Construction and prediction method and device of pipeline service life prediction model

Abstract

The invention discloses a construction and prediction method and device of a pipeline service life prediction model. The method comprises the steps that a corrosion pit depth value set of a target pipeline in multiple sampling periods is obtained, and the corrosion pit depth value set comprises a preset number of corrosion pit depth values selected from the maximum according to the sequence of the corrosion pit depth values; for each sampling period, determining a corrosion regression model corresponding to the sampling period according to the corrosion pit depth value set of the sampling period; the corrosion regression model represents the relationship between the corrosion pit depth value and the corresponding distribution probability; and fitting the corrosion regression model corresponding to each sampling period to obtain a pipeline residual life prediction model. The service life of the buried oil and gas pipeline can be scientifically and accurately predicted according to the actual corrosion condition of the buried oil and gas pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline prediction, and particularly relates to a method and device for constructing a prediction model for the service life of pipelines and a prediction method thereof. Background Art

[0002] As the basic medium in the whole process of oil and gas resource development and utilization, pipeline construction is the key in the links of oil and gas field development, storage, transportation and refining and external transportation. During the oil and gas development process, oil and gas pipelines will inevitably be corroded. Moreover, due to acid fracturing and chemical treatment often carried out during the oil and gas field development process, the corrosivity of oil and gas pipelines is increased. Corrosion will cause the wall thickness of the pipeline to decrease, the strength to decrease or stress concentration. In severe cases, it will cause pipeline leakage or explosion, which not only affects the normal transportation of natural gas, but even threatens the life and property safety of the people and the ecological environment. In addition, a large part of the pipelines in China were built a long time ago, and the corrosion resistance of pipeline steel is limited. In addition, factors such as pipeline crossing under complex geological conditions, crossing and influence of high-voltage transmission facilities, and interaction with man-made facilities such as electrified railways are constantly testing the reliability of pipelines. Therefore, the reliability analysis of corroded pipelines is urgent, and there is an urgent need for a method that can scientifically and accurately predict the service life of buried oil and gas pipelines according to the actual corrosion conditions of buried oil and gas pipelines. Summary of the Invention

[0003] The inventors of the present invention found that due to corrosion for various reasons, buried pipelines will have problems such as thinning of the pipe wall thickness, reduction of strength or stress concentration, which are likely to cause pipeline leakage or explosion. Therefore, it is very necessary to accurately predict the remaining life of the pipeline. However, there is currently no scientific and accurate method to predict the remaining life of buried pipelines. In view of the above problems, the present invention is proposed to provide a method and device for constructing a prediction model for the service life of pipelines and a prediction method thereof that can overcome or at least partially solve the above problems.

[0004] In a first aspect, an embodiment of the present invention provides a method for constructing a prediction model for the remaining life of a pipeline, including:

[0005] Obtaining a set of corrosion pit depth values for a plurality of sampling periods of a target pipeline, where the set of corrosion pit depth values includes a preset number of corrosion pit depth values selected in order of the size of the corrosion pit depth values, starting from the largest;

[0006] For each sampling period, determining a corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period; the corrosion regression model represents the relationship between the corrosion pit depth value and the corresponding distribution probability;

[0007] Fitting the corrosion regression models corresponding to each sampling period to obtain a prediction model for the remaining life of the pipeline.

[0008] In one embodiment, determining the corrosion regression model corresponding to the sampling period from the set of corrosion pit depth values according to the sampling period includes:

[0009] Calculating the distribution probability corresponding to each corrosion pit depth value in the set of corrosion pit depth values of the sampling period;

[0010] Determining the corrosion regression model corresponding to the sampling period according to each corrosion pit depth value and the corresponding distribution probability.

[0011] In one embodiment, calculating the distribution probability corresponding to each corrosion pit depth value in the set of corrosion pit depth values of the sampling period:

[0012] Calculating the distribution probability corresponding to the corrosion pit depth value through the following formula:

[0013]

[0014] In the above formula, Si is the i-th corrosion pit depth value, and M is the number of elements in the set of corrosion pit depth values.

[0015] In one embodiment, determining the corrosion regression model corresponding to the sampling period according to each corrosion pit depth value and the corresponding distribution probability includes:

[0016] Substituting each corrosion pit depth value and the corresponding distribution probability into a preset expression for solution to obtain the parameter values of the expression, and determining the corrosion regression model corresponding to the sampling period according to the parameter values of the expression.

[0017] In one embodiment, the expression is:

[0018]

[0019] In the above formula, S i represents the i-th corrosion pit depth value, P represents the distribution probability corresponding to the i-th corrosion pit depth value, and a and b represent parameters.

[0020] In one embodiment, fitting the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model includes:

[0021] Solving the corrosion regression model corresponding to each sampling period respectively according to a preset probability threshold to obtain the maximum corrosion pit depth value under each sampling period;

[0022] Determining the pipeline remaining life prediction model according to each sampling period and the corresponding maximum corrosion pit depth value.

[0023] In a second aspect, an embodiment of the present invention provides a method for predicting the remaining life of a pipeline, including:

[0024] Obtain the current wall thickness and the original wall thickness of the target pipeline;

[0025] Determine the remaining corrosion depth value of the target pipeline according to the current wall thickness and the original wall thickness;

[0026] Substitute the remaining corrosion depth value into the pipeline remaining life prediction model for solution to obtain the remaining service life of the target pipeline;

[0027] The pipeline remaining life prediction model is constructed by using the corrosion pit depth value of the target pipeline and the construction method of the foregoing pipeline remaining life prediction model.

[0028] In a third aspect, an embodiment of the present invention provides a device for constructing a pipeline remaining life prediction model, including:

[0029] A first acquisition module, configured to acquire a set of corrosion pit depth values of a target pipeline in multiple sampling periods, where the set of corrosion pit depth values includes a preset number of corrosion pit depth values selected starting from the largest in the order of the corrosion pit depth values;

[0030] A first determination module, configured to determine, for each sampling period, a corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period; the corrosion regression model represents the relationship between the corrosion pit depth value and the corresponding distribution probability;

[0031] A fitting module, configured to fit the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model.

[0032] In a fourth aspect, an embodiment of the present invention provides a device for predicting the remaining life of a pipeline, including:

[0033] A second acquisition module, configured to acquire the current wall thickness and the original wall thickness of the target pipeline;

[0034] A second determination module, configured to determine the remaining corrosion depth value of the target pipeline according to the current wall thickness and the original wall thickness;

[0035] A solution module, configured to substitute the remaining corrosion depth value into the pipeline remaining life prediction model for solution to obtain the remaining service life of the target pipeline;

[0036] The pipeline remaining life prediction model is constructed by using the corrosion pit depth value of the target pipeline and the construction method of the foregoing pipeline remaining life prediction model.

[0037] Fifth aspect, an embodiment of the present invention provides a computer storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the method for constructing the foregoing pipeline remaining life prediction model or the method for predicting the foregoing pipeline remaining life is implemented.

[0038] Sixth aspect, an embodiment of the present invention provides a terminal device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for constructing the foregoing pipeline remaining life prediction model or the method for predicting the foregoing pipeline remaining life is implemented.

[0039] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0040] For the method for constructing a pipeline remaining life prediction model provided by an embodiment of the present invention, by obtaining a preset number of corrosion pit depth values in multiple sampling periods of a target pipeline, a set of corrosion pit depth values in multiple sampling periods is obtained, and the set of corrosion pit depth values is a preset number of corrosion pit depth values selected starting from the maximum corrosion pit depth value in the order of decreasing corrosion pit depth value. According to the set of corrosion pit depth values in each sampling period, the corrosion regression model corresponding to each sampling period is respectively determined, and the corrosion regression models corresponding to each sampling period are fitted to obtain a pipeline remaining life prediction model; according to the corrosion pit depth values generated by the corrosion of the target pipeline in the early stage, a corrosion regression model is constructed to obtain the relationship between the corrosion pit depth value and the corresponding distribution probability, and then the corrosion regression models in multiple sampling periods are fitted. In other words, the corrosion pit depth values and the corresponding distribution probabilities in different corrosion periods are fitted to obtain a pipeline remaining life prediction model. The pipeline remaining life prediction model can predict the remaining life according to the actual corrosion situation of the target pipeline in the early stage, can scientifically and accurately predict the remaining service life of the target pipeline, effectively avoid pipeline leakage or explosion, ensure the lives of workers and the public, and at the same time provide scientific guidance for engineering design, construction, operation and maintenance, realizing the reduction of operation and maintenance costs and the improvement of pipeline operation safety and reliability.

[0041] For the method for predicting the pipeline remaining life provided by an embodiment of the present invention, by obtaining the current wall thickness and the original wall thickness of the target pipeline, the remaining corrosion depth value of the target pipeline is calculated. According to the remaining corrosion depth value and the pipeline remaining life prediction model constructed by using the foregoing method for constructing a pipeline remaining life prediction model, the remaining life of the target pipeline is calculated. The calculation process is simple, the operation cost is low, and the obtained result of the remaining life of the target pipeline is accurate.

[0042] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description, claims, as well as the drawings.

[0043] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0044] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. They are used together with the embodiments of the present invention to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0045] Figure 1 is a flowchart of a method for constructing a pipeline remaining life prediction model in an embodiment of the present invention;

[0046] Figure 2 is a flowchart of a method for determining a corrosion regression model corresponding to a sampling period in an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of a corrosion regression model curve in an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of a corrosion regression model curve in an embodiment of the present invention;

[0049] Figure 5 is a schematic diagram of a corrosion regression model curve in an embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of a curve of a pipeline remaining life prediction model in an embodiment of the present invention;

[0051] Figure 7 is a flowchart of a method for predicting the remaining life of a pipeline in an embodiment of the present invention;

[0052] Figure 8 is a schematic diagram of the structure of a device for constructing a pipeline remaining life prediction model in an embodiment of the invention;

[0053] Figure 9 is a schematic diagram of the structure of a device for predicting the remaining life of a pipeline in an embodiment of the invention. Detailed Embodiments

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0055] To solve the problem in the prior art that the remaining life of a pipeline cannot be scientifically and accurately predicted, an embodiment of the present invention provides a method and device for constructing and predicting a pipeline service life prediction model.

[0056] Embodiment

[0057] An embodiment of the present invention provides a method for constructing a pipeline remaining life prediction model, and its process is as Figure 1 shown, including the following steps:

[0058] Step S1: Obtain a set of corrosion pit depth values for a plurality of sampling periods of the target pipeline. The set of corrosion pit depth values includes a preset number of corrosion pit depth values selected in descending order of the corrosion pit depth values, starting from the largest.

[0059] Step S2: For each sampling period, determine a corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period. The corrosion regression model characterizes the relationship between the corrosion pit depth value and the corresponding distribution probability.

[0060] Step S3: Fit the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model.

[0061] According to the corrosion pit depth values generated by the prior corrosion of the target pipeline, a corrosion regression model is constructed to obtain the relationship between the corrosion pit depth value and the corresponding distribution probability. Then, the corrosion regression models of multiple sampling periods are fitted. In other words, the corrosion pit depth values and the corresponding distribution probabilities under different corrosion periods are fitted to obtain a pipeline remaining life prediction model. The pipeline remaining life prediction model can predict the remaining life according to the actual situation of the prior corrosion of the target pipeline, can scientifically and accurately predict the remaining service life of the target pipeline, effectively avoid pipeline leakage or explosion, ensure the lives of workers and the public, and at the same time provide scientific guidance for engineering design, construction, operation and maintenance, realizing the reduction of operation and maintenance costs and the improvement of pipeline operation safety and reliability.

[0062] In some alternative embodiments, a laser confocal microscope can be used to analyze the corrosion pit depth of the target pipeline to obtain each corrosion pit depth value. It should be noted that the corrosion of the target pipeline is continuously occurring. Obviously, the corrosion degree is different for different corrosion durations. For example, pipelines corroded for 1 year, 3 years, and 5 years respectively have different corrosion degrees. Therefore, a sampling period can be preset to collect the corrosion degree of the target pipeline in different periods. For example, taking 3 months as a sampling period, in step S1, a set of corrosion pit depth values of the target pipeline in multiple sampling periods is obtained. For example, sets of corrosion pit depth values corresponding to the previous 6 months, the previous 3 months, and the current time of the current time are obtained respectively.

[0063] For the sake of convenient description, the set of corrosion pit depth values corresponding to the previous 6 months of the current time can be called the first set of corrosion pit depth values, the set of corrosion pit depth values corresponding to the previous 3 months of the current time can be called the second set of corrosion pit depth values, and the set of corrosion pit depth values corresponding to the current time can be called the third set of corrosion pit depth values.

[0064] Among them, when obtaining the set of corrosion pit depth values, for the target pipeline in each sampling period, the corrosion pit depth values of the target pipeline are sorted in ascending order, and a preset number of corrosion pit depth values starting from the largest corrosion pit depth value are selected as the set of corrosion pit depth values. In other words, for the target pipeline in a sampling period, starting from the largest corrosion pit depth value in this sampling period, a preset number of corrosion pit depth values are selected in descending order. For example, the largest 3 corrosion pit depth values are selected. The embodiment of the present invention does not limit the selection quantity.

[0065] A specific example is used to illustrate the set of corrosion pit depth values in multiple sampling periods obtained in step S1. Taking 5 corrosion pit depth values selected in each sampling period as an example,

[0066] Refer to Table 1 below:

[0067] Table 1:

[0068]

[0069] In some alternative embodiments, for the above step S2, refer to Figure 2 as shown, it can be realized through the following steps:

[0070] Step S21: Calculate the distribution probability corresponding to each corrosion pit depth value in the set of corrosion pit depth values of the sampling period;

[0071] The distribution probability corresponding to the corrosion pit depth value is calculated through the following formula:

[0072]

[0073] In formula (1), i is the serial number arranged in ascending order of the corrosion pit depth values, Si is the i-th corrosion pit depth value, and M is the number of elements in the set of corrosion pit depth values.

[0074] For example, if the number of elements in the set of corrosion pit depth values is 3, that is, M = 3, and the smallest corrosion pit depth value in the set is 25.102, then:

[0075]

[0076] For the data in Table 1 above, calculate the distribution probability corresponding to each corrosion pit depth value in each set of corrosion pit depth values, as shown in Table 2 below:

[0077]

[0078] In some alternative embodiments, construct an expression for the corrosion regression model, and the expression is:

[0079]

[0080] In formula (2), S i represents the i-th corrosion pit depth value, P represents the distribution probability corresponding to the i-th corrosion pit depth value, and a and b represent parameters.

[0081] Step S22: Determine the corrosion regression model corresponding to the sampling period according to each corrosion pit depth value and the corresponding distribution probability.

[0082] Substitute each corrosion pit depth value and the corresponding distribution probability into a preset expression for solution to obtain the parameter values of the expression, and determine the corrosion regression model corresponding to the sampling period according to the parameter values of the expression.

[0083] Specifically, still taking the data in Table 1 and Table 2 as an example, for the first set of corrosion pit depth values, substitute the corrosion pit depth value and the corresponding distribution probability into formula (1) to solve for parameter a and parameter b, and obtain:

[0084] a = 6.5454, b = -0.1227;

[0085] Correspondingly, the corresponding corrosion regression model is:

[0086]

[0087] Taking as the ordinate and the corrosion pit depth value as the abscissa, plot the curve of this corrosion regression model, as shown in reference Figure 3 shown;

[0088] For the second set of corrosion pit depth values, substitute the corrosion pit depth values and their corresponding distribution probabilities into Equation (1) to solve for parameter a and parameter b, obtaining:

[0089] a = 5.3978, b = -0.1324;

[0090] Correspondingly, the corresponding corrosion regression model is:

[0091]

[0092] Furthermore, with as the ordinate and the corrosion pit depth value as the abscissa, plot the curve of this corrosion regression model, as shown in reference to Figure 4 ;

[0093] For the third set of corrosion pit depth values, substitute the corrosion pit depth values and their corresponding distribution probabilities into Equation (1) to solve for parameter a and parameter b, obtaining:

[0094] a = 7.8111, b = -0.282;

[0095] Correspondingly, the corresponding corrosion regression model is:

[0096]

[0097] Furthermore, with as the ordinate and the corrosion pit depth value as the abscissa, plot the curve of this corrosion regression model, as shown in reference to Figure 5 ;

[0098] In some alternative embodiments, construct an expression for the corrosion regression model, the expression being:

[0099] S i = x + yt Z ; (6)

[0100] In Equation (6), x and y represent parabola constants, z represents a power function constant, t represents time, and other parameters have been described above. Embodiments of the present invention will not elaborate on them here.

[0101] Correspondingly, the above-mentioned step S3 can be implemented in the following manner:

[0102] (1), According to a preset probability threshold, solve the corrosion regression model corresponding to each sampling period respectively to obtain the maximum corrosion pit depth value for each sampling period;

[0103] The preset probability threshold can be taken as 0.99 or 0.98. Embodiments of the present invention do not limit this;

[0104] Still taking the data in Table 1 and Table 2 as an example, when the probability threshold takes the value of 0.99 (i.e., P = 0.99), substituting P = 0.99 into Formula (3), Formula (4) and Formula (5) respectively, we can get:

[0105] -4.6 = -0.1227S i +6.5454; Solving for S i = 90.8346μm;

[0106] -4.6 = -0.1324S i +5.3978; Solving for S i = 75.5121μm;

[0107] -4.6 = -0.282S i +7.8111; Solving for S i = 44.0110μm;

[0108] The corrosion pit depth value S calculated when P = 0.90 i , represents the maximum pitting corrosion pit depth value of the target pipeline under the corresponding sampling period.

[0109] (2), According to each sampling period and the corresponding maximum corrosion pit depth value, determine the pipeline remaining life prediction model.

[0110] Substitute the maximum corrosion pit depth value and the corresponding sampling period into Formula (6), solve for the values of parameters x, y and z, substitute the values of parameters x, y and z into Formula (6), determine the pipeline remaining life prediction model. Further, taking the maximum corrosion pit depth value as the ordinate and the corresponding sampling period as the abscissa, draw the curve of the pipeline remaining life prediction model, as Figure 6 shown.

[0111] Based on the same inventive concept, the embodiment of the present invention also provides a method for predicting the remaining life of a pipeline, the process of which is as Figure 7 shown, including the following steps:

[0112] Step S71: Obtain the current wall thickness and the original wall thickness of the pipeline to be measured;

[0113] Step S72: Determine the remaining corrosion depth value of the pipeline to be measured according to the current wall thickness and the original wall thickness;

[0114] Step S73: Substitute the remaining corrosion depth value into the pipeline remaining life prediction model for solution to obtain the remaining service life of the pipeline to be measured;

[0115] The pipeline remaining life prediction model is constructed by using the corrosion pit depth value of the pipeline to be measured and the construction method of the aforementioned pipeline remaining life prediction model.

[0116] Specifically, the current wall thickness of the target steel pipe can be detected by a digital X-ray detector. The difference between the current wall thickness and the original wall thickness is the remaining corrosion depth value of the pipeline to be measured. Substitute the remaining corrosion depth value into the pipeline remaining life prediction model to solve for the value of time t, and the solution result is the remaining service life of the pipeline to be measured.

[0117] Still taking the data in Table 1 and Table 2 above as an example, if the remaining corrosion depth value is 0.5 cm, substitute 0.5 into the pipeline remaining life prediction model formula, and calculate to get t = 114.3770 months, that is, 9.5314 years.

[0118] It should be noted that the pipeline remaining life prediction model is constructed by using the aforementioned pipeline remaining life prediction model construction method. The corrosion pit depth value used in the construction process is the corrosion pit depth value of the pipeline to be measured under different adoption cycles. In fact, for buried pipelines in the actual environment, the corrosion pit depth value can be obtained and recorded in real time through relevant equipment.

[0119] Based on the same inventive concept, an embodiment of the present invention further provides a device for constructing a pipeline remaining life prediction model. The structure of the device is as Figure 8 shown, including:

[0120] The first acquisition module 81 is used to acquire a set of corrosion pit depth values of the target pipeline for multiple sampling cycles. The set of corrosion pit depth values includes a preset number of corrosion pit depth values selected in descending order of the corrosion pit depth value, starting from the largest;

[0121] The first determination module 82 is used to determine, for each sampling cycle, a corrosion regression model corresponding to the sampling cycle according to the set of corrosion pit depth values of the sampling cycle. The corrosion regression model characterizes the relationship between the corrosion pit depth value and the corresponding distribution probability;

[0122] The fitting module 83 is used to fit the corrosion regression models corresponding to each sampling cycle to obtain a pipeline remaining life prediction model.

[0123] Regarding the device for constructing a pipeline remaining life prediction model in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0124] Based on the same inventive concept, an embodiment of the present invention further provides a device for predicting the remaining life of a pipeline. The structure of the device is as Figure 9 shown, including:

[0125] The second acquisition module 91 is used to acquire the current wall thickness and the original wall thickness of the pipeline to be measured;

[0126] A second determination module 92, configured to determine a remaining corrosion depth value of a pipeline to be measured according to a current pipe wall thickness and an original pipe wall thickness;

[0127] A solution module 93, configured to substitute the remaining corrosion depth value into a pipeline remaining life prediction model for solution to obtain a remaining service life of the pipeline to be measured;

[0128] The pipeline remaining life prediction model is constructed by using the corrosion pit depth value of the pipeline to be measured and the construction method of the foregoing pipeline remaining life prediction model.

[0129] Regarding the prediction device for the remaining life of the pipeline in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0130] Unless otherwise specifically stated, terms such as processing, calculating, computing, determining, displaying, etc. may refer to actions and / or processes of one or more processing or computing systems, or similar devices, and the actions and / or processes will represent data operations and conversions of physical (such as electronic) quantities in registers or memories of the processing system into other data similarly represented as physical quantities in memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different technologies and methods. For example, the data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0131] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy.

[0132] In the above detailed description, various features are combined in a single embodiment to simplify the present disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly recited in each claim. On the contrary, as reflected in the appended claims, the present invention resides in less than all of the features of a single disclosed embodiment. Accordingly, the appended claims are hereby expressly incorporated into the detailed description, where each claim stands alone as a separate preferred embodiment of the present invention.

[0133] Those skilled in the art should also understand that all the illustrative logical blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the above-described various illustrative components, blocks, modules, circuits, and steps have been generally described in terms of their functions. Whether such a function is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Skilled technicians can implement the described functions in a flexible manner for each specific application, but such implementation decisions should not be construed as departing from the scope of protection of the present disclosure.

[0134] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software modules can be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. The ASIC can be located in a user terminal. Of course, the processor and the storage medium can also exist as discrete components in the user terminal.

[0135] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that execute the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented inside the processor or outside the processor. In the latter case, it is communicatively coupled to the processor by various means, which are well-known in the art.

[0136] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but those of ordinary skill in the art should recognize that the various embodiments can be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the manner in which this term is encompassed is similar to the term "including," as interpreted when "including" is used as a transitional word in the claims. In addition, any use of the term "or" in the claims or the specification is intended to mean "non-exclusive or."

Claims

1. A method for constructing a prediction model of the remaining life of a pipeline, characterized in that Comprising: Obtaining a set of corrosion pit depth values for a plurality of sampling periods of a target pipeline, the set of corrosion pit depth values including a preset number of corrosion pit depth values selected starting from the largest in the order of the corrosion pit depth values; For each sampling period, determining a corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period; the corrosion regression model represents the relationship between the corrosion pit depth value and the corresponding distribution probability; Fitting the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model.

2. The method according to claim 1, wherein The determining the corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period includes: Calculating the distribution probability corresponding to each corrosion pit depth value in the set of corrosion pit depth values of the sampling period; Determining the corrosion regression model corresponding to the sampling period according to each corrosion pit depth value and the corresponding distribution probability.

3. The method according to claim 2, wherein Calculating the distribution probability corresponding to each corrosion pit depth value in the set of corrosion pit depth values of the sampling period: Calculating the distribution probability corresponding to the corrosion pit depth value through the following formula: In the above formula, Si is the i-th corrosion pit depth value, and M is the number of elements in the set of corrosion pit depth values.

4. The method according to claim 3, characterized in that, The determining the corrosion regression model corresponding to the sampling period according to each corrosion pit depth value and the corresponding distribution probability includes: Substituting each corrosion pit depth value and the corresponding distribution probability into a preset expression for solution to obtain the parameter values of the expression, and determining the corrosion regression model corresponding to the sampling period according to the parameter values of the expression.

5. The method according to claim 4, wherein The expression is: In the above formula, S i represents the depth value of the i-th corrosion pit, P represents the distribution probability corresponding to the depth value of the i-th corrosion pit, and a and b represent parameters.

6. The method according to claim 5, characterized in that The fitting the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model includes: According to a preset probability threshold, respectively solving the corrosion regression models corresponding to each sampling period to obtain the maximum corrosion pit depth value under each sampling period; Determining a pipeline remaining life prediction model according to each sampling period and the corresponding maximum corrosion pit depth value.

7. A method for predicting the remaining life of a pipeline, characterized in that, Comprising: Obtaining the current wall thickness and the original wall thickness of the target pipeline; Determining the remaining corrosion depth value of the target pipeline according to the current wall thickness and the original wall thickness; Substituting the remaining corrosion depth value into the pipeline remaining life prediction model for solution to obtain the remaining service life of the target pipeline; The pipeline remaining life prediction model is constructed by using the corrosion pit depth values of the target pipeline and adopting the construction method of the pipeline remaining life prediction model according to any one of claims 1-6.

8. An apparatus for constructing a prediction model of the remaining life of a pipeline, characterized in that Comprising: A first obtaining module, configured to obtain a set of corrosion pit depth values for a plurality of sampling periods of a target pipeline, the set of corrosion pit depth values including a preset number of corrosion pit depth values selected starting from the largest in the order of the corrosion pit depth values; A first determining module, configured to, for each sampling period, determine a corrosion regression model corresponding to the sampling period according to the set of corrosion pit depth values of the sampling period; the corrosion regression model represents the relationship between the corrosion pit depth value and the corresponding distribution probability; A fitting module, configured to fit the corrosion regression models corresponding to each sampling period to obtain a pipeline remaining life prediction model.

9. A prediction device for the remaining life of a pipeline, characterized in that, Comprising: A second acquisition module, configured to acquire the current wall thickness and the original wall thickness of the target pipeline; A second determination module, configured to determine the remaining corrosion depth value of the target pipeline according to the current wall thickness and the original wall thickness; A solution module, configured to substitute the remaining corrosion depth value into a pipeline remaining life prediction model for solution to obtain the remaining service life of the target pipeline; The pipeline remaining life prediction model is constructed by using the corrosion pit depth value of the target pipeline and adopting the construction method of the pipeline remaining life prediction model according to any one of claims 1-6.

10. A computer storage medium, characterized in that, The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the construction method of the pipeline remaining life prediction model according to any one of claims 1-6 or the prediction method of the pipeline remaining life according to claim 7 is realized.

11. A terminal device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the construction method of the pipeline remaining life prediction model according to any one of claims 1-6 or the prediction method of the pipeline remaining life according to claim 7 is realized.

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