Oil and gas pipeline optical fiber early warning method and system

Abstract

The invention relates to the field of optical fiber early warning, and particularly discloses an oil and gas pipeline optical fiber early warning method and system, and the method comprises the steps: obtaining a historical vibration signal and a real-time vibration signal of an optical fiber, obtaining a vibration event model according to the historical vibration signal, substituting the real-time vibration signal into the vibration event model, and obtaining an event type of the real-time vibration signal, obtaining a vibration early warning score of the oil and gas pipeline according to the event type; acquiring a historical pressure value and a real-time pressure value of the oil and gas pipeline, calculating a pressure change rate of the oil and gas pipeline, and determining a pressure early warning score of the oil and gas pipeline; and calculating an early warning score of the oil and gas pipeline, comparing the early warning score with a set threshold value, and when the early warning score exceeds the threshold value, generating early warning information. The vibration and pressure conditions of the oil and gas pipeline can be monitored at any time, so that 24-hour real-time monitoring of the oil and gas pipeline is achieved, the intelligent degree is high, whether early warning information is generated or not is automatically judged, and the workload of workers is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of optical fiber early warning, and in particular to an optical fiber early warning method and system for oil and gas pipelines. Background Art

[0002] Oil and gas pipelines are important infrastructure that transport natural gas produced by oil and gas fields, crude oil from oil fields, and petroleum products processed by refineries from production sites to consumption sites through pipelines. It includes three types: crude oil pipelines, refined oil pipelines, and natural gas pipelines. Oil and gas pipeline transportation has the characteristics of small land occupation, strong continuity, large turnover, low cost, low loss, safety and reliability, and is the material basis and an important part of the development of the modern oil and gas industry.

[0003] In order to avoid leakage in oil and gas pipelines, inspections are usually carried out by manual and drone methods. Although this method can monitor the condition of oil and gas pipelines, it is labor-intensive and the condition of the oil and gas pipeline can only be known when personnel or drones arrive. It cannot monitor the condition of the oil and gas pipeline 24 hours a day. Summary of the invention

[0004] The object of the present invention is to provide an oil and gas pipeline optical fiber early warning method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An optical fiber early warning method for oil and gas pipelines, the method comprising: Obtain the historical vibration signal and real-time vibration signal of the optical fiber, convert the historical vibration signal into an electrical signal and perform feature extraction and modeling to obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline based on the event type; Obtain historical and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with the preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The warning score of the oil and gas pipeline is calculated based on the vibration warning score, pressure warning score and corresponding weight parameters, and the warning score is compared with the set threshold. When the warning score exceeds the threshold, a warning message is generated. The warning information includes the warning level, warning type and warning location.

[0006] As a further solution of the present invention: before the feature extraction and modeling of the electrical signal, the process also includes: normalizing, filtering and denoising the electrical signal.

[0007] As a further solution of the present invention: the steps of converting the historical vibration signal into an electrical signal and performing feature extraction and modeling to obtain a vibration event model include: Convert the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; The historical vibration electrical signal is converted into an image matrix and features are extracted to obtain a feature set, which is randomly divided into a training set and a validation set, with the ratio of the number of training sets to the number of validation sets being 7:3; The training set is trained using a convolutional neural network to obtain an initial event model; Substitute the validation set into the initial event model. If the validation passes, the initial event model is the vibration event model. If the validation fails, re-divide the training set and validation set until the validation passes.

[0008] As a further solution of the present invention: the step of calculating the pressure change rate of the oil and gas pipeline, comparing the pressure change rate with a preset change rate limit, and determining the pressure warning score of the oil and gas pipeline includes: Calculate the pressure change rate of the oil and gas pipeline, pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; The pressure change rate is compared with the preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined based on the pressure warning level.

[0009] The technical solution of the present invention also provides an oil and gas pipeline optical fiber early warning system, the system comprising: The vibration score generation module is used to obtain the historical vibration signal and real-time vibration signal of the optical fiber, convert the historical vibration signal into an electrical signal, perform feature extraction and modeling, obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline based on the event type; A pressure score generation module is used to obtain historical pressure values ​​and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with the preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The warning score generation module is used to calculate the warning score of the oil and gas pipeline based on the vibration warning score, pressure warning score and corresponding weight parameters, and compare the warning score with the set threshold. When the warning score exceeds the threshold, a warning message is generated. The warning information includes the warning level, warning type and warning location.

[0010] As a further solution of the present invention: the vibration score generation module includes: A vibration acquisition unit, used for acquiring historical vibration signals and real-time vibration signals of the optical fiber; A conversion unit, used for converting the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; A feature set generation unit is used to convert the historical vibration electrical signal into an image matrix and perform feature extraction to obtain a feature set, and randomly divide the feature set into a training set and a validation set, with the ratio of the number of the training set to the number of the validation set being 7:3; A modeling unit, used for training the training set using a convolutional neural network to obtain an initial event model; A verification unit is used to substitute the verification set into the initial event model. If the verification is passed, the initial event model is the vibration event model. If the verification is not passed, the training set and the verification set are re-divided until the verification is passed. The vibration score generating unit is used to substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline according to the event type.

[0011] As a further solution of the present invention: the stress score generating module includes: A pressure acquisition unit is used to obtain historical pressure values ​​and real-time pressure values ​​of oil and gas pipelines; The change rate calculation unit is used to calculate the pressure change rate of the oil and gas pipeline. The pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; The pressure score generating unit is used to compare the pressure change rate with a preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined according to the pressure warning level.

[0012] Compared with the prior art, the beneficial effects of the present invention are: the present invention can monitor the vibration and pressure conditions of the oil and gas pipeline at all times, thereby realizing 24-hour real-time monitoring of the oil and gas pipeline, with a high degree of intelligence and the ability to independently determine whether to generate early warning information, thereby greatly reducing the workload of the staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0014] Figure 1 This is a flowchart of the optical fiber early warning method for oil and gas pipelines.

[0015] Figure 2 This is a block diagram of the first sub-process of the optical fiber early warning method for oil and gas pipelines.

[0016] Figure 3 This is a block diagram of part of the second sub-process of the optical fiber early warning method for oil and gas pipelines.

[0017] Figure 4 This is a structural block diagram of the oil and gas pipeline optical fiber early warning system.

[0018] Figure 5 This is a structural block diagram of the vibration score generation module in the oil and gas pipeline optical fiber early warning system.

[0019] Figure 6 This is a structural block diagram of the pressure score generation module in the oil and gas pipeline optical fiber early warning system. DETAILED DESCRIPTION

[0020] At present, it is still necessary for humans to judge the situation captured by the camera before taking measures. Monitoring personnel monitor multiple cameras at the same time and rely on their experience to make judgments. Unsafe behaviors may be judged as safe behaviors due to their experience, or monitoring personnel may not be able to detect unsafe behaviors in time.

[0021] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] Embodiment 1: Figure 1 The flowchart of the optical fiber early warning method for oil and gas pipelines is shown in the following figure. In an embodiment of the present invention, an optical fiber early warning method for oil and gas pipelines is provided. The method includes: Step S100: Obtain historical vibration signals and real-time vibration signals of the optical fiber, convert the historical vibration signals into electrical signals, perform feature extraction and modeling, obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline according to the event type; Every event that threatens the oil and gas pipeline will cause the oil and gas pipeline to vibrate. If the oil and gas pipeline is broken by natural disasters, it will generate a vibration signal. If the oil and gas pipeline is broken by artificial digging, it will generate a second vibration signal. If the oil and gas pipeline is shaken, it will generate a third vibration signal. The way to obtain these signals can be based on the records of historical events, or the management personnel can directly perform the behavior (such as artificial digging on the test oil and gas pipeline), summarize all events and corresponding vibration signals, and each vibration signal corresponds to a unique event. Distributed optical fiber sensors are usually used to obtain real-time vibration signals because distributed optical fiber sensors have high sensitivity, high positioning accuracy and electromagnetic immunity. The vibration signal is then converted into an electrical signal to facilitate the server to process it. By extracting features from all electrical signals and then modeling the extracted features using specific principles, we can get a vibration event model suitable for vibration signals. We only need to substitute the vibration signal into the vibration event model to find out the specific type of event that the vibration signal corresponds to. The server pre-determines the vibration warning score for each event. For example, the vibration warning score for a natural disaster is 3, the vibration warning score for an oil and gas pipeline being artificially dug is 2.8, the vibration warning score for an oil and gas pipeline being shaken is 0.6, and so on.

[0023] Step S200: Obtain historical pressure values ​​and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with a preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The oil and gas pipeline does not appear abnormal at the beginning, but the abnormality occurs after an event (such as the oil and gas pipeline is broken or dug, the oil and gas pipeline falls off) affects the oil and gas pipeline. The pressure value of the oil and gas pipeline may change only after the event, and only when the oil and gas pipeline is disconnected or falls off will the pressure value of the oil and gas pipeline suddenly change greatly. The pressure change rate is used to characterize the degree of mutation of the oil and gas pipeline pressure value. The preset change rate can be divided into several levels: for example, it can be divided into slight level, severe level and dangerous level. When the pressure change rate is less than the preset change rate limit of the slight level, the oil and gas pipeline is not affected, and the pressure warning score is 0 at this time. When the pressure change rate is greater than or equal to the preset change rate limit of the slight level, the oil and gas pipeline is slightly affected, and the pressure warning score can be 0.3 at this time. When the pressure change rate is greater than or equal to the preset change rate limit of the severe level, the oil and gas pipeline is seriously affected, and the pressure warning score can be 0.7 at this time. When the pressure change rate is greater than or equal to the preset change rate limit of the dangerous level, the oil and gas pipeline is completely affected, and the pressure warning score can be 1 at this time.

[0024] Step S300: Calculate the warning score of the oil and gas pipeline based on the vibration warning score, the pressure warning score and the corresponding weight parameters, and compare the warning score with the set threshold. When the warning score exceeds the threshold, generate warning information, which includes the warning level, warning type and warning location.

[0025] In order to more accurately judge the situation of the oil and gas pipeline, the present invention comprehensively considers the situation of the oil and gas pipeline from the two aspects of vibration and pressure change. The opinions of experts in the field of oil and gas pipelines are collected through the expert weight method and questionnaire, and the weight parameters of the vibration warning score and the pressure warning score are determined by combining all the answers. The warning score is equal to the vibration warning score multiplied by the weight parameter of the vibration warning score plus the pressure warning score multiplied by the weight parameter of the pressure warning score. This is the comprehensive score generated by comprehensively considering the vibration and pressure changes. The set threshold is not only one, but multiple, and the warning information corresponding to different thresholds is different. When the warning score is less than the minimum threshold, it indicates that the event will not pose a threat to the oil and gas pipeline, and no warning information will be generated at this time. When the warning score is greater than or equal to the minimum threshold but less than the middle threshold, it indicates that the event will pose a slight threat to the oil and gas pipeline, which will affect the normal transportation of the oil and gas pipeline, and personnel are required to handle it. When the warning score is greater than or equal to the middle threshold but less than the highest threshold, it indicates that the event will pose a greater threat to the oil and gas pipeline, and may cause a safety accident, which requires personnel to handle it in time. When the warning score is greater than or equal to the maximum threshold, it indicates that the event will pose a complete threat to the oil and gas pipeline, which may cause major safety accidents or property losses, and personnel need to deal with it immediately.

[0026] Before the feature extraction and modeling of the electrical signal, the process also includes: normalizing, filtering and denoising the electrical signal.

[0027] After the vibration signal is converted into an electrical signal, the electrical signal may contain noise and interference signals, which will affect the quality of the features extracted later. Therefore, the electrical signal needs to be normalized, filtered and denoised. Normalization is to transform the dimensional expression into a dimensionless expression and become a scalar, thereby simplifying the subsequent calculation. Filtering is to separate the useful signal from the noise, remove the useless noise, improve the anti-interference and signal-to-noise ratio of the electrical signal, and thus improve the subsequent analysis accuracy. Here, you can choose either high-pass filtering or band-pass filtering according to the actual situation. The specific choice is based on work needs. Noise reduction is to extract the target signal from the signal interfered by noise. The target signal contains the required information. The electrical signal that has been normalized, filtered and denoised contains fewer interference signals, and the subsequent extracted features are more accurate.

[0028] Figure 2This is a partial first sub-process flowchart of the oil and gas pipeline optical fiber early warning method. The steps of converting the historical vibration signal into an electrical signal and performing feature extraction and modeling to obtain a vibration event model include: Convert the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; A vibration signal is a dynamic signal that reflects the vibration characteristics of an object or system in mechanical motion. The vibration signal measured by a distributed optical fiber sensor is an analog signal. After the distributed optical fiber sensor converts the vibration signal into an electrical signal, it is convenient to perform pre-processing such as normalization on the electrical signal, which reduces the subsequent workload and makes the extracted features more accurate.

[0029] The historical vibration electrical signal is converted into an image matrix and features are extracted to obtain a feature set, which is randomly divided into a training set and a validation set, with the ratio of the number of training sets to the number of validation sets being 7:3; Multiple vibration electrical signals are spliced ​​and then converted into an image matrix. The image matrix is ​​digital image data, which is convenient for the server to analyze and process. In computer digital image processing programs, two-dimensional arrays are usually used to store image data. The rows of the two-dimensional array correspond to the height of the image, the columns of the two-dimensional array correspond to the width of the image, and the elements of the two-dimensional array correspond to the pixels of the image. The value of the two-dimensional array element is the gray value of the pixel. Feature extraction can be regarded as extracting the feature vector of the image matrix. After feature extraction of all image matrices, the feature set is obtained. The feature set is divided into 70% training set and 30% verification set to facilitate subsequent modeling and verification.

[0030] The training set is trained using a convolutional neural network to obtain an initial event model; The feature vector is trained using a convolutional neural network to obtain classification output and classification probability. The classification output and the label of the training set are used to calculate the loss function value using the cross entropy loss function (the formula of this function is known). The convolutional neural network is then updated using the obtained loss function value and the Adam back-propagation gradient descent algorithm. When the updated convolutional neural network converges, the initial event model can be obtained.

[0031] Substitute the validation set into the initial event model. If the validation passes, the initial event model is the vibration event model. If the validation fails, re-divide the training set and validation set until the validation passes.

[0032] The feature vectors in the validation set are input into the initial event model to obtain the judgment result of the updated convolutional neural network's recognition ability. If the judgment result is up to standard, the convolutional neural network is saved. If the judgment result is not up to standard, 70% of the training set and 30% of the validation set are randomly re-divided, and the number of training times of the training set is changed until the judgment result of the convolutional neural network obtained is up to standard. The convolutional neural network that meets the standard is the initial event model.

[0033] Figure 3 This is a partial second sub-process flow chart of the oil and gas pipeline optical fiber early warning method, wherein the step of calculating the pressure change rate of the oil and gas pipeline and comparing the pressure change rate with the preset change rate limit includes: Calculate the pressure change rate of the oil and gas pipeline, pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; Subtract the pressure value of the previous moment from the real-time pressure value, and divide the difference into the pressure value of the previous moment. The result is the pressure change rate, that is, the degree of mutation of the real-time pressure value relative to the pressure value of the previous moment. A very small mutation degree indicates that the oil and gas pipeline is not affected. Only a large mutation degree indicates that a bad event has occurred in the oil and gas pipeline.

[0034] The pressure change rate is compared with the preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined based on the pressure warning level.

[0035] This preset rate of change limit is obtained by relevant personnel based on their own experience or by conducting simulation experiments when a certain event occurs. The pressure change rate is compared with the preset rate of change limit. The preset rate of change limit is not a single one, but multiple, because different events produce different pressure mutations. The pressure mutation degree of oil and gas pipeline disconnection is greater than the pressure mutation degree of oil and gas pipeline shedding, and the pressure mutation degree of oil and gas pipeline shedding is greater than the pressure mutation degree of oil and gas pipeline cracking. Because oil and gas pipelines are usually filled with natural gas or oil, the flowing natural gas or oil will exert pressure on the oil and gas pipeline wall. Oil and gas pipeline disconnection, oil and gas pipeline shedding and oil and gas pipeline cracking will all lead to natural gas or oil leakage. Disconnection represents the area of ​​natural gas or oil leakage is the surface area of ​​the oil and gas pipeline, shedding represents the area of ​​natural gas or oil leakage is the majority of the oil and gas pipeline, and cracking represents the area of ​​natural gas or oil leakage is a small part of the oil and gas pipeline, so the rate of pressure reduction is decreasing in sequence. Each preset rate of change limit will correspond to a pressure warning level. The greater the pressure mutation degree, the higher the pressure warning level and the higher the pressure warning score.

[0036] Embodiment 2: Figure 4 The following is a structural block diagram of an oil and gas pipeline optical fiber early warning system. In an embodiment of the present invention, an oil and gas pipeline optical fiber early warning system includes: The vibration score generation module is used to obtain the historical vibration signal and real-time vibration signal of the optical fiber, convert the historical vibration signal into an electrical signal, perform feature extraction and modeling, obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline based on the event type; A pressure score generation module is used to obtain historical pressure values ​​and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with the preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The warning score generation module is used to calculate the warning score of the oil and gas pipeline based on the vibration warning score, pressure warning score and corresponding weight parameters, and compare the warning score with the set threshold. When the warning score exceeds the threshold, a warning message is generated. The warning information includes the warning level, warning type and warning location.

[0037] Figure 5 The structure diagram of the vibration score generation module in the oil and gas pipeline optical fiber early warning system is shown in FIG. In the embodiment of the present invention, the vibration score generation module includes: A vibration acquisition unit, used for acquiring historical vibration signals and real-time vibration signals of the optical fiber; A conversion unit, used for converting the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; A feature set generation unit is used to convert the historical vibration electrical signal into an image matrix and perform feature extraction to obtain a feature set, and randomly divide the feature set into a training set and a validation set, with the ratio of the number of the training set to the number of the validation set being 7:3; A modeling unit, used for training the training set using a convolutional neural network to obtain an initial event model; A verification unit is used to substitute the verification set into the initial event model. If the verification is passed, the initial event model is the vibration event model. If the verification is not passed, the training set and the verification set are re-divided until the verification is passed. The vibration score generating unit is used to substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline according to the event type.

[0038] Figure 6 The following is a structural block diagram of a pressure score generation module in an oil and gas pipeline optical fiber early warning system. In an embodiment of the present invention, the pressure score generation module includes: A pressure acquisition unit is used to obtain historical pressure values ​​and real-time pressure values ​​of oil and gas pipelines; The change rate calculation unit is used to calculate the pressure change rate of the oil and gas pipeline. The pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; The pressure score generating unit is used to compare the pressure change rate with a preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined according to the pressure warning level.

[0039] The functions that can be achieved by the oil and gas pipeline optical fiber early warning method are all completed by a computer device, which includes one or more processors and one or more memories, and at least one program code is stored in the one or more memories. The program code is loaded and executed by the one or more processors to achieve the functions of the user behavior prediction method based on big data.

[0040] The processor takes out instructions from the memory one by one, analyzes the instructions, and then completes the corresponding operations according to the instruction requirements, generating a series of control commands, so that the various parts of the computer can automatically, continuously and coordinately move to become an organic whole, realize the input of programs, the input of data, and the calculation and output of results. The arithmetic operations or logical operations generated in this process are all completed by the operator; the memory includes a read-only memory (ROM), which is used to store computer programs, and a protection device is provided outside the memory.

[0041] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0042] Those skilled in the art will understand that the description of the above service equipment is merely an example and does not constitute a limitation on the terminal equipment. It may include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, etc.

[0043] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire user terminal.

[0044] The above-mentioned memory can be used to store computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as an information collection template display function, a product information release function, etc.), etc.; the data storage area can store data created according to the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to release, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0045] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the modules / units in the above-mentioned embodiment system, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can realize the functions of the above-mentioned various system embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media can include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0046] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0047] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An optical fiber early warning method for oil and gas pipelines, characterized in that: The method comprises: Obtain the historical vibration signal and real-time vibration signal of the optical fiber, convert the historical vibration signal into an electrical signal and perform feature extraction and modeling to obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline based on the event type; Obtain the historical and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with the preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The warning score of the oil and gas pipeline is calculated based on the vibration warning score, pressure warning score and corresponding weight parameters, and the warning score is compared with the set threshold. When the warning score exceeds the threshold, a warning message is generated. The warning information includes the warning level, warning type and warning location.

2. The optical fiber early warning method for oil and gas pipelines according to claim 1 is characterized in that: Before the feature extraction and modeling of the electrical signal, the process also includes: normalizing, filtering and denoising the electrical signal.

3. The optical fiber early warning method for oil and gas pipelines according to claim 1 is characterized in that: The steps of converting the historical vibration signal into an electrical signal and performing feature extraction and modeling to obtain a vibration event model include: Convert the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; The historical vibration electrical signal is converted into an image matrix and features are extracted to obtain a feature set, which is randomly divided into a training set and a validation set, with the ratio of the number of training sets to the number of validation sets being 7:3; The training set is trained using a convolutional neural network to obtain an initial event model; Substitute the validation set into the initial event model. If the validation passes, the initial event model is the vibration event model. If the validation fails, re-divide the training set and validation set until the validation passes.

4. The oil and gas pipeline optical fiber early warning method according to claim 1 or 3, characterized in that: The steps of calculating the pressure change rate of the oil and gas pipeline, comparing the pressure change rate with a preset change rate limit, and determining the pressure warning score of the oil and gas pipeline include: Calculate the pressure change rate of the oil and gas pipeline, pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; The pressure change rate is compared with the preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined based on the pressure warning level.

5. An optical fiber early warning system for oil and gas pipelines, characterized in that: The system comprises: The vibration score generation module is used to obtain the historical vibration signal and real-time vibration signal of the optical fiber, convert the historical vibration signal into an electrical signal, perform feature extraction and modeling, obtain a vibration event model, substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline based on the event type; A pressure score generation module is used to obtain historical pressure values ​​and real-time pressure values ​​of the oil and gas pipeline, calculate the pressure change rate of the oil and gas pipeline, compare the pressure change rate with the preset change rate limit, and determine the pressure warning score of the oil and gas pipeline; The warning score generation module is used to calculate the warning score of the oil and gas pipeline based on the vibration warning score, pressure warning score and corresponding weight parameters, and compare the warning score with the set threshold. When the warning score exceeds the threshold, a warning message is generated. The warning information includes the warning level, warning type and warning location.

6. The oil and gas pipeline optical fiber early warning system according to claim 5 is characterized in that: The vibration score generation module comprises: A vibration acquisition unit, used for acquiring historical vibration signals and real-time vibration signals of the optical fiber; A conversion unit, used for converting the historical vibration signal into an electrical signal to obtain a historical vibration electrical signal; A feature set generation unit is used to convert the historical vibration electrical signal into an image matrix and perform feature extraction to obtain a feature set, and randomly divide the feature set into a training set and a validation set, with the ratio of the number of the training set to the number of the validation set being 7:3; A modeling unit, used for training the training set using a convolutional neural network to obtain an initial event model; A verification unit is used to substitute the verification set into the initial event model. If the verification is passed, the initial event model is the vibration event model. If the verification is not passed, the training set and the verification set are re-divided until the verification is passed. The vibration score generating unit is used to substitute the real-time vibration signal into the vibration event model, obtain the event type of the real-time vibration signal, and obtain the vibration warning score of the oil and gas pipeline according to the event type.

7. The oil and gas pipeline optical fiber early warning system according to claim 5 or 6, characterized in that: The stress score generating module comprises: A pressure acquisition unit is used to obtain historical pressure values ​​and real-time pressure values ​​of oil and gas pipelines; The change rate calculation unit is used to calculate the pressure change rate of the oil and gas pipeline. The pressure change rate = (real-time pressure value - pressure value at the previous moment) / pressure value at the previous moment; The pressure score generating unit is used to compare the pressure change rate with a preset change rate limit. When the pressure change rate is greater than the preset change rate limit, the pressure warning level is determined, and the corresponding pressure warning score is determined according to the pressure warning level.

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