based on the corresponding temperature change time sequence. The acquisition unit is also used to select at least four spatially non-coplanar spatial nodes, and based on the time difference between the wavefront arrival times of the selected spatial nodes, combined with the travel time equations pre-built based on the soil heat diffusion model, to solve for the isochronous hyperboloids from the leakage source to each spatial node. The determination element is used to determine the three-dimensional coordinates of the leakage source based on the spatial intersections of all isochronous hyperboloids.

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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