CN112348415B - MES production scheduling delay association analysis method and system - Google Patents
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Abstract
The invention discloses a method and a system for analyzing the delay association of MES production scheduling, wherein the method comprises the following steps: constructing a potential associated production element database; based on the potential associated production element database, adopting an Apriori algorithm to output an associated item set between production scheduling delay time and the potential associated production elements; analyzing the association set to obtain potential association production element data influencing the production scheduling delay time; the production elements are regulated based on potentially relevant production element data affecting production schedule delay time. The invention utilizes the data mining method to find out hidden association between data, and finds out potential associated production elements and corresponding production scheduling delay time which can affect the production efficiency, so that technicians can adjust the production elements to improve the production efficiency as much as possible.
Description
Technical Field
The invention relates to the technical field of production scheduling delay association, in particular to a method and a system for MES production scheduling delay association analysis.
Background
The manufacturing execution system (Manufacturing Execution System, MES) can optimally manage the whole product production process from order to product completion through information transmission, timely make corresponding reactions and reports for real-time events occurring in factories, and conduct corresponding guidance and processing by using current accurate data. However, most of the existing MES systems cannot fully mine production information, and there is room for improvement.
In recent years, data mining has been increasingly used in the information industry. Data mining is a process of revealing implicit, previously unknown, and potentially valuable information from a large amount of data in a database that can be converted into useful information and knowledge by data mining the large amount of data present in the database. Current data mining is also less useful in manufacturing execution systems.
A delay in progress, known as a production scheduling delay (delay inproduction schedule), often occurs during the production process due to failure to complete a section according to the production schedule. Production scheduling delay in the discrete manufacturing process can destroy the original production plan, influence the production of the subsequent working section, and cause adverse effects on enterprise production. There is a certain correlation between the occurrence of production scheduling delay and part of potential production elements, which is an indeterminate and indirect correlation, and these correlations are mostly ignored, and even if they are noticed, they are mainly dependent on experience of technicians, and lack of scientific and reliable judgment methods, so that it is very difficult to find the correlations by adopting a manual analysis method in the face of massive big data, and meanwhile, the production efficiency of the product is reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a MES production scheduling delay association analysis method and system, so as to determine potential association elements influencing production scheduling delay time and adjust production elements according to the potential association elements.
In order to achieve the above purpose, the present invention provides a method for analyzing the delay association of MES production schedule, which comprises the following steps:
step S1: constructing a potential associated production element database;
step S2: based on the potential associated production element database, adopting an Apriori algorithm to output an associated item set between production scheduling delay time and the potential associated production elements;
step S3: analyzing the association set to obtain potential association production element data influencing the production scheduling delay time;
step S4: the production elements are regulated based on potentially relevant production element data affecting production schedule delay time.
Optionally, the building of the potential associated production element database specifically includes:
step S11: extracting data to be processed in the tire production process from a database of an MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of the potential associated production element data comprise Boolean type, analog quantity type and enumeration type;
step S12: determining production scheduling delay time according to the actual production time and the planning time;
step S13: partitioning the potential associated production element data of the analog quantity type to obtain M intervals;
step S14: numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type;
step S15: constructing a potential associated production element database; the potential associated production element database comprises ID numbers of products, production schedule delay time, analog quantity type potential associated production element data of different interval marks, boolean type potential associated production element data of different numbers and enumeration type potential associated production element data of different numbers.
Optionally, the outputting, by using an Apriori algorithm, the association term set between the production schedule delay time and the potential associated production elements based on the potential associated production element database specifically includes:
step S21: setting a minimum support and a minimum confidence;
step S22: taking each data in the potential association production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1;
step S23: rejecting candidate 1 item sets smaller than the minimum support degree to generate frequent 1 item sets;
step S24: let k=k+1, based on the potential associated production element database, connect frequent k-1 sets to generate candidate k sets; each candidate k item set is 1 item more than the candidate k-1 item set;
step S25: pruning the candidate k item set;
step S26: removing all item sets smaller than the minimum support degree from the item sets generated after pruning in the step S25, and generating frequent k item sets;
step S27: judging whether the frequent k item set is an empty set or not; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k item set is a non-empty set, returning to step S24;
step S28: and calculating the confidence coefficient of all the frequent item sets, removing the frequent item set smaller than the minimum confidence coefficient, deleting the frequent item set which does not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
Optionally, the adjusting and controlling the production element based on the potentially relevant production element data affecting the production schedule delay time specifically includes:
step S41: collecting production equipment parameters and production environment parameters in the tire production process to obtain production element data;
step S42: judging whether the range of the production element data is consistent with the range of the potential associated production element data affecting the production scheduling delay time, if so, taking the production scheduling delay time corresponding to the potential associated production element data as the estimated production scheduling delay time, and sending the estimated production scheduling delay time to a billboard display signal for alarming and the estimated production scheduling delay time; if not, return to "step S41".
The invention also provides a MES production scheduling delay association analysis system, which comprises:
the database construction module is used for constructing a potential associated production element database;
the association item set determining module is used for outputting an association item set between the production scheduling delay time and the potential association production elements by adopting an Apriori algorithm based on the potential association production element database;
the analysis module is used for analyzing the association set to obtain potential association production element data influencing the production scheduling delay time;
and the regulation and control module is used for regulating and controlling the production elements based on the potential associated production element data influencing the production schedule delay time.
Optionally, the database construction module specifically includes:
the extraction unit is used for extracting data to be processed in the tire production process from a database of the MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of the potential associated production element data comprise Boolean type, analog quantity type and enumeration type;
the production scheduling delay time determining unit is used for determining production scheduling delay time according to the actual production time and the planning time;
the partition processing unit is used for carrying out partition processing on the potential associated production element data of the analog quantity type to obtain M intervals;
the numbering processing unit is used for numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type;
a construction unit for constructing a potential associated production element database; the potential associated production element database comprises ID numbers of products, production schedule delay time, analog quantity type potential associated production element data of different interval marks, boolean type potential associated production element data of different numbers and enumeration type potential associated production element data of different numbers.
Optionally, the association item set determining module specifically includes:
a parameter determining unit for setting a minimum support and a minimum confidence;
the support degree determining unit is used for taking each data in the potential associated production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1;
the first eliminating unit is used for eliminating the candidate 1 item set smaller than the minimum support degree to generate a frequent 1 item set;
a candidate k item set determining unit, configured to make k=k+1, and connect frequent k-1 item sets to generate a candidate k item set based on the potential associated production element database; each candidate k item set is 1 item more than the candidate k-1 item set;
pruning unit, is used for pruning the said candidate k item set;
the second eliminating unit is used for eliminating all item sets smaller than the minimum support degree in the item sets generated after pruning and generating frequent k item sets;
a first judging unit, configured to judge whether the frequent k item set is an empty set; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k item set is a non-empty set, returning to a candidate k item set determining unit;
and the associated item set determining unit is used for calculating the confidence degrees of all the frequent item sets, removing the frequent item sets smaller than the minimum confidence degrees, deleting the frequent item sets which do not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
Optionally, the regulation module specifically includes:
the acquisition unit is used for acquiring production equipment parameters and production environment parameters in the tire production process to obtain production element data;
the second judging unit is used for judging whether the range of the production element data is consistent with the range of the potential associated production element data affecting the production schedule delay time, if so, taking the production schedule delay time corresponding to the potential associated production element data as the estimated production schedule delay time, and sending the estimated production schedule delay time to a billboard display signal for alarming and the estimated production schedule delay time; if not, returning to the acquisition unit.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a system for analyzing the delay association of MES production scheduling, wherein the method comprises the following steps: constructing a potential associated production element database; based on the potential associated production element database, adopting an Apriori algorithm to output an associated item set between production scheduling delay time and the potential associated production elements; analyzing the association set to obtain potential association production element data influencing the production scheduling delay time; the production elements are regulated based on potentially relevant production element data affecting production schedule delay time. The invention utilizes the data mining method to find out hidden association between data, and finds out potential associated production elements and corresponding production scheduling delay time which can affect the production efficiency, so that technicians can adjust the production elements to improve the production efficiency as much as possible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for analyzing the time delay correlation of MES production schedule according to the embodiment of the present invention;
FIG. 2 is a block diagram of a MES production schedule delay association analysis system according to an embodiment of the present invention;
FIG. 3 is a flow chart of a delay time real-time alert according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for analyzing the delay association of MES production scheduling, so as to determine potential association elements influencing the production scheduling delay time and further adjust production elements according to the potential association elements.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the invention discloses a method for analyzing the time delay association of MES production scheduling, which comprises the following steps:
step S1: and constructing a potential associated production element database.
Step S2: and outputting a correlation item set between the production scheduling delay time and the potential correlation production elements by adopting an Apriori algorithm based on the potential correlation production element database.
Step S3: and analyzing the association set to obtain potential association production element data influencing the production scheduling delay time.
Step S4: the production elements are regulated based on potentially relevant production element data affecting production schedule delay time.
The steps are discussed in detail below:
step S1: constructing a potential associated production element database, which specifically comprises the following steps:
step S11: extracting data to be processed in the tire production process from a database of an MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of the potential associated production element data comprise Boolean type, analog quantity type and enumeration type; the boolean type potential associated production element data comprises but is not limited to component attributes such as the presence or absence of plasticizer and the like, the analog type potential associated production element data comprises but is not limited to state attributes such as the rotating speed of production equipment, the time of vulcanization process and the like, and the enumeration type potential associated production element data comprises but is not limited to equipment numbers of a certain production section, production class numbers and the like.
Step S12: determining production scheduling delay time according to the actual production time and the planning time, wherein a specific calculation formula is as follows:
T＝t 0 -t 1 ；
wherein T is the production schedule delay time, T 0 For the actual production time, t 1 For the scheduled time.
Step S13: carrying out partition processing on the potential associated production element data of the analog quantity type to obtain M sections, wherein the number of M is set by actual requirements; the length calculation formula of the interval is:
wherein x is max Maximum value, x, of potentially associated production element data for analog type min For the minimum value of the analog type potential associated production element data, M is the number of intervals, and Deltax is the length of the interval.
The calculation formula of each interval range is as follows:
X m,n ∈[x min +(n-1)Δx,x min +nΔx]；
wherein X is m,n N-th interval representing m-th analog type potential associated production element data, n E [1, M]And n is an integer.
Step S14: and numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type.
Step S15: constructing a potential associated production element database; the potential associated production element database comprises the ID number of the product, the production schedule delay time and the potential associated production element data X of analog quantity types of different interval marks m,n Potentially associated production element data Y of the boolean type of different numbers i,j And potential associated production element data Z of the enumerated types with different numbers k,l . The creation of the database of potential associated production elements herein may facilitate subsequent association processing.
Step S2: and outputting a correlation item set between the production scheduling delay time and the potential correlation production elements by adopting an Apriori algorithm based on the potential correlation production element database.
The Apriori algorithm is a very classical association rule mining algorithm whose core idea is to mine frequent item sets by two phases, candidate set generation and downward closed detection of frequent item sets. The algorithm needs to screen the data by calculating the support for measuring how frequently sample X is in a given dataset and the confidence for measuring how frequently sample Y is in all occurrences of sample X. The specific formula for calculating the support degree is as follows:
where Support (X) represents the Support of sample X, σ (X) is the number of times sample X appears in a given dataset, i.e., the Support count, and N is the total number of samples in the dataset.
The specific formula for calculating the confidence coefficient is as follows:
where Confidence (x→y) represents the Confidence of sample Y in sample X, (x→y) represents the event that sample X, Y occurs simultaneously, σ (XUY) is the (XUY) support count, and σ (X) is the number of times sample X occurs in a given dataset. When the Apriori algorithm is used, the production schedule delay time and the production factors under the same working section are used as inputs.
The step S2 specifically comprises the following steps:
step S21: the minimum support and the minimum confidence are set.
Step S22: and taking each data in the potential association production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1.
Step S23: and eliminating the candidate 1 item set smaller than the minimum support degree to generate frequent 1 item sets.
Step S24: let k=k+1, based on the potential associated production element database, connect frequent k-1 sets to generate candidate k sets; each candidate k-term set is 1 more than the candidate k-1 term set.
Step S25: pruning the candidate k item set.
Step S26: and eliminating all item sets smaller than the minimum support degree in the item sets generated after pruning in the step S25, and generating frequent k item sets.
Step S27: judging whether the frequent k item set is an empty set or not; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k item set is a non-empty set, return to "step S24".
Step S28: and calculating the confidence coefficient of all the frequent item sets, removing the frequent item set smaller than the minimum confidence coefficient, deleting the frequent item set which does not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
Step S3: and analyzing the association set to obtain potential association production element data influencing the production scheduling delay time.
And obtaining potential associated production element data influencing the production schedule delay time of the product according to the associated item set, and knowing the parameter interval range of the potential associated production element data associated with the potential associated production element data, so that a technician can conveniently change production parameters in the tire production process. The parameter ranges of the potentially associated production element data associated with the production schedule being completed on schedule may be considered to have a promoting effect on the improvement of the production efficiency of the product, which should be kept as far as possible in production; the parameter range of the potential associated production element data associated with the production schedule delay can be considered to reduce the production efficiency of the product, and the occurrence of the parameter range in production should be avoided as much as possible.
Through the steps, potential associated production element data which causes production schedule delay time in the tire production process can be found out by a more scientific and reasonable method, and technical staff can adjust equipment parameters, production environment parameters and the like in the production process according to the potential associated production element data, so that the purpose of improving the production efficiency of tire products is achieved.
According to the invention, production equipment parameters and production environment parameters are regulated and controlled, production element data acquired in real time on site are compared with potential associated production element data influencing the production schedule delay time one by one, if the acquired production element data are consistent with the potential associated production element data range influencing the production schedule delay time, the production schedule delay time corresponding to the potential associated production element data can be used as the predicted delay time, and a warning signal and the predicted production schedule delay time are displayed through a billboard to remind a producer that the production plan is required to be regulated. In addition, APS (AdvancedPlanning System) can be rescheduled according to the expected production schedule delay time, and the original production schedule and schedule can be properly adjusted. The flow chart of the delay time real-time alarm is shown in fig. 3, and the specific summary steps are as follows:
step S4: the production elements are regulated based on potentially relevant production element data affecting production schedule delay time.
Step S41: collecting production equipment parameters and production environment parameters in the tire production process to obtain production element data;
step S42: judging whether the range of the production element data is consistent with the range of the potential associated production element data in the associated item set, if so, taking the production scheduling delay time corresponding to the potential associated production element data as the estimated production scheduling delay time, and sending the estimated production scheduling delay time to a billboard display signal for alarming and the estimated production scheduling delay time so as to remind a producer of adjusting the production element; if not, return to "step S41".
Step S43: judging whether to stop processing; if the processing is stopped, ending; if the processing is not stopped, the process returns to "step S41".
The production elements comprise industrial equipment data, working condition environment regulation data and production management data; the industrial equipment data comprise equipment operation data in the banburying, forming and vulcanizing processes, the working condition environment regulation and control data comprise workshop temperature and humidity, and the production management data comprise production teams and production operators.
As shown in FIG. 2, the invention also provides a MES production schedule delay association analysis system, which comprises:
a database construction module 1 for constructing a database of potentially associated production elements.
And the association item set determining module 2 is used for outputting an association item set between the production scheduling delay time and the potential association production elements by adopting an Apriori algorithm based on the potential association production element database.
And the analysis module 3 is used for analyzing the association set to obtain potential associated production element data influencing the production scheduling delay time.
And the regulation and control module 4 is used for regulating and controlling the production elements based on the potential associated production element data influencing the production schedule delay time.
As an embodiment, the database construction module 1 of the present invention specifically includes:
the extraction unit is used for extracting data to be processed in the tire production process from a database of the MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of potentially associated production element data include boolean types, analog types, and enumeration types.
And the production schedule delay time determining unit is used for determining the production schedule delay time according to the actual production time and the planning time.
And the partition processing unit is used for carrying out partition processing on the potential associated production element data of the analog quantity type to obtain M intervals.
And the numbering processing unit is used for numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type.
A construction unit for constructing a potential associated production element database; the potential associated production element database comprises ID numbers of products, production schedule delay time, analog quantity type potential associated production element data of different interval marks, boolean type potential associated production element data of different numbers and enumeration type potential associated production element data of different numbers.
As an implementation manner, the association item set determining module 2 of the present invention specifically includes:
and the parameter determining unit is used for setting the minimum support degree and the minimum confidence degree.
And the support degree determining unit is used for taking each data in the potential associated production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1.
And the first eliminating unit is used for eliminating the candidate 1 item set smaller than the minimum support degree and generating a frequent 1 item set.
A candidate k item set determining unit, configured to make k=k+1, and connect frequent k-1 item sets to generate a candidate k item set based on the potential associated production element database; each candidate k-term set is 1 more than the candidate k-1 term set.
And pruning unit for pruning the candidate k item set.
And the second eliminating unit is used for eliminating all item sets smaller than the minimum support degree in the item sets generated after pruning, and generating frequent k item sets.
A first judging unit, configured to judge whether the frequent k item set is an empty set; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k-term set is a non-empty set, a "candidate k-term set determination unit" is returned.
And the associated item set determining unit is used for calculating the confidence degrees of all the frequent item sets, removing the frequent item sets smaller than the minimum confidence degrees, deleting the frequent item sets which do not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
As an embodiment, the regulation module 4 of the present invention specifically includes:
the acquisition unit is used for acquiring production equipment parameters and production environment parameters in the tire production process to obtain production element data.
The second judging unit is used for judging whether the range of the production element data is consistent with the range of the potential associated production element data in the potential associated production element database, if so, taking the production scheduling delay time corresponding to the potential associated production element data as the estimated production scheduling delay time, and sending the estimated production scheduling delay time to a billboard display signal for alarming and the estimated production scheduling delay time; if not, returning to the acquisition unit.
The regulation and control module 4 of the present invention further comprises: a third judging unit for judging whether to stop the processing; if the processing is stopped, ending; if the processing is not stopped, the "collection unit" is returned.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. A method for MES production schedule delay association analysis, the method comprising:
step S1: constructing a potential associated production element database, which specifically comprises the following steps:
step S11: extracting data to be processed in the tire production process from a database of an MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of the potential associated production element data comprise Boolean type, analog quantity type and enumeration type;
step S12: determining production scheduling delay time according to the actual production time and the planning time;
step S13: partitioning the potential associated production element data of the analog quantity type to obtain M intervals;
step S14: numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type;
step S15: constructing a potential associated production element database; the potential associated production element database comprises ID numbers of products, production scheduling delay time, analog quantity type potential associated production element data of different interval marks, boolean type potential associated production element data of different numbers and enumeration type potential associated production element data of different numbers;
step S2: based on the potential associated production element database, adopting an Apriori algorithm to output an associated item set between production scheduling delay time and the potential associated production elements;
step S3: analyzing the association set to obtain potential association production element data influencing the production scheduling delay time;
step S4: the production elements are regulated based on potentially relevant production element data affecting production schedule delay time.
2. The MES production schedule delay association analysis method according to claim 1, wherein the outputting a set of association items between production schedule delay time and potential associated production elements using Apriori algorithm based on the potential associated production element database specifically comprises:
step S21: setting a minimum support and a minimum confidence;
step S22: taking each data in the potential association production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1;
step S23: rejecting candidate 1 item sets smaller than the minimum support degree to generate frequent 1 item sets;
step S24: let k=k+1, based on the potential associated production element database, connect frequent k-1 sets to generate candidate k sets; each candidate k item set is 1 item more than the candidate k-1 item set;
step S25: pruning the candidate k item set;
step S26: removing all item sets smaller than the minimum support degree from the item sets generated after pruning in the step S25, and generating frequent k item sets;
step S27: judging whether the frequent k item set is an empty set or not; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k item set is a non-empty set, returning to step S24;
step S28: and calculating the confidence coefficient of all the frequent item sets, removing the frequent item set smaller than the minimum confidence coefficient, deleting the frequent item set which does not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
3. The MES production schedule delay association analysis method according to claim 1, wherein the modulating the production elements based on the potentially associated production element data affecting the production schedule delay time specifically comprises:
step S41: collecting production equipment parameters and production environment parameters in the tire production process to obtain production element data;
step S42: judging whether the range of the production element data is consistent with the range of the potential associated production element data affecting the production scheduling delay time, if so, taking the production scheduling delay time corresponding to the potential associated production element data as the estimated production scheduling delay time, and sending the estimated production scheduling delay time to a billboard display signal for alarming and the estimated production scheduling delay time; if not, return to "step S41".
4. A MES production schedule delay association analysis system, the system comprising:
the database construction module is used for constructing a potential associated production element database and specifically comprises the following steps:
the extraction unit is used for extracting data to be processed in the tire production process from a database of the MES system; the data to be processed comprises different types of potential association elements, actual production time and planning time; the types of the potential associated production element data comprise Boolean type, analog quantity type and enumeration type;
the production scheduling delay time determining unit is used for determining production scheduling delay time according to the actual production time and the planning time;
the partition processing unit is used for carrying out partition processing on the potential associated production element data of the analog quantity type to obtain M intervals;
the numbering processing unit is used for numbering the potential associated production element data of the Boolean type and the potential associated production element data of the enumeration type;
a construction unit for constructing a potential associated production element database; the potential associated production element database comprises ID numbers of products, production scheduling delay time, analog quantity type potential associated production element data of different interval marks, boolean type potential associated production element data of different numbers and enumeration type potential associated production element data of different numbers;
the association item set determining module is used for outputting an association item set between the production scheduling delay time and the potential association production elements by adopting an Apriori algorithm based on the potential association production element database;
the analysis module is used for analyzing the association set to obtain potential association production element data influencing the production scheduling delay time;
and the regulation and control module is used for regulating and controlling the production elements based on the potential associated production element data influencing the production schedule delay time.
5. The MES production schedule delay association analysis system of claim 4, wherein the association set determination module specifically comprises:
a parameter determining unit for setting a minimum support and a minimum confidence;
the support degree determining unit is used for taking each data in the potential associated production element database as a candidate 1 item set, calculating the support degree of each candidate 1 item set, and setting k as the number of items in each item set, wherein k=1;
the first eliminating unit is used for eliminating the candidate 1 item set smaller than the minimum support degree to generate a frequent 1 item set;
a candidate k item set determining unit, configured to make k=k+1, and connect frequent k-1 item sets to generate a candidate k item set based on the potential associated production element database; each candidate k item set is 1 item more than the candidate k-1 item set;
pruning unit, is used for pruning the said candidate k item set;
the second eliminating unit is used for eliminating all item sets smaller than the minimum support degree in the item sets generated after pruning and generating frequent k item sets;
a first judging unit, configured to judge whether the frequent k item set is an empty set; outputting all frequent item sets if the frequent k item sets are empty sets; if the frequent k item set is a non-empty set, returning to a candidate k item set determining unit;
and the associated item set determining unit is used for calculating the confidence degrees of all the frequent item sets, removing the frequent item sets smaller than the minimum confidence degrees, deleting the frequent item sets which do not contain the production scheduling delay time, and outputting the associated item set between the production scheduling delay time and the potential associated production element data.
6. The MES production schedule delay association analysis system of claim 4, wherein the regulation module specifically comprises:
the acquisition unit is used for acquiring production equipment parameters and production environment parameters in the tire production process to obtain production element data;
the second judging unit is used for judging whether the range of the production element data is consistent with the range of the potential associated production element data affecting the production schedule delay time, if so, taking the production schedule delay time corresponding to the potential associated production element data as the estimated production schedule delay time, and sending the estimated production schedule delay time to a billboard display signal for alarming and the estimated production schedule delay time; if not, returning to the acquisition unit.
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