CN115980176B - Spherical tank quality data analysis processing method and system based on magnetic powder detection - Google Patents
Spherical tank quality data analysis processing method and system based on magnetic powder detection Download PDFInfo
- Publication number
- CN115980176B CN115980176B CN202310265165.0A CN202310265165A CN115980176B CN 115980176 B CN115980176 B CN 115980176B CN 202310265165 A CN202310265165 A CN 202310265165A CN 115980176 B CN115980176 B CN 115980176B
- Authority
- CN
- China
- Prior art keywords
- information
- partition
- spherical tank
- analysis
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
The invention discloses a spherical tank quality data analysis processing method and system based on magnetic powder detection, and relates to the field of data processing, wherein the method comprises the following steps: performing magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information; performing difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set, and determining an abnormal coefficient; determining the working relation of each partition; inputting the working relation of each subarea, the subarea information of the spherical tank and the corresponding abnormality coefficient into an abnormality analysis model to obtain the quality abnormality analysis information of the spherical tank; and determining the quality using grade of the spherical tank, and generating spherical tank analysis processing information according to the quality using grade. The quality control method and device for the spherical tank solve the technical problems that in the prior art, quality control accuracy for the spherical tank is low, and then quality control effect of the spherical tank is poor. The quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
Description
Technical Field
The invention relates to the field of data processing, in particular to a spherical tank quality data analysis processing method and system based on magnetic powder detection.
Background
The spherical tank has the advantages of small surface area, uniform internal stress, large bearing capacity and the like, and is widely applied to storage of mediums such as petroleum, natural gas, coal gas and the like, thereby playing a great role. Quality management is the prerequisite of guaranteeing spherical tank safety in utilization, along with spherical tank's wide application, carries out the continuous increase of the degree of difficulty of quality management to spherical tank. Meanwhile, the traditional spherical tank quality management mode has the defects of strong manual dependency, low automation level, poor quality management effect and the like. The research design of the method for optimizing quality management of the spherical tank has very important practical significance.
In the prior art, the quality control accuracy of the spherical tank is low, and therefore the technical problem of poor quality control effect of the spherical tank is caused.
Disclosure of Invention
The application provides a spherical tank quality data analysis processing method and system based on magnetic powder detection. The quality control method and device for the spherical tank solve the technical problems that in the prior art, quality control accuracy for the spherical tank is low, and then quality control effect of the spherical tank is poor. The multi-dimensional quality data analysis is carried out on the spherical tank, so that the quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
In view of the above problems, the present application provides a spherical tank quality data analysis processing method and system based on magnetic powder detection.
In a first aspect, the present application provides a method for analyzing and processing spherical tank quality data based on magnetic powder detection, where the method is applied to a spherical tank quality data analyzing and processing system based on magnetic powder detection, and the method includes: carrying out spherical tank structure partition according to spherical tank basic information to obtain spherical tank partition information; performing magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information; performing difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set, and determining an abnormal coefficient; performing functional analysis of each partition according to the spherical tank partition information, and determining the working relationship of each partition; inputting the working relation of each partition, the spherical tank partition information and the corresponding abnormality coefficient into an abnormality analysis model to obtain spherical tank quality abnormality analysis information; and determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade.
In a second aspect, the present application further provides a spherical tank quality data analysis processing system based on magnetic powder detection, where the system includes: the spherical tank partitioning module is used for partitioning the spherical tank structure according to the spherical tank basic information to obtain spherical tank partitioning information; the magnetic powder detection module is used for carrying out magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information; the difference analysis module is used for carrying out difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set and determining an abnormal coefficient; the functional analysis module is used for carrying out functional analysis of each partition according to the spherical tank partition information and determining the working relation of each partition; the abnormality analysis module is used for inputting the work relation of each partition, the spherical tank partition information and the corresponding abnormality coefficient into an abnormality analysis model to obtain spherical tank quality abnormality analysis information; and the processing information generation module is used for determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information and generating spherical tank analysis processing information based on the spherical tank quality use grade.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the spherical tank quality data analysis processing method based on magnetic powder detection when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, where the program is executed by a processor to implement a spherical tank quality data analysis processing method based on magnetic powder detection provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
carrying out spherical tank structure partition through spherical tank basic information to obtain spherical tank partition information; performing magnetic powder detection according to the spherical tank partition information to obtain partition magnetic conductivity detection information; determining an anomaly coefficient by performing differential analysis on the partition magnetic flux guide detection information and a preset partition magnetic flux guide set; determining the working relationship of each partition by carrying out functional analysis of each partition on the partition information of the spherical tank; inputting the working relation of each subarea, the subarea information of the spherical tank and the corresponding abnormality coefficient into an abnormality analysis model to obtain the quality abnormality analysis information of the spherical tank; and determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade. The multi-dimensional quality data analysis is carried out on the spherical tank, so that the quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a spherical tank quality data analysis processing method based on magnetic powder detection;
fig. 2 is a schematic flow chart of obtaining spherical tank partition information in the spherical tank quality data analysis processing method based on magnetic powder detection;
fig. 3 is a schematic structural diagram of a spherical tank quality data analysis processing system based on magnetic powder detection;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the device comprises a spherical tank partitioning module 11, a magnetic powder detection module 12, a difference analysis module 13, a function analysis module 14, an abnormality analysis module 15, a processing information generation module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides a spherical tank quality data analysis processing method and system based on magnetic powder detection. The quality control method and device for the spherical tank solve the technical problems that in the prior art, quality control accuracy for the spherical tank is low, and then quality control effect of the spherical tank is poor. The multi-dimensional quality data analysis is carried out on the spherical tank, so that the quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
Example 1
Referring to fig. 1, the present application provides a method for analyzing and processing spherical tank quality data based on magnetic powder detection, wherein the method is applied to a spherical tank quality data analyzing and processing system based on magnetic powder detection, and the method specifically comprises the following steps:
step S100: carrying out spherical tank structure partition according to spherical tank basic information to obtain spherical tank partition information;
further, step S100 of the present application further includes:
step S110: determining spherical tank structure information, spherical tank material information and spherical tank application information according to the spherical tank basic information;
step S120: according to the spherical tank structure information, carrying out structural element connection analysis to obtain structure partition information;
Step S130: according to the spherical tank application information, carrying out function bearing force analysis to obtain function partition information;
specifically, basic parameters of the spherical tank are acquired, and basic information of the spherical tank is obtained. The spherical tank basic information comprises spherical tank structure information, spherical tank material information and spherical tank application information. And further, carrying out structural element connection analysis based on the spherical tank structural information to obtain structural partition information. And carrying out function tolerance analysis based on the spherical tank application information to obtain the function partition information. The spherical tank structure information comprises structural parameter information such as structural composition, shape, volume, inner diameter parameter, outer diameter parameter, tank wall thickness parameter and the like of the spherical tank. The spherical tank material information comprises material parameter information such as material composition, material component content and the like of each position of the spherical tank. The spherical tank application information comprises application condition information such as storage medium type, storage medium shape, storage medium pressure, storage medium humidity and the like of the spherical tank. The structure partition information comprises a plurality of structure partitions such as a spherical tank body structure partition, a spherical tank supporting structure partition, a spherical tank operating structure partition and the like. The functional partition information comprises a plurality of functional partitions such as a spherical tank storage partition, a spherical tank pressure monitoring partition, a spherical tank heat insulation partition, a spherical tank cold insulation partition and the like. The method achieves the technical effects that reliable structural partition information and functional partition information are obtained by analyzing and partition matching the spherical tank structural information and the spherical tank application information, and a foundation is laid for the subsequent superposition, fusion and analysis of the structural partition information and the functional partition information through the spherical tank material information.
Step S140: and based on the spherical tank material information, carrying out superposition fusion analysis according to the structural partition information and the functional partition information to obtain the spherical tank partition information.
Further, as shown in fig. 2, step S140 of the present application further includes:
step S141: performing constraint condition analysis according to the spherical tank material information, and determining material constraint conditions;
step S142: according to the structural partition information and the functional partition information, conflict partition nodes are obtained;
step S143: and carrying out constraint force analysis on the conflict partition nodes according to the material constraint conditions, and determining conflict partition information based on constraint values to obtain the spherical tank partition information.
Specifically, constraint condition analysis is performed based on spherical tank material information, and material constraint conditions are obtained. The material constraint includes a plurality of constraint values. The plurality of constraint values include a plurality of material influencing parameters corresponding to the spherical tank material information. Illustratively, historical data queries are performed based on the spherical tank material information to obtain a plurality of historical spherical tank material parameters and a plurality of historical material influence parameters. And analyzing the mapping relation between the plurality of historical spherical tank material parameters and the plurality of historical material influence parameters, and arranging the plurality of historical spherical tank material parameters and the plurality of historical material influence parameters according to the mapping relation to obtain a material influence analysis database. And taking the spherical tank material information as input information, and inputting a material influence analysis database to obtain material constraint conditions.
And traversing the structural partition information and the functional partition information to perform partition comparison, and setting a plurality of structural partitions and a plurality of functional partitions with identical partitions as fusion partition nodes. And setting a plurality of structural partitions and a plurality of functional partitions with inconsistent partitions as conflict partition nodes. The conflict partition node includes a plurality of conflict partitions. The plurality of conflict partitions comprise a plurality of structure partitions and a plurality of function partitions, wherein the structure partitions are inconsistent in partition. And matching the conflict partition nodes according to a plurality of constraint values in the material constraint conditions to obtain a plurality of matched constraint values. The plurality of matching constraint values includes a plurality of constraint values corresponding to the plurality of conflicting partitions. And dividing conflict partitions with the same matching constraint value into matching conflict partitions, adding the matching conflict partitions into conflict partition information, and combining fusion partition nodes to obtain spherical tank partition information. The conflict partition information includes a plurality of matching conflict partitions. Each matching conflict partition includes multiple conflict partitions with the same matching constraint value. The spherical tank partition information includes a plurality of spherical tank partition parameters. The plurality of spherical tank partition parameters include conflicting partition information and a fusion partition node. The technical effects of overlapping, fusing and analyzing the structural partition information and the functional partition information through the spherical tank material information to obtain spherical tank partition information, and improving the adaptation degree and the reliability of spherical tank quality management are achieved.
Step S200: performing magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information;
specifically, magnetic powder detection is performed according to the spherical tank partition information, and partition magnetic conductivity detection information is obtained. The magnetic powder detection means that the spherical tank is placed in a strong magnetic field or a large current is applied to the spherical tank, so that the spherical tank is magnetized, and if defects exist on the surface of the spherical tank, the resistance of the defects to the passage of magnetic lines is large, and the magnetic lines can generate a leakage magnetic field near the defects. And (3) applying magnetic powder to the magnetized spherical tank, wherein a leakage magnetic field near the defect can absorb the magnetic powder to form a visible magnetic powder trace, so that the defect is displayed. And meanwhile, the magnetized spherical tank is subjected to magnetic conductivity detection through magnetic conductivity detection equipment, so that a plurality of magnetic conductivity detection parameters are obtained. The magnetic permeability detection device may be a magnetic permeability detector in the prior art. The partitioned magnetic flux guide detection information comprises a plurality of partitioned magnetic flux guide detection data corresponding to the spherical tank partitioned information. The magnetic conductivity detection data of each subarea comprise defect positions, defect shapes, defect areas, defect quantity and the like corresponding to the subarea parameters of each spherical tank, and magnetic conductivity parameters. The magnetic powder detection is carried out according to the spherical tank partition information, and partition magnetic conductivity detection information is obtained, so that the technical effect of accuracy in quality management of the spherical tank is improved.
Step S300: performing difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set, and determining an abnormal coefficient;
further, step S300 of the present application further includes:
step S310: establishing partition standard magnetic conductance information based on the spherical tank partition information;
step S320: obtaining a target spherical tank working requirement, and determining a tolerance coefficient according to the target spherical tank working requirement;
step S330: constructing a preset partition magnetic flux guide set according to the tolerance coefficient and the partition standard magnetic flux guide information;
step S340: acquiring magnetic flux detection equipment and detection environment information;
step S350: obtaining detection constraint conditions according to the magnetic conductance detection equipment and the detection environment information;
step S360: and carrying out detection influence analysis according to the detection constraint conditions to obtain a correction coefficient, and correcting the partition magnetic conductance detection information based on the correction coefficient.
Specifically, standard magnetic permeability parameters are set according to the spherical tank partition information, and partition standard magnetic permeability information is obtained. And determining a tolerance coefficient according to the working requirement of the target spherical tank, and combining the partition standard magnetic permeability information to obtain a preset partition magnetic permeability set. Further, the flux guide detection device and the detection environment information are set as detection constraint conditions. And obtaining a correction coefficient by detecting influence analysis on the detection constraint condition, and correcting the partition magnetic conductance detection information according to the correction coefficient.
The partition standard magnetic conductivity information comprises a plurality of standard magnetic conductivity parameters corresponding to the plurality of spherical tank partition parameters. The target spherical tank working requirements comprise working condition information such as working environment temperature, working environment humidity, working environment pressure, storage medium type, storage medium shape and the like of the spherical tank. The tolerance coefficient comprises a plurality of maximum permeability parameters corresponding to the spherical tank partition parameters. The partition standard magnetic flux guide information and the tolerance coefficient can be determined by looking up data through big data or expert setting. The preset partition magnetic permeability set comprises tolerance coefficients and partition standard magnetic permeability information. The detection environment information comprises an environment temperature parameter, an environment magnetic field parameter and an environment electric field parameter when the magnetized spherical tank is subjected to magnetic conductivity detection through the magnetic conductivity detection equipment. The detection constraint conditions comprise magnetic conduction detection equipment, detection environment information, and equipment parameters such as model specification, working voltage and the like of the magnetic conduction detection equipment. The correction coefficient can be used for representing the influence degree parameter of the detection constraint condition on the magnetic permeability detection. The higher the influence degree of the detection constraint condition on the magnetic permeability detection is, the larger the corresponding correction coefficient is.
Illustratively, a historical data query is performed based on the detection constraints, resulting in a plurality of historical detection constraints, a plurality of historical correction coefficients. And continuously self-training and learning the plurality of history detection constraint conditions and the plurality of history correction coefficients to a convergence state to obtain a detection influence evaluation model. And taking the detection constraint condition as input information, inputting a detection influence evaluation model, and carrying out detection influence analysis and detection influence parameter matching on the detection constraint condition through the detection influence evaluation model to obtain a correction coefficient. And subtracting the plurality of detected permeability parameters from the partition magnetic permeability detection information and the correction coefficient to obtain partition magnetic permeability correction information, and updating the data of the partition magnetic permeability detection information through the partition magnetic permeability correction information, so that the accuracy of the partition magnetic permeability detection information is improved.
The technical effects of obtaining the correction coefficient by detecting influence analysis on the detection constraint condition and adaptively correcting the partition magnetic permeability detection information according to the correction coefficient are achieved, so that the influence of magnetic permeability detection equipment and detection environment on magnetic permeability detection is reduced, errors of the partition magnetic permeability detection information are reduced, and the accuracy of difference analysis on the partition magnetic permeability detection information is improved.
Further, step S300 of the present application further includes:
step S370: performing linear fitting according to the preset partition magnetic conductance set to obtain a magnetic conductance curve, wherein the magnetic conductance curve comprises a standard magnetic conductance curve and a tolerance magnetic conductance curve;
step S380: performing linear fitting according to the partitioned magnetic flux guide detection information to obtain a magnetic flux guide detection curve;
step S390: and carrying out difference degree analysis by using the magnetic conductance detection curve, the standard magnetic conductance curve and the tolerance magnetic conductance curve, and distributing values according to the difference coefficients of the standard magnetic conductance curve and the tolerance magnetic conductance curve to obtain the abnormal coefficients.
Specifically, curve construction is performed based on a preset partition magnetic permeability set to obtain a magnetic permeability curve, wherein the magnetic permeability curve comprises standard magnetic permeability curves corresponding to a plurality of standard magnetic permeability parameters in the preset partition magnetic permeability set and tolerance magnetic permeability curves corresponding to a plurality of maximum magnetic permeability parameters in the preset partition magnetic permeability set. And constructing a curve based on a plurality of detection permeability parameters in the partitioned flux guide detection information to obtain a flux guide detection curve. Further, the magnetic conductance detection curve and the standard magnetic conductance curve are subjected to difference degree comparison, and a plurality of standard magnetic conductance difference degrees are obtained. And comparing the difference degree of the magnetic conductance detection curve with the tolerance magnetic conductance curve to obtain a plurality of tolerance magnetic conductance difference degrees. And weighting calculation is carried out on the standard magnetic conductance differences and the tolerance magnetic conductance differences according to the standard difference coefficient distribution value and the tolerance difference coefficient distribution value, so that a plurality of partition abnormal coefficients are obtained. Wherein the anomaly coefficient comprises a plurality of partition anomaly coefficients. The plurality of standard permeance differences includes a plurality of difference information between a plurality of standard permeability parameters and a plurality of detected permeability parameters. The plurality of tolerated flux guide differences includes a plurality of difference information between a plurality of maximum permeability parameters and a plurality of detected permeability parameters. The standard difference coefficient distribution value and the tolerance difference coefficient distribution value comprise preset and determined standard difference weight coefficients and tolerance difference weight coefficients. Illustratively, when a plurality of partition anomaly coefficients are obtained, the standard deviation coefficient distribution value is multiplied by a plurality of standard permeance differences to obtain a plurality of standard deviation calculation results. And multiplying the tolerance difference coefficient distribution value and the plurality of tolerance flux guide difference degrees to obtain a plurality of tolerance difference degree calculation results. And adding the standard difference degree calculation results and the corresponding tolerance difference degree calculation results to obtain a plurality of partition abnormal coefficients. The technical effect of providing data support for the subsequent quality anomaly analysis of the spherical tank is achieved by carrying out difference analysis on the partition magnetic permeability detection information and the preset partition magnetic permeability set to generate anomaly coefficients.
In addition, defect evaluation standard setting is carried out based on spherical tank partition information and target spherical tank working requirements, and a defect evaluation standard database is obtained. And the plurality of spherical tank defect evaluation experts evaluate a plurality of detection defect parameters in the partitioned magnetic flux guide detection information according to the defect evaluation standard database to obtain a plurality of defect evaluation coefficients, and the plurality of defect evaluation coefficients are added to the anomaly coefficients, so that the comprehensiveness of data analysis on the partitioned magnetic flux guide detection information is improved, and the reliability of spherical tank quality anomaly analysis is improved. The defect evaluation standard database comprises spherical tank partition information and a plurality of defect evaluation standard data sets corresponding to target spherical tank working requirements. Each defect evaluation standard data set comprises a plurality of preset defect characteristics and a plurality of preset defect quality influence characteristic values corresponding to each spherical tank partition parameter. The larger the influence of the preset defect characteristics on the quality of the spherical tank is, the higher the corresponding preset defect quality influence characteristic value is. For example, the predetermined defect feature includes a plurality of spherical tank surface cracks of different predetermined states. The different preset states comprise the shape, position, size and the like of the surface cracks of the spherical tank. The preset states are different, and the corresponding preset defect quality influence characteristic values are also different. The plurality of defect evaluation coefficients are parameter information for characterizing the influence of a plurality of detected defect parameters on the quality of the spherical tank. The higher the quality impact of the detected defect parameters on the spherical tank, the larger the corresponding defect evaluation coefficients.
Step S400: performing functional analysis of each partition according to the spherical tank partition information, and determining the working relationship of each partition;
further, step S400 of the present application further includes:
step S410: obtaining functional information and working environment information of each partition according to the spherical tank partition information;
step S420: determining partition connection information according to the spherical tank partition information, generating a position label and marking partition detection information;
step S430: performing functional parameter analysis according to the functional information of each partition to obtain functional index information, generating a performance label and marking partition detection information;
step S440: performing environmental impact analysis according to the working environment information to obtain environmental impact index information, and generating a functional environment label to mark partition detection information;
step S450: and carrying out block association analysis according to the position label, the performance label and the functional environment label, and determining the working relation of each partition.
Specifically, functional parameters, working environment parameters and connection parameters of the spherical tank basic information are matched according to the spherical tank partition information, and partition functional information, working environment information and partition connection information are obtained. And generating a position label based on the partition connection information, and marking the partition detection information according to the position label. Further, functional parameter analysis is performed based on the functional information of each partition, and functional index information is obtained. Based on the functional index information, generating a performance tag, and marking the partition detection information according to the performance tag. And carrying out environmental impact analysis based on the working environment information to obtain environmental impact index information. Based on the environmental impact index information, generating a functional environment label, and marking the partition detection information according to the functional environment label. And further, carrying out partition association analysis based on the position label, the performance label and the functional environment label to obtain the working relation of each partition.
Wherein each partition function information includes a plurality of partition function data. The plurality of partition function data comprises a plurality of partition action information corresponding to the plurality of spherical tank partition parameters. The working environment information comprises a plurality of partition working environment data corresponding to the partition parameters of the spherical tanks. The working environment data of each partition comprise a working environment temperature parameter, a working environment humidity parameter, a working environment pressure parameter and the like corresponding to each spherical tank partition parameter. The partition connection information comprises partition connection relations, partition connection modes and partition connection positions corresponding to the partition parameters of the spherical tanks. The location tag includes partition connection information. The partition detection information comprises partition magnetic conductivity detection information and a plurality of partition working parameter information corresponding to the partition parameters of the spherical tanks. The functional index information comprises a plurality of functional index information corresponding to each partition functional information. For example, each partition function information includes a storage role corresponding to a spherical tank storage partition. The functional index information includes a storage volume index, a storage time index, a storage pressure index, and the like corresponding to the spherical tank storage partition. The performance tag includes functional index information. The environment influence index information comprises an environment temperature influence index, an environment humidity influence index, an environment pressure influence index and the like corresponding to the working environment information. The functional environmental label includes environmental impact index information. Each partition work relation comprises a position label, a performance label, a functional environment label, and partition position association relations, partition performance association relations and partition functional environment association relations corresponding to the plurality of spherical tank partition parameters. The technical effect of improving the accuracy of quality management of the spherical tank is achieved by carrying out multidimensional functional analysis of each partition on the regional information of the spherical tank to obtain the working relationship of each partition.
Step S500: inputting the working relation of each partition, the spherical tank partition information and the corresponding abnormality coefficient into an abnormality analysis model to obtain spherical tank quality abnormality analysis information;
further, step S500 of the present application further includes:
step S510: inputting the spherical tank partition information and the corresponding abnormality coefficients into a partition abnormality analysis sub-model to obtain partition abnormality analysis information;
step S520: inputting the working relation of each partition and the abnormal analysis information of each partition into an abnormal fusion analysis sub-model to obtain spherical tank linkage abnormal analysis information;
step S530: and outputting the abnormal analysis information of each partition and the spherical tank linkage abnormal analysis information as model output results.
Step S600: and determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade.
Specifically, the spherical tank partition information and the abnormality coefficient are used as input information, and the partition abnormality analysis submodel is input to obtain the abnormality analysis information of each partition. And taking the working relation of each partition and the abnormal analysis information of each partition as input information, inputting the input information into an abnormal fusion analysis sub-model, and obtaining spherical tank linkage abnormal analysis information. And outputting the abnormal analysis information of each subarea and the abnormal analysis information of spherical tank linkage as abnormal analysis information of spherical tank quality. The abnormality analysis model comprises a partition abnormality analysis sub-model and an abnormality fusion analysis sub-model. The partition abnormality analysis information comprises spherical tank partition information, partition abnormality information corresponding to abnormality coefficients, partition abnormality reasons and partition abnormality spherical tank quality influence information. The spherical tank linkage abnormality analysis information comprises a working relation of each subarea, spherical tank abnormality information corresponding to the abnormal analysis information of each subarea, spherical tank abnormality reasons and spherical tank abnormality quality influence information. The spherical tank quality abnormality analysis information comprises abnormal analysis information of each subarea and spherical tank linkage abnormality analysis information. Illustratively, when the anomaly analysis model is obtained, historical data query is performed based on each partition work relationship, spherical tank partition information and anomaly coefficients to obtain a first construction data set and a second construction data set. The first build data set includes a plurality of historical spherical tank partition information, a plurality of historical anomaly coefficients, a plurality of historical partition anomaly analysis information. The second construction data set comprises a plurality of historical partition working relations, a plurality of historical partition abnormality analysis data and a plurality of historical spherical tank linkage abnormality analysis information. And continuously self-training and learning the first constructed data set to a convergence state to obtain the partition abnormality analysis sub-model. The partition abnormality analysis sub-model has a function of performing partition abnormality analysis on the inputted spherical tank partition information and the abnormality coefficient. And (3) continuously self-training and learning the second constructed data set to a convergence state to obtain the abnormal fusion analysis sub-model. The abnormal fusion analysis sub-model has the function of fusion abnormality analysis on the input work relation of each partition and the abnormal analysis information of each partition. The partition anomaly analysis sub-model and the anomaly fusion analysis sub-model comprise an input layer, an implicit layer and an output layer. And connecting the output layer of the partition abnormality analysis sub-model with the input layer of the abnormality fusion analysis sub-model to obtain the abnormality analysis model.
Further, based on the spherical tank quality anomaly analysis information, matching the spherical tank quality use grade, and generating spherical tank analysis processing information according to the spherical tank quality use grade. Illustratively, when the spherical tank quality use level and the spherical tank analysis processing information are obtained, historical data query is performed based on the spherical tank quality abnormality analysis information to obtain a plurality of historical spherical tank quality abnormality analysis information, a plurality of historical spherical tank quality use levels and a plurality of historical spherical tank analysis processing information. The plurality of historical spherical tank analysis processing information comprises a plurality of historical spherical tank quality management schemes corresponding to a plurality of historical spherical tank quality use grades. For example, the historical spherical tank quality management scheme corresponding to a certain historical spherical tank quality use grade comprises the steps of reducing residual stress of the spherical tank through heat treatment, spraying proper preservative on the surface of the spherical tank, regularly opening the spherical tank for inspection, repairing welding and defect treatment on the spherical tank according to a repairing process and the like. And then, analyzing the matching relation among the plurality of historical spherical tank quality abnormal analysis information, the plurality of historical spherical tank quality use grades and the plurality of historical spherical tank analysis processing information to obtain a three-dimensional mapping relation. And arranging the plurality of historical spherical tank quality anomaly analysis information, the plurality of historical spherical tank quality use grades and the plurality of historical spherical tank analysis processing information according to the three-dimensional mapping relation to obtain a spherical tank analysis processing knowledge base. And taking the spherical tank quality abnormality analysis information as input information, and inputting the input information into a spherical tank analysis processing knowledge base to obtain spherical tank quality use grade and spherical tank analysis processing information. The method and the device achieve the technical effects that the abnormal analysis of the working relation of each subarea, the subarea information of the spherical tank and the abnormal coefficient is accurately and efficiently carried out in a multi-dimensional mode through the abnormal analysis model, the accurate abnormal analysis information of the quality of the spherical tank is obtained, the quality use grade of the spherical tank and the analysis processing information of the spherical tank are matched according to the abnormal analysis information of the quality of the spherical tank, and the quality management effect of the spherical tank is improved.
In summary, the spherical tank quality data analysis processing method based on magnetic powder detection provided by the application has the following technical effects:
1. carrying out spherical tank structure partition through spherical tank basic information to obtain spherical tank partition information; performing magnetic powder detection according to the spherical tank partition information to obtain partition magnetic conductivity detection information; determining an anomaly coefficient by performing differential analysis on the partition magnetic flux guide detection information and a preset partition magnetic flux guide set; determining the working relationship of each partition by carrying out functional analysis of each partition on the partition information of the spherical tank; inputting the working relation of each subarea, the subarea information of the spherical tank and the corresponding abnormality coefficient into an abnormality analysis model to obtain the quality abnormality analysis information of the spherical tank; and determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade. The multi-dimensional quality data analysis is carried out on the spherical tank, so that the quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
2. And carrying out superposition fusion analysis on the structural partition information and the functional partition information through the spherical tank material information to obtain spherical tank partition information, thereby improving the adaptation degree and the reliability of spherical tank quality management.
3. And detecting influence analysis is carried out on the detection constraint conditions to obtain a correction coefficient, and adaptive correction is carried out on the partition magnetic conductance detection information according to the correction coefficient, so that the influence of magnetic conductance detection equipment and detection environment on magnetic conductance detection is reduced, errors of the partition magnetic conductance detection information are reduced, and the accuracy of difference analysis on the partition magnetic conductance detection information is improved.
4. And the working relationship of each partition is obtained by carrying out multidimensional functional analysis on the partition information of the spherical tank, so that the accuracy of quality management on the spherical tank is improved.
Example two
Based on the same inventive concept as the method for analyzing and processing spherical tank quality data based on magnetic powder detection in the foregoing embodiment, the invention also provides a spherical tank quality data analyzing and processing system based on magnetic powder detection, please refer to fig. 3, the system includes:
the spherical tank partitioning module 11 is used for partitioning the spherical tank structure according to the spherical tank basic information to obtain spherical tank partitioning information;
the magnetic powder detection module 12 is used for carrying out magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductance detection information;
the difference analysis module 13 is used for carrying out difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set and determining an abnormal coefficient;
The function analysis module 14 is used for carrying out function analysis of each partition according to the spherical tank partition information and determining the working relation of each partition;
the abnormality analysis module 15 is configured to input the working relationships of each partition, the partition information of the spherical tank, and the corresponding abnormality coefficients into an abnormality analysis model, so as to obtain abnormal analysis information of spherical tank quality;
and the processing information generating module 16 is used for determining the spherical tank quality use grade according to the spherical tank quality abnormality analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade.
Further, the system further comprises:
the spherical tank information determining module is used for determining spherical tank structure information, spherical tank material information and spherical tank application information according to the spherical tank basic information;
the structure partition information obtaining module is used for carrying out structural element connection analysis according to the spherical tank structure information to obtain structure partition information;
the functional partition information acquisition module is used for carrying out functional bearing analysis according to the spherical tank application information to acquire functional partition information;
And the superposition fusion analysis module is used for carrying out superposition fusion analysis according to the structural partition information and the functional partition information based on the spherical tank material information to obtain the spherical tank partition information.
Further, the system further comprises:
the material constraint condition determining module is used for carrying out constraint condition analysis according to the spherical tank material information to determine material constraint conditions;
the conflict partition node obtaining module is used for obtaining conflict partition nodes according to the structural partition information and the functional partition information;
and the spherical tank partition information determining module is used for carrying out constraint force analysis on the conflict partition nodes according to the material constraint conditions, determining conflict partition information based on constraint values and obtaining the spherical tank partition information.
Further, the system further comprises:
the partition standard magnetic flux guide information obtaining module is used for establishing partition standard magnetic flux guide information based on the spherical tank partition information;
the tolerance coefficient determining module is used for obtaining the working requirement of the target spherical tank and determining a tolerance coefficient according to the working requirement of the target spherical tank;
The preset partition magnetic flux guide set construction module is used for constructing a preset partition magnetic flux guide set according to the tolerance coefficient and the partition standard magnetic flux guide information;
the detection information acquisition module is used for acquiring magnetic flux guide detection equipment and detection environment information;
the detection constraint condition obtaining module is used for obtaining detection constraint conditions according to the magnetic conductance detection equipment and the detection environment information;
and the correction module is used for carrying out detection influence analysis according to the detection constraint conditions to obtain correction coefficients, and correcting the partition magnetic permeability detection information based on the correction coefficients.
Further, the system further comprises:
the magnetic conductance curve obtaining module is used for carrying out linear fitting according to the preset partition magnetic conductance set to obtain a magnetic conductance curve, and the magnetic conductance curve comprises a standard magnetic conductance curve and a tolerance magnetic conductance curve;
the magnetic conductance detection curve obtaining module is used for carrying out linear fitting according to the partitioned magnetic conductance detection information to obtain a magnetic conductance detection curve;
The abnormal coefficient determining module is used for carrying out difference degree analysis on the magnetic conductance detection curve, the standard magnetic conductance curve and the tolerant magnetic conductance curve, and obtaining the abnormal coefficient according to the difference coefficient distribution value of the standard magnetic conductance curve and the tolerant magnetic conductance curve.
Further, the system further comprises:
the functional environment information obtaining module is used for obtaining functional information and working environment information of each partition according to the spherical tank partition information;
the position marking module is used for determining partition connection information according to the spherical tank partition information, generating a position label and marking partition detection information;
the performance marking module is used for carrying out functional parameter analysis according to the functional information of each partition to obtain functional index information, and generating a performance label to mark the partition detection information;
the functional environment marking module is used for carrying out environment influence analysis according to the working environment information to obtain environment influence index information and generating a functional environment label to mark partition detection information;
And the partition work relation determining module is used for analyzing the block relevance according to the position label, the performance label and the functional environment label and determining the work relation of each partition.
Further, the system further comprises:
the partition abnormality analysis information acquisition module is used for inputting the spherical tank partition information and the corresponding abnormality coefficients into a partition abnormality analysis sub-model to acquire partition abnormality analysis information;
the spherical tank linkage abnormality analysis information acquisition module is used for inputting the working relation of each partition and the abnormality analysis information of each partition into an abnormality fusion analysis sub-model to acquire spherical tank linkage abnormality analysis information;
the output module is used for outputting the abnormal analysis information of each partition and the abnormal analysis information of the spherical tank linkage as model output results.
The spherical tank quality data analysis processing system based on magnetic powder detection provided by the embodiment of the invention can execute the spherical tank quality data analysis processing method based on magnetic powder detection provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example III
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing a software program, a computer executable program and a module, such as a program instruction/module corresponding to a spherical tank quality data analysis processing method based on magnetic powder detection in the embodiment of the invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements the above-mentioned spherical tank quality data analysis processing method based on magnetic particle detection.
The application provides a spherical tank quality data analysis processing method based on magnetic powder detection, wherein the method is applied to a spherical tank quality data analysis processing system based on magnetic powder detection, and the method comprises the following steps: carrying out spherical tank structure partition through spherical tank basic information to obtain spherical tank partition information; performing magnetic powder detection according to the spherical tank partition information to obtain partition magnetic conductivity detection information; determining an anomaly coefficient by performing differential analysis on the partition magnetic flux guide detection information and a preset partition magnetic flux guide set; determining the working relationship of each partition by carrying out functional analysis of each partition on the partition information of the spherical tank; inputting the working relation of each subarea, the subarea information of the spherical tank and the corresponding abnormality coefficient into an abnormality analysis model to obtain the quality abnormality analysis information of the spherical tank; and determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade. The quality control method and device for the spherical tank solve the technical problems that in the prior art, quality control accuracy for the spherical tank is low, and then quality control effect of the spherical tank is poor. The multi-dimensional quality data analysis is carried out on the spherical tank, so that the quality management accuracy of the spherical tank is improved, the quality management effect of the spherical tank is improved, and a powerful guaranteed technical effect is provided for safe use of the spherical tank.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. The spherical tank quality data analysis processing method based on magnetic powder detection is characterized by comprising the following steps of:
carrying out spherical tank structure partition according to spherical tank basic information to obtain spherical tank partition information;
performing magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information;
performing difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set, and determining an abnormal coefficient;
performing functional analysis of each partition according to the spherical tank partition information, and determining the working relationship of each partition;
Inputting the working relation of each partition, the spherical tank partition information and the corresponding abnormality coefficient into an abnormality analysis model to obtain spherical tank quality abnormality analysis information;
determining a spherical tank quality use grade according to the spherical tank quality abnormality analysis information, and generating spherical tank analysis processing information based on the spherical tank quality use grade;
before performing difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set, the method comprises the following steps:
establishing partition standard magnetic conductance information based on the spherical tank partition information;
obtaining a target spherical tank working requirement, and determining a tolerance coefficient according to the target spherical tank working requirement;
constructing a preset partition magnetic flux guide set according to the tolerance coefficient and the partition standard magnetic flux guide information;
acquiring magnetic flux detection equipment and detection environment information;
obtaining detection constraint conditions according to the magnetic conductance detection equipment and the detection environment information;
and carrying out detection influence analysis according to the detection constraint conditions to obtain a correction coefficient, and correcting the partition magnetic conductance detection information based on the correction coefficient.
2. The method of claim 1, wherein the spherical tank structure partitioning is performed according to spherical tank basic information to obtain spherical tank partitioning information, comprising:
Determining spherical tank structure information, spherical tank material information and spherical tank application information according to the spherical tank basic information;
according to the spherical tank structure information, carrying out structural element connection analysis to obtain structure partition information;
according to the spherical tank application information, carrying out function bearing force analysis to obtain function partition information;
and based on the spherical tank material information, carrying out superposition fusion analysis according to the structural partition information and the functional partition information to obtain the spherical tank partition information.
3. The method of claim 2, wherein based on the spherical tank material information, performing superposition fusion analysis according to the structural partition information and the functional partition information to obtain the spherical tank partition information, comprising:
performing constraint condition analysis according to the spherical tank material information, and determining material constraint conditions;
according to the structural partition information and the functional partition information, conflict partition nodes are obtained;
and carrying out constraint force analysis on the conflict partition nodes according to the material constraint conditions, and determining conflict partition information based on constraint values to obtain the spherical tank partition information.
4. The method of claim 1, wherein determining anomaly coefficients based on the partition permeance detection information and a difference analysis of a preset partition permeance set comprises:
Performing linear fitting according to the preset partition magnetic conductance set to obtain a magnetic conductance curve, wherein the magnetic conductance curve comprises a standard magnetic conductance curve and a tolerance magnetic conductance curve;
performing linear fitting according to the partitioned magnetic flux guide detection information to obtain a magnetic flux guide detection curve;
and carrying out difference degree analysis by using the magnetic conductance detection curve, the standard magnetic conductance curve and the tolerance magnetic conductance curve, and distributing values according to the difference coefficients of the standard magnetic conductance curve and the tolerance magnetic conductance curve to obtain the abnormal coefficients.
5. The method of claim 1, wherein performing each partition functional analysis based on the spherical tank partition information to determine each partition operational relationship comprises:
obtaining functional information and working environment information of each partition according to the spherical tank partition information;
determining partition connection information according to the spherical tank partition information, generating a position label and marking partition detection information;
performing functional parameter analysis according to the functional information of each partition to obtain functional index information, generating a performance label and marking partition detection information;
performing environmental impact analysis according to the working environment information to obtain environmental impact index information, and generating a functional environment label to mark partition detection information;
And carrying out block association analysis according to the position label, the performance label and the functional environment label, and determining the working relation of each partition.
6. The method of claim 1, wherein inputting the respective partition operational relationships, the spherical tank partition information, and the corresponding anomaly coefficients into an anomaly analysis model to obtain spherical tank quality anomaly analysis information, comprising:
inputting the spherical tank partition information and the corresponding abnormality coefficients into a partition abnormality analysis sub-model to obtain partition abnormality analysis information;
inputting the working relation of each partition and the abnormal analysis information of each partition into an abnormal fusion analysis sub-model to obtain spherical tank linkage abnormal analysis information;
and outputting the abnormal analysis information of each partition and the spherical tank linkage abnormal analysis information as model output results.
7. Spherical tank quality data analysis processing system based on magnetic powder detection, characterized in that the system comprises:
the spherical tank partitioning module is used for partitioning the spherical tank structure according to the spherical tank basic information to obtain spherical tank partitioning information;
the magnetic powder detection module is used for carrying out magnetic powder detection based on the spherical tank partition information to obtain partition magnetic conductivity detection information;
The difference analysis module is used for carrying out difference analysis according to the partition magnetic permeability detection information and a preset partition magnetic permeability set and determining an abnormal coefficient;
the functional analysis module is used for carrying out functional analysis of each partition according to the spherical tank partition information and determining the working relation of each partition;
the abnormality analysis module is used for inputting the work relation of each partition, the spherical tank partition information and the corresponding abnormality coefficient into an abnormality analysis model to obtain spherical tank quality abnormality analysis information;
the processing information generation module is used for determining the spherical tank quality use grade according to the spherical tank quality abnormal analysis information and generating spherical tank analysis processing information based on the spherical tank quality use grade;
the partition standard magnetic flux guide information obtaining module is used for establishing partition standard magnetic flux guide information based on the spherical tank partition information;
the tolerance coefficient determining module is used for obtaining the working requirement of the target spherical tank and determining a tolerance coefficient according to the working requirement of the target spherical tank;
the preset partition magnetic flux guide set construction module is used for constructing a preset partition magnetic flux guide set according to the tolerance coefficient and the partition standard magnetic flux guide information;
The detection information acquisition module is used for acquiring magnetic flux guide detection equipment and detection environment information;
the detection constraint condition obtaining module is used for obtaining detection constraint conditions according to the magnetic conductance detection equipment and the detection environment information;
and the correction module is used for carrying out detection influence analysis according to the detection constraint conditions to obtain correction coefficients, and correcting the partition magnetic permeability detection information based on the correction coefficients.
8. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor, configured to implement the spherical tank quality data analysis processing method based on magnetic powder detection according to any one of claims 1 to 6 when executing the executable instructions stored in the memory.
9. A computer-readable medium on which a computer program is stored, characterized in that the program, when executed by a processor, realizes a spherical tank quality data analysis processing method based on magnetic particle detection as claimed in any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310265165.0A CN115980176B (en) | 2023-03-20 | 2023-03-20 | Spherical tank quality data analysis processing method and system based on magnetic powder detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310265165.0A CN115980176B (en) | 2023-03-20 | 2023-03-20 | Spherical tank quality data analysis processing method and system based on magnetic powder detection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115980176A CN115980176A (en) | 2023-04-18 |
| CN115980176B true CN115980176B (en) | 2023-06-23 |
Family
ID=85958204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310265165.0A Active CN115980176B (en) | 2023-03-20 | 2023-03-20 | Spherical tank quality data analysis processing method and system based on magnetic powder detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115980176B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8244504B1 (en) * | 2007-12-24 | 2012-08-14 | The University Of North Carolina At Charlotte | Computer implemented system for quantifying stability and flexibility relationships in macromolecules |
| CN108829925A (en) * | 2018-05-07 | 2018-11-16 | 上海电力学院 | A kind of offshore wind farm collector system optimum design method considering electromagnetic environment constraint |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013221743A (en) * | 2012-04-12 | 2013-10-28 | Jfe Steel Corp | Scratch detection sensitivity adjustment method, magnetic particle inspection method, and magnetic particle inspection device in magnetic particle inspection |
| CN109142509B (en) * | 2017-06-27 | 2022-08-16 | 宝山钢铁股份有限公司 | Round steel magnetic powder flaw detection method and device |
| CN107894456B (en) * | 2017-10-30 | 2021-04-20 | 共享铸钢有限公司 | Method for detecting integral magnetic powder of casting |
| KR102304750B1 (en) * | 2020-06-24 | 2021-09-24 | 주식회사 파워인스 | Non-destructive inspection method and system based on artificial intelligence |
| CN113702391B (en) * | 2021-09-08 | 2022-08-23 | 东力智能科技有限公司 | Method and device for compositely detecting defects on surface and near surface of steel billet |
| CN115689312A (en) * | 2022-11-15 | 2023-02-03 | 中国特种设备检测研究院 | System and method for intelligently generating inspection strategy of pressure-bearing equipment based on damage |
-
2023
- 2023-03-20 CN CN202310265165.0A patent/CN115980176B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8244504B1 (en) * | 2007-12-24 | 2012-08-14 | The University Of North Carolina At Charlotte | Computer implemented system for quantifying stability and flexibility relationships in macromolecules |
| CN108829925A (en) * | 2018-05-07 | 2018-11-16 | 上海电力学院 | A kind of offshore wind farm collector system optimum design method considering electromagnetic environment constraint |
Non-Patent Citations (1)
| Title |
|---|
| 管壁长弦投影数据截断问题的迭代重建算法;刘宝东;曾理;李林升;;《仪器仪表学报》(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115980176A (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109711663B (en) | Transformer substation oil-immersed transformer state evaluation and correction method and system based on big data analysis | |
| CN109255490B (en) | KPCA-BAS-GRNN-based buried pipeline external corrosion rate prediction method | |
| CN107016236B (en) | Power grid false data injection attack detection method based on nonlinear measurement equation | |
| CN103198437A (en) | Power grid measurement data and power grid model correction method and device | |
| CN112330488B (en) | Power grid frequency situation prediction method based on transfer learning | |
| CN113268883A (en) | Method for predicting corrosion rate of submarine crude oil pipeline based on PCA-ABC-SVM model | |
| TW202100900A (en) | Intelligent learning mechanism and adaptive regulation system for liquid gas filling | |
| CN115980176B (en) | Spherical tank quality data analysis processing method and system based on magnetic powder detection | |
| CN115603320A (en) | Real-time sensing and online evaluation method and system based on wide area measurement data | |
| CN114818361A (en) | Digital twin model construction method based on application scene | |
| CN111507591B (en) | Power system state determination method, device, computer medium and storage medium | |
| CN111340352A (en) | Manufacturing system energy efficiency evaluation method based on Bayesian model | |
| Li et al. | Predicting Software Quality by Optimized BP Network Based on PSO. | |
| Yeh et al. | Using fuzzy theory in% GR&R and NDC of measurement system analysis | |
| Fradi et al. | Conflict management for mechatronic systems design | |
| Xiong et al. | Reliability Assessment of Micro Grid Power Supply System Based on D-Vine Copula Model | |
| Zhou et al. | Advanced reliability analysis method for mechanisms based on uncertain measure | |
| CN112182739A (en) | Aircraft structure non-probability credible reliability topological optimization design method | |
| CN111061191A (en) | Distributed oil-gas storage tank remote operation and maintenance system and method | |
| Liu et al. | Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade | |
| CN112241597B (en) | Identification method, equipment and storage medium for harmonic source in power system | |
| Liu et al. | Research on Power Sensing Data Quality Assessment Method Based on Hierarchical Analysis Method | |
| CN116429976B (en) | Injection production and packaging quality detection method and system | |
| Wang et al. | Filter Design for System Modeling, State Estimation and Fault Diagnosis | |
| Xie et al. | Sizing of metallic foam bubble flaws using direct current potential drop signals with the help of the neural network method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |