Method and device for predicting failure frequency of pipeline, equipment and storage medium

Abstract

The embodiment of the invention provides a method and device for predicting the failure frequency of a pipeline, equipment and a storage medium, and belongs to the field of pipeline integrity management. In the method, the future failure frequency is predicted based on the historical use failure condition of the pipeline. Individual conditions of the pipelines are fully considered, and compared with the fact that unified failure frequency is adopted for the pipelines with the same parameters or the same model, the method is more accurate and more beneficial to guiding workers to maintain and replace the pipelines, and economic losses and ecological damage caused by pipeline failure are reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of pipeline integrity management, and in particular to a method, device, equipment and storage medium for predicting the failure frequency of a pipeline. Background Art

[0002] In daily oil and gas transportation applications, there are often situations where oil and gas pipelines need to be repaired or replaced due to pipeline corrosion. During the use of oil and gas pipelines, affected by the internal and external corrosive environment, corrosion failure will inevitably occur. Pipeline corrosion failure will not only cause huge economic losses and ecological damage, but also threaten people's lives and property safety in serious cases. Therefore, how to accurately predict the failure frequency of pipelines is of great significance for the subsequent operation and management of pipelines. The failure frequency is the number of failures per unit time (such as year) of the entire pipeline.

[0003] The specific practice of the current related technology is to find the corresponding failure frequency in the pipeline failure database according to the parameters or model of the pipeline. The failure frequency is the number of failures per unit time under the unit length of the pipeline. It is corrected by combining the influencing factors of the environment in which the target pipeline is located, and a corrected failure frequency is calculated. The corrected failure frequency is multiplied by the total length of the pipeline to obtain the failure frequency of the target pipeline.

[0004] However, according to statistical analysis, it is found that using the universal failure frequency of pipelines with the same parameters or the same model cannot produce accurate results. Even after correction by the correction coefficient, the failure frequency of oil and gas pipelines cannot be accurately predicted. Summary of the invention

[0005] In order to solve the related technical problems, the embodiments of the present disclosure provide a method, device, equipment and storage medium for predicting the failure frequency of pipelines. The technical solution is as follows:

[0006] In a first aspect, a method for predicting the failure frequency of a pipeline is provided, the method comprising:

[0007] Obtain the failure frequency of the target pipeline in multiple historical periods;

[0008] Determining an initial predicted failure frequency for a target period based on the actual failure frequencies for the multiple historical periods and the prediction model;

[0009] Determine the correction factor based on the target pipeline's body condition information, internal corrosion related information, and external corrosion related information;

[0010] Based on the correction coefficient, the initial predicted failure frequency is corrected to obtain the final predicted failure frequency of the target pipeline in the target period, wherein the final predicted failure frequency is used to guide the maintenance of the target pipeline.

[0011] In a possible implementation, obtaining the failure frequencies of the target pipeline in multiple historical time periods includes:

[0012] Get the number of failures of the target pipeline in each historical period and the duration of each historical period;

[0013] Based on the number and duration of failures in each historical period, determine the failure frequency for each historical period.

[0014] In a possible implementation manner, the prediction model is a grey model.

[0015] In a possible implementation, determining the correction coefficient based on the target pipeline's body condition information, internal corrosion related information, and external corrosion related information includes:

[0016] Determining a first sub-correction coefficient based on the bulk condition information of the target pipeline;

[0017] Determining a second sub-correction coefficient based on the internal corrosion related information of the target pipeline;

[0018] Determine the third sub-correction factor based on the external corrosion related information of the target pipeline;

[0019] A correction coefficient is determined based on the first sub-correction coefficient, the second sub-correction coefficient, and the third sub-correction coefficient.

[0020] In a possible implementation, the main body status information includes at least one of: pipeline operating pressure, pipeline design wall thickness, pipeline outer diameter, pipeline operating time, pipeline yield strength, and pipeline corrosion rate.

[0021] In a possible implementation, determining the first sub-correction coefficient based on the main state information of the target pipeline includes:

[0022] Based on the formula Determine the target parameter value w, where P is the pipeline operating pressure, t is the pipeline wall thickness, D is the pipeline outer diameter, n is the pipeline operation time, δ is the pipe yield strength, and c is the pipeline corrosion rate;

[0023] Based on the value range to which w belongs, a first sub-correction coefficient is determined.

[0024] In a possible implementation, the internal corrosion related information includes: at least one of: transmission medium corrosivity information, chemical protection effect, and internal anti-corrosion layer quality information.

[0025] In a possible implementation, determining the second sub-correction coefficient based on the internal corrosion related information of the target pipeline includes:

[0026] Based on the pre-stored correspondence between the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information and the second sub-correction coefficient, as well as the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information of the target pipeline, the second sub-correction coefficient corresponding to the target pipeline is determined.

[0027] In a possible implementation, the external corrosion related information includes: at least one of: external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information.

[0028] In a possible implementation, determining the third sub-correction coefficient based on the external corrosion related information of the target pipeline includes:

[0029] Based on the pre-stored correspondence between the external medium corrosivity information, cathodic protection information, external anti-corrosion layer quality information and the third sub-correction coefficient, as well as the external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information of the target pipeline, the third sub-correction coefficient corresponding to the target pipeline is determined.

[0030] In a possible implementation, the initial predicted failure frequency is corrected based on the correction coefficient to obtain the final predicted failure frequency of the target pipeline in the target period, including:

[0031] The product of the initial predicted failure frequency and the correction coefficient is determined as the final predicted failure frequency of the target pipeline in the target time period.

[0032] In a second aspect, a device for predicting the failure frequency of a pipeline is provided, the device comprising:

[0033] An acquisition module is used to obtain the failure frequency of the target pipeline in multiple historical periods;

[0034] An initial prediction module, used to determine an initial predicted failure frequency in a target period based on the actual failure frequencies in the multiple historical periods and the prediction model;

[0035] A correction module, for determining a correction coefficient based on the body condition information, internal corrosion related information and external corrosion related information of the target pipeline;

[0036] The final prediction module is used to correct the initial predicted failure frequency based on the correction coefficient to obtain the final predicted failure frequency of the target pipeline in the target period, wherein the final predicted failure frequency is used to guide the maintenance of the target pipeline.

[0037] In a possible implementation, the acquisition module is used to:

[0038] Get the number of failures of the target pipeline in each historical period and the duration of each historical period;

[0039] Based on the number and duration of failures in each historical period, determine the failure frequency for each historical period.

[0040] In a possible implementation manner, the prediction model is a grey model.

[0041] In a possible implementation, the correction module is used to:

[0042] Determining a first sub-correction coefficient based on the bulk condition information of the target pipeline;

[0043] Determining a second sub-correction coefficient based on the internal corrosion related information of the target pipeline;

[0044] Determine the third sub-correction factor based on the external corrosion related information of the target pipeline;

[0045] A correction coefficient is determined based on the first sub-correction coefficient, the second sub-correction coefficient, and the third sub-correction coefficient.

[0046] In a possible implementation, the main body status information includes at least one of: pipeline operating pressure, pipeline design wall thickness, pipeline outer diameter, pipeline operating time, pipeline yield strength, and pipeline corrosion rate.

[0047] In a possible implementation, the correction module is used to:

[0048] Based on the formula Determine the target parameter value w, where P is the pipeline operating pressure, t is the pipeline wall thickness, D is the pipeline outer diameter, n is the pipeline operation time, δ is the pipe yield strength, and c is the pipeline corrosion rate;

[0049] Based on the value range to which w belongs, a first sub-correction coefficient is determined.

[0050] In a possible implementation, the internal corrosion related information includes: at least one of: transmission medium corrosivity information, chemical protection effect, and internal anti-corrosion layer quality information.

[0051] In a possible implementation, the correction module is used to:

[0052] Based on the pre-stored correspondence between the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information and the second sub-correction coefficient, as well as the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information of the target pipeline, the second sub-correction coefficient corresponding to the target pipeline is determined.

[0053] In a possible implementation, the external corrosion related information includes: at least one of: external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information.

[0054] In a possible implementation, the correction module is used to:

[0055] Based on the pre-stored correspondence between the external medium corrosivity information, cathodic protection information, external anti-corrosion layer quality information and the third sub-correction coefficient, as well as the external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information of the target pipeline, the third sub-correction coefficient corresponding to the target pipeline is determined.

[0056] In a possible implementation, the final prediction module is used to:

[0057] The product of the initial predicted failure frequency and the correction coefficient is determined as the final predicted failure frequency of the target pipeline in the target time period.

[0058] In a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions, and the processor executes the computer instructions stored in the memory so that the computer device executes the method provided by the first aspect and its possible implementation methods.

[0059] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program code. In response to the computer program code being executed by a computer device, the computer device executes the method provided by the first aspect and possible implementations thereof.

[0060] In a fifth aspect, a computer program product is provided. The computer program product includes a computer program code. When the computer program code is executed by a computer device, the computer device executes the method provided by the first aspect and its possible implementation methods.

[0061] In the disclosed embodiment, the future failure frequency is predicted based on the historical failure of the pipeline itself. Taking full account of the individual conditions of the pipeline, compared with adopting a unified failure frequency for pipelines with the same parameters or the same model, it is more accurate and more conducive to guiding the staff to maintain and replace the pipeline, reducing the economic losses and ecological damage caused by pipeline failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a structural schematic diagram of a computer device provided by an embodiment of the present disclosure;

[0063] Figure 2 It is a schematic diagram of a flow chart for predicting the failure frequency of a pipeline provided by an embodiment of the present disclosure;

[0064] Figure 3 It is a flow chart of obtaining the failure frequency of a target pipeline in multiple historical periods provided by an embodiment of the present disclosure;

[0065] Figure 4 It is a schematic diagram of a process for determining an initial predicted failure frequency in a target period provided by an embodiment of the present disclosure;

[0066] Figure 5 is a schematic diagram of a flow chart for determining a correction coefficient provided by an embodiment of the present disclosure;

[0067] Figure 6 It is a schematic diagram of the structure of a device for predicting the failure frequency of a pipeline provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0068] In daily oil and gas transportation applications, pipeline corrosion often causes the need to repair or replace oil and gas pipelines. During the use of oil and gas pipelines, affected by various factors, corrosion failure will inevitably occur. Pipeline corrosion failure will not only cause huge economic losses and ecological damage, but also threaten people's lives and property safety in serious cases. For example, certain substances in the soil are corrosive, and oil and gas pipelines running on the soil will be corroded by the soil, thus affecting the normal use of the pipeline.

[0069] The disclosed embodiment provides a method for predicting the failure frequency of a pipeline. The method can be applied to a computer device, which can be a terminal or a server. The terminal can be a mobile phone, a tablet computer, a notebook computer, a desktop computer, etc. The server can be a single device or a device group consisting of multiple devices.

[0070] From the perspective of hardware composition, the structure of the above computer equipment can be as follows Figure 1 As shown, it includes a processor 110 , a memory 120 and a display 130 .

[0071] The processor 110 may be a central processing unit (CPU) or a system on chip (SoC), etc. The processor 110 may be used to execute various instructions involved in the method, etc.

[0072] The memory 120 may include various volatile memories or non-volatile memories, such as a solid state disk (SSD), a dynamic random access memory (DRAM), etc. The memory 120 may be used to store pre-stored data, intermediate data, and result data in the execution of business processing, such as correction coefficient data corresponding to the evaluation result and a first sub-correction coefficient table corresponding to the value range of the target parameter.

[0073] The display 130 may be an independent screen, or a screen integrated with the terminal body, a projector, etc. The screen may be a touch screen or a non-touch screen. In the embodiment of the present disclosure, the display is used to display a corrosion status assessment table in a pipeline, etc.

[0074] The present disclosure provides a method for predicting the failure frequency of a pipeline. The processing flow of the method can be as follows: Figure 2 As shown, the following steps are included:

[0075] 201, obtaining the failure frequency of the target pipeline in multiple historical periods.

[0076] 202. Determine an initial predicted failure frequency for a target period based on actual failure frequencies for multiple historical periods and a prediction model.

[0077] 203 , determining a correction factor based on the main condition information, the internal corrosion related information, and the external corrosion related information of the target pipeline.

[0078] 204 , based on the correction coefficient, correct the initial predicted failure frequency to obtain the final predicted failure frequency of the target pipeline in the target period.

[0079] The product of the initial predicted failure frequency and the correction coefficient is determined as the final predicted failure frequency of the target pipeline in the target period. The duration of each historical period can be equal, and the duration of each target period and each historical period can be equal. The final predicted failure frequency is the predicted failure frequency finally calculated, which is used to guide the maintenance of the target pipeline. The staff can estimate the time when the failure may occur based on the failure frequency, and then perform maintenance and other work before that.

[0080] In a possible implementation, the process of obtaining the failure frequency of the target pipeline in multiple historical periods can be as follows: Figure 3 As shown, the following steps are included:

[0081] 301, obtain the number of failures of the target pipeline in each historical period and the duration of each historical period.

[0082] The number of failures of the target pipeline in each historical period is collected. For example, the number of failures of the target pipeline in the past k periods can be collected. For example, in this embodiment, the number of failures of the target pipeline in the past four years (2019-2022) is collected as 3 times, 2 times, 4 times, and 3 times respectively.

[0083] 302. Determine the actual failure frequency of each historical period based on the number and duration of failures in each historical period.

[0084] The collected number of failures is divided by the length of a single time period to convert into the actual failure frequency X(k), k=1, 2, ..., n. In this embodiment, k=4 is taken as an example, X(k)={3, 2, 4, 3}.

[0085] In a possible implementation, the process of determining the initial predicted failure frequency of the target period can be as follows: Figure 4 As shown, the following steps are included:

[0086] At 401, the obtained actual failure frequency is input into the prediction model.

[0087] The actual failure frequency obtained is counted as a set of raw data: X (0) ={X (0) (1),X (0) (2),……,X (0) (n)}. For example, in this embodiment, X (0) ={3, 2, 4, 3}. The actual failure frequencies X in multiple historical periods are obtained. (0) input into the prediction model.

[0088] 402, predicting the initial prediction failure frequency using the prediction model.

[0089] The prediction model uses the actual failure frequency to predict the trend of the future failure frequency of the target pipeline.

[0090] 403, output the initial predicted failure frequency of the target period.

[0091] Output the initial predicted failure frequency obtained by the prediction model and record the initial predicted failure frequency of the target period.

[0092] According to the prediction formula of the initial predicted failure frequency of the target pipeline, the basic failure frequency of the pipeline in the next five years is shown in the following table:

[0093] Table 1 Statistical table of initial prediction failure frequency results

[0094] years 2023 2024 2025 2026 2027 <![CDATA[X 预测 ]]> 3.96 4.56 5.26 6.06 6.98

[0095] Optionally, the prediction model in this embodiment may also be a pre-trained machine learning model, which may include a convolutional neural network, a nearest neighbor algorithm, etc.

[0096] In a possible implementation, the correction factor is determined based on the target pipeline's body condition information, internal corrosion related information, and external corrosion related information. The processing flow can be as follows: Figure 5 As shown, the following steps are included:

[0097] 501. Determine a first sub-correction coefficient based on the main state information of the target pipeline.

[0098] The target pipeline's ontological status information is the target pipeline's own status information, and the ontological status information may include: at least one of pipeline operating pressure, pipeline design wall thickness, pipeline outer diameter, pipeline operating time, pipeline yield strength, and pipeline corrosion rate. Pipeline operating pressure is the pressure that the target pipeline needs to withstand during operation. Pipeline design wall thickness is the wall thickness determined during the design process of the target pipeline. Pipeline operating time is the length of time the target pipeline is put into use. Pipeline yield strength is the critical value of the material yield of the target pipeline. Pipeline corrosion rate is the average corrosion degree of the target pipeline per unit time.

[0099] The first sub-correction coefficient can be determined by the target parameter, and the target parameter formula is:

[0100]

[0101] The target parameter value w is obtained by substituting the existing target pipeline status information into the target parameter formula, and then the first sub-correction coefficient is obtained by comparing the target parameter with 1. As shown in Table 2, when the target parameter <1.0, the first sub-correction coefficient is 0.9; when 1.0≤target parameter≤2.0, the first sub-correction coefficient is 1.0; when the target parameter>2.0, the first sub-correction coefficient is 1.1.

[0102] Among them, P is the pipeline operating pressure, the unit can be MPa (megapascal); t is the pipeline wall thickness, the unit can be mm (millimeter); D is the pipeline outer diameter, the unit can be mm (millimeter); n is the pipeline operation time, the unit can be year; δ is the pipe yield strength, the unit can be MPa (megapascal); c is the pipeline corrosion rate, the unit can be mm / a (millimeter / year).

[0103] Based on the value range to which the target parameter value belongs, a first sub-correction coefficient is determined.

[0104] Table 2 Target parameter evaluation table

[0105] w First sub-correction factor (-∞，1.0) 0.9 [1.0,2.0] 1.0 (2.0，+∞) 1.1

[0106] 502. Determine a second sub-correction coefficient based on the internal corrosion related information of the target pipeline.

[0107] The information related to the internal corrosion of the target pipeline includes at least one of the following: the transmission medium corrosiveness information, the chemical protection effect, and the internal anti-corrosion layer quality information. Among them, the transmission medium corrosiveness information is the information on the strength of the corrosiveness of the corrosive substances carried by the oil and gas itself to the target pipeline during the use of the target pipeline. The data form of the transmission medium corrosiveness information can be in numerical form, grade form (such as strong, medium, weak), etc. The chemical protection effect is the protection effect information of adding certain chemicals to the oil and gas to reduce the corrosion of the target pipeline. The data form of the chemical protection effect can be in numerical form, grade form (such as none, slightly substandard, up to standard), etc. The internal anti-corrosion layer quality information is the quality of the anti-corrosion layer added inside the target pipeline. The data form of the internal anti-corrosion layer quality information can be in numerical form, grade form (such as poor, general, good), etc.

[0108] Based on the pre-stored correspondence between the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information and the second sub-correction coefficient, as well as the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information of the target pipeline, the second sub-correction coefficient corresponding to the target pipeline is determined.

[0109] As shown in Table 3, the second sub-correction coefficient can be determined by comprehensively evaluating the corrosion information in the target pipeline. The corrosion information in the target pipeline can be information on influencing factors such as the corrosiveness of the transmission medium, the chemical protection effect, and the quality of the internal anti-corrosion layer. A comprehensive evaluation is performed on this information to obtain the corresponding second sub-correction coefficient.

[0110] Among them, the medium corrosiveness information is divided into strong, medium and weak, the chemical protection effect information is divided into none, slightly substandard and up to standard, and the internal anti-corrosion layer quality information is divided into poor, general and good. In the case where the information of the above-mentioned no less than 2 influencing factors is the worst, the second sub-correction coefficient is 1.1, and in the case where the information of the above-mentioned no less than 2 influencing factors is the best, the second sub-correction coefficient is 0.9, and in other cases, the second sub-correction coefficient is 1.

[0111] Table 3 Evaluation table of corrosion status in pipelines

[0112]

[0113] (Continued)

[0114]

[0115] 503, determining a third sub-correction coefficient based on the external corrosion related information of the target pipeline.

[0116] The information related to the external corrosion of the target pipeline includes at least one of the following: external medium corrosiveness information, cathodic protection information, and external anti-corrosion layer quality information. Among them, the external medium corrosiveness information refers to the corrosiveness of the environment in which the target pipeline is located, such as the corrosiveness of the soil. The data form of the external medium corrosiveness information can be in numerical form, grade form (such as strong, medium, weak), etc. The cathodic protection information refers to the effect information of the target pipeline as a cathode to slow down corrosion. The data form of the cathodic protection information can be in numerical form, grade form (such as none, slightly substandard, up to standard), etc. The external anti-corrosion layer quality information refers to the quality of the anti-corrosion layer added inside the target pipeline. The data form of the external anti-corrosion layer quality information can be in numerical form, grade form (such as poor, general, good), etc.

[0117] Based on the pre-stored correspondence between the external medium corrosivity information, cathodic protection information, external anti-corrosion layer quality information and the third sub-correction coefficient, as well as the external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information of the target pipeline, the third sub-correction coefficient corresponding to the target pipeline is determined.

[0118] As shown in Table 4, the third sub-correction coefficient can be determined by comprehensively evaluating the external corrosion information of the target pipeline. The external corrosion information of the target pipeline can be information on influencing factors such as the corrosiveness of the external medium, cathodic protection, and the quality of the external anti-corrosion layer. A comprehensive evaluation is performed on this information to obtain the corresponding third sub-correction coefficient.

[0119] Among them, soil corrosivity information is divided into strong, medium and weak, cathodic protection information is divided into none, slightly substandard and up to standard, and external anti-corrosion layer quality information is divided into poor, general and good. The comprehensive evaluation results are divided into three categories: light, medium and heavy. When the information of the above-mentioned influencing factors is the worst, the third sub-correction coefficient is 1.1, when the information of the above-mentioned influencing factors is the best, the third sub-correction coefficient is 0.9, and in other cases, the third sub-correction coefficient is 1.

[0120] Table 4 Pipeline external corrosion status assessment table

[0121]

[0122] (Continued)

[0123]

[0124] 504 , determine a correction coefficient based on the first sub-correction coefficient, the second sub-correction coefficient, and the third sub-correction coefficient.

[0125] The correction factor may be a product of the first sub-correction factor, the second sub-correction factor, and the third sub-correction factor.

[0126] In the disclosed embodiment, the future failure frequency is predicted based on the historical failure of the pipeline itself. Taking full account of the individual conditions of the pipeline, compared with adopting a unified failure frequency for pipelines with the same parameters or the same model, it is more accurate and more conducive to guiding the staff to maintain and replace the pipeline, reducing the economic losses and ecological damage caused by pipeline failure.

[0127] Based on the same technical concept, the embodiment of the present disclosure provides a device for predicting the failure frequency of a pipeline, which is applied to a computer device, such as Figure 6 As shown, the device comprises:

[0128] The acquisition module 610 is used to acquire the failure frequency of the target pipeline in multiple historical periods, and can specifically implement the processing functions of the above steps 201 and 301, as well as other implicit steps.

[0129] The initial prediction module 620 is used to determine the initial predicted failure frequency of the target period based on the actual failure frequencies of multiple historical periods and the prediction model. Specifically, it can implement the processing functions of the above steps 202, 401, 402, 403, and other implicit steps.

[0130] The correction module 630 is used to determine the correction coefficient based on the target pipeline's body condition information, internal corrosion related information and external corrosion related information. Specifically, it can implement the processing functions of the above steps 203, 501, 502, 503, 504, and other implicit steps.

[0131] The final prediction module 640 is used to correct the initial predicted failure frequency based on the correction coefficient to obtain the final predicted failure frequency of the target pipeline in the target period, wherein the final predicted failure frequency is used to guide the maintenance of the target pipeline. Specifically, the processing function of the above step 204 and other implicit steps can be realized.

[0132] In a possible implementation, the acquisition module 610 is used to: acquire the number of failures of the target pipeline in each historical period and the duration of each historical period; and determine the failure frequency of each historical period based on the number of failures and the duration of each historical period.

[0133] In a possible implementation, the prediction model is a grey model.

[0134] In one possible implementation, the correction module 630 is used to: determine a first sub-correction coefficient based on the main condition information of the target pipeline; determine a second sub-correction coefficient based on the internal corrosion related information of the target pipeline; determine a third sub-correction coefficient based on the external corrosion related information of the target pipeline; determine a correction coefficient based on the first sub-correction coefficient, the second sub-correction coefficient and the third sub-correction coefficient.

[0135] In a possible implementation, the main state information includes: at least one of pipeline operating pressure, pipeline design wall thickness, pipeline outer diameter, pipeline operating time, pipeline yield strength, and pipeline corrosion rate.

[0136] In a possible implementation, the correction module 630 is used to: based on the formula Determine the target parameter value w, where P is the pipeline operating pressure, t is the pipeline wall thickness, D is the pipeline outer diameter, n is the pipeline operation time, δ is the pipe yield strength, and c is the pipeline corrosion rate; determine the first sub-correction coefficient based on the value range of w.

[0137] In a possible implementation, the internal corrosion related information includes: at least one of: transmission medium corrosivity information, chemical protection effect, and internal anti-corrosion layer quality information.

[0138] In one possible implementation, the correction module 630 is used to determine the second sub-correction coefficient corresponding to the target pipeline based on the pre-stored correspondence between the transmission medium corrosivity information, chemical protection effect, inner anti-corrosion layer quality information and the second sub-correction coefficient, as well as the transmission medium corrosivity information, chemical protection effect, and inner anti-corrosion layer quality information of the target pipeline.

[0139] In a possible implementation, the external corrosion related information includes: at least one of: external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information.

[0140] In one possible implementation, the correction module 630 is used to determine the third sub-correction coefficient corresponding to the target pipeline based on the pre-stored correspondence between the external medium corrosivity information, cathodic protection information, external anti-corrosion layer quality information and the third sub-correction coefficient, as well as the external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information of the target pipeline.

[0141] In a possible implementation, the final prediction module 640 is used to determine the product of the initial predicted failure frequency and the correction coefficient as the final predicted failure frequency of the target pipeline in the target period.

[0142] The acquisition module 610, the initial prediction module 620, the correction module 630, and the final prediction module 640 may be implemented by a processor, or by a processor in conjunction with a memory and a display.

[0143] In the disclosed embodiment, the future failure frequency is predicted based on the historical failure of the pipeline itself. Taking full account of the individual conditions of the pipeline, compared with adopting a unified failure frequency for pipelines with the same parameters or the same model, it is more accurate and more conducive to guiding the staff to maintain and replace the pipeline, reducing the economic losses and ecological damage caused by pipeline failure.

[0144] When the device for predicting the failure frequency of a pipeline provided in the above embodiment is in operation, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the terminal is divided into different functional modules to complete all or part of the functions described above. In addition, the device for predicting the failure frequency of a pipeline provided in the above embodiment and the method embodiment for predicting the failure frequency of a pipeline belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0145] The embodiments of the present disclosure also provide a computer-readable storage medium. The computer-readable storage medium may be any available medium that can be stored by a computing device or a data storage device such as a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk). The computer-readable storage medium includes instructions that instruct a computing device to perform a method for business processing, or instructs a computing device to perform a method for business processing.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the failure frequency of a pipeline, characterized in that: The method comprises: Obtain the failure frequency of the target pipeline in multiple historical periods; Determining an initial predicted failure frequency for a target period based on the actual failure frequencies for the multiple historical periods and the prediction model; Determine the correction factor based on the target pipeline's body condition information, internal corrosion related information, and external corrosion related information; Based on the correction coefficient, the initial predicted failure frequency is corrected to obtain the final predicted failure frequency of the target pipeline in the target period, wherein the final predicted failure frequency is used to guide the maintenance of the target pipeline.

2. The method according to claim 1, characterized in that The obtaining of the failure frequency of the target pipeline in multiple historical periods includes: Get the number of failures of the target pipeline in each historical period and the duration of each historical period; Based on the number and duration of failures in each historical period, determine the failure frequency for each historical period.

3. The method according to claim 1, characterized in that The prediction model is a grey model.

4. The method according to claim 1, characterized in that: The correction coefficient is determined based on the target pipeline's body condition information, internal corrosion related information, and external corrosion related information, including: Determining a first sub-correction coefficient based on the bulk condition information of the target pipeline; Determining a second sub-correction coefficient based on the internal corrosion related information of the target pipeline; Determine the third sub-correction factor based on the external corrosion related information of the target pipeline; A correction coefficient is determined based on the first sub-correction coefficient, the second sub-correction coefficient, and the third sub-correction coefficient.

5. The method according to claim 4, characterized in that The main body status information includes: at least one of pipeline operating pressure, pipeline design wall thickness, pipeline outer diameter, pipeline operating time, pipeline yield strength, and pipeline corrosion rate.

6. The method according to claim 4, characterized in that The internal corrosion related information includes: at least one of: transmission medium corrosivity information, chemical protection effect, and internal anti-corrosion layer quality information.

7. The method according to claim 4, characterized in that The external corrosion related information includes: at least one of external medium corrosivity information, cathodic protection information, and external anti-corrosion layer quality information.

8. A device for predicting the failure frequency of a pipeline, characterized in that: The device comprises: An acquisition module is used to obtain the failure frequency of the target pipeline in multiple historical periods; An initial prediction module, used to determine an initial predicted failure frequency in a target period based on the actual failure frequencies in the multiple historical periods and the prediction model; A correction module, for determining a correction coefficient based on the body condition information, internal corrosion related information and external corrosion related information of the target pipeline; The final prediction module is used to correct the initial predicted failure frequency based on the correction coefficient to obtain the final predicted failure frequency of the target pipeline in the target period, wherein the final predicted failure frequency is used to guide the maintenance of the target pipeline.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory is used to store computer instructions; The processor executes the computer instructions stored in the memory to enable the computer device to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program code. In response to the computer program code being executed by a computer device, the computer device executes the method according to any one of claims 1 to 7.

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