CN114936753A - Production mold management method and management system of intelligent workshop based on MES - Google Patents
Production mold management method and management system of intelligent workshop based on MES Download PDFInfo
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Abstract
The invention relates to the technical field of data processing and intelligent production management, in particular to a production mold management method and a production mold management system of an intelligent workshop based on MES, which comprises the following steps: classifying the dies based on the matching relationship of the dies and a machine tool required by die production to obtain the production conflict degree of the machine tool; constructing a correlation diagram according to the correlation coefficient among the moulds, wherein nodes in the correlation diagram represent the moulds, and the edge weight of a connecting line among the nodes is the correlation coefficient among the moulds; and dividing the association diagram, and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines. The invention can obtain the incidence relation between a plurality of production molds and the corresponding machine tools, and is beneficial to improving the precision and the efficiency of subsequent data processing; the complex relation between a plurality of produced moulds can be quickly processed to obtain a result, the energy waste of a production machine tool is avoided to the maximum extent, and the overall efficiency of an intelligent workshop is improved.
Description
Technical Field
The invention relates to the technical field of data processing and intelligent production management, in particular to a production mold management method and a production mold management system of an intelligent workshop based on MES.
Background
In daily life, the mould can be used for a plurality of aspects, along with the appearance of different demands, the kind of mould also becomes more and more along with it, just so need carry out strict requirement to the production procedure of mould in the workshop, improves the efficiency of mould production. At present, the information communication technology, the manufacturing technology, the new energy technology and the like are promoted to be fused in a cross way all over the world, and the human society enters a new era of interconnection of everything, combination of reality and virtuality and intelligence calculation. For the mold manufacturing industry, in the process of transforming a factory from a data chemical factory to an intelligent factory, it is very critical to improve the capacity of virtual-real combination and intelligent calculation, wherein the MES system can network, transparentize, paperless and refine the workshop for producing the mold.
The problems existing in the production of the existing die are that: the produced moulds are various in variety, the structures and the sizes of the sub-moulds in each set of moulds are different, the production cycle is different, the production process is complex, and the sub-moulds need to be completed by a plurality of departments in a coordinated mode, so that the production scheduling personnel are unreasonably distributed, the working efficiency is low, and the production mould management is disordered.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a production mold management method and a production mold management system of an intelligent workshop based on MES, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a manufacturing mold management method for an MES-based intelligent workshop, where the method includes: carrying out matching classification on the produced moulds based on the matching relation of the moulds, wherein the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the dies based on a machine tool required by die production; the ratio of the number of the unmatched moulds produced by one machine tool to the total number of the moulds produced by the machine tool in one production period is the production conflict degree of the machine tool;
forming a parameter vector of the mould by the type, quality, production time consumption and delivery time of the mould, and obtaining the correlation degree between the moulds based on the parameter vector of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; constructing an association diagram according to the association coefficients among the moulds, wherein nodes in the association diagram represent the moulds, and the edge weight of a connecting line among the nodes is the association coefficient among the moulds;
the association diagram comprises a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and (4) making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
Preferably, the matching classification of the produced molds based on the mold matching relationship includes: carrying out matching classification on the dies according to the matching relation of the dies to obtain the qualification probability of each die in each set of dies, and calculating the average qualification probability of each set of dies; and setting a qualified threshold, keeping the current matching for the matched rear molds with the average qualified probability being greater than the qualified threshold, and re-classifying the matched molds in the matched rear molds with the average qualified probability being less than the qualified threshold.
Preferably, the obtaining the degree of correlation between the molds based on the parameter vector of each mold comprises: forming a mould parameter matrix by the parameter vectors of all moulds; and carrying out correlation analysis on each row of elements in the parameter matrix by using a typical correlation analysis algorithm to obtain the correlation degree between the dies.
Preferably, the obtaining of the correlation coefficient between the molds by using the degree of correlation between the molds and the degree of production conflict of the machine tool includes: if the two molds are produced by the same machine tool, the ratio of the correlation degree between the two molds to the production conflict degree of the corresponding machine tool is the correlation coefficient between the two molds; if the two molds are produced by different machine tools, the ratio of the correlation degree between the two molds to the greater production conflict degree in the production conflict degrees of the machine tools corresponding to the two molds is the correlation coefficient between the two molds.
Preferably, the energy function is:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is the mean value of the correlation degrees between the connected nodes between the ith sub-association graph and the jth sub-association graph, if the mean value of the correlation degrees between the connected nodes is smaller than a preset threshold value, the mean value of the correlation degrees is set to be a first preset value, and if the mean value of the correlation degrees between the connected nodes is larger than or equal to the preset threshold value, the mean value of the correlation degrees is set to be a second preset value;
and the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is represented.
Preferably, the making of the subsequent mold production plan according to the connected molds still existing in the divided correlation diagram and the delivery time of the connected molds includes: and obtaining nodes with connecting lines in each sub-association diagram in the divided association diagram, wherein the nodes with connecting lines in each sub-association diagram in the divided association diagram are moulds required to be produced by the machine tool corresponding to each sub-association diagram, and adjusting the production sequence of producing the moulds required to be produced by each machine tool according to the delivery time of the moulds required to be produced by each machine tool to obtain a subsequent mould production plan.
In a second aspect, another embodiment of the present invention provides a manufacturing mold management system for an MES-based intelligent plant, the system including: the classification module is used for performing matching classification on the produced moulds based on the mould matching relation, and the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the moulds based on a machine tool required by mould production; the ratio of the number of the unmatched moulds produced by one machine tool to the total number of the moulds produced by the machine tool in one production period is the production conflict degree of the machine tool;
the correlation analysis module is used for forming parameter vectors of the moulds by the type, quality, production time consumption and delivery time of the moulds and obtaining the correlation degree among the moulds on the basis of the parameter vectors of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; constructing an association diagram according to the association coefficients among the moulds, wherein nodes in the association diagram represent the moulds, and the edge weight of a connecting line among the nodes is the association coefficient among the moulds;
the production optimization module is used for enabling the association diagram to comprise a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
Preferably, the obtaining of the correlation coefficient between the molds by using the degree of correlation between the molds and the degree of production conflict of the machine tool includes: if the two molds are produced by the same machine tool, the ratio of the correlation degree between the two molds to the production conflict degree of the corresponding machine tool is the correlation coefficient between the two molds; if the two molds are produced by different machine tools, the ratio of the correlation degree between the two molds to the greater production conflict degree in the production conflict degrees of the machine tools corresponding to the two molds is the correlation coefficient between the two molds.
Preferably, the energy function is:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of negative logarithms of the side weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is the mean value of the correlation degrees between the connected nodes between the ith sub-association graph and the jth sub-association graph, if the mean value of the correlation degrees between the connected nodes is smaller than a preset threshold value, the mean value of the correlation degrees is set as a first preset value, and if the mean value of the correlation degrees between the connected nodes is larger than or equal to the preset threshold value, the mean value of the correlation degrees is set as a second preset value;
and the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is represented.
Preferably, the step of making a subsequent mold production plan according to the connected molds and the delivery times of the connected molds in the divided association map comprises: and obtaining nodes with connecting lines in each sub-association diagram in the divided association diagram, wherein the nodes with connecting lines in each sub-association diagram in the divided association diagram are moulds required to be produced by the machine tool corresponding to each sub-association diagram, and adjusting the production sequence of producing the moulds required to be produced by each machine tool according to the delivery time of the moulds required to be produced by each machine tool to obtain a subsequent mould production plan.
The embodiment of the invention at least has the following beneficial effects: the method and the device have the advantages that the incidence relations of a plurality of production molds and corresponding production machine tools can be obtained, and the accuracy and the efficiency of subsequent data processing are improved; meanwhile, the association diagram constructed by the invention comprises a plurality of sub-association diagrams, the connection lines of the nodes among the sub-association diagrams and the connection line of the node in each sub-association diagram are divided by using the association coefficient among the produced molds, the association diagram division is completed, the production sequence of the molds is obtained according to the delivery time of the molds needing to be produced, which is represented by the nodes of the connection lines, and the subsequent mold production plan is made.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a production mold management method of an intelligent MES-based workshop.
FIG. 2 is a block diagram of a production mold management system of an intelligent MES-based workshop.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, with reference to the accompanying drawings and preferred embodiments, describes specific embodiments, structures, features and effects of a manufacturing mold management method and a management system for an MES-based intelligent workshop according to the present invention. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of a production mold management method and a management system of an intelligent MES-based workshop provided by the invention in detail by combining with the accompanying drawings.
Example 1
The main application scenarios of the invention are as follows: and (3) producing a mould management scene, quantifying data generated in the mould production process, uploading the data to a management platform through an MES (manufacturing execution system), and providing reference for data analysis of the platform. Meanwhile, the MES system can transmit the acquired production and processing data in real time through the image acquisition system and the information transmission network. In the invention, the influence on the time period caused by the failure of the production machine tool is not considered, the current machine tool fails to work normally, and the corresponding mould data is directly deleted without influencing the data analysis result among other mould data.
Referring to fig. 1, a flowchart of a manufacturing mold management method for an MES-based intelligent workshop according to an embodiment of the present invention is shown, where the method includes the following steps:
the method comprises the following steps: carrying out matching classification on the produced moulds based on the matching relation of the moulds, wherein the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the moulds based on a machine tool required by mould production; the ratio of the number of the unmated molds produced by one machine tool to the total number of the molds produced by the machine tool in one production period is the production conflict degree of the machine tool.
Firstly, acquiring relevant data of all mould production processes in an intelligent mould production workshop through an MSE system, wherein the acquired data are in a production period, the length of the production period needs to be formulated according to actual conditions, and the relevant data comprise: the production number of the die, the number of a machine tool required by the production of the die, the type of the die, the quality of the die, the production time consumption of the die, the delivery time of the die and the like. The mold production quality obtains a mold image through machine vision, and the defect detection of the mold production is realized by utilizing a defect detection algorithm.
The defect detection algorithm is realized by utilizing the prior art, and the embodiment adopts the template matching algorithm to complete the defect detection and defect evaluation of all the mold images to obtain the corresponding qualification probability of all the molds
Said probability of eligibility
The acquisition method comprises the following steps: cosine similarity matching is carried out on the actual production mold image and the template image, and the similarity between the images is the qualified probability
For characterizing the quality of the produced mold.
The type of the die is mainly the production type of the die and is used for obtaining the matching relation between other dies related to a single die, and the matching relation can be determined by professionals in the field of die production to match the types of the single die. Matching and classifying the produced moulds based on the matching relationship between the moulds, wherein the moulds matched into a set are of one category; the mass of each set of die is required to be considered after matching, wherein the matching mass of each set of die is obtained according to the qualification probability of each die in each set of die, the matching mass of each set of die is the average value of the qualification probabilities of the dies in each set of die and is recorded as the average qualification probability, and the value range of the average qualification probability is [0, 1 ].
Then, the matching classification needs to be adjusted according to the average qualification probability of each set of mold, which is specifically as follows: setting a qualification threshold
Preferably, in the present embodiment
(ii) a If the average qualification probability of a set of moulds is 1, the set of moulds is completely matched; if the average qualified probability of a set of dies is more than or equal to the qualified threshold value
If the set of the mold is not completely matched, the matching is successful in the matching classification process; if the average qualified probability of a set of dies is less than the qualified threshold value
If the set of molds is not successfully matched, all the individual molds in the set of molds need to be classified again. Thus, the matching classification results of all produced moulds are obtained.
And finally, obtaining the production numbers of all the molds and the machine tool numbers required by mold production on the basis of matching and classifying the produced molds, and classifying all the molds again according to the machine tool required by each mold production to obtain a new classification result. Because the production time consumption of different molds is different in the production process, the production conflict degree of the production machine tool during the production of the molds in one production period should be considered, wherein the production conflict degree is the ratio of the number of the molds which cannot be matched and are produced by the machine tool to the total number of the molds which can be produced by the machine tool in one production period, namely the production conflict degree of the machine tool in the production period
：
Wherein the content of the first and second substances,
representing the production conflict degree of the I machine tool in the t production period;
the number of the moulds which cannot be matched and are produced by the I machine tool in the t production period is represented;
indicates the total number of I-th machine tool production molds in the t-th production cycle. The larger the number of matched molds in the molds produced by a machine tool, the smaller the production conflict of the machine tool in the production period.
Step two: forming a parameter vector of the mould by the type, quality, production time consumption and delivery time of the mould, and obtaining the correlation degree between the moulds based on the parameter vector of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; and constructing a correlation diagram according to the correlation coefficient among the moulds, wherein nodes in the correlation diagram represent the moulds, and the edge weight of a connecting line among the nodes is the correlation coefficient among the moulds.
Firstly, obtaining the type, quality, production time consumption and delivery time of all moulds produced in the production period, and using the four information to form a parameter vector of the mould, wherein the size of the parameter vector is [1, 4 ]]I.e., one row and four columns; the parameter vectors of the produced N moulds form a parameter matrix, wherein the size of the parameter matrix is [ N, 4 ]]I.e. four rows and four columns, using CCA algorithm (typical correlation analysis algorithm) to perform correlation analysis on the parameter vectors of the multiple molds in the parameter matrix, and calculating the degree of correlation between the multiple parameter vectors, i.e. the degree of correlation between different molds
。
Then, obtaining a correlation coefficient between any two molds according to the production conflict degree of a machine tool for producing the molds in a production period and the correlation degree between different produced molds; taking the die S and the die T as an example, obtaining a correlation coefficient W of the die S and the die T:
wherein W represents a correlation coefficient between the mold S and the mold T;
indicating the degree of correlation between the mold S and the mold T; c represents the degree of production conflict of the machine tool, and if the mold S and the mold T are both produced by the same machine tool in one production cycle, C is the degree of production conflict of the machine tool producing the mold S and the mold T, and if the mold S and the mold T are produced by different machine tools, C is the degree of production conflict of the machine tool having a greater degree of production conflict of the two machine tools producing the mold S and the mold T.
Finally, a correlation diagram is constructed according to the correlation coefficients of different produced molds and the corresponding relations between each machine tool and all produced molds, wherein the correlation diagram is constructed based on the data of the molds in one production cycle, the nodes in the correlation diagram represent the molds produced in the production cycle, and the edge weight of a connecting line between any two nodes in the correlation diagram is the correlation coefficient between two molds.
Step three: the association diagram comprises a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
First, after the correlation diagram is constructed in the second step, the assignment relationship between the machine tool producing the mold and the produced mold needs to be considered, so that a sub-correlation diagram needs to be divided in the correlation diagram, and the node included in one sub-correlation diagram represents the mold produced by the same machine tool. Segmenting the association graph according to the edge weight values between the nodes, and constructing an energy function E by utilizing an association graph model minimum cut algorithm:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram, namely
The negative logarithm of the weight of each edge is
W represents an edge weight;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of the negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is an average value of the correlation degrees between the ith sub-association graph and the jth sub-association graph, if the average value of the correlation degrees between the connected nodes is smaller than a preset threshold, preferably, the preset threshold value in the embodiment is 0.2, the average value of the correlation degrees is a first preset value, preferably, the first preset value in the embodiment is 0, and if the average value of the correlation degrees between the connected nodes is greater than or equal to the preset threshold, the average value of the correlation degrees is a second preset value, preferably, the second preset value in the embodiment is 1;
and the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is represented.
Then, pass through the nodeThe minimum edge weight between the nodes divides the association graph, the node connecting line with the minimum node edge weight between the initial node and the termination node in a production period is divided, in the embodiment, it is expected that in the sub-association graph and the association graph, the association coefficient between the nodes is maximum, the corresponding energy value is minimum, all the nodes are traversed according to the energy function, the nodes are divided, and finally the energy function is realized
And converging, taking the minimum value, and finishing the associated graph segmentation.
Finally, according to the result of the association diagram division, the nodes with the connecting lines are counted, and a subsequent mold production plan is made according to the result of the counting, it needs to be explained that in a mold production workshop, a machine tool production mold is a continuous production process, and the production conditions in each production cycle may be different, so the association diagram construction and the association diagram division process should also be a dynamically changing process, and the dynamic optimization of the production mold management of the intelligent workshop is completed by taking a certain fixed production cycle as a dynamic update frequency.
After the nodes with the connecting lines exist after the associated graph is divided are obtained, the nodes are connected with other nodes, the molds representing the node characteristics can form matched molds with other molds, and the molds representing the nodes are produced, so that the production conflict degree of the machine tool can be reduced to the minimum, and the production efficiency of the machine tool is greatly improved; meanwhile, information of a fourth dimension in the parameter vector of the die represented by the connected nodes, namely delivery time of the die, is also present, the production sequence of the die to be produced is determined according to the delivery time, and a subsequent die production plan is formed after the production sequence of the die to be produced by each machine tool is determined. And when the correlation diagram segmentation result is not changed in a plurality of continuous production periods, the mold production plan is not optimized and adjusted.
According to the incidence relation between the production die and the production machine tool, the optimization of the production process can be realized, the production die management chaos caused by the faults of the distribution chaos and the individual production machine tool is effectively avoided in the continuous production process, the productivity waste of the production machine tool is caused, and the optimal progress management result in the production process of the production die can be obtained.
Example 2
The present embodiment provides a system embodiment. Referring to fig. 2, a block diagram of a manufacturing mold management system for an MES-based intelligent plant according to an embodiment of the present invention is shown, where the system includes: the classification module is used for matching and classifying the produced moulds based on the matching relation of the moulds, and the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the moulds based on a machine tool required by mould production; the ratio of the number of the unmatched moulds produced by one machine tool to the total number of the moulds produced by the machine tool in one production period is the production conflict degree of the machine tool;
the correlation analysis module is used for forming parameter vectors of the moulds by the type, the quality, the production time consumption and the delivery time of the moulds and obtaining the correlation degree between the moulds based on the parameter vector of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; constructing a correlation diagram according to the correlation coefficient among the moulds, wherein nodes in the correlation diagram represent the moulds, and the edge weight of a connecting line among the nodes is the correlation coefficient among the moulds;
the production optimization module is used for enabling the association diagram to comprise a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
Preferably, the obtaining of the correlation coefficient between the molds by using the degree of correlation between the molds and the degree of production conflict of the machine tool includes: if the two molds are produced by the same machine tool, the ratio of the correlation degree between the two molds to the production conflict degree of the corresponding machine tool is the correlation coefficient between the two molds; if the two molds are produced by different machine tools, the ratio of the correlation degree between the two molds to the greater production conflict degree in the production conflict degrees of the machine tools corresponding to the two molds is the correlation coefficient between the two molds.
Preferably, the energy function is:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is the mean value of the correlation degrees between the connected nodes between the ith sub-association graph and the jth sub-association graph, if the mean value of the correlation degrees between the connected nodes is smaller than a preset threshold value, the mean value of the correlation degrees is set to be a first preset value, and if the mean value of the correlation degrees between the connected nodes is larger than or equal to the preset threshold value, the mean value of the correlation degrees is set to be a second preset value;
and the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is represented.
Preferably, the making of the subsequent mold production plan according to the connected molds still existing in the divided correlation diagram and the delivery time of the connected molds includes: and obtaining nodes with connecting lines in each sub-association diagram in the divided association diagram, wherein the nodes with connecting lines in each sub-association diagram in the divided association diagram are moulds required to be produced by the machine tool corresponding to each sub-association diagram, and adjusting the production sequence of producing the moulds required to be produced by each machine tool according to the delivery time of the moulds required to be produced by each machine tool to obtain a subsequent mould production plan.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A production mold management method of an intelligent MES-based workshop is characterized by comprising the following steps: matching and classifying the produced moulds based on the matching relation of the moulds, wherein the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the moulds based on a machine tool required by mould production; the ratio of the number of the non-matched molds produced by one machine tool to the total number of the molds produced by the machine tool in one production period is the production conflict degree of the machine tool;
forming a parameter vector of the mould by the type, quality, production time consumption and delivery time of the mould, and obtaining the correlation degree between the moulds based on the parameter vector of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; constructing a correlation diagram according to the correlation coefficient among the moulds, wherein nodes in the correlation diagram represent the moulds, and the edge weight of a connecting line among the nodes is the correlation coefficient among the moulds;
the association diagram comprises a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
2. The method for managing the production molds in the MES-based intelligent workshop according to claim 1, wherein the classification of the produced molds based on the mold matching relationship comprises: matching and classifying the dies according to the matching relation of the dies to obtain the qualification probability of each die in each set of dies, and calculating the average qualification probability of each set of dies; and setting a qualified threshold, keeping the current matching for the matched back molds with the average qualified probability larger than the qualified threshold, and reclassifying the matched molds in the matched back molds with the average qualified probability smaller than the qualified threshold.
3. The method as claimed in claim 1, wherein the obtaining of the degree of correlation between the molds based on the parameter vector of each mold comprises: forming a mould parameter matrix by using the parameter vectors of the moulds; and carrying out correlation analysis on each row of elements in the parameter matrix by using a typical correlation analysis algorithm to obtain the correlation degree between the moulds.
4. The method for managing the production molds of the MES-based intelligent workshop according to claim 1, wherein the obtaining the correlation coefficient between the molds by using the correlation degree between the molds and the production conflict degree of the machine tool comprises: if the two molds are produced by the same machine tool, the ratio of the correlation degree between the two molds to the production conflict degree of the corresponding machine tool is the correlation coefficient between the two molds; if the two molds are produced by different machine tools, the ratio of the correlation degree between the two molds to the greater production conflict degree in the production conflict degrees of the machine tools corresponding to the two molds is the correlation coefficient between the two molds.
5. The method for managing the production molds of the MES-based intelligent plant according to claim 1, wherein the energy function is:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of negative logarithms of the edge weights of the connecting nodes between the ith sub-association graph and the jth sub-association graph is the mean value of the correlation degrees between the connecting nodes between the ith sub-association graph and the jth sub-association graph, if the mean value of the correlation degrees between the connecting nodes is smaller than a preset threshold value, the mean value of the correlation degrees is set as a first preset value, and if the mean value of the correlation degrees between the connecting nodes is smaller than the preset threshold value, the mean value of the correlation degrees is set as a second preset valueSetting the mean value of the degree of correlation as a second preset value when the mean value of the degree of correlation is greater than or equal to a preset threshold value;
represents the sum of negative logarithms of the edge weights of the connecting nodes between the ith sub-association graph and the jth sub-association graph.
6. The method for managing the production molds of the MES-based intelligent workshop according to claim 1, wherein the step of making a subsequent mold production plan according to the delivery times of the connected molds and the connected molds in the segmented association diagram comprises the following steps: and obtaining nodes with connecting lines in each sub-association diagram in the divided association diagram, wherein the nodes with connecting lines in each sub-association diagram in the divided association diagram are moulds required to be produced by the machine tool corresponding to each sub-association diagram, and adjusting the production sequence of producing the moulds required to be produced by each machine tool according to the delivery time of the moulds required to be produced by each machine tool to obtain a subsequent mould production plan.
7. A manufacturing mold management system for an MES-based intelligent plant, the system comprising: the classification module is used for matching and classifying the produced moulds based on the matching relation of the moulds, and the average qualified probability of each set of moulds is greater than a qualified threshold value; classifying the moulds based on a machine tool required by mould production; the ratio of the number of the non-matched molds produced by one machine tool to the total number of the molds produced by the machine tool in one production period is the production conflict degree of the machine tool;
the correlation analysis module is used for forming parameter vectors of the moulds by the type, quality, production time consumption and delivery time of the moulds and obtaining the correlation degree among the moulds on the basis of the parameter vectors of each mould; obtaining a correlation coefficient between the dies by using the correlation degree between the dies and the production conflict degree of the machine tool; constructing a correlation diagram according to the correlation coefficient among the moulds, wherein nodes in the correlation diagram represent the moulds, and the edge weight of a connecting line among the nodes is the correlation coefficient among the moulds;
the production optimization module is used for enabling the association diagram to comprise a plurality of sub-association diagrams, and nodes in the sub-association diagrams are moulds produced by the same machine tool; obtaining an energy function by utilizing the edge weights of all nodes in each sub-association graph and the edge weights of the connection nodes between the sub-association graphs; utilizing a minimum cut algorithm to cut the association diagram, stopping cutting when an energy function is converged, and obtaining the cut association diagram; and making a subsequent mold production plan according to the molds with connecting lines in the divided association diagram and the delivery time of the molds with the connecting lines.
8. The manufacturing mold management system of an intelligent MES-based plant according to claim 7, wherein said obtaining the correlation coefficient between the molds by using the degree of correlation between the molds and the degree of production conflict of the machine tool comprises: if the two molds are produced by the same machine tool, the ratio of the correlation degree between the two molds to the production conflict degree of the corresponding machine tool is the correlation coefficient between the two molds; if the two molds are produced by different machine tools, the ratio of the correlation degree between the two molds to the greater production conflict degree in the production conflict degrees of the machine tools corresponding to the two molds is the correlation coefficient between the two molds.
9. The manufacturing mold management system of an intelligent MES-based plant according to claim 7, wherein the energy function is:
wherein E represents an energy function;
the weight of the sum of the negative logarithms of all the side weights in the I sub-correlation diagram is the reciprocal of the production conflict degree of the machine tool corresponding to the I sub-correlation diagram;
representing the sum of the negative logarithms of all the edge weights in the I sub-association graph;
the weight of the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is the mean value of the correlation degrees between the connected nodes between the ith sub-association graph and the jth sub-association graph, if the mean value of the correlation degrees between the connected nodes is smaller than a preset threshold value, the mean value of the correlation degrees is set to be a first preset value, and if the mean value of the correlation degrees between the connected nodes is larger than or equal to the preset threshold value, the mean value of the correlation degrees is set to be a second preset value;
and the sum of negative logarithms of the edge weights of the connected nodes between the ith sub-association graph and the jth sub-association graph is represented.
10. The manufacturing mold management system of an intelligent MES-based plant according to claim 7, wherein said step of making a subsequent mold production plan according to the connected molds still existing in the segmented association diagram and the delivery time of the connected molds comprises: and obtaining nodes with connecting lines in each sub-association diagram in the divided association diagram, wherein the nodes with connecting lines in each sub-association diagram in the divided association diagram are moulds required to be produced by the machine tool corresponding to each sub-association diagram, and adjusting the production sequence of producing the moulds required to be produced by each machine tool according to the delivery time of the moulds required to be produced by each machine tool to obtain a subsequent mould production plan.
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| CN117408614A (en) * | 2023-12-15 | 2024-01-16 | 阿尔卑斯系统集成(大连)有限公司 | Intelligent management system and method based on high-precision die |
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| CN117408614A (en) * | 2023-12-15 | 2024-01-16 | 阿尔卑斯系统集成(大连)有限公司 | Intelligent management system and method based on high-precision die |
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