Intelligent identification system and method for high-consequence area of oil and gas pipeline

Abstract

The invention provides an oil and gas pipeline high-consequence area intelligent identification system and method, and the system is characterized in that the system comprises a high-consequence area building extraction module which is used for extracting building data and environment data in a target area from a satellite image, and the target area is internally provided with an oil and gas pipeline; the region grade identification module is used for carrying out grade division on the target region according to the building data and the region grade standard; the high-consequence area identification module is used for performing high-consequence area range and grade division on the target area by fusing the grade data and the environment data of the target area; and the high-consequence region rendering module is used for generating a three-dimensional dynamic map according to the range and the grade of the high-consequence region of the target region.

Description

Technical Field

[0001] The present invention relates to the field of automation and control, and in particular to an intelligent identification system and method for high-consequence areas of oil and gas pipelines. Background Art

[0002] The identification of high-consequence areas of oil and gas pipelines is mainly based on the "Oil and Gas Pipeline Integrity Management Specifications". The company has compiled detailed implementation rules for the identification of high-consequence areas of oil and gas pipelines and interpreted the relevant clauses, which facilitated the subsequent work. However, due to the unclear definition of some clauses in the national standard, there are still some problems in the identification process, and even professional institutions and personnel have great differences in the understanding of some clauses.

[0003] For example, oil and gas pipelines use "general concentration of buildings with four or more floors, frequent traffic, and many underground facilities" as the main identification items for Class III high-consequence areas. However, different personnel have different understandings of "general concentration of buildings with four or more floors", which directly affects the classification, risk assessment, and on-site control of high-consequence areas. In addition, missed identification, over-identification, inaccurate boundary demarcation of high-consequence areas, and the division of a high-consequence area into multiple continuous high-consequence areas in some sections are common errors, which artificially increase or decrease the number of high-consequence areas and increase the difficulty of management.

[0004] At present, the identification of high consequence areas mainly relies on manual data collection and judgment, which has the problems of large amount of collected data and difficulty in obtaining manual identification information; inaccurate collection and long process; high personnel workload and high long-term cost; long update cycle (usually 1 year), which makes it difficult to meet the requirements of timely updating of high consequence areas when the pipeline and surrounding environment change; and high consequence area identification rules are greatly affected by personnel's knowledge level, experience and skills.

[0005] In order to solve the above problems, an intelligent identification system and method for high consequence areas of oil and gas pipelines are proposed. Summary of the invention

[0006] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art, and provide an intelligent identification system and method for high-consequence areas of oil and gas pipelines, which extracts building data and environmental data of orthophotos by using an image recognition artificial intelligence algorithm, and automatically calculates, divides, and colors high-consequence areas according to regional grades and high-consequence area calculation algorithms and national standards. It can accurately collect spatial location information of elements around oil and gas pipelines, and provide a unified and standard scale for regional grades and high-consequence area identification through a rule-based algorithm, so as to achieve a high degree of consistency in the identification results; and it can also meet the high-consequence area identification of oil and gas pipelines.

[0007] In a first aspect of the present disclosure, a system for intelligently identifying high-consequence areas in oil and gas pipelines is provided, the system comprising:

[0008] A high consequence area building extraction module is used to extract building data and environmental data in a target area from satellite images, where oil and gas pipelines are located;

[0009] A regional grade identification module, used for classifying the target area according to the building data and regional grade standards;

[0010] A high consequence area identification module, used to divide the target area into high consequence areas and levels by fusing the level data and environmental data of the target area;

[0011] The high consequence area rendering module is used to generate a three-dimensional dynamic map according to the range and level of the high consequence area in the target area.

[0012] A second aspect of the present disclosure provides a method for intelligently identifying high-consequence areas in oil and gas pipelines, comprising the following steps:

[0013] Use the High Consequence Area Building Extraction Module to extract building data and environmental data in the target area from satellite images;

[0014] Using a regional grade identification module, the target area is graded according to the building data and regional grade standards;

[0015] Using a high consequence area identification module, by fusing the level data and environmental data of the target area, the target area is divided into a high consequence area range and a level;

[0016] A high consequence area rendering module is used to generate a three-dimensional dynamic map according to the range and level of the high consequence area in the target area.

[0017] In conjunction with the second aspect, extracting building data and environmental data in the target area from the satellite image includes:

[0018] Acquire a satellite image of the target area, and use the color, texture, and geometric features of the target area in the satellite image as a first feature set;

[0019] Decomposing the geometric features in the first feature set to obtain a first building distinguishing boundary, and converting the color and texture features into the building surface reflection intensity and shadow direction to form a second feature set;

[0020] By comparing satellite images at different time periods, the changed buildings are extracted, and the second building boundary is obtained by fusing the second feature set with the second feature set;

[0021] The second building boundary is merged with the distribution data of the land cover type, roads and vegetation in the target area to generate a data extraction layer;

[0022] The data extraction layer is merged with the position coordinates of the target area to obtain the building data and environmental data in the target area.

[0023] In conjunction with the second aspect, the step of classifying the target area according to the building data and the regional grade standard includes:

[0024] Importing the building data in the target area into the regional level identification module and marking the fourth-level area;

[0025] According to the marked four-level regions, the target area is divided into a plurality of sections;

[0026] Calculate the number of buildings in the area corresponding to each section, and divide the level of the area corresponding to each section according to the number of buildings.

[0027] In conjunction with the second aspect, dividing the target area into a plurality of sections according to the marked four-level regions includes:

[0028] Removing the fourth-level region from the target region to obtain a plurality of discontinuous regions;

[0029] The oil and gas pipelines contained in the multiple discontinuous areas are regarded as corresponding multiple sections.

[0030] In conjunction with the second aspect, the calculating the number of buildings in the area corresponding to each section and grading the area corresponding to each section according to the number of buildings includes:

[0031] Extending the first preset distance on both sides of the segment to obtain an area as the corresponding area of ​​the segment;

[0032] Calculate the number of buildings in the corresponding area of ​​each segment;

[0033] Areas corresponding to the sections with a number of buildings greater than or equal to 100 are classified as third-level areas; areas corresponding to the sections with a number of buildings greater than 15 and less than 100 are classified as second-level areas; areas corresponding to the sections with a number of buildings greater than or equal to 0 and less than or equal to 15 are classified as first-level second-category areas; areas corresponding to the sections with no buildings are classified as first-level first-category areas.

[0034] In conjunction with the second aspect, the step of dividing the target area into high consequence areas and levels by fusing the level data and environmental data of the target area includes:

[0035] Importing the regional level data and environmental data of the target area into a high consequence area identification module;

[0036] Determine the fourth-level area of ​​the target area as a third-level high-consequence area, and determine the third-level area of ​​the target area as a second-level high-consequence area;

[0037] The high consequence areas are divided into different areas and levels according to the properties of the buildings and the environment in the target area.

[0038] In conjunction with the second aspect, the high consequence area range and level division according to the building properties and environmental properties in the target area includes:

[0039] Extending the two sides of the oil and gas pipeline in the target area by a second preset distance to obtain an area as a determination area;

[0040] The scope and level of the high consequence area are divided according to the building properties and environmental properties of the determination area and combined with the physical parameters of the oil and gas pipeline.

[0041] According to a third aspect of the present disclosure, an electronic device is provided, including:

[0042] one or more processors;

[0043] A storage unit is used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the above-mentioned intelligent identification methods for high-consequence areas in oil and gas pipelines.

[0044] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any of the above-mentioned intelligent identification methods for high-consequence areas in oil and gas pipelines can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural schematic diagram of an intelligent identification system for high-consequence areas in oil and gas pipelines according to an embodiment of the present disclosure;

[0046] Figure 2 A schematic diagram of a process flow of a method for intelligently identifying high-consequence areas in oil and gas pipelines according to an embodiment of the present disclosure;

[0047] Figure 3 A schematic diagram of satellite image extraction of a target area according to an embodiment of the present disclosure;

[0048] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure.

[0050] The terms used in the disclosed embodiments are only for the purpose of describing specific embodiments and are not intended to limit the disclosed embodiments. The singular forms of "a", "said" and "the" used in the disclosed embodiments and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0051] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0052] like Figure 1 FIG. 1 is a schematic diagram of the structure of the intelligent identification system for high-consequence areas of oil and gas pipelines according to an embodiment of the present disclosure. The system includes:

[0053] A high consequence area building extraction module 110 is used to extract building data and environmental data in a target area from satellite images, wherein an oil and gas pipeline is arranged in the target area;

[0054] The regional level identification module 120 is used to classify the target area according to the building data and the regional level standard;

[0055] A high consequence area identification module 130 is used to classify the target area into a high consequence area range and level by fusing the level data and environmental data of the target area;

[0056] The high consequence area rendering module 140 is used to generate a three-dimensional dynamic map according to the range and level of the high consequence area in the target area.

[0057] Specifically, the high consequence area building extraction module 110 extracts building data and environmental data in the target area from satellite images. There are oil and gas pipelines in the target area. The module uses image processing technology to identify and extract information about the building and the surrounding environment, providing basic data for subsequent analysis.

[0058] The regional level identification module 120 classifies the target area according to the regional level standard based on the building data obtained by the high-consequence area building extraction module 110. According to the information such as the number and distribution of buildings, the specific regional level of the target area is determined to be level one, level two, level three or level four.

[0059] The high consequence area identification module 130 divides the target area into high consequence areas by combining the level data and environmental data provided by the area level identification module 120. By integrating these data, the module can identify and determine the specific location and level of the high consequence area, such as the first level high consequence area, the second level high consequence area, etc.

[0060] The high consequence area rendering module 140 generates a three-dimensional dynamic map based on the range and level of the high consequence area determined by the high consequence area identification module 130. This module visualizes the data of the high consequence area for intuitive display and further analysis, so that managers can view and evaluate the situation of the high consequence area in real time.

[0061] like Figure 1 FIG. 1 is a flow chart of a method for intelligently identifying high-consequence areas in oil and gas pipelines according to an embodiment of the present disclosure. The method includes:

[0062] S201: Using the high consequence area building extraction module, extract building data and environmental data in the target area from the satellite image;

[0063] S202: using a regional grade identification module to grade the target area according to the building data and regional grade standards;

[0064] S203: using a high consequence area identification module to divide the target area into high consequence area ranges and levels by fusing the level data and environmental data of the target area;

[0065] S204: Using a high consequence area rendering module, a three-dimensional dynamic map is generated according to the range and level of the high consequence area in the target area.

[0066] Refer to the following Figure 3-Figure 4 The method is described in detail.

[0067] like Figure 3 , which is a schematic diagram of satellite image extraction of a target area according to an embodiment of the present disclosure.

[0068] In this figure, the high-consequence area building extraction module is used to identify buildings and mark them with red frames. By identifying, extracting, generating and storing building data and environmental data, data support is provided for subsequent regional level identification and high-consequence area division.

[0069] The building data includes:

[0070] Building location: the spatial coordinates or geographical location of the building;

[0071] Building type: such as residential building, commercial building, industrial building, etc.;

[0072] Building height: the number of floors or height information of the building;

[0073] Building area: the floor area or total floor area of ​​a building;

[0074] Number of buildings: The total number or density of buildings within the target area.

[0075] Environmental data includes:

[0076] Traffic information: main roads, traffic flow, transportation facilities, etc.

[0077] Natural environment characteristics: such as water bodies, green spaces, forests, etc.

[0078] Inflammable and explosive places: such as gas stations, chemical warehouses and other dangerous areas.

[0079] Underground facilities: such as underground pipelines, underground garages, etc.

[0080] Furthermore, a regional grade identification module is used to grade the target area according to the building data and regional grade standards.

[0081] Specifically, the regional classification standard is four levels: first-level category one regions, first-level category two regions, second-level regions, third-level regions and fourth-level regions.

[0082] Level 1, Category 1 area: areas where there are not frequent human activities and no permanent residents;

[0083] Level 1, Category 2 areas: areas with 15 households or less;

[0084] Secondary area: the area with more than 15 and less than 100 households;

[0085] Level III areas: areas with 100 or more households, including suburban residential areas, commercial areas, industrial areas, planned development areas, and densely populated areas that do not meet the requirements of Level IV areas;

[0086] Level 4 area: an area where buildings of four floors or above (excluding basement floors) are generally concentrated, traffic is frequent, and there are many underground facilities.

[0087] The number and location information of buildings are input into the regional level identification module. These data are the basis for the subsequent regional level division.

[0088] First, we manually marked which areas were Level 4 areas (e.g. city centers, places with many buildings and people).

[0089] Specifically, the places with common concentration, frequent traffic and many underground facilities are first projected onto the oil and gas pipelines to form multiple sections, and the areas extending 400 meters on both sides of these sections are taken as the fourth-level areas.

[0090] Furthermore, the oil and gas pipelines corresponding to the fourth-level areas are removed from the oil and gas pipelines to obtain multiple disconnected oil and gas pipelines. These remaining pipeline parts are taken as multiple small sections, and the areas extending 400 meters on both sides of these small sections are taken as the corresponding areas of the sections. The level of the corresponding area of ​​each section is calculated one by one.

[0091] Among them, the length of some sections is less than 2 kilometers. If the length of a section is less than 2 kilometers, directly calculate the number of buildings in the corresponding area of ​​the section, and then compare it with the regional level division to determine the level of this area.

[0092] Some sections are longer than 2 kilometers. If the section length is longer than 2 kilometers, start from the starting point of the section, take a 2-kilometer-long sub-section, calculate the number of buildings in the corresponding area of ​​this sub-section, and then compare the regional level divisions to determine the level of this area.

[0093] For the remaining sub-segment after being intercepted, starting from the end point of the previous sub-segment, every 10 meters is regarded as a segment, and the number of buildings in the area corresponding to the segment is calculated, and then compared with the regional level division to determine the level of this area. The levels of the regions corresponding to the obtained multiple 10-meter segments are merged into one region according to the principle that high-level regions absorb low-level regions, and the regional level of the region corresponding to the sub-segment is obtained.

[0094] It should be noted that when manually marking which areas are level 4 areas, those skilled in the art may implement it according to specific circumstances, and this embodiment does not impose any specific limitation.

[0095] Furthermore, by integrating the level data and environmental data of the target area, the target area is divided into high consequence areas and levels.

[0096] Specifically, oil and gas pipelines are divided into gas pipelines and oil pipelines.

[0097] For gas pipelines:

[0098] First, import the regional level data into the high consequence area identification module. If the data of the fourth-level region is read, it is directly determined as a third-level high consequence area. If the data of the third-level region is read, it is directly determined as a second-level high consequence area.

[0099] Furthermore, an area of ​​200 meters on each side of the pipeline is taken. If there are flammable and explosive places in the area, the flammable and explosive places are projected onto the pipeline to obtain the starting and end points of the high consequence area. Then the starting and end points are extended 200 meters forward and backward along the direction of the pipeline to obtain a new starting and end point, which is the high consequence area. This high consequence area is determined to be a secondary high consequence area.

[0100] When the diameter of the gas pipeline is greater than 762mm and the maximum allowable operating pressure is greater than 6.9MPa, take an area of ​​200 meters on both sides of the pipeline. If there are specific places in the area (such as places where people gather), project the places onto the pipeline to obtain the starting and end points of the high consequence area. Then expand the starting and end points 200 meters forward and backward along the direction of the pipeline to obtain new starting and end points, which are the high consequence areas. This high consequence area is determined to be a secondary high consequence area.

[0101] For pipes with a diameter less than 273mm and a maximum allowable operating pressure less than 1.6MPa, the same area of ​​200 meters on both sides of the pipeline is taken. If there are specific places (such as places where people gather) in the area, the places are projected onto the pipeline to determine the starting and ending points of the high consequence area. The starting and ending points extend 200 meters forward and backward along the direction of the pipeline to form a high consequence area, which is determined as a first-level high consequence area.

[0102] If the pipe diameter and pressure do not meet the above two conditions of large pipe diameter high pressure or small pipe diameter low pressure, take the area of ​​200 meters on both sides of the pipeline. If there are specific places (such as places where people gather) in the area, project the places onto the pipeline to determine the starting and ending points of the high consequence area, and extend 200 meters to determine the high consequence area. This area is determined as a first-level high consequence area.

[0103] Preferably, if two high consequence areas overlap or are separated by less than 50 meters, they are merged into a high consequence area of ​​a larger range and determined to be a higher level high consequence area.

[0104] For oil pipelines:

[0105] Import the regional level data into the high consequence area identification module. If the data of the fourth level area is read, it is directly determined as the third level high consequence area. If the data of the third level area is read, it is directly determined as the second level high consequence area.

[0106] In addition, an area of ​​200 meters on both sides of the pipeline is selected. If there are special environments such as water sources, rivers, and large reservoirs in the area, the area occupied by the special environment is projected onto the pipeline to determine the starting point and the end point. It is determined as a level 3 high consequence area.

[0107] Take an area of ​​200 meters on both sides of the pipeline. If there are special environments such as wetlands, forests, estuaries, etc. in the area, the area occupied by the special environment will be projected onto the pipeline to determine the starting and end points of the high consequence area, and it will be determined as a secondary high consequence area.

[0108] Building Clusters:

[0109] Take an area of ​​200 meters on both sides of the pipeline and aggregate the buildings in the area into clusters. If the number of buildings in a cluster in the area is greater than or equal to 50, the area occupied by the cluster of buildings is projected onto the pipeline to determine the starting and ending points, and extended by 200 meters to determine the secondary high consequence area.

[0110] Take an area of ​​50 meters on both sides of the pipeline. If there are flammable and explosive places in the area, project the place onto the pipeline to determine the starting and end points, and extend it 200 meters to determine it as a first-level high-consequence area.

[0111] Take an area of ​​50 meters on both sides of the pipeline. If there is a road network in the area, project the road network onto the pipeline to determine the starting point and end point to form a high consequence area. This area is determined as a first-level high consequence area.

[0112] If two high consequence areas overlap or are less than 50 meters apart, they will be merged into a larger high consequence area and determined to be a higher level high consequence area.

[0113] Finally, a three-dimensional dynamic map is generated according to the scope and level of the high consequence area in the target area.

[0114] Exemplarily, using a geographic information system (GIS) and 3D modeling software, the data of high-consequence areas are converted into 3D models. These models can show the ups and downs of the terrain and the three-dimensional structure of the buildings. The range and level information of the high-consequence areas are integrated into the 3D model so that areas of different levels can be clearly marked in the model.

[0115] Add dynamic features to the 3D map, such as dynamically adjusting the map perspective, zooming in and out, and displaying specific information as needed. If the scope or level of the high consequence area changes, the 3D dynamic map can be updated in real time to reflect the latest information. This ensures that the map always displays the most accurate risk information.

[0116] According to the 3D model, thematic maps are made to mark high-consequence areas of different levels and distinguish them using colors or other markers. The final 3D dynamic map is presented to users or decision makers to provide intuitive visual effects to help them understand and manage high-consequence areas.

[0117] The embodiments of the present disclosure provide an intelligent identification system and method for high-consequence areas of oil and gas pipelines, which uses an image recognition artificial intelligence algorithm to extract building data and environmental data from orthophotos, and automatically calculates, divides, and colors high-consequence areas according to regional grades and high-consequence area calculation algorithms and national standards. It can accurately collect spatial location information of elements around oil and gas pipelines, and provide a unified and standard scale for regional grades and high-consequence area identification through a rule-based algorithm, so as to achieve a high degree of consistency in the identification results; and it can also meet the requirements of high-consequence area identification for oil and gas pipelines.

[0118] The electronic device 400 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 400 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 400 and does not constitute a limitation of the electronic device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0119] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0120] The memory 402 may be an internal storage unit of the electronic device 400, for example, a hard disk or memory of the electronic device 400. The memory 402 may also be an external storage device of the electronic device 400, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 400. Further, the memory 402 may also include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store the computer program 403 and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.

[0121] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present disclosure. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0122] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0124] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0128] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. An intelligent identification system for high-consequence areas of oil and gas pipelines, characterized in that: The system comprises: A high consequence area building extraction module is used to extract building data and environmental data in a target area from satellite images, where oil and gas pipelines are located; A regional grade identification module, used for classifying the target area according to the building data and regional grade standards; A high consequence area identification module, used to divide the target area into high consequence areas and levels by fusing the level data and environmental data of the target area; The high consequence area rendering module is used to generate a three-dimensional dynamic map according to the range and level of the high consequence area in the target area.

2. An intelligent identification method for high-consequence areas of oil and gas pipelines, characterized in that: The following steps are involved: Use the High Consequence Area Building Extraction Module to extract building data and environmental data in the target area from satellite images; Using a regional grade identification module, the target area is graded according to the building data and regional grade standards; Using a high consequence area identification module, by fusing the level data and environmental data of the target area, the target area is divided into a high consequence area range and a level; A high consequence area rendering module is used to generate a three-dimensional dynamic map according to the range and level of the high consequence area in the target area.

3. The method according to claim 2, characterized in that Extracting building data and environmental data in the target area from satellite images includes: Acquire a satellite image of the target area, and use the color, texture, and geometric features of the target area in the satellite image as a first feature set; Decomposing the geometric features in the first feature set to obtain a first building distinguishing boundary, and converting the color and texture features into the building surface reflection intensity and shadow direction to form a second feature set; By comparing satellite images at different time periods, the changed buildings are extracted, and the second building boundary is obtained by fusing the second feature set with the second feature set; The second building boundary is merged with the distribution data of the land cover type, roads and vegetation in the target area to generate a data extraction layer; The data extraction layer is merged with the position coordinates of the target area to obtain the building data and environmental data in the target area.

4. The method according to claim 2, characterized in that: The classifying the target area according to the building data and the regional grade standard comprises: Importing the building data in the target area into the regional level identification module and marking the fourth-level area; According to the marked four-level regions, the target area is divided into a plurality of sections; Calculate the number of buildings in the area corresponding to each section, and divide the level of the area corresponding to each section according to the number of buildings.

5. The method according to claim 4, characterized in that The target area is divided into a plurality of sections according to the marked four-level regions, including: Removing the fourth-level region from the target region to obtain a plurality of discontinuous regions; The oil and gas pipelines contained in the multiple discontinuous areas are regarded as corresponding multiple sections.

6. The method according to claim 4, characterized in that The calculating the number of buildings in the area corresponding to each section and classifying the area corresponding to each section according to the number of buildings includes: Extending the first preset distance on both sides of the segment to obtain an area as the corresponding area of ​​the segment; Calculate the number of buildings in the corresponding area of ​​each segment; Areas corresponding to the sections with a number of buildings greater than or equal to 100 are classified as third-level areas; areas corresponding to the sections with a number of buildings greater than 15 and less than 100 are classified as second-level areas; areas corresponding to the sections with a number of buildings greater than or equal to 0 and less than or equal to 15 are classified as first-level second-category areas; areas corresponding to the sections with no buildings are classified as first-level first-category areas.

7. The method according to claim 2, characterized in that: The step of dividing the target area into high consequence areas and levels by fusing the level data and environmental data of the target area comprises: Importing the regional level data and environmental data of the target area into a high consequence area identification module; Determine the fourth-level area of ​​the target area as a third-level high-consequence area, and determine the third-level area of ​​the target area as a second-level high-consequence area; The high consequence areas are divided into different areas and levels according to the properties of the buildings and the environment in the target area.

8. The method according to claim 7, characterized in that The high consequence area range and level division according to the building properties and environmental properties in the target area includes: Extending the two sides of the oil and gas pipeline in the target area by a second preset distance to obtain an area as a determination area; The scope and level of the high consequence area are divided according to the building properties and environmental properties of the determination area and combined with the physical parameters of the oil and gas pipeline.

9. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the intelligent identification method for high-consequence areas in oil and gas pipelines according to any one of claims 2 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the intelligent identification method of high consequence areas in oil and gas pipelines according to any one of claims 2 to 8.

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