CN116882723A - Tobacco shredding big data and AI information processing method - Google Patents
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Abstract
The invention discloses a tobacco shred making big data and AI information processing method, which relates to the technical field of tobacco processing and manufacturing and artificial intelligence, finds a cutting point for improving the production quality and efficiency of shred making, and finally solves the technical problem of improving the efficiency. The method comprises the following steps: inputting different data according to the AI information processing requirement; in a graphical man-machine interaction environment, large data and AI application flows are built through data aggregation, data preprocessing and an artificial intelligent model by using graphical, dragging and what you see is what you get modes; after the big data and AI application flow set up in the second step is completed, the user uses the modeling flow to run an engine, and runs and debugs the big data and AI application flow; and obtaining a big data information processing result and visually displaying the big data information processing result. The invention improves the production quality of the silk making; under the production plan management scene, based on the intelligent scheduling of silk production of this platform realization, improve silk production efficiency.
Description
Technical Field
The invention relates to the technical field of tobacco processing manufacture and artificial intelligence, in particular to a tobacco shredding big data and AI information processing method.
Background
With the rapid development of sensor technology, semiconductor manufacturing process and communication technology in recent years, big data and artificial intelligence (Artificial Intelligence, AI) technology has been widely used, and has a great influence on society, civilian life and various industries, and the traditional manufacturing industry has also obtained a huge space for technical improvement and upgrading. For tobacco shred production, the sensor of the production line is abundant, the automation degree is high, and the method has the unique advantages in the application of big data and AI technology; meanwhile, due to the complexity of the production flow and the process, how to effectively utilize big data and AI technology has certain technical difficulty and challenges.
The combination of tobacco shredding with big data and AI technology has at least the following challenges: first, wire production is a continuous process, which itself has higher complexity than discrete processes, and also has higher difficulty when combined with AI algorithms; second, tobacco quality identification relies in large part on sensory evaluation by the authenticator, a qualitative indicator that is not computer-friendly; thirdly, although the data in the silk making process is more, the quality of the data accumulated at present is low, the available data is not more, and the requirements of an AI algorithm cannot be met. This is a common practice in various industries, and it is difficult to accurately grasp the data requirements of large data and AI techniques before they are not used.
Disclosure of Invention
The invention aims to provide a new technical scheme of a tobacco shred making big data and AI information processing method, find a cutting point for improving the production quality and efficiency of shred making, and finally solve the technical problem of improving the efficiency.
The technical scheme of the invention is as follows:
a tobacco shred big data and AI information processing method comprises the following steps:
step one: inputting different data according to the AI information processing requirement;
step two: in a graphical man-machine interaction environment, large data and AI application flows are built through data aggregation, data preprocessing and an artificial intelligent model by using graphical, dragging and what you see is what you get modes;
step three: after the big data and AI application flow set up in the second step is completed, the user uses the modeling flow to run an engine, and runs and debugs the big data and AI application flow;
step four: and obtaining a big data information processing result and visually displaying the big data information processing result.
Preferably, if the problem of error reporting or operation failure occurs in the third step, the second step is returned to modify and rerun until the big data and the AI application flow are correctly operated.
Preferably, the data aggregation in the second step includes:
step two-1: according to different scenes, loading data from data sources of each service system in different modes;
step two-2: after the data of each service system in the step two-1 are loaded, extracting the data from the loaded data, scattering the loaded data, and reading the data of each field of each service system;
step two-3: after each field data output by each original service system is obtained in the step two-2, the extracted data are converged by using a data type and relationship identification algorithm;
step two-4: and generating a table based on the data convergence of the step two-3, and converting the result into csv and arff formats.
Preferably, the data type and relation recognition algorithm in the step two-3 comprises the following steps:
step A: identifying the type of the data field for the data field in the data field set; reading the data content of the field; calculating the data base of the field;
the if radix is close to 0, then this field is a constant variable;
the if radix is less than a threshold, then this field is a type variable;
if radix is greater than a threshold and conforms to date format, then this field is a date variable;
if radix is greater than a threshold and does not conform to date format, then this field is a continuous variable;
and (B) step (B): identifying the relationship among the data tables, for data table 1, data table 2in data table set, for data field A in data table 1 data field set; for data field B in data table 2; respectively calculating the cardinal similarity of the two data fields; respectively calculating the content similarity of the two data fields; if the two data tables are all larger than a certain threshold value, the two data tables have an external key relation, and the external key relation is A < - > B;
step C: the automatic mapping of each data field to the body model takes a data table with a label field as a reference table C, a data table D in and a data table set E, an if data table D and the reference table C have an external key relation, and then uses a left connection mode to splice D onto C and delete D from the data table set E; and outputting the machine learning sample data ontology model filled with the data.
Preferably, the data preprocessing in the second step includes: format conversion, unit conversion, outlier screening.
Preferably, the artificial intelligence model in the second step includes:
copying the data set into N copies, and changing the time through space;
splitting each data set into K parts, and performing folding cross validation;
training a model for each fold of data;
stacking integration is performed on the K.times.N models.
Preferably, the modeling flow operation engine in the third step includes: running, running all, canceling running, canceling all running functions; and an operation state signal lamp is arranged, and red, yellow and green are respectively used for representing the states of successful operation, waiting for operation and failure operation. .
Preferably, the second step further includes: correlation analysis; the correlation analysis is based on Pearson, spearman, kendall three coefficients.
Preferably, the correlation analysis of three coefficients: firstly judging the data type, and if the data is a continuous variable and can be subjected to parameter test, using a Pearson coefficient; if the data is sequential measurement data and there is no parametric test, the Spearman coefficients are used; if an ordered classification variable and no parameter check, kendall coefficients are used.
Preferably, the method further comprises: an intelligent scheduling algorithm; the intelligent scheduling algorithm comprises the following steps: modeling the execution time of each production task, optimizing the target, modeling the constraint and solving the four steps by a solver.
The invention has the beneficial effects that: according to the invention, based on tobacco shred production big data and an AI development platform, the correlation analysis of tobacco shred quality and technological parameters is realized, and the tobacco shred production quality is improved in an auxiliary way; under the production plan management scene, based on the intelligent scheduling of silk production of this platform realization, improve silk production efficiency.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flow chart of a tobacco shredding big data and AI information processing method.
Fig. 2 is a data aggregation flow chart of a tobacco shredding big data and AI information processing method.
Fig. 3 is an artificial intelligence model diagram of a tobacco shredding big data and AI information processing method.
Fig. 4 is a flowchart of analysis of correlation between tobacco shred quality and process parameters in a tobacco shred manufacturing big data and AI information processing method.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
As shown in fig. 1, a method for processing big data and AI information of tobacco shred production comprises the following steps:
step one: inputting different data according to the AI information processing requirement;
step two: in a graphical man-machine interaction environment, large data and AI application flows are built through data aggregation, data preprocessing and an artificial intelligent model by using graphical, dragging and what you see is what you get modes;
step three: after the big data and AI application flow set up in the second step is completed, the user uses the modeling flow to run an engine, and runs and debugs the big data and AI application flow; and (3) returning to the second step for modification and rerun until the big data and the AI application flow run correctly.
Step four: and obtaining a big data information processing result and visually displaying the big data information processing result.
As shown in fig. 2, the data aggregation in the second step includes:
step two-1: according to different scenes, loading data from data sources of each service system in different modes; generally, the business systems all have their own databases from which data can be loaded using a database access interface; for a service system provided with a Restful calling interface, acquiring data through an HTTP request and loading the data; for a business system without a database access interface or a Restful interface, a custom development dedicated interface is required to access and load data of the business system.
Step two-2: after the data of each service system in the step two-1 are loaded, extracting the data from the loaded data, scattering the loaded data, and reading the data of each field of each service system;
step two-3: after each field data output by each original service system is obtained in the step two-2, the extracted data are converged by using a data type and relationship identification algorithm;
step two-4: and generating a table based on the data convergence of the step two-3, and converting the result into csv and arff formats.
The data type and relation recognition algorithm in the step two-3 comprises the following steps:
step A: identifying the type of the data field for the data field in the data field set; reading the data content of the field; calculating the data base of the field;
the if radix is close to 0, then this field is a constant variable;
the if radix is less than a threshold, then this field is a type variable;
if radix is greater than a threshold and conforms to date format, then this field is a date variable;
if radix is greater than a threshold and does not conform to date format, then this field is a continuous variable;
and (B) step (B): identifying the relationship among the data tables, for data table 1, data table 2in data table set, for data field A in data table 1 data field set; for data field B in data table 2; respectively calculating the cardinal similarity of the two data fields; respectively calculating the content similarity of the two data fields; if the two data tables are both larger than a certain threshold value, the two data tables have a foreign key relation, and the foreign key relation is A < - > B.
Step C: the automatic mapping of each data field to the body model takes a data table with a label field as a reference table C, a data table D in and a data table set E, an if data table D and the reference table C have an external key relation, and then uses a left connection mode to splice D onto C and delete D from the data table set E; and outputting the machine learning sample data ontology model filled with the data.
The data preprocessing in the second step comprises the following steps: format conversion, unit conversion, outlier screening. For example, the time "2023, 06, and 01" is uniformly modified to "2023-06-01". The unit conversion includes unified conversion of various measurement units, such as unified conversion of money into renminbi, unified conversion of length into meters, and the like. Outlier screening refers to cleaning data for significant errors, such as a person older than 100 years old, a person's birthday older than 1923, etc.
As shown in fig. 3, the artificial intelligence model in the second step includes:
copying the data set into N copies, and changing the time through space;
splitting each data set into K parts, and performing folding cross validation;
training a model for each fold of data;
stacking integration is performed on the K.times.N models.
The modeling flow operation engine in the third step comprises the following steps: running, running all, canceling running, canceling all running functions; and an operation state signal lamp is arranged, and red, yellow and green are respectively used for representing the states of successful operation, waiting for operation and failure operation. .
As shown in fig. 4, the second step further includes: correlation analysis; the correlation analysis is based on Pearson, spearman, kendall three coefficients. Correlation analysis of three coefficients: firstly judging the data type, and if the data is a continuous variable and can be subjected to parameter test, using a Pearson coefficient; if the data is sequential measurement data and there is no parametric test, the Spearman coefficients are used; if an ordered classification variable and no parameter check, kendall coefficients are used.
The second step also comprises the following steps: an intelligent scheduling algorithm;
definition 1 (intelligent scheduling problem): assuming only one production line and sufficient raw materials, the order arrives evenly, and the order is ordered to minimize inventory and overdue delivery costs. Let the working time of the production line per day be h hours, the minimum production unit be mpn, the production period of the minimum production unit be mpt, the yield per unit time be pn, and the stock cost be cpd. Let the existing order set O { O 0 ，o 1 ...,o m-1 }，Order o k The attribute set of (1) is { id } k ,pdt k ,adt k ,region k ,num k ,type k ,cpd k }. Wherein, id k For order number pdt k Adt for promised delivery time k To schedule delivery time, region k For delivery location, num k Type for the number of goods k Cpd is the size model of the goods k Is overdue delivery cost. In the intelligent scheduling problem, a production batch sequence P { P is obtained according to the prediction of the existing order and the future order 0 ,p 1 ,...,p n-1 And, in the case where the constraint condition is satisfied, the objective function value is minimized. Wherein, the objective function and constraint conditions are as follows:
objective function:wherein (1)>Representing the sum of inventory costs for all orders, adt k Representing the planned delivery date of order k, pdt k Representing the promised delivery date of order k, crd k Representing the inventory cost of order k, the inventory cost being zero if the actual delivery date is later than the promised delivery date; />Representing the sum of the overdue delivery costs for all orders,cpd k for the overdue delivery cost of order k, the overdue delivery cost is zero when the actual delivery date is earlier than the promised delivery date. Alpha and beta are adjustable weight coefficients for inventory costs and overdue delivery costs, respectively.
Constraint conditions: the intelligent scheduling problem needs to satisfy the following 2 constraint conditions simultaneously:
(1) The production lot sequence P satisfies the condition
(2)Production batch->Or->
The intelligent scheduling algorithm comprises the following steps: modeling the execution time of each production task, optimizing the target, modeling the constraint and solving the four steps by a solver.
(1) Modeling execution time of each production task
The start and end times of each task are represented using the following sets;
S1＝[model.addVar(vtype＝"I",name＝"S[％s]"％(i))for i in range(n)]
E1＝[model.addVar(vtype＝"I",name＝"E[％s]"％(i))for i in range(n)]
S2＝[model.addVar(vtype＝"I",name＝"S2[％s]"％(i))for i in range(n)]
E2＝[model.addVar(vtype＝"I",name＝"E2[％s]"％(i))for i in range(n)]
...
Sn＝[model.addVar(vtype＝"I",name＝"S2[％s]"％(i))for i in range(n)]
En＝[model.addVar(vtype＝"I",name＝"E2[％s]"％(i))for i in range(n)]
wherein each task is divided into n processes, si and Ei are used for describing the starting time and the ending time of the ith process of each task respectively, and n is the total number of production tasks.
(2) Optimization objective modeling
The optimization target is set to be the shortest in the last working procedure time of all tasks, and the modeling is as follows:
model.setObjective(E2[n-1],"minimize")
(3) Constraint modeling
The following constraints are added:
1) Each task can be arranged only 1 time;
2) Only 1 task is arranged at a certain position;
modeling was as follows:
for i in range(n):
model.addCons(quicksum(A[i][j]for j in range(n))＝＝1)
model.addCons(quicksum(A[j][i]for j in range(n))＝＝1)
determining the duration of each task
for i in range(n):
model.addCons(E1[i]-S1[i]-quicksum(L1[j]*A[j][i]for j in range(n))＝＝0)
model.addCons(E2[i]-S2[i]-quicksum(L2[j]*A[j][i]for j in range(n))＝＝0)
...
model.addCons(En[i]-Sn[i]-quicksum(L2[j]*A[j][i]for j in range(n))＝＝0)
Start time of 0 #
model.addCons(S1[0]＝＝0)
# execution of each task 1 is started before execution 2, each task must be started after the end of the previous task
for i in range(n):
model.addCons(S2[i]-E1[i]>＝0)
model.addCons(S3[i]-E2[i]>＝0)
...
model.addCons(Sn[i]-En-1[i]>＝0)
for i in range(n-1):
model.addCons(S1[i+1]-E1[i]＝＝0)
model.addCons(S2[i+1]-E2[i]＝＝0)
...
model.addCons(Sn[i+1]-En-1[i]＝＝0)
model.addCons(S2[i+1]-E2[i]>＝0)
model.addCons(S3[i+1]-E3[i]>＝0)
...
model.addCons(Sn[i+1]-En[i]>＝0)
(4) Solver
After the construction of the scheduling business model is completed, the model is input into a third party solver, and the scheduling business model is operated to obtain a scheduling result.
While certain specific embodiments of the invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. A tobacco shred making big data and AI information processing method is characterized in that the method comprises the following steps:
step one: inputting different data according to the AI information processing requirement;
step two: in a graphical man-machine interaction environment, large data and AI application flows are built through data aggregation, data preprocessing and an artificial intelligent model by using graphical, dragging and what you see is what you get modes;
step three: after the big data and AI application flow set up in the second step is completed, the user uses the modeling flow to run an engine, and runs and debugs the big data and AI application flow;
step four: and obtaining a big data information processing result and visually displaying the big data information processing result.
2. The method for processing big data and AI information of tobacco according to claim 1, wherein the problem of error reporting or operation failure occurs in the third step, and the second step is returned to modify and rerun until the big data and AI application flow run correctly.
3. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the data aggregation in the second step includes:
step two-1: according to different scenes, loading data from data sources of each service system in different modes;
step two-2: after the data of each service system in the step two-1 are loaded, extracting the data from the loaded data, scattering the loaded data, and reading the data of each field of each service system;
step two-3: after each field data output by each original service system is obtained in the step two-2, the extracted data are converged by using a data type and relationship identification algorithm;
step two-4: and generating a table based on the data convergence of the step two-3, and converting the result into csv and arff formats.
4. The method for processing big data and AI information of tobacco according to claim 1, wherein the data type and relation recognition algorithm in step two-3 comprises the steps of:
step A: identifying the type of the data field for the data field in the data field set; reading the data content of the field; calculating the data base of the field;
the if radix is close to 0, then this field is a constant variable;
the if radix is less than a threshold, then this field is a type variable;
if radix is greater than a threshold and conforms to date format, then this field is a date variable;
if radix is greater than a threshold and does not conform to date format, then this field is a continuous variable;
and (B) step (B): identifying the relationship among the data tables, for data table 1, data table 2in data table set, for data field A in data table 1 data field set; for data field B in data table 2; respectively calculating the cardinal similarity of the two data fields; respectively calculating the content similarity of the two data fields; if the two data tables are all larger than a certain threshold value, the two data tables have an external key relation, and the external key relation is A < - > B;
step C: the automatic mapping of each data field to the body model takes a data table with a label field as a reference table C, a data table D in and a data table set E, an if data table D and the reference table C have an external key relation, and then uses a left connection mode to splice D onto C and delete D from the data table set E; and outputting the machine learning sample data ontology model filled with the data.
5. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the data preprocessing in the second step includes: format conversion, unit conversion, outlier screening.
6. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the artificial intelligence model in the second step comprises:
copying the data set into N copies, and changing the time through space;
splitting each data set into K parts, and performing folding cross validation;
training a model for each fold of data;
stacking integration is performed on the K.times.N models.
7. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the modeling flow operation engine in the third step includes: running, running all, canceling running, canceling all running functions; and an operation state signal lamp is arranged, and red, yellow and green are respectively used for representing the states of successful operation, waiting for operation and failure operation. .
8. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the second step further comprises: correlation analysis; the correlation analysis is based on Pearson, spearman, kendall three coefficients.
9. The method for processing big data and AI information of tobacco shred production according to claim 8, wherein the correlation analysis of three coefficients: firstly judging the data type, and if the data is a continuous variable and can be subjected to parameter test, using a Pearson coefficient; if the data is sequential measurement data and there is no parametric test, the Spearman coefficients are used; if an ordered classification variable and no parameter check, kendall coefficients are used.
10. The method for processing big data and AI information of tobacco shred production according to claim 1, wherein the second step further comprises: an intelligent scheduling algorithm; the intelligent scheduling algorithm comprises the following steps: modeling the execution time of each production task, optimizing the target, modeling the constraint and solving the four steps by a solver.
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