Pipeline reliability evaluation method, electronic device, and storage medium

Abstract

The application provides a pipeline reliability evaluation method, an electronic device and a storage medium, relates to the field of pipeline safety, and can solve the problem that the pipeline reliability evaluation result is inaccurate. The method comprises the following steps: determining a pipeline performance parameter and an efficiency ratio of the pipeline, the pipeline performance parameter being used for indicating a parameter affecting the performance of the pipeline, and the efficiency ratio being used for representing the ratio between the actual gas conveying capacity of the pipeline and the gas consumption demand of a user; a function relationship between the pipeline performance parameter and the efficiency ratio is constructed based on the pipeline performance parameter and the efficiency ratio; and the reliability index of the pipeline at a preset time is determined based on the function relationship. The application can accurately describe the continuous change process of the reliability of the pipeline, improve the accuracy of the reliability evaluation result, and make the evaluation result more consistent with the engineering practice.

Description

Technical Field

[0001] This application relates to the field of pipeline safety, and in particular to a pipeline reliability assessment method, electronic equipment, and storage medium. Background Technology

[0002] Gas pipelines are the "lifeline" of energy supply, and their reliability is directly related to the stable operation of social production and residents' lives.

[0003] Currently, pipeline reliability assessments typically rely on two-state reliability theory and multi-state reliability theory. However, the assessment results obtained under these two theories are inaccurate. Summary of the Invention

[0004] This application provides a pipeline reliability assessment method, electronic device, and storage medium, which can improve the accuracy of reliability assessment results.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a pipeline reliability assessment method, which includes: determining pipeline performance parameters and pipeline efficiency ratio, wherein the pipeline performance parameters are used to indicate parameters affecting pipeline performance, and the efficiency ratio is used to characterize the ratio between the actual gas transport capacity of the pipeline and the user's gas consumption demand; constructing a functional relationship between the pipeline performance parameters and efficiency ratio based on the pipeline performance parameters and efficiency ratio; and determining the pipeline reliability index at a preset time based on the functional relationship.

[0006] In conjunction with the first aspect mentioned above, one possible implementation involves constructing a functional relationship between pipeline performance parameters and efficiency ratio based on these parameters. This includes: fitting a preset basis function using the least squares method to obtain the functional relationship based on the pipeline performance parameters and efficiency ratio; or fitting a regression model containing a time trend term with the pipeline performance parameters as independent variables and the efficiency ratio as dependent variable to obtain the functional relationship; or calculating a random variable function based on the Darcy-Weisbach formula and the nodal pressure balance equation, and then performing probability density transfer on the random variable function to obtain the functional relationship.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the reliability index of the pipeline at a preset time is determined based on the functional relationship, including: determining the complementary cumulative distribution function of the efficiency ratio based on the functional relationship; taking the derivative of the complementary cumulative distribution function to obtain the probability density function of the efficiency ratio; determining the expectation of the probability density function of the efficiency ratio at the preset time, and determining the expectation as the reliability index.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the pipeline performance parameters include the pipeline roughness, the gas source pressure fluctuation parameters within the pipeline, the user load dynamic fluctuation parameters, and the pipeline leakage probability. The gas source pressure fluctuation parameters within the pipeline are used to characterize the changes in internal pressure and gas flow rate of the pipeline, and the user load dynamic fluctuation parameters are used to characterize the changes in user gas consumption.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, determining the pipeline efficiency ratio includes: taking the actual gas pressure on the user side as greater than or equal to the maximum allowable pressure and the gas flow rate in the pipeline as less than or equal to the maximum allowable flow rate of the pipeline as constraints, and taking the maximization of the pipeline gas supply as the optimization objective, determining the actual gas flow rate transported by the pipeline; and determining the efficiency ratio as the ratio between the actual gas flow rate transported by the pipeline and the user's gas consumption demand value.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: performing partial derivative calculations on the complementary cumulative distribution function of the efficiency ratio to obtain a sensitivity index, which is used to indicate the degree of influence of pipeline performance parameters on reliability indicators.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: taking the gas inventory in the pipeline as a first preset range, the compressor power as less than or equal to the rated maximum power, and the actual gas supply to the user as greater than or equal to the user's minimum gas demand as constraints, and taking the maximization of the total economic benefits of gas supply determined by the probability density function based on the efficiency ratio as the optimization objective, to obtain the adjustment time of the gas supply strategy.

[0012] Secondly, this application provides a pipeline reliability assessment device, which includes: a determining unit for determining pipeline performance parameters and pipeline efficiency ratio, wherein the pipeline performance parameters are used to indicate parameters affecting pipeline performance, and the efficiency ratio is used to characterize the ratio between the actual gas transport capacity of the pipeline and the user's gas consumption demand; a constructing unit for constructing a functional relationship between the pipeline performance parameters and the efficiency ratio based on the pipeline performance parameters and the efficiency ratio; and an assessment unit for determining the pipeline reliability index at a preset time based on the functional relationship.

[0013] In conjunction with the second aspect above, in one possible implementation, the construction unit is used to: fit a preset basis function using the least squares method based on pipeline performance parameters and efficiency ratio to obtain a functional relationship; or, fit a regression model containing a time trend term with pipeline performance parameters as independent variables and efficiency ratio as dependent variable to obtain a functional relationship; or, calculate a random variable function based on the Darcy-Weisbach formula and the nodal pressure balance equation, and perform probability density transfer on the random variable function to obtain a functional relationship.

[0014] In conjunction with the second aspect above, in one possible implementation, the evaluation unit is used to: determine the complementary cumulative distribution function of the efficiency ratio based on the functional relationship; differentiate the complementary cumulative distribution function to obtain the probability density function of the efficiency ratio; determine the expectation of the probability density function of the efficiency ratio at a preset time, and determine the expectation as a reliability index.

[0015] In conjunction with the second aspect above, in one possible implementation, the pipeline performance parameters include the pipeline roughness, the gas source pressure fluctuation parameters inside the pipeline, the user load dynamic fluctuation parameters, and the pipeline leakage probability. The gas source pressure fluctuation parameters inside the pipeline are used to characterize the changes in internal pressure and gas flow rate of the pipeline, and the user load dynamic fluctuation parameters are used to characterize the changes in user gas consumption.

[0016] In conjunction with the second aspect above, in one possible implementation, the determining unit is used to: determine the actual gas flow rate of the pipeline with the constraints that the actual gas pressure on the user side is greater than or equal to the maximum allowable pressure and the gas flow rate in the pipeline is less than or equal to the maximum allowable flow rate of the pipeline, and with the optimization objective of maximizing the gas supply in the pipeline; and determine the efficiency ratio as the ratio between the actual gas flow rate of the pipeline and the user's gas consumption demand value.

[0017] In conjunction with the second aspect above, in one possible implementation, the device further includes: a calculation unit for performing partial derivative calculations on the complementary cumulative distribution function of the efficiency ratio to obtain a sensitivity index, wherein the sensitivity index is used to indicate the degree of influence of pipeline performance parameters on reliability indicators.

[0018] In conjunction with the second aspect above, in one possible implementation, the device further includes: an adjustment unit, configured to: obtain the adjustment time of the gas supply strategy under the constraints that the gas inventory in the pipeline is within a first preset range, the compressor power is less than or equal to the rated maximum power, and the actual gas supply to the user is greater than or equal to the user's minimum gas demand, and with the optimization objective of maximizing the total economic benefit of gas supply determined by the probability density function based on the efficiency ratio.

[0019] Thirdly, this application provides an electronic device, including: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the pipeline reliability assessment method as described in the first aspect and any possible implementation of the first aspect.

[0020] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the pipeline reliability assessment method as described in the first aspect and any possible implementation thereof.

[0021] Fifthly, this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the pipeline reliability assessment method as described in the first aspect and any possible implementation thereof.

[0022] In a sixth aspect, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the pipeline reliability assessment method as described in the first aspect and any possible implementation thereof.

[0023] Specifically, the chip provided in this application also includes a memory for storing computer programs or instructions.

[0024] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the device, or it may be packaged separately from the processor of the device; this application does not impose any limitation on this.

[0025] In a seventh aspect, this application provides a pipeline reliability assessment system, comprising: a pipeline reliability assessment apparatus, wherein the pipeline reliability assessment apparatus is used to perform the pipeline reliability assessment method as described in the first aspect and any possible implementation thereof.

[0026] The descriptions of aspects two through seven in this application can be referenced to the detailed description of aspect one; and the beneficial effects of the descriptions of aspects two through seven can be referenced to the analysis of the beneficial effects of aspect one, which will not be repeated here.

[0027] In this application, the name of the aforementioned pipeline reliability assessment device does not limit the equipment or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.

[0028] These or other aspects of this application will become more readily apparent in the following description.

[0029] The pipeline reliability assessment method provided in this application constructs a functional relationship between pipeline performance parameters and efficiency ratio, and then obtains pipeline reliability indicators based on this functional relationship. Compared with pipeline reliability assessment under discrete conditions, it can accurately describe the continuous change process of pipeline reliability, more realistically reflect the actual situation, improve the accuracy of reliability assessment results, and make the assessment results more consistent with engineering practice. Attached Figure Description

[0030] Figure 1A flowchart of a pipeline reliability assessment method provided in this application embodiment; Figure 2 A comparative schematic diagram of reliability assessment provided for an embodiment of this application; Figure 3 A schematic diagram of a pipeline reliability assessment system module provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a pipeline reliability assessment device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the hardware structure of a pipeline reliability assessment device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0033] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0034] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0035] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0036] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0037] Gas pipelines are the "lifeline" of energy supply, and their reliability is directly related to the stable operation of social production and residents' lives.

[0038] Currently, the evaluation of gas pipeline network reliability mainly relies on traditional reliability theories, including two-state reliability theory and multi-state reliability theory. Two-state reliability theory divides the gas pipeline system into two discrete states: "completely normal" and "completely failed," using only the efficiency ratio g=1 as the sole dividing threshold. This model can only determine whether the system meets basic gas demand and cannot characterize the continuous degradation / surplus conditions of g in the range of 0-1 and g>1. While this method is simple to calculate, it is too coarse for complex systems like gas pipeline networks. In actual operation, the actual performance of the gas pipeline network (such as flow rate and pressure) changes continuously. Between fully meeting demand and complete failure, there are numerous "degraded operation" states (such as reduced flow rate and pressure). Two-state reliability theory cannot describe this continuous performance degradation process, leading to inaccurate evaluation results and difficulty in supporting preventative maintenance and refined scheduling decisions.

[0039] Multistate reliability theory introduces multiple discrete intermediate states, representing an improvement over binary reliability theory. However, its state division is still inherently discrete. For gas pipeline networks with continuously varying performance, the state division of multistate reliability theory is subjective and cannot accurately characterize the probability distribution of the gas pipeline network system at arbitrary performance levels, thus limiting its evaluation accuracy.

[0040] In view of this, the pipeline reliability assessment method provided in this application constructs a functional relationship between pipeline performance parameters and efficiency ratio, and then obtains pipeline reliability index based on this functional relationship. Compared with the reliability assessment of pipelines under discrete conditions, it can accurately describe the continuous change process of pipeline reliability, more realistically reflect the actual situation, improve the accuracy of reliability assessment results, and make the assessment results more consistent with engineering practice.

[0041] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0042] It should be noted that the various embodiments of this application can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0043] Figure 1 This is a flowchart illustrating a pipeline reliability assessment method provided in an embodiment of this application. Figure 1As shown, the pipeline reliability assessment method includes: S101. Determine the pipeline performance parameters and the pipeline efficiency ratio. The pipeline performance parameters are used to indicate the parameters that affect the pipeline performance.

[0044] The efficiency ratio is used to characterize the ratio between the actual gas transport capacity of the pipeline and the gas consumption demand of the user.

[0045] S102. Based on pipeline performance parameters and efficiency ratio, construct the functional relationship between pipeline performance parameters and efficiency ratio.

[0046] For example, a functional relationship between pipeline performance parameters and efficiency ratio can be constructed using probabilistic methods (e.g., Monte Carlo simulation, first-order second-moment method, or analytical derivation).

[0047] S103. Based on the functional relationship, determine the reliability index of the pipeline at a preset time.

[0048] For example, the functional relationship is a function of time t. Based on this functional relationship, the reliability index at any given time can be determined. For example, the preset time can be a future time, the current time, or a past time; this embodiment of the application does not limit this.

[0049] The pipeline reliability assessment method provided in this application constructs a functional relationship between pipeline performance parameters and efficiency ratio, and then obtains pipeline reliability indicators based on this functional relationship. Compared with pipeline reliability assessment under discrete conditions, it can accurately describe the continuous change process of pipeline reliability, more realistically reflect the actual situation, improve the accuracy of reliability assessment results, and make the assessment results more consistent with engineering practice.

[0050] In some embodiments, the above-mentioned pipeline performance parameters include pipeline roughness, gas source pressure fluctuation parameters within the pipeline, user load dynamic fluctuation parameters, and pipeline leakage probability.

[0051] Corrosion, scaling, and deposits on the inner wall of the pipeline can directly affect the friction coefficient of the pipe section, which in turn can lead to changes in the roughness of the pipeline, reducing the pipeline's transport capacity and end pressure, and thus affecting the pipeline's efficiency ratio.

[0052] The gas source pressure fluctuation parameter within the pipeline is used to characterize the changes in pressure and gas flow rate inside the pipeline. Understandably, compressor failures, gas source switching, and changes in gas source field capacity can cause variations in gas pressure and flow rate within the pipeline, thus affecting the gas source pressure fluctuation parameter. These variations directly impact the pipeline's efficiency ratio.

[0053] User load dynamic fluctuation parameters are used to characterize the changes in user gas consumption. Understandably, factors such as seasonal peak shaving, instantaneous gas consumption surges by industrial users, and changes in residential gas consumption patterns can cause dynamic fluctuations in user load, which in turn affect the user load dynamic fluctuation parameters. These parameters directly determine the actual gas transport capacity of the pipeline, i.e., directly determine the pipeline's efficiency ratio.

[0054] Pipeline leakage probability is used to characterize the likelihood of a pipeline leaking within a preset time period. For example, pipeline leakage can cause a drop in local pipeline pressure and a loss of gas flow, which can severely impact the gas supply capacity of the entire gas transmission network, leading to changes in pipeline efficiency. Furthermore, pipeline leakage probability can also be used to refine the hydraulic model of the pipeline network.

[0055] By clarifying the pipeline performance parameters, we can identify which parameters affect the pipeline's efficiency ratio, thus providing a parameter basis for the subsequent process of determining the functional relationship between pipeline performance parameters and pipeline efficiency ratio.

[0056] In some embodiments, determining the pipeline performance parameters includes: determining the pipeline roughness; determining the gas source pressure fluctuation parameters within the pipeline; determining the user load dynamic fluctuation parameters; and determining the pipeline leakage probability.

[0057] For example, the pipe roughness k at different time points of the same pipeline can be obtained through historical operational data, experimental data, or engineering experience (including intelligent pipeline cleaning machine detection data, historical data from the supervisory control and data acquisition (SCADA) system, internal inspection reports, meteorological load curves, and pipeline integrity management records, etc.). ), k( ), ..., k( A power function model is used to construct a functional relationship between the pipe roughness and time. For example, the pipe roughness satisfies the following formula 1.

[0058] Formula 1 In the formula, This represents the roughness of the pipe at time t; The initial roughness of the pipe is represented by ; a and b are degradation parameters, which are obtained by fitting historical data using the least squares method.

[0059] To determine the probability distribution characteristics of pipeline roughness, roughness samples were collected from m pipelines of the same corrosion level at the same time point. , , ..., The collected pipe roughness samples are subjected to a normality test. If the test passes, it indicates that the pipe roughness follows a log-normal distribution. .in, and These are the mean and standard deviation of the logarithmic roughness, respectively. and It changes with time t.

[0060] The probability distribution of the pipe roughness described above is used to represent the probability density that the pipe roughness is a specific value at a given time t. For example, P(k(t)≤0.5mm) represents the probability that the pipe roughness does not exceed 0.5mm at time t.

[0061] For example, continuous pressure monitoring data of the gas source node is acquired through a SCADA system. , , ..., The sampling interval was 15 minutes to 1 hour. Outliers and trend terms were identified from the collected sample data, and the random components of the retained gas source pressure fluctuations were determined as the gas source pressure fluctuation parameters in the pipeline.

[0062] After removing outliers and long-term trend terms, the real-time monitoring values ​​of the remaining random components are the actual values ​​of the gas source pressure fluctuations. These values ​​are directly substituted into the subsequent pipeline hydraulic model and efficiency ratio function calculations. The gas source pressure fluctuation parameters within the pipeline follow a time-varying Weibull probability distribution; the Weibull probability distribution is only used to describe the random fluctuation patterns of the gas source pressure fluctuation parameters within the pipeline and to generate simulation samples, and does not directly participate in hydraulic calculations.

[0063] Based on SCADA time-series data within a rolling time window, the shape parameter β(t) and scale parameter η(t) are updated hourly using the maximum likelihood estimation method. This makes the gas source pressure fluctuation parameter a dynamic variable that changes with time t, thus integrating it into the time-varying functional relationship between pipeline performance parameters and efficiency ratio. The probability distribution of the gas source pressure fluctuation parameter within the pipeline thus satisfies Equation 2.

[0064] Formula 2 In the formula, p represents the pressure of the gas inside the pipe. This represents the random fluctuation characteristics of the gas pressure within the pipeline within the normal operating range. The time-varying shape parameter β(t) characterizes the concentration of gas source pressure fluctuations within the pipeline. β(t) < 1 indicates early failure-type fluctuations (such as instability during the initial startup of a compressor), β(t) = 1 indicates an exponential distribution (memoryless fluctuations), and β(t) > 1 indicates normal fluctuations (during the stable operation phase of the compressor). η(t) is a time-varying scaling parameter.

[0065] For example, by obtaining hourly user load dynamic fluctuation parameters of user nodes. , , ..., The time span is at least one year. The dynamic fluctuation parameters of user load are divided into a trend component T(t), a seasonal component S(t), a daily cycle component W(t), and a random component Rd(t), i.e., D(t) = T(t) + S(t) + W(t) + Rd(t). The trend component T(t) is obtained through linear regression fitting, the seasonal component S(t) through Fourier series fitting, and the daily cycle component W(t) through 24-hour moving average fitting. The random component Rd(t) is obtained by substituting the user load dynamic fluctuation parameters D(t), trend component T(t), seasonal component S(t), and daily cycle component W(t) into the model. The random component is then defined as the user load dynamic fluctuation parameter. An autoregressive integrated moving average (ARIMA) model is established for the user load dynamic fluctuation parameter. .

[0066] in, Let i be the i-th autoregressive coefficient. Let j be the moving average coefficient. Let be the white noise sequence at time t, p be the autoregressive order of the ARIMA model, representing the order of influence of historical load data on the current load, and q be the moving average order of the ARIMA model, representing the fitting order of the random white noise sequence.

[0067] This model can be used to characterize the non-steady-state characteristics of users' gas load changes over time. By fitting the trend component, seasonal fluctuations, daily periodic variations, and random fluctuation components, it can be used to predict users' gas demand and its probability distribution at any future time.

[0068] For example, the probability of pipeline leakage can be calculated based on a pre-trained corrosion / crack defect rate model. For instance, for pipeline j, the probability of leakage at time t can be calculated using the pre-trained corrosion / crack defect rate model. .

[0069] In some embodiments, determining the efficiency ratio of the pipeline includes: determining the actual gas flow rate of the pipeline with the user's actual gas pressure being greater than or equal to the maximum allowable pressure and the gas flow rate in the pipeline being less than or equal to the maximum allowable flow rate of the pipeline as constraints, and maximizing the gas supply of the pipeline as the optimization objective; and determining the efficiency ratio as the ratio between the actual gas flow rate of the pipeline and the user's gas consumption demand.

[0070] For example, the actual flow rate of gas transported by the pipeline satisfies the following formula 3.

[0071] Formula 3 in, This represents the gas supply volume of the i-th gas source node. k represents the user node. j represents each segment of the pipeline. Let be the minimum allowed pressure for the k-th user node. Let be the maximum allowable flow rate of the j-th pipe segment, which is determined by the pipe strength and flow velocity limits.

[0072] The efficiency ratio of the pipeline satisfies the following formula 4.

[0073] Formula 4: G=C / B In the formula, G represents the efficiency ratio of the pipeline; C represents the actual flow rate of gas transported by the pipeline; and B represents the user's gas consumption demand.

[0074] Wherein, when G=1 is the critical threshold, it means that the actual gas flow rate delivered by the pipeline just meets the user's gas consumption demand; when G>1, it means that the actual gas flow rate delivered by the pipeline is greater than the user's gas consumption demand, that is, the gas pipeline system has spare capacity; when G<1, it means that the actual gas flow rate delivered by the pipeline is less than the user's gas consumption demand, then the gas pipeline system is in a degraded operation state (for example, there are problems such as gas shortage, pressure decay).

[0075] This clarifies how the pipeline's efficiency ratio is calculated. By using the pipeline's efficiency ratio to reflect whether it meets user needs, and using this as the core indicator for judging pipeline reliability, a user-demand-oriented evaluation system can be achieved.

[0076] In some embodiments, the above-described functional relationship between pipeline performance parameters and efficiency ratio, based on pipeline performance parameters and efficiency ratio, includes any of the following: (1) Based on the pipeline performance parameters and efficiency ratio, the preset basis function is fitted by the least squares method to obtain the functional relationship.

[0077] For example, the preset basis functions can be constructed based on at least one of power functions, polynomial functions, and exponential functions.

[0078] For example, power functions, polynomials, or exponential functions are selected as basis functions. Considering the interaction terms of pipeline performance parameters, the least squares method is used as the fitting criterion to obtain a nonlinear functional relationship between pipeline performance parameters and efficiency ratio. Furthermore, in the process of fitting the pipeline performance parameters and efficiency ratio, a coefficient of determination R² ≥ 0.95 is used as the criterion for judging the goodness of fit.

[0079] (2) Using pipeline performance parameters as independent variables and efficiency ratio as dependent variable, the regression model containing time trend term is fitted to obtain the functional relationship.

[0080] For example, using pipeline performance parameters as independent variables and efficiency ratio as the dependent variable, a multiple regression model including a time trend term is fitted to obtain the coefficients of the multiple regression model. Based on the coefficients of the multiple regression model, a functional relationship is constructed. Furthermore, significant influencing factors are screened using stepwise regression to quantify the contribution coefficients of each pipeline performance parameter to the efficiency ratio, thus obtaining the functional relationship.

[0081] (3) Based on the Darcy-Weisbach formula and the nodal pressure balance equation, the pipeline performance parameters and efficiency ratio are calculated to obtain the random variable function. The probability density transfer of the random variable function is performed to obtain the functional relationship.

[0082] The above method can theoretically deduce the mapping relationship between pipeline performance parameters and efficiency ratio, and is applicable to simple pipeline network structures with a clear distribution of pipeline performance parameters.

[0083] For example, taking pipeline performance parameters including pipeline roughness, pipeline gas source pressure fluctuation parameters, user load dynamic fluctuation parameters, and pipeline leakage probability as examples, the pipeline performance parameters and pipeline efficiency ratio satisfy the following formula 5.

[0084] Formula 5 In the formula, G represents the efficiency ratio of the pipeline; C represents the actual flow rate of gas transported by the pipeline; B represents the user's gas consumption demand; and k represents the roughness of the pipeline. The parameter represents the gas source pressure fluctuation within the pipeline; D represents the user load dynamic fluctuation parameter; and L represents the pipeline leakage probability.

[0085] This not only clarifies the functional relationship between pipeline performance parameters and efficiency ratio, but also allows G(t) in the above formula to be an explicit time-varying function containing the time variable t, which can directly characterize the quantitative influence of performance parameters on efficiency ratio at different times. This eliminates the need for repeated complex pipeline hydraulic iteration calculations and significantly improves the calculation efficiency of subsequent reliability indicators.

[0086] It should be noted that when constructing the functional relationship between pipeline performance parameters and efficiency ratio, the sample data of "pipeline performance parameters - efficiency ratio" used can be obtained from actual measurements or generated through simulations using methods such as Monte Carlo. The pipeline performance parameters satisfy their respective probability distribution functions during the simulation process.

[0087] In some embodiments, the above-mentioned determination of the pipeline reliability index at a preset time based on the functional relationship includes: determining the complementary cumulative distribution function of the efficiency ratio based on the functional relationship; taking the derivative of the complementary cumulative distribution function to obtain the probability density function of the efficiency ratio; determining the expectation of the probability density function of the efficiency ratio at the preset time, and determining the expectation as the reliability index.

[0088] For example, the complementary cumulative distribution function of the efficiency ratio satisfies the following formula 6.

[0089] Formula 6 In the formula, The complementary cumulative distribution function represents the efficiency ratio. The cumulative distribution function represents the efficiency ratio G(t). .Right now It can be used to characterize the probability that the efficiency ratio is not lower than g at time t, i.e. . In this context, g is a continuous independent variable, obtained through... It can accurately quantify the probability distribution at any t and / or any g, using It can represent the probability distribution across different efficiency ratio ranges.

[0090] Understandable It can be used to characterize the probability that the actual flow rate of gas transported in the pipeline at time t is not less than c, i.e. .

[0091] For example, the probability density function of the efficiency ratio satisfies the following formula 7.

[0092] Formula 7 In the formula, The probability density function representing the efficiency ratio; The complementary cumulative distribution function represents the efficiency ratio. Let G(t) be the cumulative distribution function representing the efficiency ratio.

[0093] The above The probability density function used to characterize the efficiency ratio G(t) of a pipeline within any interval (e.g., [0.8, 0.9]) reflects the distribution of the efficiency ratio at different levels. For example, the probability density function of the efficiency ratio... The larger the value, the higher the probability that the pipeline's efficiency ratio is near g.

[0094] For example, the reliability index satisfies the following formula 8.

[0095] Formula 8 In the formula, Indicates reliability index; g represents efficiency ratio; The probability density function representing the efficiency ratio.

[0096] Reliability indicators can quantify the average efficiency level of a pipeline at time t, reflecting the overall gas supply capacity reserve of the pipeline. For example, E[G]=1.1875 indicates that the pipeline has an average of 18.75% spare gas transmission capacity.

[0097] By using indicators such as expectation and probability density function in continuous states, the reliability of pipelines under different efficiency ratios can be fully revealed, providing rich information support for refined management and decision-making.

[0098] In some embodiments, the above method further includes: performing partial derivative calculations on the complementary cumulative distribution function of the efficiency ratio to obtain a sensitivity index, which is used to indicate the degree of influence of pipeline performance parameters on reliability indicators.

[0099] For example, the sensitivity index satisfies the following formula 9.

[0100] Formula 9 In the formula, This represents the sensitivity index of the i-th pipeline performance parameter. This represents the standard deviation of the i-th pipeline performance parameter. The complementary cumulative distribution function represents the efficiency ratio.

[0101] The higher the sensitivity index value, the greater the impact of the pipeline performance parameter on the reliability index, and the corresponding pipe section is the weak point of the pipeline. For different pipe sections, when the sensitivity index is greater than 5%, that pipe section needs to be subject to key monitoring and maintenance.

[0102] By identifying sensitivity indicators, we can provide direction for pipeline optimization, avoid blind optimization, and reduce decision-making costs.

[0103] In some embodiments, the above method further includes: taking the gas storage in the pipeline as a first preset range, the compressor power as less than or equal to the rated maximum power, and the actual gas supply to the user as greater than or equal to the user's minimum gas demand as constraints, and taking the maximization of the total economic benefits of gas supply determined by the probability density function based on the efficiency ratio as the optimization objective, to obtain the adjustment time of the gas supply strategy.

[0104] For example, the adjustment time of the gas supply strategy satisfies the following formula 10.

[0105] Formula 10 In the formula, This represents the economic benefit function corresponding to an efficiency ratio of g. For example, when g > 0.9, the gas supply strategy results in no loss for industrial users; when 0.8 ≤ g ≤ 0.9, the gas supply strategy reduces the number of low-priority users; and when g < 0.8, the gas supply strategy reduces the number of industrial users.

[0106] The probability density function representing the efficiency ratio. This represents the amount of gas in the pipeline at time t. Indicates the lower limit of the first preset range; This indicates the upper limit of the first preset range. This indicates the compressor power. This indicates the rated maximum power. This indicates the actual amount of gas supplied to the user. This indicates the user's minimum gas consumption requirement.

[0107] For example, if R(1.05,t)<95%, the adjustment time calculated based on Formula 9 above is 48 hours, then liquefied natural gas (LNG) peak shaving will be started 48 hours in advance.

[0108] Understandably, the adjustment time calculated under the above constraints is the optimal adjustment time.

[0109] By determining the adjustment time of the gas supply strategy, the adjustment time can be predicted, achieving optimal gas allocation at different efficiency ratios and reducing peak shaving costs and the risk of gas supply interruption.

[0110] Furthermore, the complementary cumulative distribution function based on the efficiency ratio can also determine the probability of the pipeline maintaining an efficiency ratio threshold. The probability of the pipeline maintaining an efficiency ratio threshold can be used to achieve risk early warning, supporting the transformation from "passive emergency repair" to "proactive prevention" operation and maintenance mode, preventing problems before they occur.

[0111] For example, the probability of the pipeline maintaining an efficiency ratio threshold satisfies the following formula 11.

[0112] Formula 11 In the formula, This represents the efficiency ratio threshold.

[0113] For example, when =1.1, RG(1.1,t)=0.92 indicates that there is a 92% probability of maintaining a gas delivery capacity margin of more than 10% at time t.

[0114] By combining the probability of pipeline performance maintaining a threshold with reliability indicators, it is possible to predict the degradation pattern of pipeline performance and thus provide early warnings. For example, when E[G(t)] drops below 1.1 or RG(1.05,t) drops below 95%, a preventative maintenance warning is triggered.

[0115] In addition, it can combine sensitivity indicators to formulate a maintenance priority list, giving priority to maintaining pipe sections with high sensitivity and severe degradation, thereby maximizing system reliability with minimal maintenance costs.

[0116] In some embodiments, the method further includes: determining the degradation time based on Monte Carlo simulation, wherein the degradation time is used to characterize the time when the efficiency ratio is less than the failure threshold; and determining the probability change based on the degradation time, wherein the probability change is used to characterize the probability change in pipeline reliability caused by the change in the efficiency ratio of the pipeline at time t.

[0117] For example, G(t) generated by Monte Carlo simulation is reduced from the initial value of the efficiency ratio to the failure threshold (e.g., g). th The continuous degradation trajectory (=0.6) is used to obtain the degradation time. The probability change satisfies the following formula 12.

[0118] Formula 12 In the formula, Used to characterize the probabilistic change in pipeline reliability during performance degradation. The failure threshold represents the efficiency ratio. This represents the initial value of the efficiency ratio. This indicates that at time t, the pipeline efficiency ratio is not lower than the initial efficiency ratio. The probability of. This indicates that the pipeline efficiency ratio at time t is not lower than the failure threshold. The probability of.

[0119] Formula 12 can be used to calculate the probability change in system reliability as the pipeline efficiency ratio degrades from the initial state to the failure threshold, thus quantifying the reliability loss caused by performance degradation.

[0120] Using the above formula for probability change, the reliability improvement effect of pipelines under a specified performance threshold (e.g., g=0.9) after adding equipment such as compressors, pipe sections or pressure regulating stations can be accurately evaluated, avoiding over-investment or under-investment caused by the binary conclusion of traditional methods that only evaluate g≥1.

[0121] In addition, in some embodiments, the above method further includes: determining the pressure at the end of the pipeline network.

[0122] For example, the end of a pipeline network typically refers to the node with the lowest pressure in the pipeline. This could be the node furthest from the gas source or the node with the highest elevation. Pipeline network end nodes directly impact gas supply security and the operation of user-end equipment.

[0123] For example, the pressure at the end of the pipeline network, calculated based on the Darcy-Weisbach formula and the nodal pressure balance equation, satisfies the following formula 13.

[0124] Formula 13 In the formula, This represents the pressure at the end of the pipeline network at the node containing the i-th pipeline. The initial pressure of the gas source. Let be the friction coefficient of the j-th pipe segment (and the roughness of the pipe segment). kj, Reynolds number (Related) , , These are the length, inner diameter, and cross-sectional area of ​​the j-th pipe segment, respectively. Let ρ be the flow rate of the j-th pipeline segment, ρ be the density of natural gas, and g be the acceleration due to gravity.

[0125] Among them, the coefficient of friction The Colebrook-White formula is used for calculation. That is, the coefficient of friction satisfies the following formula 14.

[0126] Formula 14 After determining the pressure at the end of the pipeline network, a second functional relationship between the pressure at the end of the pipeline network and the pipeline performance parameters can be determined based on the pressure at the end of the pipeline network. That is, the second functional relationship satisfies the following formula 15.

[0127] Formula 15 In the formula, PP(t) represents the pressure at the end of the pipeline at time t; k represents the roughness of the pipeline; The parameter represents the gas source pressure fluctuation within the pipeline; D represents the user load dynamic fluctuation parameter; and L represents the pipeline leakage probability.

[0128] Furthermore, the complementary cumulative distribution function of the pressure at the end of the pipeline network can be determined based on the second functional relationship. .based on , representing the probability that the pressure at the end of the pipeline network is not lower than p at time t.

[0129] In some embodiments, the method further includes determining the maximum permissible pressure of the pipeline.

[0130] For example, the maximum permissible pressure of a pipeline is calculated based on wall thickness, pipe diameter, material, and yield strength. The maximum permissible pressure of a pipeline satisfies the following formula 16.

[0131] Formula 16 in, This indicates the maximum allowable pressure of the pipeline. δ is the yield strength of the pipe material, δ is the pipe wall thickness, F is the design coefficient (usually taken as 0.72), and B is the outer diameter of the pipe.

[0132] As a concrete example, in a gas pipeline network, the peak gas demand B is 80,000 cubic meters per hour, and the actual gas flow rate C is a random variable following a normal distribution N(9.5, 0.5²) (unit: 10,000 cubic meters per hour). Since the efficiency ratio G = C / B, and C follows a normal distribution, the efficiency ratio G also follows a normal distribution G ~ N(1.1875, 0.0625²). Therefore, the complementary cumulative distribution function of the efficiency ratio R(g) = P(G ≥ g), which is a curve that continuously varies with g. At this point, the reliability index E[G] = 1.1875, indicating that the pipeline has an average of 18.75% spare gas transmission capacity.

[0133] To calculate the probability that the pipeline network is in a "stressful operating condition" (G < 1.1), we need to substitute the cumulative distribution function of the efficiency ratio. From this, we can obtain:

[0134] Where F() is the cumulative distribution function of the standard normal distribution, which is the probability density function of the efficiency ratio. In the interval ( It is obtained by integrating over ∞, 1.1).

[0135] When P(G<1.1)=8%, it indicates that the operator needs to strengthen the monitoring of the pipeline and consider adjusting the gas supply strategy of the pipeline to avoid further deterioration of the pipeline performance.

[0136] When relying on the traditional two-state reliability theory, reliability can only be calculated with g=1 as the only fixed threshold, resulting in a single result of P(G≥1)≈99.87%. It is not possible to evaluate any continuous thresholds such as g=0.8, g=0.9, g=1.1.

[0137] Among them, the evaluation result obtained by relying on the two-state reliability theory is only 0.9987, while the complementary cumulative distribution function that relies on the efficiency ratio yields a continuous curve.

[0138] Figure 2 This is a schematic diagram illustrating a reliability assessment comparison provided for an embodiment of this application. For example... Figure 2As shown, G~N(1.1875,0.0625²), E[G]=1.1875, P(G<1.1)=0.08. Based on this, we can obtain R(1.1)=0.92. However, relying on the traditional two-state reliability theory, we can obtain R(1)=0.998.

[0139] The pipeline reliability assessment method provided in this application can achieve: 1. Threshold continuity: It can calculate the reliability of any efficiency ratio threshold g, and fully characterize the pipeline's state across the entire range of "redundant operation, degraded operation, and near failure", rather than just judging the single boundary point of g=1.

[0140] 2. State Refinement: Accurately describes the continuous degradation process of pipeline performance and captures degraded conditions such as "slight gas shortage and pressure decay" that are ignored by the two-state model (such as the tense condition of P(G<1.1)=8% in this case).

[0141] 3. Diversified decision support: Based on continuous indicators, it can realize refined management such as weak link identification, early warning, dynamic scheduling, and renovation and expansion assessment. Traditional two-state models can only give a single reliability value and cannot support preventive maintenance and dynamic scheduling.

[0142] Furthermore, this application also provides a pipeline reliability assessment system capable of evaluating pipeline reliability. This system acquires real-time operational data from a SCADA system every 5 minutes, updates the probability distribution model of pipeline performance parameters, and updates the complementary cumulative distribution function of efficiency ratio and reliability indices, thereby achieving real-time monitoring and prediction of system reliability. When RG(0.95,t) < 0.95 is predicted, control measures such as pressure regulation, gas source switching, or compressor load adjustment are automatically triggered to restore system performance to a safe level. Based on pipeline detection data and a corrosion rate model, the remaining service life of the pipeline is calculated. Remaining service life assessment indices are determined based on the remaining service life of the pipeline. A dynamic maintenance priority list is generated based on the remaining service life assessment indices and sensitivity indices.

[0143] The aforementioned pipeline reliability assessment system can be combined with existing pipeline network simulation software and data acquisition and SCADA system to conduct assessments using real-time or historical data. It is easy to integrate into actual pipeline network operation and management platforms and has broad engineering application prospects.

[0144] In some embodiments, the pipeline reliability assessment system described above is divided in a modular manner. Figure 3 This is a schematic diagram of a pipeline reliability assessment system module provided in an embodiment of this application.

[0145] The pipeline reliability assessment system includes a parameter input module, a probability distribution modeling module, a continuous state reliability calculation engine module, and a result output and visualization module.

[0146] The parameter input module includes the pressure at the end of the pipeline, the actual flow rate of gas transported in the pipeline, the user's gas consumption demand, the roughness of the pipeline, the gas source pressure fluctuation parameters in the pipeline, the user's load dynamic fluctuation parameters, and the pipeline leakage probability.

[0147] The probability distribution modeling module includes a probability distribution model corresponding to the roughness of the pipeline, a probability distribution model corresponding to the gas source pressure fluctuation parameters in the pipeline, and a probability distribution model corresponding to the dynamic fluctuation parameters of the user load.

[0148] The continuous reliability calculation engine module includes the functional relationship between pipeline performance parameters and efficiency ratio, reliability indicators, probability density function of efficiency ratio, and probability of pipeline maintaining efficiency ratio threshold.

[0149] The results output and visualization module includes sensitivity indicators, gas supply strategy adjustment time, early warning, and probability change.

[0150] Pipeline performance is characterized by indicators from different dimensions, with efficiency ratio as the core analytical indicator. This allows for the analysis of key uncertainty parameters and the impact mechanisms of each parameter. This application embodiment can divide the pipeline reliability assessment device into functional modules or functional units according to the above method example. For example, each function can be divided into a separate functional module or functional unit, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or in software functional modules or functional units. The module or unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0151] Figure 4 This is a schematic diagram of the structure of a pipeline reliability assessment device 40 provided in an embodiment of this application. The pipeline reliability assessment device 40 includes: a determination unit 401, used to determine pipeline performance parameters and pipeline efficiency ratio, wherein the pipeline performance parameters are used to indicate parameters affecting pipeline performance, and the efficiency ratio is used to characterize the ratio between the actual gas transport capacity of the pipeline and the user's gas consumption demand; a construction unit 402, used to construct a functional relationship between the pipeline performance parameters and the efficiency ratio based on the pipeline performance parameters and the efficiency ratio; and an assessment unit 403, used to determine the reliability index of the pipeline at a preset time based on the functional relationship.

[0152] In one possible implementation, the construction unit 402 is used to: fit a preset basis function using the least squares method based on pipeline performance parameters and efficiency ratio to obtain a functional relationship; or, fit a regression model containing a time trend term with pipeline performance parameters as independent variables and efficiency ratio as dependent variable to obtain a functional relationship; or, calculate a random variable function based on the Darcy-Weisbach formula and the nodal pressure balance equation, and perform probability density transfer on the random variable function to obtain a functional relationship.

[0153] In one possible implementation, the evaluation unit 403 is used to: determine the complementary cumulative distribution function of the efficiency ratio based on the functional relationship; obtain the probability density function of the efficiency ratio by differentiating the complementary cumulative distribution function; determine the expectation of the probability density function of the efficiency ratio at a preset time, and determine the expectation as a reliability index.

[0154] In one possible implementation, the pipeline performance parameters include pipeline roughness, gas source pressure fluctuation parameters within the pipeline, user load dynamic fluctuation parameters, and pipeline leakage probability. The gas source pressure fluctuation parameters within the pipeline are used to characterize the changes in internal pressure and gas flow rate, while the user load dynamic fluctuation parameters are used to characterize the changes in user gas consumption.

[0155] In one possible implementation, the determining unit 401 is used to: determine the actual gas flow rate of the pipeline with the constraints that the actual gas pressure on the user side is greater than or equal to the maximum allowable pressure and the gas flow rate in the pipeline is less than or equal to the maximum allowable flow rate of the pipeline, and with the optimization objective of maximizing the gas supply in the pipeline; and determine the ratio between the actual gas flow rate of the pipeline and the user's gas consumption demand as the efficiency ratio.

[0156] In one possible implementation, the above-mentioned device further includes: a calculation unit for performing partial derivative calculations on the complementary cumulative distribution function of the efficiency ratio to obtain a sensitivity index, which is used to indicate the degree of influence of pipeline performance parameters on reliability indicators.

[0157] In one possible implementation, the device further includes an adjustment unit, which is used to obtain the adjustment time of the gas supply strategy under the constraints that the gas inventory in the pipeline is within a first preset range, the compressor power is less than or equal to the rated maximum power, and the actual gas supply to the user is greater than or equal to the user's minimum gas demand, and with the optimization objective of maximizing the total economic benefit of gas supply determined by the probability density function based on the efficiency ratio.

[0158] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0159] When implemented in hardware, the various modules of the pipeline reliability assessment device can be integrated into, for example... Figure 5 The hardware structure of the pipeline reliability assessment device shown is implemented. Specifically, as... Figure 5 As shown, the basic hardware structure of the pipeline reliability assessment device is introduced.

[0160] Figure 5 This is a schematic diagram of the hardware structure of a pipeline reliability assessment device provided in an embodiment of this application. Figure 5 As shown, the pipeline reliability assessment device includes at least one processor 501, a communication line 502, and at least one communication interface 504, and may also include a memory 503. The processor 501, memory 503, and communication interface 504 are connected via the communication line 502.

[0161] The processor 501 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0162] Communication line 502 may include a path for transmitting information between the aforementioned components.

[0163] The communication interface 504 is used to communicate with other devices or communication networks. It can use any transceiver-like device, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0164] The memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of including or storing desired program code having the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0165] In one possible design, the memory 503 can exist independently of the processor 501, meaning the memory 503 can be an external memory of the processor 501. In this case, the memory 503 can be connected to the processor 501 via a communication line 502 to store execution instructions or application code, and its execution is controlled by the processor 501 to implement the pipeline reliability assessment method provided in the following embodiments of this application. In another possible design, the memory 503 can also be integrated with the processor 501, meaning the memory 503 can be an internal memory of the processor 501. For example, the memory 503 can be a cache, which can be used to temporarily store some data and instruction information.

[0166] As one possible implementation, processor 501 may include one or more CPUs, for example Figure 5 CPU0 and CPU1 in the example. As another possible implementation, the pipeline reliability assessment device may include multiple processors, such as... Figure 5 The processors 501 and 507 are included. As another possible implementation, the pipeline reliability assessment device may also include an output device 505 and an input device 506.

[0167] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the pipeline reliability assessment method described in the above method embodiments.

[0168] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the pipeline reliability assessment method in the method flow shown in the above method embodiments.

[0169] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires; a portable computer disk drive; a hard disk drive; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); a register; a hard disk drive; an optical fiber; a compact disc read-only memory (CD-ROM); an optical storage device; a magnetic storage device; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0170] Since the pipeline reliability assessment device, computer-readable storage medium, and computer program product in the embodiments of this application can be applied to the above method, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0174] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A pipeline reliability assessment method, characterized in that, The method includes: Determine pipeline performance parameters and pipeline efficiency ratio. The pipeline performance parameters are used to indicate parameters that affect pipeline performance, and the efficiency ratio is used to characterize the ratio between the actual gas transport capacity of the pipeline and the user's gas consumption demand. Based on the pipeline performance parameters and the efficiency ratio, a functional relationship between the pipeline performance parameters and the efficiency ratio is constructed; Based on the aforementioned functional relationship, the reliability index of the pipeline at a preset time is determined.

2. The method according to claim 1, characterized in that, The step of constructing a functional relationship between the pipeline performance parameters and the efficiency ratio based on the pipeline performance parameters and the efficiency ratio includes: Based on the pipeline performance parameters and the efficiency ratio, the functional relationship is obtained by fitting a preset basis function using the least squares method; or... Using the pipeline performance parameters as independent variables and the efficiency ratio as the dependent variable, a regression model including a time trend term is fitted to obtain the functional relationship; or, Based on the Darcy-Weisbach formula and the nodal pressure balance equation, random variable functions are calculated for the pipeline performance parameters and the efficiency ratio. The probability density transfer of the random variable functions is then performed to obtain the functional relationship.

3. The method according to claim 1, characterized in that, Determining the reliability index of the pipeline at a preset time based on the functional relationship includes: Based on the aforementioned functional relationship, the complementary cumulative distribution function of the efficiency ratio is determined; The probability density function of the efficiency ratio is obtained by differentiating the complementary cumulative distribution function. The expected value of the probability density function of the performance ratio at the preset time is determined, and the expected value is determined as the reliability index.

4. The method according to claim 1, characterized in that, The pipeline performance parameters include pipeline roughness, gas source pressure fluctuation parameters within the pipeline, user load dynamic fluctuation parameters, and pipeline leakage probability. The gas source pressure fluctuation parameters within the pipeline are used to characterize the changes in internal pressure and gas flow rate, while the user load dynamic fluctuation parameters are used to characterize the changes in user gas consumption.

5. The method according to claim 1, characterized in that, The determination of the pipeline's efficiency ratio includes: With the constraints that the actual gas pressure on the user side is greater than or equal to the maximum allowable pressure and the gas flow rate in the pipeline is less than or equal to the maximum allowable flow rate of the pipeline, and with the optimization objective of maximizing the gas supply capacity of the pipeline, the actual gas flow rate transported by the pipeline is determined. The efficiency ratio is defined as the ratio between the actual flow rate of gas transported by the pipeline and the user's gas consumption demand.

6. The method according to claim 3, characterized in that, The method further includes: The partial derivative of the complementary cumulative distribution function of the efficiency ratio is calculated to obtain a sensitivity index, which is used to indicate the degree of influence of the pipeline performance parameters on the reliability index.

7. The method according to claim 3, characterized in that, The method further includes: With constraints that the gas inventory in the pipeline is within a first preset range, the compressor operating power is less than or equal to the rated maximum power, and the actual gas supply to the user is greater than or equal to the user's minimum gas demand, and with the optimization objective of maximizing the total economic benefit of gas supply determined based on the probability density function of the efficiency ratio, the adjustment time of the gas supply strategy is obtained.

8. An electronic device, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being configured to run computer programs or instructions to implement the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-7.

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