A pipeline leak range prediction method and device

Abstract

The embodiment of the application provides a pipeline leakage range prediction method and device, which is suitable for a pipeline leakage range prediction system, and comprises the following steps: acquiring a pipeline whole-line map and remote sensing images of the pipeline whole-line map, determining a high-risk leakage area from the pipeline whole-line map according to topographic features of the remote sensing images; determining a pipeline leakage point and a leakage fluid volume of the pipeline in the high-risk leakage area; taking the pipeline leakage point as a starting point, calculating a flow distribution coefficient of each sub-area in the high-risk leakage area according to elevation data of the high-risk leakage area; and predicting a pipeline leakage range in the high-risk leakage area according to the pipeline leakage point, the leakage fluid volume and the flow distribution coefficient of each sub-area. The above method is used for accurately and quickly predicting a pipeline leakage range, so that a leakage control strategy can be quickly taken, and pollution and loss of the environment caused by a leakage accident are reduced.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and apparatus for predicting the range of pipeline leakage. Background Technology

[0002] Leaks are one of the major malfunctions in oil pipeline operations, causing severe environmental impacts and substantial economic losses. Geological disasters are also a contributing factor to oil pipeline leaks. For example, due to the complex geological conditions in mountainous areas, landslides and mudslides are frequent occurrences, posing a significant potential threat to oil pipelines. Currently, there are no effective methods to address such leaks.

[0003] Therefore, there is an urgent need for a method and device for predicting the extent of pipeline leaks, so as to accurately and quickly predict the extent of pipeline leaks, enable the rapid implementation of leak control strategies, and reduce the environmental pollution and losses caused by leak accidents. Summary of the Invention

[0004] This application provides a method and apparatus for predicting the range of pipeline leaks, which can accurately and quickly predict the range of pipeline leaks, enabling rapid implementation of leak control strategies to reduce environmental pollution and losses caused by leak accidents.

[0005] In a first aspect, in one or more embodiments, this application provides a method for predicting the extent of pipeline leakage, applicable to a pipeline leakage extent prediction system, comprising:

[0006] Acquire a full-line map of the pipeline and remote sensing images of the full-line map of the pipeline; based on the topographic features of the remote sensing images, identify high-risk leakage areas from the full-line map of the pipeline.

[0007] Determine the pipeline leak point and the amount of leaking fluid in the high-risk leak area;

[0008] Starting from the pipeline leak point, the flow distribution coefficient of each sub-region in the high-risk leak area is calculated based on the elevation data of the high-risk leak area.

[0009] The extent of pipeline leakage in the high-risk leakage area is predicted based on the pipeline leak point, the amount of leaked fluid, and the flow distribution coefficient of each sub-region.

[0010] The above method acquires a full-line map of the pipeline and remote sensing imagery of the pipeline. Based on the topographic features of the remote sensing imagery, high-risk leakage areas are identified from the full-line pipeline map. This ensures that the topographic features of the acquired full-line pipeline map are up-to-date, further guaranteeing the accuracy of identifying high-risk leakage areas. Based on the elevation data of the high-risk leakage areas, the flow distribution coefficients of each sub-region within the high-risk leakage area, starting from the pipeline leak point, are determined. Furthermore, the pipeline leakage range is predicted based on the leakage fluid volume and the flow distribution coefficients of each sub-region within the high-risk leakage area. Thus, effective control can be implemented based on the predicted pipeline leakage range, reducing environmental pollution and losses caused by leakage accidents.

[0011] In one or more embodiments, high-risk leakage areas are identified from the pipeline map based on the topographic features of the remotely sensed imagery, including:

[0012] Based on the topographic features of the remote sensing image, pipeline branches are determined from the full pipeline map;

[0013] For any pipeline branch, determine the historical disaster information and disaster prediction information of the pipeline layout area of ​​the pipeline branch; if the similarity between the historical disaster information and the disaster prediction information meets the set conditions, then determine the pipeline layout area as a high-risk leakage area; wherein, the disaster prediction information includes weather prediction information and geological disaster prediction information, and the historical disaster information includes geological disaster information and weather information when a geological disaster occurs.

[0014] In the above method, the similarity between disaster prediction information and historical disaster information of the pipeline deployment area can determine whether a leak is likely to occur in that area. If the similarity is greater than a set threshold, it indicates that the pipeline deployment area has a high probability of leakage, and is therefore a high-risk leak area. In this way, high-risk leak areas can be accurately predicted.

[0015] In one or more embodiments, the flow distribution coefficient of each sub-region within the high-risk leakage area is calculated based on the elevation data of the high-risk leakage area, including:

[0016] Based on the elevation data of the high-risk leakage area, the depression area of ​​the high-risk leakage area is determined;

[0017] The depression area was filled in to obtain the elevation data after the filling process.

[0018] Based on the elevation data after the leveling process, the flow distribution coefficient of each sub-region in the high-risk leakage area is calculated.

[0019] In the above method, depressions are identified based on the elevation data of the high-risk leakage area, and these depressions are filled in to obtain the elevation data after filling. Thus, calculating the flow distribution coefficients of each sub-region within the high-risk leakage area based on the filled elevation data ensures the continuity of fluid flow during the calculation process and improves the accuracy of the calculated flow distribution coefficients.

[0020] In one or more embodiments, before determining the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area, the method includes: dividing the high-risk leakage area into grids to obtain each cell corresponding to the high-risk leakage area; determining the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area includes: for any cell, taking the cell as the center cell of a 3*3 cell grid, and the 8 cells surrounding the center cell as neighboring cells; determining a first elevation value of the center cell in the elevation data, and a second elevation value of the neighboring cells in the elevation data; obtaining a relative slope based on the difference between the first elevation value and the second elevation value; if there is a relative slope less than 0 among the relative slopes of the center cell and each neighboring cell, then a depression area exists in the 3*3 cell grid.

[0021] In the above method, the high-risk leakage area is divided into sub-regions, with each grid cell serving as a sub-region. The flow distribution coefficient of each cell is calculated until the flow distribution coefficient of cells in the same direction is 0 or close to 0. The area formed by connecting these cells with flow distribution coefficients of 0 or close to 0 can then be considered the pipeline leakage range. Specifically, using a 3x3 cell approach, the relative slope between the central cell and its eight neighboring cells is obtained. This determines whether the central cell is a depression area. Similarly, this process is applied to all cells within the high-risk leakage area, analyzing each cell individually using a 3x3 cell approach to identify all depression areas within the high-risk leakage area. These depression areas can then be filled in to ensure the accuracy of the obtained flow distribution coefficients.

[0022] In one or more embodiments, the depression area is filled to obtain the elevation data after filling, including: for any isolated depression area, a third elevation value is assigned to the center cell to obtain the preliminary filled elevation data, wherein the isolated depression area is a depression area where the maximum value of the relative slope between the center cell and each neighboring cell is less than 0, and the third elevation value is the lowest second elevation value among all second elevation values;

[0023] Based on the preliminary leveled elevation data, an interconnected depression area is determined. The interconnected depression area is a depression area composed of multiple ring-shaped cells and cells within the ring. The multiple ring-shaped cells are connected to form a closed loop. There is at least one cell within the closed loop. The elevation value of any cell within the loop is lower than a fourth elevation value. The fourth elevation value is the lowest elevation value among the multiple ring-shaped cells.

[0024] The fourth elevation value is assigned to the cell within the ring.

[0025] In the above method, if the maximum relative slope between the central cell and its neighboring cells is less than 0, then the first elevation value of the central cell is determined to be less than the second elevation value of any neighboring cell. That is, if the central cell is the lowest in the 3x3 cell grid, then it is confirmed as an isolated depression area within the 3x3 cell grid. Analytically, simulating fluid flow trends, to ensure fluid can flow outside the depression, the lowest second elevation value among the eight neighboring cells is used as the third elevation value. This third elevation value is assigned to the central cell, so that when simulating fluid flow trends, the fluid in the central cell can flow to the lowest neighboring cell. This ensures the normal processing and analysis of pipeline leakage range prediction. After preliminary filling of the isolated depression area, preliminary filled elevation data is obtained. At this stage, the preliminary filled elevation data may still contain interconnected depression areas, which are then identified. Similarly, to simulate fluid flow trends and allow fluid to flow outside the interconnected depression area, the lowest value among the ring-shaped cells is selected and designated as the fourth elevation value. This fourth elevation value is then assigned to the cells within the ring. When simulating fluid flow trends, fluid can flow from this inner ring cell to the lowest ring-shaped cell. This ensures the accurate prediction and analysis of pipeline leakage range.

[0026] In one or more embodiments, obtaining a relative slope based on the difference between the first elevation value and the second elevation value includes:

[0027]

[0028]

[0029] Among them, G x,y (m,n) is the relative gradient between the central cell and the (m,n)th neighboring cell, N x,y (m,n) is the difference between the first elevation value of the central cell and the second elevation value of the (m,n)th neighboring cell, L x,y (m,n) is the distance between the center cell and its (m,n)th neighboring cell. For cells in the horizontal or vertical direction, Lx,y The value of (m,n) is 1. For diagonal elements, L x,y The value of (m,n) is

[0030] In the above method, the relative slope is determined by processing the cells in a 3x3 grid, based on the positional relationship, distance, and elevation value of each cell. This method results in higher accuracy of the relative slope.

[0031] In one or more embodiments, each cell corresponding to the high-risk leakage area corresponds to a sub-area within the high-risk leakage area, and the flow allocation coefficient is calculated as follows:

[0032]

[0033] Where, d i Let tanβ be the fluid distribution coefficient of the i-th neighboring cell corresponding to the leaking fluid. i Let be the slope gradient from the center cell to the i-th neighboring cell, where the i-th slope gradient is the ratio of the relative slope of the i-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; p is the leakage fluid allocation weight (p > 0 as a condition, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives); the i-th neighboring cell is any one of the eight neighboring cells; tanβ j Let Q be the slope gradient from the center cell to the j-th neighboring cell, where the j-th slope gradient is the ratio of the relative slope of the j-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; i Q is the weighting factor for the contour length of the i-th neighboring cell. j The contour length weighting factor is the length of the contour line in the j-th neighboring cell.

[0034] In the above method, each cell corresponding to the high-risk leakage area corresponds to a sub-region within that high-risk leakage area. The 3x3 cell approach can still be used to determine the slope gradient from the central cell to each neighboring cell (the slope gradient of a neighboring cell is the ratio of its relative slope to the sum of the relative slopes of its eight neighboring cells. In other words, the slope gradient of a neighboring cell characterizes the relative slope between that cell and the others. To put it simply, a larger slope gradient means a greater effect of gravitational potential energy, and thus a greater impact on the flow distribution coefficient). This allows for a more accurate flow distribution coefficient. Furthermore, p is added as the leakage fluid distribution weight (p > 0, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives) and Q is added as the contour length weighting factor. By considering the magnitude of the relative slope and the contour length weighting factor of the neighboring cells, an even more accurate flow distribution coefficient can be obtained.

[0035] Secondly, in one or more embodiments, this application provides a pipeline leakage range prediction device, applicable to a pipeline leakage range prediction system, comprising:

[0036] The acquisition module is used to acquire a full-line map of the pipeline and remote sensing images of the full-line map of the pipeline, and to determine high-risk leakage areas from the full-line map of the pipeline based on the topographic features of the remote sensing images.

[0037] The determination module is used to determine the pipeline leak point and the leakage fluid volume in the high-risk leak area; starting from the pipeline leak point, it calculates the flow distribution coefficient of each sub-region in the high-risk leak area based on the elevation data of the high-risk leak area; and predicts the pipeline leak range in the high-risk leak area based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficient of each sub-region.

[0038] Thirdly, in one or more embodiments, this application also provides a computing device, comprising: a memory for storing a program; and a processor for invoking the program stored in the memory and executing the method described in various possible designs of the first aspect according to the obtained program.

[0039] Fourthly, in one or more embodiments, this application also provides a computer-readable non-volatile storage medium including a computer-readable program that, when read and executed by a computer, causes the computer to perform the method described in various possible designs of the first aspect.

[0040] These or other implementations of this application will become clearer and easier to understand in the following description of the embodiments. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A schematic diagram of an architecture for predicting the range of pipeline leakage provided in an embodiment of this application;

[0043] Figure 2 A schematic flowchart illustrating a pipeline leakage range prediction method provided in an embodiment of this application;

[0044] Figure 3 A schematic flowchart illustrating a pipeline leakage range prediction method provided in an embodiment of this application;

[0045] Figure 4 This is a schematic diagram of a pipeline leakage range prediction device provided in an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Oil transportation refers to the transport of abundant but low-demand oil and gas resources from one location to a region with higher demand but relatively scarce resources, using various methods (mainly pipelines). Pipeline transportation of oil and gas significantly reduces transportation time and costs compared to other methods. In pipeline transportation of oil and gas, the pipeline must first be constructed, starting from areas with low demand or oil and gas extraction sites, and then extending to regions with higher demand but relatively scarce resources. Consequently, some pipelines may be laid in dangerous and complex areas, such as mountainous regions with complex terrain. The geological conditions in these areas may lead to frequent geological disasters, such as landslides and mudslides. These disasters pose a significant threat to the safe transport of oil through pipelines. Once a leak occurs, it has an extremely detrimental impact on the ecological environment of the mountainous area. If the mountainous area also contains rivers, the damage caused by the pipeline leak is incalculable. Therefore, based on this existing problem, in order to reduce the pollution of the ecological environment caused by pipeline leaks, this application provides a pipeline leak range prediction technology in one or more embodiments. This technology is used to accurately and quickly predict the range of pipeline leaks, enabling rapid implementation of leak control strategies to reduce the pollution and losses caused by leak accidents. It should be noted that the pipeline leakage range prediction technology of this application involves fluid leakage. The fluid can be any kind of fluid. The example of oil in the above application does not limit the implementation of this solution. It is understood that any kind of fluid transported by pipeline can be predicted by the pipeline leakage range prediction technology of this application. All implementation methods within the concept of this application should be within the protection scope of this solution.

[0048] Based on the above, embodiments of this application provide a system architecture for a pipeline leakage range prediction system, such as... Figure 1 As shown, it includes: a data acquisition module, a data analysis module, and an early warning module. Specifically, the data acquisition module is used to acquire a complete map of the pipeline and remote sensing imagery of the complete pipeline map.

[0049] In one example, a full-line pipeline map can be obtained from the national pipeline management platform, the pipeline management department's platform, etc. The latest full-line pipeline map can be obtained from the corresponding platform, website, or application as needed. Remote sensing imagery can be obtained from the national satellite data management platform, remote sensing marketplace, or related websites. The latest remote sensing imagery can also be obtained from the corresponding platform, website, or application as needed. It should be noted that there are no specific restrictions on the sources from which the full-line pipeline map and remote sensing imagery are obtained.

[0050] The data analysis module is used to identify high-risk leakage areas from the pipeline map based on the topographic features of remote sensing images, determine the pipeline leak point and the leakage fluid volume in the high-risk leakage area; calculate the flow distribution coefficient of each sub-region in the high-risk leakage area based on the elevation data of the pipeline leak point as the starting point; and predict the pipeline leakage range in the high-risk leakage area based on the pipeline leak point, leakage fluid volume, and flow distribution coefficient of each sub-region.

[0051] In one example, based on the elevation data of the high-risk leak area and the elevation data of the pipeline leak point, sub-regions within the high-risk leak area with elevations higher than the pipeline leak point can be considered to have a flow allocation coefficient of 0. Sub-regions within the high-risk leak area with elevations lower than the pipeline leak point can have their flow allocation coefficients determined based on the ratio of their elevation data to that of the pipeline leak point, and the distance between the area and the pipeline leak point. Specifically, the smaller the ratio of the area's elevation data to that of the pipeline leak point, the smaller the flow allocation coefficient; the larger the ratio, the larger the flow allocation coefficient; the greater the distance between the area and the pipeline leak point, the smaller the flow allocation coefficient; and the smaller the distance, the larger the flow allocation coefficient. The influence of the ratio of the distance to the pipeline leak point and the elevation data on the flow allocation coefficient can be established using exponential, power, and trigonometric functions.

[0052] In another example, the high-risk leak area can be filled in first, allowing the data analysis module to determine the leak trend based on the fluid flow direction. To ensure continuous fluid flow during analysis, the elevation data of the depressions within the high-risk leak area can be filled in. First, the high-risk leak area can be divided into grids to obtain the corresponding cells. For any given cell, the cell is designated as the center cell of a 3x3 grid, and the eight cells surrounding it are designated as neighboring cells. This creates a "cell" for data analysis. The first elevation value of the center cell and the second elevation value of each neighboring cell are determined. Further, the relative slope is calculated based on the difference between the first and second elevation values, continuing until eight relative slopes are obtained between the center cell and each neighboring cell. If any of these eight relative slopes is less than 0, the area containing the center cell is considered a depression. This confirms the depression areas within the high-risk leak area. High-risk leak areas can be categorized into two types: isolated depressions and interconnected depressions. The method for filling these depressions involves first filling isolated depressions to obtain preliminary elevation data for the filled high-risk leak area. Then, interconnected depressions are filled within the preliminarily filled high-risk leak area. Specifically:

[0053] For any isolated depression area (where the maximum relative slope between the central cell and its neighboring cells is less than 0, the 3x3 cell is identified as an isolated depression area), the central cell is filled by assigning the third elevation value (the lowest of the second elevation values) to it. This completes the initial filling of the central cell, yielding preliminary filled elevation data. Based on this preliminary filled elevation data, interconnected depression areas are identified (depression areas composed of multiple ring-shaped cells and cells within the rings, where multiple ring-shaped cells connect to form a closed loop, and at least one cell within the loop contains a cell whose elevation value is lower than the fourth elevation value, which is the lowest of the multiple ring-shaped cells). The fourth elevation value is assigned to the cell within the loop. This completes the filling of this depression area. Thus, the elevation data for each sub-area (cell) of the high-risk leakage area after filling are obtained.

[0054] Furthermore, the flow distribution coefficient can be analyzed based on the elevation data of each sub-region (cell) of the high-risk leakage area after leveling, the location of each cell, the pipeline leak point, and the leakage fluid volume. The flow distribution coefficient is calculated as follows:

[0055]

[0056] Where, d iLet tanβ be the fluid distribution coefficient of the i-th neighboring cell corresponding to the leaking fluid. i Let be the slope gradient from the center cell to the i-th neighboring cell, where the i-th slope gradient is the ratio of the relative slope of the i-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; p is the leakage fluid allocation weight (p > 0 as a condition, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives); the i-th neighboring cell is any one of the eight neighboring cells; tanβ j Let Q be the slope gradient from the center cell to the j-th neighboring cell, where the j-th slope gradient is the ratio of the relative slope of the j-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; i Q is the weighting factor for the contour length of the i-th neighboring cell. j The contour length weighting factor is the length of the contour line in the j-th neighboring cell.

[0057] In the two examples above, based on the positional relationship between each cell and the pipe leak point, the cells adjacent to the leak point are analyzed first, and then one or more cells are analyzed outwards sequentially. Specifically, the fluid volume of a cell = flow distribution coefficient * the fluid volume of the cell above it (the cell closest to the leak point). Thus, the extent of the pipe leak can be determined based on whether the fluid volume of a cell is 0 or close to 0.

[0058] Additionally, it should be noted that the two examples above are merely illustrative of how to calculate the flow distribution coefficient of each sub-region within a high-risk leakage area based on its elevation data, and how to predict the pipeline leakage range within the high-risk leakage area based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficient of each sub-region. They do not limit the specific implementation methods. It is understood that any implementation method based on the same concept is within the scope of protection of this solution.

[0059] Based on the above system architecture, embodiments of this application provide a method for predicting the range of pipeline leakage, such as... Figure 2 As shown, a pipeline leakage range prediction system includes:

[0060] Step 201: Obtain a full pipeline map and a remote sensing image of the full pipeline map; based on the topographic features of the remote sensing image, identify high-risk leakage areas from the full pipeline map.

[0061] Step 202: Determine the pipeline leak point and the amount of leaking fluid in the high-risk leak area;

[0062] Here, a high-risk leakage area can be an area prone to disasters such as landslides, mudslides, or earthquakes. There are no restrictions on the specific methods for determining a high-risk leakage area.

[0063] Pipeline leak points can be pipeline locations corresponding to areas with severe landslides, areas with severe debris flows, or areas where the ground is prone to fracturing or arching during earthquakes. There are no restrictions on the specific methods for determining pipeline leak points.

[0064] The amount of fluid leaking from a pipeline can be determined based on relevant information such as the fluid flow rate at the leak point, the size of the leak point, the pipeline material, the time since the pipeline was laid, the possible disaster types and severity in high-risk leak areas, etc. There are no specific restrictions on the analysis method for the amount of fluid leaking from a pipeline.

[0065] Step 203: Starting from the pipeline leak point, calculate the flow distribution coefficient of each sub-region in the high-risk leak area based on the elevation data of the high-risk leak area;

[0066] Here, the sub-regions can be divided based on elevation data of areas along any fluid flow direction (e.g., the pipe leak point can be considered as a dot, with each 30° / 10° / 40° / 5° angle corresponding to a fluid flow direction; or it can be non-uniform angles, such as alternating 30° and 10° angles, each corresponding to a fluid flow direction). For example, if the elevation data shows a decreasing trend along a fluid flow direction, it constitutes a sub-region.

[0067] Furthermore, the flow distribution coefficient of the sub-region can be determined based on the ratio of the elevation data of the sub-region to the elevation data of the pipeline leak point, as well as the distance between the sub-region and the pipeline leak point.

[0068] The sub-region division method can also be to divide the high-risk leakage area into cells and determine the flow distribution coefficient for that cell. There are no specific restrictions on the sub-region division method here.

[0069] Step 204: Predict the pipeline leakage range in the high-risk leakage area based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficient of each sub-region.

[0070] The above method acquires a full-line map of the pipeline and remote sensing imagery of the pipeline. Based on the topographic features of the remote sensing imagery, high-risk leakage areas are identified from the full-line pipeline map. This ensures that the topographic features of the acquired full-line pipeline map are up-to-date, further guaranteeing the accuracy of identifying high-risk leakage areas. Based on the elevation data of the high-risk leakage areas, the flow distribution coefficients of each sub-region within the high-risk leakage area, starting from the pipeline leak point, are determined. Furthermore, the pipeline leakage range is predicted based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficients of each sub-region within the high-risk leakage area. Thus, effective control can be implemented based on the predicted pipeline leakage range, reducing environmental pollution and losses caused by leakage accidents.

[0071] Based on the above method and process, this application provides another method for predicting the range of pipeline leaks. Step 201: Determine high-risk leak areas from the pipeline map based on the topographic features of the remote sensing image, including: determining pipeline branches from the pipeline map based on the topographic features of the remote sensing image; for any pipeline branch, determining historical disaster information and disaster prediction information of the pipeline layout area of ​​the pipeline branch; if the similarity between the historical disaster information and the disaster prediction information meets a set condition, then the pipeline layout area is determined to be a high-risk leak area; wherein, the disaster prediction information includes weather prediction information and geological disaster prediction information, and the historical disaster information includes geological disaster information and weather information at the time of the geological disaster. That is, a high-risk leak area can be determined based on the similarity between the disaster prediction information and the historical disaster information. When the similarity is greater than a set threshold, the disaster-affected area can be considered a high-risk leak area. Correspondingly, in the above system architecture, the data analysis module can be used to obtain historical disaster information and disaster prediction information. In one example, historical disaster information can be obtained from the national disaster data management platform, the regional disaster information management platform, and relevant websites; the specific channels for obtaining historical disaster information are not limited here. Disaster prediction information can be obtained from the national disaster prediction platform, the regional disaster prediction platform, and relevant websites; the specific channels for obtaining disaster prediction information are not limited here.

[0072] Based on the above method and process, this application provides another method for predicting the range of pipeline leaks. Step 203: Calculate the flow distribution coefficient of each sub-region within the high-risk leak area based on the elevation data of the high-risk leak area. This includes: determining the depression areas within the high-risk leak area based on the elevation data; filling the depression areas to obtain the filled elevation data; and calculating the flow distribution coefficient of each sub-region within the high-risk leak area based on the filled elevation data. In one example, the data analysis module can fill the depressions in the elevation data to obtain the filled elevation data. This ensures that when analyzing fluid flow based on this elevation data, the fluid flow path starting from the pipeline leak point is continuous. This prevents the fluid flow path from being interrupted due to low elevation data in the depression areas, thus failing to accurately reflect the fluid flow path and leading to inaccurate prediction of the pipeline leak range. In other words, it can improve the accuracy of pipeline leak range prediction.

[0073] Based on the above method and process, this application provides another method for predicting the range of pipeline leakage. Before determining the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area, the method includes: dividing the high-risk leakage area into grids to obtain each cell corresponding to the high-risk leakage area; determining the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area, including: for any cell, taking the cell as the center cell of a 3*3 grid, and the 8 cells surrounding the center cell as neighboring cells; determining the first elevation value of the center cell in the elevation data, and the second elevation value of the neighboring cells in the elevation data; obtaining the relative slope based on the difference between the first elevation value and the second elevation value; if there is a relative slope less than 0 among the relative slopes of the center cell and each neighboring cell, then there is a depression area in the 3*3 grid. In other words, the method for identifying depressions can be as follows: divide the high-risk leakage area into grid cells, using 3x3 cells as "units," and determine the depression area based on the relationship between the elevation data of the central cell and its neighboring cells. That is, if there is a relative slope less than 0 between the central cell and its neighboring cells, then a depression area exists within the 3x3 cell. This method can quickly and easily identify depression areas.

[0074] Based on the above methods and procedures, this application provides a method for filling depressions, which fills the depression area to obtain the elevation data after filling, including:

[0075] For any isolated depression area, the third elevation value is assigned to the central cell to obtain the preliminary filled elevation data. The isolated depression area is the depression area where the maximum value of the relative slope between the central cell and each neighboring cell is less than 0. The third elevation value is the lowest second elevation value among all the second elevation values.

[0076] Based on the preliminary leveled elevation data, an interconnected depression area is determined. The interconnected depression area is a depression area composed of multiple ring-shaped cells and cells within the ring. The multiple ring-shaped cells are connected to form a closed loop. There is at least one cell within the closed loop. The elevation value of any cell within the loop is lower than a fourth elevation value. The fourth elevation value is the lowest elevation value among the multiple ring-shaped cells.

[0077] The fourth elevation value is assigned to the cell within the ring. That is, preliminary leveling: if the maximum value of the relative slope between the central cell and each neighboring cell in the depression area is less than 0, then the lowest second elevation value among the neighboring cells is selected, and this second elevation value is assigned to the first elevation value of the central cell to obtain the preliminary leveling elevation data.

[0078] A second leveling process is performed based on the initially leveled elevation data: If multiple looping cells connect to form a closed loop, and at least one cell within the closed loop has an elevation value lower than a fourth elevation value (the lowest among the looping cells), then the looping cells and the cells within the loop constitute an interconnected depression area. The fourth elevation value is then assigned to the cell within the loop, completing the leveling of the interconnected depression area. This ensures that the elevation of the fluid flow path, originating from the pipeline leak point, always descends from high to low, conforming to the fluid flow trend and guaranteeing the accuracy of the predicted pipeline leak range.

[0079] Based on the above methods and processes, this application provides a relative slope, which is obtained by calculating the relative slope based on the difference between the first elevation value and the second elevation value, including:

[0080]

[0081]

[0082] Among them, G x,y (m,n) is the relative gradient between the central cell and the (m,n)th neighboring cell, N x,y (m,n) is the difference between the first elevation value of the central cell and the second elevation value of the (m,n)th neighboring cell, L x,y (m,n) is the distance between the center cell and its (m,n)th neighboring cell. For cells in the horizontal or vertical direction, Lx,y The value of (m,n) is 1. For diagonal elements, L x,y The value of (m,n) is In other words, the relative slope is obtained by considering both the elevation values ​​and the distance relationships between cells. This ensures that the relative slope incorporates both elevation and distance factors, guaranteeing the accuracy of the analysis of depression areas based on the relative slope, as well as the accuracy of the flow distribution coefficient calculation.

[0083] Based on the above methods and processes, this application embodiment provides a relative gradient, where each cell corresponding to the high-risk leakage area corresponds to a sub-area within the high-risk leakage area, and the flow distribution coefficient is calculated as follows:

[0084]

[0085] Where, d i Let tanβ be the fluid distribution coefficient of the i-th neighboring cell corresponding to the leaking fluid. i Let be the slope gradient from the center cell to the i-th neighboring cell, where the i-th slope gradient is the ratio of the relative slope of the i-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; p is the leakage fluid allocation weight (p > 0 as a condition, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives); the i-th neighboring cell is any one of the eight neighboring cells; tanβ j Let Q be the slope gradient from the center cell to the j-th neighboring cell, where the j-th slope gradient is the ratio of the relative slope of the j-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; i Q is the weighting factor for the contour length of the i-th neighboring cell. j The contour length weighting factor is set to the j-th neighboring cell. Here, the slope ratio is the ratio of the relative slope of the corresponding neighboring cell to the sum of the relative slopes of the eight neighboring cells. The relative slope can characterize the height relationship between the central cell and the neighboring cells, as well as the degree of height difference. Correspondingly, the slope ratio can characterize the height relationship between the central cell and the neighboring cells, the degree of height difference (slope), and the slope relationship between each neighboring cell and the central cell. A leakage fluid allocation weight p is set, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives. Q is also set to represent the contour length weighting factor of the cell. In this way, the obtained flow allocation coefficient fully considers the slope, slope relationship, elevation relationship, fluid flow pattern, and contour factors of the cells, resulting in high accuracy of the flow allocation coefficient and further improving the accuracy of pipeline prediction results. Wherein, Q... iThe contour length weighting factor for the i-th neighboring cell is defined as follows: If Q i =1 / 2, the elevation of the i-th neighboring cell is lower than that of the center cell, and it is located in the horizontal or vertical direction; if The elevation of the i-th neighboring cell is lower than that of the center cell, and it is located in the diagonal direction; if Q i =0, the elevation of the i-th neighboring cell is not lower than that of the center cell; Q j The contour length weighting factor for the j-th neighboring cell is defined as follows: If Q j =1 / 2, the elevation of the j-th neighboring cell is lower than that of the center cell, and it is located in the horizontal or vertical direction; if The elevation of the j-th neighboring cell is lower than that of the center cell, and it is located diagonally; if Q j =0, the elevation of the j-th neighboring cell is not lower than that of the center cell.

[0086] Based on the above system architecture and various methodologies, this application provides a method for predicting the extent of pipeline leakage. The fluid leakage rate is set to 10e+6, which can be determined based on relevant information, such as the frequency of disasters in high-risk leakage areas, pipeline flow rate, and historical fluid leakage data. Figure 3 As shown, it includes:

[0087] Step 301: Obtain a full-line map of the pipeline and a remote sensing image of the full-line map of the pipeline.

[0088] Step 302: Based on the geomorphic features of the remote sensing image, determine the pipeline branches from the full pipeline map.

[0089] Step 303: For any pipeline branch, obtain historical disaster information and disaster prediction information of the pipeline layout area of ​​that pipeline branch.

[0090] Step 304: If the similarity between historical disaster information and disaster prediction information meets the set conditions, then the pipeline layout area is determined to be a high-risk leakage area; otherwise, it is a non-high-risk leakage area.

[0091] Step 305: For high-risk leakage areas, divide the high-risk leakage areas into grids to obtain the corresponding cells of the high-risk leakage areas.

[0092] Step 306: Obtain the relative slope between the center cell and each neighboring cell. The center cell is defined as a 3x3 cell, and the eight cells surrounding it are considered neighboring cells.

[0093] Step 307: Does the relative gradient between the center cell and its neighboring cells have a maximum value less than 0? If yes, proceed to step 308; otherwise, use the cells outside the 3x3 grid of the center cell as the center cell and proceed to step 306.

[0094] The cycle from step 306 to step 307 to step 306 continues until the edge cell of the high-risk leakage area is the center cell, at which point the relative slope calculation ends.

[0095] Step 308: Identify isolated depression areas within the 3x3 cell. Step 309: Perform preliminary leveling on the isolated depression areas and obtain elevation data for the high-risk leakage areas after preliminary leveling.

[0096] Step 310: Based on the elevation data of the high-risk leakage area after preliminary leveling, obtain the relative slope of each cell.

[0097] Step 311: Does a closed loop exist where multiple connected cells form a loop, and at least one cell within the loop has an elevation value lower than a fourth elevation value, which is the lowest elevation value among the multiple connected cells? If yes, proceed to step 312; otherwise, based on the flow direction, determine the closed loop using cells outside the loop and proceed to step 310.

[0098] The cycle from step 310 to step 311 to step 310 continues until the edge cell of the high-risk leakage area is a ring cell, at which point the relative slope calculation ends.

[0099] Step 312: Determine the interconnected depression area formed by the multiple ring cells and the cells within the ring.

[0100] Step 313: Fill in the interconnected depression area and obtain the elevation data of the high-risk leakage area after filling. Step 314: Calculate the flow distribution coefficient for each cell based on the elevation data of the filled high-risk leakage area.

[0101] Step 315: Set the leakage fluid volume to 10e+6.

[0102] Step 316: Initialize the flow matrix. Each element in the flow matrix can represent a cell, which corresponds to the flow allocation coefficient of that cell.

[0103] Step 317: Predict the extent of pipeline leakage in high-risk leakage areas based on the pipeline leak point, leakage fluid volume, and flow distribution coefficient of each cell.

[0104] Here, in one example, the fluid volume of the next cell is equal to the fluid volume of the previous cell multiplied by the flow distribution coefficient of that next cell. For instance, the fluid volume of the cell containing the pipe leak is 10e+6. The fluid volume of the cell following the leak is then equal to 10e+6 multiplied by the flow distribution coefficient of that next cell. In this way, the fluid volume of each cell can be obtained sequentially. Based on the fluid volume of each cell, the pipe leak diffusion area can be determined, further defining the extent of the pipe leak.

[0105] It should be noted that the above method and steps are not unique. For example, steps 306 to 308 can be used to obtain all isolated depressions in the high-risk leak area (in this case, at step 307, the relative slope calculation ends when the edge cell of the high-risk leak area is the center cell), and then preliminary filling of the depressions can be performed to obtain preliminary filled elevation data. Steps 310 to 312 can be used to obtain all interconnected depressions in the high-risk leak area (in this case, at step 311, the relative slope calculation ends when the edge cell of the high-risk leak area is a ring cell), and then depression filling can be performed to obtain filled elevation data, and the distribution coefficient of each cell can be calculated. Alternatively, starting from the pipeline leak point, in the diffusion direction, with a set number of cells as the calculation radius, after calculating the relative slope for each set number of cells, isolated depressions can be identified, and preliminary filling can be performed to obtain preliminary filled elevation data. Starting from the pipeline leak point, and using a predetermined number of cells as the calculation radius in the diffusion direction, after calculating the relative slope for each predetermined number of cells, the interconnected depression area is identified, filled, and the filled elevation data is obtained, and the distribution coefficient is calculated. That is, the above method is merely one or more embodiments of this solution and does not limit the specific implementation of this solution. Under the same concept, modifications can be made based on the above process, and these modifications should be within the scope of protection of this application.

[0106] Based on the same inventive concept, this application provides a pipeline leakage range prediction device, applicable to pipeline leakage range prediction systems, such as... Figure 4 As shown, it includes:

[0107] The acquisition module 401 is used to acquire a full-line map of the pipeline and remote sensing images of the full-line map of the pipeline;

[0108] The determination module 402 is used to determine high-risk leakage areas from the pipeline map based on the topographic features of the remote sensing image; determine the pipeline leak point and the leakage fluid volume in the high-risk leakage area; calculate the flow distribution coefficient of each sub-region in the high-risk leakage area based on the elevation data of the high-risk leakage area, using the pipeline leak point as the starting point; and predict the pipeline leakage range in the high-risk leakage area based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficient of each sub-region.

[0109] In one or more embodiments, the determining module 402 is specifically configured to: determine pipeline branches from the pipeline full-line map based on the geomorphic features of the remote sensing image; for any pipeline branch, determine historical disaster information of the pipeline layout area of ​​the pipeline branch and disaster prediction information of the pipeline layout area; if the similarity between the historical disaster information and the disaster prediction information meets a set condition, then determine the pipeline layout area as a high-risk leakage area; wherein, the disaster prediction information includes weather prediction information and geological disaster prediction information, and the historical disaster information includes geological disaster information and weather information when a geological disaster occurs.

[0110] In one or more embodiments, the determining module 402 is specifically configured to: determine the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area; fill the depression area to obtain the elevation data after filling; and calculate the flow distribution coefficient of each sub-area in the high-risk leakage area based on the elevation data after filling.

[0111] In one or more embodiments, the determining module 402 is further configured to: divide the high-risk leakage area into grids to obtain each cell corresponding to the high-risk leakage area; determine the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area, including: for any cell, taking the cell as the center cell of a 3*3 cell, and the 8 cells surrounding the center cell as neighboring cells; determining the first elevation value of the center cell in the elevation data, and the second elevation value of the neighboring cells in the elevation data; obtaining the relative slope based on the difference between the first elevation value and the second elevation value; if there is a relative slope less than 0 among the relative slopes of the center cell and each neighboring cell, then there is a depression area in the 3*3 cell.

[0112] In one or more embodiments, the determining module 402 is specifically used to assign a third elevation value to the central cell for any isolated depression area to obtain preliminary filled elevation data. The isolated depression area is a depression area where the maximum value of the relative slope between the central cell and each neighboring cell is less than 0, and the third elevation value is the lowest second elevation value among all second elevation values.

[0113] Based on the preliminary leveled elevation data, an interconnected depression area is determined. The interconnected depression area is a depression area composed of multiple ring-shaped cells and cells within the ring. The multiple ring-shaped cells are connected to form a closed loop. There is at least one cell within the closed loop. The elevation value of any cell within the loop is lower than a fourth elevation value. The fourth elevation value is the lowest elevation value among the multiple ring-shaped cells.

[0114] The fourth elevation value is assigned to the cell within the ring.

[0115] In one or more embodiments, obtaining a relative slope based on the difference between the first elevation value and the second elevation value includes:

[0116]

[0117]

[0118] Among them, G x,y (m,n) is the relative gradient between the central cell and the (m,n)th neighboring cell, N x,y (m,n) is the difference between the first elevation value of the central cell and the second elevation value of the (m,n)th neighboring cell, L x,y (m,n) is the distance between the center cell and its (m,n)th neighboring cell. For cells in the horizontal or vertical direction, L x,y The value of (m,n) is 1. For diagonal elements, L x,y The value of (m,n) is

[0119] In one or more embodiments, each cell corresponding to the high-risk leakage area corresponds to a sub-area within the high-risk leakage area, and the flow allocation coefficient is calculated as follows:

[0120]

[0121] Where, d i Let tanβ be the fluid distribution coefficient of the i-th neighboring cell corresponding to the leaking fluid. iLet be the slope gradient from the center cell to the i-th neighboring cell, where the i-th slope gradient is the ratio of the relative slope of the i-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; p is the leakage fluid allocation weight (p > 0 as a condition, indicating that the larger the absolute value of the relative slope, the more flow the lower the cell receives); the i-th neighboring cell is any one of the eight neighboring cells; tanβ j Let Q be the slope gradient from the center cell to the j-th neighboring cell, where the j-th slope gradient is the ratio of the relative slope of the j-th neighboring cell to the sum of the relative slopes of the eight neighboring cells; i Q is the weighting factor for the contour length of the i-th neighboring cell. j The contour length weighting factor is the length of the contour line in the j-th neighboring cell.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should 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.

[0124] 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.

[0125] 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.

[0126] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for predicting the range of pipeline leakage, characterized in that, Suitable for pipeline leak range prediction systems, including: Acquire a full-line map of the pipeline and remote sensing images of the full-line map of the pipeline; based on the topographic features of the remote sensing images, identify high-risk leakage areas from the full-line map of the pipeline. Determine the pipeline leak point and the amount of leaking fluid in the high-risk leak area; Starting from the pipeline leak point, the flow distribution coefficient of each sub-region in the high-risk leak area is calculated based on the elevation data of the high-risk leak area. The extent of pipeline leakage in the high-risk leakage area is predicted based on the pipeline leak point, the amount of leaked fluid, and the flow distribution coefficient of each sub-region.

2. The method as described in claim 1, characterized in that, Based on the topographic features of the remote sensing imagery, high-risk leakage areas are identified from the pipeline map, including: Based on the topographic features of the remote sensing image, pipeline branches are determined from the full pipeline map; For any pipeline branch, determine the historical disaster information and disaster prediction information of the pipeline layout area of ​​the pipeline branch; if the similarity between the historical disaster information and the disaster prediction information meets the set conditions, then determine the pipeline layout area as a high-risk leakage area; wherein, the disaster prediction information includes weather prediction information and geological disaster prediction information, and the historical disaster information includes geological disaster information and weather information when a geological disaster occurs.

3. The method as described in claim 1, characterized in that, Based on the elevation data of the high-risk leakage area, the flow distribution coefficient of each sub-region within the high-risk leakage area is calculated, including: Based on the elevation data of the high-risk leakage area, the depression area of ​​the high-risk leakage area is determined; The depression area was filled in to obtain the elevation data after the filling process. Based on the elevation data after the leveling process, the flow distribution coefficient of each sub-region in the high-risk leakage area is calculated.

4. The method as described in claim 3, characterized in that, Before determining the depression area of ​​the high-risk leakage area based on the elevation data of the high-risk leakage area, the following steps are included: The high-risk leakage area is divided into grids to obtain the corresponding cells for the high-risk leakage area; The depression areas of the high-risk leakage area are determined based on the elevation data of the high-risk leakage area, including: For any given cell, treat that cell as 3. The center cell of cell 3, and the eight cells surrounding the center cell are considered as neighboring cells; Determine the first elevation value of the center cell in the elevation data, and the second elevation value of the neighboring cells in the elevation data; The relative slope is obtained based on the difference between the first elevation value and the second elevation value; If any of the relative slopes between the central cell and its neighboring cells is less than 0, then the 3 There is a depression area in cell 3.

5. The method as described in claim 4, characterized in that, The depression area is filled in to obtain the elevation data after filling, including: For any isolated depression area, the third elevation value is assigned to the central cell to obtain the preliminary filled elevation data. The isolated depression area is the depression area where the maximum value of the relative slope between the central cell and each neighboring cell is less than 0. The third elevation value is the lowest second elevation value among all the second elevation values. Based on the preliminary leveled elevation data, an interconnected depression area is determined. The interconnected depression area is a depression area composed of multiple ring-shaped cells and cells within the ring. The multiple ring-shaped cells are connected to form a closed loop. There is at least one cell within the closed loop. The elevation value of any cell within the loop is lower than a fourth elevation value. The fourth elevation value is the lowest elevation value among the multiple ring-shaped cells. The fourth elevation value is assigned to the cell within the ring.

6. The method as described in claim 4, characterized in that, The relative slope is obtained based on the difference between the first elevation value and the second elevation value, including: ,in, ; in, The central cell and the first The relative slope between neighboring cells The first elevation value of the central cell and the second... The difference between the second elevation values ​​of the neighboring cells The central cell and the first The distance between neighboring cells, for cells in the horizontal or vertical direction, The value is 1, for elements in the diagonal direction. The value is .

7. The method as described in claim 5, characterized in that, Each cell corresponding to the high-risk leakage area corresponds to a sub-area within the high-risk leakage area. The flow allocation coefficient is calculated as follows: in, For the leaking fluid corresponding to the first Fluid distribution coefficients of neighboring cells, For the center cell to the first The slope gradient of the i-th neighboring cell, wherein the i-th slope gradient is the slope gradient of the i-th neighboring cell. The ratio of the relative slope of 8 neighboring cells to the sum of the relative slopes of 8 neighboring cells; Assign weights to the leaking fluid, and The first Each neighboring cell is any one of the eight neighboring cells; The j-th slope gradient from the center cell to the j-th neighboring cell is the ratio of the relative slope of the j-th neighboring cell to the sum of the relative slopes of the eight neighboring cells. For the first The contour length weighting factor of each neighboring cell The contour length weighting factor is the length of the contour line in the j-th neighboring cell.

8. A device for predicting the range of pipeline leakage, characterized in that, Suitable for pipeline leak range prediction systems, including: The acquisition module is used to acquire a full-line map of the pipeline and remote sensing images of the full-line map of the pipeline; The determination module is used to identify high-risk leakage areas from the pipeline map based on the topographic features of the remote sensing image; determine the pipeline leak point and the leakage fluid volume in the high-risk leakage area; calculate the flow distribution coefficient of each sub-region in the high-risk leakage area based on the elevation data of the high-risk leakage area, using the pipeline leak point as the starting point; and predict the pipeline leakage range in the high-risk leakage area based on the pipeline leak point, the leakage fluid volume, and the flow distribution coefficient of each sub-region.

9. A computing device, characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method according to any one of claims 1 to 7.

10. A computer-readable non-volatile storage medium, characterized in that, Includes computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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