A gas pipeline emergency material demand dynamic prediction method and system

Abstract

This invention belongs to the field of gas pipeline failure prediction and analysis technology, and specifically relates to a method and system for dynamically predicting the emergency material demand of gas pipelines. The method includes: collecting gas pipeline failure data; calculating the pipeline failure probability based on the data; establishing a demand prediction model based on the failure probability; using the demand prediction model to predict the emergency material demand for gas pipelines, generating an initial prediction result; and optimizing the initial prediction result to form a target prediction result. This invention dynamically adjusts the predicted emergency material demand for gas pipelines based on the production management foundation of gas operating enterprises, meeting the needs of different gas operating enterprises and possessing strong practical applicability.

Description

Technical Field

[0001] This invention belongs to the field of gas pipeline failure prediction and analysis technology, and specifically relates to a method and system for dynamic prediction of emergency material demand for gas pipelines. Background Technology

[0002] With rapid economic development and accelerated urbanization, the demand for gas in urban areas is constantly increasing, greatly promoting the development of gas companies. As the number of urban gas pipelines continues to increase on a large scale, the probability of safety accidents caused by pipeline failures is also constantly increasing. This places increasingly higher demands on the emergency response and handling management of gas companies, making the availability of sufficient emergency supplies for gas pipelines crucial for timely emergency rescue and repair operations.

[0003] Currently, most gas companies rely on experience to configure their emergency gas pipeline material reserves, which has several drawbacks:

[0004] 1) Some pipe materials and fittings are not fully configured, making it impossible to immediately organize emergency repair work and effectively guarantee the supply of urban gas;

[0005] 2) Some pipes and fittings are required in large quantities, which on the one hand ties up a lot of the gas company’s funds, and on the other hand, emergency reserve materials that have not been used for a long time may exceed their shelf life or be in poor condition when needed, which may affect emergency rescue operations.

[0006] Therefore, establishing an optimal method for predicting and analyzing the demand for emergency materials for gas pipelines based on the different basic conditions of gas pipeline production management of different gas operating companies, and using reasonable management devices to optimize their circulation and renewal, is of great significance for ensuring gas emergency safety management.

[0007] Current research on emergency material demand analysis, both domestically and internationally, largely focuses on calculations and reserve demand analysis based on established material allocation standards. Research on emergency material demand forecasting based on the potential for gas pipeline accidents across different gas companies is relatively limited. Furthermore, emergency rescue management needs have not been integrated with the daily construction and management of gas companies into a cohesive system for relevant management research. Summary of the Invention

[0008] To address the above problems, this invention provides a method for dynamically predicting the emergency material demand of gas pipelines, the method comprising:

[0009] Collect gas pipeline failure data and calculate the pipeline failure probability based on the gas pipeline failure data;

[0010] Based on the pipeline failure probability, a demand forecasting model is established;

[0011] The demand forecasting model is used to predict the emergency material demand for gas pipelines, generating initial forecast results;

[0012] The initial prediction results are optimized to form the target prediction results.

[0013] Preferably, before collecting the gas pipeline failure data, the method further includes: classifying and statistically analyzing the basic data of the gas pipeline.

[0014] Preferably, the classified statistical basic data of gas pipelines includes:

[0015] Organize the types of emergency supplies for gas pipelines;

[0016] In-service gas pipelines are classified, and the total length of gas pipelines with different years of operation is calculated.

[0017] Preferably, the process includes collecting gas pipeline failure data and calculating the pipeline failure probability based on the gas pipeline failure data, including:

[0018] Based on the failure history data of the enterprise under test and the pre-collected failure data of the gas industry, the probability of pipeline failure of the enterprise under test is calculated.

[0019] Preferably, the calculation of the pipeline failure probability of the enterprise under test includes:

[0020] The pipeline failure data is categorized.

[0021] Calculate the pipeline failure probability of the enterprise under test under different influencing factors.

[0022] Preferably, the calculation of the pipeline failure probability of the enterprise under test under different influencing factors includes:

[0023] Calculate the average mobile failure rate of gas pipelines with different service lifespans due to intrinsic failure.

[0024] Calculate the failure rate of gas pipelines caused by external influences.

[0025] Preferably, the calculation of the intrinsic failure average moving failure rate of gas pipelines with different service life includes:

[0026] The essential failure rate of gas pipelines with different years of operation was summarized and statistically analyzed.

[0027] The mean mobility failure rate of the intrinsic failure is calculated based on the intrinsic failure rate.

[0028] Preferably, the initial prediction result is optimized to form the target prediction result, including:

[0029] Calculate the annual turnover rate of emergency gas pipeline materials for the enterprise under test, corresponding to the construction materials, and calculate the minimum inventory requirement of construction materials for the enterprise under test.

[0030] The initial forecast results are adjusted based on the annual turnover rate and minimum inventory requirements to obtain the target forecast results.

[0031] This invention also proposes a dynamic forecasting system for emergency material demand in gas pipelines, the system comprising:

[0032] The data acquisition module is used to collect gas pipeline failure data and calculate the pipeline failure probability based on the gas pipeline failure data.

[0033] A construction module is used to establish a demand prediction model based on the pipeline failure probability;

[0034] The prediction module is used to predict the emergency material demand for gas pipelines using the demand prediction model and generate initial prediction results.

[0035] An optimization module is used to optimize the initial prediction results to form a target prediction result.

[0036] Preferably, the system further includes:

[0037] The classification module is used to classify and statistically analyze basic data on gas pipelines.

[0038] Preferably, the classification module is used to classify and statistically analyze basic data of gas pipelines, including:

[0039] The classification module is used to organize the types of emergency supplies for gas pipelines;

[0040] In-service gas pipelines are classified, and the total length of gas pipelines with different years of operation is calculated.

[0041] Preferably, the acquisition module is used to acquire gas pipeline failure data and calculate the pipeline failure probability based on the gas pipeline failure data, including:

[0042] The data acquisition module is used to calculate the pipeline failure probability of the enterprise under test based on the enterprise's historical failure data and pre-collected gas industry failure data.

[0043] Preferably, the optimization module is used to optimize the initial prediction result to form a target prediction result, including:

[0044] The optimization module is used to calculate the annual turnover rate of emergency gas pipeline materials for the enterprise under test and the corresponding engineering construction materials, as well as to calculate the minimum inventory requirements of engineering construction materials for the enterprise under test.

[0045] The initial forecast results are adjusted based on the annual turnover rate and minimum inventory requirements to obtain the target forecast results.

[0046] The present invention also proposes an electronic device, comprising:

[0047] Processor and memory;

[0048] The processor invokes the computer program stored in the memory to execute any of the above-described methods for dynamic prediction of emergency material demand for gas pipelines.

[0049] The present invention also proposes a computer-readable storage medium.

[0050] The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform any of the above-described methods for dynamic prediction of emergency material demand for gas pipelines.

[0051] The present invention has the following beneficial effects:

[0052] (1) This invention dynamically adjusts the forecast of emergency material demand for gas pipelines based on the production management foundation of gas operating enterprises, which meets the needs of different gas operating enterprises and has strong practical applicability.

[0053] (2) This invention uses historical failure data to conduct predictive analysis of the demand for different materials in the gas pipeline system, so as to fully guarantee gas emergency rescue and reduce inventory funds.

[0054] (3) This invention matches emergency supplies with engineering construction materials, further optimizes the amount of inventory materials, and improves the reliability of the essential state of emergency supplies.

[0055] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This diagram illustrates a method for dynamically predicting emergency material demand for gas pipelines in an embodiment of the present invention.

[0058] Figure 2 This invention illustrates a flowchart of the statistical analysis of basic data for emergency materials analysis of gas pipelines in an embodiment of the present invention.

[0059] Figure 3 This invention presents a flowchart illustrating the failure probability analysis for different years, different laying environments, and different installation conditions in an embodiment of the invention.

[0060] Figure 4 This invention illustrates a flowchart of different emergency material demand prediction and analysis methods in an embodiment of the invention.

[0061] Figure 5 This invention illustrates a flowchart of an emergency material optimization method based on the production management of gas operating companies, as shown in an embodiment of the invention.

[0062] Figure 6 This invention presents a statistical chart showing the mileage of municipal gas steel pipelines according to different years of operation in an embodiment of the invention.

[0063] Figure 7 This invention presents a statistical chart showing the cumulative number of intrinsic failures over the past five years for different years of operation.

[0064] Figure 8 This diagram shows the calculation results of pipeline failure rates at different service lifespans in an embodiment of the present invention.

[0065] Figure 9 This diagram illustrates a dynamic prediction system for emergency material demand in gas pipelines, as shown in an embodiment of the present invention.

[0066] Figure 10 A diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0067] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0068] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0069] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0070] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0071] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0072] like Figure 1 As shown, this invention proposes a method for dynamic prediction of emergency material demand for gas pipelines, the method comprising:

[0073] S1 Classification Statistics of Basic Gas Pipeline Data;

[0074] S2 collects gas pipeline failure data and calculates the pipeline failure probability based on the gas pipeline failure data;

[0075] S3 establishes a demand forecasting model based on the pipeline failure probability;

[0076] S4 uses the demand forecasting model to predict the emergency material demand for gas pipelines and generates initial forecast results;

[0077] S5 optimizes the initial prediction result to form the target prediction result.

[0078] Specifically, the S1 category statistical data on basic gas pipeline data includes:

[0079] S11 Organizes the types of emergency supplies for gas pipelines;

[0080] S12 classifies in-service gas pipelines and calculates the total length of gas pipelines with different years of operation.

[0081] like Figure 2 As shown in this embodiment, the present invention uses a classification and statistical method to classify and statistically analyze the basic data of a gas pipeline of a gas operating company. Its core is to organize the types and potential needs of emergency materials for gas pipelines.

[0082] 1) Establish basic data for three major categories: pipes, fittings, and valves.

[0083] ① Pipe Material Category: Subcategories are created based on material and external anti-corrosion coating. Within each subcategory, further subcategories are listed by pipe outer diameter. The actual in-service pipe length for each corresponding item is statistically analyzed, using C as the unit of measurement. sn (where n represents different sub-items), unit: km;

[0084] ② Pipe Fittings Category: Subcategories are created based on material and fitting type. Within each subcategory, further subcategories are listed by outer diameter. The quantity of fittings is based on the number of fittings per kilometer of pipeline length. Municipal pipeline fittings index (A) s ), courtyard pipe fittings specifications (A) t ), to make approximate statistics, with J zn (n represents different sub-items), unit: number;

[0085] ③ Valve Category: Subcategories are created based on material and valve type. The number of valves is based on actual statistics, using F... sn (n represents distinct sub-items), unit: number.

[0086] 2) In-service gas pipelines are divided into two categories: municipal and residential. The total length L of pipelines with different service years is calculated according to the service years of the pipelines, in km, as shown in Table 1.

[0087] Table 1

[0088]

[0089] Statistics on the percentage of municipal pipeline length p s ,unit:%:

[0090]

[0091] The percentage of courtyard pipe length p t ,unit:%:

[0092]

[0093] S2 collects gas pipeline failure data and calculates the pipeline failure probability based on the gas pipeline failure data, including:

[0094] S21 calculates the pipeline failure probability of the enterprise under test based on the enterprise's historical failure data and pre-collected gas industry failure data.

[0095] Specifically, S21 calculates the pipeline failure probability of the enterprise under test, including:

[0096] S211 classifies the pipeline failure data;

[0097] S212 calculates the pipeline failure probability of the enterprise under test under different influencing factors.

[0098] Specifically, S212 calculates the pipeline failure probability of the enterprise under test under different influencing factors, including:

[0099] S2121 The calculation of the pipeline failure probability of the enterprise under test under different influencing factors includes:

[0100] S2122 calculates the average mobile failure rate of the intrinsic failure of gas pipelines with different years of operation;

[0101] S2123 calculates the failure rate of gas pipelines caused by external influences.

[0102] Specifically, S2122 calculates the average moving failure rate of inherent failures in gas pipelines with different service lifespans, including:

[0103] S21221 summarizes and statistically analyzes the intrinsic failure rate of gas pipelines with different years of operation;

[0104] S21222 Calculates the intrinsic failure mean migration failure rate based on the intrinsic failure rate.

[0105] In this embodiment, as Figure 3 As shown, this invention summarizes the failure data of a gas company over the past 5 years and categorizes them according to the direct causes of failure. First, it establishes two major categories: municipal and courtyard pipelines; then it divides them into two subcategories: failures affected by external factors and failures inherent in the pipeline system.

[0106] 1) Calculate the pipeline failure probability under different influencing factors.

[0107] 1) Calculate the intrinsic failure rate of gas pipelines of different ages.

[0108] The data on inherent failures of pipeline systems are summarized and statistically analyzed according to the number of years the pipeline has been in operation when the failure occurs, as shown in Table 2 - Statistics of the Number of Inherent Failures of Pipelines with Different Years of Operation N (Total in the Past 5 Years).

[0109] Table 2

[0110]

[0111] 2) Calculate the average moving failure rate of pipelines with different service life.

[0112] 1))) The average rate of intrinsic failure and migration failure of municipal gas pipelines for different service years η sn

[0113] η sn —Overall average failure rate over five years for municipal gas pipelines that have been in operation for N years, km -1 ·a -1 ;

[0114] For ease of explanation, the calculation formula uses the average migration failure rate (η) of municipal gas pipelines with a service life of 1 year. s1 For example.

[0115]

[0116] The numerator represents the total number of municipal gas pipelines that have experienced fundamental failures over the past five years, as shown in the statistics table; the denominator represents the total mileage of municipal gas pipelines corresponding to the years of operation of the failure data, accumulated sequentially up to L. sn .

[0117] 2))) Average migration failure rate η of courtyard gas pipelines with different service life due to inherent failure. tn .

[0118] η tn —Overall average failure rate of courtyard pipelines after N years of operation (km) -1 ·a -1

[0119] For ease of explanation, the calculation formula uses the average failure rate η of the courtyard pipeline after 1 year of operation. t1 For example.

[0120]

[0121] The numerator represents the total number of essential failures of courtyard gas pipelines of different service years in the past 5 years as shown in the statistics table; the denominator represents the total mileage of courtyard gas pipelines corresponding to the years of operation of the failure data, accumulated sequentially up to L. tn .

[0122] 3) Calculate the failure rate of the gas pipeline due to external influences (target setpoint);

[0123] Failures caused by external factors such as third-party damage or geological disasters are classified and statistically analyzed as a whole.

[0124] 1) Calculate the gas pipeline failure rate η caused by external factors affecting the gas operating company. q ;

[0125] Statistics on the failure rate η of gas pipelines caused by external factors in gas companies over the past 5 years qn n = 1, 2, 3, 4, 5, where n is selected from the nearest to the furthest year.

[0126] If η q1 The value is the smallest, and η qn Arranged from largest to smallest n value, the overall values ​​show a decreasing trend, then η q =η q1 ;

[0127] If η q1 The value is the smallest, but η qn Arranged from largest to smallest n value, the overall values ​​show an fluctuating trend.

[0128]

[0129] If η q1 The value is not the minimum, but η qn Arranged from largest to smallest n value, the overall values ​​show an fluctuating trend.

[0130] η q =maxη qn (6)

[0131] 2))) The average level of the domestic gas industry η over the past five years h ;

[0132] 3))) The failure rate η of gas pipelines caused by external influences w Assignment:

[0133] η w =max(η q ,η h (7)

[0134] like Figure 4 As shown, in this embodiment, the present invention can perform analysis and calculation for S3 according to the following steps:

[0135] 1) Predict the failure rate of different types (municipal, courtyard) gas pipelines;

[0136] 1) Predicted overall failure rate η of municipal gas pipelines s Unit: km -1 ·a -1 :

[0137]

[0138] 2)) Predicted overall failure rate η of courtyard gas pipeline t Unit: km -1 ·a -1 :

[0139]

[0140] 2) Statistically record the length of pipes, fittings, and valves used in historical emergency response events;

[0141] 1) Pipe consumption

[0142] Differentiate between municipal gas pipelines and courtyard gas pipelines, and take the maximum historical pipe material consumption (regardless of specifications or models).

[0143] C sl —Maximum consumption of pipe materials for a single emergency repair of municipal gas pipelines, in meters;

[0144] C tl —Maximum consumption of pipe material for a single emergency repair of a courtyard gas pipeline, unit: m;

[0145] 2) Pipe fitting consumption

[0146] Differentiate between municipal gas pipelines and courtyard gas pipelines, and take the maximum historical consumption of pipe fittings (regardless of specifications or models).

[0147] H s —Maximum consumption of pipe fittings in a single emergency repair of municipal gas pipelines, unit: pieces;

[0148] H t —Maximum consumption of pipe fittings for a single emergency repair of a courtyard gas pipeline, unit: pieces;

[0149] 3) Valve consumption

[0150] Differentiate between municipal gas pipelines and courtyard gas pipelines, and take the maximum historical valve consumption (regardless of specifications or models).

[0151] F s —Maximum valve consumption during a single emergency repair of a municipal gas pipeline, unit: valves;

[0152] F t —Maximum valve consumption for a single emergency repair of a gas pipeline in a courtyard, unit: valve;

[0153] 3) Establish a demand forecasting model

[0154] 1) Establish a pipe demand forecasting model

[0155] C xn =L sn ×p s ×η s ×C sl +L sn ×p t ×η t ×C tl (10)

[0156] Among them, C xn —Emergency demand forecast for the nth type of pipe, unit: m; L sn — Actual statistical length of the nth type of pipe, unit: km; p s —Percentage of municipal gas pipelines, in %; p t —Percentage of gas pipeline in the courtyard, in %; η s —Predicted overall failure rate of municipal gas pipelines, unit: km -1 ·a -1 η t —Predicted overall failure rate of courtyard gas pipelines, unit: km -1 ·a -1 C sl —Maximum consumption of pipe materials in a single emergency repair of municipal gas pipelines, unit: m; C tl —Maximum consumption of pipe material for a single emergency repair of a courtyard gas pipeline, unit: m;

[0157] 2) Establish a pipe fitting demand forecasting model

[0158] 1))) Calculate the consumption index of pipe fittings for a single emergency repair

[0159] Maximum consumption index of pipe fittings in a single emergency repair of municipal gas pipelines:

[0160]

[0161] Maximum consumption index of pipe fittings in a single emergency repair of courtyard gas pipeline:

[0162]

[0163] 2)))Calculate the consumption value J of pipe fittings in a single emergency repair

[0164] Municipal gas pipeline demand forecast indicators:

[0165] J s =max(A s B s (13)

[0166] Municipal gas pipeline demand forecast indicators:

[0167] J t =max(A t B t (14)

[0168] 3) Calculate the predicted demand for pipe fittings, and round up to the nearest integer based on the calculation result.

[0169] J xn =(L sn ×ps ×η s ×C sl ×J s +L sn ×p t ×η t ×C tl ×J t ) / 1000 (15)

[0170] Among them, J xn —Emergency demand forecast for the nth type of pipe fitting, unit: pieces;

[0171] 3) Establish a valve demand forecasting model

[0172] Based on municipal gas pipelines and courtyard gas pipelines, calculate the maximum number of times (X) of different types of valves requiring emergency repairs and replacements annually. sn (Municipal), X tn (patio).

[0173] F xn =F s ×X sn ×p s +F t ×X tn ×p t (16)

[0174] Among them, F xn —Predicted emergency demand for the nth type of valve, unit: number of valves.

[0175] S5 optimizes the initial prediction result to form the target prediction result, including:

[0176] S51 calculates the annual turnover rate of emergency gas pipeline materials for the enterprise under test, corresponding to the construction materials, and calculates the minimum inventory requirement for construction materials of the enterprise under test.

[0177] S52 adjusts the initial forecast based on the annual turnover rate and minimum inventory requirement to obtain the target forecast result.

[0178] like Figure 5 As shown in this embodiment, the calculated emergency material demand value for gas pipelines is based on the annual emergency rescue demand. According to the production and construction management status of gas operating companies, the demand reserve can be further optimized based on the actual situation of the flow management of engineering construction materials.

[0179] 1) Calculate the annual turnover rate of emergency materials for gas pipelines corresponding to the construction materials for the project.

[0180] 1) Pipe material turnover rate in engineering construction:

[0181]

[0182] Among them, T cn —Annual turnover rate of the nth type of pipe, in times; M cn —Annual outbound amount for the nth type of pipe, unit: ten thousand yuan; K cn —Year-end inventory value of the nth type of pipe, unit: RMB 10,000;

[0183] 2) Turnover rate of pipe fittings in engineering construction:

[0184]

[0185] Among them, T jn —Annual turnover rate of the nth type of pipe fitting, in times; M jn —Annual outbound amount for the nth type of pipe fitting, unit: ten thousand yuan; K jn —Year-end inventory amount of type n pipe fittings, unit: RMB 10,000;

[0186] 3) Valve turnover rate in engineering construction:

[0187]

[0188] Among them, T fn —Annual turnover rate of the nth type of valve, in times; M fn —Annual outbound amount for valve type n, unit: ten thousand yuan; K fn —Year-end inventory value of valve type n, unit: RMB 10,000;

[0189] 2) Calculate the minimum inventory requirements for construction materials.

[0190] 1) Minimum inventory requirements for pipe materials in engineering construction:

[0191]

[0192] Among them, X cn —Minimum inventory requirement for the nth type of pipe material in engineering construction, unit: m; L cn —The estimated total length of the nth type of pipe used in the annual construction project, in meters; T cn —Annual turnover rate of the nth type of pipe, in times;

[0193] 2) Minimum inventory requirements for pipe fittings in engineering construction:

[0194]

[0195] Among them, X jn —Minimum inventory requirement for the nth type of pipe fitting in engineering construction, unit: pieces; L cn —The estimated total length of the nth type of pipe used in the annual construction project, in meters; T cn—Annual turnover rate of the nth type of pipe, in times; A s —Municipal pipeline fittings per kilometer, unit: fittings / km; A t —Indicators for pipe fittings per kilometer of courtyard pipelines, unit: fittings / km; p s —Percentage of municipal gas pipelines, in %; p t —Percentage of gas pipeline in the yard, unit: %; L jn —The estimated total number of type n pipe fittings in the annual construction projects, in units of: units; T jn —Annual turnover rate of the nth type of pipe fitting, in times.

[0196] 3) Minimum inventory requirements for valves in engineering construction:

[0197]

[0198] Among them, X fn —Minimum inventory requirement for the construction of valve type n, unit: units; L fn —The estimated total number of valves of type n in the annual construction project, in units of: units; T fn —Annual turnover rate of the nth type of valve, in times.

[0199] 3) Based on the management foundation of gas production and operation enterprises, the emergency material reserve requirements for gas pipelines can be further optimized.

[0200] 1) Operating enterprises with sound material circulation management systems and a high level of informatization

[0201] 1)))Optimized emergency reserve requirements for pipe materials:

[0202] C' xn =min(C xn ,X cn ) (twenty three)

[0203] Among them, C' xn —Emergency demand forecast for the nth type of pipe material after optimization, in meters (C) xn —Predicted annual emergency demand for the nth type of pipe material based on failure, in meters; X cn —Minimum inventory requirement for the nth type of pipe in engineering construction, unit: m.

[0204] 2)))Optimized emergency reserve requirements for pipe fittings:

[0205] J' xn =min(J xn ,X jn ) (twenty four)

[0206] Among them, J' xn—Emergency demand forecast for the nth type of pipe fitting after optimization, unit: units; C jn —Annual emergency demand forecast based on the nth type of pipe fitting that has failed, unit: pieces; X jn —Minimum inventory requirement for the construction of the nth type of pipe fitting, unit: piece.

[0207] 3)))Optimized valve emergency reserve requirements:

[0208] F x ' n =min(F xn ,X fn (25)

[0209] Among them, F x ' n —Emergency demand forecast for the nth type of valve after optimization, unit: number; F jn —Annual emergency demand forecast based on the nth type of valve failure, unit: number; F jn —Minimum inventory requirement for the construction of the nth type of valve, unit: pieces.

[0210] The optimized emergency supplies should be managed and circulated together with the engineering construction materials, and a basic emergency demand quantity must be reserved.

[0211] 2) Businesses with inadequate material circulation management systems and poor information technology levels.

[0212] Emergency material requirements for gas pipelines are calculated based on C. xn J xn F xn .

[0213] However, emergency materials should be replaced and circulated at least once a year, depending on the progress of the project, to ensure the reliability of the inherent state of the emergency materials.

[0214] Example

[0215] To make the objectives, technical approach, and advantages of this invention clearer, the key technical points of this invention are described in detail below with reference to specific embodiments. Due to the large number of types involved, for the sake of simplicity, only a certain type of municipal gas steel pipeline is used as an example to calculate the failure rate prediction and demand prediction parts, and all data are matched to only a single influencing factor.

[0216] Step 1:

[0217] Statistics of municipal gas steel pipelines of a gas company based on years of operation and mileage are as follows: Figure 6 As shown.

[0218] Statistics based on pipe type, fittings, and valve type (example):

[0219] ① Pipes

[0220] Material: Steel pipe; External anti-corrosion layer: Three-layer PE; Specification: D159×6; Quantity: 950km.

[0221] ② Pipe fittings

[0222] D159 is calculated based on a pipe fitting (elbow) index of 5 pieces / km; material: steel; type: elbow; quantity: 4750 pieces.

[0223] ③ Valves

[0224] Material: Steel; Type: Embedded earth valve; Quantity: 195 units.

[0225] Step 2:

[0226] ① A summary of statistical failure data for the past 5 years is shown in Table 4.

[0227] Table 4

[0228] years 2022 2021 2020 2019 2018 total Number of failures 20 23 30 34 42 149

[0229] According to the failure factor classification, the pipeline system had a total of 122 intrinsic failures and 27 failures caused by external environmental factors.

[0230] ②Inherent Pipeline Failure Analysis

[0231] Based on the pipeline's operational lifespan at the time of failure, a cumulative failure data analysis is performed, as follows: Figure 7 As shown.

[0232] Combining Table 5 with formula (3), the result is as follows: Figure 8 As shown.

[0233] ③ Failure analysis affected by external environment

[0234] The number of intrinsic failures caused by external environmental factors in the past 5 years is shown in Table 5.

[0235] Table 5

[0236] years 2022 2021 2020 2019 2018 total Number of failures / times 3 23 30 34 42 27 <![CDATA[η qn ]]> 0.00164 0.00338 0.00290 0.00361 0.00436

[0237] According to η qn Numerical values, arranged from 2018 to 2022, η q1 The smallest value is η, but the overall data shows a fluctuating trend, therefore η q Average value: 0.00318km -1 ·a -1 .

[0238] Collect statistics on the average level η of the domestic gas industry over the past five years h Assume η h It is 0.005km-1 ·a -1 .

[0239] At this point, the failure rate affected by the external environment is taken as η. w =0.005km -1 ·a -1 .

[0240] Step 3:

[0241] 1) Predict the overall failure rate η of municipal gas steel pipelines using formula (8) s According to calculations, η s =0.0184+0.005=0.0189.

[0242] 2) Statistics on the consumption of municipal pipeline materials during historical emergency response events.

[0243] Pipe material: C sl Take the maximum consumption value of 5m / time.

[0244] Pipe fittings: H s Take the maximum value of 2 elbows (using elbows as an example only) per cycle.

[0245] Valve: F s Take the maximum value, 1 per time.

[0246] 3) Calculate the emergency material requirements for the D159 municipal gas steel pipeline.

[0247] ① Pipes

[0248] Using formula (10), since data is only extracted from municipal pipelines, p is not calculated. s p t C was calculated xn It is 35.91m.

[0249] ② Pipe fittings (DN150 steel elbows)

[0250] The maximum consumption index for a single emergency repair of pipe fittings was calculated using formula (11). The calculated value of B is... s The consumption rate is 400 units / km, and the consumption index for elbows per kilometer in engineering construction is 5 units / km. Let J be the value. s The density is 400 units / km. Using formula (15), J is calculated to be... xn There are 15.

[0251] ③ Valves (DN150 steel embedded earth valve)

[0252] The maximum number of valve replacements per year for municipal gas pipelines is 3. Using formula (16), F is calculated. xn There are 3.

[0253] Step 4:

[0254] 1) Calculate the turnover rate and inventory requirements of construction materials.

[0255] The calculation process is omitted as it involves a large amount of assumed data.

[0256] Calculations show that the minimum inventory requirement for D159 three-layer PE anti-corrosion steel pipe projects is 100m; the minimum inventory requirement for DN150 steel elbow projects is 20 units; and the minimum inventory requirement for DN150 steel buried earth valve projects is 2 units.

[0257] 2) Optimize the emergency material requirements for gas pipelines

[0258] ① Operating enterprises with sound material circulation management systems and a high level of informatization

[0259] After optimization, the emergency reserve requirement for D159 three-layer PE anti-corrosion steel pipe is 35.91m (rounded to 36); the emergency reserve requirement for DN150 steel elbow is 15 units; and the emergency reserve requirement for DN150 steel buried earth valve is 2 units.

[0260] The optimized emergency supplies should be managed and circulated together with the engineering construction materials, but the basic emergency demand must be guaranteed.

[0261] ② Businesses with inadequate material circulation management systems and poor information technology levels

[0262] The emergency reserve requirement for D159 three-layer PE anti-corrosion steel pipes is 35.91m (rounded to 36m); the emergency reserve requirement for DN150 steel elbows is 15 units; and the emergency reserve requirement for DN150 steel buried earth valves is 3 units.

[0263] Emergency materials should be replaced and circulated at least once a year, depending on the progress of the project, to ensure the reliability of the inherent state of the emergency materials.

[0264] The calculation examples listed in the specific implementation are simplified versions of gas pipelines managed by gas companies, providing a clear and concise explanation. Gas companies can utilize the method of this invention and electronic devices to comprehensively optimize emergency supplies for gas pipelines, based on their own production and management conditions. This ensures gas safety while effectively utilizing company funds and improving management efficiency.

[0265] like Figure 9 As shown, this invention also proposes a dynamic forecasting system for emergency material demand in gas pipelines, the system comprising:

[0266] Classification module 10 is used for classifying and statistically analyzing basic data on gas pipelines;

[0267] The acquisition module 20 is used to acquire gas pipeline failure data and calculate the pipeline failure probability based on the gas pipeline failure data.

[0268] Module 30 is used to build a demand prediction model based on the pipeline failure probability;

[0269] Prediction module 40 is used to predict the emergency material demand for gas pipelines using the demand prediction model and generate initial prediction results;

[0270] The optimization module 50 is used to optimize the initial prediction result to form the target prediction result.

[0271] like Figure 10 As shown, corresponding to the above-described dynamic forecasting method for emergency material demand in gas pipelines, this invention also provides an electronic device. Since the embodiment of this device is similar to the above-described method embodiment, the description is relatively simple; please refer to the description in the above-described method embodiment section for relevant details. The device described below is merely illustrative. This device may include: a processor 1, a memory 2, a communication bus (i.e., the aforementioned device bus), and a lookup engine. The processor 1 and memory 2 communicate with each other via the communication bus and communicate with external systems via a communication interface. The processor 1 can call logical instructions in the memory 2 to execute the dynamic forecasting method for emergency material demand in gas pipelines.

[0272] Furthermore, the logical instructions in the aforementioned memory 2 can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0273] On the other hand, embodiments of the present invention also provide a processor-readable storage medium storing a computer program 3, which, when executed by a processor 1, implements the dynamic prediction method for emergency material demand of gas pipelines provided in the above embodiments.

[0274] The processor-readable storage medium can be any available medium or data storage device that the processor 1 can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0275] Those skilled in the art should understand that, despite the detailed description of the present invention with reference to the foregoing embodiments, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically predicting the emergency material requirements of a gas pipeline, characterized in that, The method includes: Collect gas pipeline failure data and calculate pipeline failure probability based on the gas pipeline failure data, including: calculating the pipeline failure probability of the enterprise under test based on the enterprise's historical failure data and pre-collected gas industry failure data; Based on the pipeline failure probability, a demand forecasting model is established; the demand forecasting model includes a pipe material demand forecasting model, a pipe fitting demand forecasting model, and a valve demand forecasting model, and the expression for the pipe material demand forecasting model is: The expression for the pipe fitting demand forecasting model is: The expression for the valve demand prediction model is: ,in, —Emergency demand forecast for the nth type of pipe; —The actual statistical length of the nth type of pipe; —Percentage of municipal gas pipelines; —Percentage of gas pipeline in the courtyard; —Predicted overall failure rate of municipal gas pipelines; —Predicted overall failure rate of courtyard gas pipelines; —Maximum consumption of pipe materials for a single emergency repair of municipal gas pipelines; —Maximum consumption of pipe materials for a single emergency repair of a courtyard gas pipeline; —Emergency demand forecast for the nth type of pipe fitting; —Forecast of emergency demand for the nth type of valve; —Demand forecast indicators for municipal gas pipelines; —Demand forecast indicators for municipal gas pipelines; —Maximum consumption of valves during a single emergency repair of municipal gas pipelines; —Maximum consumption of valves during a single emergency repair of a gas pipeline in a courtyard; —The maximum number of times different types of valves were replaced during emergency repairs in municipal gas pipelines annually; —Statistics on the maximum number of times different types of valves were replaced during the annual emergency repair of courtyard gas pipelines; The demand forecasting model is used to predict the emergency material demand for gas pipelines, generating initial forecast results; The initial forecast results are optimized to form the target forecast results, including: calculating the annual turnover rate of the emergency gas pipeline materials corresponding to the engineering construction materials of the enterprise under test, and calculating the minimum inventory requirement of the engineering construction materials of the enterprise under test; adjusting the initial forecast results based on the annual turnover rate and the minimum inventory requirement to obtain the target forecast results.

2. The method for dynamic prediction of emergency material demand for gas pipelines according to claim 1, characterized in that, Before collecting gas pipeline failure data, the process also includes: classifying and statistically analyzing basic gas pipeline data.

3. The method for dynamic prediction of emergency material demand for gas pipelines according to claim 2, characterized in that, The categorized statistical basic data for gas pipelines includes: Organize the types of emergency supplies for gas pipelines; In-service gas pipelines are classified, and the total length of gas pipelines with different years of operation is calculated.

4. The method for dynamic prediction of emergency material demand for gas pipelines according to claim 1, characterized in that, The calculation of the pipeline failure probability of the enterprise under test includes: The pipeline failure data is categorized. Calculate the pipeline failure probability of the enterprise under test under different influencing factors.

5. The method for dynamic prediction of emergency material demand for gas pipelines according to claim 4, characterized in that, The calculation of the pipeline failure probability of the enterprise under test under different influencing factors includes: Calculate the average mobile failure rate of gas pipelines with different service lifespans due to intrinsic failure. Calculate the failure rate of gas pipelines caused by external influences.

6. The method for dynamic prediction of emergency material demand for gas pipelines according to claim 5, characterized in that, The calculation of the intrinsic failure average mobile failure rate of gas pipelines with different service life includes: The essential failure rate of gas pipelines with different years of operation was summarized and statistically analyzed. The mean mobility failure rate of the intrinsic failure is calculated based on the intrinsic failure rate.

7. A gas pipeline emergency material demand dynamic prediction system, characterized in that, The system includes: The acquisition module is used to collect gas pipeline failure data and calculate the pipeline failure probability based on the gas pipeline failure data. The acquisition module is used to calculate the pipeline failure probability of the enterprise under test based on the enterprise's historical failure data and pre-collected gas industry failure data. The construction module is used to establish a demand forecasting model based on the pipeline failure probability; the demand forecasting model includes a pipe material demand forecasting model, a pipe fitting demand forecasting model, and a valve demand forecasting model, and the expression of the pipe material demand forecasting model is: The expression for the pipe fitting demand forecasting model is: The expression for the valve demand prediction model is: ,in, —Emergency demand forecast for the nth type of pipe; —The actual statistical length of the nth type of pipe; —Percentage of municipal gas pipelines; —Percentage of gas pipeline in the courtyard; —Predicted overall failure rate of municipal gas pipelines; —Predicted overall failure rate of courtyard gas pipelines; —Maximum consumption of pipe materials for a single emergency repair of municipal gas pipelines; —Maximum consumption of pipe materials for a single emergency repair of a courtyard gas pipeline; —Emergency demand forecast for the nth type of pipe fitting; —Forecast of emergency demand for the nth type of valve; —Demand forecast indicators for municipal gas pipelines; —Demand forecast indicators for municipal gas pipelines; —Maximum consumption of valves during a single emergency repair of municipal gas pipelines; —Maximum consumption of valves during a single emergency repair of a gas pipeline in a courtyard; —The maximum number of times different types of valves were replaced during emergency repairs in municipal gas pipelines annually; —Statistics on the maximum number of times different types of valves were replaced during the annual emergency repair of courtyard gas pipelines; The prediction module is used to predict the emergency material demand for gas pipelines using the demand prediction model and generate initial prediction results. An optimization module is used to optimize the initial prediction results to form a target prediction result. The optimization module is used to calculate the annual turnover rate of the emergency materials for gas pipelines of the enterprise under test and the corresponding engineering construction materials, and to calculate the minimum inventory requirement of the engineering construction materials of the enterprise under test. The initial prediction results are adjusted according to the annual turnover rate and the minimum inventory requirement to obtain the target prediction result.

8. The dynamic forecasting system for emergency material demand of gas pipelines according to claim 7, characterized in that, The system also includes: The classification module is used to classify and statistically analyze basic data on gas pipelines.

9. The dynamic forecasting system for emergency material demand in gas pipelines according to claim 8, characterized in that, The classification module is used to classify and statistically analyze basic data on gas pipelines, including: The classification module is used to organize the types of emergency supplies for gas pipelines; In-service gas pipelines are classified, and the total length of gas pipelines with different years of operation is calculated.

10. An electronic device, comprising: include: Processor and memory; The processor invokes the computer program stored in the memory to execute the dynamic prediction method for emergency material demand of gas pipelines as described in any one of claims 1 to 6.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the dynamic prediction method for emergency material demand of gas pipelines as described in any one of claims 1 to 6.

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