Oil pipeline high consequence area identification method, electronic device and computer readable storage medium

Abstract

The application provides a high-consequence area identification method for an oil pipeline, an electronic device and a computer readable storage medium. The high-consequence area identification method for the oil pipeline comprises obtaining topographic information collected by a sensor carried by an aircraft flying along the oil pipeline when the aircraft hovers at a plurality of preset monitoring points for a preset time length. According to the topographic information of the preset monitoring points, attribute information of the oil pipeline and meteorological information, a potential influence radius is determined. According to the potential influence radius, a potential influence area is determined. According to the potential influence area and population prediction information in the potential influence area, a high-consequence area is determined. Thus, the accuracy is higher.

Description

Technical Field

[0001] This application relates to the field of oil pipeline identification, and in particular to a method, electronic device and computer-readable storage medium for identifying high-consequence zones of oil pipelines. Background Technology

[0002] With the gradual development of science and technology, the coverage of oil pipelines is constantly expanding, and the safety risks are also increasing. Among them, high-consequence zones of oil pipelines refer to areas along the pipeline route where leaks may cause significant casualties, property damage, or environmental destruction. Accurate identification of high-consequence zones of oil pipelines can effectively reduce safety risks and protect the safety of people and property.

[0003] Some methods for identifying high-consequence zones in oil pipelines use a preset radius of influence to delineate potential impact areas, which makes it difficult to accurately reflect the actual situation of natural gas leaks and diffusion. This results in discrepancies between the identified high-consequence zones and the actual situation, leading to poor accuracy. Summary of the Invention

[0004] This application provides a highly accurate method for identifying high-consequence zones in oil pipelines, an electronic device, and a computer-readable storage medium.

[0005] This application provides a method for identifying high-consequence zones in oil pipelines, including: The terrain information collected by the sensors on the aircraft when the aircraft flying along the oil pipeline hovers at multiple preset monitoring points for a preset duration is obtained. Based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and the meteorological information, the potential impact radius is determined; Based on the potential impact radius, the potential impact area is determined; High-consequence areas are identified based on the potential impact area and population projections within that area.

[0006] Furthermore, determining the potential impact radius based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and meteorological information includes: Based on the attribute information of the oil pipeline, the amount of leakage kinetic energy released is determined; Based on the meteorological information, the diffusion attenuation factor is determined; The potential influence radius of the preset monitoring point is determined based on the terrain information, the amount of leakage kinetic energy released, and the diffusion attenuation factor of the preset monitoring point.

[0007] Furthermore, the attribute information of the oil pipeline includes at least one of the following: the material of the oil pipeline, the design pressure of the oil pipeline, the service life of the oil pipeline, the upper limit of the design life of the oil pipeline, and the corrosion rate assessment index of the oil pipeline; and / or The meteorological information includes at least one of the following: atmospheric stability level, surface wind speed at the preset monitoring point, and wind direction at the preset monitoring point.

[0008] Furthermore, determining the leakage kinetic energy release based on the attribute information of the oil pipeline includes: Based on the attribute information of the oil pipeline, the leakage kinetic energy release E is determined using the following formula: , in, The material and pressure coupling constant of the oil pipeline is given. The design pressure of the oil pipeline is [insert pressure here]. The service life of the oil pipeline. This represents the upper limit of the design life of the oil pipeline. This is the corrosion rate assessment index for the oil pipeline.

[0009] Furthermore, determining the diffusion attenuation factor based on the meteorological information includes: Based on the meteorological information, the diffusion attenuation factor D is determined using the following formula: , in, These are the empirical fitting coefficients. The ground wind speed at the preset monitoring point. This refers to the atmospheric stability level.

[0010] Further, determining the potential influence radius of the preset monitoring point based on the terrain information, the amount of leakage kinetic energy released, and the diffusion attenuation factor of the preset monitoring point includes: Based on the terrain information, leakage kinetic energy release, and diffusion attenuation factor of the preset monitoring point, the potential influence radius of the preset monitoring point is calculated using the following formula. : , in, and Where E is the terrain correction factor, E is the leakage kinetic energy release, and D is the diffusion attenuation factor. The slope angle at the preset monitoring point is obtained based on the terrain information of the preset monitoring point.

[0011] Furthermore, the meteorological information includes the wind direction at the preset monitoring point; determining the potential impact area based on the potential impact radius includes: Using the input risk point as the vertex and the wind direction at the risk detection point as the axis of symmetry, the potential influence area is determined by combining the set angle value and the potential influence radius of the preset monitoring point closest to the risk point.

[0012] Furthermore, the method for identifying high-consequence zones in the oil pipeline also includes: The area defining the oil pipeline and its surrounding area is divided into multiple continuous local sections; The step of determining high-consequence areas based on the potential impact area and population projection information within the potential impact area includes: Based on the potential impact area, population projections within the potential impact area, and the distribution information of sensitive facilities, the risk index of the local segment is determined; If the risk index of the local segment is not less than the risk threshold, the local segment is determined to be a high-consequence area. If the risk index of the local segment is less than the risk threshold, the local segment is determined to be a non-high-consequence area.

[0013] This application provides an electronic device, which includes a memory and a processor. The memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the high-consequence zone identification method for oil pipelines as described in any of the above embodiments when executing the computer instructions.

[0014] This application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-consequence zone identification method for oil pipelines as described in any of the above embodiments.

[0015] This application provides a method for identifying high-consequence zones of oil pipelines, including acquiring terrain information collected by sensors mounted on an aircraft. Based on the terrain information of preset monitoring points, the attribute information of the oil pipeline, and meteorological information, a potential impact radius is determined. This potential impact radius can be adjusted based on changes in terrain information, oil pipeline attribute information, and meteorological information, making the identified high-consequence zones more closely resemble the actual situation and improving accuracy.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] Figure 1 The diagram shown is a flowchart of a high-consequence zone identification method for oil pipelines according to an embodiment of this application; Figure 2 As shown Figure 1 A sub-flowchart of the high-consequence zone identification method for oil pipelines in the illustrated embodiment; Figure 3 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment; Figure 4 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment; Figure 5 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment; Figure 6 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment; Figure 7 The diagram shown is a structural schematic of the high-consequence zone identification device for oil pipelines of this application. Figure 8 The diagram shown is a schematic diagram of the electronic device of this application. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0020] This application provides a method, electronic device, and computer-readable storage medium for identifying high-consequence zones of oil pipelines. The method for identifying high-consequence zones of oil pipelines provided by this application includes: acquiring terrain information collected by sensors onboard the aircraft when it hovers at multiple preset monitoring points for a preset duration along the oil pipeline; determining a potential impact radius based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and meteorological information; determining a potential impact area based on the potential impact radius; and determining a high-consequence zone based on the potential impact area and population prediction information within the potential impact area.

[0021] This application discloses a method, electronic device, and computer-readable storage medium for identifying high-consequence zones of oil pipelines. The method includes acquiring terrain information collected by sensors mounted on an aircraft. Based on the terrain information of preset monitoring points, the attribute information of the oil pipeline, and meteorological information, a potential impact radius is determined. This potential impact radius can be adjusted based on changes in terrain information, oil pipeline attribute information, and meteorological information, making the determined high-consequence zone more closely resemble the actual situation and thus more accurate.

[0022] To better understand the technical solution of this application, the high-consequence zone identification method, electronic equipment, and computer-readable storage medium of this application will be described in detail below with reference to the accompanying drawings. Unless otherwise specified, the features in the following embodiments and implementations can be combined with each other.

[0023] Figure 1 The diagram shows a flowchart of a high-consequence zone identification method for oil pipelines according to an embodiment of this application. See also... Figure 1 As shown, the high-consequence zone identification method for oil pipelines in this application includes steps S1 to S4.

[0024] Step S1: Obtain terrain information collected by sensors on the aircraft as it hovers at multiple preset monitoring points for a preset duration along the oil pipeline. The aircraft may include a drone.

[0025] In one embodiment, the aircraft is equipped with multiple types of sensors, including lidar, a visible light camera, and an infrared thermal imager. The lidar is used to collect terrain information. The visible light camera is used to collect visible light image information. The infrared thermal imager is used to collect infrared imaging information. Unlike manual inspections or single-sensor methods, which suffer from blind spots, time delays, and data fragmentation, this application, by equipping multiple types of sensors, can acquire diverse information, providing a high-quality data foundation for subsequent high-consequence area identification.

[0026] During flight, the aircraft can use an inertial navigation system (INS) to record timestamps and a global positioning system (GPS) to record position coordinates. The timestamps recorded by the INS and the position coordinates recorded by the GPS are bound together to serve as a unified spatiotemporal reference for multiple types of sensors. Terrain information, visible light image information, and infrared imaging information, along with timestamps and position coordinates, can be stored in the aircraft's storage devices.

[0027] In one embodiment, the high-consequence zone identification method for oil pipelines further includes: step S5, acquiring visible light image information and infrared imaging information collected by the sensors on the aircraft at set collection intervals within a preset time period when the aircraft hovers at multiple preset monitoring points for a preset duration along the oil pipeline.

[0028] In one embodiment, the method for identifying high-consequence zones of oil pipelines further includes dividing the area covered by the aircraft flight into multiple grid cells.

[0029] Visible light image information, infrared imaging information, and terrain information from multiple preset monitoring points are mapped to corresponding grid cells. Each grid cell may contain at least the following types of information: visible light image information, infrared imaging information, and terrain information.

[0030] Visible light image information, infrared imaging information and terrain information in the same grid cell are fused to determine the comprehensive sensing feature value of each grid cell, so as to obtain a comprehensive sensing feature map containing multiple grid cells.

[0031] In this embodiment, the aircraft is equipped with three types of sensors: lidar, a visible light camera, and an infrared thermal imager. The comprehensive sensing feature value of each grid cell g is determined using the following formula. : , in, These are the weighting coefficients. Let be the normalized response value of the i-th type of sensor in grid cell g.

[0032] These are the weighting coefficients. This ensures the confidence level of various sensors under the current external environment, and can be dynamically adjusted based on factors such as light intensity, atmospheric visibility, and wind speed disturbances. This effectively overcomes recognition errors caused by changes in lighting, weather interference, and differences in sensor performance, improving the spatial consistency and semantic reliability of the comprehensive sensing feature map, and making subsequent risk assessments more robust and accurate.

[0033] Step S2: Determine the potential impact radius based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and meteorological information.

[0034] Figure 2 As shown Figure 1 A sub-flowchart of the high-consequence zone identification method for oil pipelines in the illustrated embodiment. See also... Figure 2 As shown, in one embodiment, step S2 includes steps S21 to S23.

[0035] Step S21: Determine the amount of leakage kinetic energy released based on the attribute information of the oil pipeline.

[0036] In one embodiment, step S2 further includes: obtaining attribute information of the oil pipeline. This attribute information can be obtained from the oil pipeline operation and management system.

[0037] In one embodiment, the attribute information of the oil pipeline includes at least one of the following: the material of the oil pipeline, the design pressure of the oil pipeline, the service life of the oil pipeline, the upper limit of the design life of the oil pipeline, and the corrosion rate assessment index of the oil pipeline.

[0038] In one embodiment, step S21 includes: determining the leakage kinetic energy release amount E based on the attribute information of the oil pipeline using the following formula: , in, The material and pressure coupling constant of the oil pipeline is given. The design pressure of the oil pipeline is [insert pressure here]. The service life of the oil pipeline. This represents the upper limit of the design life of the oil pipeline. This is the corrosion rate assessment index for the oil pipeline.

[0039] This represents the material-pressure coupling constant of the oil pipeline. It can reflect the sensitivity of the oil pipeline material to energy release under high pressure. In one embodiment, the material-pressure coupling constant of the oil pipeline can be determined based on the pipeline material.

[0040] Step S22: Determine the diffusion attenuation factor based on meteorological information.

[0041] In one embodiment, step S2 further includes: acquiring meteorological information about the oil pipeline. In one embodiment, the meteorological information is acquired through a meteorological service interface. In another embodiment, the multiple sensors also include an anemometer. Meteorological information collected by sensors on the aircraft is acquired when the aircraft, flying along the oil pipeline, hovers at multiple preset monitoring points for a preset duration.

[0042] In one embodiment, the meteorological information includes at least one of atmospheric stability level, surface wind speed at a preset monitoring point, and wind direction at the preset monitoring point.

[0043] In one embodiment, step S22 includes: determining the diffusion attenuation factor D based on meteorological information using the following formula: , in, These are the empirical fitting coefficients. The ground wind speed at the preset monitoring point. This refers to the atmospheric stability level.

[0044] These are the empirical fitting coefficients. It can reflect the influence of wind speed and atmospheric stability on diffusion attenuation, and its value can be 0.05 to 0.10.

[0045] Step S23: Determine the potential influence radius of the preset monitoring point based on the terrain information, leakage kinetic energy release amount, and diffusion attenuation factor of the preset monitoring point.

[0046] In one embodiment, the terrain slope angle of the preset monitoring point is determined based on the terrain information of the preset monitoring point.

[0047] In one embodiment, step S23 includes: calculating the potential influence radius of the preset monitoring point using the following formula based on the terrain information, leakage kinetic energy release amount, and diffusion attenuation factor of the preset monitoring point. : , in, and Where E is the terrain correction factor, E is the leakage kinetic energy release, and D is the diffusion attenuation factor. The slope angle at the preset monitoring point is obtained based on the terrain information of the preset monitoring point.

[0048] and This is the terrain correction factor. and It can reflect the moderating effect of macroscopic topographic features and topographic slope angle on the range of leakage impact. The value can range from 1.0 to 1.5. The value can range from 0.3 to 0.8.

[0049] Thus, the potential impact radius of the preset monitoring point is determined based on the attribute information of the oil pipeline, the actual terrain information, and meteorological information. Unlike the preset impact radius in related technologies, the potential impact radius of this application embodiment can adapt to the environmental conditions of the preset monitoring point, which can better fit the actual accident impact range and determine the potential impact radius more accurately.

[0050] Step S3: Determine the potential impact area based on the potential impact radius.

[0051] In one embodiment, step S3 includes: using the input risk point as the vertex, and the wind direction at the risk detection point as the axis of symmetry, combining the set angle value and the potential influence radius of the preset monitoring point closest to the risk point, to determine the potential influence area. In this embodiment, the set angle value can be 60°. This makes the determined potential influence area more accurate.

[0052] In one embodiment, the method for identifying high-consequence zones in an oil pipeline further includes steps S6 to S7.

[0053] Step S6: Determine the current population information based on all visible light image information and infrared imaging information collected within a preset time period.

[0054] Figure 3 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines in the illustrated embodiment. See also Figure 3 As shown, in one embodiment, step S6 includes steps S61 to S62: Step S61: Determine the population information within the grid cell based on the visible light image information and infrared imaging information within the grid cell.

[0055] In one embodiment, based on visible light image information and infrared imaging information collected at set intervals within a grid cell, a moving object detection algorithm is used to identify moving objects to determine the population count within that grid cell. In this embodiment, the set collection time can be 1 second. This achieves automatic conversion from visible light image information and infrared imaging information to population count information within the grid cell, resulting in more accurate population count information.

[0056] Step S62: Based on the population quantity information within the grid cell, determine the population density information within that grid cell to obtain a current population density map containing multiple grid cells, which serves as the current population information.

[0057] In one embodiment, the population density information within the grid cell g is determined using the following formula. : , in, Let g be the actual surface area of ​​the grid cell. This provides population information for grid cell g. In this embodiment, each grid cell g is a 10m × 10m square grid. The actual surface area of ​​each grid cell g can be calculated using a projected coordinate system, thus ensuring spatial consistency of population density information.

[0058] Step S7: Obtain population forecast information based on current population information and social activity information.

[0059] Figure 4 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment. See also Figure 4 As shown, in one embodiment, step S7 includes steps S71 to S72.

[0060] Step S71: Based on the current population information, predict the future population information for future moments.

[0061] In one embodiment, past population density maps are obtained for each time point during L inspection days of the same season and week type in the past, to obtain a past population density sequence. .

[0062] The population density map of each time before the future time of each inspection day is extracted from the past population density sequence as input, and the population density map of the future time of the inspection day is used as the label to construct a training sample set of L inputs and labels.

[0063] The training sample set is then input into the neural network for training to obtain the trained neural network.

[0064] In one embodiment, step S71 includes: obtaining a past population density map of any past season and week type.

[0065] The difference between the population density in each grid cell of the past population density map and the current population density map is compared with a set population density threshold.

[0066] If the difference in population density within each grid cell is no greater than a set population density threshold, then the future population density map for a future time can be predicted based on either the past or current population density map, serving as future population information. Either the past or current population density map can be input into a trained neural network to predict the future population density map for a future time, serving as future population information.

[0067] If the difference in population density within at least one grid cell exceeds a set population density threshold, then the future population density map is predicted based on the current population density map, and this prediction is used as future population information. The current population density map can be input into a trained neural network to predict the future population density map, which is then used as future population information.

[0068] Step S72: Correct the future population information based on social activity information to obtain population prediction information.

[0069] In one embodiment, step S72 includes: Based on social activity information, the population density within the grid cells of the future population density map is corrected, and the predicted population density within each grid cell is determined to obtain a population prediction map, which serves as population prediction information.

[0070] The corrected population prediction density within each grid cell is determined using the following formula. : , in, and For adjustment coefficients, This represents the population density within grid cell g in the future population density map. This is a holiday marker. This is a signal for announcing a large-scale event.

[0071] and These values ​​reflect the moderating effect of holidays and large-scale events on population density, and are determined based on the functional category of the grid cell. In one embodiment, based on the comprehensive sensing feature map, and according to remote sensing imagery, POI data, and spatial rules, a deep learning model identifies the functional category of each grid cell. Functional categories include, but are not limited to, schools, hospitals, nursing homes, and residential areas.

[0072] It can be determined based on statutory holidays, weekends, and regional festivals, and its value can be a discrete code or a normalized score. It can be generated based on activity type, scale, and geographical relevance, and its value range is usually set to [0,1] to represent activity popularity. In this way, it breaks through the limitation of related technologies that rely solely on static population data, making the determination of population prediction information more accurate.

[0073] Step S4: Based on the potential impact area and the population projection information within the potential impact area, identify high-consequence areas.

[0074] Figure 5 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment. See also Figure 5 As shown, in one embodiment, the high-consequence zone identification method for oil pipelines further includes dividing the oil pipeline and its surrounding area into multiple continuous local segments. Each local segment has the same length.

[0075] Step S4 includes steps S41 to S43.

[0076] Step S41: Determine the risk index of a local segment based on the potential impact area, population projections within the potential impact area, and the distribution information of sensitive facilities.

[0077] Figure 6 As shown Figure 1 Another sub-flowchart of the high-consequence zone identification method for oil pipelines shown in the embodiment. See also Figure 6 As shown, in one embodiment, step S41 includes: Step S411: Determine the risk index for each grid cell based on the potential impact area, population projection information within the potential impact area, and distribution information of sensitive facilities.

[0078] In one embodiment, step S411 includes: determining the grid cells covering the potential impact area in the comprehensive sensing feature map through spatial overlay analysis; Identify whether sensitive facilities exist within each grid cell; If a sensitive setting exists within a grid cell, determine the type of the sensitive facility; The risk index for each grid cell g is determined using the following formula. : , in, and Weights are assigned to population facilities based on their risk profiles. For population projection information, the population density within a grid cell is set. If grid cell g falls within the coverage area of ​​the potential impact zone, then... ,otherwise If grid cell g contains sensitive settings, then set the sensitive facility weights. ,otherwise Weight of different sensitive facilities different.

[0079] Step S412: Calculate the average risk index of all grid cells contained in each local segment based on the risk index of each grid cell, and use it as the risk index of the local segment.

[0080] In one embodiment, step S412 includes: The average risk index of all grid cells contained in the m-th local segment is calculated using the following formula. : , in, The set of all grid cells g contained in this local segment. Let g be the number of all grid cells contained in this local segment. Thus, by spatially coupling population projection information, sensitive facility distribution information, and potential impact areas, and using the risk contribution weights of population and facilities to construct a local segment risk index, a quantitative fusion and spatially refined expression of different risk factors are achieved, resulting in high reliability in identifying high-consequence areas.

[0081] In one embodiment, the method for identifying high-consequence zones in oil pipelines further includes defining a risk threshold using the following formula. : , in, The set benchmark threshold, This refers to the population density within a grid cell in the population projection information. This represents the average population density of this local area during the same period in history. This is the sensitivity coefficient. Because different sections have different risk thresholds, the identification of high-consequence areas becomes more accurate.

[0082] Step S42: If the risk index of a local segment is not less than the risk threshold, the local segment is determined to be a high-consequence area.

[0083] Step S43: If the risk index of a local segment is less than the risk threshold, the local segment is determined to be a non-high-consequence area.

[0084] In one embodiment, an identification report can be output, which includes the starting and ending chainages of the high-consequence zone, the coordinate range of the high-consequence zone, the risk level, the cause of the hazard, and recommended measures. The identification report is then sent to the pipeline integrity management platform.

[0085] In one embodiment, for each local segment identified as a high-consequence zone, the risk evolution trend of that local segment at a future preset time t is calculated using the following formula. : , in, and These are the weights for the rate of change over time and the impact of the event, respectively. This represents the risk index for this local section. The set of all grid cells g contained in this local segment. The population density within grid cell g in the population projection information. This is a probability index of a temporary large-scale event occurring in the grid cell g of this local segment at a future time t.

[0086] Based on the risk evolution trend and the risk index of local areas, combined with weather forecast information and social activity information, risk management strategies are determined.

[0087] In one embodiment, information such as risk index, risk evolution trend, meteorological information and social activity information of local sections identified as high-consequence areas can be collected to determine high-consequence scenarios (e.g., "high risk + upward trend + blizzard + Spring Festival").

[0088] The system identifies scenarios in a pre-defined rule base that match the high-consequence scenario and generates risk management strategies. These strategies include, but are not limited to, inspection strategies, early warning strategies, and resource scheduling strategies. This ensures that a suitable risk management strategy exists for each local segment identified as a high-consequence area. The high-consequence area identification method and the corresponding risk management strategy for each identified local segment are stored in a database. This approach, while meeting industry standards, allows for dynamic adjustment of the risk threshold based on actual circumstances, thus matching different local segments and improving the accuracy of high-consequence area identification.

[0089] Based on the same concept as the method described above, this application also proposes a high-consequence zone identification device 100 for oil pipelines. Figure 7 The diagram shown is a structural schematic of the high-consequence zone identification device 100 for oil pipelines according to this application. (See attached diagram.) Figure 7 As shown, the high-consequence zone identification device 100 for oil pipelines includes the following modules: The information acquisition module 10 is used to acquire terrain information collected by the sensors on the aircraft when the aircraft hovers at multiple preset monitoring points for a preset duration along the oil pipeline.

[0090] The potential impact radius determination module 20 is used to determine the potential impact radius based on the terrain information of preset monitoring points, the attribute information of the oil pipeline, and meteorological information. The potential impact area determination module 30 is used to determine the potential impact area based on the potential impact radius; The high-consequence zone identification module 40 is used to identify high-consequence zones based on the potential impact area and population projection information within the potential impact area.

[0091] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, which can achieve the same technical effect, and will not be repeated here.

[0092] Figure 8 The diagram shown is a schematic representation of the electronic device 50 of this application. See also... Figure 8 As shown, this application embodiment also provides an electronic device 50, which includes a memory 51 and a processor 52. The memory 51 is used to store computer instructions that can be run on the processor 52, and the processor 52 is used to implement the high-consequence zone identification method of the oil pipeline as described above when executing the computer instructions.

[0093] In one embodiment, the electronic device 50 may further include memory 53 and interface 54. The electronic device 50 may also include other hardware depending on the specific application.

[0094] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the high-consequence zone identification method for oil pipelines as described above.

[0095] This application also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the high-consequence zone identification method for oil pipelines as described above.

[0096] This application also provides a computer program stored in a computer-readable storage medium, which, when executed by a processor, causes the processor to perform the high-consequence zone identification method for oil pipelines as described above.

[0097] This application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented using any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0098] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for identifying high-consequence zones in oil pipelines, characterized in that, include: The terrain information collected by the sensors on the aircraft when the aircraft flying along the oil pipeline hovers at multiple preset monitoring points for a preset duration is obtained. Based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and the meteorological information, the potential impact radius is determined; Based on the potential impact radius, the potential impact area is determined; High-consequence areas are identified based on the potential impact area and population projections within that area.

2. The method for identifying high-consequence zones in oil pipelines according to claim 1, characterized in that, The step of determining the potential impact radius based on the terrain information of the preset monitoring points, the attribute information of the oil pipeline, and meteorological information includes: Based on the attribute information of the oil pipeline, the amount of leakage kinetic energy released is determined; Based on the meteorological information, the diffusion attenuation factor is determined; The potential influence radius of the preset monitoring point is determined based on the terrain information, the amount of leakage kinetic energy released, and the diffusion attenuation factor of the preset monitoring point.

3. The method for identifying high-consequence zones in oil pipelines according to claim 2, characterized in that, The attribute information of the oil pipeline includes at least one of the following: the material of the oil pipeline, the design pressure of the oil pipeline, the service life of the oil pipeline, the upper limit of the design life of the oil pipeline, and the corrosion rate assessment index of the oil pipeline; and / or The meteorological information includes at least one of the following: atmospheric stability level, surface wind speed at the preset monitoring point, and wind direction at the preset monitoring point.

4. The method for identifying high-consequence zones in oil pipelines according to claim 2, characterized in that, Determining the leakage kinetic energy release based on the attribute information of the oil pipeline includes: Based on the attribute information of the oil pipeline, the leakage kinetic energy release E is determined using the following formula: ， in, The material and pressure coupling constant of the oil pipeline is given. The design pressure of the oil pipeline is [insert pressure here]. The service life of the oil pipeline. This represents the upper limit of the design life of the oil pipeline. This is the corrosion rate assessment index for the oil pipeline.

5. The method for identifying high-consequence zones in oil pipelines according to claim 2, characterized in that, The determination of the diffusion attenuation factor based on the meteorological information includes: Based on the meteorological information, the diffusion attenuation factor D is determined using the following formula: ， in, These are the empirical fitting coefficients. The ground wind speed at the preset monitoring point. This refers to the atmospheric stability level.

6. The method for identifying high-consequence zones in oil pipelines according to claim 2, characterized in that, The step of determining the potential influence radius of the preset monitoring point based on the terrain information, the amount of leakage kinetic energy released, and the diffusion attenuation factor of the preset monitoring point includes: Based on the terrain information, leakage kinetic energy release, and diffusion attenuation factor of the preset monitoring point, the potential influence radius of the preset monitoring point is calculated using the following formula. : ， in, and Where E is the terrain correction factor, E is the leakage kinetic energy release, and D is the diffusion attenuation factor. The slope angle at the preset monitoring point is obtained based on the terrain information of the preset monitoring point.

7. The method for identifying high-consequence zones in oil pipelines according to claim 1, characterized in that, The meteorological information includes the wind direction at the preset monitoring points; The step of determining the potential impact area based on the potential impact radius includes: Using the input risk point as the vertex and the wind direction at the risk detection point as the axis of symmetry, the potential influence area is determined by combining the set angle value and the potential influence radius of the preset monitoring point closest to the risk point.

8. The method for identifying high-consequence zones in oil pipelines according to claim 1, characterized in that, The method for identifying high-consequence zones in oil pipelines also includes: The area defining the oil pipeline and its surrounding area is divided into multiple continuous local sections; The step of determining high-consequence areas based on the potential impact area and population projection information within the potential impact area includes: Based on the potential impact area, population projections within the potential impact area, and the distribution information of sensitive facilities, the risk index of the local segment is determined; If the risk index of the local segment is not less than the risk threshold, the local segment is determined to be a high-consequence area. If the risk index of the local segment is less than the risk threshold, the local segment is determined to be a non-high-consequence area.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory is used to store computer instructions that can be executed on the processor. The processor is used to implement the high-consequence zone identification method for oil pipelines according to any one of claims 1 to 8 when executing the computer instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the high-consequence zone identification method for oil pipelines as described in any one of claims 1 to 8.

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