CN118350619B - Intelligent full-flow production management method and management system thereof - Google Patents
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Abstract
The invention provides an intelligent whole-flow production management method and a management system thereof, which relate to the technical field of intelligent whole-flow production management and comprise the following steps: obtaining a correction feature set based on the obtained initial feature set of the product produced by each process at the current moment; obtaining the quality coefficient of the product produced by each process at the current moment based on the correction feature set; obtaining the quality index of each process at the current moment based on the basic information of each process at the current moment and the quality coefficient of the produced product; acquiring an energy consumption index and a yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment; and obtaining a process management correction result of each process of the whole production process based on the process analysis matrix. The invention realizes the specific analysis of each procedure of the whole process production, and improves the production efficiency and the product quality of the whole process production more efficiently.
Description
Technical Field
The invention relates to the technical field of intelligent full-flow production management systems, in particular to an intelligent full-flow production management method and an intelligent full-flow production management system.
Background
At present, a production line, also called an assembly line, is an industrial production mode, namely, the production of a complete product is divided into a plurality of working procedures, the working procedures are connected through a conveying belt, and each working procedure only focuses on the work of processing a certain segment so as to improve the work efficiency and the yield.
However, the existing intelligent full-flow production management method and the management system thereof only improve the production efficiency by adjusting a processing station with higher processing efficiency on a production line into a full-function station, do not specifically analyze each procedure of full-flow production, do not obtain a specific mode of optimizing or correcting each procedure in the full-flow production, and are difficult to improve the production efficiency and the product quality of the full-flow production more efficiently. For example, publication number is CN115345514a, and patent name is "an intelligent full-flow production management system and control method thereof", the method comprises the following steps: according to the condition of predetermineeing, adjust the higher processing station of machining efficiency into full function station on the production line, promote the machining efficiency of the processing procedure that the processing station corresponds that machining efficiency is low through these full function stations, reduce the time that this processing procedure took in whole production line to the work load between each processing station is balanced, avoids production line to appear some processing stations and leads to the processing procedure to use too long because of machining efficiency is low, and the idle condition of some processing stations, and then makes the machining efficiency of whole production line promote. However, this patent only improves the production efficiency by adjusting the processing station with higher processing efficiency on the production line to a full-function station, and does not specifically analyze each process in the full-process production, and does not obtain a specific way to optimize or correct each process in the full-process production.
Therefore, the invention provides an intelligent whole-flow production management method and a management system thereof, which are used for carrying out specific analysis on each procedure of whole-flow production to obtain a specific mode of optimizing or correcting each procedure in the whole production flow, and more effectively improving the production efficiency and the product quality of the whole-flow production.
Disclosure of Invention
The invention provides an intelligent whole-flow production management method and a management system thereof, which are used for more accurately obtaining an initial feature set of a produced product of each procedure at the current moment according to preset equipment, correcting the initial feature set of the produced product of each procedure at the current moment, obtaining a corrected feature set of the produced product of each procedure at the current moment, eliminating the influence of unqualified products on subsequent processing, accurately obtaining the quality coefficient of the produced product of each procedure at the current moment according to the preset standard feature set of the produced product of each procedure and the corrected feature set of the produced product of each procedure at the current moment, facilitating the calculation of the subsequent quality index, more accurately obtaining the level coefficient of each procedure at the current moment according to basic information of each procedure at the current moment, the quality index of each process at the current moment is calculated more accurately according to the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information, the construction of a subsequent process analysis matrix is facilitated, the energy consumption index and the yield index of each process at the current moment are obtained more accurately according to the basic information of each process at the current moment, the process analysis matrix of the whole production process at the current moment is constructed according to the energy consumption index, the yield index and the quality index of each process at the current moment, the subsequent analysis of the correction mode of each process is facilitated, the analysis of the correction mode of each process in the whole production process at the current moment is carried out according to the process analysis matrix of the whole production process at the current moment, the process management correction result of each process of the whole production process is obtained, the specific mode of optimizing or correcting each process in the whole production process at the current moment is achieved, the production efficiency and the product quality of the whole process production are improved.
The invention provides an intelligent whole-flow production management method, which comprises the following steps:
s1: acquiring characteristics of the produced products of each process in the full production flow at the current moment based on preset equipment, acquiring an initial characteristic set of the produced products of each process at the current moment, correcting the initial characteristic set of the produced products of each process at the current moment, and acquiring a corrected characteristic set of the produced products of each process at the current moment;
S2: obtaining a quality coefficient of the output product of each process at the current moment based on a preset standard feature set of the output product of each process and a preset correction feature set of the output product of each process at the current moment;
s3: acquiring basic information of each process at the current moment, acquiring a level coefficient of each process at the current moment based on the basic information of each process at the current moment, and calculating a quality index of each process at the current moment based on the level coefficient of each process at the current moment, a quality coefficient of a produced product and the basic information;
S4: acquiring an energy consumption index and a yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment;
s5: and analyzing the correction mode of each process in the full production flow at the current moment based on the process analysis matrix of the full production flow at the current moment to obtain a process management correction result of each process of the full production flow.
Preferably, the intelligent whole-flow production management method comprises the following steps of S1: based on the preset equipment, carrying out feature acquisition on the output product of each process in the full production flow at the current moment, obtaining an initial feature set of the output product of each process at the current moment, correcting the initial feature set of the output product of each process at the current moment, and obtaining a corrected feature set of the output product of each process at the current moment, wherein the method comprises the following steps:
S101: acquiring all appearance structure sizes of the produced products of each process in the whole production flow at the current moment based on preset equipment, constructing a set by taking all appearance structure sizes of the produced products of each process at the current moment as set elements, and taking the constructed set as an initial feature set of the produced products of each process at the current moment, wherein the positions of the set elements are determined by the acquisition time sequence of the thickness of the products at the preset product positions;
s102: and correcting the initial feature set of the output product of each process at the current moment to obtain the corrected feature set of the output product of each process at the current moment.
Preferably, the intelligent whole-flow production management method, S102: correcting the initial feature set of the output product of each process at the current moment to obtain the corrected feature set of the output product of each process at the current moment, wherein the method comprises the following steps:
s1021: acquiring a historical initial feature set of a produced product of each process at a plurality of moments in a preset time period before the current moment;
S1022: the average value of all the set elements with the same ordinal numbers in all the historical initial feature sets of the produced products of each procedure is calculated and used as the standard set element with the corresponding ordinal numbers in the historical initial feature sets, the difference value between each set element in the initial feature sets of the produced products of each procedure at the current moment and the standard set element with the same ordinal numbers as the corresponding set element in the historical initial feature sets of the produced products of the corresponding procedure is calculated, all the elements of the initial feature sets with the difference value within the preset difference value threshold range are replaced with the standard set element with the same ordinal numbers as the corresponding set element, the initial feature set after the elements are replaced is used as the correction feature set, and otherwise, the initial feature set with the difference value not within the preset difference value threshold range is used as the correction feature set.
Preferably, the intelligent whole-flow production management method comprises the following steps of: obtaining the quality coefficient of the output product of each process at the current moment based on the preset standard feature set of the output product of each process and the correction feature set of the output product of each process at the current moment, wherein the quality coefficient comprises the following steps:
all working procedures in the whole production flow are defined according to the production time sequence, the ordinal number is defined by increasing from 1, and ordinal number definition results are obtained;
Calculating a quality coefficient of the produced product of each process at the current moment based on the ordinal definition result, the average value of all set elements in the preset standard feature set of the produced product of each process and the average value of all set elements in the corrected feature set of the produced product of each process at the current moment, wherein the quality coefficient comprises the following steps:
;
Wherein, Is the current timeThe quality coefficient of the product produced by the working procedure is dimensionless,Is the current timeThe value of the mean value of the set elements of the corrected feature set of the output product of the process,Is the preset firstThe number of the mean value of the set elements of the standard feature set of the produced product of the process,Is the preset firstThe value of the maximum value of all set elements in the standard feature set of the produced product of the process,Is the current timeThe value of the minimum value of all set elements in the corrected feature set of the output product of the process,Is the preset firstStandard feature sets of the produced products of the previous process,Is the current timeAnd (3) correcting the characteristic set of the output product of the procedure.
Preferably, the intelligent whole-flow production management method acquires basic information of each procedure at the current moment, and acquires a horizontal coefficient of each procedure at the current moment based on the basic information of each procedure at the current moment, including:
Acquiring basic information of each process at the current moment, wherein the basic information of each process at the current moment comprises standard working hours of each process at the current moment, energy consumption of each process at the current moment, yield of each process at the current moment, number of workers of each process at the current moment, number of advanced workers in each process at the current moment, number of intermediate workers in each process at the current moment and number of primary workers in each process at the current moment;
obtaining the quotient of the number of the advanced workers in each process at the current moment and the number of the workers in each process at the current moment, and multiplying the quotient by a preset advanced proportional coefficient to obtain an advanced worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of middle-level workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset middle-level proportional coefficient to obtain a middle-level worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of primary workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset primary proportional coefficient to obtain a primary worker level coefficient of each process at the current moment;
And summing the high-grade worker level coefficient, the medium-grade worker level coefficient and the primary worker level coefficient of each process at the current moment, and taking the sum value as the level coefficient of each process at the current moment.
Preferably, the intelligent whole-flow production management method calculates a quality index of each procedure at the current moment based on a level coefficient of each procedure at the current moment, a quality coefficient of a produced product and basic information, and comprises the following steps:
Calculating the quality coefficient of each process at the current moment based on the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information, wherein the calculating comprises the following steps:
;
Wherein, Is the current timeThe quality index of the previous working procedure is dimensionless,Is the current timeThe number of workers in the process,Is natural constantAnd takes a value of 2.718,Is the current timeThe horizontal coefficient of the process,Is the current timeThe quality coefficient of the product produced by the previous procedure,Is the current timeNumerical values of standard man-hours of the procedure;
normalizing the quality coefficients of all the procedures at the current moment, and taking the normalized value of the quality coefficient of each procedure as the quality index of each procedure at the current moment.
Preferably, the intelligent whole-flow production management method obtains the energy consumption index and the yield index of each process at the current moment based on the basic information of each process at the current moment, and comprises the following steps:
acquiring the energy consumption and the yield of each process at the current moment from the basic information of each process at the current moment;
Normalizing the quotient of the energy consumption of all the procedures at the current moment and the standard energy consumption, and taking the value obtained after normalizing the quotient of each procedure as the energy consumption index of each procedure at the current moment;
Normalizing the values of the yields of all the procedures at the current moment, and taking the value obtained after normalizing the yield of each procedure as the yield index of each procedure at the current moment.
Preferably, the intelligent whole-process production management method constructs a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment, and comprises the following steps:
;
Wherein, A matrix is analyzed for the process of the full production flow at the current moment,Is the energy consumption index of the 1 st working procedure at the current moment,Is the current timeThe energy consumption index of the working procedure is calculated,Is the current timeThe energy consumption index of the working procedure is calculated,Is the total number of the working procedures of the whole production flow,Is the quality index of the 1 st working procedure at the current moment,Is the current timeThe quality index of the process is that,Is the current timeThe quality index of the process is that,Is the yield index of the 1 st working procedure at the current moment,Is the current timeThe yield index of the process is calculated,Is the current timeAnd (5) yield index of the process.
Preferably, the intelligent whole-flow production management method, S5: based on a process analysis matrix of the full production process at the current moment, correcting each process in the full production process at the current moment to obtain a process management correction result of each process in the full production process, wherein the process management correction result comprises the following steps:
S501: determining the maximum value of elements in each column of matrix elements in a process analysis matrix of the full production process at the current moment;
s502: when the minimum value of the elements of the single-row matrix elements is an energy consumption index, reducing the product conveying speed of the corresponding process of the corresponding row matrix elements by a preset conveying speed to obtain a process management correction result of the corresponding process, when the minimum value of the elements of the row matrix elements is a quality index, increasing the number of advanced workers of the corresponding process of the corresponding row matrix elements by a preset number of people to obtain a process management correction result of the corresponding process, and when the minimum value of the elements of the row matrix elements is a yield index, increasing the product conveying speed of the corresponding process of the row matrix elements by the preset conveying speed to obtain a process management correction result of the corresponding process.
The present invention provides an intelligent full-process production management system for executing any one of the intelligent full-process production management methods of embodiments 1 to 9, comprising:
The correction module is used for obtaining the characteristics of the produced products of each process in the whole production flow at the current moment based on the preset equipment, obtaining an initial characteristic set of the produced products of each process at the current moment, correcting the initial characteristic set of the produced products of each process at the current moment, and obtaining a corrected characteristic set of the produced products of each process at the current moment;
The quality coefficient module is used for obtaining the quality coefficient of the output product of each process at the current moment based on a preset standard feature set of the output product of each process and a preset correction feature set of the output product of each process at the current moment;
The quality index module is used for acquiring basic information of each process at the current moment, acquiring a horizontal coefficient of each process at the current moment based on the basic information of each process at the current moment, and calculating a quality index of each process at the current moment based on the horizontal coefficient of each process at the current moment, a quality coefficient of a product produced by each process at the current moment and the basic information of each process at the current moment;
The process analysis matrix module is used for obtaining the energy consumption index and the yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment;
The correction mode acquisition module is used for analyzing the correction mode of each process in the full production flow at the current moment based on the process analysis matrix of the full production flow at the current moment to obtain the process management correction result of each process of the full production flow.
Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of obtaining an initial feature set of a produced product of each process at the current moment more accurately according to preset equipment, correcting the initial feature set of the produced product of each process at the current moment, obtaining a corrected feature set of the produced product of each process at the current moment, eliminating the influence of unqualified products on subsequent processing, obtaining a quality coefficient of the produced product of each process at the current moment accurately according to the preset standard feature set of the produced product of each process at the current moment and the corrected feature set of the produced product of each process at the current moment, facilitating calculation of a subsequent quality index, obtaining a level coefficient of each process at the current moment more accurately according to basic information of each process at the current moment, calculating a quality index of each process at the current moment more accurately according to the level coefficient of each process at the current moment, the quality coefficient of the produced product and basic information, facilitating construction of a subsequent process analysis matrix, obtaining an energy consumption index and a yield index of each process at the current moment more accurately according to the basic information of each process at the current moment, facilitating calculation of a full-process production process analysis matrix according to a full-process production process analysis mode, and full-process production process quality analysis of each process at the current moment, and full-process production process, and realizing full-process quality analysis of each full-process in full-process, and full-process production process, and full-process quality analysis of each process, and full-process production process quality matrix according to full-process, and full-process quality analysis of each process is optimized according to a full-process.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objects and other advantages of the application may be realized and obtained by means of the instrumentalities particularly pointed out in the written description of the application.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flowchart of an intelligent whole-process production management method according to an embodiment of the present invention;
FIG. 2 is a flowchart showing the step S1 according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of an intelligent whole-process production management system according to an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Examples
The invention provides an intelligent full-flow production management method, which comprises the following steps of:
s1: acquiring characteristics of the produced products of each process in the full production flow at the current moment based on preset equipment, acquiring an initial characteristic set of the produced products of each process at the current moment, correcting the initial characteristic set of the produced products of each process at the current moment, and acquiring a corrected characteristic set of the produced products of each process at the current moment;
S2: obtaining a quality coefficient of the output product of each process at the current moment based on a preset standard feature set of the output product of each process and a preset correction feature set of the output product of each process at the current moment;
s3: acquiring basic information of each process at the current moment, acquiring a level coefficient of each process at the current moment based on the basic information of each process at the current moment, and calculating a quality index of each process at the current moment based on the level coefficient of each process at the current moment, a quality coefficient of a produced product and the basic information;
S4: acquiring an energy consumption index and a yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment;
s5: and analyzing the correction mode of each process in the full production flow at the current moment based on the process analysis matrix of the full production flow at the current moment to obtain a process management correction result of each process of the full production flow.
In this embodiment, the preset device is a preset device, such as a laser measurement device, for obtaining characteristics of the output product of each process in the full production process at the current moment.
In this embodiment, the whole production flow is a flow of all the processes of the pipeline process in the industrial production.
In this embodiment, the output product of each process is the output product of each process through the pipeline.
In this embodiment, the feature acquisition is the apparent structural size of the output product produced by each process of the pipeline acquired based on the preset equipment.
In this embodiment, the initial feature set is the set constructed from all of the apparent structural dimensions of the output product produced by each process through the pipeline.
In this embodiment, the correction is a process of replacing or not processing the set elements in the initial feature set of the produced product of each process at the current time to obtain a new feature set.
In this embodiment, the corrected feature set is a new feature set obtained by replacing or not processing set elements in the initial feature set of the produced product of each process at the current time.
In this embodiment, the preset standard feature set of the output product of each process is a preset standard feature set of the output product of each process determined by a preset standard product of each process.
In this embodiment, the quality coefficient of the output product of each process at the current time is a coefficient reflecting the product appearance condition of the output product produced by each process of the assembly line at the current time, and a larger quality coefficient indicates a better quality control of the output product appearance (a smaller difference from the appearance of the standard product).
In this embodiment, the basic information of each process at the current time is specific information about production of each process at the current time, including standard man-hour of each process at the current time, energy consumption of each process at the current time, yield of each process at the current time, number of workers of each process at the current time, number of advanced workers of each process at the current time, number of intermediate workers of each process at the current time, and number of primary workers of each process at the current time.
In this embodiment, the horizontal coefficient for each process at the present time is a coefficient reflecting the overall processing capacity for each process at the present time, and the higher the horizontal coefficient, the stronger the overall processing capacity for the corresponding process.
In this embodiment, the quality index of each process at the current time is a value reflecting the quality condition of the output product produced by each process at the current time, and a larger quality index indicates a better quality condition of the output product.
In this embodiment, the energy consumption index and the yield index of each process at the current time are values reflecting the energy consumption condition and the yield condition of each process at the current time.
In this embodiment, the process analysis matrix of the full production process at the current moment is a matrix constructed by taking the energy consumption index, the yield index and the quality index of all the processes at the current moment as matrix elements, and the optimization and correction of the full production process can be obtained according to the constructed matrix.
In this embodiment, the correction mode is a specific mode for optimizing or correcting each process in the whole production flow at the current time obtained based on the process analysis matrix.
In this embodiment, the process management correction result of each process is a result obtained by obtaining a correction method for each process in the full production flow at the current time.
The beneficial effects of the technology are as follows: the method comprises the steps of obtaining an initial feature set of a produced product of each process at the current moment more accurately according to preset equipment, correcting the initial feature set of the produced product of each process at the current moment, obtaining a corrected feature set of the produced product of each process at the current moment, eliminating the influence of unqualified products on subsequent processing, obtaining a quality coefficient of the produced product of each process at the current moment accurately according to the preset standard feature set of the produced product of each process at the current moment and the corrected feature set of the produced product of each process at the current moment, facilitating calculation of a subsequent quality index, obtaining a level coefficient of each process at the current moment more accurately according to basic information of each process at the current moment, calculating a quality index of each process at the current moment more accurately according to the level coefficient of each process at the current moment, the quality coefficient of the produced product and basic information, facilitating construction of a subsequent process analysis matrix, obtaining an energy consumption index and a yield index of each process at the current moment more accurately according to the basic information of each process at the current moment, facilitating calculation of a full-process production process analysis matrix according to a full-process production process analysis mode, and full-process production process quality analysis of each process at the current moment, and full-process production process, and realizing full-process quality analysis of each full-process in full-process, and full-process production process, and full-process quality analysis of each process, and full-process production process quality matrix according to full-process, and full-process quality analysis of each process is optimized according to a full-process.
Example 2:
On the basis of the embodiment 1, an intelligent full-flow production management method, S1: based on the preset equipment, carrying out feature acquisition on the produced products of each process in the full production flow at the current moment, obtaining an initial feature set of the produced products of each process at the current moment, correcting the initial feature set of the produced products of each process at the current moment, and obtaining a corrected feature set of the produced products of each process at the current moment, referring to fig. 2, including:
S101: acquiring all appearance structure sizes of the produced products of each process in the whole production flow at the current moment based on preset equipment, constructing a set by taking all appearance structure sizes of the produced products of each process at the current moment as set elements, and taking the constructed set as an initial feature set of the produced products of each process at the current moment, wherein the positions of the set elements are determined by the acquisition time sequence of the thickness of the products at the preset product positions;
s102: and correcting the initial feature set of the output product of each process at the current moment to obtain the corrected feature set of the output product of each process at the current moment.
In this embodiment, the dimension of the appearance structure is the product thickness of the output product of each process at a plurality of preset product positions (preset product thickness acquisition positions of the output product of each process), the preset product positions of the output product of each process are different, for example, the thickness at one preset position on the front surface of an object is the perpendicular to the plane of the outer surface of the object at the preset position, and the distance between the intersection point of the perpendicular and the other surface of the object and the position is the thickness.
In this embodiment, the position of the aggregation element is determined by the acquisition timing of the product thickness at the preset product position before and after the position of the aggregation element is determined by the time sequence of the product thickness acquisition at the preset product position for the output product of each process, and the earlier the time sequence is, the earlier the position of the aggregation element is.
The beneficial effects of the technology are as follows: the method comprises the steps of acquiring the thickness of a product according to preset equipment, obtaining an initial feature set of the produced product of each process at the current moment more accurately, correcting the initial feature set of the produced product of each process at the current moment, and obtaining a corrected feature set of the produced product of each process at the current moment.
Example 3:
Based on embodiment 2, the intelligent full-flow production management method, S102: correcting the initial feature set of the output product of each process at the current moment to obtain the corrected feature set of the output product of each process at the current moment, wherein the method comprises the following steps:
s1021: acquiring a historical initial feature set of a produced product of each process at a plurality of moments in a preset time period before the current moment;
S1022: the average value of all the set elements with the same ordinal numbers in all the historical initial feature sets of the produced products of each procedure is calculated and used as the standard set element with the corresponding ordinal numbers in the historical initial feature sets, the difference value between each set element in the initial feature sets of the produced products of each procedure at the current moment and the standard set element with the same ordinal numbers as the corresponding set element in the historical initial feature sets of the produced products of the corresponding procedure is calculated, all the elements of the initial feature sets with the difference value within the preset difference value threshold range are replaced with the standard set element with the same ordinal numbers as the corresponding set element, the initial feature set after the elements are replaced is used as the correction feature set, and otherwise, the initial feature set with the difference value not within the preset difference value threshold range is used as the correction feature set.
In this embodiment, the preset time period is a preset time period (i.e. data in the historical initial feature set) for determining the historical initial feature set of the output product of each process, for example, 10min.
In this embodiment, the historical initial feature set is a set constructed by all the appearance structure dimensions of the produced product of each process at a time within a preset time period before the current time.
In this embodiment, the same ordinal number is the same position of the set element in the set (the position of the set element in all elements from left to right) in all the historical initial feature sets of the output product of each process.
In this embodiment, the mean is the average of all the same ordinal collection elements in all the historical initial feature sets of the produced product for each process.
In this embodiment, the standard set element is the average of all the set elements of the same ordinal number in all the historical initial feature sets of the produced product of each process (e.g., the average of all the geometric elements of ordinal number 2 in all the historical initial feature sets of the produced product of the first process).
In this embodiment, the preset difference threshold range is a preset difference threshold range for determining whether to correct or not process the elements of the initial feature set, for example (2, ++ infinity A kind of electronic device.
The beneficial effects of the technology are as follows: and correcting the initial feature set of the output product of each process at the current moment according to the historical initial feature set of the output product of each process, so as to obtain the corrected feature set of the output product of each process at the current moment, eliminate the influence of unqualified products on subsequent processing, and facilitate the calculation of subsequent quality coefficients.
Example 4:
on the basis of the embodiment 1, an intelligent full-flow production management method, S2: obtaining the quality coefficient of the output product of each process at the current moment based on the preset standard feature set of the output product of each process and the correction feature set of the output product of each process at the current moment, wherein the quality coefficient comprises the following steps:
all working procedures in the whole production flow are defined according to the production time sequence, the ordinal number is defined by increasing from 1, and ordinal number definition results are obtained;
Calculating a quality coefficient of the produced product of each process at the current moment based on the ordinal definition result, the average value of all set elements in the preset standard feature set of the produced product of each process and the average value of all set elements in the corrected feature set of the produced product of each process at the current moment, wherein the quality coefficient comprises the following steps:
;
Wherein, Is the current timeThe quality coefficient of the product produced by the working procedure is dimensionless,Is the current timeThe value of the mean value of the set elements of the corrected feature set of the output product of the process,Is the preset firstThe number of the mean value of the set elements of the standard feature set of the produced product of the process,Is the preset firstThe value of the maximum value of all set elements in the standard feature set of the produced product of the process,Is the current timeThe value of the minimum value of all set elements in the corrected feature set of the output product of the process,Is the preset firstStandard feature sets of the produced products of the previous process,Is the current timeAnd (3) correcting the characteristic set of the output product of the procedure.
The beneficial effects of the technology are as follows: and precisely obtaining the quality coefficient of the output product of each process at the current moment according to the preset standard feature set of the output product of each process and the correction feature set of the output product of each process at the current moment, so as to facilitate the calculation of the subsequent quality index.
Example 5:
On the basis of embodiment 1, the intelligent full-process production management method obtains basic information of each process at the current moment, and obtains a horizontal coefficient of each process at the current moment based on the basic information of each process at the current moment, including:
Acquiring basic information of each process at the current moment, wherein the basic information of each process at the current moment comprises standard working hours of each process at the current moment, energy consumption of each process at the current moment, yield of each process at the current moment, number of workers of each process at the current moment, number of advanced workers in each process at the current moment, number of intermediate workers in each process at the current moment and number of primary workers in each process at the current moment;
obtaining the quotient of the number of the advanced workers in each process at the current moment and the number of the workers in each process at the current moment, and multiplying the quotient by a preset advanced proportional coefficient to obtain an advanced worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of middle-level workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset middle-level proportional coefficient to obtain a middle-level worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of primary workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset primary proportional coefficient to obtain a primary worker level coefficient of each process at the current moment;
And summing the high-grade worker level coefficient, the medium-grade worker level coefficient and the primary worker level coefficient of each process at the current moment, and taking the sum value as the level coefficient of each process at the current moment.
In this embodiment, the standard man-hour of each process at the current time is the man-hour consumed by each process at the current time to produce a product under standard working conditions.
In this embodiment, the energy consumption of each process at the current time is the energy consumed by each process at the current time to produce a piece of output product (the total amount of energy consumed by each process in a preset time before the current time divided by the amount of output product produced in the preset time).
In this embodiment, the yield per process at the present time is the total number of produced products per process per unit time (1 h) before the present time.
In this embodiment, the number of workers per process at the present time is the sum of the numbers of advanced workers, intermediate workers, and primary workers per process at the present time.
In this embodiment, the advanced worker is, for example, a worker who works in the process for more than 2000 hours.
In this embodiment, the primary worker is, for example, a worker working in this process for a man-hour of less than 300 hours.
In this embodiment, the intermediate workers are the remaining workers that remove the advanced workers and the primary workers for each process at the present time.
In this embodiment, the preset high-level proportionality coefficient is a determined specific gravity of the ratio of the number of high-level workers set in advance in the horizontal coefficient of the corresponding process.
In this embodiment, the advanced worker level coefficient is a coefficient reflecting the processing capacity (or work capacity) of all advanced workers on each process at the present time.
In this embodiment, the preset intermediate scale factor is a determined specific gravity of the preset ratio of the number of intermediate workers in the horizontal factor of the corresponding process.
In this embodiment, the intermediate worker level coefficient is a coefficient reflecting the processing capacity (or work capacity) of all intermediate workers on each process at the present time.
In this embodiment, the preset primary scale factor is a determined specific gravity of the ratio of the number of low-level workers set in advance in the level factor of the corresponding process.
In this embodiment, the primary worker level coefficient is a coefficient reflecting all primary worker processing capacities (or work capacities) on each process at the present time.
The beneficial effects of the technology are as follows: according to the basic information of each process at the current moment, the horizontal coefficient of each process at the current moment is obtained more accurately, and the embodiment provides a method for calculating the horizontal coefficient of each process at the current moment according to the number of workers of each process at the current moment and the number of workers of each level.
Example 6:
On the basis of embodiment 1, the intelligent full-process production management method calculates a quality index of each process at the current moment based on a level coefficient of each process at the current moment, a quality coefficient of a produced product and basic information, and comprises the following steps:
Calculating the quality coefficient of each process at the current moment based on the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information, wherein the calculating comprises the following steps:
;
Wherein, Is the current timeThe quality index of the previous working procedure is dimensionless,Is the current timeThe number of workers in the process,Is natural constantAnd takes a value of 2.718,Is the current timeThe horizontal coefficient of the process,Is the current timeThe quality coefficient of the product produced by the previous procedure,Is the current timeNumerical values of standard man-hours of the procedure;
normalizing the quality coefficients of all the procedures at the current moment, and taking the normalized value of the quality coefficient of each procedure as the quality index of each procedure at the current moment.
In this embodiment, the quality coefficients are normalized to: the ratio of the difference between the quality coefficient and the minimum value among the quality coefficients of all the processes at the current time to the difference between the maximum value and the minimum value among the quality coefficients of all the processes at the current time is calculated.
The beneficial effects of the technology are as follows: and the quality index of each process at the current moment is calculated more accurately according to the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information, so that the construction of a subsequent process analysis matrix is facilitated.
Example 7:
on the basis of embodiment 1, the intelligent full-process production management method obtains the energy consumption index and the yield index of each process at the current moment based on the basic information of each process at the current moment, and comprises the following steps:
acquiring the energy consumption and the yield of each process at the current moment from the basic information of each process at the current moment;
Normalizing the quotient of the energy consumption of all the procedures at the current moment and the standard energy consumption, and taking the value obtained after normalizing the quotient of each procedure as the energy consumption index of each procedure at the current moment;
Normalizing the values of the yields of all the procedures at the current moment, and taking the value obtained after normalizing the yield of each procedure as the yield index of each procedure at the current moment.
In this embodiment, the standard energy consumption is the maximum energy consumption per process in a state where no correction is required.
The beneficial effects of the technology are as follows: and the energy consumption index and the yield index of each process at the current moment are more accurately obtained according to the basic information of each process at the current moment, so that the construction of a subsequent process analysis matrix is facilitated.
Example 8:
On the basis of embodiment 1, the intelligent full-process production management method constructs a process analysis matrix of the full-process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment, and comprises the following steps:
;
Wherein, A matrix is analyzed for the process of the full production flow at the current moment,Is the energy consumption index of the 1 st working procedure at the current moment,Is the current timeThe energy consumption index of the working procedure is calculated,Is the current timeThe energy consumption index of the working procedure is calculated,Is the total number of the working procedures of the whole production flow,Is the quality index of the 1 st working procedure at the current moment,Is the current timeThe quality index of the process is that,Is the current timeThe quality index of the process is that,Is the yield index of the 1 st working procedure at the current moment,Is the current timeThe yield index of the process is calculated,Is the current timeAnd (5) yield index of the process.
The beneficial effects of the technology are as follows: and constructing a process analysis matrix of the whole production process at the current moment according to the energy consumption index, the yield index and the quality index of each process at the current moment, so that the subsequent analysis of the correction mode of each process is facilitated.
Example 9:
Based on the embodiment 1, the intelligent full-flow production management method, S5: based on a process analysis matrix of the full production process at the current moment, correcting each process in the full production process at the current moment to obtain a process management correction result of each process in the full production process, wherein the process management correction result comprises the following steps:
S501: determining the maximum value of elements in each column of matrix elements in a process analysis matrix of the full production process at the current moment;
s502: when the minimum value of the elements of the single-row matrix elements is an energy consumption index, reducing the product conveying speed of the corresponding process of the corresponding row matrix elements by a preset conveying speed to obtain a process management correction result of the corresponding process, when the minimum value of the elements of the row matrix elements is a quality index, increasing the number of advanced workers of the corresponding process of the corresponding row matrix elements by a preset number of people to obtain a process management correction result of the corresponding process, and when the minimum value of the elements of the row matrix elements is a yield index, increasing the product conveying speed of the corresponding process of the row matrix elements by the preset conveying speed to obtain a process management correction result of the corresponding process.
In this embodiment, the element maximum is the maximum of each column of matrix elements in the process analysis matrix.
In this embodiment, the preset conveyance speed is a conveyance speed value set in advance to adjust the conveyance speed of the product, for example, 2m/s.
In this embodiment, the preset number of people is a preset number of people for increasing the number of advanced workers in a certain process.
The beneficial effects of the technology are as follows: and analyzing the correction mode of each process in the full production flow at the current moment according to the process analysis matrix of the full production flow at the current moment to obtain the process management correction result of each process of the full production flow.
Example 10:
the present invention provides an intelligent full-process production management system for executing any one of the intelligent full-process production management methods of embodiments 1 to 9, referring to fig. 3, including:
The correction module is used for obtaining the characteristics of the produced products of each process in the whole production flow at the current moment based on the preset equipment, obtaining an initial characteristic set of the produced products of each process at the current moment, correcting the initial characteristic set of the produced products of each process at the current moment, and obtaining a corrected characteristic set of the produced products of each process at the current moment;
The quality coefficient module is used for obtaining the quality coefficient of the output product of each process at the current moment based on a preset standard feature set of the output product of each process and a preset correction feature set of the output product of each process at the current moment;
The quality index module is used for acquiring basic information of each process at the current moment, acquiring a horizontal coefficient of each process at the current moment based on the basic information of each process at the current moment, and calculating a quality index of each process at the current moment based on the horizontal coefficient of each process at the current moment, a quality coefficient of a product produced by each process at the current moment and the basic information of each process at the current moment;
The process analysis matrix module is used for obtaining the energy consumption index and the yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment;
The correction mode acquisition module is used for analyzing the correction mode of each process in the full production flow at the current moment based on the process analysis matrix of the full production flow at the current moment to obtain the process management correction result of each process of the full production flow.
The beneficial effects of the technology are as follows: the method comprises the steps of obtaining an initial feature set of a produced product of each process at the current moment more accurately according to preset equipment, correcting the initial feature set of the produced product of each process at the current moment, obtaining a corrected feature set of the produced product of each process at the current moment, eliminating the influence of unqualified products on subsequent processing, obtaining a quality coefficient of the produced product of each process at the current moment accurately according to the preset standard feature set of the produced product of each process at the current moment and the corrected feature set of the produced product of each process at the current moment, facilitating calculation of a subsequent quality index, obtaining a level coefficient of each process at the current moment more accurately according to basic information of each process at the current moment, calculating a quality index of each process at the current moment more accurately according to the level coefficient of each process at the current moment, the quality coefficient of the produced product and basic information, facilitating construction of a subsequent process analysis matrix, obtaining an energy consumption index and a yield index of each process at the current moment more accurately according to the basic information of each process at the current moment, facilitating calculation of a full-process production process analysis matrix according to a full-process production process analysis mode, and full-process production process quality analysis of each process at the current moment, and full-process production process, and realizing full-process quality analysis of each full-process in full-process, and full-process production process, and full-process quality analysis of each process, and full-process production process quality matrix according to full-process, and full-process quality analysis of each process is optimized according to a full-process.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (3)
1. An intelligent full-flow production management method is characterized by comprising the following steps:
s1: acquiring characteristics of the produced products of each process in the full production flow at the current moment based on preset equipment, acquiring an initial characteristic set of the produced products of each process at the current moment, correcting the initial characteristic set of the produced products of each process at the current moment, and acquiring a corrected characteristic set of the produced products of each process at the current moment;
S2: obtaining a quality coefficient of the output product of each process at the current moment based on a preset standard feature set of the output product of each process and a preset correction feature set of the output product of each process at the current moment;
s3: acquiring basic information of each process at the current moment, acquiring a level coefficient of each process at the current moment based on the basic information of each process at the current moment, and calculating a quality index of each process at the current moment based on the level coefficient of each process at the current moment, a quality coefficient of a produced product and the basic information;
S4: acquiring an energy consumption index and a yield index of each process at the current moment based on the basic information of each process at the current moment, and constructing a process analysis matrix of the whole production process at the current moment based on the energy consumption index, the yield index and the quality index of each process at the current moment;
S5: analyzing each process in the full production flow at the current moment in a correction mode based on a process analysis matrix of the full production flow at the current moment to obtain a process management correction result of each process of the full production flow;
The method for correcting the initial feature set of the output product of each process at the current moment comprises the following steps:
Acquiring a historical initial feature set of a produced product of each process at a plurality of moments in a preset time period before the current moment;
The average value of all the set elements with the same ordinal numbers in all the historical initial feature sets of the produced products of each procedure is calculated and used as the standard set element with the corresponding ordinal numbers in the historical initial feature sets, the difference value between each set element in the initial feature sets of the produced products of each procedure at the current moment and the standard set element with the same ordinal numbers as the corresponding set element in the historical initial feature sets of the produced products of the corresponding procedure is calculated, all the elements of the initial feature sets with the difference value within the preset difference value threshold range are replaced with the standard set element with the same ordinal numbers as the corresponding set element, the initial feature set after the elements are replaced is used as the correction feature set, and otherwise, the initial feature set with the difference value not within the preset difference value threshold range is used as the correction feature set;
Wherein S2: obtaining the quality coefficient of the output product of each process at the current moment based on the preset standard feature set of the output product of each process and the correction feature set of the output product of each process at the current moment, wherein the quality coefficient comprises the following steps:
all working procedures in the whole production flow are defined according to the production time sequence, the ordinal number is defined by increasing from 1, and ordinal number definition results are obtained;
Calculating a quality coefficient of the produced product of each process at the current moment based on the ordinal definition result, the average value of all set elements in the preset standard feature set of the produced product of each process and the average value of all set elements in the corrected feature set of the produced product of each process at the current moment, wherein the quality coefficient comprises the following steps:
;
Wherein, Is the current timeThe quality coefficient of the product produced by the working procedure is dimensionless,Is the current timeThe value of the mean value of the set elements of the corrected feature set of the output product of the process,Is the preset firstThe number of the mean value of the set elements of the standard feature set of the produced product of the process,Is the preset firstThe value of the maximum value of all set elements in the standard feature set of the produced product of the process,Is the current timeThe value of the minimum value of all set elements in the corrected feature set of the output product of the process,Is the preset firstStandard feature sets of the produced products of the previous process,Is the current timeA correction feature set of a product produced by the previous procedure;
the method for obtaining the basic information of each process at the current moment, and obtaining the horizontal coefficient of each process at the current moment based on the basic information of each process at the current moment comprises the following steps:
Acquiring basic information of each process at the current moment, wherein the basic information of each process at the current moment comprises standard working hours of each process at the current moment, energy consumption of each process at the current moment, yield of each process at the current moment, number of workers of each process at the current moment, number of advanced workers in each process at the current moment, number of intermediate workers in each process at the current moment and number of primary workers in each process at the current moment;
obtaining the quotient of the number of the advanced workers in each process at the current moment and the number of the workers in each process at the current moment, and multiplying the quotient by a preset advanced proportional coefficient to obtain an advanced worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of middle-level workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset middle-level proportional coefficient to obtain a middle-level worker level coefficient of each process at the current moment;
Obtaining the quotient of the number of primary workers in each process at the current moment and the number of workers in each process at the current moment, and multiplying the quotient by a preset primary proportional coefficient to obtain a primary worker level coefficient of each process at the current moment;
Summing the high-level worker level coefficient, the medium-level worker level coefficient and the primary worker level coefficient of each process at the current moment, and taking the sum value as the level coefficient of each process at the current moment;
The method for calculating the quality index of each process at the current moment based on the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information comprises the following steps:
Calculating the quality coefficient of each process at the current moment based on the level coefficient of each process at the current moment, the quality coefficient of the produced product and the basic information, wherein the calculating comprises the following steps:
;
Wherein, Is the current timeThe quality index of the previous working procedure is dimensionless,Is the current timeThe number of workers in the process,Is a logarithmic function of the natural constant e, and takes a value of 2.718,Is the current timeThe horizontal coefficient of the process,Is the current timeThe quality coefficient of the product produced by the previous procedure,Is the current timeNumerical values of standard man-hours of the procedure;
normalizing the quality coefficients of all the procedures at the current moment, and taking the value of the normalized quality coefficient of each procedure as the quality index of each procedure at the current moment;
the method for obtaining the energy consumption index and the yield index of each process at the current moment based on the basic information of each process at the current moment comprises the following steps:
acquiring the energy consumption and the yield of each process at the current moment from the basic information of each process at the current moment;
Normalizing the quotient of the energy consumption of all the procedures at the current moment and the standard energy consumption, and taking the value obtained after normalizing the quotient of each procedure as the energy consumption index of each procedure at the current moment;
Normalizing the values of the yields of all the procedures at the current moment, and taking the value obtained after normalizing the yield of each procedure as the yield index of each procedure at the current moment;
Wherein, S5: based on a process analysis matrix of the full production process at the current moment, correcting each process in the full production process at the current moment to obtain a process management correction result of each process in the full production process, wherein the process management correction result comprises the following steps:
S501: determining the maximum value of elements in each column of matrix elements in a process analysis matrix of the full production process at the current moment;
s502: when the minimum value of the elements of the single-row matrix elements is an energy consumption index, reducing the product conveying speed of the corresponding process of the corresponding row matrix elements by a preset conveying speed to obtain a process management correction result of the corresponding process, when the minimum value of the elements of the row matrix elements is a quality index, increasing the number of advanced workers of the corresponding process of the corresponding row matrix elements by a preset number of people to obtain a process management correction result of the corresponding process, and when the minimum value of the elements of the row matrix elements is a yield index, increasing the product conveying speed of the corresponding process of the row matrix elements by the preset conveying speed to obtain a process management correction result of the corresponding process.
2. The intelligent full-process production management method according to claim 1, wherein S1: based on the preset equipment, carrying out feature acquisition on the output product of each process in the full production flow at the current moment, obtaining an initial feature set of the output product of each process at the current moment, correcting the initial feature set of the output product of each process at the current moment, and obtaining a corrected feature set of the output product of each process at the current moment, wherein the method comprises the following steps:
acquiring all appearance structure sizes of the produced products of each process in the whole production flow at the current moment based on preset equipment, constructing a set by taking all appearance structure sizes of the produced products of each process at the current moment as set elements, and taking the constructed set as an initial feature set of the produced products of each process at the current moment, wherein the positions of the set elements are determined by the acquisition time sequence of the thickness of the products at the preset product positions;
and correcting the initial feature set of the output product of each process at the current moment to obtain the corrected feature set of the output product of each process at the current moment.
3. The intelligent full-process production management method according to claim 1, wherein the construction of the process analysis matrix of the full-process at the current time based on the energy consumption index, the yield index and the quality index of each process at the current time comprises:
;
Wherein, A matrix is analyzed for the process of the full production flow at the current moment,Is the current timeThe energy consumption index of the working procedure is calculated,Is the current timeThe energy consumption index of the working procedure is calculated,Is the current timeThe energy consumption index of the working procedure is calculated,Is the total number of the working procedures of the whole production flow,Is the current timeThe quality index of the process is that,Is the current timeThe quality index of the process is that,Is the current timeThe quality index of the process is that,Is the current timeThe yield index of the process is calculated,Is the current timeThe yield index of the process is calculated,Is the current timeAnd (5) yield index of the process.
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| BE1025126B1 (en) * | 2017-08-28 | 2018-11-05 | Automation & Robotics Sa | REAL-TIME ONLINE QUALITY AUDIT METHOD OF A DIGITAL PROCESS FOR THE MANUFACTURE OF OPHTHALMIC LENSES |
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| BE1025126B1 (en) * | 2017-08-28 | 2018-11-05 | Automation & Robotics Sa | REAL-TIME ONLINE QUALITY AUDIT METHOD OF A DIGITAL PROCESS FOR THE MANUFACTURE OF OPHTHALMIC LENSES |
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