Oil and gas pipeline abnormity diagnosis method, device and equipment and storage medium

Abstract

The invention discloses an oil and gas pipeline abnormity diagnosis method, device and equipment and a storage medium, and the method comprises the steps: presetting the types of corrosion characteristics of an oil and gas pipeline and a characteristic value calculation rule of the characteristic values of all types of corrosion characteristics; presetting the level number of abnormal levels and a grading rule of the characteristic value of each corrosion characteristic in a single abnormal level; presetting a weight value of each corrosion feature; generating a reference feature standard cloud of each corrosion feature; generating a reference virtual cloud corresponding to the abnormal level; calculating a current characteristic value corresponding to each corrosion characteristic according to the current corrosion data, and generating a current characteristic standard cloud; generating a current virtual cloud from all the current feature standard clouds through a preset weighting algorithm; determining the current abnormal level of the oil and gas pipeline by calculating the maximum similarity between the current virtual cloud and each reference virtual cloud; according to the method, the current overall abnormal degree of the pipeline can be accurately evaluated, so that an effective technical support can be provided for intelligent diagnosis and early warning of the pipeline.

Description

Technical Field

[0001] The present invention relates to the field of pipeline safety technology, and in particular to an oil and gas pipeline abnormality diagnosis method, device, equipment and storage medium. Background Art

[0002] In the oil industry, pipeline transportation is the most economical and reasonable, and it is also the main mode of oil and gas transportation in my country. During the long-term operation of pipelines, internal and external corrosion will lead to pipeline failure, causing huge economic losses and seriously affecting people's production and life.

[0003] At present, the diagnosis and analysis of corrosion data of oil and gas pipelines plays a very important role in the field of pipeline safety. It has become an important tool for summarizing, summarizing and predicting to eliminate pipeline safety hazards and ensure the safe and smooth operation of pipeline networks.

[0004] With the rise of intelligent cathodic protection and internal and external pipeline corrosion monitoring technologies in the field of oil and gas pipelines, the collection of oil and gas pipeline corrosion data has become more abundant and diverse.

[0005] The existing intelligent cathodic protection and pipeline internal and external corrosion monitoring technologies obtain the required data according to a variety of different detection purposes, thereby conducting their own evaluations on the working conditions of oil and gas pipelines in multiple different dimensions.

[0006] The inventors have found through research that the pipeline abnormality diagnosis and analysis method in the prior art still has at least the following defects:

[0007] It is not possible to accurately assess the overall degree of abnormality in the pipeline.

[0008] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0009] The purpose of the present invention is to accurately evaluate the overall abnormality of a pipeline.

[0010] The present invention provides a method for diagnosing abnormalities in oil and gas pipelines, comprising the steps of:

[0011] S11. Preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0012] S12, presetting the number of abnormal levels, and setting the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level;

[0013] S13, presetting weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion;

[0014] S14, generating reference feature standard clouds of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature values ​​of the corrosion features; and fusing all reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal levels respectively;

[0015] S15, obtaining current corrosion data of the oil and gas pipeline, and calculating current characteristic values ​​corresponding to each of the corrosion characteristics according to the characteristic data calculation rules;

[0016] S16, generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and fusing all the current characteristic standard clouds through the preset weighting algorithm to generate a current virtual cloud;

[0017] S17, respectively calculating the similarity between the current virtual cloud and each of the reference virtual clouds, and determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0018] Preferably, in the present invention, the grading rules for setting the characteristic values ​​of each type of corrosion characteristics within a single abnormal level include:

[0019] Threshold ranges or level definition rules are determined for characteristic values ​​of each type of corrosion characteristic within a single anomaly level.

[0020] Preferably, in the present invention, the preset weight values ​​corresponding to the various types of corrosion characteristics include the steps of:

[0021] S131. Construct a weight judgment matrix according to the analytic hierarchy process;

[0022] S132, judging the importance of each type of corrosion feature according to the 9-scale method, and calculating the weight value of each type of corrosion feature by the following formula;

[0023]

[0024] Among them, w i is the weight value of the i-th corrosion feature, a ij is the comparison between the i-th corrosion feature and the j-th corrosion feature;

[0025] S133, verifying the consistency of the weight values ​​of the various corrosion characteristics by the following formula;

[0026]

[0027] Among them, |εij | can be expressed as: When ε<0.1, the consistency meets the requirements.

[0028] Preferably, in the present invention, the generation of reference characteristic standard clouds of each of the corrosion characteristics by a forward cloud generator comprises the steps of:

[0029] S141, determining a cloud model including digital features of expected Ex, entropy En, excess entropy He and number of cloud droplets N;

[0030] S142, generating a normally distributed random number En' with entropy En as expectation and super entropy He as standard deviation;

[0031] S143, generate a normal random number x with Ex as expectation and En' as standard deviation n ,in,

[0032] (n=1,2,……,N);

[0033] S144, x n As a cloud droplet, determine x n The corresponding membership degree y n ,

[0034] S145, through (x n ,y n ) Determine the conversion between cloud droplets and corresponding membership qualitatively and quantitatively;

[0035] S146. Repeat the above steps S141-S145 to generate N cloud droplets.

[0036] Preferably, in the present invention, the step of fusing all reference feature standard clouds in the same abnormal level by using an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal level respectively includes:

[0037] The reference virtual cloud is generated by calculation using the following formula:

[0038]

[0039]

[0040]

[0041] Where m is the number of corrosion features; Ex1, Ex2, …Ex m is the expectation of each reference feature standard cloud; En1, En2, …En m is the entropy of each reference characteristic standard cloud; He1, He2, ...He m is the super entropy of each reference characteristic standard cloud.

[0042] Preferably, in the present invention, the calculating the similarity between the current virtual cloud and each of the reference virtual clouds comprises the steps of:

[0043] S171. Generate an E in the current virtual cloud n For expectation, H e is a normal random number with standard deviation E nn ;

[0044] S172, generate E in the current virtual cloud x As expected, E nn A normal random number with standard deviation x n ;

[0045] S173, the normal random number x n Substitute the reference virtual cloud C corresponding to the abnormal level K k In the equation, we can calculate Among them, μ n is the similarity of a single point cloud;

[0046] S174, by repeating steps S172 and S173, generate N μ n , and calculate the similarity between the current virtual cloud and each of the reference virtual clouds:

[0047] Preferably, in the present invention, determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline includes:

[0048] According to the current virtual cloud similarity δ k , determining the reference virtual cloud having the highest similarity to the current virtual cloud; and determining the abnormality level corresponding to the reference virtual cloud as the current abnormality level of the oil and gas pipeline.

[0049] Preferably, in the present invention, the corrosion characteristics of the grading rules by setting the threshold range as the characteristic value include:

[0050] The degree of AC interference, soil corrosivity and the condition of the anti-corrosion layer.

[0051] Preferably, in the present invention, the corrosion characteristics of the grading rules of the characteristic values ​​by setting the grade definition rules include:

[0052] Cathodic protection effect and pipe corrosion condition.

[0053] Preferably, in the present invention, the calculation rule of the characteristic value R of soil corrosivity includes:

[0054] R=R1+R2+R3+R4+R5+R6+R7+R8;

[0055] Among them, R1 is the soil natural corrosion potential score, R2 is the soil resistivity score, R3 is the soil moisture score, R4 is the soil pH score, R5 is the soil redox potential score, R6 is the soil geology score, R7 is the soil salt content score, and R8 is the soil Cl - Content score.

[0056] In another aspect of the present invention, there is also provided an oil and gas pipeline abnormality diagnosis device, comprising:

[0057] A corrosion feature setting unit is used to preset the categories of corrosion features of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion features; the categories of corrosion features include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0058] An abnormality level setting unit, used to preset the number of abnormality levels and set the grading rules of the characteristic values ​​of each type of corrosion characteristics within a single abnormality level;

[0059] A weight value setting unit, used to preset weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion;

[0060] A reference virtual cloud generation unit is used to generate a reference feature standard cloud of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature value of the corrosion feature; and to fuse all the reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal level respectively;

[0061] A current characteristic value calculation unit, used to obtain current corrosion data of the oil and gas pipeline, and calculate the current characteristic value corresponding to each of the corrosion characteristics according to the characteristic data calculation rules;

[0062] A current virtual cloud generation unit is used to generate a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and to fuse all the current characteristic standard clouds through the preset weighting algorithm to generate a current virtual cloud;

[0063] The abnormality level determination unit is used to respectively calculate the similarity between the current virtual cloud and each reference virtual cloud, and determine the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0064] On the other hand, an oil and gas pipeline abnormality diagnosis method and device is provided. The oil and gas pipeline abnormality diagnosis device includes a computer program stored on a medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the methods described in the above aspects and achieves the same technical effects.

[0065] In another aspect of the embodiment of the present invention, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, each step of the oil and gas pipeline abnormality diagnosis method as described in any one of the above items is implemented.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention provides an oil and gas pipeline abnormality diagnosis method, which makes full use of pipeline corrosion data generated during the operation of the oil and gas pipeline, presets the categories of corrosion characteristics of the oil and gas pipeline, the characteristic value calculation rules of the characteristic values ​​of the characteristic values ​​of various corrosion characteristics, presets the number of levels of abnormal levels and the grading rules of the characteristic values ​​of various corrosion characteristics in a single abnormal level; generates a reference characteristic standard cloud of each corrosion characteristic through a forward cloud generator according to the grading characteristics of the characteristic values ​​of the corrosion characteristics; then, all reference characteristic standard clouds in the same abnormal level are fused through an addition and averaging algorithm to generate a reference virtual cloud corresponding to the abnormal level; when performing diagnosis, after obtaining the current corrosion data of the oil and gas pipeline, the present invention calculates the current characteristic values ​​corresponding to each corrosion characteristic through a preset characteristic data calculation rule, and generates a current virtual cloud according to a reference virtual cloud generation method; then, respectively calculates the similarity between the current virtual cloud and each reference virtual cloud, and determines the abnormal level of the reference virtual cloud with the highest similarity as the current abnormal level of the oil and gas pipeline, thereby realizing dynamic comprehensive diagnosis of the abnormal level of the oil and gas pipeline.

[0068] Since the reference virtual cloud generated by the present invention not only makes full use of pipeline corrosion data, but also integrates the potential relationship between various corrosion characteristics, the reference virtual cloud can accurately evaluate the current overall abnormality of the pipeline, thereby providing effective technical support for pipeline intelligent diagnosis and early warning.

[0069] In addition, the present invention also takes into account the different times for acquiring data through intelligent monitoring, internal detection, external detection, etc., and can support dynamic diagnosis of changes in single data or multiple data to achieve flexible configuration.

[0070] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, and to make the above and other purposes, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only 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.

[0072] Figure 1 is a step diagram of the oil and gas pipeline abnormality diagnosis method of the present invention;

[0073] Figure 2 is a schematic diagram of the standard cloud described in the present invention;

[0074] Figure 3 is a schematic diagram of the reference virtual cloud described in the present invention;

[0075] Figure 4 is a schematic diagram of the positional relationship of each virtual cloud in the present invention;

[0076] Figure 5 It is a structural schematic diagram of the oil and gas pipeline abnormality diagnosis device of the present invention;

[0077] Figure 6 It is a structural schematic diagram of the oil and gas pipeline abnormality diagnosis equipment described in the present invention. DETAILED DESCRIPTION

[0078] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0079] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0080] In this document, the terms "first", "second", etc. are used to distinguish two different elements or parts, and are not used to limit a specific position or relative relationship. In other words, in some embodiments, the terms "first", "second", etc. can also be interchangeable.

[0081] Embodiment 1

[0082] In order to improve the utilization rate of pipeline corrosion data and provide strong technical support for pipeline abnormality diagnosis, such as Figure 1 As shown, in an embodiment of the present invention, a method for diagnosing abnormalities in an oil and gas pipeline is provided, comprising the steps of:

[0083] S11. Preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0084] In the embodiment of the present invention, the categories of corrosion characteristics of the oil and gas pipelines are extracted by summarizing the various corrosion data of the oil and gas pipelines; the various corrosion data acquisition methods of the oil and gas pipelines include intelligent monitoring, internal detection and external detection, etc. Among them, the corrosion data collected by intelligent monitoring include power-off potential, AC interference voltage, soil natural corrosion potential, soil resistivity, soil moisture content and soil pH value, etc.; the corrosion data collected by internal detection include defect quantification size; the corrosion data collected by external detection include anti-corrosion layer resistivity, soil redox potential, soil geology, soil salt content and soil Cl- content, etc.

[0085] In the embodiment of the present invention, the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics include the setting of characteristic value calculation rules of cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition, wherein:

[0086] The calculation rule of the characteristic value of the cathodic protection effect is set by the power-off potential. The cathodic protection potential E without IR drop IRfree The following formula cathodic protection rules should be met:

[0087] E a ≤E IRfree ≤E b

[0088] Among them, Ea is the limiting critical potential, which should usually be no less than -1.20V (vs. CSE); Eb is the minimum protection potential when the metal corrosion rate is less than 0.01mm / a, which must reach at least -0.85V (vs. CSE).

[0089] The calculation rules for the characteristic value of the AC interference degree are set by the AC interference voltage. When the AC interference voltage at any point on the pipeline is lower than 4V, no AC interference protection measures need to be taken. When it is higher than this value, the AC density should be used for evaluation and classification. The AC density can be calculated according to the following formula:

[0090]

[0091] Wherein, JAC is the evaluated AC current density, in amperes per square meter (A / m2), V is the average value of the effective value of the AC interference voltage, in volts (V), ρ is the soil resistivity, in ohms (Ω·m), and d is the diameter of the damage point, which is usually taken as 0.0113 based on the most serious AC corrosion, in meters (m).

[0092] The calculation rules of characteristic values ​​of soil corrosivity are based on natural corrosion potential, soil resistivity, soil moisture content, soil pH value, soil redox potential, soil geology, soil salt content, soil Cl - The soil corrosivity characteristic value R is calculated by the following formula:

[0093] R=R1+R2+R3+R4+R5+R6+R7+R8

[0094] Among them, R1 is the soil natural corrosion potential score, R2 is the soil resistivity score, R3 is the soil moisture score, R4 is the soil pH score, R5 is the soil redox potential score, R6 is the soil geology score, R7 is the soil salt content score, and R8 is the soil Cl - Content score, the scores of various soil parameters are shown in Table 1:

[0095] Table 1:

[0096]

[0097]

[0098]

[0099] For the calculated characteristic value R of soil corrosivity, the corresponding relationship between the characteristic value and the abnormal level can be determined according to the set threshold range delineation method.

[0100] The characteristic value calculation rules for the pipe corrosion condition can be set according to the pipeline defect applicability evaluation, and the pipeline defects can be divided into four levels according to their severity: immediate response, planned response, monitoring and use, and no defects.

[0101] The characteristic value calculation rule of the anti-corrosion layer condition is set by the anti-corrosion layer resistivity, where the unit of the anti-corrosion layer resistivity is expressed in kΩ·m 2 .

[0102] S12, presetting the number of abnormal levels, and setting the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level;

[0103] Preferably, the abnormality level in the embodiment of the present invention may include four levels, namely, level I (very urgent), level II (urgent), level III (relatively urgent) and level IV (general);

[0104] Within a certain abnormal level, it is also necessary to further set the grading rules for the characteristic values ​​of each corrosion feature, that is, to determine the conditions that should be met when the characteristic value of a certain corrosion feature belongs to this abnormal level.

[0105] In the embodiment of the present invention, the grading rules of the characteristic values ​​of the Yin-Yang protection effect, that is, the corresponding relationship between the evaluation result of the Yin-Yang protection effect and the abnormal level can be shown in Table 2;

[0106] Table 2: Rating rules for characteristic values ​​of the cathodic protection effect

[0107]

[0108] In the embodiment of the present invention, the grading rule of the characteristic value of the AC interference degree, that is, the corresponding relationship between the calculation result of the AC interference degree and the abnormal level can be shown in Table 3;

[0109] Table 3: Classification rules for characteristic values ​​of AC interference level JAC

[0110] Corrosion characteristics Level IV Grade III Level II Level I AC interference level JAC<30 30≤JAC<70 70≤JAC<100 JAC≥100

[0111] In the embodiment of the present invention, for the characteristic value R of the calculated soil corrosivity, the corresponding relationship between the characteristic value R and the abnormal level can be determined according to the threshold range definition method in the following table (Table 4).

[0112] Table 4: Rating rules for characteristic values ​​of soil corrosivity R

[0113] Corrosion characteristics Level IV Grade III Level II Level I Soil corrosivity 0≤R≤5 5＜R≤11 11＜R≤19 19＜R≤32

[0114] In the embodiment of the present invention, the correspondence between the four levels of pipe body corrosion conditions and the abnormal level can be shown in Table 5;

[0115] Table 5: Rating rules for characteristic values ​​of pipe corrosion conditions

[0116] Corrosion characteristics Level IV Grade III Level II Level I Pipe corrosion condition No defects Monitoring use Plan Response Immediate response

[0117] In the embodiment of the present invention, the range of the characteristic value of the anti-corrosion layer condition and the corresponding relationship between the abnormal level can be shown in Table 6;

[0118] Table 6: Rating rules for characteristic values ​​of corrosion protection layer condition

[0119]

[0120]

[0121] Specifically, in an embodiment of the present invention, the grading rules for the characteristic values ​​of various types of corrosion characteristics within a single abnormal level are set, including: determining the threshold range or level definition rules for the characteristic values ​​of various types of corrosion characteristics within a single abnormal level; wherein the characteristic value of the cathodic protection effect and the characteristic value of the pipe body corrosion condition can be set as the level definition rule, and the characteristic value of the AC interference degree, the characteristic value of the soil corrosiveness, and the characteristic value of the anti-corrosion layer condition can be set as the threshold range. Referring to Tables 2 to 6, after determining the corresponding relationship between the characteristic value of each pipeline corrosion characteristic and the abnormal level, the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level can be obtained, and a table combining the abnormal level and characteristic value grading rules of various types of corrosion characteristics can be obtained, as shown in Table 7:

[0122] Table 7:

[0123]

[0124] In practical applications, the abnormal level of each type of corrosion feature can be determined through the above table according to the characteristic value of each corrosion feature; if the characteristic value of the cathodic protection effect E IRfree is -1.12V (vs.CSE), which fully complies with the cathodic protection criteria, and the abnormal level of this corrosion feature is level IV; if the AC interference level is J AC If the value is 45, the abnormal level of AC interference is Level III; if the severity of pipe body defects is the planned response, the level of pipe body corrosion condition is Level II; if the resistivity of the anti-corrosion layer is 82, the level of the anti-corrosion layer condition is Level III.

[0125] S13, presetting weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion;

[0126] In an embodiment of the present invention, the weight values ​​corresponding to various corrosion features are set according to the influence of each corrosion feature on pipeline corrosion, including the steps of:

[0127] S131. Construct a weight judgment matrix according to the analytic hierarchy process;

[0128] In the embodiment of the present invention, when constructing the weight judgment matrix according to the hierarchical analysis method, it is assumed that m corrosion features have relative importance to pipeline corrosion, and the i-th corrosion feature is compared with other corrosion features, and its relative importance is a ij ; In this way, an m-order matrix can be constructed to solve the priority weight of each corrosion feature. This matrix is ​​called a weight analysis judgment matrix, also called a judgment matrix, denoted by A = (a ij )m×m, it can also be represented by an array of m rows and m columns; the weight analysis judgment matrix A=(a ij )m×m can be specifically expressed as follows:

[0129]

[0130] S132, judging the importance of each type of corrosion feature according to the 9-scale method, and calculating the weight value of each type of corrosion feature by the following formula;

[0131]

[0132] Among them, w i is the weight value of the i-th corrosion feature, a ij Compare the i-th corrosion feature with the j-th corrosion feature.

[0133] The 9-scale method is used to judge the importance of various corrosion characteristics. If the scale is 1, it means that the two corrosion characteristics are equally important; if the scale is 1.3161, it means that compared with the two corrosion characteristics, one corrosion characteristic is slightly more important than the other; if the scale is 1.7321, it means that compared with the two corrosion characteristics, one corrosion characteristic is more important than the other; if the scale is 3, it means that compared with the two corrosion characteristics, one corrosion characteristic is obviously more important than the other; if the scale is 5.1966, it means that compared with the two corrosion characteristics, one corrosion characteristic is strongly more important than the other; if the scale is 9, it means that compared with the two corrosion characteristics, one corrosion characteristic is extremely important than the other.

[0134] S133, verifying the consistency of the weight values ​​of the various corrosion characteristics by the following formula;

[0135]

[0136] Among them, |ε ij | can be expressed as: When ε<0.1, the consistency meets the requirements.

[0137] In the embodiment of the present invention, the relative importance of each corrosion feature can be obtained by the hierarchical analysis method and the 9-scale method. However, the hierarchical analysis method allows independent individuals to make separate judgments. At this time, the consistency of the matrix composed of corrosion features cannot be guaranteed; therefore, a consistency check must be performed to ensure that the results are consistent with the actual situation.

[0138] In addition, if the judgment of corrosion feature i compared with corrosion feature j is a ij , then the judgment a of the comparison between corrosion feature j and corrosion feature i is ji =1 / a ij , the weight value of each corrosion feature i can be determined by the calculation formula in step S132.

[0139] S14, generating a reference feature standard cloud of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature values ​​of the corrosion features; and generating a reference virtual cloud corresponding to the abnormal level by adding and averaging all the reference feature standard clouds of the same abnormal level through an algorithm of adding and averaging;

[0140] In the embodiment of the present invention, the grading rule includes the threshold range or level definition rule of the characteristic value of the corrosion feature; referring to Table 7, in each abnormal level (level IV, level III, level II and level I), there are corresponding threshold ranges or level definition rules of the characteristic values ​​of 5 kinds of corrosion features in the level; thus, for each abnormal level, 5 reference characteristic standard clouds can be generated; then, by fusing all the reference characteristic standard clouds in the same abnormal level through the addition and averaging algorithm, a reference virtual cloud is generated; in the embodiment of the present invention, the abnormal level is set to 4 levels, and 4 reference virtual clouds will be generated. Based on the grading rule, the grading characteristics in the embodiment of the present invention include the level number corresponding to the corrosion feature, and the upper and lower limits of the threshold range of the characteristic value in a certain abnormal level, or the grading definition determined according to the level definition rule (such as no defect, monitoring use, planned response or immediate response, etc.).

[0141] refer to Figure 2 In a specific example, the reference characteristic standard cloud in the embodiment of the present invention may include four digital features, namely, expected Ex, entropy En, super entropy He and cloud droplet number N, wherein expected Ex, entropy En and super entropy He may have upper and lower limits, namely [L max , L min ], the three digital characteristic parameters are calculated according to the following formula:

[0142]

[0143] In the above formula, z is a constant, which can be selected according to the fuzzy threshold of the variable, and the value can be set to 0.01, 0.02 or 1.00.

[0144] After determining the three digital features of the reference characteristic standard cloud, Ex, entropy En and super entropy He, and also determining the required number of cloud droplets N, the forward cloud generator will generate corresponding cloud droplets according to the inference rules. By inputting the digital features of the expected Ex, entropy En and super entropy He, the quantitative positions of all cloud droplets in the domain space and the membership degree y represented by any cloud droplet are obtained. n .

[0145] Furthermore, in an embodiment of the present invention, the specific steps of generating a reference feature standard cloud by a forward cloud generator may be as follows:

[0146] S141, determining a cloud model including digital features of expected Ex, entropy En, excess entropy He and number of cloud droplets N;

[0147] S142, generating a normally distributed random number En' with entropy En as expectation and super entropy He as standard deviation;

[0148] S143, generate a normal random number x with Ex as expectation and En' as standard deviation n ,in,

[0149] (n=1,2,……,N);

[0150] S144, x n As a cloud droplet, determine x n The corresponding membership degree y n ,

[0151] S145, through (x n ,y n ) Determine the conversion between cloud droplets and corresponding membership qualitatively and quantitatively;

[0152] S146. Repeat the above steps S141-S145 to generate N cloud droplets.

[0153] Further, in the embodiment of the present invention, the step of fusing all reference feature standard clouds in the same abnormal level through an addition and averaging algorithm to generate a reference virtual cloud corresponding to the abnormal level may specifically include:

[0154] The reference virtual cloud is generated by calculation using the following formula:

[0155]

[0156]

[0157]

[0158] Where m is the number of corrosion features; Ex1, Ex2, …Ex m is the expectation of each reference feature standard cloud; En1, En2, …En m is the entropy of each reference characteristic standard cloud; He1, He2, ...He m is the super entropy of each reference characteristic standard cloud.

[0159] In a specific example, four reference virtual clouds corresponding to four abnormal levels can be obtained as follows: Figure 3 shown.

[0160] S15, obtaining current corrosion data of the oil and gas pipeline, and calculating current characteristic values ​​corresponding to each of the corrosion characteristics according to the characteristic data calculation rules;

[0161] In the embodiment of the present invention, the current corrosion data of the oil and gas pipeline acquired in real time is used to calculate the current characteristic value of each corrosion characteristic according to the set corrosion characteristic category and characteristic value calculation rule of each corrosion characteristic.

[0162] S16, generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and generating a current virtual cloud through the preset weighting algorithm for all the current characteristic standard clouds;

[0163] In the embodiment of the present invention, the current feature standard cloud is generated by referring to the generation method of the feature standard cloud; there are five types of corrosion features, so five current feature standard clouds are generated respectively; all the current feature standard clouds are generated into the current virtual cloud through the set corrosion data weighting algorithm.

[0164] According to the abnormal level of the current m (5) pipeline corrosion characteristics, m current characteristic standard clouds C1 (Ex1, En1, He1), C2 (Ex2, En2, He2), ..., C m (Ex m ,En m , He m ), and generate the current virtual cloud C(Ex, En, He) through calculation:

[0165]

[0166]

[0167]

[0168] Where: w1, w2, ..., w m is the pipeline corrosion feature weight value.

[0169] S17, respectively calculating the similarity between the current virtual cloud and each of the reference virtual clouds, and determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0170] In an embodiment of the present invention, calculating the similarity between the current virtual cloud and each of the reference virtual clouds comprises the following steps:

[0171] S171. Generate an E in the current virtual cloud n For expectation, H e is a normal random number with standard deviation E nn ;

[0172] S172, generate E in the current virtual cloud x As expected, E nn A normal random number with standard deviation x n ;

[0173] S173, the normal random number x n Substitute the reference virtual cloud C corresponding to the abnormal level K k In the equation, we can calculate Among them, μ n is the similarity of a single point cloud;

[0174] S174, by repeating steps S172 and S173, generate N μ n , and calculate the similarity between the current virtual cloud and each of the reference virtual clouds:

[0175] Further, in the embodiment of the present invention, determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline includes:

[0176] According to the current virtual cloud similarity δ k , determining the reference virtual cloud having the highest similarity to the current virtual cloud; and determining the abnormality level corresponding to the reference virtual cloud as the current abnormality level of the oil and gas pipeline.

[0177] In the embodiment of the present invention, the generated current virtual cloud is assumed to be C(4.30, 0.4167, 0.125), and the generated current virtual cloud is compared with the reference virtual cloud C k (Ex k ,En k , He k ) Similarity δ k The calculation shows that the similarity δ3 with the reference virtual cloud C3 is the largest, so the current abnormality level of the oil and gas pipeline is level III.

[0178] Intuitive, reference Figure 4 , Figure 4 is Figure 3 On the basis of the four reference virtual clouds, the schematic diagram of the position relationship of each virtual cloud after the current virtual cloud is generated is further shown; it can be seen from the figure that the current virtual cloud is closest to the reference virtual cloud of level III; the position relationship of the virtual cloud can characterize the similarity, so the current abnormal level of the oil and gas pipeline can be determined as level III. The following is an explanation of the embodiment of the present invention in conjunction with a specific application scenario:

[0179] Firstly, the corrosion data of oil and gas pipelines are obtained through intelligent monitoring, internal detection and external detection. Then, the corrosion data are classified and summarized to extract various corrosion characteristics including cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition. According to the grading characteristics of the characteristic values ​​of the corrosion characteristics, the reference characteristic standard cloud of each corrosion characteristic can be generated through the forward cloud generator; and all the reference characteristic standard clouds in the same abnormal level are generated into a reference virtual cloud corresponding to the abnormal level through a preset weighted algorithm.

[0180] According to the preset characteristic value calculation rules of various corrosion characteristics, the current characteristic values ​​corresponding to various corrosion characteristics are obtained; then, according to each current characteristic value, the current characteristic standard cloud is generated by the forward cloud generator, and all current characteristic standard clouds are used to generate the current virtual cloud through the preset weighted algorithm, and the similarity between the current virtual cloud and each reference virtual cloud is calculated respectively, and the abnormal level of the reference virtual cloud with the highest similarity is determined as the current abnormal level of the oil and gas pipeline. Suppose the current virtual cloud C (4.30, 0.4167, 0.125), the reference virtual clouds C1 (1.25, 0.4167, 0.125), C2 (3.75, 0.4167, 0.125), C3 (3.75, 0.4167, 0.125), C4 (6.25, 0.4167, 0.125), by respectively calculating the similarity δ between the current virtual cloud C and each reference virtual cloud C1, C2, C3, C4 k , the similarity δ3 with the reference virtual cloud C3 is the largest, therefore, the abnormality level of the reference virtual cloud C3 is determined as the current abnormality level of the oil and gas pipeline.

[0181] In summary, the embodiment of the present invention provides an oil and gas pipeline abnormality diagnosis method, which makes full use of the pipeline corrosion data generated during the operation of the oil and gas pipeline, presets the categories of the corrosion characteristics of the oil and gas pipeline, the characteristic value calculation rules of the characteristic values ​​of the characteristic values ​​of each type of corrosion characteristics, presets the number of levels of abnormal levels and the grading rules of the characteristic values ​​of each type of the corrosion characteristics within a single abnormal level; generates a reference characteristic standard cloud of each corrosion characteristic through a forward cloud generator according to the grading characteristics of the characteristic values ​​of the corrosion characteristics; then, all the reference characteristic standard clouds in the same abnormal level are merged by adding and averaging to generate a reference virtual cloud corresponding to the abnormal level; when performing diagnosis, after obtaining the current corrosion data of the oil and gas pipeline in the embodiment of the present invention, the current characteristic values ​​corresponding to each corrosion characteristic are calculated by the preset characteristic data calculation rules, and the current virtual cloud is generated according to the reference virtual cloud generation method; then, the similarity between the current virtual cloud and each reference virtual cloud is calculated respectively, and the abnormal level of the reference virtual cloud with the highest similarity is determined as the current abnormal level of the oil and gas pipeline, thereby realizing dynamic comprehensive diagnosis of the abnormal level of the oil and gas pipeline.

[0182] Since the reference virtual cloud generated by the embodiment of the present invention not only makes full use of the pipeline corrosion data, but also integrates the potential relationship between various corrosion characteristics, the reference virtual cloud can accurately evaluate the current overall abnormality of the pipeline, thereby providing effective technical support for pipeline intelligent diagnosis and early warning.

[0183] In addition, the embodiments of the present invention also take into account that the time for acquiring data by means of intelligent monitoring, internal detection, external detection, etc. is different, and can support the diagnosis of changes in a single data or multiple data to achieve flexible configuration.

[0184] Embodiment 2

[0185] Corresponding to the method embodiment, another aspect of the embodiment of the present invention further provides an oil and gas pipeline abnormality diagnosis device. Figure 5 The schematic diagram of the structure of the oil and gas pipeline abnormality diagnosis device provided by the embodiment of the present invention is shown. Figure 1 The device corresponding to the oil and gas pipeline abnormality diagnosis method described in the corresponding embodiment, that is, implemented by means of a virtual device Figure 1 In the corresponding embodiment of the oil and gas pipeline abnormality diagnosis method, each virtual module constituting the oil and gas pipeline abnormality diagnosis device can be executed by an electronic device, such as a network device, a terminal device or a server. Specifically, the oil and gas pipeline abnormality diagnosis device in the embodiment of the present invention includes:

[0186] The corrosion characteristic setting unit 01 is used to preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0187] The abnormality level setting unit 02 is used to preset the number of abnormality levels and set the grading rules of the characteristic values ​​of each type of corrosion characteristics within a single abnormality level;

[0188] The weight value setting unit 03 is used to preset the weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the influence degree of each corrosion characteristic on pipeline corrosion;

[0189] The reference virtual cloud generation unit 04 is used to generate a reference feature standard cloud of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature value of the corrosion feature; and to fuse all the reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal level respectively;

[0190] The current characteristic value calculation unit 05 is used to obtain the current corrosion data of the oil and gas pipeline, and calculate the current characteristic value corresponding to each corrosion characteristic according to the characteristic data calculation rule;

[0191] The current virtual cloud generation unit 06 is used to generate a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and to fuse all the current characteristic standard clouds through the preset weighting algorithm to generate a current virtual cloud;

[0192] The abnormality level determination unit 07 is used to respectively calculate the similarity between the current virtual cloud and each reference virtual cloud, and determine the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0193] It should be noted that the specific implementation and technical effects of the oil and gas pipeline abnormality diagnosis device in the embodiment of the present invention can be referred to Figure 1 The corresponding oil and gas pipeline abnormality diagnosis method will not be described in detail here.

[0194] Embodiment 3

[0195] Corresponding to the method embodiment, in the embodiment of the present invention, an oil and gas pipeline abnormality diagnosis device, such as a terminal, a server, etc., is also provided. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto.

[0196] An example of a hardware structure block diagram of an oil and gas pipeline abnormality diagnosis device provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, it may include:

[0197] Processor 1, communication interface 2, memory 3 and communication bus 4;

[0198] The processor 1, the communication interface 2, and the memory 3 communicate with each other via the communication bus 4;

[0199] Optionally, the communication interface 2 may be an interface of a communication module, such as an interface of a GSM module;

[0200] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0201] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0202] The processor 1 is specifically used to execute the computer program stored in the memory 3 to perform the following steps:

[0203] S11. Preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0204] S12, presetting the number of abnormal levels, and setting the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level;

[0205] S13, presetting weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion;

[0206] S14, generating reference feature standard clouds of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature values ​​of the corrosion features; and fusing all reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal levels respectively;

[0207] S15, obtaining current corrosion data of the oil and gas pipeline, and calculating current characteristic values ​​corresponding to each of the corrosion characteristics according to the characteristic data calculation rules;

[0208] S16, generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and fusing all the current characteristic standard clouds through the preset weighting algorithm to generate a current virtual cloud;

[0209] S17, respectively calculating the similarity between the current virtual cloud and each of the reference virtual clouds, and determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0210] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the abnormality diagnosis method of oil and gas pipeline provided by the embodiment of the present invention.

[0211] Embodiment 4

[0212] In an embodiment of the present invention, a storage medium is further provided, which may store a program suitable for execution by a processor, wherein the program is used to:

[0213] S11. Preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition;

[0214] S12, presetting the number of abnormal levels, and setting the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level;

[0215] S13, presetting weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion;

[0216] S14, generating reference feature standard clouds of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature values ​​of the corrosion features; and fusing all reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal levels respectively;

[0217] S15, obtaining current corrosion data of the oil and gas pipeline, and calculating current characteristic values ​​corresponding to each of the corrosion characteristics according to the characteristic data calculation rules;

[0218] S16, generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; and fusing all the current characteristic standard clouds through the preset weighting algorithm to generate a current virtual cloud;

[0219] S17, respectively calculating the similarity between the current virtual cloud and each of the reference virtual clouds, and determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

[0220] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0221] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the methods provided by other embodiments of the present invention.

[0222] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0223] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0224] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0225] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0226] It should be understood that in the embodiments of the present application, the various embodiments and features can be combined with each other to solve the aforementioned technical problems.

[0227] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, which is stored in a storage medium and includes several instructions for 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 methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0228] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for diagnosing abnormalities in oil and gas pipelines, characterized in that: Includes steps: S11. Preset the categories of corrosion characteristics of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion characteristics; the categories of corrosion characteristics include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition; S12, presetting the number of abnormal levels, and setting the grading rules for the characteristic values ​​of each type of corrosion characteristics within a single abnormal level; S13, presetting weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion; S14, generating reference feature standard clouds of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature values ​​of the corrosion features; and fusing all reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to each abnormal level respectively; S15, obtaining current corrosion data of the oil and gas pipeline, and calculating current characteristic values ​​corresponding to each of the corrosion characteristics according to the characteristic data calculation rules; S16, generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; And all current characteristic standard clouds are merged through the preset weighting algorithm to generate the current virtual cloud; S17, respectively calculating the similarity between the current virtual cloud and each of the reference virtual clouds, and determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

2. The oil and gas pipeline abnormality diagnosis method according to claim 1, characterized in that: The grading rules for setting the characteristic values ​​of various types of corrosion characteristics within a single abnormal level include: Threshold ranges or level definition rules are determined for characteristic values ​​of each type of corrosion characteristic within a single anomaly level.

3. The oil and gas pipeline abnormality diagnosis method according to claim 1, characterized in that: The preset weight values ​​corresponding to the various corrosion features include the steps of: S131. Construct a weight judgment matrix according to the analytic hierarchy process; S132, judging the importance of each type of corrosion feature according to the 9-scale method, and calculating the weight value of each type of corrosion feature by the following formula; Among them, w i is the weight value of the i-th corrosion feature, a ij is the comparison between the i-th corrosion feature and the j-th corrosion feature; S133, verifying the consistency of the weight values ​​of the various corrosion characteristics by the following formula; Among them, |ε ij | can be expressed as: When ε<0.1, the consistency meets the requirements.

4. The oil and gas pipeline abnormality diagnosis method according to claim 1, characterized in that: The method of generating a reference characteristic standard cloud of each corrosion characteristic by a forward cloud generator comprises the steps of: S141, determining a cloud model including digital features of expected Ex, entropy En, excess entropy He and number of cloud droplets N; S142, generating a normally distributed random number En' with entropy En as expectation and super entropy He as standard deviation; S143, generate a normal random number x with Ex as expectation and En' as standard deviation n , where (n=1,2,……,N); S144, x n As a cloud droplet, determine x n The corresponding membership degree y n , S145, through (x n ,y n ) Determine the conversion between cloud droplets and corresponding membership qualitatively and quantitatively; S146. Repeat the above steps S141-S145 to generate N cloud droplets.

5. The oil and gas pipeline abnormality diagnosis method according to claim 4, characterized in that: The method of fusing all reference feature standard clouds in the same abnormal level by using an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal level respectively includes: The reference virtual cloud is generated by calculation using the following formula: Where m is the number of corrosion features; Ex1, Ex2, …Ex m is the expectation of each reference feature standard cloud; En1, En2, …En m is the entropy of each reference characteristic standard cloud; He1, He2, ...He m is the super entropy of each reference characteristic standard cloud.

6. The oil and gas pipeline abnormality diagnosis method according to claim 1, characterized in that: The calculating of the similarity between the current virtual cloud and each reference virtual cloud comprises the steps of: S171. Generate an E in the current virtual cloud n For expectation, H e is a normal random number with standard deviation E nn ; S172, generate E in the current virtual cloud x As expected, E nn A normal random number with standard deviation x n ; S173, the normal random number x n Substitute the reference virtual cloud C corresponding to the abnormal level K k In the equation, we can calculate Among them, μ n is the similarity of a single point cloud; S174, by repeating steps S172 and S173, generate N μ n , and calculate the similarity between the current virtual cloud and each reference virtual cloud:

7. The oil and gas pipeline abnormality diagnosis method according to claim 6, characterized in that: The step of determining the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline includes: According to the current virtual cloud similarity δ k , determining the reference virtual cloud having the highest similarity to the current virtual cloud; and determining the abnormality level corresponding to the reference virtual cloud as the current abnormality level of the oil and gas pipeline.

8. The oil and gas pipeline abnormality diagnosis method according to claim 2, characterized in that: The corrosion characteristics that are graded as feature values ​​by setting threshold ranges include: The degree of AC interference, soil corrosivity and the condition of the anti-corrosion layer.

9. The oil and gas pipeline abnormality diagnosis method according to claim 8, characterized in that: The corrosion characteristics that are used as the grading rules for characteristic values ​​by setting the grade definition rules include: Cathodic protection effect and pipe corrosion condition.

10. The oil and gas pipeline abnormality diagnosis method according to claim 1, characterized in that: The calculation rules of the characteristic value R of soil corrosivity include: R=R1+R2+R3+R4+R5+R6+R7+R8; Among them, R1 is the soil natural corrosion potential score, R2 is the soil resistivity score, R3 is the soil moisture score, R4 is the soil pH score, R5 is the soil redox potential score, R6 is the soil geology score, R7 is the soil salt content score, and R8 is the soil Cl - Content score.

11. An oil and gas pipeline abnormality diagnosis device, characterized in that: include: A corrosion feature setting unit is used to preset the categories of corrosion features of oil and gas pipelines and the characteristic value calculation rules of the characteristic values ​​of various corrosion features; the categories of corrosion features include cathodic protection effect, AC interference degree, soil corrosivity, pipe body corrosion condition and anti-corrosion layer condition; An abnormality level setting unit, used to preset the number of abnormality levels and set the grading rules of the characteristic values ​​of each type of corrosion characteristics within a single abnormality level; A weight value setting unit, used to preset weight values ​​corresponding to each type of corrosion characteristics; the weight values ​​are determined according to the degree of influence of each corrosion characteristic on pipeline corrosion; A reference virtual cloud generation unit is used to generate a reference feature standard cloud of each of the corrosion features through a forward cloud generator according to the grading characteristics of the feature value of the corrosion feature; and to fuse all the reference feature standard clouds of the same abnormal level through an addition and averaging algorithm to generate reference virtual clouds corresponding to the abnormal level respectively; A current characteristic value calculation unit, used to obtain current corrosion data of the oil and gas pipeline, and calculate the current characteristic value corresponding to each of the corrosion characteristics according to the characteristic data calculation rules; A current virtual cloud generating unit, used for generating a current characteristic standard cloud through a forward cloud generator according to each of the current characteristic values; And all current characteristic standard clouds are merged through the preset weighting algorithm to generate the current virtual cloud; The abnormality level determination unit is used to respectively calculate the similarity between the current virtual cloud and each reference virtual cloud, and determine the abnormality level of the reference virtual cloud with the highest similarity as the current abnormality level of the oil and gas pipeline.

12. An oil and gas pipeline abnormality diagnosis device, characterized in that: include: Memory for storing computer programs; A processor is used to call and execute the computer program to implement the steps of the oil and gas pipeline abnormality diagnosis method as described in any one of claims 1-10.

13. A storage medium, characterized in that: It comprises a software program, wherein the software program is suitable for a processor to execute the steps of the oil and gas pipeline abnormality diagnosis method as claimed in any one of claims 1 to 10.

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