WO2024046216A1

WO2024046216A1 - Method and apparatus for monitoring safety along pipeline, and storage medium

Info

Publication number: WO2024046216A1
Application number: PCT/CN2023/114819
Authority: WO
Inventors: 蔡永军; 陈朋超; 王海明
Original assignee: 国家石油天然气管网集团有限公司
Priority date: 2022-09-01
Filing date: 2023-08-25
Publication date: 2024-03-07
Also published as: CN115435247A

Abstract

Disclosed in the present application are a method and apparatus for monitoring safety along a pipeline, and a storage medium. The method comprises: monitoring a vibration signal along a pipeline in real time; when the vibration signal is collected along the pipeline, collecting random microscopic feature data of the vibration signal in real time; when the microscopic feature data meets a preset condition, acquiring macroscopic feature data corresponding to the vibration signal; according to the microscopic feature data and the macroscopic feature data, determining an uncertainty probability of a third-party activity causing the vibration signal; and finally outputting an alarm signal according to the uncertainty probability. According to the present application, the vibration signal along the pipeline is monitored in real time, and the uncertainty probability that the third-party activity causing the vibration signal along the pipeline is a destructive event is determined by combining the microscopic feature data and macroscopic feature data corresponding to the vibration signal, so that targeted early warning is performed, the accuracy of determining the vibration signal is improved, and the situation of alarm flooding is reduced, so as to realize effective early warning.

Description

Methods, devices and storage media for monitoring safety along pipelines

Technical field

This application relates to the technical field of safe operation of oil and gas pipelines, and specifically to a method, device and storage medium for monitoring safety along pipelines.

Background technique

Alarm is to give clear information about deterministic events and remind relevant personnel to carry out work according to the planned response procedures. In the field of oil and gas pipeline safety monitoring, the pipeline leakage monitoring system gives a deterministic alarm on whether the pipeline is leaking, which requires on-site confirmation. The early warning is a description of uncertainty. It gives a description of the possibility that the pipeline may encounter damage events in the future. It does not indicate that the pipeline damage event has occurred or will definitely occur.

Since the Australian FFT Company invented the optical fiber vibration sensing method based on Mach-Zehnder interference, vibration sensing and positioning technology of large linear structures has been applied. With the continuous evolution of optical fiber sensing technology, the University of Southampton in the UK invented vibration sensing technology based on coherent back-rayleigh scattered light interference and has been widely used.

At present, safety early warning systems based on pipeline accompanying optical cables that sense vibration signals along the pipeline are commonly used in areas prone to third-party damage and high-consequence areas along pipelines. For the identification method of third-party damage signals along the pipeline, it mainly uses microscopic frequency information for target recognition to distinguish mechanical, manual and other characteristic information. However, the pipeline is an open space with frequent third-party activities and crosses with the road, which introduces too many vibration signals to the vibration monitoring based on the pipeline accompanying optical cable. There are too many vibration signals along the pipeline. The optical fiber early warning system collects various vibration signals and identifies them through simple microscopic vibration signals. This will cause the optical fiber early warning system to overflow with alarms and exceed the processing capabilities of the operators, thus directly causing The system is unavailable.

At present, it is difficult to determine which third-party activities around the pipeline are destructive events that will cause pipeline damage and which are non-destructive events with relatively low risks, using vibration signals alone. Therefore, it is difficult to provide targeted early warning. However, the method used in the existing technology to provide early warning for third-party damage activities along the pipeline has the problem of low accuracy of detection results and prone to flooding of alarms.

Contents of the invention

The purpose of the embodiments of this application is to provide a method, device and storage medium for monitoring safety along pipelines, so as to solve the problem of accurate detection results of the methods used in the prior art for early warning of third-party activities along pipelines. It has low sensitivity and can easily cause the problem of alarm flooding.

In order to achieve the above objectives, the first aspect of this application provides a method for monitoring safety along pipelines, including:

Real-time monitoring of vibration signals along the pipeline;

When vibration signals are detected along the pipeline, microscopic feature data corresponding to the vibration signals are collected in real time;

When the microscopic characteristic data meets the preset conditions, the macroscopic characteristic data corresponding to the vibration signal is obtained;

Determine the uncertainty probability of third-party activities that trigger vibration signals based on macro-feature data and micro-feature data;

Alarm signals are output based on uncertainty probabilities.

In the embodiment of this application, the microscopic characteristic data includes:

The vibration intensity of the vibration signal, the influence length of the vibration signal and the duration of the vibration signal.

In the embodiment of the present application, when the microscopic characteristic data meets the preset conditions, obtaining the macroscopic characteristic data corresponding to the vibration signal includes at least one of the following:

When the vibration intensity of the vibration signal reaches the first intensity threshold and is less than the second intensity threshold, obtain macroscopic feature data corresponding to the vibration signal;

When the influence length of the vibration signal reaches the first length threshold, obtain the macroscopic feature data corresponding to the vibration signal;

When the action duration of the vibration signal reaches the first duration threshold, macroscopic feature data corresponding to the vibration signal is obtained.

In the embodiment of the present application, the macroscopic characteristic data includes the occurrence time of the vibration signal and the occurrence location of the vibration signal.

In the embodiment of this application, determining the uncertainty probability of third-party activities that trigger vibration signals based on macroscopic feature data and microscopic feature data includes:

Determine the microscopic probability corresponding to the vibration signal based on the microscopic characteristic data;

Determine the macro probability corresponding to the vibration signal based on the macro feature data;

Determine the uncertainty probability of third-party activities that trigger vibration signals based on microscopic and macroscopic probabilities.

In the embodiment of this application, the uncertainty probability satisfies formula (1):
P＝H×Pw×PH; (1)

Among them, P is the uncertainty probability, H is the failure consequence, Pw is the microscopic probability corresponding to the vibration signal, and PH is the macroscopic probability corresponding to the vibration signal.

In the embodiment of this application, the microscopic probability satisfies formula (2):
Pw＝[P(A)+P(L)]×(ΔA+ΔL)×PS; (2)

Among them, Pw is the microscopic probability corresponding to the vibration signal, P(A) is the vibration intensity of the vibration signal, P(L) is the influence length of the vibration signal, ΔA is the change in the vibration intensity of the vibration signal, ΔL is the influence of the vibration signal The change in length, PS is the recognition probability.

In the embodiment of this application, the macroscopic probability satisfies formula (3):
PH＝P(FM)+P(FT); (3)

Among them, PH is the macroscopic probability corresponding to the vibration signal, P(FM) is the probability of a destructive event occurring at the location where the vibration signal occurs, and P(FT) is the probability of a destructive event occurring at the time when the vibration signal occurs.

The second aspect of this application provides a device for monitoring safety along pipelines, which is characterized by including:

memory configured to store instructions; and

The processor is configured to call the instructions from the memory and when executing the instructions, can implement the above-mentioned method for monitoring safety along the pipeline.

A third aspect of the present application provides a machine-readable storage medium, which is characterized in that instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the above-mentioned method for monitoring safety along a pipeline.

Through the above technical solution, the vibration signal along the pipeline is monitored in real time. When there is a vibration signal along the pipeline, random microscopic feature data of the vibration signal is collected in real time. When the microscopic feature data meets the preset conditions, the vibration signal is obtained. The corresponding macro feature data is combined with the micro feature data and macro feature data to determine the uncertainty probability of the third-party activity that triggers the vibration signal, and finally an alarm signal is output based on the uncertain new probability. This application monitors the vibration signals along the pipeline in real time and combines the micro-feature data and macro-feature data corresponding to the vibration signal to determine the uncertainty probability that the third-party activity that triggers the vibration signal along the pipeline is a destructive event, thereby providing targeted early warning and improving This improves the accuracy of vibration signal judgment, reduces alarm flooding, and achieves effective early warning.

Other features and advantages of the embodiments of the present application will be described in detail in the following detailed description.

Description of drawings

The drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the description. Together with the following specific implementation modes, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the attached picture:

Figure 1 is a flow chart of a method for monitoring safety along a pipeline provided by an embodiment of the present application;

Figure 2 is a flow chart of a method for monitoring safety along a pipeline provided by a specific embodiment of the present application;

Figure 3 is a structural block diagram of a device for monitoring safety along a pipeline provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the The accompanying drawings clearly and completely describe the technical solutions in the embodiments of the present application. It should be understood that the specific implementations described here are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. . Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

It should be noted that if there are directional instructions (such as up, down, left, right, front, back...) in the embodiments of the present application, the directional instructions are only used to explain the position of a certain posture (as shown in the accompanying drawings). The relative positional relationship, movement conditions, etc. between the components under the display). If the specific posture changes, the directional indication will also change accordingly.

In addition, if there are descriptions involving “first”, “second”, etc. in the embodiments of this application, the descriptions of “first”, “second”, etc. are only for descriptive purposes and shall not be understood as indications or implications. Its relative importance or implicit indication of the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In addition, the technical solutions in various embodiments can be combined with each other, but it must be based on the realization by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that such a combination of technical solutions does not exist. , nor is it within the scope of protection required by this application.

Figure 1 is a flow chart of a method for monitoring safety along a pipeline provided by an embodiment of the present application. As shown in Figure 1, the embodiment of the present application provides a method for monitoring safety along a pipeline. The method may include the following steps:

S101. Real-time monitoring of vibration signals along the pipeline;

S102. When a vibration signal is detected along the pipeline, the microscopic characteristic data corresponding to the vibration signal is collected in real time;

S103. When the microscopic characteristic data meets the preset conditions, obtain the macroscopic characteristic data corresponding to the vibration signal;

S104. Determine the uncertainty probability of third-party activities that cause vibration signals based on macroscopic characteristic data and microscopic characteristic data;

S105. Output the alarm signal according to the uncertainty probability.

In the embodiment of this application, first, the processor can monitor the vibration signals along the pipeline in real time to determine whether there is any third-party activity. Among them, vibration signals can be monitored through sensors and other methods. When a vibration signal is detected along the pipeline, the microscopic characteristic data corresponding to the vibration signal is collected in real time. Since third-party activities are often persistent after the occurrence, and the microscopic characteristic data corresponding to the vibration signal changes, so in the vibration signal If it still exists, it is necessary to collect microscopic characteristic data corresponding to the vibration signal in real time. Among them, the microscopic characteristic data may include the vibration intensity of the vibration signal, the influence length of the vibration signal, and the action duration of the vibration signal.

Secondly, the processor can determine in real time whether the acquired micro-feature data meets the preset conditions. When the micro-feature data meets the preset conditions, the macro-feature data corresponding to the vibration signal is obtained. Among them, the micro-feature data meeting the preset conditions means that the micro-feature data meets the set threshold conditions, and the size of the threshold is set according to the actual situation. In one example, the threshold corresponding to the influence length of the vibration signal may be the first length threshold, and the threshold corresponding to the action duration of the vibration signal may be the first duration threshold. In another example, since the vibration intensity of the vibration signal may directly produce destructive effects, the thresholds corresponding to the vibration intensity of the vibration signal may be a first intensity threshold, a second intensity threshold, and a third intensity threshold; where , the third intensity threshold is greater than the second intensity threshold, and the second intensity threshold is greater than the first intensity threshold.

When the micro-feature data meets the set conditions, the possibility of third-party activities being destructive events increases. At this time, it is necessary to obtain the macro-feature data corresponding to the vibration signal, and further determine the third-party activities based on the macro-feature data. Macroscopic feature data includes the time when the vibration signal occurs and the location where the vibration signal occurs. Combining the micro-feature data and macro-feature data corresponding to the vibration signal, the uncertainty probability of the third-party activity causing the vibration signal is determined, and an alarm signal is output based on the uncertainty probability. Among them, uncertainty probability refers to the possibility of a third-party activity being a destructive event.

In this embodiment of the present application, the processor can determine the danger level of the pipeline based on the uncertainty probability, and then output an alarm signal in a targeted manner. In one example, different levels of alarm signals can be output based on different levels of danger. Due to the different time and geographical conditions in which third-party activities occur, when the third-party activities are destructive events The consequences are also different. Therefore, the alarm signals need to be classified according to the actual situation and the consequences of the destructive event. For example, according to the high-consequence area analysis method, the area along the pipeline can be divided into ordinary areas and three-level high-consequence areas. The three-level high-consequence areas are level I, II, and III respectively, among which level III is the highest. For different vibration signal occurrences, area, and the alarm signals are graded according to the consequences of damage. For example, alarm signals can be divided into level one, level two and level three, with level one being the highest and level three being the lowest; the uncertainty probability threshold corresponding to the level one alarm can be the first threshold, and the uncertainty probability threshold corresponding to the level two alarm. It can be a second threshold, and the uncertainty probability threshold corresponding to the three-level alarm can be a third threshold. The first threshold is greater than the second threshold, and the second threshold is greater than the third threshold. For Level III high-consequence areas and Level II high-consequence areas, it is obvious that the three thresholds for Level III high-consequence areas all correspond to less than the three thresholds for Level II high-consequence areas. The specific value of the threshold is set according to the actual situation of each region.

Through the above technical solution, the vibration signal along the pipeline is monitored in real time. When there is a vibration signal along the pipeline, random microscopic feature data of the vibration signal is collected in real time. When the microscopic feature data meets the preset conditions, the vibration signal is obtained. The corresponding macro feature data is combined with the micro feature data and macro feature data to determine the uncertainty probability of the third-party activity that triggers the vibration signal, and finally an alarm signal is output based on the uncertain new probability. The embodiment of the present application monitors the vibration signals along the pipeline in real time, and combines the micro-feature data and macro-feature data corresponding to the vibration signal to determine the uncertainty probability that the third-party activity that triggers the vibration signal along the pipeline is a destructive event, thereby providing targeted early warning. , improves the accuracy of judgment of vibration signals, reduces alarm flooding, and achieves effective early warning.

In the embodiment of this application, microscopic feature data may include:

Specifically, the intensity of the vibration signal refers to the amplitude of the vibration signal, that is, the size of the vibration signal, which can be used to determine the intensity of the third party's activities. In one example, the change amount ΔA of the vibration intensity of the vibration signal can be obtained in real time, and the change in the degree of risk can be judged by the change amount of the vibration intensity of the vibration signal. When ΔA is a positive value, the risk increases; when ΔA is a negative value case, the risk is reduced. The influence length L of the vibration signal refers to the size of the influence range of the vibration signal, which can be used to judge the proximity of the vibration and the intensity of the activity. In one example, the change amount ΔL of the vibration signal's influence length can be obtained in real time, and the change in the risk level can be judged by the change amount of the vibration signal's influence length. When the vibration influence length L increases, the increase in ΔL indicates that the vibration source is constantly approaching. , the risk increases; the vibration influence length L decreases, and the decrease in ΔL indicates that the vibration source is moving away and the risk decreases; a positive value of ΔL indicates an increase in risk, and a negative value of ΔL indicates a decrease in risk. The duration of the vibration signal refers to the duration of the vibration signal, which is the length of time from when the vibration signal is detected to when each microscopic data is collected. By collecting microscopic data in real time including the vibration intensity of the vibration signal, the length of influence of the vibration signal, and the duration of the vibration signal, and then conducting real-time data analysis on the data, it can be determined whether the vibration signal is continuing to occur and whether the vibration source is approaching or far away. It is helpful to judge the evolution of third-party activities, so as to determine what treatment methods to adopt to improve safety along the pipeline.

In the embodiment of the present application, when the microscopic characteristic data meets the preset conditions, obtaining the macroscopic characteristic data corresponding to the vibration signal may include at least one of the following:

1) When the vibration intensity of the vibration signal reaches the first intensity threshold and is less than the second intensity threshold, obtain the macroscopic feature data corresponding to the vibration signal;

2) When the influence length of the vibration signal reaches the first length threshold, obtain the macroscopic feature data corresponding to the vibration signal;

3) When the action duration of the vibration signal reaches the first duration threshold, obtain the macroscopic feature data corresponding to the vibration signal.

Specifically, the preset conditions refer to conditions that need to be further combined with macroscopic characteristic data to judge the nature of the third-party activity that triggers the vibration signal. Due to the frequent third-party activities along the pipeline, there are a variety of vibration signals. If the nature of the third-party activities that cause vibration signals is judged only through microscopic data, the accuracy of the judgment results will be low and it is easy to cause alarm flooding. It is necessary to further combine the macro characteristic data to analyze the vibration signals. Make judgments. Therefore, in the embodiment of the present application, when the microscopic characteristic data meets the preset conditions, the macroscopic characteristic data corresponding to the vibration signal is obtained. In the embodiment of the present application, the preset condition can be based on the vibration intensity of the vibration signal, the influence length of the vibration signal, and/or the action time of the vibration signal. Long threshold is set.

The threshold value of the vibration intensity of the vibration signal may include a first intensity threshold A ₀ , a second intensity threshold A ₁ and a third intensity threshold A ₂ . The third intensity threshold A ₂ is greater than the second intensity threshold A ₁ . The second intensity threshold A ₁ Greater than the first intensity threshold A ₀ , the first intensity threshold A ₀ , the second intensity threshold A ₁ and the third intensity threshold A ₂ are explained as follows:

1) The first intensity threshold A ₀ refers to the value that the vibration intensity of the vibration signal must reach to enable the processor to start the uncertain judgment program;

2) The second intensity threshold A ₁ refers to the value that the processor must control to output the vibration intensity of the important reminder signal vibration signal;

3) The third intensity threshold A ₂ refers to a value that is required for the processor to control the vibration intensity of the vibration signal that outputs the danger alarm signal.

Among them, the uncertainty judgment procedure refers to determining the probability that the third-party activity that triggers the vibration signal is a destructive event through microscopic characteristic data combined with macroscopic characteristic data.

Among them, the threshold of the influence length of the vibration signal and the threshold of the action duration of the vibration signal are explained as follows:

1) The threshold value of the influence length of the vibration signal may be the first length threshold L ₀ . The first length threshold L ₀ refers to the value that the processor needs to reach to start the uncertain judgment program of the influence length of the vibration signal.

2) The threshold value of the action duration of the vibration signal may be the first duration threshold value t ₀ . The first duration threshold value t ₀ refers to the value that must be reached for the processor to start the uncertain judgment program of the action duration of the vibration signal.

In this embodiment of the present application, microscopic feature data that satisfies preset conditions includes at least one of the following:

1) The vibration intensity of the vibration signal reaches the first intensity threshold A ₀ and is less than the second intensity threshold A ₁ ;

2) The influence length of the vibration signal reaches the first intensity threshold L ₀ ;

3) The action duration of the vibration signal reaches the first duration threshold t ₀ .

In the embodiment of the present application, when the vibration intensity of the vibration signal reaches the second intensity threshold _A1 and is less than the third intensity threshold _A2 , it indicates that a strong vibration source has entered, and the processor outputs the first reminder information. The reminder information can be an important reminder; when the vibration intensity of the vibration signal reaches the third threshold _A2 , the processor outputs the second reminder information, and the second reminder information can be a danger alarm. The above thresholds are set according to the actual conditions of the surrounding environment of the pipeline. Judging the microscopic characteristic data first, and then obtaining the macroscopic characteristic data when the preset conditions are met, will help improve the system's discrimination efficiency, improve the system's response speed, and further improve the safety along the pipeline.

In the embodiment of the present application, the macroscopic feature data may include the occurrence time of the vibration signal and the occurrence location of the vibration signal.

Specifically, the occurrence time of the vibration signal refers to the time domain information when the vibration signal occurs, including which season the vibration signal occurs and which time period of the day or night the vibration signal occurs. Based on the actual situation, the probability that the third-party activity causing the vibration signal is a destructive event is different for different occurrence times. For example, if a vibration signal caused by a third-party activity is detected at night, the probability that the third-party activity is a destructive event is greater than the probability that a vibration signal detected during the day is caused by a destructive event under the same conditions. In actual situations, the activities that occur along pipelines are generally human activities. If the frequency of human activities decreases at night, the probability of destructive events occurring along pipelines at night increases, such as theft of transported goods in pipelines. The location where the vibration signal occurs refers to the regional information when the vibration signal occurs, including the soil quality and land use properties where the vibration signal occurs. Among them, soil quality mainly refers to the hardness of the soil. Different soil quality will affect the propagation strength of vibration signals. The harder the soil quality, the greater the propagation strength of vibration signals, and the higher the possibility that vibration signals will have a damaging impact on pipelines; the nature of land use mainly Refers to the type of land, such as roads, farmland, rivers, etc. By collecting the macro data characteristics corresponding to vibration signals, it is helpful to improve the judgment of the nature of third-party activity events that cause vibration signals along the pipeline, and realize alarm analysis of vibration events along the pipeline, which can effectively reduce alarm data, thereby achieving high quality And targeted alarms are issued to improve safety along the pipeline.

Among them, the process of determining the location of the vibration signal includes:

First, the principle of optical fiber phase sensitivity optical time domain reflection is used to measure the vibration signal and determine the position of the vibration signal along the length of the optical fiber. Secondly, the correction relationship table between the fiber and the ground position is used to determine the actual position of the vibration signal on the ground. The actual position is the location where the vibration signal occurs. The correction relationship table between the fiber and the ground position includes the correspondence between the actual location on the ground and the length of the fiber. The land use at the location where the vibration signal occurs can also be determined through the ground location. , high-consequence areas and other characteristics.

In the embodiment of this application, determining the uncertainty probability of third-party activities that trigger vibration signals based on macroscopic feature data and microscopic feature data may include:

Specifically, the uncertainty probability of the third-party activity that causes the vibration signal is further determined by combining the macro feature data and the micro feature data. First, the micro probability corresponding to the vibration signal is determined based on the micro feature data. In one example, real-time collected microscopic feature data can be used to perform uncertainty analysis of destructive events through a recognition algorithm, and the recognition probability PS can be output. Specifically: the recognition probability PS is determined by analyzing the frequency, intensity, energy, etc. of the vibration signal. The basic implementation method for the recognition probability obtained by feature analysis is: first, obtain the characteristics of the vibration signal. The characteristics can be vibration signal characteristics such as vibration intensity, frequency, bandwidth, energy, duration, and length of influence. Secondly, the signal obtained by the test is Sample labeling is used to train machine learning algorithms to obtain recognition accuracy. The recognition accuracy is the recognition probability PS. Furthermore, the recognition probability is combined with the microscopic feature data to obtain the microscopic probability corresponding to the vibration signal. Then, the macroscopic probability corresponding to the vibration signal is determined based on the obtained macroscopic characteristic data. In one example, the macroscopic feature data includes the occurrence of the vibration signal and the location of the vibration signal. The probability of a destructive time occurring at that time is summed with the probability of a destructive event occurring at that location, so that the vibration signal corresponds to macroscopic probability. Finally, the uncertainty probability of the third-party activity that caused the vibration signal is determined based on the micro-probability and macro-probability corresponding to the vibration signal and the failure consequences of the pipe section where the vibration signal occurs. The uncertainty probability is the product of the three. Among them, the failure consequences of the pipe section where the vibration signal occurs are determined by combining the location of the vibration signal with high-consequence area evaluation and risk evaluation. By combining micro-feature data and macro-feature data of vibration signals to perform uncertainty analysis on third-party activities that cause vibration events, it is helpful to improve the accuracy of system judgment and achieve targeted alarms.

In the embodiment of this application, the uncertainty probability can satisfy formula (1):
P＝H×Pw×PH; (1)

Specifically, the failure consequences can be determined by the unknown occurrence of vibration signals combined with the results of high consequence area evaluation and risk assessment. The failure consequences are the impact if a failure occurs, which can be quantified. The more serious the consequences, the greater the magnitude of the failure consequences. The higher it is, the highest is 1. The microscopic probability corresponding to the vibration signal is determined through the microscopic characteristic data. The macroscopic probability corresponding to the vibration signal is determined through macroscopic characteristic data.

In the embodiment of this application, the microscopic probability can satisfy formula (2):
Pw＝[P(A)+P(L)]×(ΔA+ΔL)×PS; (2)

Specifically, the microscopic probability corresponding to the vibration signal is determined by the microscopic characteristic data. The real-time collected microscopic feature data is used to analyze the uncertainty of destructive events through the recognition algorithm, and the recognition probability PS is output. The microscopic probability is the product of the OR operation of the vibration intensity of the vibration signal and the influence length of the vibration signal, the sum operation of the change amount of the vibration intensity of the vibration signal, the change amount of the influence length of the vibration signal, and the identification probability.

In the embodiment of this application, the macro probability can satisfy formula (3):
PH＝P(FM)+P(FT); (3)

Specifically, the macroscopic probability corresponding to the vibration signal is the sum of the probability of a destructive event occurring at the location where the vibration signal occurs and the probability of a destructive event occurring at the location where the vibration signal occurs. Among them, the numerical source of P(FM) and P(FT) and the offset position and the statistical value of the event are the prior probabilities. Collect historical data of the locations where destructive events occur along the pipeline and count correlation values to obtain the probability of destructive events occurring at different locations; collect historical data of when destructive events occur and count correlation values to obtain the probability of destructive events occurring at different times. probability of time. Since human activities have certain particularities, such as spring plowing in farmland in spring, third-party activities cannot be judged as destructive time at this time. Therefore, the use nature of the land in the location where the vibration signal occurs must be combined with the time when the vibration signal occurs. Make judgments. Relevant statistical values are determined by relevant management departments based on historical data. For example, if a similar location occurs once, P(FM) = 0.1, twice is 0.2, 5 times is 0.6, and more than 10 times is 0.9. For time statistics, the drilling and oil stealing time is 0.9 from 0:00-5:00, 0.3 from 8:00-18:00, 0.7 from 20:00-24:00, and the remaining time periods are set to 0.5. Specifically, the judgment is made based on the actual conditions in various areas along the pipeline.

Figure 2 is a flow chart of a method for monitoring safety along a pipeline provided by a specific embodiment of the present application. As shown in Figure 2, in a specific embodiment, it includes:

S201. Detect vibration signals along the pipeline;

S202. Obtain the microscopic characteristic data A, L, t corresponding to the vibration signal in real time;

S203. Determine whether A is greater than or equal to A ₂ . If the judgment result is yes, enter S204. If the judgment result is no, enter S205;

S204. Output danger alarm;

S205. Determine whether A is greater than or equal to A _1. If the judgment result is yes, enter S206. If the judgment result is no, enter S207;

S206. Output important reminders;

S207. Determine whether A is less than A ₀ or whether L is less than L ₀ or whether t is less than t ₀ . If the judgment result is yes, enter S202. If the judgment result is no, enter S208;

S208. Start the uncertainty determination program;

S209. Obtain macroscopic feature data M and T corresponding to the vibration signal;

S210. Determine the comprehensive uncertainty probability (the probability that the vibration signal is a destructive event) based on the microscopic characteristic data and macroscopic characteristic data;

S211. Send an alarm signal of corresponding level according to probability.

In a specific implementation, the uncertainty analysis method is used for the pipeline optical fiber safety early warning system, and the vibration signals in each time slice are processed separately. According to the high-consequence area analysis method, the pipeline is divided into ordinary areas and three-level high-consequence areas, which are levels I, II, and III respectively, with level III being the highest.

Each area along the pipeline is divided into 5 types according to road intersection, farmland, river wetland, adjacent village, ordinary, etc., and the probability of the area is divided by the historical event occurrence rate; the occurrence time period is divided into 2 periods according to day and night. The probability is one level higher than during the day;

Vibration signals use machine learning algorithms to classify event types, and are divided into different levels according to the recognized types.

The microscopic characteristic amplitude, influence length and variation of the vibration signal are used to calculate the microscopic characteristic in addition to the recognition algorithm.

Finally, alarm analysis is performed based on microscopic characteristics, algorithm identification results, high-consequence area evaluation, and regional and time period results.

For example: the microscopic characteristic amplitude of a place is A>A ₀ , and the influence length is L>L _0. A and L will become longer at one time and shorter at the other. The impact time t lasts for a long time and is recognized by the algorithm as a mechanical operation. The location where the macroscopic feature occurs is farmland, the soil is soft, the occurrence time is spring, and the time is daytime. According to statistical rules, this time and place is farming, and the probability of damage is extremely low. High-fruit area identification farmland is not a high-fruit area, low-effect area. Based on comprehensive calculations, the area does not require alarm and is judged to be silent.

Through uncertainty analysis, macro-probability and high-consequence area analysis methods are integrated on the basis of statistical feature analysis and algorithm identification to realize alarm analysis of vibration events along the pipeline, which can effectively reduce the number of alarms. Especially the recognition of farmland cultivation and vehicle passing.

Figure 3 is a structural block diagram of a device for monitoring safety along a pipeline provided by an embodiment of the present application. As shown in Figure 3, this embodiment of the present application provides a device for monitoring safety along pipelines, which may include:

Memory 310 configured to store instructions; and

The processor 320 is configured to call instructions from the memory 310 and when executing the instructions, can implement the above-mentioned method for monitoring safety along the pipeline.

Specifically, in this embodiment of the present application, the processor 320 may be configured to:

Real-time monitoring of vibration signals along the pipeline;

Alarm signals are output based on uncertainty probabilities.

Further, the processor 320 can also be configured to:

Micro-feature data include:

Further, the processor 320 can also be configured to:

When the microscopic characteristic data meets the preset conditions, obtaining the macroscopic characteristic data corresponding to the vibration signal includes at least one of the following:

Further, the processor 320 can also be configured to:

The uncertainty probability of determining the third-party activity causing the vibration signal based on the macro-feature data and micro-feature data includes:

Embodiments of the present application also provide a machine-readable storage medium. Instructions are stored on the machine-readable storage medium. The instructions are used to cause the machine to execute the above-mentioned method for monitoring safety along a pipeline.

Those skilled in the art will understand that embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for implementing the functions specified in one process or processes of the flowchart and/or one block or blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-volatile memory in computer-readable media, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media includes both persistent and non-volatile, removable and non-removable media that can be implemented by any method or technology for storage of information. Information may be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory. (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cassettes, tape disk storage or other magnetic storage devices or any other non-transmission medium can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes Other elements are not expressly listed or are inherent to the process, method, article or equipment. Without further limitation, an element qualified by the statement "comprises a..." does not exclude the presence of additional identical elements in the process, method, good, or device that includes the element.

The above are only examples of the present application and are not used to limit the present application. To those skilled in the art, various modifications and variations may be made to this application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application shall be included in the scope of the claims of this application.

Claims

A method for monitoring safety along pipelines, characterized by including:

Real-time monitoring of vibration signals along the pipeline;

When a vibration signal is detected along the pipeline, microscopic characteristic data corresponding to the vibration signal is collected in real time;

When the microscopic characteristic data meets the preset conditions, obtain the macroscopic characteristic data corresponding to the vibration signal;

Determine the uncertainty probability of a third-party activity that triggers the vibration signal based on the macroscopic feature data and the microscopic feature data;

An alarm signal is output based on the uncertainty probability.
A method for monitoring safety along pipelines according to claim 1, wherein the microscopic characteristic data includes:

The vibration intensity of the vibration signal, the influence length of the vibration signal and the action duration of the vibration signal.
A method for monitoring safety along pipelines according to claim 2, characterized in that, when the microscopic characteristic data meets preset conditions, obtaining the macroscopic characteristic data corresponding to the vibration signal includes the following: At least one of:

When the vibration intensity of the vibration signal reaches the first intensity threshold and is less than the second intensity threshold, obtain macroscopic feature data corresponding to the vibration signal;

When the influence length of the vibration signal reaches the first length threshold, obtain macroscopic feature data corresponding to the vibration signal;

When the action duration of the vibration signal reaches the first duration threshold, macroscopic feature data corresponding to the vibration signal is obtained.
A method for monitoring safety along pipelines according to claim 1, wherein the macroscopic characteristic data includes occurrence time characteristics of the vibration signal and occurrence location characteristics of the vibration signal.
A method for monitoring safety along pipelines according to claim 1, characterized in that the uncertainty of third-party activities that trigger the vibration signal is determined based on the macroscopic feature data and the microscopic feature data. Probabilities include:

Determine the microscopic probability corresponding to the vibration signal according to the microscopic characteristic data;

Determine the macro probability corresponding to the vibration signal according to the macro feature data;

The uncertainty probability of the third-party activity causing the vibration signal is determined based on the microscopic probability and the macroscopic probability.
A method for monitoring safety along pipelines according to claim 1, characterized in that the uncertainty probability satisfies formula (1):
P＝H×Pw×PH; (1)

Among them, P is the uncertainty probability, H is the failure consequence, Pw is the microscopic probability corresponding to the vibration signal, and PH is the macroscopic probability corresponding to the vibration signal.
The method according to claim 6, characterized in that the microscopic probability satisfies formula (2):
Pw＝[P(A)+P(L)]×(ΔA+ΔL)×PS; (2)

Among them, Pw is the microscopic probability corresponding to the vibration signal, P(A) is the vibration intensity of the vibration signal, P(L) is the influence length of the vibration signal, ΔA is the change in vibration intensity of the vibration signal quantity, ΔL is the change in the influence length of the vibration signal, and PS is the recognition probability.
The method according to claim 6, characterized in that the macroscopic probability satisfies formula (3):
PH＝P(FM)+P(FT); (3)

Among them, PH is the macroscopic probability corresponding to the vibration signal, and P(FM) is the occurrence position of the vibration signal. The probability of a destructive event, P(FT), is the probability of a destructive event occurring at the time when the vibration signal occurs.
A device for monitoring safety along pipelines, which is characterized by including:

memory configured to store instructions; and

A processor configured to call the instructions from the memory and when executing the instructions is capable of implementing a method for monitoring safety along a pipeline according to any one of claims 1 to 8.
A machine-readable storage medium, characterized in that instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute a method for monitoring pipelines along a pipeline according to any one of claims 1 to 8. Safe method.