Dynamic prediction method and system for emergency material demand of gas pipeline

Abstract

The invention belongs to the technical field of gas pipeline failure prediction analysis, and particularly relates to a dynamic prediction method and system for emergency material requirements of a gas pipeline. The method comprises the steps that gas pipeline failure data are collected, and the pipeline failure probability is calculated according to the gas pipeline failure data; establishing a demand prediction model according to the pipeline failure probability; using the demand prediction model to predict the emergency material demand of the gas pipeline, and generating an initial prediction result; and optimizing the initial prediction result to form a target prediction result. According to the method, the gas pipeline emergency material demand prediction quantity is dynamically adjusted according to the production management basis of the gas operating enterprise, the requirements of different gas operating enterprises are met, and the method has high practical applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas pipeline failure prediction and analysis, and in particular relates to a method and system for dynamically predicting emergency material demand for gas pipelines. Background Art

[0002] With the rapid development of the economy and the acceleration of urbanization, the demand for urban gas has continued to increase, which has greatly promoted the development of gas operating companies. With the continuous large-scale increase in urban gas pipelines, the probability of safety accidents caused by gas pipeline failures is also increasing. The emergency response and disposal management requirements of gas operating companies are becoming increasingly high. The configuration of sufficient gas pipeline emergency materials is very critical for timely emergency rescue and repair operations.

[0003] At present, the emergency material reserves of gas pipelines of gas operating enterprises are mostly configured according to experience, and there are:

[0004] 1) Some pipes and fittings are not fully configured, and emergency rescue operations cannot be organized immediately, which cannot effectively guarantee the supply of urban gas;

[0005] 2) Some pipes and fittings are configured in large quantities, which, on the one hand, occupy a large amount of funds of the gas company; on the other hand, the emergency spare materials that have not been used for a long time may exceed the shelf life or be in poor condition when they are needed, thus affecting emergency rescue operations.

[0006] Therefore, how to establish the most optimized gas pipeline emergency material demand forecasting and analysis method based on the different basic conditions of gas pipeline production management of different gas operating enterprises, and adopt reasonable management devices to optimize and ensure its circulation and update, is of great significance to ensure gas emergency safety management.

[0007] At present, the research on emergency material demand analysis at home and abroad is mostly focused on the calculation and reserve demand analysis based on the established different material allocation standards. There are few studies on emergency material demand forecasting based on the possible accidents in the gas pipelines of different gas operating companies. At the same time, the emergency rescue management needs and the daily construction management of gas operating companies have not been integrated into a system to conduct relevant management research. Summary of the invention

[0008] In view of the above problems, the present invention provides a method for dynamically predicting emergency material demand of a gas pipeline, the method comprising:

[0009] Collecting gas pipeline failure data, and calculating pipeline failure probability based on the gas pipeline failure data;

[0010] Establishing a demand forecasting model based on the pipeline failure probability;

[0011] Using the demand forecasting model to forecast the demand for emergency materials in the gas pipeline, and generating an initial forecasting result;

[0012] The initial prediction result is optimized to form a target prediction result.

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

[0014] Preferably, the classified statistics of gas pipeline basic data include:

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

[0016] Classify the gas pipelines in service and calculate the total length of gas pipelines with different years of operation.

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

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

[0019] Preferably, the calculating of the pipeline failure probability of the enterprise to be tested includes:

[0020] classifying the pipeline failure data;

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

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

[0023] Calculate the average moving failure rate of essential failure of gas pipelines with different operating years;

[0024] Calculate the failure rate of gas pipelines due to external influences.

[0025] Preferably, the calculation of the average moving failure rate of essential failure of gas pipelines with different operating years includes:

[0026] Summarize and calculate the essential failure rates of gas pipelines with different operating years;

[0027] The essential failure average moving failure rate is calculated according to the essential failure rate.

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

[0029] Calculate the annual turnover rate of the gas pipeline emergency materials of the enterprise under test corresponding to the engineering construction materials, and calculate the minimum inventory requirements of the engineering construction materials of 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] The present invention also provides a gas pipeline emergency material demand dynamic prediction system, the system comprising:

[0032] A collection module, 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 according to the pipeline failure probability;

[0034] A prediction module, used to predict the demand for emergency materials in the gas pipeline using the demand prediction model and generate an initial prediction result;

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

[0036] Preferably, the system further comprises:

[0037] Classification module, used to classify and count basic data of gas pipelines.

[0038] Preferably, the classification module is used to classify and count the basic data of the gas pipeline, including:

[0039] The classification module is used to sort out the types of emergency materials in gas pipelines;

[0040] Classify the gas pipelines in service and calculate the total length of gas pipelines with different years of operation.

[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 acquisition module is used to calculate the pipeline failure probability of the enterprise under test based on the failure history data of the enterprise under test and the 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 the gas pipeline emergency materials of the enterprise under test corresponding to the engineering construction materials, and calculate the minimum inventory requirements of the engineering construction materials of 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 provides an electronic device, comprising:

[0047] Processor and memory;

[0048] The processor calls the computer program stored in the memory to execute any one of the above-mentioned methods for dynamically predicting emergency material demand for gas pipelines.

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

[0050] The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute any of the above-mentioned methods for dynamically predicting emergency material requirements for gas pipelines.

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

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

[0053] (2) The present invention uses historical failure data to carry out forecast analysis of different material requirements in the gas pipeline system, comprehensively ensuring gas emergency rescue while reducing inventory funds;

[0054] (3) The present invention matches the flow of emergency supplies with that of engineering construction materials, further optimizes the quantity of inventory materials, and improves the reliability of the essential state of emergency supplies.

[0055] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A diagram showing a method for dynamically predicting emergency material demand for a gas pipeline in an embodiment of the present invention is shown;

[0058] Figure 2 A flow chart showing basic data statistical analysis of emergency material analysis of gas pipelines in an embodiment of the present invention is shown;

[0059] Figure 3 A flowchart of different failure probability analysis in different years and different laying environments according to an embodiment of the present invention is shown;

[0060] Figure 4 A flow chart of different emergency material demand forecasting and analysis methods in an embodiment of the present invention is shown;

[0061] Figure 5 A flow chart of an emergency material optimization method based on the production management situation of a gas business enterprise in an embodiment of the present invention is shown;

[0062] Figure 6 A statistical diagram of the mileage of municipal gas steel quality pipelines according to different years of operation in an embodiment of the present invention is shown;

[0063] Figure 7 A statistical graph showing the cumulative number of essential failures in the past five years at different operating ages according to the embodiment of the present invention is shown;

[0064] Figure 8 A graph showing the calculation results of pipeline failure rates at different operating years in an embodiment of the present invention;

[0065] Fig. 9 A diagram showing a dynamic forecasting system for emergency material demand of a gas pipeline in an embodiment of the present invention is shown;

[0066] Fig.10 A diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0067] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware units or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0069] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

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

[0071] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or sub-modules is not necessarily limited to those steps or sub-modules explicitly listed, but may include other steps or sub-modules not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0072] like Figure 1 As shown, the present invention proposes a method for dynamically predicting emergency material demand of a gas pipeline, the method comprising:

[0073] S1 classified statistics of basic data of gas pipelines;

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

[0075] S3 establishes a demand forecasting model according to the pipeline failure probability;

[0076] S4 uses the demand forecasting model to forecast the demand for emergency materials in the gas pipeline and generate an initial forecast result;

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

[0078] Specifically, S1 classifies and collects basic data on gas pipelines, including:

[0079] S11 Organize the types of emergency materials for gas pipelines;

[0080] S12 classifies the gas pipelines in service 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 adopts classification statistics to classify and count the basic data of the gas pipeline of a gas operating enterprise, the core of which is to organize the types of emergency materials and potential demands for the gas pipeline.

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

[0083] ① Pipe material category: Create sub-catalogs by material and external anti-corrosion layer, and sub-items by pipeline outer diameter under the sub-catalog, and count the actual length of pipelines in service for the corresponding items, with C sn Indicates (n represents different subdivisions), unit: km;

[0084] ② Pipe fittings category: create sub-catalogs by material and pipe fitting type, sub-catalogs by pipe fitting outer diameter, pipe fitting quantity by per kilometer pipeline length Pipe fitting quantity index: Municipal pipeline pipe fitting index (A s ), courtyard pipe fittings index (A t ), make approximate statistics, with J zn Indicates (n represents different subdivision items), unit: piece;

[0085] ③ Valve categories: Create sub-categories by material and valve type. The number of valves is based on actual statistics. sn Indicates (n represents different sub-items), unit: piece.

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

[0087] Table 1

[0088]

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

[0090]

[0091] Statistics on the proportion of courtyard pipeline length p t ,unit:%:

[0092]

[0093] S2 collects gas pipeline failure data, and calculates 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 failure history data of the enterprise under test and the pre-collected gas industry failure data.

[0095] Specifically, the pipeline failure probability of the enterprise under test is calculated in S21, 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 calculates the pipeline failure probability of the enterprise under test under different influencing factors, including:

[0100] S2122 Calculate the average moving failure rate of essential failure of gas pipelines with different operating years;

[0101] S2123 Calculates the failure rate of gas pipelines due to external influences.

[0102] Specifically, S2122 calculates the average moving failure rate of essential failure of gas pipelines with different operating years, including:

[0103] S21221 summarizes and calculates the essential failure rates of gas pipelines with different operating years;

[0104] S21222 calculates the essential failure average moving failure rate based on the essential failure rate.

[0105] In this embodiment, Figure 3 As shown, the present invention summarizes the failure data of a gas company in the past five years and classifies them according to the direct causes of failure. First, two categories of municipal and courtyard pipelines are established; then they are divided into two sub-categories: failures affected by external factors and essential failures of the pipeline system.

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

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

[0108] The data on essential failures of the pipeline system are summarized and counted according to the years of operation of the pipeline when the failure occurs, as shown in Table 2 - Statistics of the number of essential failures of pipelines with different years of operation N (total amount in the past five years).

[0109] Table 2

[0110]

[0111] 2)) Calculate the average moving failure rate of pipeline essential failure with different operating years

[0112] 1))) Average moving failure rate η of municipal gas pipelines with different operating years of pipeline essential failure sn

[0113] η sn —The five-year average failure rate of municipal gas pipelines put into operation for N years, km -1 ·a -1 ;

[0114] For the sake of convenience, the calculation formula is based on the average moving failure rate of municipal gas pipelines with a one-year operation period (η s1 ) for example.

[0115]

[0116] The numerator is the total number of essential failures of municipal gas pipelines of different ages in the statistical table in the past five years; the denominator is the total mileage of municipal gas pipelines in operation corresponding to the failure data, which is accumulated forward to L sn .

[0117] 2))) Average moving failure rate η of essential failure of courtyard gas pipelines with different operating years tn .

[0118] η tn ——Five-year average failure rate of courtyard pipelines after N years of operation, km -1 ·a -1

[0119] For the sake of convenience, the calculation formula is based on the average moving failure rate η of the courtyard pipeline with a service life of 1 year. t1 Give an example.

[0120]

[0121] The numerator is the total number of essential failures of courtyard gas pipelines of different ages in the statistical table in the past five years; the denominator is the total mileage of courtyard gas pipelines in operation years corresponding to the failure data, which is accumulated forward to L tn .

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

[0123] Failures caused by external influences such as third-party damage, geological disasters, etc. are classified and counted as a whole.

[0124] 1))) Calculate the gas pipeline failure rate η caused by external influences in the gas business enterprise q ;

[0125] Statistics on the failure rate of gas pipelines caused by external influences in gas companies in the past five years η qn , n = 1, 2, 3, 4, 5, the value of n is taken from the nearest to the farthest in terms of years.

[0126] If η q1 The value is the smallest, and η qn Arrange the n values ​​from large to small, and the overall value shows a downward trend, then η q =η q1 ;

[0127] If η q1 The value is the smallest, but η qn Arrange the n values ​​from large to small, and the overall value shows an ups and downs trend.

[0128]

[0129] If η q1 The value is not the minimum, but η qn Arrange the n values ​​from large to small, and the overall value shows an ups and downs trend.

[0130] η q =maxη qn (6)

[0131] 2))) The average level of the domestic gas industry in 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 analyze and calculate S3 according to the following steps:

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

[0136] 1)) Prediction of comprehensive failure rate η of municipal gas pipelines s , unit: km -1 ·a -1 :

[0137]

[0138] 2)) Prediction of comprehensive failure rate η of courtyard gas pipeline t , unit: km -1 ·a -1 :

[0139]

[0140] 2) Count the length of pipes, pipe fittings and valves used in historical emergency rescue events;

[0141] 1)) Pipe consumption

[0142] Distinguish between municipal gas pipelines and courtyard gas pipelines, and take the maximum value of historical pipe consumption (regardless of specifications and models)

[0143] C sl —The maximum consumption of pipes for a single emergency repair of municipal gas pipelines, unit: m;

[0144] C tl —The maximum consumption of pipes for a single emergency repair of the courtyard gas pipeline, unit: m;

[0145] 2)) Pipe fittings consumption

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

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

[0148] H t —The maximum consumption of pipe fittings for a single emergency repair of the courtyard gas pipeline, unit: piece;

[0149] 3)) Valve consumption

[0150] Distinguish between municipal gas pipelines and courtyard gas pipelines, and take the maximum value of historical consumption valve volume (regardless of specification and model)

[0151] F s —The maximum consumption of valves for a single emergency repair of municipal gas pipelines, unit: pieces;

[0152] F t —The maximum consumption of valves for a single emergency repair of the courtyard gas pipeline, unit: piece;

[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 —The actual statistical length of the nth type of pipe, unit: km; p s —Percentage of municipal gas pipelines, unit: %; p t —Percentage of courtyard gas pipelines, unit: %; η s —Predicted comprehensive failure rate of municipal gas pipelines, unit: km -1 ·a -1 ; η t —Predicted comprehensive failure rate of courtyard gas pipelines, unit: km -1 ·a -1 ; C sl —Maximum consumption of pipes for a single emergency repair of municipal gas pipelines, unit: m; C tl —The maximum consumption of pipes for a single emergency repair of the courtyard gas pipeline, unit: m;

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

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

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

[0160]

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

[0162]

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

[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 forecast demand for pipe fittings and round up the 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 fittings, unit: piece;

[0171] 3)) Establish a valve demand forecasting model

[0172] According to municipal gas pipelines and courtyard gas pipelines, the maximum number of times of emergency repair and replacement of different types of valves in an annual survey is X sn (Municipal Affairs), 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: piece.

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

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

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

[0178] like Figure 5 As shown, in this embodiment, the above-calculated gas pipeline emergency material demand value is a demand based on the annual emergency rescue demand. According to the production, construction and management status of the gas business enterprise, the demand reserve can be further optimized based on the actual situation of engineering construction material circulation management.

[0179] 1) Calculate the annual turnover rate of gas pipeline emergency materials corresponding to construction materials

[0180] 1))Turnover rate of engineering construction pipes:

[0181]

[0182] Among them, T cn —Annual turnover rate of the nth type of pipe, unit: times; M cn —Annual delivery amount of the nth type of pipe, unit: 10,000 yuan; K cn —The year-end inventory amount of the nth type of pipe, unit: ten thousand yuan;

[0183] 2)) Turnover rate of pipe fittings for construction projects:

[0184]

[0185] Among them, T jn —Annual turnover rate of the nth type of pipe fittings, unit: times; M jn —Annual delivery amount of the nth type of pipe fittings, unit: 10,000 yuan; K jn —The year-end inventory amount of the nth type of pipe fittings, unit: ten thousand yuan;

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

[0187]

[0188] Among them, T fn —Annual turnover rate of the nth type of valve, unit: times; M fn —Annual delivery amount of the nth type of valve, unit: 10,000 yuan; K fn —The year-end inventory amount of the nth type of valve, unit: ten thousand yuan;

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

[0190] 1)) Minimum inventory requirement for engineering construction pipes:

[0191]

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

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

[0194]

[0195] Among them, X jn —The minimum inventory requirement for the nth type of pipe fittings engineering construction, unit: piece; L cn —Estimated total length of the nth type of pipe in the annual construction project, unit: m; T cn—Annual turnover rate of the nth type of pipe, unit: times; A s —Indicators of pipe fittings per kilometer of municipal pipelines, unit: pieces / km; A t —Index of pipe fittings per kilometer of courtyard pipeline, unit: pieces / km; p s —Percentage of municipal gas pipelines, unit: %; p t —Percentage of courtyard gas pipeline, unit: %; L jn —The total number of the nth type of pipe fittings in the annual construction project is expected to be, unit: piece; T jn —Annual turnover rate of the nth type of pipe fittings, unit: times.

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

[0197]

[0198] Among them, X fn —The minimum inventory requirement for the nth type of valve engineering construction, unit: piece; L fn —The total number of valves of the nth type in the annual construction project is expected to be, unit: piece; T fn —Annual turnover rate of the nth type of valve, unit: times.

[0199] 3) Combined with the basic management situation of gas production and operation enterprises, the demand for emergency material reserves in gas pipelines can be further optimized

[0200] 1)) Enterprises with a sound material circulation management system and a high level of informatization

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

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

[0203] Among them, C' xn —Emergency demand forecast after optimization of the nth pipe material, unit: m; C xn —Annual emergency demand forecast of the nth type of pipe based on failure, unit: m; X cn —Minimum inventory requirement for the nth type of pipe 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 of the nth type of pipe fitting after optimization, unit: piece; C jn —Annual emergency demand forecast of the nth type of pipe fittings based on failure, unit: piece; X jn —Minimum inventory requirement for the nth type of pipe fittings project construction, unit: piece.

[0207] 3))) Optimized emergency reserve requirements for valves:

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

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

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

[0211] 2)) Enterprises with an imperfect material circulation management system and a poor level of informatization

[0212] The emergency material demand for gas pipelines is calculated based on C xn , J xn 、F xn .

[0213] However, emergency materials should be replaced and circulated at least once a year based on the construction situation to ensure the reliability of their essential state.

[0214] Example

[0215] In order to make the purpose, significance, technical route and advantages of the present invention more clearly explained, the key technical points of the present invention are described in detail in combination with specific embodiments. Because there are many types involved, in order to simplify the description, only a certain type of municipal gas steel pipeline is used as an example to calculate the failure rate prediction and demand prediction part, and all data only match a single influencing factor.

[0216] Step 1:

[0217] The mileage of a gas company's municipal gas steel pipeline is calculated by years of operation. Figure 6 shown.

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

[0219] ①Pipe

[0220] Material: steel pipe; outer anti-corrosion layer: three-layer PE; specification: D159×6; quantity: 950km.

[0221] ②Pipe fittings

[0222] D159 is calculated based on the pipe fittings (elbows) index per kilometer of pipeline: 5 pieces / km; material: steel; type: elbow; quantity: 4750 pieces.

[0223] ③Valve

[0224] Material: Steel; Type: Buried Earth Valve; Quantity: 195 pcs.

[0225] Step 2:

[0226] ① The statistical failure data for the past five years is summarized 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 classification of failure factors, the number of intrinsic failures of the pipeline system accumulated to 122 times, and the number of failures caused by external environmental influences was 27 times.

[0230] ②Essential pipeline failure analysis

[0231] According to the operation years of the pipeline when the failure occurs, the cumulative failure data analysis is performed as follows: Figure 7 shown.

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

[0233] ③ Failure analysis affected by external environment

[0234] The number of essential failures affected by the external environment in the past five 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 Values, ranked from 2018 to 2022, η q1 The minimum, but the data as a whole shows an ups and downs trend, so η q Take the average value: 0.00318km -1 ·a -1 .

[0238] Collect statistics on the average level of the domestic gas industry in the past five years h , assuming that η h 0.005km-1 ·a -1 .

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

[0240] Step:3:

[0241] 1) Using formula (8) to predict the comprehensive failure rate η of municipal gas steel pipelines s , after calculation, η s =0.0184+0.005=0.0189.

[0242] 2) Statistics on municipal pipeline material consumption in historical emergency response incidents

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

[0244] Pipe fittings: H s Take the maximum value elbow (only elbow as an example) 2 times / time.

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

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

[0247] ①Pipe

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

[0249] ②Pipe fittings (DN150 steel elbow)

[0250] Formula (11) is used to calculate the maximum consumption index of pipe fittings for a single emergency repair. After calculation, B s The elbow consumption index per kilometer of engineering construction is 5 / km. s =400 / km, using formula (15), after calculation, J xn There are 15 of them.

[0251] ③Valve (DN150 steel buried globe valve)

[0252] The maximum number of valve replacements per year for municipal gas pipelines is 3. Using formula (16), we can calculate F xn For 3.

[0253] Step 4:

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

[0255] There are many assumed data involved, so the relevant calculation process is skipped.

[0256] According to calculations, the minimum inventory requirement for D159 three-layer PE anti-corrosion steel pipe project construction is 100m; the minimum inventory requirement for DN150 steel elbow project construction is 20; and the minimum inventory requirement for DN150 steel buried globe valve project construction is 2.

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

[0258] ① Enterprises with a sound material circulation management system and a high level of informatization

[0259] After optimization, the emergency reserve demand for D159 three-layer PE anti-corrosion steel pipes is 35.91m (rounded to 36); the emergency reserve demand for DN150 steel elbows is 15; and the emergency reserve demand for DN150 steel buried globe valves is 2.

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

[0261] ② Enterprises with an imperfect material circulation management system and a poor level of informatization

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

[0263] Emergency materials should be replaced and circulated at least once a year based on the construction situation to ensure the reliability of their essential state.

[0264] The calculation examples listed in the specific implementation are simple and clear descriptions of the gas pipelines managed by the gas operating enterprises after simplification. The gas operating enterprises can use the method and electronic equipment of the present invention to optimize the emergency materials of the gas pipelines in a comprehensive system according to their own production management basic conditions. While ensuring gas safety, the production and operation management funds of the enterprises can be effectively used to improve management efficiency.

[0265] like Fig. 9 As shown, the present invention also proposes a gas pipeline emergency material demand dynamic prediction system, the system comprising:

[0266] Classification module 10, used for classifying and counting basic data of gas pipelines;

[0267] A collection module 20, used to collect gas pipeline failure data and calculate pipeline failure probability based on the gas pipeline failure data;

[0268] A construction module 30 is used to establish a demand prediction model according to the pipeline failure probability;

[0269] A prediction module 40, used to predict the demand for emergency materials in the gas pipeline using the demand prediction model and generate an initial prediction result;

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

[0271] like Fig.10 As shown, corresponding to the above-mentioned gas pipeline emergency material demand dynamic prediction method, the present invention also provides an electronic device diagram. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. Please refer to the description of the above-mentioned method embodiment for relevant matters. The device described below is only schematic. The device may include: a processor (processor) 1, a memory (memory) 2 and a communication bus (i.e., the above-mentioned device bus) and a search engine, wherein the processor 1 and the memory 2 communicate with each other through the communication bus and communicate with the outside through the communication interface. The processor 1 can call the logic instructions in the memory 2 to execute the gas pipeline emergency material demand dynamic prediction method.

[0272] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a storage chip, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0273] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, the method for dynamically predicting emergency material demand for gas pipelines provided in the above-mentioned embodiments is implemented.

[0274] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0275] Those skilled in the art should understand that although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein; and these modifications or replacements 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 emergency material demand of a gas pipeline, characterized in that: The method comprises: Collecting gas pipeline failure data, and calculating pipeline failure probability based on the gas pipeline failure data; Establishing a demand forecasting model based on the pipeline failure probability; Using the demand forecasting model to forecast the demand for emergency materials in the gas pipeline, and generating an initial forecasting result; The initial prediction result is optimized to form a target prediction result.

2. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 1 is characterized in that: Before collecting the gas pipeline failure data, the method further includes: classifying and counting the basic data of the gas pipeline.

3. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 2 is characterized in that: The classified statistics of gas pipeline basic data include: Organize the types of emergency materials for gas pipelines; Classify the gas pipelines in service and calculate the total length of gas pipelines with different years of operation.

4. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 1, characterized in that: Collecting gas pipeline failure data, and calculating pipeline failure probability based on the gas pipeline failure data, including: Based on the failure history data of the enterprise to be tested and the pre-collected failure data of the gas industry, the pipeline failure probability of the enterprise to be tested is calculated.

5. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 4 is characterized in that: The calculation of pipeline failure probability of the enterprise to be tested includes: classifying the pipeline failure data; Calculate the pipeline failure probability of the enterprise under test under different influencing factors.

6. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 5 is characterized in that: The calculation of pipeline failure probability of the enterprise under test under different influencing factors includes: Calculate the average moving failure rate of essential failure of gas pipelines with different operating years; Calculate the failure rate of gas pipelines due to external influences.

7. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 6 is characterized in that: The calculation of the average moving failure rate of essential failure of gas pipelines with different operating years includes: Summarize and calculate the essential failure rates of gas pipelines with different operating years; The essential failure average moving failure rate is calculated according to the essential failure rate.

8. The method for dynamic prediction of emergency material demand of gas pipeline according to claim 1, characterized in that: Optimizing the initial prediction result to form a target prediction result includes: Calculate the annual turnover rate of the gas pipeline emergency materials of the enterprise under test corresponding to the engineering construction materials, and calculate the minimum inventory requirements of the engineering construction materials of the enterprise under test; The initial forecast results are adjusted based on the annual turnover rate and minimum inventory requirements to obtain the target forecast results.

9. A gas pipeline emergency material demand dynamic prediction system, characterized in that: The system comprises: A collection module, used to collect gas pipeline failure data, and calculate the pipeline failure probability based on the gas pipeline failure data; A construction module is used to establish a demand prediction model according to the pipeline failure probability; A prediction module, used to predict the demand for emergency materials in the gas pipeline using the demand prediction model and generate an initial prediction result; The optimization module is used to optimize the initial prediction result to form a target prediction result.

10. The gas pipeline emergency material demand dynamic prediction system according to claim 9 is characterized in that: The system further comprises: Classification module, used to classify and count basic data of gas pipelines.

11. The gas pipeline emergency material demand dynamic prediction system according to claim 10, characterized in that: The classification module is used to classify and count the basic data of gas pipelines, including: The classification module is used to sort out the types of emergency materials in gas pipelines; Classify the gas pipelines in service and calculate the total length of gas pipelines with different years of operation.

12. The gas pipeline emergency material demand dynamic prediction system according to claim 9, characterized in that: The acquisition module is used to acquire gas pipeline failure data and calculate pipeline failure probability based on the gas pipeline failure data, including: The acquisition module is used to calculate the pipeline failure probability of the enterprise under test based on the failure history data of the enterprise under test and the pre-collected gas industry failure data.

13. The gas pipeline emergency material demand dynamic prediction system according to claim 9, characterized in that: The optimization module is used to optimize the initial prediction result to form a target prediction result, including: The optimization module is used to calculate the annual turnover rate of the gas pipeline emergency materials of the enterprise under test corresponding to the engineering construction materials, and calculate the minimum inventory requirements of the engineering construction materials of the enterprise under test; The initial forecast results are adjusted based on the annual turnover rate and minimum inventory requirements to obtain the target forecast results.

14. An electronic device, characterized in that: include: Processor and memory; The processor calls the computer program stored in the memory to execute the method for dynamically predicting emergency material demand for a gas pipeline as described in any one of claims 1 to 8.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the method for dynamically predicting emergency material demand for a gas pipeline as described in any one of claims 1 to 8.

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