Oil and gas pipeline risk analysis method and device

Abstract

The invention provides an oil and gas pipeline risk analysis method and device. The method comprises the steps that detection data of a first oil and gas pipeline in a preset period, detection data of the first oil and gas pipeline in a historical preset period and detection data of multiple second oil and gas pipelines in the preset period are acquired; determining a first characteristic value of the detection data of the first oil and gas pipeline in the preset period according to the detection data of the first oil and gas pipeline in the preset period and a corresponding preset target value; determining a second characteristic value according to the detection data of the first oil and gas pipeline in the preset period and the detection data of the first oil and gas pipeline in the historical preset period; determining a third characteristic value according to the detection data of the first oil and gas pipeline in the preset period and the detection data of the plurality of second oil and gas pipelines in the preset period; and determining a first oil and gas pipeline risk grade by using the oil and gas pipeline risk analysis model. The oil and gas pipeline risk analysis method can perform effective risk analysis on the oil and gas pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas storage and transportation and surface engineering, and particularly to a method and device for risk analysis of oil and gas pipelines. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention described in the claims. The description herein is not admitted to be prior art merely because it is included in this section.

[0003] Pipeline transportation is currently the main transportation method for natural gas and oil transportation, with advantages such as low transportation cost, less land occupation, fast construction, large oil and gas transportation volume, less transportation loss, convenient management, and easy realization of remote centralized monitoring; when oil and gas pipelines are in operation, they are easily affected by factors such as corrosion, third-party construction, and natural disasters, and there is a risk of causing the failure of oil and gas pipelines; the media transported in oil and gas pipelines are all flammable and explosive substances, and if the oil and gas pipelines fail, it will cause natural gas and oil leakage and trigger safety accidents. Therefore, it is necessary to conduct risk analysis on oil and gas pipelines to provide support for the maintenance of oil and gas pipelines; the prior art uses the experience of technicians to prevent the risks of oil and gas pipelines, wasting a large amount of human resources and being unable to effectively analyze the risks of oil and gas pipelines. Summary of the Invention

[0004] In an embodiment of the present invention, a method for risk analysis of oil and gas pipelines is proposed to effectively analyze the risks of oil and gas pipelines, save human resources, and provide technical support for the maintenance of oil and gas pipelines, including:

[0005] Obtain the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within a preset period; wherein, the second oil and gas pipeline is an oil and gas pipeline of the same type as the first oil and gas pipeline;

[0006] According to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value, determine the first characteristic value of the detection data of the first oil and gas pipeline within a preset period;

[0007] According to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period, determine the second characteristic value of the detection data of the first oil and gas pipeline within a preset period;

[0008] According to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period, determine the third characteristic value of the detection data of the first oil and gas pipeline within a preset period;

[0009] Input the first eigenvalue, second eigenvalue, and third eigenvalue of the detection data of the first oil and gas pipeline within a preset period into the oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is obtained by training a machine learning model based on the first eigenvalue, second eigenvalue, third eigenvalue of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

[0010] In an embodiment of the present invention, an oil and gas pipeline risk analysis device is proposed, which is used to effectively analyze the risk of oil and gas pipelines, save human resources, and provide technical support for the maintenance of oil and gas pipelines. The device includes:

[0011] A data acquisition module, configured to acquire the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within a preset period; wherein, the second oil and gas pipeline is an oil and gas pipeline of the same type as the first oil and gas pipeline;

[0012] A first determination module, configured to determine the first eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value;

[0013] A second determination module, configured to determine the second eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period;

[0014] A third determination module, configured to determine the third eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period;

[0015] A risk analysis module, configured to input the first eigenvalue, second eigenvalue, and third eigenvalue of the detection data of the first oil and gas pipeline within a preset period into the oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is obtained by training a machine learning model based on the first eigenvalue, second eigenvalue, third eigenvalue of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

[0016] In an embodiment of the present invention, a computer device is proposed, which 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, an oil and gas pipeline risk analysis method is implemented.

[0017] In an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, an oil and gas pipeline risk analysis method is implemented.

[0018] In an embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program, and when the computer program is executed by a processor, an oil and gas pipeline risk analysis method is implemented.

[0019] The oil and gas pipeline risk analysis method and device proposed in the embodiments of the present invention can solve the problems in the prior art that the risk of oil and gas pipelines is prevented through the experience of technicians, wasting a large amount of human resources and being unable to effectively analyze the risk of oil and gas pipelines. In the embodiments of the present invention, the detection data of the first oil and gas pipeline in a preset period, the detection data of the first oil and gas pipeline in a historical preset period, and the detection data of a plurality of second oil and gas pipelines in a preset period are obtained; wherein, the second oil and gas pipeline is an oil and gas pipeline of the same type as the first oil and gas pipeline; according to the detection data of the first oil and gas pipeline in the preset period and the corresponding preset target value, a first characteristic value of the detection data of the first oil and gas pipeline in the preset period is determined; according to the detection data of the first oil and gas pipeline in the preset period and the detection data of the first oil and gas pipeline in the historical preset period, a second characteristic value of the detection data of the first oil and gas pipeline in the preset period is determined; according to the detection data of the first oil and gas pipeline in the preset period and the detection data of a plurality of second oil and gas pipelines in the preset period, a third characteristic value of the detection data of the first oil and gas pipeline in the preset period is determined; the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline in the preset period are input into an oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model according to the first characteristic value, the second characteristic value, the third characteristic value of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels. The embodiments of the present invention can effectively analyze the risk of oil and gas pipelines, save human resources, and provide technical support for the maintenance of oil and gas pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of the oil and gas pipeline risk analysis method in the embodiments of the present invention;

[0022] Figure 2It is a specific example diagram of the oil and gas pipeline risk analysis method in the embodiment of the present invention;

[0023] Figure 3 It is a specific example diagram of the oil and gas pipeline risk analysis method in the embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the oil and gas pipeline risk analysis device in the embodiment of the present invention;

[0025] Figure 5 It is a schematic diagram of the computer device in the embodiment of the present invention. Detailed implementation manners

[0026] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer and more understandable, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0027] The term "and / or" in this article only describes an associated relationship and means that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of multiple or any combination of at least two of multiple. For example, including at least one of A, B, and C can represent any one or more elements selected from the set composed of A, B, and C.

[0028] In the description of this specification, the terms "comprising", "including", "having", "containing", etc. are all open-ended terms, that is, they are intended to include but not limited to. The descriptions referring to terms such as "one embodiment", "one specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. 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 or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0029] Next, with reference to several representative embodiments of the present invention, the principles and spirits of the present invention will be elaborated in detail.

[0030] Figure 1 It is a flow schematic diagram of the oil and gas pipeline risk analysis method of the embodiment of the present invention. As Figure 1 shown, the method includes:

[0031] Step 101: Obtain the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within the preset period; wherein, the second oil and gas pipelines are oil and gas pipelines of the same type as the first oil and gas pipeline.

[0032] Step 102: Determine the first characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the corresponding preset target value.

[0033] Step 103: Determine the second characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of the first oil and gas pipeline within the historical preset period.

[0034] Step 104: Determine the third characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of multiple second oil and gas pipelines within the preset period.

[0035] Step 105: Input the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model based on the first characteristic value, the second characteristic value, the third characteristic value of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

[0036] by Figure 1As can be seen from the flow shown, in the embodiment of the present invention, the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within a preset period are obtained; wherein, the second oil and gas pipelines are oil and gas pipelines of the same type as the first oil and gas pipeline; according to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value, a first characteristic value of the detection data of the first oil and gas pipeline within a preset period is determined; according to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period, a second characteristic value of the detection data of the first oil and gas pipeline within a preset period is determined; according to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period, a third characteristic value of the detection data of the first oil and gas pipeline within a preset period is determined; the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period are input into an oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model according to the first characteristic value, the second characteristic value, the third characteristic value of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels. The embodiment of the present invention can effectively analyze the risks of oil and gas pipelines, analyze the risk improvement effects of oil and gas pipelines, save human resources, and provide technical support for the maintenance of oil and gas pipelines.

[0037] In order to more clearly explain the above oil and gas pipeline risk analysis method, the following will be described in detail in combination with each step.

[0038] In an embodiment of the present invention, the detection data includes: internal detection data and external detection data;

[0039] Among them, the internal detection data includes: one or any combination of the number of internal corrosion defects, the number of abnormal girth weld defects, the number of dents, and the number of deformation defects;

[0040] The external detection data includes: one or any combination of the number of external corrosion defects, the number of damaged oil and gas pipeline auxiliary facilities, the number of buildings within a preset range from the oil and gas pipeline, the number of natural disaster sensitive points, and the stray current interference rate; wherein, the present invention can select the preset range to be five meters.

[0041] In one embodiment of the present invention, the risk analysis method for oil and gas pipelines described in the present invention can be used to conduct risk analysis on oil and gas pipelines in 4 scenarios. Referring to Table 1, they are the risk analysis of a single oil and gas pipeline in a preset period, the annual risk analysis of a single oil and gas pipeline, the risk analysis of an oil and gas pipeline management unit in a preset period, and the annual risk analysis of an oil and gas pipeline management unit. Taking Scenario 1 as an example: Obtain the detection data of the oil and gas pipeline in the preset period, the detection data of the oil and gas pipeline in the historical preset period, and the detection data of multiple oil and gas pipelines of the same type in the preset period; According to the detection data of the oil and gas pipeline in the preset period and the corresponding preset target value (compared with the target), determine the first characteristic value of the detection data of the first oil and gas pipeline in the preset period; According to the detection data of the oil and gas pipeline in the preset period and the detection data of the oil and gas pipeline in the historical preset period (longitudinal comparison), determine the second characteristic value of the detection data of the first oil and gas pipeline in the preset period; According to the detection data of the oil and gas pipeline in the preset period and the detection data of multiple oil and gas pipelines of the same type in the preset period (horizontal comparison), determine the third characteristic value of the detection data of the first oil and gas pipeline in the preset period.

[0042] Table 1

[0043]

[0044] In one embodiment of the present invention, according to the detection data of the first oil and gas pipeline in the preset period and the corresponding preset target value, determining the first characteristic value of the detection data of the first oil and gas pipeline in the preset period includes:

[0045] Determine the difference between the detection data of the first oil and gas pipeline in the preset period and the corresponding preset target value, and set the difference as the first characteristic value of the detection data of the first oil and gas pipeline in the preset period.

[0046] Specifically, calculate the difference between the detection data of the first oil and gas pipeline in the preset period and the corresponding preset target value. There are three comparison results between the detection data of the first oil and gas pipeline in the preset period and the corresponding preset target value: lower than the target, equal to the target, and higher than the target; When it is lower than the target, the first characteristic value can be set to 0; If it is equal to the target, the first characteristic value can be set to 1; If it is higher than the target, the first characteristic value can be set to 2.

[0047] Figure 2 It is a specific example diagram of the risk analysis method for oil and gas pipelines in an embodiment of the present invention.

[0048] In one embodiment of the present invention, referring to Figure 2 , according to the detection data of the first oil and gas pipeline in the preset period and the detection data of the first oil and gas pipeline in the historical preset period, determining the second characteristic value of the detection data of the first oil and gas pipeline in the preset period includes:

[0049] Step 201: Determine the change rate of the detection data of the first oil and gas pipeline within a preset period compared to the detection data of the first oil and gas pipeline within a historical preset period.

[0050] Step 202: Determine the change rate as the second eigenvalue of the detection data of the first oil and gas pipeline within the preset period.

[0051] In specific implementation, if the change rate is less than 0, it indicates a regression compared to the detection data of the first oil and gas pipeline within the historical preset period, and the second eigenvalue is set to 0; if the change rate is equal to 0, it indicates a flat level compared to the detection data of the first oil and gas pipeline within the historical preset period, and the second eigenvalue is set to 1; if the change rate is greater than 0, it indicates progress compared to the detection data of the first oil and gas pipeline within the historical preset period, and the second eigenvalue is set to 2.

[0052] Figure 3 It is a specific example diagram of the oil and gas pipeline risk analysis method in an embodiment of the present invention.

[0053] In an embodiment of the present invention, with reference to Figure 3 , determine the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of multiple second oil and gas pipelines within the preset period, including:

[0054] Step 301: Determine the distribution interval of the detection data of the second oil and gas pipelines according to the detection data of the multiple second oil and gas pipelines within the preset period.

[0055] Step 302: Determine the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the distribution interval of the detection data of the second oil and gas pipelines.

[0056] In specific implementation, determine the mean value and standard deviation of the detection data of the multiple second oil and gas pipelines within the preset period, and determine the distribution interval of the detection data of pipelines of the same type according to the mean value and standard deviation.

[0057] In an embodiment of the present invention, determine the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the distribution interval of the detection data of the second oil and gas pipelines, including:

[0058] If the detection data of the first oil and gas pipeline within the preset period is less than the minimum value of the distribution interval of the detection data of the second oil and gas pipelines, determine the first preset value as the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period;

[0059] If the detection data of the first oil and gas pipeline within a preset period falls within the distribution range of the detection data of the second oil and gas pipeline, the second preset value is determined as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period;

[0060] If the detection data of the first oil and gas pipeline within a preset period is greater than the maximum value of the distribution range of the detection data of the second oil and gas pipeline, the third preset value is determined as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period; wherein, the first preset value is less than the second preset value, and the second preset value is less than the third preset value. In the present invention, the first preset value is 0, the second preset value is 1, and the third preset value is 2.

[0061] In another embodiment of the present invention, the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period can be determined with reference to Table 2; specifically, the detection data of the first oil and gas pipeline within a preset period can be compared with the corresponding preset target value to determine the first characteristic value; the detection data of the first oil and gas pipeline within a preset period can be compared with the detection data of the first oil and gas pipeline within a historical preset period to determine the second characteristic value; the distribution range of the detection data of the same type of oil and gas pipeline is determined by using the detection data of multiple second oil and gas pipelines within a preset period, and the detection data of the first oil and gas pipeline within a preset period is compared with the distribution range of the detection data of the same type of oil and gas pipeline to determine the third characteristic value. The present invention analyzes the risks of oil and gas pipelines more comprehensively from three dimensions: the target completion situation, the longitudinal comparison with itself, and the horizontal comparison with the same type of oil and gas pipelines.

[0062] Table 2

[0063]

[0064] In an embodiment of the present invention, before inputting the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period into the oil and gas pipeline risk analysis model, it further includes: normalizing the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period.

[0065] In an embodiment of the present invention, the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period are input into the oil and gas pipeline risk analysis model to determine the first oil and gas pipeline risk level; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model according to the first characteristic value, the second characteristic value, the third characteristic value of the detection data of the historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

[0066] In the present invention, in addition to the number of internal corrosion defects, the number of abnormal girth weld defects, the number of dents, and the number of deformation defects, the internal inspection data also includes a variety of other internal inspection data related to the risk of oil and gas pipelines; in addition to the number of external corrosion defects, the number of damaged oil and gas pipeline auxiliary facilities, the number of buildings with a distance from the oil and gas pipeline within a preset range, the number of natural disaster sensitive points, and the stray current interference rate, the external inspection data also includes a variety of other external inspection data related to the risk of oil and gas pipelines.

[0067] In another embodiment of the present invention, after obtaining the inspection data of the oil and gas pipeline within a preset period, the inspection data of the oil and gas pipeline within a historical preset period, and the inspection data of multiple oil and gas pipelines of the same type within a preset period, referring to Table 3, according to the comparison situation in three dimensions, the risk level of the oil and gas pipeline is judged, and optimization and improvement suggestions are put forward for the next risk analysis plan. For oil and gas pipelines, the areas that need to be optimized and improved are the data that perform lower than the preset target, show a decline compared with the previous period, and are lower than the minimum value of the distribution interval.

[0068] Table 3

[0069]

[0070] In one embodiment of the present invention, the inspection data of the oil and gas pipeline in this period is compared longitudinally with the inspection data of the previous period. Taking the "number of internal corrosion defects of the oil and gas pipeline detected in this period" as an example, the number of internal corrosion defects of the pipeline detected in this period of this oil and gas pipeline is 647, and the number of internal corrosion defects of the pipeline body in the previous period is 1228. Therefore, the longitudinal comparison result of the "number of internal corrosion defects of the pipeline body detected in this period" with the previous period is "progress compared with the previous period". According to this method, the comparison work of other data with the integrity management situation of the previous period is completed. The inspection data of this pipeline in this period is compared horizontally with the inspection data of the same type of oil and gas pipeline in the preset period: taking the "average number of external corrosion defects per kilometer of the pipeline body detected in this period" as an example, the average number of external corrosion defects per kilometer of the pipeline body detected in this period of this oil and gas pipeline is 2.12 per km, and the average value of the average number of external corrosion defects per kilometer of the pipeline body detected in this period of the same type of oil and gas pipeline is 103.23 per km. Therefore, the horizontal comparison result of the "average number of external corrosion defects per kilometer of the pipeline body detected in this period" with the same type of oil and gas pipeline is "higher than the average value". According to this method, the comparison work of other inspection data with the same type of oil and gas pipeline is completed.

[0071] The method provided by the present invention can analyze the risks of oil and gas pipelines and optimize the management plans for oil and gas pipelines. According to the comparison of the three dimensions of oil and gas pipelines, optimization and improvement suggestions for the next risk analysis are put forward. For oil and gas pipelines, the areas that need to be optimized and improved are the pipeline risk management work links corresponding to the detection data that are lower than the preset target value, regress compared with the previous cycle, and are lower than the average level.

[0072] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0073] The implementation of the oil and gas pipeline risk analysis device can refer to the implementation of the above method, and the repeated parts will not be elaborated. The terms "module" or "unit" used hereinafter can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0074] Based on the same inventive concept, the present invention also proposes an oil and gas pipeline risk analysis device, as Figure 4 shown, the device includes:

[0075] A data acquisition module 401, configured to acquire the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within a preset period; wherein, the second oil and gas pipelines are oil and gas pipelines of the same type as the first oil and gas pipeline;

[0076] A first determination module 402, configured to determine a first characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value;

[0077] A second determination module 403, configured to determine a second characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period;

[0078] A third determination module 404, configured to determine a third characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period;

[0079] A risk analysis module 405 is configured to input the first eigenvalue, the second eigenvalue, and the third eigenvalue of the detection data of the first oil and gas pipeline within a preset period into an oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline. The oil and gas pipeline risk analysis model is trained for a machine learning model based on the first eigenvalue, the second eigenvalue, the third eigenvalue of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

[0080] In an embodiment of the present invention, the detection data includes internal detection data and external detection data.

[0081] The internal detection data includes one or any combination of the number of internal corrosion defects, the number of abnormal girth weld defects, the number of dents, and the number of deformation defects.

[0082] The external detection data includes one or any combination of the number of external corrosion defects, the number of damaged oil and gas pipeline auxiliary facilities, the number of buildings within a preset range from the oil and gas pipeline, the number of natural disaster sensitive points, and the stray current interference rate.

[0083] In an embodiment of the present invention, the first determination module 402 is specifically configured to:

[0084] Determine the difference between the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value, and determine the difference as the first eigenvalue of the detection data of the first oil and gas pipeline within a preset period.

[0085] In an embodiment of the present invention, the second determination module 403 is specifically configured to:

[0086] Determine the change rate of the detection data of the first oil and gas pipeline within a preset period compared to the detection data of the first oil and gas pipeline within a historical preset period, and determine the change rate as the second eigenvalue of the detection data of the first oil and gas pipeline within a preset period.

[0087] In an embodiment of the present invention, the third determination module 404 is specifically configured to:

[0088] Determine the distribution interval of the detection data of the second oil and gas pipeline according to the detection data of multiple second oil and gas pipelines within a preset period.

[0089] Determine the third eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the distribution interval of the detection data of the second oil and gas pipeline.

[0090] In an embodiment of the present invention, the third determination module 404 is specifically configured to:

[0091] If the detection data of the first oil and gas pipeline within a preset period is less than the minimum value of the distribution range of the detection data of the second oil and gas pipeline, determine the first preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period;

[0092] If the detection data of the first oil and gas pipeline within a preset period falls within the distribution range of the detection data of the second oil and gas pipeline, determine the second preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period;

[0093] If the detection data of the first oil and gas pipeline within a preset period is greater than the maximum value of the distribution range of the detection data of the second oil and gas pipeline, determine the third preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period; wherein, the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

[0094] In an embodiment of the present invention, it further includes:

[0095] A normalization processing module, configured to perform normalization processing on the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period before inputting the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model.

[0096] It should be noted that although several modules of the oil and gas pipeline risk analysis device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-described modules can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0097] Based on the foregoing inventive concept, as Figure 5 shown, the present invention also proposes a computer device 500, including a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and executable on the processor 502. When the processor 502 executes the computer program 503, the foregoing oil and gas pipeline risk analysis method is implemented.

[0098] Based on the foregoing inventive concept, the present invention proposes a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the foregoing oil and gas pipeline risk analysis method is implemented.

[0099] Based on the foregoing inventive concept, the present invention proposes a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the oil and gas pipeline risk analysis method is implemented.

[0100] The oil and gas pipeline risk analysis method and device proposed by the embodiments of the present invention can solve the problems in the prior art that the risk of oil and gas pipelines is prevented through the experience of technicians, wasting a large amount of human resources and being unable to effectively analyze the risk of oil and gas pipelines. The embodiments of the present invention obtain the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of a plurality of second oil and gas pipelines within a preset period; wherein, the second oil and gas pipelines are oil and gas pipelines of the same type as the first oil and gas pipeline; determine the first characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the corresponding preset target value; determine the second characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of the first oil and gas pipeline within the historical preset period; determine the third characteristic value of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of a plurality of second oil and gas pipelines within the preset period; input the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model to determine the first oil and gas pipeline risk level; wherein, the oil and gas pipeline risk analysis model is obtained by training a machine learning model according to the first characteristic value, the second characteristic value, the third characteristic value of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels. The embodiments of the present invention can effectively analyze the risk of oil and gas pipelines, save human resources, and provide technical support for the maintenance of oil and gas pipelines.

[0101] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing in the process Figure 1 one process or multiple processes and / or blocksFigure 1 a device for the functions specified in one or more boxes

[0103] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one Figure 1 process or a plurality of processes and / or boxes Figure 1 a box or a plurality of boxes

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or a plurality of processes and / or boxes Figure 1 a box or a plurality of boxes

[0105] In the specific embodiments described above, the purpose, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for risk analysis of oil and gas pipelines, characterized in that, Including: Obtain the detection data of the first oil and gas pipeline within a preset period, the detection data of the first oil and gas pipeline within a historical preset period, and the detection data of multiple second oil and gas pipelines within a preset period; wherein, the second oil and gas pipelines are oil and gas pipelines of the same type as the first oil and gas pipeline. Determine the first characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value. Determine the second characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period. Determine the third characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period. Input the first characteristic value, second characteristic value, and third characteristic value of the detection data of the first oil and gas pipeline within a preset period into the oil and gas pipeline risk analysis model to determine the risk level of the first oil and gas pipeline; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model according to the first characteristic value, second characteristic value, third characteristic value of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

2. The method according to claim 1, wherein The detection data includes: internal detection data and external detection data. Among them, the internal detection data includes: one or any combination of the number of internal corrosion defects, the number of abnormal girth weld defects, the number of dents, and the number of deformation defects. The external detection data includes: one or any combination of the number of external corrosion defects, the number of damaged oil and gas pipeline auxiliary facilities, the number of buildings within a preset range from the oil and gas pipeline, the number of natural disaster sensitive points, and the stray current interference rate.

3. The method according to claim 1, wherein Determining the first characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value includes: Determine the difference between the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value, and determine the difference as the first characteristic value of the detection data of the first oil and gas pipeline within a preset period.

4. The method according to claim 1, characterized in that, Determining the second characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of the first oil and gas pipeline within a historical preset period includes: Determine the change rate of the detection data of the first oil and gas pipeline within a preset period compared to the detection data of the first oil and gas pipeline within a historical preset period, and determine the change rate as the second characteristic value of the detection data of the first oil and gas pipeline within a preset period.

5. The method according to claim 1, wherein Determining the third characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the detection data of multiple second oil and gas pipelines within a preset period includes: Determine the distribution interval of the detection data of the second oil and gas pipelines according to the detection data of multiple second oil and gas pipelines within a preset period. Determine the third eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within the preset period and the distribution range of the detection data of the second oil and gas pipeline.

6. The method according to claim 5, characterized in that Determining the third eigenvalue of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within the preset period and the distribution range of the detection data of the second oil and gas pipeline includes: If the detection data of the first oil and gas pipeline within the preset period is less than the minimum value of the distribution range of the detection data of the second oil and gas pipeline, determine the first preset value as the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period; If the detection data of the first oil and gas pipeline within the preset period falls within the distribution range of the detection data of the second oil and gas pipeline, determine the second preset value as the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period; If the detection data of the first oil and gas pipeline within the preset period is greater than the maximum value of the distribution range of the detection data of the second oil and gas pipeline, determine the third preset value as the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period; wherein, the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

7. The method according to claim 1, characterized in that Before inputting the first eigenvalue, second eigenvalue, and third eigenvalue of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model, it further includes: Normalize the first eigenvalue, second eigenvalue, and third eigenvalue of the detection data of the first oil and gas pipeline within the preset period.

8. An oil and gas pipeline risk analysis device, characterized in that, It includes: A data acquisition module for acquiring the detection data of the first oil and gas pipeline within the preset period, the detection data of the first oil and gas pipeline within the historical preset period, and the detection data of multiple second oil and gas pipelines within the preset period; wherein, the second oil and gas pipeline is an oil and gas pipeline of the same type as the first oil and gas pipeline; A first determination module for determining the first eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the corresponding preset target value; A second determination module for determining the second eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of the first oil and gas pipeline within the historical preset period; A third determination module for determining the third eigenvalue of the detection data of the first oil and gas pipeline within the preset period according to the detection data of the first oil and gas pipeline within the preset period and the detection data of multiple second oil and gas pipelines within the preset period; A risk analysis module for inputting the first eigenvalue, second eigenvalue, and third eigenvalue of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model to determine the first oil and gas pipeline risk level; wherein, the oil and gas pipeline risk analysis model is trained on a machine learning model based on the first eigenvalue, second eigenvalue, third eigenvalue of the detection data of historical oil and gas pipelines, and the corresponding historical oil and gas pipeline risk levels.

9. The device according to claim 8, characterized in that, The detection data includes: internal detection data and external detection data; Among them, the internal detection data includes: the number of internal corrosion defects, the number of abnormal girth weld defects, the number of dents, the number of deformation defects, or any combination thereof; The external detection data includes: the number of external corrosion defects, the number of damaged oil and gas pipeline auxiliary facilities, the number of buildings whose distance from the oil and gas pipeline is within a preset range, the number of natural disaster sensitive points, the stray current interference rate, or any combination thereof.

10. The device according to claim 8, characterized in that, The first determination module is specifically configured to: Determine the difference between the detection data of the first oil and gas pipeline within a preset period and the corresponding preset target value, and determine the difference as the first characteristic value of the detection data of the first oil and gas pipeline within the preset period.

11. The device according to claim 8, wherein The second determination module is specifically configured to: Determine the change rate of the detection data of the first oil and gas pipeline within a preset period compared to the detection data of the first oil and gas pipeline within a historical preset period, and determine the change rate as the second characteristic value of the detection data of the first oil and gas pipeline within the preset period.

12. The device according to claim 8, characterized in that, The third determination module is specifically configured to: Determine the distribution interval of the detection data of the second oil and gas pipeline according to the detection data of multiple second oil and gas pipelines within a preset period; Determine the third characteristic value of the detection data of the first oil and gas pipeline within a preset period according to the detection data of the first oil and gas pipeline within a preset period and the distribution interval of the detection data of the second oil and gas pipeline.

13. The device according to claim 12, wherein The third determination module is specifically configured to: If the detection data of the first oil and gas pipeline within a preset period is less than the minimum value of the distribution interval of the detection data of the second oil and gas pipeline, determine the first preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period; If the detection data of the first oil and gas pipeline within a preset period falls within the distribution interval of the detection data of the second oil and gas pipeline, determine the second preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period; If the detection data of the first oil and gas pipeline within a preset period is greater than the maximum value of the distribution interval of the detection data of the second oil and gas pipeline, determine the third preset value as the third characteristic value of the detection data of the first oil and gas pipeline within the preset period; where the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

14. The device according to claim 8, characterized in that, It further includes: A normalization processing module, configured to perform normalization processing on the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within a preset period before inputting the first characteristic value, the second characteristic value, and the third characteristic value of the detection data of the first oil and gas pipeline within the preset period into the oil and gas pipeline risk analysis model.

15. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 7.

17. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 7.

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