An intrusion monitoring method, system, device and medium for oil and gas pipelines

Abstract

The present invention discloses an intrusion monitoring method, system, device and medium for oil and gas pipelines, relating to the technical field of oil and gas pipeline monitoring. The method includes: Step S1, obtaining at least one piece of current monitoring data for the oil and gas pipeline; Step S2, for each piece of the current monitoring data, inputting the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determining whether an intrusion event has occurred through the target digital twin model. The oil and gas pipeline includes multiple pipeline segments. By using the target digital twin model for the oil and gas pipeline to analyze and judge the obtained current monitoring data, it is determined whether an intrusion event has occurred in the pipeline segment corresponding to the current monitoring data, which is convenient for subsequent alarm to the operating enterprise of the oil and gas pipeline according to the intrusion matter, and for the operating enterprise to make timely decisions according to the location where the intrusion event occurs, so as to prevent the occurrence of accidents and disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas pipeline monitoring, and in particular to an intrusion monitoring method, system, device and medium for oil and gas pipelines. Background Art

[0002] According to the statistical analysis of long-distance oil and gas pipeline accidents at home and abroad, surface excavation above the pipeline is one of the main threats to the safe operation of pipelines. With the rapid development of social economy, especially the acceleration of the urbanization process, the surrounding environment of China's long-distance oil and gas pipelines is changing with each passing day. Surface excavations and occupation activities above the pipelines are increasing accordingly, and pipeline failure accidents caused thereby occur from time to time. Excavating and taking soil near the pipeline will directly damage the pipeline anti-corrosion layer or the outer wall of the pipeline at least, resulting in external corrosion of the pipeline and a decrease in its pressure-bearing capacity; at worst, it will damage the pipeline and cause the pipeline to rupture and leak, leading to environmental pollution and even combustion and explosion accidents. The indirect impact on the pipeline is that the stability of the soil around the pipeline changes. In the rainy season, it may cause soil layer dislocation and soil collapse, making the pipeline unevenly stressed and facing potential safety hazards.

[0003] In order to ensure pipeline safety, when excavating and taking soil and constructing buildings and structures, it is necessary to first identify whether there are oil and gas pipelines nearby. If there are oil and gas pipelines, relevant legal provisions need to be strictly followed. For example, when carrying out the following construction operations, an application needs to be submitted to the relevant department: construction operations that cross or span pipelines; within 550 meters on each side of the pipeline route center line and within 100 meters around pipeline ancillary facilities, new construction, reconstruction, and expansion of railways, highways, and canals, erection of underground cables and optical cables, and setting of safety grounding bodies and lightning protection grounding bodies; within 200 meters on each side of the pipeline route center line and within 500 meters of pipeline ancillary facilities, blasting, seismic exploration, or engineering excavation, engineering drilling, and mining.

[0004] At present, for threats such as occupation, soil taking, excavation, and construction by third parties occurring along and above the pipeline, various means are adopted for daily patrol and threat monitoring of oil and gas pipelines. The main method is a combination of manual patrol and drone patrol. For key pipeline sections, a video monitoring system, a leakage monitoring and safety warning system need to be installed to effectively monitor external activities and timely issue early warnings of pipeline damage and leakage. The daily patrol of oil and gas pipelines is usually carried out at a certain cycle, such as twice a day. However, for locations such as high-consequence areas with intensive population, areas with high human activity, and areas prone to geological disasters, the patrol frequency needs to be increased. Satellite photos of the earth can help pipeline operators timely discover changes in the terrain and landforms along the pipeline, identify terrain changes such as landslides, mudslides, and ground settlement along the pipeline, and landform changes such as hydraulic damage, exposed pipelines, and occupation.

[0005] At present, for the daily patrol and threat monitoring of oil and gas pipelines, the daily patrol of the pipeline route is intermittent. The optical fiber early warning technology is troubled by the inability to accurately locate parallel optical cables and the problem of cable breakage. The leakage monitoring system can only monitor after a leakage occurs. The video monitoring system only monitors high-consequence areas and cannot cover the entire pipeline. The high-definition satellite camera technology has strict requirements for terrain and landforms. In case of high-altitude obstacles, foggy weather or oil theft by drilling in the fields, it cannot effectively monitor either. The above-mentioned various technologies are limited by the problems of monitoring accuracy and positioning accuracy, and often have missed reports, false alarms and misjudgments. Under the current technical background, they can neither "complement each other" to comprehensively and real-time monitor the pipeline status, nor visually display the threats of third-party intrusion experienced by the pipeline. Third-party intrusion incidents of pipelines occur from time to time. How to accurately perceive and locate in the first time to prevent the occurrence of accidents and the expansion of consequences is still a difficult problem that the pipeline industry has not been able to completely solve. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that the existing methods for monitoring oil and gas pipeline intrusion have low efficiency in determining whether an intrusion event occurs, which affects the subsequent rapid positioning of the location where the intrusion event occurs and the corresponding handling of the intrusion event. To solve this technical problem, the present invention provides a method, system, device and medium for monitoring oil and gas pipeline intrusion.

[0007] The technical solution of the present invention to solve the above technical problems is as follows:

[0008] A method for monitoring oil and gas pipeline intrusion, comprising:

[0009] Step S1, obtaining at least one piece of current monitoring data for the oil and gas pipeline;

[0010] Step S2, for each piece of the current monitoring data, inputting the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determining whether an intrusion event occurs through the target digital twin model.

[0011] The beneficial effect of the present invention is that by using the target digital twin model for the oil and gas pipeline to analyze and judge the obtained current monitoring data, it can quickly determine whether an intrusion event occurs in the oil and gas pipeline and the location where the intrusion event occurs, which is convenient for subsequent alarm to the operating enterprise of the oil and gas pipeline according to the intrusion matter, and for the operating enterprise to make timely decisions according to the location where the intrusion event occurs, so as to prevent the occurrence of accidents and disasters and prevent the expansion of accidents and the occurrence of secondary disasters.

[0012] On the basis of the above technical solution, the present invention can also be improved as follows.

[0013] Further, the step S1 includes:

[0014] The current monitoring data is obtained by an image-based acquisition device or a digital-based acquisition device, where the image-based acquisition device includes any one of a drone, a camera, and a satellite monitoring device, and the digital-based acquisition device includes any one of an optical fiber monitoring device and a leak detection device;

[0015] For each of the current monitoring data, if the current monitoring data is obtained by the image-based acquisition device, the current monitoring data is image-based monitoring data, and if the current monitoring data is obtained by the digital-based acquisition device, the current monitoring data is digital-based monitoring data.

[0016] The beneficial effect of adopting the above further solution is that through various intelligent monitoring technologies, integrated all-round intelligent monitoring is achieved, facilitating real-time perception of the risk of third-party intrusion into oil and gas pipelines.

[0017] Further, the target digital twin model is determined through the following steps:

[0018] Step A1, obtain multi-source attribute data of the oil and gas pipeline, where the multi-source attribute data includes static constant data and dynamic variable data;

[0019] Step A2, perform big data analysis and processing on the multi-source attribute data to determine the static constant data and the dynamic variable data;

[0020] Step A3, according to the static constant data and the dynamic variable data, use data twin technology to construct a pipeline digital twin model;

[0021] Step A4, obtain the target training data of the oil and gas pipeline, where the target training data includes at least one historical monitoring data corresponding to each of the multiple pipeline segments corresponding to the oil and gas pipeline, and the intrusion monitoring result corresponding to each historical monitoring data. For each historical monitoring data, the intrusion monitoring result is that an intrusion has occurred or not occurred in the intrusion area corresponding to the historical monitoring data;

[0022] Step A5, use the target training data to train the pipeline digital twin model to obtain the target digital twin model.

[0023] The beneficial effect of adopting the above further solution is that by constructing the target digital twin model corresponding to the oil and gas pipeline, it is convenient to quickly and accurately determine whether an intrusion event has occurred.

[0024] Further, for each of the pipeline segments, the historical monitoring data is determined through the following steps:

[0025] Taking the center point of the pipeline segment as the center of the circle, determine the intrusion area corresponding to the pipeline segment according to a preset reference radius;

[0026] Obtain the original historical monitoring data corresponding to the pipeline segment, where the original historical monitoring data includes the location information of the pipeline segment, the importance level of the pipeline segment, the maintenance required for the pipeline segment, the number of intruders who invaded the pipeline segment, the basic information of the intruders who invaded the pipeline segment, the tool information carried by the intruders who invaded the pipeline segment, and the distance between the intruders and the boundary of the intrusion area corresponding to the pipeline segment;

[0027] Perform noise reduction processing on the original historical monitoring data to obtain the historical monitoring data corresponding to the pipeline segment.

[0028] The beneficial effect of adopting the above further solution is that by processing each obtained original historical monitoring data, the historical monitoring data corresponding to each original historical monitoring data is obtained, which is convenient for training the pipeline digital twin model according to the historical monitoring data and its corresponding intrusion monitoring results, and ensures the accuracy of the target digital twin model in determining whether an intrusion event occurs.

[0029] Further, the method further includes:

[0030] For each of the current monitoring data, determine whether the current monitoring data is image-based monitoring data or digital-based monitoring data;

[0031] For each of the current monitoring data, if the current monitoring data is image-based monitoring data, use the current monitoring data as type-one monitoring data, and if the current monitoring data is digital-based monitoring data, use the current monitoring data as type-two monitoring data;

[0032] The step S2 includes:

[0033] For each of the type-one monitoring data, input the type-one monitoring data into the target digital twin model, and determine whether an intrusion event occurs through the target digital twin model;

[0034] For each of the type-two monitoring data, input the type-two monitoring data into the target digital twin model, and determine whether an intrusion event occurs through the target digital twin model.

[0035] Further, for each of the type-one monitoring data, the step of inputting the type-one monitoring data into the target digital twin model and determining whether an intrusion event occurs through the target digital twin model includes:

[0036] Step B1: For each piece of the first type of monitoring data, when there are people in the first type of monitoring data, use the first type of monitoring data as the target processing data;

[0037] Step B2: For each piece of the target processing data, according to the pipeline maintenance strategy preset for the oil and gas pipeline, determine whether the people appearing in the target processing data are for normal work scheduling. If so, determine that no intrusion event has occurred; if not, execute Step B3;

[0038] Step B3: Input the target processing data into the target digital twin model to determine the event alarm value corresponding to the target processing data;

[0039] Step B4: If the event alarm value is greater than or equal to the preset event alarm threshold, determine that an intrusion event has occurred; if the event alarm value is less than the event alarm threshold, determine that no intrusion event has occurred;

[0040] For each piece of the second type of monitoring data, inputting the second type of monitoring data into the target digital twin model and determining whether an intrusion event has occurred through the target digital twin model includes:

[0041] Step C1: For each piece of the second type of monitoring data, determine the parameters corresponding to the second type of monitoring data;

[0042] Step C2: For each piece of the second type of monitoring data, input the second type of monitoring data into the target digital twin model, and determine whether an intrusion event has occurred according to the second type of monitoring data, the parameters corresponding to the second type of monitoring data, and the preset parameter alarm threshold corresponding to the parameters;

[0043] In Step B3, inputting the target processing data into the target digital twin model to determine the event alarm value corresponding to the target processing data includes:

[0044] Determine the target pipeline section corresponding to the target processing data;

[0045] According to the pipeline maintenance strategy, determine the repair situation to be carried out on the target pipeline section;

[0046] According to the target processing data, determine the type of the target pipeline section and the relevant information of the person to be verified. The person to be verified is the person in the target processing data, and the relevant information includes the position situation between the person to be verified and the intrusion area corresponding to the pipeline section, the total number of the person to be verified, and the tool-carrying situation of the person to be verified;

[0047] Input the target processing data into the target digital twin model, and determine the event alarm value corresponding to the target processing data according to the to-be-repaired condition of the target pipeline section, the type of the target pipeline section, and the relevant information.

[0048] The beneficial effects of adopting the above further solution are as follows: For image-based monitoring data, when there are personnel in the image-based monitoring data, first determine whether the personnel in the image-based monitoring data are staff members. When it is determined that the personnel in the image-based monitoring data are non-staff members, determine the event alarm value corresponding to the image-based monitoring data through the target digital twin model. According to the event alarm value and the event alarm threshold, it can be determined whether an intrusion event has occurred; for digital-based monitoring data, according to the digital-based monitoring data, the parameters corresponding to the digital-based monitoring data, and the preset parameter alarm threshold, it can be determined whether an intrusion event has occurred; through the calculated event alarm value and the obtained digital-based monitoring data, it can be quickly determined whether an intrusion event has occurred. After it is determined that an intrusion event has occurred, subsequent alarms are sent to the operating enterprise of the oil and gas pipeline according to the intrusion matters. Through the timely decision-making of the operating enterprise, it is convenient for the staff to carry out timely processing and prevention, and can prevent the occurrence of accidents and disasters and prevent the expansion of accidents and the occurrence of secondary disasters.

[0049] Further, the method further includes:

[0050] For each piece of the current monitoring data, determine whether there is a misjudgment event according to the judgment result corresponding to the current monitoring data, and the judgment result is that an intrusion event has occurred or an intrusion event has not occurred;

[0051] If there is the misjudgment event, update the target digital twin model according to the current monitoring data corresponding to the misjudgment event.

[0052] The beneficial effects of adopting the above further solution are as follows: According to the current monitoring data and the judgment result corresponding to the misjudgment event, re-evaluate the entire solution to determine whether there are problems, so as to modify the target digital twin model, reduce the adverse effects caused by misjudgment, avoid the occurrence of misjudgment situations, achieve accurate prediction, and ensure the rapid identification of accidents and disasters.

[0053] To solve the above technical problems, the present invention also provides an intrusion monitoring system for an oil and gas pipeline, including:

[0054] A data acquisition module, configured to acquire at least one piece of current monitoring data for the oil and gas pipeline;

[0055] An intrusion judgment module, configured to input, for each piece of the current monitoring data, the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determine whether an intrusion event has occurred through the target digital twin model.

[0056] To solve the above technical problems, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the intrusion monitoring method for an oil and gas pipeline as described above is implemented.

[0057] To solve the above technical problems, the present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the intrusion monitoring method for an oil and gas pipeline as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flowchart of the intrusion monitoring method for an oil and gas pipeline in the present invention;

[0059] Figure 2 It is a schematic structural diagram of the intrusion monitoring system for an oil and gas pipeline in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0061] Embodiment 1

[0062] As Figure 1 shown, this embodiment provides an intrusion monitoring method for an oil and gas pipeline, including:

[0063] Step S1, obtaining at least one piece of current monitoring data for the oil and gas pipeline;

[0064] Step S2, for each piece of the current monitoring data, inputting the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determining whether an intrusion event occurs through the target digital twin model.

[0065] Among them, the current monitoring data refers to the monitoring data obtained at different positions of the oil and gas pipeline; the intrusion event specifically refers to an accident and disaster event that may occur or is occurring in the oil and gas pipeline, such as: oil and gas pipeline rupture, oil and gas leakage, and major environmental pollution events caused by natural disasters such as floods, mudslides, landslides, and earthquakes, and a large amount of oil and gas leakage in the oil and gas pipeline caused by third-party damage, pipeline corrosion, etc., which is likely to cause fire, explosion, or major environmental pollution events.

[0066] Optionally, step S1 includes:

[0067] Acquiring the current monitoring data through an image acquisition device or a digital acquisition device, wherein the image acquisition device includes any one of a drone, a camera, and a satellite monitoring device, and the digital acquisition device includes any one of a fiber optic monitoring device and a leak detection device;

[0068] For each of the current monitoring data, if the current monitoring data is obtained through the image acquisition device, then the current monitoring data is image monitoring data; if the current monitoring data is obtained through the digital acquisition device, then the current monitoring data is digital monitoring data.

[0069] Optionally, the target digital twin model is determined by the following steps:

[0070] Step A1: Acquire multi-source attribute data of the oil and gas pipeline, wherein the multi-source attribute data includes static constant data and dynamic variable data. In this embodiment, the multi-source attribute data includes basic attributes of the oil and gas pipeline, physical properties of the transported oil and gas medium, hydraulic data, thermal data, and production operation data.

[0071] Step A2: performing big data analysis on the multi-source attribute data to determine static constant data and dynamic variable data. In this embodiment, the static constant data is data generated when no liquid flows within the oil and gas pipeline, and the dynamic variable data is data generated when liquid flows within the oil and gas pipeline.

[0072] Step A3, constructing a pipeline digital twin model using data twin technology based on the static constant data and the dynamic variable data; the purpose of distinguishing the static constant data from the dynamic variable data in this method is to construct a pipeline digital twin model based on the static constant data and the dynamic variable data, so that the target digital twin model finally obtained based on the pipeline digital twin model can accurately predict the risks of the oil and gas pipeline under different states, thereby ensuring the accuracy of oil and gas pipeline monitoring; wherein, the digital twin is to make full use of physical models, sensor updates, operation history and other data, integrate multi-disciplinary, multi-physical quantity, multi-scale, multi-probability simulation processes, complete mapping in virtual space, thereby reflecting the entire life cycle process of the corresponding physical equipment. In this method, the use of digital twin technology to infer the subsequent development of the oil and gas pipeline based on changes in existing data is an existing technology and will not be elaborated here;

[0073] Step A4: Obtain the target training data of the oil and gas pipeline. The target training data includes at least one historical monitoring data corresponding to each of the multiple pipeline segments of the oil and gas pipeline, and the intrusion monitoring result corresponding to each historical monitoring data. For each historical monitoring data, the intrusion monitoring result is that an intrusion has occurred or not occurred in the intrusion area corresponding to the historical monitoring data. In this embodiment, the target training data is the data of the oil and gas pipeline and its along - line obtained by image - type acquisition devices and digital - type acquisition devices.

[0074] Step A5: Use the target training data to train the pipeline digital twin model to obtain the target digital twin model.

[0075] Optionally, for each pipeline segment, the historical monitoring data is determined through the following steps:

[0076] Take the center point of the pipeline segment as the center of a circle, and determine the intrusion area corresponding to the pipeline segment according to a preset reference radius.

[0077] Obtain the original historical monitoring data corresponding to the pipeline segment. The original historical monitoring data includes the location information of the pipeline segment, the importance level of the pipeline segment, the maintenance - required situation of the pipeline segment, the number of intruders invading the pipeline segment, the basic information of the intruders invading the pipeline segment, the tool information carried by the intruders invading the pipeline segment, and the distance between the intruders and the boundary of the intrusion area corresponding to the pipeline segment. Among them, the importance level and the maintenance - required situation of the pipeline segment are determined according to the oil - transfer attribute of the pipeline segment.

[0078] Perform noise reduction processing on the original historical monitoring data to obtain the historical monitoring data corresponding to the pipeline segment.

[0079] Optionally, the method further includes:

[0080] For each current monitoring data, determine whether the current monitoring data is image - type monitoring data or digital - type monitoring data.

[0081] For each current monitoring data, if the current monitoring data is image - type monitoring data, regard the current monitoring data as type - one monitoring data; if the current monitoring data is digital - type monitoring data, regard the current monitoring data as type - two monitoring data.

[0082] Step S2 includes:

[0083] For each type - one monitoring data, input the type - one monitoring data into the target digital twin model, and determine whether an intrusion event has occurred through the target digital twin model.

[0084] For each of the second - type monitoring data, input the second - type monitoring data into the target digital twin model, and determine whether an intrusion event has occurred through the target digital twin model.

[0085] Optionally, for each of the first - type monitoring data, the step of inputting the first - type monitoring data into the target digital twin model and determining whether an intrusion event has occurred through the target digital twin model includes:

[0086] Step B1: For each of the first - type monitoring data, when there are people in the first - type monitoring data, it indicates that there may be people intruding into the corresponding intrusion area, then take the first - type monitoring data as the target processing data.

[0087] Step B2: For each of the target processing data, according to the pipeline maintenance strategy preset for the oil - gas pipeline, determine whether the people appearing in the target processing data are for normal work scheduling. Normal work scheduling refers to the work arrangements made by relevant units for the oil - gas pipeline. If so, it indicates that the people appearing will not cause an intrusion event, then determine that no intrusion event has occurred. If not, then execute Step B3.

[0088] Step B3: Input the target processing data into the target digital twin model, and determine the event alarm value corresponding to the target processing data. The calculation formula of the event alarm value is as follows:

[0089]

[0090] In the above formula, F1 represents the event alarm value, S IJ represents the attribute value of the type of the pipeline segment corresponding to the target processing data, R IJ represents the reference radius, M IJ represents the distance between the person and the boundary of the intrusion area corresponding to the pipeline segment, represents the position situation between the person and the intrusion area corresponding to the pipeline segment, N IJ represents the total number of people, Q IJ represents the tool - carrying situation of the people, L IJ represents the to - be - repaired situation of the pipeline segment; in this embodiment, the types of the pipeline segment include the main road and the branch road. When the type of the pipeline segment is the main road, the value of S IJ is 2. When the type of the pipeline segment is the branch road, the value of S IJ is 1; if the person does not enter the intrusion area corresponding to the pipeline segment, the value of is 0; Q IJ the value of is the quantity carried by the person; when the pipeline segment is in a to - be - repaired state, L IJtakes a value of 2. When the pipeline section is not in the state to be repaired, L IJ takes a value of 1;

[0091] Step B4: If the event alarm value is greater than or equal to the preset event alarm threshold, it is determined that an intrusion event has occurred; if the event alarm value is less than the event alarm threshold, it is determined that no intrusion event has occurred.

[0092] For each of the second - type monitoring data, inputting the second - type monitoring data into the target digital twin model and determining whether an intrusion event has occurred through the target digital twin model includes:

[0093] Step C1: For each of the second - type monitoring data, determine the parameter corresponding to the second - type monitoring data, where the parameter represents the attribute to which the second - type monitoring data belongs.

[0094] Step C2: For each of the second - type monitoring data, input the second - type monitoring data into the

[0095] target digital twin model. According to the second - type monitoring data, the parameter corresponding to the second - type monitoring data, and the preset parameter alarm threshold corresponding to the parameter, determine whether an intrusion event has occurred. In this embodiment, if the second - type monitoring data is greater than or equal to the preset parameter alarm threshold corresponding to the parameter of the second - type monitoring data, it is determined that an intrusion event has occurred; if the second - type monitoring data is less than the preset parameter alarm threshold corresponding to the parameter of the second - type monitoring data, it is determined that no intrusion event has occurred.

[0096] In the step B3, inputting the target processing data into the target digital twin model and determining the event alarm value corresponding to the target processing data includes:

[0097] Determine the target pipeline section corresponding to the target processing data;

[0098] According to the pipeline maintenance strategy, determine the situation of the target pipeline section to be repaired;

[0099] According to the target processing data, determine the type of the target pipeline section and the relevant information of the person to be verified. The person to be verified is the person in the target processing data, and the relevant information includes the position situation between the person to be verified and the intrusion area corresponding to the pipeline section, the total number of the persons to be verified, and the tool - carrying situation of the persons to be verified.

[0100] Input the target processing data into the target digital twin model. According to the situation of the target pipeline section to be repaired, the type of the target pipeline section, and the relevant information, determine the event alarm value corresponding to the target processing data.

[0101] Optionally, the method further includes:

[0102] For each of the current monitoring data, according to the judgment result corresponding to the current monitoring data, determine whether there is a judgment error event, where the judgment result is that an intrusion event occurs or an intrusion event does not occur, and the judgment error event indicates that the judgment result of determining whether an intrusion event occurs based on the current monitoring data is incorrect;

[0103] If there is the judgment error event, update the target digital twin model according to the current monitoring data corresponding to the judgment error event.

[0104] Optionally, the method further includes:

[0105] When it is determined that an intrusion event occurs, alarm the operating enterprise of the oil and gas pipeline, and the operating enterprise determines the location corresponding to the intrusion event according to the current monitoring data and displays it in a visual manner, so as to facilitate the operating enterprise to make decisions in a timely manner and prevent the occurrence of accidents and disasters.

[0106] Optionally, the method further includes:

[0107] Use a tablet and sensors to achieve intelligent patrol by people. The tablet solves the problems of edge computing and data transmission, the sensors solve the problems of seeing and hearing, people act as mobile carriers to move in the station yard, the positioning device built in the tablet realizes the recording of the movement trajectory, the tablet pushes the work that needs to be carried out at this position according to the position, and displays the maintenance and repair operation plans of various devices in real time. The wireless camera records the operation steps in real time to realize operation and recording according to the regulations and intelligent video monitoring.

[0108] Embodiment 2

[0109] Based on the same principle as the intrusion monitoring method for oil and gas pipelines described in the above Embodiment 1, this embodiment provides an intrusion monitoring system for oil and gas pipelines, as Figure 2 shown, including:

[0110] A data acquisition module, configured to acquire at least one current monitoring data for an oil and gas pipeline;

[0111] An intrusion judgment module, configured to input, for each of the current monitoring data, the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determine whether an intrusion event occurs through the target digital twin model.

[0112] Among them, the data acquisition module obtains the current monitoring data through an image acquisition device or a digital acquisition device. The image acquisition device includes any one of an unmanned aerial vehicle, a camera, and a satellite monitoring device, and the digital acquisition device includes any one of an optical fiber monitoring device and a leakage detection device;

[0113] For each of the current monitoring data, if the current monitoring data is obtained through the image acquisition device, the current monitoring data is image monitoring data, and if the current monitoring data is obtained through the digital acquisition device, the current monitoring data is digital monitoring data;

[0114] For each of the current monitoring data, determine whether the current monitoring data is image monitoring data or digital monitoring data;

[0115] For each of the current monitoring data, if the current monitoring data is image monitoring data, use the current monitoring data as one type of monitoring data, and if the current monitoring data is digital monitoring data, use the current monitoring data as two types of monitoring data.

[0116] Among them, the system further includes a model construction module for constructing the target digital twin model;

[0117] The model construction module includes:

[0118] The first unit is used to obtain the multi-source attribute data of the oil and gas pipeline, and the multi-source attribute data includes static constant data and dynamic variable data;

[0119] The second unit is used to perform big data analysis and processing on the multi-source attribute data to determine static constant data and dynamic variable data;

[0120] The third unit is used to construct a pipeline digital twin model using data twin technology based on the static constant data and the dynamic variable data;

[0121] The fourth unit is used to obtain the target training data of the oil and gas pipeline. The target training data includes at least one historical monitoring data corresponding to each of the multiple pipeline segments corresponding to the oil and gas pipeline, and the intrusion monitoring result corresponding to each historical monitoring data. For each historical monitoring data, the intrusion monitoring result is that an intrusion has occurred or not occurred in the intrusion area corresponding to the historical monitoring data;

[0122] The fifth unit is used to train the pipeline digital twin model using the target training data to obtain the target digital twin model.

[0123] Among them, the fourth unit includes:

[0124] The first sub-unit is used to determine, for each of the pipeline segments, an intrusion area corresponding to the pipeline segment with the center point of the pipeline segment as the center and according to a preset reference radius.

[0125] The second sub-unit is used to obtain, for each of the pipeline segments, the original historical monitoring data corresponding to the pipeline segment, where the original historical monitoring data includes the position information of the pipeline segment, the importance level of the pipeline segment, the maintenance required situation of the pipeline segment, the number of intruders invading the pipeline segment, the basic information of the intruders invading the pipeline segment, the tool information carried by the intruders invading the pipeline segment, and the distance between the intruders and the boundary of the intrusion area corresponding to the pipeline segment.

[0126] The third sub-unit is used to perform noise reduction processing on the original historical monitoring data corresponding to each of the pipeline segments to obtain the historical monitoring data corresponding to the pipeline segment.

[0127] Among them, the intrusion judgment module includes a first intrusion judgment module and a second intrusion judgment module.

[0128] The first intrusion judgment module is used to input, for each of the first type of monitoring data, the first type of monitoring data into the target digital twin model, and determine whether an intrusion event occurs through the target digital twin model.

[0129] The second intrusion judgment module is used to input, for each of the second type of monitoring data, the second type of monitoring data into the target digital twin model, and determine whether an intrusion event occurs through the target digital twin model.

[0130] Among them, the first intrusion judgment module includes:

[0131] The first processing unit is used to, for each of the first type of monitoring data, when there are people in the first type of monitoring data, use the first type of monitoring data as target processing data.

[0132] The second processing unit is used to, for each of the target processing data, determine whether the people appearing in the target processing data are normal work schedules according to the pipeline maintenance strategy preset for the oil and gas pipeline. If so, it is determined that no intrusion event occurs. If not, the third processing unit is executed.

[0133] The third processing unit is used to input the target processing data into the target digital twin model to determine the event alarm value corresponding to the target processing data.

[0134] A fourth processing unit, configured to determine that an intrusion event has occurred if the event alarm value is greater than or equal to a preset event alarm threshold; and determine that no intrusion event has occurred if the event alarm value is less than the event alarm threshold.

[0135] Wherein, the second intrusion determination module includes:

[0136] A fifth processing unit, configured to determine, for each of the second-class monitoring data, a parameter corresponding to the second-class monitoring data;

[0137] A sixth processing unit, configured to input, for each of the second-class monitoring data, the second-class monitoring data into the target digital twin model, and determine whether an intrusion event has occurred according to the second-class monitoring data, the parameter corresponding to the second-class monitoring data, and a preset parameter alarm threshold corresponding to the parameter.

[0138] Wherein, the third processing unit is specifically configured to:

[0139] Determine a target pipeline section corresponding to the target processing data;

[0140] Determine a situation to be repaired for the target pipeline section according to the pipeline maintenance strategy;

[0141] Determine the type of the target pipeline section and relevant information of the person to be verified according to the target processing data, where the person to be verified is the person in the target processing data, and the relevant information includes the location situation between the person to be verified and the intrusion area corresponding to the pipeline section, the total number of the persons to be verified, and the tool-carrying situation of the persons to be verified;

[0142] Input the target processing data into the target digital twin model, and determine an event alarm value corresponding to the target processing data according to the situation to be repaired for the target pipeline section, the type of the target pipeline section, and the relevant information.

[0143] Wherein, the system further includes a result identification module, configured to:

[0144] For each of the current monitoring data, determine whether there is a judgment error event according to a judgment result corresponding to the current monitoring data, where the judgment result is that an intrusion event has occurred or no intrusion event has occurred;

[0145] If there is the judgment error event, update the target digital twin model according to the current monitoring data corresponding to the judgment error event.

[0146] Embodiment III

[0147] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the intrusion monitoring method for oil and gas pipelines as described in Embodiment 1.

[0148] Embodiment 4

[0149] This embodiment provides a computer-readable storage medium with a computer program stored thereon. When the computer program is executed by a processor, it implements the intrusion monitoring method for oil and gas pipelines as described in Embodiment 1.

[0150] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0151] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0152] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An intrusion monitoring method for oil and gas pipelines, characterized in that, Including: Step S1, obtaining at least one piece of current monitoring data for the oil and gas pipeline; Step S2, for each piece of the current monitoring data, inputting the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determining whether an intrusion event has occurred through the target digital twin model; The target digital twin model is determined through the following steps: Step A1, obtaining multi-source attribute data of the oil and gas pipeline, where the multi-source attribute data includes static constant data and dynamic variable data; Step A2, performing big data analysis and processing on the multi-source attribute data to determine the static constant data and dynamic variable data; Step A3, according to the static constant data and the dynamic variable data, using data twin technology to construct a pipeline digital twin body model; Step A4, obtaining target training data for the oil and gas pipeline, where the target training data includes at least one piece of historical monitoring data corresponding to each of a plurality of pipeline segments corresponding to the oil and gas pipeline, and an intrusion monitoring result corresponding to each piece of the historical monitoring data. For each piece of the historical monitoring data, the intrusion monitoring result is that an intrusion has occurred or not occurred in the intrusion area corresponding to the historical monitoring data; Step A5, training the pipeline digital twin body model with the target training data to obtain a target digital twin model.

2. The method according to claim 1, wherein The step S1 includes: Obtaining the current monitoring data through an image-based acquisition device or a digital-based acquisition device. The image-based acquisition device includes any one of an unmanned aerial vehicle, a camera, and a satellite monitoring device, and the digital-based acquisition device includes any one of an optical fiber monitoring device and a leak detection device; For each piece of the current monitoring data, if the current monitoring data is obtained through the image-based acquisition device, the current monitoring data is image-based monitoring data, and if the current monitoring data is obtained through the digital-based acquisition device, the current monitoring data is digital-based monitoring data.

3. The method according to claim 1, wherein For each pipeline segment, the historical monitoring data is determined through the following steps: Taking the center point of the pipeline segment as the center of a circle, determining the intrusion area corresponding to the pipeline segment according to a preset reference radius; Obtaining the original historical monitoring data corresponding to the pipeline segment, where the original historical monitoring data includes the position information of the pipeline segment, the importance level of the pipeline segment, the maintenance required situation of the pipeline segment, the number of intruders invading the pipeline segment, the basic information of the intruders invading the pipeline segment, the tool information carried by the intruders invading the pipeline segment, and the distance between the intruders and the boundary of the intrusion area corresponding to the pipeline segment; Performing noise reduction processing on the original historical monitoring data to obtain the historical monitoring data corresponding to the pipeline segment.

4. The method according to claim 1, characterized in that The method further includes: For each piece of the current monitoring data, determining whether the current monitoring data is image-based monitoring data or digital-based monitoring data; For each of the current monitoring data, if the current monitoring data is image-based monitoring data, the current monitoring data is regarded as one type of monitoring data; if the current monitoring data is digital-based monitoring data, the current monitoring data is regarded as another type of monitoring data. The step S2 includes: For each of the first type of monitoring data, input the first type of monitoring data into the target digital twin model, and determine whether an intrusion event has occurred through the target digital twin model; For each of the second type of monitoring data, input the second type of monitoring data into the target digital twin model, and determine whether an intrusion event has occurred through the target digital twin model.

5. The method according to claim 4, characterized in that For each of the first type of monitoring data, the process of inputting the first type of monitoring data into the target digital twin model and determining whether an intrusion event has occurred through the target digital twin model includes: Step B1, for each of the first type of monitoring data, when there are people in the first type of monitoring data, regard the first type of monitoring data as target processing data; Step B2, for each of the target processing data, according to the pipeline maintenance strategy preset for the oil and gas pipeline, determine whether the people appearing in the target processing data are for normal work scheduling. If so, determine that no intrusion event has occurred; if not, execute Step B3; Step B3, input the target processing data into the target digital twin model to determine the event alarm value corresponding to the target processing data; Step B4, if the event alarm value is greater than or equal to the preset event alarm threshold, determine that an intrusion event has occurred; if the event alarm value is less than the event alarm threshold, determine that no intrusion event has occurred; For each of the second type of monitoring data, the process of inputting the second type of monitoring data into the target digital twin model and determining whether an intrusion event has occurred through the target digital twin model includes: Step C1, for each of the second type of monitoring data, determine the parameters corresponding to the second type of monitoring data; Step C2, for each of the second type of monitoring data, input the second type of monitoring data into the target digital twin model, and determine whether an intrusion event has occurred according to the second type of monitoring data, the parameters corresponding to the second type of monitoring data, and the preset parameter alarm threshold corresponding to the parameters; In the step B3, when inputting the target processing data into the target digital twin model to determine the event alarm value corresponding to the target processing data, it includes: Determine the target pipeline section corresponding to the target processing data; According to the pipeline maintenance strategy, determine the situation of the target pipeline section to be repaired; According to the target processing data, determine the type of the target pipeline section and the relevant information of the personnel to be verified. The personnel to be verified are the people in the target processing data, and the relevant information includes the position situation between the personnel to be verified and the intrusion area corresponding to the pipeline section, the total number of the personnel to be verified, and the tool-carrying situation of the personnel to be verified. Input the target processing data into the target digital twin model, and determine the event alarm value corresponding to the target processing data according to the to-be-repaired situation of the target pipeline segment, the type of the target pipeline segment, and the relevant information.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: For each of the current monitoring data, determine whether there is a judgment error event according to the judgment result corresponding to the current monitoring data, where the judgment result is that an intrusion event occurs or does not occur; If there is the judgment error event, update the target digital twin model according to the current monitoring data corresponding to the judgment error event.

7. An intrusion monitoring system for oil and gas pipelines, characterized in that, An intrusion monitoring method for an oil and gas pipeline according to claim 1, the system includes: A data acquisition module for acquiring at least one current monitoring data for an oil and gas pipeline; An intrusion judgment module for inputting each of the current monitoring data into a pre-constructed target digital twin model for the oil and gas pipeline, and determining whether an intrusion event occurs through the target digital twin model.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the intrusion monitoring method for an oil and gas pipeline according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it implements the intrusion monitoring method for an oil and gas pipeline according to any one of claims 1 to 6.

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