CN114778684A - Steel pipe performance evaluation method and system based on service scene - Google Patents
Steel pipe performance evaluation method and system based on service scene Download PDFInfo
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Abstract
The invention discloses a steel pipe performance evaluation method and system based on a service scene, and relates to the field of artificial intelligence, wherein the method comprises the following steps: obtaining a first standard detection result of a first standard steel pipe by using a Hall sensor; calculating to obtain a first standard approximate entropy; constructing a steel pipe damage database; acquiring first damage data of a first service steel pipe, traversing the steel pipe damage database to acquire first adaptive data, reversely matching the first damaged steel pipe, and calculating a first damage approximate entropy; comparing and determining the first damage degree, and using the first damage degree as the damage degree of the first service steel pipe; a first performance evaluation is generated. The method solves the problems of poor evaluation accuracy and low reliability in the prior art based on experience judgment of the actual performance of the steel pipe, and also solves the technical problems of complex detection method and low evaluation efficiency in the performance detection of the steel pipe by using a nondestructive detection technology. The technical effects of improving the performance evaluation accuracy and the efficiency of the steel pipe in a service scene are achieved.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a service scene-based steel pipe performance evaluation method and system.
Background
In case of failure of the steel pipe in a service scene, the safety of the pipeline structure is affected, and meanwhile, the leakage of contents can be caused, so that the environment is affected. In the prior art, when the performance of the steel pipe in a service scene is evaluated, the performance of the steel pipe is usually detected by using a nondestructive detection technology, and the technical problems that the detection method is complex, the detection precision is poor, and the actual performance of the steel pipe cannot be quickly and accurately evaluated exist. In addition, some enterprises only carry out regular maintenance on the steel pipes in service, and related technical personnel subjectively evaluate the performance of the steel pipes based on experience and the like, and once the service environment of the steel pipes is severe, the steel pipes are easily subjected to wrong evaluation, so that serious consequences are caused. Therefore, the intelligent performance evaluation of the steel pipe in the service scene has important significance for effectively predicting the failure probability and the residual service life of the steel pipe and judging whether the steel pipe needs to be maintained or replaced and the like on the basis.
However, in the prior art, related technicians regularly maintain the steel pipes in service, and judge the actual performance of the steel pipes based on experience, so that the problems of poor evaluation accuracy and low reliability exist, and in addition, the nondestructive detection technology is used for detecting the performance of the steel pipes, so that the technical problems of complex detection method and low evaluation efficiency exist.
Disclosure of Invention
The invention aims to provide a service scene-based steel pipe performance evaluation method and system, which are used for solving the problems of poor evaluation accuracy and low reliability in the prior art that related technical personnel regularly maintain a steel pipe in service and judge the actual performance of the steel pipe based on experience, and in addition, the technical problems of complex detection method and low evaluation efficiency in the steel pipe performance detection by using a nondestructive detection technology.
In view of the above problems, the invention provides a method and a system for evaluating the performance of a steel pipe based on a service scene.
In a first aspect, the present invention provides a service scenario-based steel pipe performance evaluation method, which is implemented by a service scenario-based steel pipe performance evaluation system, wherein the method includes: performing quality detection on the first standard steel pipe by using a Hall sensor to obtain a first standard detection result; calculating the complexity of the first standard detection result according to the approximate entropy algorithm idea to obtain a first standard approximate entropy; constructing a steel pipe damage database based on the big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes; obtaining first damage data of a first service steel pipe, traversing the steel pipe damage database based on the first damage data to obtain first adaptation data, reversely matching the first damaged steel pipe according to the first adaptation data, and calculating a first damage approximate entropy of the first damaged steel pipe; comparing the first damage approximate entropy with the first standard approximate entropy, determining a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe; generating a first performance assessment based on the first damage level.
In another aspect, the present invention further provides a steel pipe service scenario-based steel pipe performance evaluation system, configured to execute the steel pipe service scenario-based steel pipe service scenario performance evaluation method according to the first aspect, where the system includes: a first obtaining unit: the first obtaining unit is used for detecting the quality of the first standard steel pipe by using the Hall sensor to obtain a first standard detection result; a second obtaining unit: the second obtaining unit is used for calculating the complexity of the first standard detection result according to the idea of approximate entropy algorithm to obtain a first standard approximate entropy; a first building unit: the first construction unit is used for constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes; a third obtaining unit: the third obtaining unit is used for obtaining first damage data of a first service steel pipe, traversing the steel pipe damage database based on the first damage data, and obtaining first adaptive data; the first calculation unit: the first calculation unit is used for reversely matching a first damaged steel pipe according to the first adaptation data and calculating a first damage approximate entropy of the first damaged steel pipe; a first setting unit: the first setting unit is used for comparing the first damage approximate entropy with the first standard approximate entropy, determining a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe; a first generation unit: the first generation unit is used for generating a first performance evaluation according to the first damage degree.
In a third aspect, an electronic device comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first aspect through calling.
In a fourth aspect, a computer program product comprises a computer program and/or instructions which, when executed by a processor, implement the steps of the method of any one of the first aspect described above.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
1. the quality of the standard steel pipe qualified by quality inspection is detected by using the Hall sensor, and the standard approximate entropy is determined by using the idea of the approximate entropy algorithm, so that the aim of providing standards and bases for subsequent judgment of the performance condition of the steel pipe in a service scene is fulfilled; a steel pipe damage database is constructed through big data, and the effect of providing fitting and reference data for the damage evaluation of the steel pipe in a service scene is achieved; through comparison calculation of the approximate entropy, the performance loss degree of the steel pipe in the service scene is determined, and the technical effect of improving the performance evaluation accuracy and efficiency of the steel pipe in the service scene is achieved.
2. By integrating the approximate entropy value, the surface scanning image processing result, the knocking sound signal condition and the like of the service steel pipe, the comprehensive performance detection and evaluation of the inside and outside of the service steel pipe are realized, and the technical effect of improving the accuracy and reliability of the performance evaluation result is achieved.
3. Through taboo search global iteration optimization, the highest adaptation degree of local loss data is avoided, the quality of adaptation data is improved, the accuracy of subsequent steel pipe performance evaluation in a service scene is further ensured, and the technical effect of improving the reliability of the steel pipe performance in the service scene in the system intelligent evaluation is achieved.
4. By presetting the taboo period, the technical effects of controlling the taboo search optimization accuracy, reasonably controlling the optimization time and saving the system calculation time on the basis of ensuring the moderate system calculation amount are achieved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without inventive efforts based on the provided drawings.
FIG. 1 is a schematic flow chart of a service scene-based steel pipe performance evaluation method according to the present invention;
FIG. 2 is a schematic flow chart illustrating a process of adjusting the first performance evaluation according to the first residual strength to generate a second performance evaluation in the steel pipe performance evaluation method based on the service scenario of the present invention;
FIG. 3 is a schematic flow chart illustrating the adjustment of the second performance evaluation according to the first fitting degree in the method for evaluating the performance of a steel pipe based on a service scene according to the present invention;
FIG. 4 is a schematic flow chart illustrating the process of outputting the obtained first steel pipe damage data as the first adaptive data in the steel pipe performance evaluation method based on the service scenario according to the present invention;
FIG. 5 is a schematic structural diagram of a steel pipe performance evaluation system based on a service scene according to the present invention;
FIG. 6 is a schematic diagram of an exemplary electronic device of the present invention;
description of the reference numerals:
a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a first calculating unit 15, a first setting unit 16, a first generating unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The invention provides a service scene-based steel pipe performance evaluation method and system, and solves the problems that in the prior art, related technical personnel regularly maintain a steel pipe in service, the actual performance of the steel pipe is judged based on experience, evaluation accuracy is poor, and reliability is low, and in addition, the technical problems that a detection method is complex and evaluation efficiency is low due to the fact that nondestructive detection technology is used for steel pipe performance detection. The technical effects of improving the performance evaluation accuracy and the efficiency of the steel pipe in a service scene are achieved.
In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
The invention provides a steel pipe performance evaluation method based on a service scene, which is applied to a steel pipe performance evaluation system based on the service scene, wherein the method comprises the following steps: performing quality detection on the first standard steel pipe by using a Hall sensor to obtain a first standard detection result; calculating the complexity of the first standard detection result according to the idea of approximate entropy algorithm to obtain a first standard approximate entropy; constructing a steel pipe damage database based on the big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes; the method comprises the steps of obtaining first damage data of a first service steel pipe, traversing in a steel pipe damage database based on the first damage data to obtain first adaptation data, reversely matching the first damaged steel pipe according to the first adaptation data, and calculating first damage approximate entropy of the first damaged steel pipe; comparing the first damage approximate entropy with the first standard approximate entropy to determine a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe; generating a first performance assessment based on the first damage level.
Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.
Example one
Referring to the accompanying drawing 1, the invention provides a steel pipe performance evaluation method based on a service scene, wherein the method is applied to a steel pipe performance evaluation system based on the service scene, and the method specifically comprises the following steps:
step S100: detecting the quality of the first standard steel pipe by using a Hall sensor to obtain a first standard detection result;
specifically, the steel pipe performance evaluation method based on the service scene is applied to the steel pipe performance evaluation system based on the service scene, steel pipe data in the service scene can be detected by using intelligent setting, and the performance of the steel pipe in the service scene is comprehensively evaluated by combining historical steel pipe use data, defect conditions and the like. The Hall sensor is a commonly used sensor in the nondestructive testing technology, and compared with an ultrasonic sensor, the Hall sensor has the advantages that the detection method is simple, a full mixture agent is not required to be arranged between a steel pipe and the sensor, the shape of an object to be monitored is not required to be changed, and meanwhile, the subsequent use performance of the Hall sensor is not influenced, so that the steel pipe in a service scene is easy to intelligently detect. The intelligent data detection and sensing of the first standard steel pipe are realized through the Hall sensor, and the quality detection of the first standard steel pipe is realized. The first standard steel pipe is any steel pipe which is unused, qualified in quality inspection and consistent with the subsequent steel pipe product process to be intelligently evaluated, production requirements, use environment and the like. By detecting the quality of the first standard steel pipe and obtaining the corresponding first standard detection result, the technical effects of providing an accurate and effective data base for the performance of the subsequent intelligent evaluation standard steel pipe and further improving the reliability of performance evaluation are achieved.
Step S200: calculating the complexity of the first standard detection result according to the idea of approximate entropy algorithm to obtain a first standard approximate entropy;
specifically, the approximate entropy algorithm refers to a non-negative number to represent the complexity of a certain time series, and can measure the probability of generating a new pattern in the time series. Wherein, the larger the probability of generating a new pattern, the more complex the sequence, that is, the more approximate entropy corresponding to the more complex time sequence is. And processing the first standard detection result by using an approximate entropy algorithm idea to obtain waveform data of the first standard steel pipe, and performing complexity calculation, for example, using matlab degree to analyze complexity. Furthermore, the complexity data obtained through intelligent analysis and calculation is used as the first standard approximate entropy of the first standard steel pipe, and the technical effect of providing a reference standard for subsequent evaluation of the to-be-tested service steel pipe is achieved.
Step S300: constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes;
further, step S300 of the present invention further includes:
step S310: training according to the history damaged steel pipe record to obtain a steel pipe damage type support vector machine;
step S320: building a steel pipe damage set, wherein the steel pipe damage set comprises a plurality of damaged steel pipes;
step S330: sequentially collecting the characteristics of each damaged steel pipe in the damaged steel pipes, inputting the characteristics as input information into the steel pipe damage type support vector machine, and respectively obtaining a plurality of damage types of the damaged steel pipes;
step S340: according to the damage types, the damaged steel pipes are divided and combined to obtain a first combined result, wherein the first combined result comprises a first type steel pipe damage set and a second type steel pipe damage set;
step S350: and constructing the steel pipe damage database according to the first type steel pipe damage set and the second type steel pipe damage set.
Specifically, first, a steel pipe damage type support vector machine is trained based on data such as the damage condition, the service life, and the failure cause of each damaged steel pipe in the history of steel pipe use. The steel pipe damage type support vector machine is an intelligent model capable of intelligently judging the type of the steel pipe defects based on the defect and loss data and the like of various steel pipes. Further, a steel pipe damage set is established, wherein the steel pipe damage set comprises a plurality of damaged steel pipes, and damage data of each damaged steel pipe are random. And then intelligently analyzing the damage types of all damaged steel pipes by using the steel pipe damage type support vector machine obtained by training, and combining the damaged steel pipes belonging to the same damage type into a set, namely, respectively obtaining a first type steel pipe damage set and a second type steel pipe damage set. And finally, performing union operation on the steel pipe sets of all damage types to obtain the steel pipe damage database.
By constructing a database of damaged steel pipes with various damage types and different damage degrees, the technical aim of providing a wider matching range for the steel pipes in a subsequent service scene to be evaluated is achieved, and the matching degree is improved.
Step S400: acquiring first damage data of a first service steel pipe, and traversing in the steel pipe damage database based on the first damage data to acquire first adaptive data;
specifically, a Hall sensor is utilized to intelligently detect the damage condition of the steel pipe to be detected and evaluated, wherein the steel pipe is in service, namely the first service steel pipe is subjected to damage detection, so that the first damage data is obtained, and further, based on the first damage data, searching and matching are performed in the constructed steel pipe damage database, so that the damage data with the highest matching degree is used as the first adaptive data and is output. The first adaptive data refers to all relevant data of the existing steel pipes with the highest fitting degree and the most consistent with the first service steel pipe, such as steel pipe damage types, damage degrees, specific damage parameters and the like in the steel pipe damage database. The first adaptation data are obtained through searching, the most similar damage data are adapted to the service steel pipe, and the technical effect of improving the accuracy of the follow-up performance evaluation result is achieved.
Step S500: reversely matching a first damaged steel pipe according to the first adaptive data, and calculating a first damage approximate entropy of the first damaged steel pipe;
step S600: comparing the first damage approximate entropy with the first standard approximate entropy, determining a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe;
specifically, the damaged steel pipe corresponding to the first fitting data is reversely matched, and the damaged steel pipe is recorded as a first damaged steel pipe. That is, a steel pipe for which damage data, damage type, and damage condition have been accurately evaluated is used as a substitute for the first service steel pipe. Further, the first damage approximate entropy of the first damaged steel pipe obtained through calculation is the damage approximate entropy of the first service steel pipe. And comparing the first damage approximate entropy and the first standard approximate entropy for calculation, for example, calculating a ratio of the first damage approximate entropy to the first standard approximate entropy, and the like, to obtain the first damage degree, so as to achieve a goal of performing damage quantitative evaluation on the first service steel pipe. Namely, the calculated first damage degree is recorded as the damage degree of the first service steel pipe. The technical aim of replacing the problem that the damage condition of the steel pipe in a service scene cannot be accurately evaluated by using the existing accurate test detection and visual quantitative damage data is achieved.
Step S700: and generating a first performance evaluation according to the first damage degree.
Specifically, based on the first damage degree, the damage of the first service steel pipe after use, namely after the first service steel pipe starts to be in service, is quantified, so that the performance of the first service steel pipe is reliably evaluated. The quality of the standard steel pipe qualified by quality inspection is detected by using the Hall sensor, and the standard approximate entropy is determined by using the idea of the approximate entropy algorithm, so that the aim of providing standards and bases for subsequent judgment of the performance condition of the steel pipe in a service scene is fulfilled; a steel pipe damage database is established through big data, and the effect of providing fitting and reference data for the damage evaluation of the steel pipe in a service scene is achieved; through comparison calculation of the approximate entropy, the performance loss degree of the steel pipe in the service scene is determined, and the technical effect of improving the performance evaluation accuracy and efficiency of the steel pipe in the service scene is achieved.
Further, as shown in fig. 2, the present invention further includes step S800:
step S810: collecting first basic information of the first service steel pipe;
step S820: scanning the first service steel pipe by using a three-dimensional scanner to obtain a first scanning image, and processing the first scanning image to obtain a first processing result;
step S830: determining first service data according to the first processing result;
step S840: and calculating first residual strength according to the first basic information and the first service data, and adjusting the first performance evaluation according to the first residual strength to generate a second performance evaluation.
Further, step S820 of the present invention further includes:
step S821: segmenting the first scanning image based on a preset segmentation scheme to obtain a first segmentation result, wherein the first segmentation result comprises a plurality of image blocks;
step S822: extracting the plurality of image blocks in the horizontal and vertical directions based on a preset step length to form a to-be-processed image block set, wherein the to-be-processed image block set comprises a plurality of to-be-processed image blocks;
step S823: sequentially performing feature analysis on the image blocks to be processed to form a first feature analysis result set, wherein the first feature analysis result set comprises a plurality of feature analysis results;
step S824: and screening and determining a first characteristic analysis result according to the plurality of characteristic analysis results, and taking the first characteristic analysis result as the first processing result.
Specifically, first basic information of the first service steel pipe, including steel pipe material, thickness, diameter, service time, service environment conditions and the like, is collected on the basis of a steel pipe use record, production conditions and the like. And then, scanning the first service steel pipe by using a three-dimensional scanner, wherein the scanned first scanning image is used as a basis for analyzing the service damage condition of the first service steel pipe. And further based on the intelligent processing result of the first scanned image, calculating to obtain corresponding first service data. The first service data comprise damage and defect data of the steel pipe in the service process, wherein the damage and defect data comprise defect length, defect depth and the like. And finally, determining the first residual strength of the first service steel pipe by using a calculation method of corrosion and residual strength of the steel pipe based on the first basic information and the first service data. Further, the first performance evaluation is adjusted based on the first residual intensity data, and the adjusted result is the second performance evaluation.
Further, when the first scanned image obtained by scanning is processed and analyzed, the first scanned image is firstly segmented based on a preset segmentation scheme, so that a first segmentation result of the first scanned image is obtained. The first division result includes a plurality of divided image blocks, that is, the first scanned image is obtained by splicing the plurality of image blocks. The preset segmentation scheme is a segmentation scheme which is made by the system based on the size of the steel pipe, the size of the scanned image, the processing precision requirement and the like. For example, if the processing and analysis precision is required to be high, more fine segmentation with shorter side length is performed, so that the number of obtained image blocks is also larger, and the amount of images analyzed and processed by a subsequent system is larger. And further, extracting the plurality of image blocks obtained by division in the horizontal and vertical directions based on a preset step length, wherein all the extracted image blocks form an image block set to be processed. Wherein the set of to-be-processed image blocks comprises a plurality of to-be-processed image blocks. The preset step length refers to the interval quantity of the extracted image blocks which is determined in advance by the system and can be automatically adjusted according to the precision requirement. And finally, sequentially performing characteristic analysis on the plurality of image blocks to be processed, wherein the characteristic analysis comprises image color characteristics, image texture characteristics, image structure characteristics and the like, and results obtained by all the characteristic analysis form the first characteristic analysis result set. And screening and determining a first characteristic analysis result according to the plurality of characteristic analysis results, and taking the first characteristic analysis result as the first processing result. The first characteristic analysis result refers to a result that the deviation between the characteristic condition and the standard steel pipe is the largest, namely the damage condition is the most serious.
The method has the advantages that the part corresponding to the image with the most serious damage in the scanned image of the service steel pipe is used as the processing result of the service steel pipe, and the aims of determining the damage of the steel pipe and evaluating the performance based on the barrel theory are achieved.
Further, as shown in fig. 3, the present invention further includes step S850:
step S851: sequentially carrying out a knocking test on the first service steel pipe and the first standard steel pipe based on a preset knocking scheme;
step S852: carrying out real-time sound collection on a knocking test by using a sound sensor to respectively obtain a first service sound signal and a first standard sound signal;
step S853: sequentially generating a first service signal curve of the first service sound signal and a first standard signal curve of the first standard sound signal;
step S854: fitting the first service signal curve with the first standard signal curve to obtain a first fitting degree;
step S855: the second performance assessment is adjusted according to the first fitness.
Specifically, the preset knocking scheme refers to a scheme which is formulated by a system and used for detecting and judging knocking sound of the steel pipe, and includes related knocking parameters such as knocking strength and knocking frequency. The technical goal of carrying out a knocking test on the first service steel pipe and the first standard steel pipe and then determining the damage condition of the service steel pipe based on the difference analysis of the knocked sound signals is achieved based on the preset knocking scheme. When the knocking test is carried out, a sound sensor is utilized to carry out real-time sound collection on the knocking test, wherein a signal obtained by knocking a first service steel pipe is the first service sound signal, and a signal obtained by knocking a first standard steel pipe is the first standard sound signal. And then respectively carrying out curve fitting on the two sound signals so as to obtain fitting degree data of the two curves, namely the first fitting degree. And finally, adjusting the second performance evaluation according to the first fitting degree. That is, the higher the first fitting degree is, the smaller the damage of the corresponding first service steel pipe is, that is, the better the performance is, and the second performance evaluation is corrected and adjusted based on the first fitting degree.
The method is based on the sound signals obtained after the same knocking test between the steel pipe and the standard steel pipe which is not actually used in the service scene is compared, and the two signal curves are fitted, so that the difference quantification target of the steel pipe in the service scene compared with the standard steel pipe which is not used is achieved, and the technical effect of improving the performance evaluation reliability is achieved based on the second performance evaluation result obtained before the verification and adjustment based on image processing and the like.
Further, as shown in fig. 4, step S400 of the present invention further includes:
step S410: extracting first steel pipe damage data of the steel pipe damage database, and taking the first steel pipe damage data as the first adaptive data;
step S420: calculating to obtain a first matching index according to the first damage data and the first steel pipe damage data;
step S430: constructing a first neighborhood of the first adaptive data based on a preset neighborhood scheme, wherein the first neighborhood comprises multiple groups of candidate steel pipe damage data;
step S440: sequentially calculating matching indexes of the first damage data and the multiple groups of candidate steel pipe damage data to form a plurality of matching indexes;
step S450: comparing the plurality of matching indexes, and screening a first optimal matching index of the first neighborhood;
step S460: if the first optimal matching index is superior to the first matching index, reversely matching candidate steel pipe damage data of the first optimal matching index, recording the candidate steel pipe damage data as second steel pipe damage data, and replacing the first steel pipe damage data with the second steel pipe damage data;
step S470: and if the iteration optimization reaches a preset iteration number, outputting the obtained first steel pipe damage data as the first adaptive data.
Specifically, first, any one set of damage data of the steel pipe damage database, that is, the first steel pipe damage data, is randomly extracted, the first steel pipe damage data is temporarily used as the first fitting data, and the first matching index is calculated.
Further, a first neighborhood of the first steel pipe damage data is constructed based on a preset neighborhood scheme, wherein the first neighborhood comprises multiple groups of steel pipe damage data. The preset neighborhood scheme refers to a neighborhood range determination scheme preset after system comprehensive analysis. For example, the first fitting data is used as a center, damage data with similar circumference of 10 units is used as a neighborhood, and the like. In the same method, the multiple matching indexes of the multiple groups of steel pipe damage data are sequentially calculated, the multiple matching indexes are traversed and compared, and the optimal matching index in the first neighborhood is obtained through screening. And then, judging whether the first optimal matching index is superior to the first matching index. And when the first optimal matching index is superior to the first matching index, reversely matching the steel pipe damage data of the first optimal matching index, recording the matched steel pipe damage data as second steel pipe damage data, and simultaneously taking the second steel pipe damage data as the first adapting data, namely, when the matching index in the neighborhood is superior to the initially set matching index of the first adapting data, replacing the previously set first adapting data with the steel pipe damage data corresponding to the matching index in the neighborhood. And finally, carrying out iteration optimization for multiple times, and when the iteration optimization reaches a preset iteration time, taking the obtained first steel pipe damage data as the first adaptive data.
The first adaptive data are obtained through global iterative optimization, so that the local optimal solution for tripping is achieved, the quality of the optimal solution is improved, the steel pipe damage data and the damage condition of the first service steel pipe are ensured to be the most consistent and the similarity is the highest, the accuracy of subsequent steel pipe performance evaluation under the service scene is ensured, and the technical effect of improving the reliability of the steel pipe performance under the service scene of system intelligent evaluation is achieved.
Further, step S460 of the present invention further includes:
step S461: marking the first steel pipe damage data and the second steel pipe damage data in a taboo manner in sequence, and marking the first steel pipe damage data and the second steel pipe damage data as a first taboo mark and a second taboo mark respectively;
step S462: sequentially calculating the taboo duration of the first taboo mark and the second taboo mark to obtain a first taboo duration and a second taboo duration;
step S463: when the first taboo duration meets a preset taboo duration limit, removing the first taboo mark of the first steel pipe damage data;
step S464: and when the second tabu duration meets the preset tabu period limit, removing the second tabu mark of the second steel pipe damage data.
Specifically, when seeking the first fitting data, a group of steel pipe damage data is randomly selected at an initial stage, and after the data is used as the first fitting data, contraindication marking is carried out on the data. In addition, after the steel pipe damage data with the matching index superior to that of the first fitting data is obtained based on the neighborhood of the first fitting data, the steel pipe damage data in the neighborhood is set as the first fitting data, and in the same way, taboo marking is performed on the first fitting data. That is, the first steel pipe damage data and the second steel pipe damage data are sequentially marked with taboo marks, which are respectively marked with a first taboo mark and a second taboo mark. Further, the time length of each group of the first steel pipe damage data marked as the taboo is calculated respectively, when the taboo time length exceeds a preset taboo period, the system automatically removes the taboo, and after that, the steel pipe damage data with the taboo marks removed can be optimally compared again. The preset taboo period is determined after the system is comprehensively analyzed based on the optimization space scale, the optimization precision requirement and the like. The shorter the preset taboo period is, the more easily the optimization cycle occurs, and the longer the preset taboo period is, the longer the number of times, the amount of calculation, and the like of the system optimization calculation are.
By presetting the taboo period, the technical effects of controlling the taboo search optimization accuracy, reasonably controlling the optimization time and saving the system calculation time on the basis of ensuring the moderate system calculation amount are achieved.
In summary, the steel pipe performance evaluation method based on the service scene provided by the invention has the following technical effects:
1. the quality of the standard steel pipe qualified by quality inspection is detected by using the Hall sensor, and the standard approximate entropy is determined by using the idea of the approximate entropy algorithm, so that the aim of providing standards and bases for subsequent judgment of the performance condition of the steel pipe in a service scene is fulfilled; a steel pipe damage database is constructed through big data, and the effect of providing fitting and reference data for the damage evaluation of the steel pipe in a service scene is achieved; through comparison calculation of the approximate entropy, the performance loss degree of the steel pipe in the service scene is determined, and the technical effect of improving the performance evaluation accuracy and efficiency of the steel pipe in the service scene is achieved.
2. By integrating the approximate entropy value, the surface scanning image processing result, the knocking sound signal condition and the like of the service steel pipe, the comprehensive performance detection and evaluation of the inside and outside of the service steel pipe are realized, and the technical effect of improving the accuracy and reliability of the performance evaluation result is achieved.
3. Through taboo search global iterative optimization, the highest degree of adaptation of local loss data is skipped, the quality of adaptation data is improved, the accuracy of subsequent evaluation of the performance of the steel pipe in a service scene is further ensured, and the technical effect of improving the reliability of the performance of the steel pipe in the service scene through system intelligent evaluation is achieved.
4. By presetting the taboo period, the technical effects of controlling the taboo search optimization accuracy, reasonably controlling the optimization time and saving the system calculation time on the basis of ensuring the moderate system calculation amount are achieved.
Example two
Based on the same inventive concept as the steel pipe performance evaluation method based on the service scene in the foregoing embodiment, the present invention further provides a steel pipe performance evaluation system based on the service scene, referring to fig. 5, where the system includes:
the first obtaining unit 11 is used for detecting the quality of the first standard steel pipe by using a Hall sensor to obtain a first standard detection result;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform complexity calculation on the first standard detection result according to an approximate entropy algorithm idea, so as to obtain a first standard approximate entropy;
the first construction unit 13 is used for constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain first damage data of a first service steel pipe, and traverse through the steel pipe damage database based on the first damage data to obtain first fitting data;
the first calculation unit 15 is configured to reversely match a first damaged steel pipe according to the first adaptation data, and calculate a first damage approximate entropy of the first damaged steel pipe;
a first setting unit 16, where the first setting unit 16 is configured to compare the first damage approximate entropy with the first standard approximate entropy, determine a first damage degree of the first damaged steel pipe, and use the first damage degree as the damage degree of the first service steel pipe;
a first generating unit 17, the first generating unit 17 being configured to generate a first performance evaluation according to the first damage degree.
Further, the system further comprises:
the first acquisition unit is used for acquiring first basic information of the first service steel pipe;
the fourth obtaining unit is used for scanning the first service steel pipe by using a three-dimensional scanner to obtain a first scanning image, and processing the first scanning image to obtain a first processing result;
the first determining unit is used for determining first service data according to the first processing result;
and the second generating unit is used for calculating a first residual strength according to the first basic information and the first service data, adjusting the first performance evaluation according to the first residual strength and generating a second performance evaluation.
Further, the system further comprises:
a fifth obtaining unit, configured to segment the first scan image based on a preset segmentation scheme to obtain a first segmentation result, where the first segmentation result includes a plurality of image blocks;
the first composition unit is used for extracting the plurality of image blocks in the horizontal and vertical directions based on a preset step length to form a to-be-processed image block set, wherein the to-be-processed image block set comprises a plurality of to-be-processed image blocks;
the second composition unit is used for sequentially performing feature analysis on the plurality of image blocks to be processed to form a first feature analysis result set, wherein the first feature analysis result set comprises a plurality of feature analysis results;
and the second setting unit is used for screening and determining a first feature analysis result according to the plurality of feature analysis results, and taking the first feature analysis result as the first processing result.
Further, the system further comprises:
the first execution unit is used for sequentially carrying out a knocking test on the first service steel pipe and the first standard steel pipe based on a preset knocking scheme;
the sixth obtaining unit is used for collecting real-time sound of the knocking test by using the sound sensor and respectively obtaining a first service sound signal and a first standard sound signal;
a third generating unit, configured to sequentially generate a first service signal curve of the first service sound signal and a first standard signal curve of the first standard sound signal;
a seventh obtaining unit, configured to fit the first service signal curve and the first standard signal curve to obtain a first fitting degree;
a second execution unit to adjust the second performance evaluation according to the first fitness.
Further, the system further comprises:
the eighth obtaining unit is used for training and obtaining a steel pipe damage type support vector machine according to the historical damage steel pipe record;
the system comprises a first building unit, a second building unit and a third building unit, wherein the first building unit is used for building a steel pipe damage set, and the steel pipe damage set comprises a plurality of damaged steel pipes;
a ninth obtaining unit, configured to sequentially acquire characteristics of each damaged steel pipe in the plurality of damaged steel pipes, input the characteristics to the steel pipe damage type support vector machine as input information, and obtain a plurality of damage types of the plurality of damaged steel pipes, respectively;
a tenth obtaining unit, configured to divide and combine the multiple damaged steel pipes according to the damage types to obtain a first combined result, where the first combined result includes a first type steel pipe damage set and a second type steel pipe damage set;
and the second construction unit is used for constructing the steel pipe damage database according to the first type steel pipe damage set and the second type steel pipe damage set.
Further, the system further comprises:
the second setting unit is used for extracting first steel pipe damage data of the steel pipe damage database and taking the first steel pipe damage data as the first adaptive data;
an eleventh obtaining unit, configured to calculate and obtain a first matching index according to the first damage data and the first steel pipe damage data;
a third construction unit, configured to construct a first neighborhood of the first fitting data based on a preset neighborhood scheme, where the first neighborhood includes multiple sets of candidate steel pipe damage data;
the third composition unit is used for sequentially calculating the matching indexes of the first damage data and the multiple groups of candidate steel pipe damage data to form a plurality of matching indexes;
a first screening unit, configured to compare the plurality of matching indices and screen a first optimal matching index of the first neighborhood;
a first replacement unit, configured to, if the first optimal matching index is superior to the first matching index, reversely match candidate steel pipe damage data of the first optimal matching index, record the candidate steel pipe damage data as second steel pipe damage data, and replace the first steel pipe damage data with the second steel pipe damage data;
and the third setting unit is used for outputting the obtained first steel pipe damage data as the first adaptive data if the iteration optimization reaches a preset iteration number.
Further, the system further comprises:
a fourth setting unit, configured to mark the first steel pipe damage data and the second steel pipe damage data as a first taboo mark and a second taboo mark in sequence, respectively;
a twelfth obtaining unit, configured to calculate tabu durations of the first tabu mark and the second tabu mark in sequence, and obtain a first tabu duration and a second tabu duration;
the first removing unit is used for removing the first taboo mark of the first steel pipe damage data when the first taboo duration meets a preset taboo duration limit;
and the second removing unit is used for removing the second taboo mark of the second steel pipe damage data when the second taboo duration meets the preset taboo period limit.
In the present description, each embodiment is described in a progressive manner, and the main point of description of each embodiment is different from that of the other embodiments, and the steel pipe performance evaluation method based on the service scenario in the first embodiment of fig. 1 and the specific example are also applicable to the steel pipe performance evaluation system based on the service scenario of the present embodiment. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the present invention is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present invention.
Based on the inventive concept of the service scene-based steel pipe performance evaluation method in the foregoing embodiment, the present invention further provides a service scene-based steel pipe performance evaluation system, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the foregoing service scene-based steel pipe performance evaluation methods.
Where in fig. 6 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The invention provides a service scene-based steel pipe performance evaluation method, which is applied to a service scene-based steel pipe performance evaluation system, wherein the method comprises the following steps: performing quality detection on the first standard steel pipe by using a Hall sensor to obtain a first standard detection result; calculating the complexity of the first standard detection result according to the approximate entropy algorithm idea to obtain a first standard approximate entropy; constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes; the method comprises the steps of obtaining first damage data of a first service steel pipe, traversing in a steel pipe damage database based on the first damage data to obtain first adaptation data, reversely matching the first damaged steel pipe according to the first adaptation data, and calculating first damage approximate entropy of the first damaged steel pipe; comparing the first damage approximate entropy with the first standard approximate entropy to determine a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe; generating a first performance assessment based on the first damage level. The problems that in the prior art, related technicians regularly maintain the steel pipe in service and judge the actual performance of the steel pipe based on experience, evaluation accuracy is poor and reliability is low are solved, and in addition, the nondestructive detection technology is used for detecting the performance of the steel pipe, so that the technical problems that a detection method is complex and evaluation efficiency is low are solved. The technical effects of improving the performance evaluation accuracy and the efficiency of the steel pipe in a service scene are achieved.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first embodiment through calling.
The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, carry out the steps of the method of any one of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also encompass such modifications and variations.
Claims (10)
1. A steel pipe performance evaluation method based on a service scene is characterized in that the method is applied to a steel pipe performance evaluation system based on the service scene, the system is in communication connection with a Hall sensor, and the method comprises the following steps:
detecting the quality of the first standard steel pipe by using a Hall sensor to obtain a first standard detection result;
calculating the complexity of the first standard detection result according to the approximate entropy algorithm idea to obtain a first standard approximate entropy;
constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes;
acquiring first damage data of a first service steel pipe, and traversing in the steel pipe damage database based on the first damage data to acquire first adaptive data;
reversely matching a first damaged steel pipe according to the first adaptive data, and calculating a first damage approximate entropy of the first damaged steel pipe;
comparing the first damage approximate entropy with the first standard approximate entropy to determine a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe;
generating a first performance assessment based on the first damage level.
2. The method of claim 1, wherein the method further comprises:
collecting first basic information of the first service steel pipe;
scanning the first service steel pipe by using a three-dimensional scanner to obtain a first scanning image, and processing the first scanning image to obtain a first processing result;
determining first service data according to the first processing result;
and calculating first residual strength according to the first basic information and the first service data, and adjusting the first performance evaluation according to the first residual strength to generate a second performance evaluation.
3. The method of claim 2, wherein said processing said first scanned image to obtain a first processing result comprises:
segmenting the first scanning image based on a preset segmentation scheme to obtain a first segmentation result, wherein the first segmentation result comprises a plurality of image blocks;
extracting the plurality of image blocks in the horizontal and vertical directions based on a preset step length to form a to-be-processed image block set, wherein the to-be-processed image block set comprises a plurality of to-be-processed image blocks;
sequentially performing feature analysis on the image blocks to be processed to form a first feature analysis result set, wherein the first feature analysis result set comprises a plurality of feature analysis results;
and screening and determining a first characteristic analysis result according to the plurality of characteristic analysis results, and taking the first characteristic analysis result as the first processing result.
4. The method of claim 3, wherein the generating a second performance evaluation further comprises:
sequentially carrying out a knocking test on the first service steel pipe and the first standard steel pipe based on a preset knocking scheme;
carrying out real-time sound collection on the knocking test by using a sound sensor to respectively obtain a first service sound signal and a first standard sound signal;
sequentially generating a first service signal curve of the first service sound signal and a first standard signal curve of the first standard sound signal;
fitting the first service signal curve with the first standard signal curve to obtain a first fitting degree;
the second performance assessment is adjusted according to the first fitness.
5. The method of claim 1, wherein the building a steel pipe damage database based on big data comprises:
training according to the history damaged steel pipe record to obtain a steel pipe damage type support vector machine;
building a steel pipe damage set, wherein the steel pipe damage set comprises a plurality of damaged steel pipes;
sequentially collecting the characteristics of each damaged steel pipe in the damaged steel pipes, inputting the characteristics into the steel pipe damage type support vector machine as input information, and respectively obtaining a plurality of damage types of the damaged steel pipes;
according to the damage types, the damaged steel pipes are divided and combined to obtain a first combined result, wherein the first combined result comprises a first type steel pipe damage set and a second type steel pipe damage set;
and constructing the steel pipe damage database according to the first type steel pipe damage set and the second type steel pipe damage set.
6. The method of claim 1, wherein said traversing in said steel pipe damage database based on said first damage data to obtain first fitting data comprises:
extracting first steel pipe damage data of the steel pipe damage database, and taking the first steel pipe damage data as the first adaptive data;
calculating to obtain a first matching index according to the first damage data and the first steel pipe damage data;
constructing a first neighborhood of the first adaptive data based on a preset neighborhood scheme, wherein the first neighborhood comprises multiple groups of candidate steel pipe damage data;
sequentially calculating matching indexes of the first damage data and the multiple groups of candidate steel pipe damage data to form a plurality of matching indexes;
comparing the plurality of matching indexes, and screening a first optimal matching index of the first neighborhood;
if the first optimal matching index is superior to the first matching index, reversely matching candidate steel pipe damage data of the first optimal matching index, recording the candidate steel pipe damage data as second steel pipe damage data, and replacing the first steel pipe damage data with the second steel pipe damage data;
and if the iteration optimization reaches the preset iteration times, outputting the obtained first steel pipe damage data as the first adaptive data.
7. The method of claim 6, wherein said back-matching candidate steel pipe damage data of said first optimal match index is denoted as second steel pipe damage data, comprising:
sequentially marking the first steel pipe damage data and the second steel pipe damage data as a first contraindication mark and a second contraindication mark;
calculating the taboo duration of the first taboo mark and the second taboo mark in sequence to obtain a first taboo duration and a second taboo duration;
when the first taboo duration meets a preset taboo period limit, removing the first taboo mark of the first steel pipe damage data;
and when the second taboo duration meets the preset taboo period limit, removing the second taboo mark of the second steel pipe damage data.
8. A steel pipe performance evaluation system based on a service scene is applied to the method of any one of claims 1 to 7, and the system comprises:
a first obtaining unit: the first obtaining unit is used for detecting the quality of the first standard steel pipe by using the Hall sensor to obtain a first standard detection result;
a second obtaining unit: the second obtaining unit is used for calculating the complexity of the first standard detection result according to the idea of approximate entropy algorithm to obtain a first standard approximate entropy;
a first building unit: the first construction unit is used for constructing a steel pipe damage database based on big data, wherein the steel pipe damage database comprises damage data of a plurality of damaged steel pipes;
a third obtaining unit: the third obtaining unit is used for obtaining first damage data of a first service steel pipe, and traversing the steel pipe damage database based on the first damage data to obtain first adaptive data;
the first calculation unit: the first calculation unit is used for reversely matching a first damaged steel pipe according to the first adaptive data and calculating a first damage approximate entropy of the first damaged steel pipe;
a first setting unit: the first setting unit is used for comparing the first damage approximate entropy with the first standard approximate entropy, determining a first damage degree of the first damaged steel pipe, and taking the first damage degree as the damage degree of the first service steel pipe;
a first generation unit: the first generation unit is used for generating a first performance evaluation according to the first damage degree.
9. An electronic device comprising a processor and a memory;
the memory is used for storing;
the processor is used for executing the method of any one of claims 1-7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.
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| 黄炳琼等: "近似熵在钢管无损检测实验信号分析中的应用", 《实验技术与管理》 * |
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| CN116309556B (en) * | 2023-05-15 | 2023-07-21 | 安徽数智建造研究院有限公司 | Steel member finished product quality management method based on machine vision technology |
| CN117236572A (en) * | 2023-11-14 | 2023-12-15 | 深圳市共安实业发展有限公司 | Method and system for evaluating performance of dry powder extinguishing equipment based on data analysis |
| CN117236572B (en) * | 2023-11-14 | 2024-03-29 | 深圳市共安实业发展有限公司 | Method and system for evaluating performance of dry powder extinguishing equipment based on data analysis |
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