Pipeline identification test method, system and equipment in power station boiler range

Abstract

The invention discloses a pipeline identification test method, system and equipment in a power station boiler range, and relates to the technical field of pipeline identification test, and the method comprises the steps: obtaining an identification test result; constructing a pipeline evaluation index system according to the identification test result; determining an influence factor set and an evaluation grade corresponding to each influence factor according to the pipeline evaluation index system; establishing a fuzzy relation matrix based on the influence factor set and the evaluation level corresponding to each influence factor; determining a weight vector of each influence factor, and determining a fuzzy comprehensive evaluation result vector based on the weight vector of each influence factor and the fuzzy relation matrix; and analyzing the fuzzy comprehensive evaluation result vector to obtain a final evaluation result. According to the method, the safety state of the pipeline can be mastered, major potential safety hazards and weak points of the pipeline system can be found in time, and a scientific and effective theoretical basis is provided for formulating schemes such as overhaul, transformation and replacement.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline identification inspection, and in particular to a pipeline identification inspection method, system and equipment within the scope of a power station boiler. Background Art

[0002] In recent years, accidents have occurred one after another in the pipelines of cogeneration boilers and self-owned power station boilers, causing great losses and social impact. In the follow-up investigation, some regions have successively found the following problems: (1) The design, manufacture, installation, repair and modification, use management, inspection and testing of pipelines within the scope of some boilers have not strictly implemented boiler safety technical specifications and relevant standards; (2) The actual manufacturing unit of pipeline component materials (including pipes and pipe fittings, hereinafter referred to as boiler pipes) within the scope of power station boilers is unknown or there are doubts, the relevant information is incomplete or the authenticity of the information cannot be traced, and some enterprises have actually supplied boiler pipes that do not meet the agreed technical conditions of the order or even counterfeit imported pipe materials. In order to prevent systemic risks and effectively eliminate safety hazards, the State Administration for Market Regulation and the National Energy Administration and other departments have issued documents many times, requiring power companies to entrust qualified inspection and testing institutions to conduct identification inspections.

[0003] However, there is no systematic discussion at home and abroad on how to scientifically and accurately carry out pipeline identification inspections within the scope of power plant boilers. There are only some simple statements in the "Technical Supervision Regulations for Metals in Thermal Power Plants" (DL / T 438-2023), which only include some routine inspection and testing items. The relevant technical fields are blank, and the technical system is in urgent need of establishment, which is difficult to meet the growing urgent needs of boiler users for the investigation of pipeline hidden dangers within the scope of power plant boilers and the accurate identification of the current safety status. Therefore, in order to ensure the safe production of generator sets, a complete and reliable set of identification inspection technologies for the investigation of pipeline safety hazards within the scope of power plant boilers and the determination of the current safety status needs to be established. Summary of the invention

[0004] The purpose of the present invention is to provide a pipeline identification inspection method, system and equipment within the scope of a power station boiler, which can grasp the safety status of the pipeline, promptly discover major safety hazards and weak points of the pipeline system, and provide a scientific and effective theoretical basis for the formulation of maintenance, transformation, replacement and other plans.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] In a first aspect, the present invention provides a pipeline identification inspection method within the scope of a power plant boiler, the pipeline identification inspection method within the scope of a power plant boiler comprising:

[0007] Obtain identification test results; the identification test results include: pipe identification test results, defective weld joint identification test results and piping identification test results.

[0008] According to the identification test results, a pipeline evaluation index system is constructed; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators.

[0009] According to the pipeline evaluation index system, a set of influencing factors and an evaluation level corresponding to each influencing factor are determined.

[0010] A fuzzy relationship matrix is ​​established based on the set of influencing factors and the evaluation level corresponding to each influencing factor.

[0011] The weight vector of each influencing factor is determined, and based on the weight vector of each influencing factor and the fuzzy relationship matrix, a fuzzy comprehensive evaluation result vector is determined.

[0012] The fuzzy comprehensive evaluation result vector is analyzed to obtain a final evaluation result.

[0013] In a second aspect, the present invention provides a pipeline identification inspection system within the scope of a power plant boiler, the pipeline identification inspection system within the scope of a power plant boiler comprising:

[0014] The identification inspection result acquisition module is used to obtain the identification inspection results; the identification inspection results include: pipe identification inspection results, defective welding joint identification inspection results and pipe system identification inspection results.

[0015] The pipeline evaluation index system construction module is used to construct a pipeline evaluation index system according to the identification test results; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators.

[0016] The influencing factor set and evaluation level determination module is used to determine the influencing factor set and the evaluation level corresponding to each influencing factor according to the pipeline evaluation index system.

[0017] The fuzzy relationship matrix establishment module is used to establish a fuzzy relationship matrix based on the influencing factor set and the evaluation level corresponding to each influencing factor.

[0018] The fuzzy comprehensive evaluation result vector determination module is used to determine the weight vector of each influencing factor, and determine the fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix.

[0019] The analysis module is used to analyze the fuzzy comprehensive evaluation result vector to obtain the final evaluation result.

[0020] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0021] The present invention provides a pipeline identification inspection method, system and equipment within the scope of a power plant boiler. First, the identification inspection results are obtained, and a pipeline evaluation index system is constructed according to the identification inspection results; secondly, according to the pipeline evaluation index system, a set of influencing factors and the evaluation level corresponding to each influencing factor are determined; based on the set of influencing factors and the evaluation level corresponding to each influencing factor, a fuzzy relationship matrix is ​​established; then, the weight vector of each influencing factor is determined, and based on the weight vector of each influencing factor and the fuzzy relationship matrix, a fuzzy comprehensive evaluation result vector is determined; finally, the fuzzy comprehensive evaluation result vector is analyzed to obtain the final evaluation result. The present invention establishes a multi-scale comprehensive evaluation index system for pipeline safety risks based on multi-source data fusion analysis, comprehensively applies a hierarchical fuzzy comprehensive evaluation method, and puts forward opinions and suggestions for improving the safety status or quality level of pipelines. By applying this technology, the safety status of pipelines can be mastered, major safety hazards and weak points of pipeline systems can be discovered in a timely manner, and a scientific and effective theoretical basis can be provided for the formulation of maintenance, transformation, replacement and other plans, so as to solve the bottleneck technical problems in the industry that the actual performance of pipelines is inconsistent with the design requirements and the safety status is difficult to accurately evaluate, so as to achieve both safety and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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.

[0023] Figure 1 This is a diagram of the application environment of a pipeline identification inspection method within a power plant boiler in one embodiment of the present invention.

[0024] Figure 2 A schematic flow chart of a pipeline identification inspection method within a power plant boiler provided in accordance with an embodiment of the present invention.

[0025] Figure 3 A schematic diagram of the functional modules of a pipeline identification inspection system within the scope of a power plant boiler provided in one embodiment of the present invention.

[0026] Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] The pipeline identification inspection method within the scope of the power plant boiler provided by the embodiment of the present invention can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 may send the identification inspection result to the server 104, and the identification inspection result includes: the identification inspection result of the pipe material, the identification inspection result of the defective welded joint, and the identification inspection result of the pipe system; after the server 104 receives the identification inspection result, for the identification inspection result, the server 104 constructs a pipeline evaluation index system according to the identification inspection result; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators; according to the pipeline evaluation index system, determine the influencing factor set and the evaluation level corresponding to each influencing factor; establish a fuzzy relationship matrix based on the influencing factor set and the evaluation level corresponding to each influencing factor; determine the weight vector of each influencing factor, and determine the fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix; analyze the fuzzy comprehensive evaluation result vector to obtain the final evaluation result. The server 104 may feedback the final evaluation result obtained to the terminal 102. In addition, in some embodiments, the pipeline identification inspection method within the power plant boiler range can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform the pipeline identification inspection on the identification inspection result, or the server 104 can obtain the identification inspection result from the data storage system and perform the pipeline identification inspection on the identification inspection result.

[0030] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0031] In an exemplary embodiment, Figure 2 As shown, a method for identifying the inspection of pipelines within the scope of a power plant boiler is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present invention, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S6. Among them:

[0032] S1: Obtain identification test results; the identification test results include: identification test results of pipe materials, identification test results of defective weld joints and identification test results of piping systems.

[0033] S2: Constructing a pipeline evaluation index system based on the identification test results; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators.

[0034] S3: According to the pipeline evaluation index system, determine a set of influencing factors and an evaluation level corresponding to each influencing factor.

[0035] S4: Establishing a fuzzy relationship matrix based on the set of influencing factors and the evaluation level corresponding to each influencing factor.

[0036] S5: Determine the weight vector of each influencing factor, and determine the fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix.

[0037] S6: Analyze the fuzzy comprehensive evaluation result vector to obtain the final evaluation result.

[0038] Implementing the above-mentioned steps S1 to S6, the present invention is based on basic inspection technologies such as chemical inspection and nondestructive testing of pipes within the scope of power station boilers, and carries out identification inspection of power station boiler pipes, identification inspection of defective welding joints of power station boilers, and identification inspection of power station boiler pipe systems based on multi-defect and different structural coupling analysis in the order of "parent material-welded joint-pipe system", constructs a pipeline identification inspection evaluation index system, establishes a fuzzy relationship matrix, and combines multi-source heterogeneous data for fusion analysis, and finally forms a pipeline identification inspection method within the scope of power station boilers. Through the present invention, the safety status of pipelines can be mastered, major safety hazards and weak points of pipeline systems can be discovered in time, and a scientific and effective theoretical basis can be provided for the formulation of maintenance, transformation, replacement and other plans, solving the industry bottleneck technical problems of inconsistency between actual pipeline performance and design requirements and difficulty in accurately evaluating safety status, so as to achieve both safety and economy.

[0039] In an exemplary embodiment, in step S1, determining the result of the tube identification test specifically includes:

[0040] A1: Characterize the microstructure of boiler tubes with different service times, especially the elbows, tees and other high-temperature parts of low-hardness P91 and P92 main steam pipes and reheating hot section steam pipes, analyze their organizational evolution and performance degradation laws, such as martensitic lath degradation, subcrystal nucleation and growth, precipitation phase ripening laws, etc., and reveal their creep damage mechanism.

[0041] A2: Test and analyze the conventional mechanical properties (room temperature tensile strength, high temperature tensile strength, impact toughness, etc.) of samples with different hardness and aging conditions to study their changing patterns.

[0042] A3: Use a high temperature endurance testing machine to test low hardness P91 and P92 materials, and select the Larson-Miller parameter method or isotherm extrapolation method to study the corresponding relationship between heat-resistant steel materials with different hardness and aging conditions and creep residual life.

[0043] The determination of the identification test results of defective welded joints specifically includes:

[0044] B1: Test the creep performance of pipeline welded joints, analyze their microstructure evolution and performance degradation laws, including the formation and growth of creep voids, the ripening law of precipitated phases, etc., to reveal their creep damage mechanism.

[0045] B2: Carry out the evaluation of the suitability of pipeline welded joints with crack defects, and use the finite element method to analyze the changes in the stress and strain fields at the crack tip.

[0046] B3: Based on the nondestructive testing data of defective welded joints in pipelines (including area defects such as cracks and volume defects such as slag inclusions and pores), the interaction between different defects is evaluated from a dynamic perspective.

[0047] B4: Analyze the influence of welding process parameters on the heat affected zone, especially the fine grain zone, and characterize the formation and ripening of carbides in the welded joint during the creep damage evolution process and its mechanism of action on type IV cracking.

[0048] The determination of the piping identification test results specifically includes:

[0049] C1: Determine the main damage mode based on the design parameters and operating conditions of the pipeline. Combined with the data from basic tests such as physical and chemical testing and non-destructive testing and the evaluation results of long-term service performance, systematically analyze and dynamically evaluate the weights of hardness, metallographic structure, mechanical properties, cracks, volume defects and other influencing factors in multiple defects.

[0050] C2: Evaluate the structural characteristics (such as longitudinal welds, etc.) of the main feedwater pipeline and the reheat cold section steam pipeline as well as the possible damage modes.

[0051] C3: Couple the different structural forms of pipelines within the scope of the power station boiler, such as straight pipes, elbows (tubes), welds, tees, reducers, etc., and simultaneously consider the influence of the piping layout and supports on the piping stress, and carry out piping identification inspection.

[0052] In another exemplary embodiment of the present invention, based on the identification test results of the above-mentioned different parts and ranges, a pipeline evaluation index system is condensed, including core indicators (5), secondary indicators (18) and tertiary indicators (40), as shown in Table 1.

[0053] Table 1 Pipeline evaluation index system

[0054]

[0055]

[0056] Due to the frequent deep peak regulation, rapid load change and long-term service of thermal power units in new power systems, there are many factors that affect pipeline safety, and there are often hierarchical differences between the factors. The present invention adopts hierarchical fuzzy comprehensive evaluation theory, analyzes the various factors affecting pipeline safety based on multi-source heterogeneous data at multiple levels, and uses fuzzy calculation to determine the safety level of the pipeline in the current state. The specific process is as follows:

[0057] (1) Determine the set of influencing factors for pipeline identification inspection.

[0058] The index system constructed by the present invention includes five core indicators: material performance deviation, welding joint defects, pipe system stress, structural form and operating conditions. The influencing factor set X is as follows:

[0059] X = {x1, x2, x3, x4, x5} (1);

[0060] Among them, x1 is the material performance deviation, x2 is the welding joint defect, x3 is the piping stress, x4 is the structural form, and x5 is the operating condition.

[0061] (2) Determine the evaluation level set Y.

[0062] Y = {y1, y2, y3, y4, y5} (2);

[0063] Among them, y1 is safe, y2 is basically safe, y3 is low risk, y4 is medium risk, and y5 is high risk.

[0064] The present invention selects 5 evaluation levels, namely safety, basic safety, low risk, medium risk, and high risk.

[0065] First, the single factor x in the pipeline identification test influencing factor set is i(i=1,2,···,5) for single factor evaluation, from factor x i Focus on the impact of this factor on the evaluation level y j The membership degree of (j=1,2,···,5) is R ij , so we get the i-th factor x i The single factor evaluation set of:

[0066] R i =(R i1 ,R i2 ,···,R i5 ) (3);

[0067] In this way, the evaluation set of the five influencing factors constructs a total evaluation matrix, namely the fuzzy relationship matrix R:

[0068]

[0069] Among them, R ij is the membership degree of the i-th influencing factor to the j-th evaluation level, i=1,2,···,5, j=1,2,···,5.

[0070] Table 2 is a grade evaluation table, please refer to Table 1 for details.

[0071] Table 2 Rating table

[0072]

[0073]

[0074] Since each influencing factor is not equally important for pipeline identification inspection, the performance of each single factor has different effects on the overall performance. Therefore, before synthesizing the fuzzy comprehensive evaluation result vector of pipeline identification inspection, its weight vector must be determined, which is generally selected based on expert experience.

[0075] W = (w1, w2, w3, w4, w5) (5);

[0076] Among them, W is the weight vector of influencing factors, w1 is the weight vector of material performance deviation, w2 is the weight vector of welding joint defects, w3 is the weight vector of piping stress, w4 is the weight vector of structural form, and w5 is the weight vector of operating conditions.

[0077] By integrating different rows with the weight vector W of the influencing factors, the membership degree of the pipeline identification inspection to each level fuzzy subset can be obtained from the overall point of view, that is, the fuzzy comprehensive evaluation result vector D. The calculation formula of the fuzzy comprehensive evaluation result vector is:

[0078]

[0079] Among them, formula (6) is called fuzzy transformation, is the symbol of the fuzzy synthesis operator, W is the weight vector of the influencing factors, w1 is the weight vector of the material performance deviation, w2 is the weight vector of the welding joint defect, w3 is the weight vector of the pipe system stress, w4 is the weight vector of the structural form, w5 is the weight vector of the operating condition, R is the fuzzy relationship matrix, R ij is the membership of the i-th influencing factor to the j-th evaluation level, i=1,2,···,5, j=1,2,···,5, D1 is the membership of the evaluation level of safety; D2 is the membership of the evaluation level of basic safety; D3 is the membership of the evaluation level of low risk; D4 is the membership of the evaluation level of medium risk; D5 is the membership of the evaluation level of high risk.

[0080] Based on the quality and safety perspective, the present invention starts from the most dangerous factor in the fuzzy synthesis result vector, uses a binary tree classification algorithm to determine the safety level of the pipeline in the current state, and gives the final evaluation result, as shown below:

[0081] If D5>0, the pipeline identification inspection result is "high risk".

[0082] If D5=0, D4>0, the pipeline identification inspection result is "medium risk".

[0083] If D5=D4=0, D3>0, the pipeline identification inspection result is "low risk".

[0084] If D5=D4=D3=0, D2>0, the pipeline identification test result is "basically safe".

[0085] If D5=D4=D3=D2=0, D1>0, the pipeline identification test result is "safe".

[0086] The present invention establishes a multi-scale comprehensive evaluation index system for pipeline safety risk based on multi-source data fusion analysis, comprehensively applies the hierarchical fuzzy comprehensive evaluation method, and puts forward opinions and suggestions for improving the safety status or quality level of pipelines. By applying this technology, the safety status of pipelines can be mastered, major safety hazards and weak points of pipeline systems can be discovered in a timely manner, and a scientific and effective theoretical basis can be provided for the formulation of maintenance, transformation, replacement and other plans, solving the bottleneck technical problems in the industry where the actual performance of pipelines is inconsistent with the design requirements and the safety status is difficult to accurately evaluate, so as to achieve both safety and economy.

[0087] The present invention also provides an application scenario, which applies the above-mentioned pipeline identification inspection method within the scope of a power plant boiler. Specifically: The pipeline identification inspection method within the scope of a power plant boiler provided in this embodiment can be applied in a pipeline identification inspection scenario. The pipeline identification inspection scenario includes: data acquisition link, pipeline evaluation index system construction link, influencing factor combination and evaluation level determination link, fuzzy relationship matrix establishment link, fuzzy comprehensive evaluation result vector determination link and analysis link. According to the obtained identification inspection results, a pipeline evaluation index system is constructed; the identification inspection results include: pipe identification inspection results, defective welding joint identification inspection results and pipe system identification inspection results; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators; then according to the pipeline evaluation index system, a set of influencing factors and the evaluation level corresponding to each influencing factor are determined; based on the set of influencing factors and the evaluation level corresponding to each influencing factor, a fuzzy relationship matrix is ​​established; the weight vector of each influencing factor is determined, and based on the weight vector of each influencing factor and the fuzzy relationship matrix, a fuzzy comprehensive evaluation result vector is determined; finally, the fuzzy comprehensive evaluation result vector is analyzed to obtain the final evaluation result.

[0088] Based on the same inventive concept, the embodiment of the present invention also provides a pipeline identification inspection system within the scope of a power plant boiler for implementing the pipeline identification inspection method within the scope of a power plant boiler involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the pipeline identification inspection system within the scope of a power plant boiler provided below can refer to the limitations of the pipeline identification inspection method within the scope of a power plant boiler above, and will not be repeated here.

[0089] In an exemplary embodiment, Figure 3 As shown, a pipeline identification inspection system within the scope of a power plant boiler is provided, and the pipeline identification inspection system within the scope of a power plant boiler includes:

[0090] The identification inspection result acquisition module M1 is used to obtain the identification inspection results; the identification inspection results include: pipe identification inspection results, defective welding joint identification inspection results and pipe system identification inspection results.

[0091] The pipeline evaluation index system construction module M2 is used to construct a pipeline evaluation index system according to the identification test results; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators.

[0092] The influencing factor set and evaluation level determination module M3 is used to determine the influencing factor set and the evaluation level corresponding to each influencing factor according to the pipeline evaluation index system.

[0093] The fuzzy relationship matrix establishing module M4 is used to establish a fuzzy relationship matrix based on the influencing factor set and the evaluation level corresponding to each influencing factor.

[0094] The fuzzy comprehensive evaluation result vector determination module M5 is used to determine the weight vector of each influencing factor, and determine the fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix.

[0095] The analysis module M6 is used to analyze the fuzzy comprehensive evaluation result vector to obtain the final evaluation result.

[0096] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store identification inspection results. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a pipeline identification inspection method within the scope of a power plant boiler is implemented.

[0097] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the computer device to which the scheme of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0101] The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A pipeline identification inspection method within the scope of a power plant boiler, characterized in that: The pipeline identification inspection methods within the scope of the power station boiler include: Obtaining identification test results; the identification test results include: identification test results of pipe materials, identification test results of defective weld joints and identification test results of piping systems; According to the identification test results, a pipeline evaluation index system is constructed; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators; According to the pipeline evaluation index system, determine a set of influencing factors and an evaluation level corresponding to each influencing factor; Based on the set of influencing factors and the evaluation level corresponding to each influencing factor, a fuzzy relationship matrix is ​​established; Determine a weight vector of each influencing factor, and determine a fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix; The fuzzy comprehensive evaluation result vector is analyzed to obtain a final evaluation result.

2. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The core indicators include: material performance deviation, welding joint defects, piping stress, structural form and operating conditions; The secondary indicators include: chemical composition, delivery status, mechanical properties, structural stability, creep life of welded joints, evaluation of welded joint suitability for use, IV cracks, medium effects, external loads, temperature changes, stress concentration, size and shape, pipeline structure, weld arrangement, flexible operation, safety valve failure, over-temperature and over-pressure; The three-level indicators include: mass fraction of each element, chemical composition deviation of finished product, heat treatment status / heat treatment system of steel pipe, tensile strength, lower yield strength or specified plastic extension strength, elongation after fracture, impact absorption energy, hardness, 10 5 h Endurance strength, non-metallic inclusions, grain size, microstructure, high temperature creep performance of welded joints, high temperature endurance performance of welded joints, area defects such as cracks, volume defects such as slag inclusions and pores, defect interactions, damage tolerance, welding process parameters, post-weld heat treatment, carbides and second phases, welding residual stress, working pressure, support and hanger specifications, installation position and quantity, temperature fluctuations, load changes, number of furnace starts and stops, deformation, thermal expansion obstruction, outer diameter and wall thickness, curvature, out-of-roundness, straight pipes, elbows, tees, reducers, longitudinal girth welds, minimum load for deep peak regulation, maximum variable load rate, safety valve installation problems, safety valve leakage, over-temperature amplitude and cumulative time, and over-pressure amplitude and cumulative time.

3. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The evaluation levels include: safe, basically safe, low risk, medium risk and high risk.

4. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The expression of the fuzzy relationship matrix is: Among them, R is the fuzzy relationship matrix, R ij is the membership degree of the i-th influencing factor to the j-th evaluation level, i=1,2,···,5, j=1,2,···,5.

5. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The expression of the weight vector of each influencing factor is: W = (w1, w2, w3, w4, w5); Among them, W is the weight vector of influencing factors, w1 is the weight vector of material performance deviation, w2 is the weight vector of welding joint defects, w3 is the weight vector of piping stress, w4 is the weight vector of structural form, and w5 is the weight vector of operating conditions.

6. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The calculation formula of the fuzzy comprehensive evaluation result vector is: in, is the symbol of the fuzzy synthesis operator, W is the weight vector of the influencing factors, w1 is the weight vector of the material performance deviation, w2 is the weight vector of the welding joint defect, w3 is the weight vector of the pipe system stress, w4 is the weight vector of the structural form, w5 is the weight vector of the operating condition, R is the fuzzy relationship matrix, R ij is the membership of the i-th influencing factor to the j-th evaluation level, i=1,2,···,5, j=1,2,···,5, D1 is the membership of the evaluation level of safety; D2 is the membership of the evaluation level of basic safety; D3 is the membership of the evaluation level of low risk; D4 is the membership of the evaluation level of medium risk; D5 is the membership of the evaluation level of high risk, and D is the fuzzy comprehensive evaluation result vector.

7. The method for testing the pipeline identification of a power plant boiler according to claim 1 is characterized in that: The fuzzy comprehensive evaluation result vector is analyzed by using a binary tree classification algorithm to obtain a final evaluation result.

8. The method for testing the pipeline identification within the scope of a power plant boiler according to claim 1 is characterized in that: The fuzzy comprehensive evaluation result vector is analyzed to obtain the final evaluation result, which specifically includes: If D5>0, the pipeline identification inspection result is high risk; If D5=0, D4>0, the pipeline identification inspection result is medium risk; If D5=D4=0, D3>0, the pipeline identification inspection result is low risk; If D5=D4=D3=0, D2>0, the pipeline identification test result is basically safe; If D5=D4=D3=D2=0, D1>0, then the pipeline identification test result is safe; Among them, D1 is the membership degree of the evaluation level of safety; D2 is the membership degree of the evaluation level of basic safety; D3 is the membership degree of the evaluation level of low risk; D4 is the membership degree of the evaluation level of medium risk; D5 is the membership degree of the evaluation level of high risk.

9. A pipeline identification inspection system within the scope of a power station boiler, characterized in that: The pipeline identification inspection system within the scope of the power station boiler includes: An identification inspection result acquisition module is used to acquire identification inspection results; the identification inspection results include: identification inspection results of pipes, identification inspection results of defective welded joints and identification inspection results of pipe systems; A pipeline evaluation index system construction module is used to construct a pipeline evaluation index system according to the identification test results; the evaluation index system includes: a number of core indicators; each of the core indicators includes a number of secondary indicators and a number of tertiary indicators; An influencing factor set and evaluation level determination module, used to determine an influencing factor set and an evaluation level corresponding to each influencing factor according to the pipeline evaluation index system; A fuzzy relationship matrix establishment module, used to establish a fuzzy relationship matrix based on the influencing factor set and the evaluation level corresponding to each influencing factor; A fuzzy comprehensive evaluation result vector determination module, used to determine the weight vector of each influencing factor, and determine the fuzzy comprehensive evaluation result vector based on the weight vector of each influencing factor and the fuzzy relationship matrix; The analysis module is used to analyze the fuzzy comprehensive evaluation result vector to obtain the final evaluation result.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the pipeline identification inspection method within the scope of a power plant boiler as described in any one of claims 1-8.

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