CN-121998236-A - Steel analysis laboratory intelligent monitoring management system based on multisource data fusion

CN121998236ACN 121998236 ACN121998236 ACN 121998236ACN-121998236-A

Abstract

The invention belongs to the technical field of industrial automation and intelligent manufacturing, and particularly discloses a steel analysis laboratory intelligent monitoring management system based on multi-source data fusion, which comprises a parameter monitoring module, a procedure judging module, a path decision module, a deviation analyzing module, a deviation propagation judging module and a health evaluating module; according to the invention, the running state parameters and the material quality parameters of the steel samples in each process are collected in real time, the preset steel quality detection standard is combined to judge whether the current process is qualified, the problem that the equipment monitoring and the quality detection are mutually disjointed is solved, meanwhile, the state deviation degree of the previous process and the real-time monitoring parameters of the subsequent process are subjected to fusion analysis, the quality influence evaluation of the process is established, the propagation path of the quality deviation between the processes is dynamically tracked, and the process set value of the subsequent process is accurately adjusted based on the deviation of the previous process, so that the prior intervention of the quality risk is realized.

Inventors

Tan Zezhuo
WANG HONGYUE
LEI AIDI
ZHANG BIN
LI SHUXIN
ZHANG BOXIANG
LI CHUNYU
XU ZHILONG
HUANG PING

Assignees

鞍钢广州汽车钢有限公司

Dates

Publication Date: 20260508
Application Date: 20251225

Claims (10)

1. Steel analysis laboratory intelligent monitoring management system based on multisource data fusion, which is characterized by comprising: The parameter monitoring module monitors equipment operation state parameters of the steel sample in the current working procedure and material quality parameters of the steel sample in real time; The process judging module is used for judging whether the current process is qualified or not according to the equipment running state parameters and the material quality parameters and the preset steel quality detection standard; The path decision module is used for judging whether the steel sample can continuously execute the subsequent process if the current process is unqualified, triggering an equipment maintenance instruction if the steel sample is judged to be incapable of continuously executing, and triggering monitoring and analysis of the subsequent process if the steel sample is not qualified; the deviation analysis module is used for calculating the state deviation degree of the steel sample based on the material quality parameters and the potential influence of the material quality parameters on the subsequent process if the current process is qualified; The deviation propagation judging module dynamically tracks the propagation path of the quality deviation among the working procedures based on the state deviation degree of the steel sample, and comprehensively analyzes whether the subsequent working procedures are qualified or not by combining the real-time monitoring parameters of the subsequent working procedures; And the health evaluation module is used for determining the operation qualification degree of the equipment in each process based on the qualification judgment result of each process and generating a corresponding maintenance strategy.
2. The intelligent monitoring and management system of the steel analysis laboratory based on multi-source data fusion of claim 1, wherein the determining whether the current process is qualified comprises: comparing the equipment operation state parameters of corresponding equipment of the steel sample in the current working procedure with the reference equipment operation parameter ranges of the corresponding equipment, and judging that the process conditions of the current working procedure are qualified when the equipment operation state parameters are all in the reference equipment operation parameter ranges; When the equipment running state parameters are not in the range of the reference equipment running parameters, judging that the process conditions of the current working procedure are unqualified; Comparing the material quality parameters of the steel sample with the corresponding standard quality detection standard ranges in the steel quality detection standards, and judging that the product characteristics of the current process are qualified when all the material quality parameters are in the standard quality detection standard ranges; when the quality parameters of the materials are not in the standard range of the reference quality detection, judging that the product characteristics of the current working procedure are unqualified; if the technological conditions and the product characteristics of the current working procedure are qualified, the current working procedure is judged to be qualified, otherwise, the current working procedure is judged to be unqualified.
3. The intelligent monitoring and management system for steel analysis laboratories based on multi-source data fusion according to claim 2, wherein the determining whether the steel sample can continue to perform the subsequent process comprises: If the product characteristics of the current process are judged to be unqualified, judging that the steel sample cannot continue to execute the subsequent process; and if the product characteristics of the current process are judged to be qualified, judging that the steel sample can continue to execute the subsequent process.
4. The intelligent monitoring and management system for steel analysis laboratories based on multi-source data fusion according to claim 1, wherein the calculating the state deviation degree of the steel sample comprises: q1, comparing a measured value of each material quality parameter with a reference quality detection standard interval of each material quality parameter; q2, if the measured value is larger than the upper limit value of the standard interval of the reference quality detection, calculating the ratio of the absolute value of the part of the measured value exceeding the upper limit value to the distance from the upper limit value of the standard interval of the reference quality detection to the central value, and adding the ratio to a preset punishment coefficient to obtain a parameter deviation value of the quality parameter of the material; q3, if the measured value is smaller than the lower limit value of the standard interval of the reference quality detection, calculating the ratio of the absolute value of the part of the measured value lower than the lower limit value to the distance from the central value of the standard interval of the reference quality detection to the lower limit value, and adding the ratio to a preset punishment coefficient to obtain a parameter deviation value of the quality parameter of the material; q4, if the measured value is positioned in the standard interval of the reference quality detection, calculating the absolute value of the measured value deviating from the central value of the standard interval of the reference quality detection, and the ratio of the absolute value to half of the total width of the interval, and taking the absolute value as the parameter deviation value of the material quality parameter; q5, setting a weight coefficient of each material quality parameter based on the deviation direction of each material quality parameter; Q6, calculating by weighting fusion based on the parameter deviation value and the weight coefficient of each material quality parameter to obtain the state deviation degree of the steel sample.
5. The intelligent monitoring and management system for steel analysis laboratories based on multi-source data fusion according to claim 4, wherein the setting the weight coefficient of each material quality parameter comprises: Based on the deviation direction of each material quality parameter, acquiring a basic weight value of each material quality parameter through a preset deviation direction-basic weight mapping relation; And summing the basic weight values of the quality parameters of the materials to obtain comprehensive basic weight values, and taking the ratio of the basic weight values of the quality parameters of the materials to the comprehensive basic weight values as the weight coefficient of the quality parameters of the materials.
6. The intelligent monitoring and management system of the steel analysis laboratory based on multi-source data fusion of claim 1, wherein the comprehensive analysis of whether the subsequent process is qualified comprises: Y1, carrying out negative influence compensation on process setting of subsequent procedures based on the state deviation degree to obtain a dynamic set value of the material characteristic parameter; y2, driving equipment of subsequent procedures to process based on the dynamic set value of the material characteristic parameter, and monitoring and obtaining the actual material quality parameter of the processed steel sample; y3, comparing the actual material quality parameter with a reference quality detection standard thereof, and simultaneously comparing the equipment operation state parameter of the corresponding equipment in the subsequent procedure with the reference equipment operation parameter range thereof; and Y4, comprehensively judging whether the subsequent process is qualified or not based on the comparison result of the two steps.
7. The intelligent monitoring and management system of the steel analysis laboratory based on multi-source data fusion of claim 6, wherein the calculation process of the dynamic set value of the material characteristic parameter is as follows: Multiplying the state deviation degree by a preset process sensitivity coefficient to obtain a negative influence factor; Subtracting the negative influence factor from the value 1 to obtain a correction coefficient; acquiring a material characteristic parameter process setting reference value of a subsequent process based on a preset process rule; And multiplying the material characteristic parameter process setting reference value by the correction coefficient to obtain a material characteristic parameter dynamic setting value.
8. The intelligent monitoring and management system for the steel analysis laboratory based on multi-source data fusion according to claim 1, wherein the determining the operation qualification degree of each process device comprises: w1, screening out qualified judgment records of all process conditions in a preset statistical period based on qualified judgment results of all the processes to form a qualified process record set of all the processes; W2, calculating the record number of qualified process record sets of each process, and taking the ratio of the record number to the total steel sample number in a preset statistical period as the process qualification ratio of each process equipment; w3, evaluating the quality failure rate of each process equipment based on the qualified process record set; w4, evaluating the average inheritance deviation degree of each process equipment based on the state deviation degree of each steel sample in each process in the qualified process record set; and W5, performing product calculation on the process qualification ratio, the quality failure rate and the average inheritance deviation degree of each process device to obtain the operation qualification degree of each process device.
9. The intelligent monitoring and management system for steel analysis laboratories based on multi-source data fusion according to claim 8, wherein the evaluating the quality failure rate of each process equipment comprises: Traversing the qualified process record set, counting the product characteristic judging result, accumulating the unqualified record number of the product characteristic judging, marking as failure times, counting the total record number of the qualified process record set, and marking as the total number of qualified processes; and carrying out ratio operation on the failure times and the total times of the qualified process, and calculating to obtain the quality failure rate of each process device.
10. The intelligent monitoring and management system for steel analysis laboratories based on multi-source data fusion according to claim 8, wherein the evaluating the average inheritance deviation degree of each process equipment comprises: The corresponding state deviation degree of each steel sample in the qualified process record set when entering the current working procedure is obtained, and the state deviation degree of the steel sample in the first working procedure is defined as a reference value zero; Accumulating and summing all acquired state deviation degrees to obtain a total deviation degree; and dividing the total deviation degree and the total number of records of the qualified process record set, and calculating to obtain the average inheritance deviation degree of each process device.

Description

Steel analysis laboratory intelligent monitoring management system based on multisource data fusion Technical Field The invention belongs to the technical field of industrial automation and intelligent manufacturing, and relates to an intelligent monitoring management system of a steel analysis laboratory based on multi-source data fusion. Background In the production process of the steel industry, a steel analysis laboratory is responsible for continuously monitoring and evaluating performance indexes of steel samples in each processing procedure. With the continuous development of intelligent manufacturing technology, the traditional quality detection and equipment monitoring mode has difficulty in meeting the high requirements on the quality stability of the whole process and the predictability of the running state of equipment. The quality detection data of each process are stored and processed independently, and an effective data interaction and association analysis mechanism is lacked, so that the propagation path and influence degree of quality deviation of a preceding process in a subsequent process cannot be tracked and quantitatively evaluated. And meanwhile, the existing system fails to carry out fusion analysis on the running state parameters of the equipment and the quality parameters of materials, so that the comprehensive efficiency of the equipment is difficult to accurately reflect, and the maintenance decision of the equipment is lack of predictability. In addition, in terms of flow path planning and process anomaly handling of steel samples, the prior art scheme still mainly relies on experience of operators to judge, and cannot comprehensively determine flow paths of the steel samples based on multi-source data collected in real time and cross-process quality influence, so that the consistency of production efficiency improvement and quality control cannot be guaranteed. Disclosure of Invention In view of this, in order to solve the problems presented in the above background art, a steel analysis laboratory intelligent monitoring management system based on multi-source data fusion is now proposed. The intelligent monitoring management system for the steel analysis laboratory based on multi-source data fusion comprises a parameter monitoring module, wherein the parameter monitoring module is used for monitoring equipment running state parameters of a steel sample in the current working procedure and material quality parameters of the steel sample in real time. And the process judging module is used for judging whether the current process is qualified or not based on the equipment running state parameters and the material quality parameters and combining preset steel quality detection standards. And the path decision module is used for judging whether the steel sample can continue to execute the subsequent process if the current process is unqualified, triggering the equipment maintenance instruction if the steel sample is judged to be unable to continue to execute, and otherwise triggering the monitoring and analysis of the subsequent process. And the deviation analysis module is used for calculating the state deviation degree of the steel sample based on the material quality parameter and the potential influence of the material quality parameter on the subsequent process if the current process is qualified. And the deviation propagation judging module dynamically tracks the propagation path of the quality deviation among the working procedures based on the state deviation degree of the steel sample, and comprehensively analyzes whether the subsequent working procedures are qualified or not by combining the real-time monitoring parameters of the subsequent working procedures. And the health evaluation module is used for determining the operation qualification degree of the equipment in each process based on the qualification judgment result of each process and generating a corresponding maintenance strategy. Compared with the prior art, the method has the beneficial effects that (1) the method judges whether the current working procedure is qualified or not by collecting the equipment running state parameters and the material quality parameters of the steel samples in each working procedure in real time and combining the preset steel quality detection standard, thereby solving the problem that the equipment monitoring and the quality detection are mutually disjointed in the traditional system and realizing the comprehensive and accurate assessment of the working procedure state. (2) According to the invention, the circulation path of the steel sample is determined according to the product characteristic qualification judgment result of the current working procedure, so that the dependence on manual experience is eliminated, the invalid circulation of unqualified products is further reduced, and meanwhile, the smooth transmission of the qualified steel sample in the production flow is ensu