Pipeline reliability assessment method, device, equipment and readable storage medium

Abstract

The present application discloses a pipeline reliability assessment method, device, equipment and readable storage medium, which relates to the field of long-distance pipelines. The method includes: obtaining the average failure frequency data of the target pipeline, the average failure frequency data is used to indicate the number of accidents that occurred in the target pipeline per unit length in the historical time; obtaining the historical failure possibility data of the target pipeline, the historical failure possibility data is the possibility of an accident occurring in the target pipeline predicted by a preset risk assessment method in the historical time; using the average failure frequency data and the historical failure possibility data as variable data, performing curve fitting to obtain a target curve, the target curve is used to indicate the relationship between the number of accidents occurring in the target pipeline and the predicted possibility of an accident occurring in the target pipeline in the historical time; determining the reliability data of the target pipeline according to the target curve, the reliability data is used to evaluate the current use status of the target pipeline. The accuracy and efficiency of pipeline reliability calculation are improved.

Description

Technical Field

[0001] The present application relates to the field of long-distance pipelines, and in particular to a pipeline reliability assessment method, device, equipment and readable storage medium. Background Art

[0002] Reliability is the inherent statistical law of things, which characterizes the ability of the research object to complete the predetermined function under specified working conditions. Reliability is a numerical representation of reliability, representing the reliability level of a product or system.

[0003] When determining the reliability of long-distance pipelines, the sampling analytical method is often used to calculate the pipeline reliability. According to the factors that cause pipeline failure, a variety of limit state calculation models are established. Monte Carlo sampling of no less than 10 to the fifth power is carried out for each failure point. The sampling data is input into the calculation model, and the reliability of the pipeline is obtained as the output.

[0004] However, due to the large number of failure factors, a large number of calculation models need to be established when calculating the reliability in this way. There are no effective research results for the calculation models corresponding to some failure factors, that is, the types of failure factors that can be included in the calculation are limited, and the accuracy of pipeline reliability calculation is relatively low. In addition, due to the large number of sampled data required and the large amount of calculation, the calculation efficiency is relatively low. Summary of the invention

[0005] The embodiment of the present application provides a pipeline reliability assessment method, device, equipment and readable storage medium, which can improve the accuracy and calculation efficiency of pipeline reliability calculation. The technical solution is as follows:

[0006] In one aspect, a pipeline reliability assessment method is provided, which is applied to a computer device, and the method comprises:

[0007] Obtaining average failure frequency data of a target pipeline, wherein the average failure frequency data is used to indicate the number of accidents that occurred in a unit length of the target pipeline in a historical period;

[0008] Acquire historical failure possibility data of the target pipeline, wherein the historical failure possibility data is the possibility of the accident occurring in the target pipeline predicted by a preset risk assessment method within the historical time;

[0009] The average failure frequency data and the historical failure probability data are used as variable data to perform curve fitting to obtain a target curve, wherein the target curve is used to represent the relationship between the number of accidents occurring in the target pipeline within the historical time and the predicted probability of the accident occurring in the target pipeline;

[0010] The reliability data of the target pipeline is determined according to the target curve, and the reliability data is used to evaluate the current usage status of the target pipeline.

[0011] In an optional embodiment, the method of using the average failure frequency data and the historical failure probability data as variable data to perform curve fitting to obtain a target curve includes:

[0012] Based on the historical failure possibility data, determining a first score set by a first preset value method, wherein the first score data in the first score set corresponds to the concentration degree of the historical failure possibility data;

[0013] Based on the average failure frequency data, determining a failure frequency value set by a second preset value taking method, wherein the failure frequency values ​​in the failure frequency value set are obtained by correcting the average failure frequency data;

[0014] The target curve is obtained by fitting the first score set and the failure frequency value set.

[0015] In an optional embodiment, determining the first score set based on the historical failure probability data using a first preset value method includes:

[0016] Obtaining a corresponding distribution histogram based on the historical failure probability data;

[0017] Obtaining a log-normal distribution curve according to the distribution histogram fitting;

[0018] Determine the mathematical expectation value corresponding to the lognormal distribution curve;

[0019] Based on the mathematical expectation value, the first score set is determined using a first preset value selection method.

[0020] In an optional embodiment, obtaining average failure frequency data of the target pipeline includes:

[0021] Acquire historical failure data of the target pipeline, where the historical failure data is used to indicate the number of accidents that occurred in the target pipeline during the historical period;

[0022] The average failure frequency data of the target pipeline is determined according to the historical failure data.

[0023] In an optional embodiment, determining the reliability data of the target pipeline according to the target curve includes:

[0024] Determining failure frequency data corresponding to the target pipeline according to the target curve, wherein the failure frequency data is used to evaluate the probability of failure of the target pipeline;

[0025] The reliability data is determined from the failure frequency data.

[0026] In an optional embodiment, determining the failure frequency data corresponding to the target pipeline according to the target curve includes:

[0027] Dividing the target pipeline into at least two pipe sections;

[0028] Determine a pipe segment score corresponding to the pipe segment according to the preset risk assessment method, wherein the pipe segment score is used to represent the failure possibility of the pipe segment determined by the preset risk assessment method;

[0029] Determine the pipe segment failure frequency data corresponding to the pipe segment score according to the target curve;

[0030] The failure frequency data corresponding to the target pipeline is obtained from the pipe section failure frequency data.

[0031] In an optional embodiment, the preset risk assessment method includes at least one assessment method selected from the group consisting of a risk matrix, a safety checklist method, a Kent scoring method, a probability risk assessment method, a fault tree analysis method, an event number analysis method, and a numerical simulation method.

[0032] In another aspect, a pipeline reliability assessment device is provided, the device comprising:

[0033] An acquisition module, used to acquire average failure frequency data of a target pipeline, wherein the average failure frequency data is used to represent the number of accidents that occurred in a unit length of the target pipeline in a historical period;

[0034] The acquisition module is further used to acquire historical failure possibility data of the target pipeline, wherein the historical failure possibility data is the possibility of the accident occurring in the target pipeline predicted by a preset risk assessment method within the historical time;

[0035] A fitting module, used to use the average failure frequency data and the historical failure probability data as variable data to perform curve fitting to obtain a target curve, wherein the target curve is used to represent the relationship between the number of accidents occurring in the target pipeline within the historical time and the predicted probability of the accident occurring in the target pipeline;

[0036] A determination module is used to determine the reliability data of the target pipeline according to the target curve, and the reliability data is used to evaluate the current usage status of the target pipeline.

[0037] On the other hand, a computer device is provided, characterized in that the computer device includes a processor and a memory, the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement any pipeline reliability assessment method described in the embodiments of the present application.

[0038] On the other hand, a computer-readable storage medium is provided, wherein at least one program code is stored in the computer-readable storage medium, and the program code is loaded and executed by a processor to implement any pipeline reliability assessment method described in the embodiments of the present application.

[0039] In another aspect, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the pipeline reliability assessment method described in any of the above embodiments.

[0040] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0041] Based on the average failure frequency data and historical failure probability data of the target pipeline, curve fitting is performed to obtain a target curve that represents the relationship between the number of accidents that occurred in the target pipeline in the historical time and the predicted probability of accidents occurring in the target pipeline. The reliability data of the target pipeline is then determined based on the curve to implement the reliability assessment of the target pipeline and determine the current usage status of the target pipeline. In the reliability assessment process of the target pipeline, there is no need to design a complex calculation model or obtain a large amount of sampling data. Since it is based on historical failure probability data, the accuracy of the reliability calculation is high and the calculation efficiency can also be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 is a structural block diagram of a computer device provided by an exemplary embodiment of the present application;

[0044] Figure 2 is a flow chart of a pipeline reliability assessment method provided by an exemplary embodiment of the present application;

[0045] Figure 3 is a flow chart of a pipeline reliability assessment method provided by another exemplary embodiment of the present application;

[0046] Figure 4 is a distribution histogram of pipeline failure possibility data provided by an exemplary embodiment of the present application;

[0047] Figure 5 is a target curve schematic diagram provided by an exemplary embodiment of the present application;

[0048] Figure 6 It is a structural block diagram of a pipeline reliability assessment device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0050] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0051] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0052] First, in order to make the scheme shown in the embodiment of the present application easier to understand, several terms and formulas appearing in the embodiment of the present application are explained below:

[0053] Failure frequency: The commonly used failure frequency data performance indicator refers to the number of accidents or failures that occur per cumulative unit length of the pipeline within a time interval. Let X be the number of events, that is, the failure events of the pipeline system within a time interval (t-t0); L is the length of the pipeline system (all or part of the pipeline network); E is the system inclusion, that is, the newly added pipelines in the system, please refer to formula 1, and f is the failure frequency, please refer to formula 2.

[0054] Formula 1:

[0055] Formula 2: f = X / E

[0056] Failure possibility: The determination and calculation analysis of pipeline failure possibility is one of the core tasks of pipeline risk management. Pipeline failure refers to the physical changes in which the pipeline loses the original design function or causes certain losses, including leakage, damage or performance degradation. The analysis of the overall failure possibility of the pipeline is carried out by analyzing various risk factors. Among them, there are seven major risk factors for the causes of pipeline failure: third-party damage, pipe body defects, internal corrosion, external corrosion, stress corrosion, natural geological disasters, and misoperation. The score of the failure possibility indicator can be used to reflect the size of the failure possibility and has no actual physical meaning.

[0057] Risk assessment method: It is a method that can comprehensively consider multiple pipeline failure factors and is used to evaluate the pipeline risk level. The core evaluation calculation formula of this method is "risk = failure possibility × failure consequence". According to the degree of quantification of pipeline risk assessment results, risk assessment methods can be divided into three categories: qualitative methods, semi-quantitative methods and quantitative methods. The evaluation results of qualitative methods are generally risk levels or other qualitative descriptions; semi-quantitative methods are generally indicator system methods, and the results are a numerical value, which is used to indicate the level of risk and is dimensionless. The results of quantitative methods are generally also numerical values ​​indicating the level of risk, but this numerical value has practical significance and has dimensions.

[0058] The main pipeline risk assessment methods are shown in Table 1. Among them, compared with the qualitative risk assessment method, the quantitative risk assessment method has more objective results, but the accuracy of the data is more difficult to guarantee, so it is mostly suitable for risk assessment in individual cases; on the other hand, the qualitative risk assessment method is more mature, but the evaluation results are more subjective, and the evaluation results of different evaluators may vary greatly. In the embodiment of this application, the relevant data results of the semi-quantitative risk assessment are used to evaluate the reliability of the pipeline.

[0059] Table 1

[0060]

[0061]

[0062] Reliability: Reliability is a probabilistic measure of the reliability of the engineering object being analyzed. The reliability of components and the overall project is calculated based on the combination of elements and components. It is a basic indicator to characterize the reliability of components or the overall project. The reliability evaluation of oil and gas pipeline projects mainly focuses on the pipeline line part, pump (compressor) station and automatic control system. Among them, the evaluation of the line part is currently based on the corresponding failure rate prediction.

[0063] For example, the pipeline reliability assessment method shown in the embodiment of the present application can be applied to a computer device, see Figure 1 , Figure 1It is a block diagram of a computer device structure provided by an exemplary embodiment of the present application. The computer device 101 includes a processor 102 and a memory 103. The memory 103 stores at least one instruction. The instruction is loaded and executed by the processor 102 to implement the pipeline reliability assessment method as described in each method embodiment of the present application.

[0064] In the present application, the computer device 101 is an electronic device capable of processing data and completing calculations. The computer device 101 includes two major parts: a hardware system and a software system. The computer device 101 can read the pipeline information stored in the memory 103, as well as the historical failure possibility data and historical failure data corresponding to the pipeline, organize and calculate the above data, obtain the pipeline reliability evaluation result, and output the energy consumption evaluation result.

[0065] The processor 102 may include one or more processing cores. The processor 102 uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the computer device 101 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 103, and calling data stored in the memory 103.

[0066] The memory 103 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 103 includes a non-transitory computer-readable storage medium. The memory 103 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 103 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as calculation of average failure frequency data of a target pipeline, curve fitting, etc.), instructions for implementing the following various method embodiments, etc.; the data storage area may store data involved in the following various method embodiments, etc.

[0067] Please refer to Figure 2 , which shows a flow chart of a pipeline reliability assessment method provided in an embodiment of the present application. The method can be applied in the computer device shown above. The method includes:

[0068] Step 201, obtaining average failure frequency data of a target pipeline.

[0069] In the embodiment of the present application, taking the target pipeline as an oil and gas pipeline as an example, in the early stage of pipeline service and after a long period of service, failure accidents may occur due to external interference, corrosion, pipe materials and construction quality, leading to fire, explosion, poisoning, and may cause major casualties, economic losses and environmental pollution. Therefore, failure analysis is an important task in various stages of pipeline scientific research, design, construction, and operation. Among them, failure analysis refers to a measure to analyze and diagnose the failure mode, mechanism, and cause in a timely manner after a pipeline failure accident occurs, which is of great significance to reducing pipeline failure accidents and preventing similar accidents from recurring. Failure frequency data is an important indicator of failure analysis, and the average failure frequency data is used to indicate the number of accidents that occurred in a unit length of the target pipeline in historical time.

[0070] In one example, the above average failure frequency data is determined by the historical failure data of the target pipeline. Schematically, the historical failure data of the target pipeline is obtained, wherein the historical failure data is used to indicate the number of accidents that occurred in the target pipeline in the historical time; the average failure frequency data of the target pipeline is determined according to the historical failure data. Optionally, the historical failure data can be directly read from the memory, that is, the memory stores the pipeline information and the corresponding failure data of the pipeline in previous years; optionally, the historical failure data can also be input by a technician, which is not limited here.

[0071] Step 202, obtaining historical failure probability data of the target pipeline.

[0072] In the embodiment of the present application, the historical failure probability data is the probability of an accident occurring in the target pipeline predicted by a preset risk assessment method within a historical time. Schematically, the preset risk assessment method includes at least one assessment method selected from the group consisting of a risk matrix, a safety checklist method, a Kent scoring method, a probability risk assessment method, a fault tree analysis method, an event number analysis method, and a numerical simulation method. Among them, the risk matrix and the safety checklist method are stereotyped methods; the Kent scoring method is a semi-quantitative method; the probability risk assessment, the fault tree analysis method, the event tree analysis method, and the numerical simulation method are quantitative methods.

[0073] In one example, a semi-quantitative method is selected as the preset risk assessment method, that is, the Kent scoring method is used as the preset risk assessment method in the embodiment of the present application. The principle of the Kent scoring method is to determine the factors that lead to pipeline accidents, score each factor according to certain rules to obtain the index of each factor, and then add them up to obtain the sum of the indexes that characterize the possibility of failure; then combine the danger of the pipeline medium and the surrounding environment to evaluate the leakage impact index with serious consequences of the accident; finally, divide the two to obtain the relative risk coefficient, where the larger the result, the safer the pipeline. Optionally, historical failure possibility data can be obtained through data accumulated by risk assessment application software.

[0074] Indicatively, the failure possibility is divided into seven indicators according to the factors affecting the safe operation of the pipeline, namely, third-party damage, external corrosion, internal corrosion, manufacturing and construction defects, geological disasters, misoperation, and leakage consequences. Please refer to Table 2. Each indicator is assigned a score from the two perspectives of threat events and protection effectiveness (multiple sub-indicators). The calculated failure possibility is between 0 and 0.9, where the closer to 0.9, the greater the failure possibility. Please refer to Table 3, which shows the explanation of the pipeline failure possibility score.

[0075] Table 2

[0076]

[0077]

[0078] Table 3

[0079] Serial number Points Risk Level 1 0～0.1 Low risk 2 0.1～0.15 Average 3 0.15～0.25 Medium risk 4 0.25～0.35 Higher risk 5 0.35～.045 High risk 6 0.45～0.9 High risk

[0080] Optionally, the historical failure possibility data can be directly read from the memory, that is, the memory stores the pipeline information and the historical failure possibility data corresponding to the pipeline; optionally, the historical failure possibility data can also be input by a technician, which is not limited here.

[0081] Step 203: Use the average failure frequency data and the historical failure probability data as variable data to perform curve fitting to obtain a target curve.

[0082] The target curve is used to represent the relationship between the number of accidents that occurred in the target pipeline and the predicted probability of an accident occurring in the target pipeline in the historical time. The target curve is used to analyze the corresponding relationship between the probability distribution of the failure probability score and the actual failure frequency of the pipeline (times / 1,000 km / year). Schematically, the target curve is obtained by the point drawing method, that is, the "failure probability-failure frequency" curve within the historical time period is obtained. Optionally, the above historical time period can be the previous year of the current year of the target pipeline, or it can be the entire time that the target pipeline has been in service.

[0083] Step 204, determining reliability data of the target pipeline according to the target curve.

[0084] In the embodiment of the present application, the reliability data is used to evaluate the current usage status of the target pipeline. Schematically, when calculating the reliability data, the failure probability score corresponding to each pipe section of the target pipeline is obtained using a preset risk assessment method, the failure frequency of each pipe section is obtained on the target curve, and then the failure frequency data of the entire pipeline is calculated by the failure frequency of each pipe section. The frequency value represents the failure probability of the target pipeline, and the reliability data of the entire pipeline can be obtained. That is, the failure frequency data corresponding to the target pipeline is determined according to the target curve, wherein the failure frequency data is used to evaluate the probability of failure of the target pipeline; the above reliability data is determined by the failure frequency data.

[0085] In one example, a method for determining failure frequency data corresponding to a target pipeline is as follows: dividing the target pipeline into at least two pipe sections; determining a pipe section score corresponding to the pipe section according to a preset risk assessment method, wherein the pipe section score is used to represent the failure possibility of the pipe section determined by the preset risk assessment method; determining pipe section failure frequency data corresponding to the pipe section score according to a target curve; and obtaining failure frequency data corresponding to the target pipeline from the pipe section failure frequency data.

[0086] In summary, the pipeline reliability assessment method provided in the embodiment of the present application performs curve fitting based on the average failure frequency data and historical failure probability data of the target pipeline, thereby obtaining a target curve for representing the relationship between the number of accidents that occurred in the target pipeline in the historical time and the predicted probability of an accident occurring in the target pipeline, and then determining the reliability data of the target pipeline based on the curve, thereby implementing the reliability assessment of the target pipeline and determining the current usage status of the target pipeline. On the one hand, this method can make full use of part of the evaluation data in the historical risk assessment results, and the amount of accumulated historical data is huge and real, which can improve the credibility of the reliability calculation results; on the other hand, in the process of evaluating the reliability of the target pipeline, there is no need to design a complex calculation model or obtain a large amount of sampling data, and the calculation efficiency can also be improved.

[0087] Please refer to Figure 3 , which shows a flow chart of a pipeline reliability assessment method provided by another embodiment of the present application, the method comprising:

[0088] Step 301, obtaining historical failure data of the target pipeline.

[0089] In the embodiment of the present application, the historical failure data stored in the memory is obtained, and the historical failure data is used to indicate the number of accidents that occurred in the target pipeline in the historical time. The historical time can be a historical time period, such as within 2015, or it can be the historical service time of the target pipeline. Schematically, the memory also stores the pipeline mileage of the target pipeline. For example, the target pipeline is a long-distance oil and gas pipeline, and the total pipeline mileage is 5.3×104 km, historical failure data in the historical time period, that is, the cumulative number of oil and gas pipeline accidents in 2015 was 22 times.

[0090] Step 302: Determine average failure frequency data of the target pipeline based on historical failure data.

[0091] The average failure frequency data of the target pipeline is determined from the stored historical failure data, where the average failure frequency data = historical failure data / (total pipeline mileage*time). From the historical failure data and total pipeline mileage in the above example, the average failure frequency data of the target pipeline in 2015 is 0.415×10 -3 , the unit is times / (km*year).

[0092] Step 303, obtaining historical failure probability data of the target pipeline.

[0093] The historical failure probability data of the target pipeline is the probability of the target pipeline occurring the accident predicted by the preset risk assessment method in the historical time. In the embodiment of the present application, the historical failure probability data is obtained by a semi-quantitative risk assessment method. In one example, the number of target pipeline sections in 2015 is 171, and the failure probability data of these 171 pipeline sections in 2015 are shown in Table 4.

[0094] Table 4

[0095]

[0096]

[0097]

[0098] Step 304: Determine a first score set based on the historical failure probability data using a first preset value selection method.

[0099] In an embodiment of the present application, a corresponding distribution histogram is obtained based on historical failure probability data, a log-normal distribution curve is obtained by fitting the distribution histogram, and a mathematical expectation value corresponding to the log-normal distribution curve is determined. Based on the above mathematical expectation value, a first score set is determined using a first preset value-taking method, wherein the first score data in the first score set corresponds to the degree of concentration of the historical failure probability data.

[0100] For an example, see Figure 4, the historical failure probability data 402 in Table 4 are equally grouped from 0 to 0.9 with a group interval of 0.1 as the x-axis, and the number density 403 as the y-axis, and a distribution histogram 400 of the pipeline failure probability data is drawn, and a logarithmic normal distribution curve 401 of the pipeline failure probability data is fitted on the distribution histogram. According to the curve, the mathematical expectation of the curve E(t)=0.283658 is obtained by formula 3 (wherein, the mean value μ of the variable logarithm=-1.48196, and the standard deviation σ of the variable logarithm=0.666295). The first score set is determined by a first preset value-taking method. Schematically, the first preset value-taking method can be to take the target number of values ​​as the first score data at a preset interval x (wherein 0.01≤x≤0.1) before and after the above-mentioned mathematical expectation to obtain the first score set. For example, seven first score data are taken: 0, E(t)-2x, E(t)-x, E(t), E(t)+x, E(t)+2x, and 0.9, and x=0.1 to obtain the first score set {0, 0.09, 0.19, 0.29, 0.39, 0.49, 0.9}.

[0101] Formula 3:

[0102] Step 305: Based on the average failure frequency data, determine a failure frequency value set using a second preset value selection method.

[0103] In an illustrative manner, the second preset value method may be to correct the average failure frequency data by a preset correction factor to obtain multiple failure frequency values, and obtain a failure frequency value set from the above failure frequency values, that is, the failure frequency values ​​in the failure frequency value set are corrected by the average failure frequency data. The correction factor is obtained based on the corresponding relationship between the failure probability data and the risk level, wherein a failure probability of 0 directly corresponds to a failure frequency of 0.01×10 -3 ,As shown in Table V, the failure frequency values ​​corresponding to each failure possibility data are obtained.

[0104] Table 5

[0105]

[0106] Step 306: fitting the first score set and the failure frequency value set to obtain a target curve.

[0107] Please refer to Figure 5, in a rectangular coordinate system 500, with the first score set (dimensionless) of the target pipeline as the x-axis 501, and the failure frequency value (unit: times / (km*year)) as the y-axis 502, points (0, f1), (E(t)-2x, f2), (E(t)-x, f3), (E(t), f), (E(t)+x, f4), (E(t)+2x, f5), and (0.9, f6) are respectively made in the coordinate system, and a target curve 503, i.e., a "failure probability-failure frequency" curve graph, is fitted based on these seven points, and a curve formula corresponding to the target curve (y=0.0017x 3 -0.0025x 2 +0.002x+2×10 -5 ).

[0108] Step 307, determining the reliability data of the target pipeline according to the target curve.

[0109] After the target curve is obtained, the reliability data of the target pipeline can be determined by the target curve. Schematically, the current number of pipe segments of the target pipeline is obtained, and the failure probability score of each current pipe segment is determined according to the preset risk assessment method, and the failure frequency value corresponding to each current pipe segment is determined based on the target curve, and the current failure frequency value of the target pipe segment is determined according to the failure frequency value corresponding to each pipe segment, and the frequency value represents the failure probability of the pipeline. Finally, the reliability of the target pipeline is obtained according to the relationship between the reliability of the target pipeline and the failure probability.

[0110] In one example, the current number of pipe segments of the target pipeline is 93. The semi-quantitative risk assessment method is used to conduct the current risk assessment of the target pipeline, and the failure probability data of each pipe segment during the assessment process are determined. Based on the target curve (y = 0.0017x 3 -0.0025x 2 +0.002x+2×10 -5 ), the failure frequency of each pipe section is calculated based on the failure probability data of each pipe section, as shown in Table VI.

[0111] Table 6

[0112]

[0113]

[0114]

[0115] Then, according to Formula 4, the failure frequency F of the entire pipeline is obtained by using the weighted average method, and the frequency value is used to represent the failure probability of the pipeline, where F is the failure frequency of the target pipeline, and F i is the failure frequency of each pipe segment, li is the length of each pipe section, and L is the length of the target pipeline. The calculated value is F = 0.000349244. The sum of reliability and failure probability is 1, so the reliability of the target pipeline is 0.999650756.

[0116] Formula 4: F = ∑(F i *l i ) / L

[0117] In summary, the pipeline reliability assessment method provided in the embodiment of the present application performs curve fitting based on the average failure frequency data and historical failure probability data of the target pipeline, thereby obtaining a target curve for representing the relationship between the number of accidents that occurred in the target pipeline in the historical time and the predicted probability of an accident occurring in the target pipeline, and then determining the reliability data of the target pipeline based on the curve, thereby implementing the reliability assessment of the target pipeline and determining the current usage status of the target pipeline. On the one hand, this method can make full use of part of the evaluation data in the historical risk assessment results, and the amount of accumulated historical data is huge and real, which can improve the credibility of the reliability calculation results; on the other hand, in the process of evaluating the reliability of the target pipeline, there is no need to design a complex calculation model or obtain a large amount of sampling data, and the calculation efficiency can also be improved.

[0118] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0119] Please refer to Figure 6 , Figure 6 This is a structural block diagram of a pipeline reliability assessment device provided by an exemplary embodiment of the present application. The pipeline reliability assessment device can be implemented as all or part of a computer device through software, hardware, or a combination of both. The device includes:

[0120] An acquisition module 601 is used to acquire average failure frequency data of a target pipeline, where the average failure frequency data is used to represent the number of accidents that occurred in a unit length of the target pipeline in a historical period;

[0121] The acquisition module 601 is further used to acquire historical failure possibility data of the target pipeline, wherein the historical failure possibility data is the possibility of the accident occurring in the target pipeline predicted by a preset risk assessment method within the historical time;

[0122] A fitting module 602 is used to use the average failure frequency data and the historical failure probability data as variable data to perform curve fitting to obtain a target curve, wherein the target curve is used to represent the relationship between the number of accidents occurring in the target pipeline within the historical time and the predicted probability of the accident occurring in the target pipeline;

[0123] The determination module 603 is used to determine the reliability data of the target pipeline according to the target curve, and the reliability data is used to evaluate the current use status of the target pipeline.

[0124] In an optional embodiment, the fitting module 602 includes:

[0125] a determining unit, configured to determine a first score set based on the historical failure possibility data by a first preset value method, wherein the first score data in the first score set corresponds to the concentration degree of the historical failure possibility data;

[0126] The determining unit is further configured to determine a failure frequency value set based on the average failure frequency data by a second preset value taking method, wherein the failure frequency values ​​in the failure frequency value set are obtained by correcting the average failure frequency data;

[0127] The fitting module is further used to fit the target curve using the first score set and the failure frequency value set.

[0128] In an optional embodiment, the determining unit is further configured to obtain a corresponding distribution histogram based on the historical failure probability data;

[0129] The determining unit is further used to obtain a log-normal distribution curve according to the distribution histogram fitting;

[0130] The determining unit is further used to determine the mathematical expectation value corresponding to the lognormal distribution curve;

[0131] The determining unit is further configured to determine the first score set based on the mathematical expectation value using a first preset value selection method.

[0132] In an optional embodiment, the acquisition module 601 is further used to acquire historical failure data of the target pipeline, where the historical failure data is used to indicate the number of accidents that occurred in the target pipeline within the historical time;

[0133] The determination module 603 is further configured to determine average failure frequency data of the target pipeline according to the historical failure data.

[0134] In an optional embodiment, the determination module 603 is further used to determine the failure frequency data corresponding to the target pipeline according to the target curve, and the failure frequency data is used to evaluate the probability of failure of the target pipeline;

[0135] The determination module 603 is further configured to determine the reliability data from the failure frequency data.

[0136] In an optional embodiment, the determination model 603 is further used to divide the target pipeline into at least two pipe sections;

[0137] The determination model 603 is further used to determine a pipe segment score corresponding to the pipe segment according to the preset risk assessment method, wherein the pipe segment score is used to represent the failure possibility of the pipe segment determined by the preset risk assessment method;

[0138] The determination model 603 is also used to determine the pipe segment failure frequency data corresponding to the pipe segment score according to the target curve;

[0139] The determination model 603 is also used to obtain the failure frequency data corresponding to the target pipeline from the pipe section failure frequency data.

[0140] In an optional embodiment, the preset risk assessment method includes at least one assessment method selected from the group consisting of a risk matrix, a safety checklist method, a Kent scoring method, a probability risk assessment method, a fault tree analysis method, an event number analysis method, and a numerical simulation method.

[0141] In summary, the pipeline reliability assessment device provided in the embodiment of the present application performs curve fitting based on the average failure frequency data and historical failure probability data of the target pipeline, thereby obtaining a target curve for representing the relationship between the number of accidents that occurred in the target pipeline in the historical time and the predicted probability of the target pipeline having an accident, and then determines the reliability data of the target pipeline based on the curve, implements the reliability assessment of the target pipeline, and determines the current usage status of the target pipeline. On the one hand, this method can make full use of part of the evaluation data in the historical risk assessment results, and the accumulated historical data is huge and real, which can improve the credibility of the reliability calculation results; on the other hand, in the process of evaluating the reliability of the target pipeline, there is no need to design a complex calculation model, nor to obtain a large amount of sampling data, and the calculation efficiency can also be improved.

[0142] An embodiment of the present application further provides a computer-readable medium storing at least one instruction, wherein the at least one instruction is loaded and executed by the processor to implement the pipeline reliability assessment method as described in the above embodiments.

[0143] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0144] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A pipeline reliability assessment method, characterized in that: Applied in a computer device, the method comprises: Obtaining average failure frequency data of a target pipeline, wherein the average failure frequency data is used to indicate the number of accidents that occurred in a unit length of the target pipeline in a historical period; Acquire historical failure possibility data of the target pipeline, wherein the historical failure possibility data is the possibility of the accident occurring in the target pipeline predicted by a preset risk assessment method within the historical time; Obtaining a corresponding distribution histogram based on the historical failure probability data; Obtaining a log-normal distribution curve according to the distribution histogram fitting; Determine the mathematical expectation value corresponding to the lognormal distribution curve; Based on the mathematical expectation value, a first score set is determined by a first preset value method, wherein the first score data in the first score set corresponds to the concentration degree of the historical failure possibility data; Based on the average failure frequency data, determining a failure frequency value set by a second preset value taking method, wherein the failure frequency values ​​in the failure frequency value set are obtained by correcting the average failure frequency data; A target curve is obtained by fitting the first score set and the failure frequency value set, wherein the target curve is used to represent the relationship between the number of accidents occurring in the target pipeline and the predicted probability of the accident occurring in the target pipeline within the historical time; The reliability data of the target pipeline is determined according to the target curve, and the reliability data is used to evaluate the current usage status of the target pipeline.

2. The method according to claim 1, characterized in that The obtaining of average failure frequency data of the target pipeline includes: Acquire historical failure data of the target pipeline, where the historical failure data is used to indicate the number of accidents that occurred in the target pipeline during the historical period; The average failure frequency data of the target pipeline is determined according to the historical failure data.

3. The method according to claim 1, characterized in that Determining the reliability data of the target pipeline according to the target curve includes: Determining failure frequency data corresponding to the target pipeline according to the target curve, wherein the failure frequency data is used to evaluate the probability of failure of the target pipeline; The reliability data is determined from the failure frequency data.

4. The method according to claim 3, characterized in that The step of determining the failure frequency data corresponding to the target pipeline according to the target curve includes: Dividing the target pipeline into at least two pipe sections; Determine a pipe segment score corresponding to the pipe segment according to the preset risk assessment method, wherein the pipe segment score is used to represent the failure possibility of the pipe segment determined by the preset risk assessment method; Determine the pipe segment failure frequency data corresponding to the pipe segment score according to the target curve; The failure frequency data corresponding to the target pipeline is obtained from the pipe section failure frequency data.

5. The method according to claim 1, characterized in that The preset risk assessment method includes at least one assessment method selected from the group consisting of a risk matrix, a safety checklist method, a Kent scoring method, a probability risk assessment method, a fault tree analysis method, an event number analysis method, and a numerical simulation method.

6. A pipeline reliability assessment device, characterized in that: The device comprises: An acquisition module, used to acquire average failure frequency data of a target pipeline, wherein the average failure frequency data is used to represent the number of accidents that occurred in a unit length of the target pipeline in a historical period; The acquisition module is further used to acquire historical failure possibility data of the target pipeline, wherein the historical failure possibility data is the possibility of the accident occurring in the target pipeline predicted by a preset risk assessment method within the historical time; A determination unit, configured to obtain a corresponding distribution histogram based on the historical failure probability data; The determining unit is further used to obtain a log-normal distribution curve according to the distribution histogram fitting; The determining unit is further used to determine the mathematical expectation value corresponding to the lognormal distribution curve; The determining unit is further configured to determine a first score set based on the mathematical expectation value using a first preset value selection method, wherein the first score data in the first score set corresponds to the concentration degree of the historical failure probability data; The determining unit is further configured to determine a failure frequency value set based on the average failure frequency data by a second preset value taking method, wherein the failure frequency values ​​in the failure frequency value set are obtained by correcting the average failure frequency data; A fitting module, used for fitting a target curve by using the first score set and the failure frequency value set, wherein the target curve is used for representing the relationship between the number of accidents occurring in the target pipeline within the historical time and the predicted possibility of the accident occurring in the target pipeline; A determination module is used to determine the reliability data of the target pipeline according to the target curve, and the reliability data is used to evaluate the current usage status of the target pipeline.

7. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the pipeline reliability assessment method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the program code is loaded and executed by a processor to implement the pipeline reliability assessment method according to any one of claims 1 to 5.

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