CN113420157A - Steel product surface longitudinal crack defect traceability analysis method based on knowledge graph - Google Patents

Steel product surface longitudinal crack defect traceability analysis method based on knowledge graph Download PDF

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CN113420157A
CN113420157A CN202110584742.3A CN202110584742A CN113420157A CN 113420157 A CN113420157 A CN 113420157A CN 202110584742 A CN202110584742 A CN 202110584742A CN 113420157 A CN113420157 A CN 113420157A
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张云贵
刘自恒
李卓卿
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Automation Research and Design Institute of Metallurgical Industry
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Abstract

A method for analyzing the traceability of surface longitudinal crack defects of steel products based on a knowledge graph belongs to the technical field of steel product quality tracing. The method comprises the steps of adopting a top-down knowledge map construction route, carrying out knowledge combing by means of industry experts in the aspect of longitudinal crack defects on the surface of the steel product, designing a body of the traceability knowledge map of the longitudinal crack defects on the surface of the steel product, and establishing an entity and a relation of the knowledge map by combining actual data of enterprises to construct a cross-process traceability knowledge map of the longitudinal crack defects on the surface of the steel product. The method has the advantages that the knowledge graph visualization technology is used for representing the transmission relation between the quality defects and the quality events of the whole process, the limitation of the process is broken, and the cross-process traceability analysis of the longitudinal crack defects on the surface of the steel product is realized.

Description

Steel product surface longitudinal crack defect traceability analysis method based on knowledge graph
Technical Field
The invention belongs to the technical field of quality tracing of steel products, and particularly relates to a knowledge graph-based method for tracing and analyzing surface longitudinal crack defects of steel products.
Technical Field
In the original tracing of the quality defect of the surface longitudinal crack of the steel product, the whole production flow of the steel product with the quality defect of the surface longitudinal crack is obtained through the correlation of specific fields or product ID in a relational database, and then the source tracing is carried out through corresponding data analysis. The technical personnel need to clearly know the complex association relationship among the data tables and the meaning and the association influence of each production parameter recorded in the data tables. However, the cause of the formation of the surface longitudinal crack defect is complex, and the influence of other quality events without parameter record and the transmission effect of the quality events among all the processes due to heredity and relevance are ignored to a certain extent by analyzing the data.
Disclosure of Invention
The invention aims to provide a method for analyzing the traceability of the surface longitudinal crack defects of steel products based on an intellectual map, which describes the relationship between quality events occurring in the steel production process and the final surface longitudinal crack defects of the steel products by using a graph mode, and is more reasonable and more flexible instead of simply describing the relationship by using a relational database table. The field association of the table structure in the original relational database is replaced by a graph mode, a steel product quality defect cause knowledge graph is constructed by means of expert knowledge from top to bottom, the expert knowledge is normalized through an ontology, and the expert knowledge is precipitated. The knowledge graph visualization technology is used for representing the transmission relation between the quality defects and the quality events of the whole process, so that the limitation of the process is broken, and the cross-process traceability analysis of the longitudinal crack defects on the surface of the steel product is realized.
The method adopts a top-down knowledge map construction route, carries out knowledge carding by means of industry experts in the aspect of longitudinal crack defects on the surface of the steel product, designs a body of the traceability knowledge map of the longitudinal crack defects on the surface of the steel product, and creates entities and relations of the knowledge map by combining actual data of enterprises to construct the traceability knowledge map of the longitudinal crack defects on the surface of the steel product across processes. The method specifically comprises the following steps:
(1) constructing a source tracing knowledge map body of the surface longitudinal crack defects of the steel product based on expert experience knowledge;
(2) based on the ontology and the actual production condition, constructing an entity example and a relation example to form a traceability knowledge map of the surface longitudinal crack defects of the steel product: the example corresponding to the model in the knowledge graph body is a specific event which can occur in the process of processing and producing the steel product. For example: entities corresponding to the quality event model are working steps of steel products in the production process, such as 'converter slag tapping', 'converter reblowing' in the converter process, and 'LF argon static blowing time inconsistency' in the LF refining process; the entity corresponding to the abnormal event model is an abnormal situation which may occur in each process step (quality event entity), such as "excessive converter tapping temperature drop", "2 times of converter reblowing" and the like which may occur in a converter process. And (3) building entities, wherein each entity corresponds to a certain ontology model, and then establishing specific relations among the entities for all the entities according to the relations among the ontology models.
(3) According to the established knowledge graph entity network, the possible cause path of the surface longitudinal crack quality defect of the steel product is searched: inputting quality defects of steel products, such as surface longitudinal cracks, acquiring all entity nodes and relations pointing to the surface longitudinal crack nodes to form a map network with the surface longitudinal crack nodes as the center. And then inputting screening conditions according to actual conditions, performing attribute filtration on entity types and relationship types appearing in the network, and acquiring a cause path possibly causing surface longitudinal crack defects. For example, when a billet number of a product steel product with a surface longitudinal crack defect is input, a processing procedure for processing the billet number steel is obtained, and all cause paths which may cause the surface longitudinal crack in the processing procedure of the billet number steel product are screened.
(4) And analyzing by combining actual production data and events based on the searched possible paths, and reasoning out the real cause of the quality defect: according to the blank number of the steel product with the quality defect, on one hand, all processing procedures of the blank number product and abnormal events possibly occurring in the processing procedures are obtained, and then a production process parameter model and a judgment rule which can cause the abnormal events are obtained; on the other hand, the actual production parameters of the steel product are obtained from the production process database of the enterprise and are matched with the judgment rule, so that the path of the quality defect caused by the unmatched production rule is obtained. For example, the billet number of the steel product with the surface longitudinal crack defect is 200900011, the surface longitudinal crack is selected from the quality defects, and the billet number is input 20092300011, obtaining the product through systematic retrieval, wherein in the production process of the product, in the working procedure processing link of a converter with the furnace number of 20092301, the temperature drop is overlarge in the tapping process of the converter, and T isEnd point temperature-TTemperature of baleThe temperature is 73 ℃ and is 65 ℃ higher than the critical value of the judging rule endpoint, so that the surface longitudinal crack defect occurs. Therefore, the analysis is output (decision rule { rule description: "at converter tapping, T)End point temperature-TTemperature of bale≥ 65 ℃ ", rule id: 10}) ->(parameter: [ "T [) parameterEnd point temperature”,”TTemperature of bale”]Metadata { converter monitoring sensor database } }) ->(abnormal event name: converter tapping temperature drop is too large; occurrence position: all casting blanks produced by the converter, belonging to equipment: converter equipment name; belonging to procedure: converter)>(quality event name: converter tapping temperature drop, belonging to the process: converter->(quality defect) (defect name: "surface longitudinal crack", belonging to the process: "continuous casting") the cause path;
generating a cross-process cause key path for the longitudinal crack defects on the surface of the steel product through attribute filtering, and outputting a specific quality event chain; meanwhile, a steel product quality defect traceability key path flow chart is established based on experience probability and data drive, the cause probability of each possible traceability path is quantitatively analyzed, and support is provided for quality decision.
The traceability analysis knowledge graph of the longitudinal crack defects on the surface of the steel product comprises five knowledge graph ontology models and interrelations. The specific ontology model is as follows:
quality defect model: refers to the quality defect event which can occur in the steel product, such as the defect of 'surface longitudinal crack'. The quality defect ontology model comprises two attributes of a defect name and a process to which the defect name belongs. The out-of-relationship "produce" points to the failed product model, with the relationship being weighted. Certain quality defects can cause product failure of steel products in the product using process, and the weight is the influence degree of the product defects on the product failure.
Quality event model: refers to the process events in the steel product processing process. The quality event ontology model comprises two attributes of a quality event name and a process to which the quality event belongs. The relation 'result' points to a quality defect model, the relation is accompanied by a weight, a certain quality event in the process of processing the steel product can cause the appearance of a certain quality defect, and the weight refers to the influence degree on the quality defect; the relation "conduct" points to the relation, the relation is accompanied by a weight, each process is composed of a plurality of quality events, the quality events in different processes have mutual influence, and the weight refers to the influence degree between different quality events.
An abnormal event model: refers to the abnormal situation that may occur in a certain quality event in the process of processing steel products. The abnormal event model comprises four attributes of the name, the occurrence position, the equipment and the process of the abnormal event, and the relation of 'belonging' to the pointing quality event model.
Parameter model: the method refers to an influence parameter for judging whether an abnormal event occurs in the process of processing the steel product. The judgment rule model comprises two attributes of parameter names and metadata, and the output relation points to the abnormal event. Metadata attributes record information such as the location of the parameter in the database table and the access method or interface. There are cases where a plurality of parameters jointly determine the occurrence of an abnormal event.
Judging a rule model: refers to the decision rule of the corresponding parameter. The judgment rule model comprises two attributes of rule description and rule ID, and the relationship points to the parameter model. The rule ID is the serial number of a rule in the rule base.
Drawings
FIG. 1 is a body model diagram of a knowledge map for tracing analysis of quality defects of longitudinal cracks on the surface of a steel product.
FIG. 2 is a knowledge graph entity node and relationship graph constructed based on a knowledge graph ontology model.
Detailed Description
The technical solution of the present invention will be described in more detail below with reference to the accompanying drawings:
the method of the invention is based on a knowledge graph. A knowledge graph is essentially a semantic network in which nodes represent entities or concepts and edges represent various semantic relationships between entities/concepts. Each model in the knowledge graph ontology is a concept in an abstract meaning, and entity nodes corresponding to the ontology models are the appearance of the ontology concept in reality.
The method adopts a top-down mode to construct the source-tracing analysis knowledge graph of the longitudinal crack defects on the surface of the steel product, so that the ontology model of the knowledge graph and the relation between the models need to be designed and defined. And summarizing cross-process causes and related quality events of the surface longitudinal crack defects of the steel products by using experts in the related field, and establishing an ontology and defining a relation between the ontologies in a manual modeling mode.
The traceability knowledge map body of the longitudinal crack defects on the surface of the steel product comprises a quality defect model, a quality event model, an abnormal event model, a parameter model, a judgment rule model, a failure product model, a solution model and relations among the models. The model specific information has been detailed in the summary of the invention.
And after the knowledge map ontology model is designed, constructing a cross-process steel product surface longitudinal crack defect traceability knowledge map based on the ontology model and in combination with a data source in a steel enterprise production database. As shown in fig. 2.
The following describes embodiments by way of a specific example:
the quality defect model refers to a quality defect event which can occur in a steel product, and a corresponding entity node can be established (a: quality defect { defect name: "surface longitudinal crack", belonging to the procedure: "continuous casting");
in the production of steel products, the steel products undergo a series of processing steps and steps. The quality event model refers to process events that are passed through during the process. The corresponding quality event entity node (b: { quality event name: "crystallizer liquid level does not meet", belonging process: "converter") can be established, and meanwhile, according to the knowledge graph body model, the relationship of (b) - > (a) exists, and the influence degree relationship weight is 0.6. Because different process steps exist in the same process and the represented quality events also have mutual influence, the method can establish a quality event entity node (c: { quality event name: 'single casting blank pulling speed fluctuation', belonging to the process: 'continuous casting'), and has (c) - > (a) according to the body model relation, and the relation weight is 0.4; meanwhile, according to the actual production link sequence of the steel product, the relation weight is 0.4, wherein the relation is (c) - > (b).
In the process of processing steel products, abnormal conditions may occur in a certain quality event. Therefore, an abnormal event entity node (d: an abnormal event { the name of the abnormal event: '0 < the pulling speed fluctuation < 0.1 m/min', 'all casting blanks produced by the furnace', the equipment, the continuous casting machine and the process are established), and according to the body model relationship, the relationship (d) - > (c) exists, and the influence weight is 0.2.
The influencing parameters of whether the abnormal event occurs are the actual pulling speed maximum value and the pulling speed minimum value, so that the parameters (e: parameter name:
Figure RE-GDA0003212099910000061
metadata { continuous casting machine monitoring sensor database } } according to the ontology model relationship, the existence (e) ->(d)。
The rule decision model refers to a decision rule of parameters corresponding to the occurrence of an abnormal event, so that for parameters related to the entity node (e), a corresponding rule is used for deciding the entity node (f: decision rule { rule description: "during continuous casting, for a single casting blank, pulling fluctuation of a continuous casting machine is between 0.1m/min and 0.25m/min,
Figure RE-GDA0003212099910000062
rule id: 88}) in combination with (f) ->(e)。
All data related to the entity nodes are derived from the actual situation of the iron and steel enterprises in the actual production and processing process, and the data source is mainly semi-structured data. And establishing the steel enterprise data into a CSV format semi-structured data table based on the knowledge graph ontology model and the relation rule. And compiling scripts to extract knowledge, selectively extracting fields in the CSV table into attributes and access relations of entity nodes, and performing knowledge mapping on the entity attributes after information extraction and the defined body attributes to complete the construction of the iron product surface longitudinal crack defect traceability knowledge map. Wherein, the number of the quality defect entity nodes is 1, the number of the quality event entity nodes is 59, the number of the abnormal event entity nodes is 119, the number of the parameter entity nodes is 60, and the number of the judgment rule instances is 93.
And establishing an abstract relation between a Cypher graph database query language and actual use requirements for the constructed tracing knowledge graph of the surface longitudinal crack defects of the steel product.
And (3) carrying out product traceability analysis on the product with the surface longitudinal crack defect by means of a knowledge graph:
firstly, the billet number of the steel product with the surface longitudinal crack defect is obtained, so that the processing steps of the billet number product and the production parameters in the corresponding processing equipment monitoring sensor are known.
Taking the steel product with the product blank number of "200900011" as an example, the converter process number of "20092301" and the production process parameters of the product are obtained. And then, performing attribute filtering in the knowledge graph, and filtering out quality events belonging to the converter process, abnormal events belonging to the converter process and related parameters. This resulted in all the causative paths of surface longitudinal crack defects associated with the "200900011" billet product. And finally, analyzing, and matching the production process parameters of the '20092301' furnace number with the production rules to which the parameter entity nodes belong. If the determination rule is not satisfied, it means that the corresponding production process parameter is abnormal in the production process, and finally the surface longitudinal crack defect is caused to appear. And deducing a real cause causing the defect, and outputting a cause path pointing to the surface longitudinal crack defect entity node from the entity node of the judgment rule so as to provide support for quality decision.

Claims (2)

1. A method for analyzing the traceability of surface longitudinal crack defects of steel products based on a knowledge graph is characterized by comprising the following steps:
(1) constructing a source tracing knowledge map body of the surface longitudinal crack defects of the steel product based on expert experience knowledge;
(2) based on the ontology and the actual production condition, constructing an entity example and a relation example to form a traceability knowledge map of the surface longitudinal crack defects of the steel product: the example corresponding to the model in the knowledge graph body is a specific event which can occur in the process of processing and producing the steel product. For example: the entity corresponding to the quality event model is the working steps of the steel product in the production process, and comprises converter slag tapping, converter supplementary blowing and LF argon static blowing time in an LF refining process which are not consistent; the entity corresponding to the abnormal event model is an abnormal condition which may occur to the quality event entity in each process step, and comprises 'the excessive reduction of the tapping temperature of the converter' and 'the 2 times of additional blowing of the converter' which may occur to the converter process; building entities, wherein each entity corresponds to a certain ontology model, and then building specific relations among the entities for all the entities according to the relations among the ontology models;
(3) according to the established knowledge graph entity network, the possible cause path of the surface longitudinal crack quality defect of the steel product is searched: inputting quality defects of the steel product, such as surface longitudinal cracks, acquiring all entity nodes and relations pointing to the surface longitudinal crack nodes to form a map network taking the surface longitudinal crack nodes as a center; then inputting screening conditions according to actual conditions, performing attribute filtration on entity types and relationship types appearing in the network, and acquiring a cause path possibly causing surface longitudinal crack defects; inputting the blank number of the product steel product with the surface longitudinal crack defect, and obtaining a processing procedure for processing the blank number steel, thereby screening all cause paths which can cause the surface longitudinal crack in the processing procedure of the blank number steel product.
(4) And analyzing by combining actual production data and events based on the searched possible paths, and reasoning out the real cause of the quality defect: according to the blank number of the steel product with the quality defect, on one hand, all processing procedures of the blank number product and abnormal events possibly occurring in the processing procedures are obtained, and then a production process parameter model and a judgment rule which can cause the abnormal events are obtained; on the other hand, the actual production parameters of the steel product are obtained from the production process database of the enterprise and are matched with the judgment rule, so that a path of the quality defect caused by the unmatched production rule is obtained; selecting surface stringers in quality defectsCracks are obtained through systematic retrieval, and in the production process of the product, in the working procedure processing link of the converter, the temperature drop in the tapping process of the converter is overlarge, and T isEnd point temperature-TTemperature of baleThe temperature is 73 ℃ and is 65 ℃ higher than the critical value of the judging rule end point, so that the surface longitudinal crack defect occurs; output after analysis (decision rule { rule description: "at tapping of converter, TEnd point temperature-TTemperature of bale≥ 65 ℃ ", rule id: 10}) ->(parameter: [ "T [) parameterEnd point temperature”,”TTemperature of bale”]Metadata { converter monitoring sensor database } }) ->(abnormal event name: converter tapping temperature drop is too large; occurrence position: all casting blanks produced by the converter, belonging to equipment: converter equipment name; belonging to procedure: converter)>(quality event name: converter tapping temperature drop, belonging to the process: converter->(quality defect) (defect name: "surface longitudinal crack", belonging to the process: "continuous casting") the cause path;
generating a cross-process cause key path for the longitudinal crack defects on the surface of the steel product through attribute filtering, and outputting a specific quality event chain; meanwhile, a steel product quality defect traceability key path flow chart is established based on experience probability and data drive, the cause probability of each possible traceability path is quantitatively analyzed, and support is provided for quality decision.
2. The method for analyzing the traceability of the surface longitudinal crack defects of the steel product based on the knowledge graph according to claim 1,
the traceability analysis knowledge graph of the longitudinal crack defects on the surface of the steel product comprises five knowledge graph ontology models and interrelations. The specific ontology model is as follows:
quality defect model: the method comprises the following steps that quality defect events which can occur in steel products are referred to, surface longitudinal cracks are referred to, a quality defect body model comprises two attributes of a defect name and a process which the quality defect body model belongs to, a relation of generation points to a failure product model, and a weight is attached to the relation; certain quality defects can cause product failure of steel products in the product using process, and the weight is the influence degree of the product defects on the product failure;
quality event model: refers to the process events in the steel product processing process. The quality event ontology model comprises two attributes of a quality event name and a process to which the quality event name belongs; the relation 'result' points to a quality defect model, the relation is accompanied by a weight, a certain quality event in the process of processing the steel product can cause the appearance of a certain quality defect, and the weight refers to the influence degree on the quality defect; the relation "conduction" points to the relation, the relation is accompanied by a weight, each process consists of a plurality of quality events, the quality events in different processes have mutual influence, and the weight refers to the influence degree among different quality events;
an abnormal event model: the method refers to abnormal conditions which may occur in a certain quality event in the process of processing the steel products; the abnormal event model comprises four attributes of the name, the occurrence position, the equipment and the process of the abnormal event, and the relation of belonging to the pointing quality event model is shown;
parameter model: the method comprises the steps of judging whether an abnormal event occurs in the process of processing the steel product or not according to an influence parameter, judging whether a rule model comprises two attributes of a parameter name and metadata, and enabling a relation to point to the abnormal event; metadata attributes record information such as the location of the parameter in the database table and the access method or interface. The situation that a plurality of parameters jointly determine the generation of an abnormal event exists;
judging a rule model: and the judgment rule model comprises two attributes of rule description and rule ID, the relationship points to the parameter model, and the rule ID is the serial number of a rule in the rule base.
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CN114943415A (en) * 2022-04-15 2022-08-26 上海波士内智能科技有限公司 Metal welding defect root cause analysis method based on knowledge graph
CN114881474A (en) * 2022-05-09 2022-08-09 山东大学 Tire full life cycle quality tracing method and system based on knowledge graph
CN114969364A (en) * 2022-05-23 2022-08-30 冶金自动化研究设计院有限公司 Knowledge graph construction method of enterprise production material tracking system
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CN116523039B (en) * 2023-04-26 2024-02-09 华院计算技术(上海)股份有限公司 Continuous casting knowledge graph generation method and device, storage medium and terminal

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