CN117071046B - Intelligent processing management system for automatic galvanization barrel plating production line - Google Patents
Intelligent processing management system for automatic galvanization barrel plating production line Download PDFInfo
- Publication number
- CN117071046B CN117071046B CN202311315451.XA CN202311315451A CN117071046B CN 117071046 B CN117071046 B CN 117071046B CN 202311315451 A CN202311315451 A CN 202311315451A CN 117071046 B CN117071046 B CN 117071046B
- Authority
- CN
- China
- Prior art keywords
- production line
- value
- product
- processing
- target
- 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
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 394
- 238000012545 processing Methods 0.000 title claims abstract description 156
- 238000007747 plating Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 claims abstract description 130
- 239000012071 phase Substances 0.000 claims abstract description 104
- 238000001514 detection method Methods 0.000 claims abstract description 86
- 238000004458 analytical method Methods 0.000 claims abstract description 61
- 239000007791 liquid phase Substances 0.000 claims abstract description 41
- 239000007788 liquid Substances 0.000 claims abstract description 40
- 238000011156 evaluation Methods 0.000 claims abstract description 17
- 230000000007 visual effect Effects 0.000 claims abstract description 14
- 239000002351 wastewater Substances 0.000 claims abstract description 13
- 239000007789 gas Substances 0.000 claims description 110
- 238000003754 machining Methods 0.000 claims description 41
- 238000004364 calculation method Methods 0.000 claims description 20
- 230000005856 abnormality Effects 0.000 claims description 13
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 12
- 230000007613 environmental effect Effects 0.000 claims description 10
- 230000000903 blocking effect Effects 0.000 claims description 9
- 229910001385 heavy metal Inorganic materials 0.000 claims description 8
- 239000012535 impurity Substances 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 8
- 239000007787 solid Substances 0.000 claims description 8
- 239000010815 organic waste Substances 0.000 claims description 7
- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 claims description 6
- 229910021529 ammonia Inorganic materials 0.000 claims description 6
- 229910000041 hydrogen chloride Inorganic materials 0.000 claims description 6
- IXCSERBJSXMMFS-UHFFFAOYSA-N hydrogen chloride Substances Cl.Cl IXCSERBJSXMMFS-UHFFFAOYSA-N 0.000 claims description 6
- 239000000779 smoke Substances 0.000 claims description 6
- 238000005246 galvanizing Methods 0.000 description 17
- 150000002500 ions Chemical class 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 5
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 3
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000009713 electroplating Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- CTQNGGLPUBDAKN-UHFFFAOYSA-N O-Xylene Chemical compound CC1=CC=CC=C1C CTQNGGLPUBDAKN-UHFFFAOYSA-N 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 150000002894 organic compounds Chemical class 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000004065 wastewater treatment Methods 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
- 239000008096 xylene Substances 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25D—PROCESSES FOR THE ELECTROLYTIC OR ELECTROPHORETIC PRODUCTION OF COATINGS; ELECTROFORMING; APPARATUS THEREFOR
- C25D21/00—Processes for servicing or operating cells for electrolytic coating
- C25D21/12—Process control or regulation
-
- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25D—PROCESSES FOR THE ELECTROLYTIC OR ELECTROPHORETIC PRODUCTION OF COATINGS; ELECTROFORMING; APPARATUS THEREFOR
- C25D3/00—Electroplating: Baths therefor
- C25D3/02—Electroplating: Baths therefor from solutions
- C25D3/22—Electroplating: Baths therefor from solutions of zinc
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
Abstract
The invention belongs to the technical field of supervision of barrel plating production lines, in particular to a processing intelligent management system for a galvanization automatic barrel plating production line, which comprises an intelligent management platform, a barrel plating production line evaluation module, a production line operation matching module, a production line operation detection analysis module, a product quality visual detection module and a production line gas-liquid management and control module; according to the invention, before each galvanization automatic barrel plating production line is operated, the galvanization automatic barrel plating production line is evaluated and graded, corresponding operators are reasonably distributed to the galvanization automatic barrel plating production line, the management difficulty is reduced, the production efficiency and the production safety are guaranteed, the operating condition of the production line and the processing quality condition of the product are reasonably analyzed in the operation process of the production line, the processing area of the corresponding galvanization automatic barrel plating production line is subjected to gas phase detection and the discharged wastewater is subjected to liquid phase detection, and the gas-liquid management and control condition of the production line is timely fed back, so that the management difficulty is further reduced, and the processing efficiency, the product quality and the processing safety are remarkably improved.
Description
Technical Field
The invention relates to the technical field of barrel plating production line supervision, in particular to a processing intelligent management system for a galvanization automatic barrel plating production line.
Background
The automatic galvanization barrel plating production line is a production line for electroplating, can reduce the workload of operators and improve the production efficiency, and can realize a series of treatment operations of the product to be treated by sequentially conveying the product to be treated to corresponding process equipment in the running process of the automatic galvanization barrel plating production line, so that the processing of the corresponding product can be carried out on a large scale;
at present, when the galvanized automatic barrel plating production line is processed and managed, each production line cannot be evaluated and classified before running and corresponding operators are reasonably distributed to the production line, and the running condition of the production line and the processing quality condition of the product cannot be reasonably analyzed in the running process of the production line, so that the processing efficiency and the product quality are not guaranteed, the gas-liquid control condition of the production line is not fed back in time, the management difficulty is further increased, and the processing safety is not facilitated;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a processing intelligent management system for a galvanization automatic barrel plating production line, which solves the problems that the prior art cannot evaluate and classify each production line before running and reasonably distribute corresponding operators to each production line, the running condition of the production line and the processing quality condition of the product cannot be reasonably analyzed in the running process of the production line, the gas-liquid control condition of the production line is difficult to timely feed back, the processing efficiency, the product quality and the production safety are not guaranteed, and the management difficulty is high.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a processing intelligent management system for a galvanization automatic barrel plating production line comprises an intelligent management platform, a barrel plating production line evaluation module, a production line operation matching module, a production line operation detection analysis module, a product quality visual detection module and a production line gas-liquid management and control module;
the barrel plating production line evaluation module is used for analyzing the production line condition of the automatic barrel plating production line of the galvanization, dividing the automatic barrel plating production line into an unstable production line, a good grade production line or a good grade production line according to the analysis, and sending evaluation division information to the production line operation matching module through the intelligent management platform; the production line operation matching module analyzes corresponding operators for galvanization operation, so as to divide the corresponding operators into non-mature operators, good operators or good operators, distributes operators matched with the corresponding galvanized automatic barrel-plating production line before the corresponding operators are processed, and sends personnel matching information to the production line management terminal through the intelligent management platform;
the production line operation detection analysis module analyzes and evaluates the operation processing conditions of the corresponding galvanization automatic barrel plating production line, so as to generate a production line operation qualified signal or a production line operation unqualified signal of the corresponding galvanization automatic barrel plating production line and send the signal to the intelligent management platform;
The product quality visual detection module is used for visually detecting the processed product and collecting a product image thereof, carrying out pretreatment operations comprising image enhancement, denoising and segmentation on the collected product image, extracting characteristics comprising the size, the shape, the color and the texture of the product from the pretreated image, comparing the extracted characteristics with preset quality standards corresponding to the product one by one so as to judge whether the corresponding product meets the quality requirements, and generating a processing quality qualified signal or a processing quality unqualified signal corresponding to a galvanization automatic barrel plating production line through analysis and sending the processing quality qualified signal or the processing quality unqualified signal to the intelligent management platform;
the production line gas-liquid control module carries out gas phase detection on a processing area of a corresponding automatic barrel plating production line for galvanizing, and carries out liquid phase detection on wastewater discharged by the processing area, so that a gas-liquid control qualified signal or a gas-liquid control unqualified signal is generated through analysis and is sent to the intelligent management platform; the intelligent management platform generates corresponding early warning information when receiving the production line operation unqualified signal, the processing quality unqualified signal or the gas-liquid management and control unqualified signal, and sends the corresponding early warning information to the production line management terminal.
Further, the specific operation process of the barrel plating production line evaluation module comprises the following steps:
the method comprises the steps of obtaining a galvanization automatic barrel plating production line to be managed, and marking the corresponding galvanization automatic barrel plating production line as a target production line i, wherein i is a natural number larger than 1; the method comprises the steps that a product galvanization abnormal rate and a product line operation failure rate of a target production line i in a monitoring period are collected, the product galvanization abnormal rate and the product line operation failure rate are respectively compared with a preset product galvanization abnormal rate threshold value and a preset product line operation failure rate threshold value, and if the product galvanization abnormal rate or the product line operation failure rate exceeds the corresponding preset threshold value, the target production line i is divided into unstable production lines;
if the product galvanization abnormality rate and the production line operation failure rate do not exceed the corresponding preset thresholds, acquiring the time of stopping the production line i caused by the production line failure of the supervision period target production line i and marking the time as single failure stopping time, summing all the single failure stopping time to obtain the total failure stopping time, and analyzing and calculating the total failure stopping time, the product galvanization abnormality rate and the production line operation failure rate to obtain a production line decision value;
comparing the production line decision value of the target production line i with a preset production line decision value range in a numerical mode, and dividing the target production line i into unstable production lines if the production line decision value exceeds the maximum value of the preset production line decision value range; if the production line decision value is within the preset production line decision value range, dividing the target production line i into good-grade production lines; if the line decision value does not exceed the minimum value of the preset line decision value range, dividing the target line i into a top-grade line; and sending the evaluation division information of the target production line i to a production line operation matching module through the intelligent management platform.
Further, the specific operation process of the production line operation matching module comprises the following steps:
all operators performing galvanization operation are obtained, the corresponding operators are marked as target operators t, and t is a natural number larger than 1; acquiring the product rejection rate and the operation error value of a target person t in the processing process in the supervision period, respectively comparing the product rejection rate and the operation error value with a preset product rejection rate threshold value and a preset operation error value threshold value, and dividing the target person t into non-mature operators if the product rejection rate or the operation error value exceeds the corresponding preset threshold value;
if the product rejection rate and the misoperation value do not exceed the corresponding preset thresholds, collecting the working time of the target personnel t in the galvanization automatic barrel plating production line and the operation damage value data of the target personnel t in the supervision period, and carrying out numerical calculation on the working time, the operation damage value data, the product rejection rate and the misoperation value of the target personnel t to obtain an operation decision value; the operation decision value is compared with a preset operation decision value range in a numerical mode, if the operation decision value exceeds the maximum value of the preset operation decision value range, the target personnel t is divided into non-mature operators, if the operation decision value is in the preset operation decision value range, the target personnel t is divided into good operators, and if the operation decision value does not exceed the minimum value of the preset operation decision value range, the target personnel t is divided into the good operators;
When the operator of the target production line i is allocated, if the target production line i is an unstable production line, preferentially allocating the operator to the unstable production line i, and not allocating good operators and non-mature operators to the target production line i; if the target production line i is a good-grade production line, an operator or a good operator is allocated to the target production line i, and a non-cooked operator is not allocated to the target production line i; if the target production line i is an excellent production line, an excellent operator, a good operator or a non-ripe operator is allocated to the target production line i, and the non-ripe operator is preferentially arranged to the excellent production line.
Further, the specific operation process of the production line operation detection and analysis module comprises the following steps:
when a target production line i is galvanized, acquiring all machining procedures of the target production line i, acquiring machining position deviation data and machining duration deviation data of a product in a corresponding machining procedure in the machining process, respectively comparing the machining position deviation data and the machining duration deviation data with corresponding preset machining position deviation data thresholds and preset machining duration deviation data thresholds, and judging that the machining operation of the corresponding product in the corresponding procedure is qualified if the machining position deviation data and the machining duration deviation data do not exceed the corresponding preset thresholds; otherwise, judging that the corresponding product is unqualified in the processing operation of the corresponding procedure;
If the processing operation of all the processing procedures of the corresponding product in the target production line i is qualified, marking the whole processing process of the corresponding product as an obstacle-free process; acquiring the total processing amount of a product of a target production line i in unit time, acquiring the processing amount of the product corresponding to an obstacle-free process, marking the processing amount as an obstacle-free processing amount, and calculating the ratio of the obstacle-free processing amount to the total processing amount of the product to obtain an obstacle-free product coefficient; marking the corresponding machining process of the target production line i as a smooth process or a blocking process through process performance detection analysis, and calculating the ratio of the number of the smooth processes to the number of the blocking processes to obtain a barrier-free process coefficient;
carrying out analysis and calculation on the barrier-free product coefficient and barrier-free process coefficient of the target production line i to obtain a production line operation coefficient, and carrying out numerical comparison on the production line operation coefficient and a preset production line operation coefficient threshold; if the production line operation coefficient exceeds a preset production line operation coefficient threshold value, generating a production line operation qualified signal; if the production line operation coefficient does not exceed the preset production line operation coefficient threshold value, a production line operation disqualification signal is generated.
Further, the specific analysis procedure of the process performance detection analysis is as follows:
If the processing operation of the product processed by the corresponding processing procedure in unit time is qualified, marking the corresponding processing procedure as a smooth procedure; otherwise, collecting all processing position deviation data and processing time length deviation data corresponding to the processing procedure in unit time, carrying out summation calculation on all processing position deviation data and taking an average value to obtain a processing position deviation value, and carrying out summation calculation on all processing time length deviation data and taking an average value to obtain a processing time deviation value; collecting the number percentage of unqualified products of the processing operation corresponding to the processing procedure in unit time, and marking the number percentage as an unqualified operation coefficient;
performing numerical calculation on the machining position deviation value, the machining time deviation value and the unqualified operation coefficient of the corresponding machining process to obtain a process detection value, and performing numerical comparison on the process detection value and a corresponding preset process detection threshold value; if the process detection value exceeds a preset process detection threshold, marking the corresponding processing process as a blocking process; and if the process detection value does not exceed the preset process detection threshold value, marking the corresponding processing process as a smooth process.
Further, the analysis process of the product quality visual detection module comprises the following steps:
When the corresponding product processed by the target production line i meets the quality requirement, marking the corresponding product as a compliant product; when the corresponding product processed by the target production line i is judged to be inconsistent with the quality requirement, collecting the characteristic quantity of the corresponding product which does not meet the corresponding quality standard, marking the characteristic quantity as a non-standard characteristic value, carrying out numerical comparison on the non-standard characteristic value and a preset non-standard characteristic threshold value, and marking the corresponding product as a high-bias product if the non-standard characteristic value exceeds the preset non-standard characteristic threshold value;
if the non-standard feature value does not exceed the preset non-standard feature threshold, marking the corresponding feature which does not reach the corresponding quality standard as a bad feature, acquiring a feature influence value of the preset corresponding bad feature, summing the feature influence values of all bad features of the corresponding product to obtain a feature bad value, comparing the feature bad value with the preset feature bad threshold in a numerical mode, marking the corresponding product as a high-bias product if the feature bad value exceeds the preset feature bad threshold, and marking the corresponding product as a low-bias product if the feature bad value does not exceed the preset feature bad threshold;
acquiring the number of the compliant products, the number of the high-bias products and the number of the low-bias products in the products processed by the target production line i in unit time, and analyzing and calculating the number of the compliant products, the number of the high-bias products and the number of the low-bias products to obtain a quality analysis value; comparing the quality analysis value with a preset quality analysis threshold value, and generating a processing quality disqualification signal of the target production line i if the quality analysis value exceeds the preset quality analysis threshold value; and if the quality analysis value does not exceed the preset quality analysis threshold value, generating a processing quality qualified signal of the target production line i.
Further, the specific operation process of the production line gas-liquid control module comprises the following steps:
setting a plurality of detection points in a processing area of a target production line i, collecting hydrogen chloride data, ammonia data, organic waste gas data and smoke concentration data of the detection points, collecting environmental temperature data of the detection points, calculating a difference value between the environmental temperature data and a preset proper environmental temperature value, and taking an absolute value to obtain ring temperature deviation data; carrying out numerical calculation on hydrogen chloride data, ammonia data, organic waste gas data, smoke concentration data and ring temperature deviation data of detection points to obtain a gas phase value of a production line;
comparing the gas phase value of the production line of the detection point with the range of the gas phase value of the preset environment, and marking the detection point as a gas phase bad point if the gas phase value of the production line exceeds the maximum value of the range of the gas phase value of the preset production line; if the gas phase value of the production line is within the range of the gas phase value of the preset production line, marking the detection point as a gas phase benign point; if the gas phase value of the production line does not exceed the minimum value of the gas phase range of the preset production line, marking the detection point as a gas phase unimpeded point; if a gas phase bad point exists in the processing area of the target production line i, a gas phase judgment symbol QX-1 is given to the gas phase bad point;
If no gas phase bad points exist in the processing area of the target production line i, calculating the ratio of the number of gas phase bad points to the number of gas phase unimpeded points to obtain a gas phase representation value, summing the gas phase values of the production line of all detection points, taking an average value to obtain a gas phase average value, and calculating the values of the gas phase representation value and the gas phase average value to obtain a gas phase live value; comparing the gas phase live value with a preset gas phase live threshold value, and if the gas phase live value exceeds the preset gas phase live threshold value, giving a gas phase judgment symbol QX-1 to the gas phase live value; if the gas phase live value does not exceed the preset gas phase live threshold value, a gas phase judgment symbol QX-2 is given to the gas phase live value;
and giving a liquid phase judgment symbol YX-1 or YX-2 to the target production line i through liquid discharge supervision analysis, generating a gas-liquid control qualified signal when QX-2 ∈YX-2 is generated, and generating a gas-liquid control unqualified signal when the other conditions are the same.
Further, the specific analysis process of the drainage supervision analysis is as follows:
the method comprises the steps of collecting heavy metal ion data, solid impurity data, ph deviation data and color deviation data in wastewater discharged by a target production line i, and carrying out numerical calculation on the heavy metal ion data, the solid impurity data, the ph deviation data and the color deviation data to obtain a liquid phase live value; comparing the liquid phase live value with a preset liquid phase live threshold value, and if the liquid phase live value exceeds the preset liquid phase live threshold value, giving a liquid phase judgment symbol YX-1 to the liquid phase live value; if the liquid phase live value does not exceed the preset liquid phase live threshold value, a liquid phase judgment symbol YX-2 is assigned to the liquid phase live value.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the operation processing conditions of the corresponding galvanizing automatic barrel plating production lines are analyzed and evaluated so as to master the operation conditions of the respective galvanizing automatic barrel plating production lines in detail, the processed products are subjected to visual detection and analysis so as to judge whether the corresponding products meet the quality requirements, processing quality qualified signals or processing quality unqualified signals of the corresponding galvanizing automatic barrel plating production lines are generated so as to master the product processing quality conditions of the respective galvanizing automatic barrel plating production lines in detail, the processing areas of the corresponding galvanizing automatic barrel plating production lines are subjected to gas phase detection, and the discharged waste water is subjected to liquid phase detection so as to master the gaseous environment conditions of the production areas of the corresponding galvanizing automatic barrel plating production lines and the pollution conditions of the discharged waste liquid, so that the management difficulty is reduced, corresponding management personnel can timely make targeted management measures, and the production efficiency, the product quality and the production safety of the galvanizing automatic barrel plating production lines are effectively ensured;
2. according to the invention, the production line conditions of the galvanizing automatic barrel plating production line are analyzed, so that the galvanizing automatic barrel plating production line is divided into an unstable production line, a good grade production line or a good grade production line, the intelligent management platform sends the evaluation division information of each galvanizing automatic barrel plating production line to the production line operation matching module, the production line operation matching module analyzes corresponding operators for galvanizing operation, so that the corresponding operators are divided into non-mature operators, good operators or good operators, operators matched with the corresponding galvanizing automatic barrel plating production line are distributed to the corresponding operators before the corresponding galvanizing automatic barrel plating production line is processed, and the personnel distribution is more reasonable, thereby being beneficial to ensuring the safe, stable and efficient operation of each production line.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of the second and third embodiments of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the intelligent processing management system for the galvanization automatic barrel plating production line provided by the invention comprises an intelligent management platform, a production line operation detection and analysis module, a product quality visual detection module and a production line gas-liquid management and control module, wherein the intelligent management platform is in communication connection with the production line operation detection and analysis module, the product quality visual detection module and the production line gas-liquid management and control module;
the production line operation detection analysis module analyzes and evaluates the operation processing conditions of the corresponding galvanization automatic barrel plating production lines, so as to generate production line operation qualified signals or production line operation unqualified signals of the corresponding galvanization automatic barrel plating production lines and send the signals to the intelligent management platform, so that the operation conditions of each galvanization automatic barrel plating production line can be mastered in detail, the inspection maintenance and management measure adjustment of the corresponding production lines can be conveniently and timely carried out, and the product processing quality and efficiency can be guaranteed; the specific operation process of the production line operation detection and analysis module is as follows:
When a target production line i is galvanized, all machining procedures (including cleaning, electroplating, dewatering and the like) of the target production line i are acquired, and machining position deviation data and machining duration deviation data of the product in the corresponding machining procedures in the machining process are acquired, wherein the machining position deviation data and the machining duration deviation data are data values representing the degree of deviation of the machining position and the machining duration from the corresponding standard position and the standard duration; respectively carrying out numerical comparison on the processing position deviation data and the processing time length deviation data and corresponding preset processing position deviation data threshold values and preset processing time length deviation data threshold values, and judging that the processing operation of the corresponding product in the corresponding working procedure is qualified if the processing position deviation data and the processing time length deviation data do not exceed the corresponding preset threshold values; otherwise, judging that the corresponding product is unqualified in the processing operation of the corresponding procedure;
if the processing operation of all the processing procedures of the corresponding product in the target production line i is qualified, marking the whole processing process of the corresponding product as an obstacle-free process; acquiring the total processing amount of a product of a target production line i in unit time, acquiring the processing amount of the product corresponding to an obstacle-free process, marking the processing amount as an obstacle-free processing amount, and calculating the ratio of the obstacle-free processing amount to the total processing amount of the product to obtain an obstacle-free product coefficient; the larger the value of the barrier-free product coefficient is, the more stable the processing of the target production line i is in unit time;
The corresponding processing procedure of the target production line i is marked as a smooth procedure or a blocking procedure through procedure performance detection and analysis, specifically: if the processing operation of the product processed by the corresponding processing procedure in unit time is qualified, marking the corresponding processing procedure as a smooth procedure; otherwise, collecting all processing position deviation data and processing time length deviation data corresponding to the processing procedure in unit time, carrying out summation calculation on all processing position deviation data and taking an average value to obtain a processing position deviation value, and carrying out summation calculation on all processing time length deviation data and taking an average value to obtain a processing time deviation value; collecting the number percentage of unqualified products of the processing operation corresponding to the processing procedure in unit time, and marking the number percentage as an unqualified operation coefficient;
numerical calculation is carried out on a machining deviation value WP, a machining deviation value QP and a disqualified operation coefficient HS corresponding to a machining procedure through a formula GX=ep1+ep2+ep3+HS to obtain a procedure detection value GX, wherein ep1, ep2 and ep3 are preset weight coefficients, and ep3 is more than ep2 and more than ep1 and more than 0; further, the larger the value of the process detection value GX is, the more unsmooth the operation state in the corresponding process is indicated; comparing the process detection value GX with a corresponding preset process detection threshold value; if the process detection value GX exceeds a preset process detection threshold, marking the corresponding processing process as a blocking process; if the process detection value GX does not exceed the preset process detection threshold value, marking the corresponding processing process as a smooth process;
Calculating the ratio of the number of fluent procedures to the number of blocking procedures to obtain an unobstructed procedure coefficient, and analyzing and calculating an unobstructed product coefficient WGi and an unobstructed procedure coefficient WQi of a target production line i to obtain a production line operation coefficient CXi through a formula CXi =re 1 WGi +re2 WQi, wherein re1 and re2 are preset weight coefficients, and re2 is larger than re1 and larger than 0; and, the larger the value of the line operation coefficient CXi, the better the operation processing condition of the target line i is overall; comparing the line operation coefficient CXi with a preset line operation coefficient threshold value; if the production line operation coefficient CXi exceeds a preset production line operation coefficient threshold value, a production line operation qualified signal is generated; if the line operational coefficient CXi does not exceed the preset line operational coefficient threshold, a line operational failure signal is generated.
The product quality visual detection module is used for visually detecting the processed product and collecting a product image thereof, carrying out pretreatment operations including image enhancement, denoising, segmentation and the like on the collected product image, extracting characteristics including product size, shape, color, texture and the like from the pretreated image, comparing the extracted characteristics with preset quality standards corresponding to the product one by one, judging whether the corresponding product meets quality requirements or not according to the characteristics, effectively detecting the processed product and accurately feeding back the quality condition of the processed product, and generating a processing quality qualified signal or a processing quality unqualified signal corresponding to the galvanization automatic barrel plating production line through analysis and sending the processing quality unqualified signal or the processing quality unqualified signal to the intelligent management platform so as to master the processing quality condition of the product of each galvanization automatic barrel plating production line in detail, thereby facilitating the timely inspection maintenance and management measure adjustment of the corresponding production line and further ensuring the processing quality and efficiency of the product; the analysis process of the product quality visual detection module is as follows:
When the corresponding product processed by the target production line i meets the quality requirement, marking the corresponding product as a compliant product; when judging that the corresponding product processed by the target production line i does not meet the quality requirement, acquiring the characteristic quantity of the corresponding product which does not meet the corresponding quality standard, marking the characteristic quantity as a non-standard characteristic value, comparing the non-standard characteristic value with a preset non-standard characteristic threshold value in a numerical mode, and marking the corresponding product as a high-bias product if the non-standard characteristic value exceeds the preset non-standard characteristic threshold value, wherein the quality of the corresponding product is extremely poor;
if the non-standard feature value does not exceed the preset non-standard feature threshold, marking the corresponding feature which does not reach the corresponding quality standard as a bad feature, acquiring a preset feature influence value of the corresponding bad feature, wherein the values of the feature influence values are larger than zero and are recorded and stored in an intelligent management platform in advance; summing the characteristic influence values of all the bad characteristics of the corresponding product to obtain a characteristic bad value, wherein the larger the numerical value of the characteristic bad value is, the worse the quality of the corresponding product is indicated; the method comprises the steps of comparing a characteristic bad value with a preset characteristic bad threshold value, marking a corresponding product as a high-bias product if the characteristic bad value exceeds the preset characteristic bad threshold value, and marking the corresponding product as a low-bias product if the characteristic bad value does not exceed the preset characteristic bad threshold value;
Obtaining the quantity of the compliant products, the quantity of the high-bias products and the quantity of the low-bias products in the products processed by the target production line i in unit time, and analyzing and calculating the quantity of the compliant products HGi, the quantity of the high-bias products TGi and the quantity of the low-bias products PGi by the formula ZFi = (fp1+HGi+fp2+TGi+fp3+ PGi)/(HGi+TGi+ PGi) to obtain a quality analysis value ZFi; wherein fp1, fp2, fp3 are preset weight coefficients, fp2 > fp3 > fp1 > 0; and, the larger the value of the quality analysis value ZFi, the worse the production quality condition of the target production line i is indicated; comparing the quality analysis value ZFi with a preset quality analysis threshold value, and generating a processing quality disqualification signal of the target production line i if the quality analysis value ZFi exceeds the preset quality analysis threshold value; if the quality analysis value ZFi does not exceed the preset quality analysis threshold, a processing quality qualification signal for the target line i is generated.
The production line gas-liquid control module carries out gas phase detection on a processing area corresponding to the automatic barrel plating production line and carries out liquid phase detection on the discharged wastewater, so that a gas-liquid control qualified signal or a gas-liquid control unqualified signal is generated through analysis and sent to an intelligent management platform, and the gaseous environment condition of the production area corresponding to the automatic barrel plating production line and the pollution condition of the discharged waste liquid are mastered; the intelligent management platform generates corresponding early warning information when receiving the production line operation failure signal, the processing quality failure signal or the gas-liquid management failure signal, and sends the corresponding early warning information to the production line management terminal, so that corresponding management personnel can timely make targeted management measures, and the production efficiency, the product quality and the production safety of the galvanization automatic barrel plating production line are effectively ensured; the specific operation process of the production line gas-liquid control module is as follows:
Setting a plurality of detection points in a processing area of a target production line i, and collecting hydrogen chloride data, ammonia data, organic waste gas data and smoke concentration data of the detection points, wherein the organic waste gas data mainly represent data values of concentration of organic compounds such as benzene, toluene and xylene; collecting environmental temperature data of a detection point, calculating a difference value between the environmental temperature data and a preset proper environmental temperature value, and taking an absolute value to obtain ring temperature deviation data;
performing numerical calculation on the hydrogen chloride data LQi, the ammonia data AQi, the organic waste gas data YFi, the smoke concentration data YCi and the ring temperature deviation data HWi of the detection points through a formula CQi =fd1× LQi +fd2× AQi +fd3× YFi +fd4× YCi +fd5× HWi to obtain a production line gas phase value CQi; wherein fd1, fd2, fd3, fd4 and fd5 are preset weight coefficients, and the values of fd1, fd2, fd3, fd4 and fd5 are all larger than zero; and, the larger the line gas phase value CQi, the worse the gas phase condition of the corresponding detection point in the target line i;
comparing the line gas phase value CQi of the detection point with a preset environment gas phase value range, and marking the detection point as a gas phase bad point if the line gas phase value CQi exceeds the maximum value of the preset line gas phase value range; if the line gas phase value CQi is within the preset line gas phase value range, marking the detection point as a gas phase benign point; if the line gas phase value CQi does not exceed the minimum value of the preset line gas phase range, marking the detection point as a gas phase unimpeded point; if a gas phase bad point exists in the processing area of the target production line i, which indicates that the environment condition of the processing area of the target production line i is good as a whole, a gas phase judgment symbol QX-1 is given to the processing area of the target production line i;
If no gas phase severe points exist in the processing area of the target production line i, calculating the ratio of the number of gas phase benign points to the number of gas phase unimpeded points to obtain a gas phase representation value, summing the production line gas phase values of all detection points and taking an average value to obtain a gas phase average value, and carrying out numerical calculation on the gas phase representation value QBi and the gas phase average value QPI through a formula QKi =rh1× QBi +rh2×QPI to obtain a gas phase live value QKi; wherein, rh1 and rh2 are preset weight coefficients, and rh1 is more than rh2 is more than 0; and, the larger the value of the gas phase live value QKi, the worse the overall processing region environmental condition of the target production line i; comparing the gas phase live value QKi with a preset gas phase live threshold value, and if the gas phase live value QKi exceeds the preset gas phase live threshold value, giving a gas phase judgment symbol QX-1 to the gas phase live value; if the gas phase live value QKi does not exceed the preset gas phase live threshold value, a gas phase judgment symbol QX-2 is given to the gas phase live value QKi;
and assigning a liquid phase judgment symbol YX-1 or YX-2 to the target production line i by liquid discharge supervision analysis, specifically: the method comprises the steps of collecting heavy metal ion data, solid impurity data, ph deviation data and color deviation data in wastewater discharged by a target production line i, wherein the heavy metal ion data and the solid impurity data are data values representing the concentration of heavy metal ions and the content of solid impurities, and the ph deviation data and the color deviation data are data values representing the ph deviation degree of the wastewater and the color deviation degree of the wastewater; numerical calculation is performed on heavy metal ion data ZJi, solid impurity data GZi, ph deviation data PSi and color deviation data RJi through a formula YKi =rq1× ZJi +rq2× GZi +rq3×psi+rq4× RJi to obtain a liquid phase live value YKi; wherein rq1, rq2, rq3 and rq4 are preset weight coefficients, and the values of rq1, rq2, rq3 and rq4 are all larger than zero;
The larger the value of the liquid phase live value YKi is, the stronger the waste water pollution of the discharged waste water is, and the worse the waste water treatment effect is; comparing the liquid phase live value YKi with a preset liquid phase live threshold value, and if the liquid phase live value YKi exceeds the preset liquid phase live threshold value, giving a liquid phase judgment symbol YX-1 to the liquid phase live value; if the liquid phase live value YKi does not exceed the preset liquid phase live threshold value, a liquid phase judgment symbol YX-2 is given to the liquid phase live value YKi; intersection analysis is carried out on a gas phase judgment symbol and a liquid phase judgment symbol at the corresponding moment of the target production line i, and if QX-2 n YX-2 is generated, which indicates that the environment of a production area and the performance condition of discharged wastewater are better, a gas-liquid control qualified signal is generated; and generating a gas-liquid control disqualification signal under the other conditions.
Embodiment two: as shown in fig. 2, the difference between the present embodiment and embodiment 1 is that the intelligent management platform is in communication connection with the barrel plating production line evaluation module, and the barrel plating production line evaluation module is configured to analyze the production line status of the automatic barrel plating production line, so as to divide the automatic barrel plating production line into an unstable production line, a good grade production line or a superior grade production line, and send evaluation division information to the intelligent management platform, so that a manager can grasp the production line status of each automatic barrel plating production line in detail, thereby facilitating the manager to perform processing supervision of corresponding intensity and reasonably set management measures, and facilitating the manager to allocate operators to each automatic barrel plating production line; the specific operation process of the barrel plating production line evaluation module is as follows:
The method comprises the steps of obtaining a galvanization automatic barrel plating production line to be managed, and marking the corresponding galvanization automatic barrel plating production line as a target production line i, wherein i is a natural number larger than 1; collecting a product galvanization abnormality rate and a production line operation fault rate of a target production line i in a monitoring period, wherein the product galvanization abnormality rate is a data value representing the number of abnormal products in a ratio, and the production line operation fault rate is a data value representing the number of times of fault occurrence; respectively carrying out numerical comparison on the product galvanization abnormality rate and the production line operation failure rate as well as a preset product galvanization abnormality rate threshold and a preset production line operation failure rate threshold, and if the product galvanization abnormality rate or the production line operation failure rate exceeds the corresponding preset threshold, indicating that the operation condition of the target production line i in the monitoring period is poor, dividing the target production line i into unstable production lines;
if the product galvanization abnormality rate and the production line operation failure rate do not exceed the corresponding preset thresholds, collecting the time of stopping the production line i caused by the production line failure of the supervision period target production line, marking the time as single failure stopping time, summing all the single failure stopping time to obtain the total failure stopping time, and analyzing and calculating the total failure stopping time TSi, the product galvanization abnormality rate DY and the production line operation failure rate CGi through a formula CJi=a1+a2+DYI+a3×CGi to obtain a production line decision value CJi; wherein a1, a2 and a3 are preset weight coefficients, and the values of a1, a2 and a3 are all larger than zero; and, the larger the value of the line decision value CJi is, the worse the running condition of the target line i in the monitoring period is indicated;
Comparing the production line decision value CJi of the target production line i with a preset production line decision value range in a numerical value mode, and dividing the target production line i into unstable production lines if the production line decision value CJi exceeds the maximum value of the preset production line decision value range, which indicates that the running condition of the target production line i in a monitoring period is poor; if the production line decision value CJi is within the preset production line decision value range, indicating that the running condition of the target production line i in the monitoring period is general, dividing the target production line i into good-grade production lines; if the line decision value CJi does not exceed the minimum value of the preset line decision value range, indicating that the running condition of the target line i in the monitoring period is good, dividing the target line i into the top-grade lines.
Embodiment III: as shown in fig. 2, the difference between this embodiment and embodiments 1 and 2 is that the intelligent management platform is in communication connection with the production line operation matching module, the intelligent management platform sends the evaluation division information of each galvanization automatic barrel plating production line to the production line operation matching module, the production line operation matching module analyzes the corresponding operators performing the galvanization operation, so as to divide the corresponding operators into non-mature operators, good operators or preferred operators, and distribute the operators matched with the corresponding operators to the corresponding operators before the corresponding galvanization automatic barrel plating production line is processed, and send the personnel matching information to the production line management terminal through the intelligent management platform, so that the personnel allocation of each galvanization automatic barrel plating production line is facilitated for the manager, and the personnel allocation is more reasonable, thereby being beneficial to ensuring the safe, stable and efficient operation of each production line; the specific operation process of the production line operation matching module is as follows:
All operators performing galvanization operation are obtained, the corresponding operators are marked as target operators t, and t is a natural number larger than 1; acquiring the product rejection rate and the operation error value of a target person t in the processing process in a supervision period, wherein the product rejection rate refers to the data value of the number of the rejected products of the target person t accounting for the specific size, and the operation error value refers to the data value of the number of times of operation errors of the target person t in the processing process; respectively comparing the product rejection rate and the misoperation value with a preset product rejection rate threshold value and a preset misoperation value threshold value, and dividing the target person t into non-mature operators if the product rejection rate or the misoperation value exceeds the corresponding preset threshold value, which indicates that the operation of the target person t is not skilled;
if the product rejection rate and the misoperation value do not exceed the corresponding preset thresholds, collecting the working time of the target personnel t in the galvanization automatic barrel plating production line and collecting the operation damage value data of the target personnel t in the supervision period, wherein the operation damage value data refer to the data value of the loss amount of the production line caused by the misoperation and other problems of the target personnel t; numerical calculation is carried out on the working time length GSi, the operation damage value data ZSi, the product rejection rate CFi and the operation error value CWi of the target person t through a formula CYt =b2×zsi+b3×cfi+b4×cwi/(b1× GSi +1.258) to obtain an operation decision value CYt; wherein b1, b2, b3 and b4 are preset proportionality coefficients, and the values of b1, b2, b3 and b4 are all larger than zero;
It should be noted that, the larger the value of the operation decision value CYt is, the worse the operation performance of the target person t is; comparing the operation decision value CYt with a preset operation decision value range, if the operation decision value CYt exceeds the maximum value of the preset operation decision value range, indicating that the operation performance condition of the target person t is poor, dividing the target person t into non-mature operators, if the operation decision value CYt is in the preset operation decision value range, dividing the target person t into good operators, and if the operation decision value CYt does not exceed the minimum value of the preset operation decision value range, indicating that the operation performance condition of the target person t is good, dividing the target person t into good operators;
when the operator of the target production line i is allocated, if the target production line i is an unstable production line, preferentially allocating the operator to the unstable production line i, and not allocating good operators and non-mature operators to the target production line i; if the target production line i is a good-grade production line, an operator or a good operator is allocated to the target production line i, and a non-cooked operator is not allocated to the target production line i; if the target production line i is a top-grade production line, an operator, a good operator or a non-mature operator is allocated to the target production line i, and the non-mature operator is preferentially arranged to the top-grade production line, so that safe and stable operation of each galvanization automatic barrel plating production line is guaranteed.
The working principle of the invention is as follows: when the automatic galvanization automatic barrel plating production line is used, the operation processing conditions of the corresponding automatic galvanization barrel plating production line are analyzed and evaluated through the production line operation detection and analysis module, so that a production line operation qualified signal or a production line operation unqualified signal of the corresponding automatic galvanization barrel plating production line is generated, and the operation conditions of all the automatic galvanization barrel plating production lines are mastered in detail; the product quality visual detection module performs visual detection analysis on the processed product to judge whether the corresponding product meets the quality requirement, and generates a processing quality qualified signal or a processing quality unqualified signal corresponding to the automatic galvanization production line through analysis so as to grasp the product processing quality status of each automatic galvanization production line in detail; the production line gas-liquid control module carries out gas phase detection on the processing area of the corresponding automatic galvanizing production line and carries out liquid phase detection on the discharged wastewater, and accordingly gas-liquid control qualified signals or gas-liquid control unqualified signals are generated through analysis, so that the gaseous environment condition of the production area of the corresponding automatic galvanizing production line and the pollution condition of the discharged waste liquid are mastered, the management difficulty is reduced, corresponding management personnel can timely carry out targeted management measures, and the production efficiency, the product quality and the production safety of the automatic galvanizing production line are effectively guaranteed.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The intelligent processing management system for the galvanization automatic barrel plating production line is characterized by comprising an intelligent management platform, a barrel plating production line evaluation module, a production line operation matching module, a production line operation detection analysis module, a product quality visual detection module and a production line gas-liquid management and control module;
the barrel plating production line evaluation module is used for analyzing the production line condition of the automatic barrel plating production line of the galvanization, dividing the automatic barrel plating production line into an unstable production line, a good grade production line or a good grade production line according to the analysis, and sending evaluation division information to the production line operation matching module through the intelligent management platform; the production line operation matching module analyzes corresponding operators performing galvanization operation, and distributes operators matched with the corresponding galvanization automatic barrel plating production line before the corresponding galvanization automatic barrel plating production line is processed;
The production line operation detection analysis module analyzes and evaluates the operation processing conditions of the corresponding galvanization automatic barrel plating production line, so as to generate a production line operation qualified signal or a production line operation unqualified signal of the corresponding galvanization automatic barrel plating production line and send the signal to the intelligent management platform; the product quality visual detection module generates a processing quality qualified signal or a processing quality unqualified signal corresponding to the galvanization automatic barrel plating production line through analysis and sends the processing quality qualified signal or the processing quality unqualified signal to the intelligent management platform; the production line gas-liquid control module generates a gas-liquid control qualified signal or a gas-liquid control disqualification signal through analysis and sends the gas-liquid control qualified signal or the gas-liquid control disqualification signal to the intelligent management platform; the intelligent management platform generates corresponding early warning information when receiving a production line operation failure signal, a processing quality failure signal or a gas-liquid management failure signal, and sends the corresponding early warning information to a production line management terminal;
the concrete operation process of the barrel plating production line evaluation module comprises the following steps:
the method comprises the steps of obtaining a galvanization automatic barrel plating production line to be managed, and marking the corresponding galvanization automatic barrel plating production line as a target production line i, wherein i is a natural number larger than 1; collecting the product galvanization abnormality rate and the production line operation failure rate of the target production line i in the monitoring period, and dividing the target production line i into unstable production lines if the product galvanization abnormality rate or the production line operation failure rate exceeds a corresponding preset threshold value;
If the product galvanization abnormality rate and the production line operation failure rate do not exceed the corresponding preset thresholds, acquiring the time of stopping the production line i caused by the production line failure of the supervision period target production line i and marking the time as single failure stopping time, summing all the single failure stopping time to obtain the total failure stopping time, and analyzing and calculating the total failure stopping time, the product galvanization abnormality rate and the production line operation failure rate to obtain a production line decision value;
if the line decision value exceeds the maximum value of the preset line decision value range, dividing the target line i into unstable lines; if the production line decision value is within the preset production line decision value range, dividing the target production line i into good-grade production lines; if the line decision value does not exceed the minimum value of the preset line decision value range, dividing the target line i into a top-grade line; the evaluation division information of the target production line i is sent to a production line operation matching module through an intelligent management platform;
the specific operation process of the production line operation matching module comprises the following steps:
all operators performing galvanization operation are obtained, the corresponding operators are marked as target operators t, and t is a natural number larger than 1; acquiring the product rejection rate and the operation error value of a target person t in the processing process in the supervision period, and dividing the target person t into non-mature operators if the product rejection rate or the operation error value exceeds a corresponding preset threshold value;
If the product rejection rate and the misoperation value do not exceed the corresponding preset thresholds, collecting the working time of the target personnel t in the galvanization automatic barrel plating production line and the operation damage value data of the target personnel t in the supervision period, and carrying out numerical calculation on the working time, the operation damage value data, the product rejection rate and the misoperation value of the target personnel t to obtain an operation decision value; dividing the target person t into non-mature operators if the operation decision value exceeds the maximum value of the preset operation decision value range, dividing the target person t into good operators if the operation decision value is within the preset operation decision value range, and dividing the target person t into good operators if the operation decision value does not exceed the minimum value of the preset operation decision value range;
when the operator of the target production line i is allocated, if the target production line i is an unstable production line, preferentially allocating the operator to the unstable production line i, and not allocating good operators and non-mature operators to the target production line i; if the target production line i is a good-grade production line, an operator or a good operator is allocated to the target production line i, and a non-cooked operator is not allocated to the target production line i; if the target production line i is an excellent production line, an excellent operator, a good operator or a non-cooked operator is allocated to the target production line i, and the non-cooked operator is preferentially arranged to the excellent production line;
The specific operation process of the production line operation detection and analysis module comprises the following steps:
when the target production line i is galvanized, all machining procedures of the target production line i are acquired, machining position deviation data and machining duration deviation data of the product in the corresponding machining procedures in the machining process are acquired, and if the machining position deviation data and the machining duration deviation data do not exceed the corresponding preset threshold values, the machining operation of the corresponding product in the corresponding procedures is judged to be qualified; otherwise, judging that the corresponding product is unqualified in the processing operation of the corresponding procedure;
if the processing operation of all the processing procedures of the corresponding product in the target production line i is qualified, marking the whole processing process of the corresponding product as an obstacle-free process; acquiring the total processing amount of a product of a target production line i in unit time, acquiring the processing amount of the product corresponding to an obstacle-free process, marking the processing amount as an obstacle-free processing amount, and calculating the ratio of the obstacle-free processing amount to the total processing amount of the product to obtain an obstacle-free product coefficient; marking the corresponding machining process of the target production line i as a smooth process or a blocking process through process performance detection analysis, and calculating the ratio of the number of the smooth processes to the number of the blocking processes to obtain a barrier-free process coefficient;
Analyzing and calculating the barrier-free product coefficient and barrier-free process coefficient of the target production line i to obtain a production line operation coefficient, and generating a production line operation qualified signal if the production line operation coefficient exceeds a preset production line operation coefficient threshold; if the production line operation coefficient does not exceed the preset production line operation coefficient threshold value, generating a production line operation disqualification signal;
the specific analysis process of the process performance detection analysis is as follows:
if the processing operation of the product processed by the corresponding processing procedure in unit time is qualified, marking the corresponding processing procedure as a smooth procedure; otherwise, collecting all processing position deviation data and processing time length deviation data corresponding to the processing procedure in unit time, carrying out summation calculation on all processing position deviation data and taking an average value to obtain a processing position deviation value, and carrying out summation calculation on all processing time length deviation data and taking an average value to obtain a processing time deviation value; collecting the number percentage of unqualified products of the processing operation corresponding to the processing procedure in unit time, and marking the number percentage as an unqualified operation coefficient;
performing numerical calculation on the machining position deviation value, the machining time deviation value and the unqualified operation coefficient of the corresponding machining process to obtain a process detection value, and marking the corresponding machining process as a blocking process if the process detection value exceeds a preset process detection threshold; otherwise, marking the corresponding processing procedure as a smooth procedure;
The analysis process of the product quality visual detection module comprises the following steps:
when the corresponding product processed by the target production line i meets the quality requirement, marking the corresponding product as a compliant product; when the corresponding product processed by the target production line i is judged to be not in accordance with the quality requirement, acquiring the characteristic quantity of the corresponding product which does not meet the corresponding quality standard, marking the characteristic quantity as a non-standard characteristic value, and marking the corresponding product as a high-bias product if the non-standard characteristic value exceeds a preset non-standard characteristic threshold value;
if the non-standard feature value does not exceed the preset non-standard feature threshold, marking the corresponding feature which does not reach the corresponding quality standard as a bad feature, acquiring a feature influence value of the preset corresponding bad feature, summing the feature influence values of all bad features of the corresponding product to obtain a feature bad value, if the feature bad value exceeds the preset feature bad threshold, marking the corresponding product as a high-bias product, and if the feature bad value does not exceed the preset feature bad threshold, marking the corresponding product as a low-bias product;
acquiring the number of the compliant products, the number of the high-bias products and the number of the low-bias products in the products processed by the target production line i in unit time, and analyzing and calculating the number of the compliant products, the number of the high-bias products and the number of the low-bias products to obtain a quality analysis value; if the quality analysis value exceeds a preset quality analysis threshold value, generating a processing quality disqualification signal of the target production line i; otherwise, generating a processing quality qualified signal of the target production line i;
The specific operation process of the production line gas-liquid control module comprises the following steps:
setting a plurality of detection points in a processing area of a target production line i, collecting hydrogen chloride data, ammonia data, organic waste gas data and smoke concentration data of the detection points, collecting environmental temperature data of the detection points, calculating a difference value between the environmental temperature data and a preset proper environmental temperature value, and taking an absolute value to obtain ring temperature deviation data; carrying out numerical calculation on hydrogen chloride data, ammonia data, organic waste gas data, smoke concentration data and ring temperature deviation data of detection points to obtain a gas phase value of a production line;
if the gas phase value of the production line exceeds the maximum value of the gas phase value range of the preset production line, marking the detection point as a gas phase bad point; if the gas phase value of the production line is within the range of the gas phase value of the preset production line, marking the detection point as a gas phase benign point; if the gas phase value of the production line does not exceed the minimum value of the gas phase range of the preset production line, marking the detection point as a gas phase unimpeded point; if a gas phase bad point exists in the processing area of the target production line i, a gas phase judgment symbol QX-1 is given to the gas phase bad point;
if no gas phase bad points exist in the processing area of the target production line i, calculating the ratio of the number of gas phase bad points to the number of gas phase unimpeded points to obtain a gas phase representation value, summing the gas phase values of the production line of all detection points, taking an average value to obtain a gas phase average value, and calculating the values of the gas phase representation value and the gas phase average value to obtain a gas phase live value; if the gas phase live value exceeds a preset gas phase live threshold value, a gas phase judgment symbol QX-1 is given to the gas phase live value; otherwise, a gas phase judgment symbol QX-2 is given to the gas phase judgment symbol;
And assigning a liquid phase decision symbol YX-1 or YX-2 to the target production line i by liquid discharge supervision analysis; when QX-2 ∈YX-2 is generated, a gas-liquid control qualified signal is generated, and under the other conditions, a gas-liquid control unqualified signal is generated;
the specific analysis process of the drainage supervision analysis is as follows:
the method comprises the steps of collecting heavy metal ion data, solid impurity data, ph deviation data and color deviation data in wastewater discharged by a target production line i, and carrying out numerical calculation on the heavy metal ion data, the solid impurity data, the ph deviation data and the color deviation data to obtain a liquid phase live value; if the liquid phase live value exceeds a preset liquid phase live threshold value, a liquid phase judgment symbol YX-1 is given to the liquid phase live value; if the liquid phase live value does not exceed the preset liquid phase live threshold value, a liquid phase judgment symbol YX-2 is assigned to the liquid phase live value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311315451.XA CN117071046B (en) | 2023-10-12 | 2023-10-12 | Intelligent processing management system for automatic galvanization barrel plating production line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311315451.XA CN117071046B (en) | 2023-10-12 | 2023-10-12 | Intelligent processing management system for automatic galvanization barrel plating production line |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117071046A CN117071046A (en) | 2023-11-17 |
| CN117071046B true CN117071046B (en) | 2024-01-12 |
Family
ID=88713637
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311315451.XA Active CN117071046B (en) | 2023-10-12 | 2023-10-12 | Intelligent processing management system for automatic galvanization barrel plating production line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117071046B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117737815B (en) * | 2024-02-21 | 2024-06-11 | 山东裕得金属制品股份有限公司 | Intelligent operation quality early warning system suitable for electroplating bath impurity removal device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017161991A (en) * | 2016-03-07 | 2017-09-14 | 三菱重工業株式会社 | Quality evaluation system, quality evaluation method and program |
| CN107312993A (en) * | 2016-04-27 | 2017-11-03 | 天津轧冷轧薄板有限公司 | Galvanization production line zinc layers specification early warning system |
| CN107608328A (en) * | 2017-10-27 | 2018-01-19 | 东莞市希锐自动化科技股份有限公司 | A kind of tele-control system of surface-treatment line |
| CN109457291A (en) * | 2018-09-29 | 2019-03-12 | 昆山阿尔玛电子设备有限公司 | Electroplating assembly line monitoring system |
| CN114757473A (en) * | 2022-03-09 | 2022-07-15 | 长春富晟安创汽车零部件有限公司 | Remote online management method and system based on electroplating production line |
| CN116184926A (en) * | 2023-04-21 | 2023-05-30 | 山东力乐新材料有限公司 | Full-automatic intelligent production line flow detection system for plastic hollow plate |
| CN116700186A (en) * | 2023-07-11 | 2023-09-05 | 滁州学院 | Electrical control cabinet processing quality detection system based on big data detection |
-
2023
- 2023-10-12 CN CN202311315451.XA patent/CN117071046B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017161991A (en) * | 2016-03-07 | 2017-09-14 | 三菱重工業株式会社 | Quality evaluation system, quality evaluation method and program |
| CN107312993A (en) * | 2016-04-27 | 2017-11-03 | 天津轧冷轧薄板有限公司 | Galvanization production line zinc layers specification early warning system |
| CN107608328A (en) * | 2017-10-27 | 2018-01-19 | 东莞市希锐自动化科技股份有限公司 | A kind of tele-control system of surface-treatment line |
| CN109457291A (en) * | 2018-09-29 | 2019-03-12 | 昆山阿尔玛电子设备有限公司 | Electroplating assembly line monitoring system |
| CN114757473A (en) * | 2022-03-09 | 2022-07-15 | 长春富晟安创汽车零部件有限公司 | Remote online management method and system based on electroplating production line |
| CN116184926A (en) * | 2023-04-21 | 2023-05-30 | 山东力乐新材料有限公司 | Full-automatic intelligent production line flow detection system for plastic hollow plate |
| CN116700186A (en) * | 2023-07-11 | 2023-09-05 | 滁州学院 | Electrical control cabinet processing quality detection system based on big data detection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117071046A (en) | 2023-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117071046B (en) | Intelligent processing management system for automatic galvanization barrel plating production line | |
| CN106079892B (en) | A kind of PCB paste solder printing procedure quality intelligent monitor system and method | |
| CN111738308A (en) | Dynamic threshold detection method for monitoring index based on clustering and semi-supervised learning | |
| CN113448298B (en) | Data acquisition system for automatic production equipment | |
| CN113762604B (en) | Industrial Internet big data service system | |
| CN115941529B (en) | Cable tunnel detection method and system based on robot | |
| CN117075528B (en) | Mobile phone accessory rotating assembly line monitoring system based on data processing | |
| CN105116301B (en) | A kind of data auxiliary judgment method based on dynamic statistics | |
| CN117689119B (en) | Intelligent building site safety supervision method and system based on Internet of things | |
| CN104794771B (en) | One kind patrols inspection automatic planning and a device | |
| CN113934536B (en) | Data acquisition method facing edge calculation | |
| CN117238388B (en) | Electroplating solution monitoring system for composite electroplating based on data analysis | |
| CN117335570B (en) | Visual monitoring system and method for panoramic information of elastic power distribution network | |
| CN207516235U (en) | Heavy metal pollution risk on-line early warning system in a kind of water | |
| CN113471864A (en) | Transformer substation secondary equipment field maintenance device and method | |
| CN105158610A (en) | Screening processing method of transformer state early warning data suspected value | |
| CN112504709B (en) | High-efficient concentrated quick-witted operation monitoring system | |
| CN110339938A (en) | A kind of digitlization coal separation system | |
| CN116049098A (en) | Operational data characteristic analysis method suitable for judging operational state of power distribution network | |
| CN112734251A (en) | Visual neural network monitoring system for intelligent water management platform | |
| Kaartinen et al. | Image analysis based control of zinc flotation-A multi-camera approach | |
| CN109102001A (en) | A kind of gene improve the rotor on-line fault diagnosis method of neural network | |
| CN113159605B (en) | Chemical device intelligent alarm method and system based on logistic regression | |
| CN117592753B (en) | Production supervision control system based on knowledge graph | |
| CN210657170U (en) | Electrical control and monitoring system of high-purity indium preparation device |
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 |