CN113361661A - Modeling method and device for data cooperation capability evaluation - Google Patents
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Abstract
The application discloses a modeling method and a device for evaluating data synergy capability, wherein the modeling method comprises the following steps: constructing a variable acquisition model, wherein the variable acquisition model is used for acquiring values of a plurality of variables according to the acquired process data, and the variables comprise process precision and process accuracy; constructing a variable ability evaluation model, wherein the ability evaluation model is used for calculating the ability evaluation score of the corresponding variable according to the value of the variable; constructing a coordination ability acquisition model, wherein the coordination ability acquisition model is used for calculating a data coordination ability score according to the ability evaluation scores of a plurality of variables; combining the variable acquisition model, the variable capability evaluation model and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model; and training the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model. The data coordination capacity is evaluated by adopting a plurality of variables, and the data coordination capacity in the production process is objectively, scientifically and accurately described.
Description
Technical Field
The application relates to the technical field of production and manufacturing, in particular to a modeling method and device for evaluating data synergy.
Background
The data synergy ability of the cigarette cut tobacco product refers to the degree that the product of the previous procedure can meet the processing requirement of the next procedure, such as: inlet moisture content, outlet temperature, outlet moisture content, and the like. In the process of cross-process production, various factors such as the properties of incoming materials, the environment temperature and humidity, the cabinet feeding and distributing mode and the like can influence the product. Aiming at the cross-workshop data synergy, in order to meet the quality requirements of the rolling and wrapping, the indexes of the finished tobacco shreds of each brand, such as the whole shred rate, the shred breaking rate, the water content, the filling value, the temperature and the like, must be stably controlled, and the stable control of the indexes depends on the stability, the control and the synergy of processing parameters in the shred making process, so that the homogenization of the product quality can be realized.
The research on the data synergy of the cut tobacco products is a complex subject, and the improvement of the data synergy is a key ring for improving the quality of finished cut tobacco. In the production flow of tobacco shred manufacturing, tobacco leaves are processed into finished tobacco shreds from raw materials, multiple cross-process production is completed, the finished tobacco shreds are fed to a package through wind power, and cross-department production is completed. In each cross-process and cross-department production process, collaborative production data are generated. Currently, in the tobacco industry, the evaluation on the data synergistic capability of the cut tobacco products is less, most of the evaluation is only limited to the traditional single-process quality index management, and the comprehensive evaluation on more production links is lacked.
At present, the evaluation of the data synergistic ability of the cut tobacco products is mainly aimed at the production link of 'loosening and moisture regaining, feeding, storing leaves and drying cut tobacco', and the process ability index C of the water content after cutting is setpkThe standard of (3) can simply evaluate the moisture of the incoming material, and the evaluation on the synergistic capability of the product data can be completed. The evaluation method only considers the process control capacity of a single process or single characteristics, has a small evaluation range and is not fine enough, the value of cooperative data cannot be mined, the processing data of each process in the silk making are isolated from each other, the data of the whole silk making line does not cooperate, the evaluation of the cooperative capacity of cross-department data is lacked, and the improvement of the cooperative capacity of production data and the homogenization level of products is not facilitated.
And, when the process precision CpAnd process accuracy CaAll show large equidirectional deviation, the existing process capability index CpkThe calculation process of (a) will make these two deviations cancel each other to some extent, and thus fail to correctly reflect the process instability status.
Disclosure of Invention
The application provides a modeling method and device for evaluating data cooperation capacity, which are used for evaluating the data cooperation capacity by adopting a plurality of variables and objectively, scientifically and accurately describing the data cooperation capacity in the production process.
The application provides a modeling method for evaluating data cooperation capability, which comprises the following steps:
constructing a variable acquisition model, wherein the variable acquisition model is used for acquiring values of a plurality of variables according to the acquired process data, and the variables comprise process precision and process accuracy;
constructing a variable ability evaluation model, wherein the ability evaluation model is used for calculating the ability evaluation score of the corresponding variable according to the value of the variable;
constructing a coordination ability acquisition model, wherein the coordination ability acquisition model is used for calculating a data coordination ability score according to the ability evaluation scores of a plurality of variables;
combining the variable acquisition model, the variable capability evaluation model and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model;
and training the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
Preferably, the constructing of the variable acquisition model includes preprocessing the collected process data to obtain preprocessed process data.
Preferably, the constructing the variable acquisition model further comprises taking the preprocessed process data as a sample, calculating a standard deviation and a mean of the sample, and further calculating values of a plurality of variables of the sample.
Preferably, constructing the capability evaluation model of the variables comprises:
obtaining a membership function corresponding to each variable and parameters thereof through a fuzzy algorithm;
and calculating the capability evaluation score of the corresponding variable according to the parameters of the membership function and the value of the variable.
Preferably, the capability evaluation score of the variable is calculated using a percentile capability index function.
Preferably, the data synergy score F is calculated using the following formula
Wherein f is1Ability evaluation score, f, indicating process precision2The ability evaluation score, which represents the accuracy of the process, and α represents the weight of the precision of the process.
The application also provides a modeling device for evaluating the data cooperation capability, which comprises a variable acquisition model construction module, a variable capability evaluation model construction module, a cooperation capability acquisition model construction module, a pre-training model construction module and a training module;
the variable acquisition model construction module acquires values of a plurality of variables according to the acquired process data, wherein the variables comprise process precision and process accuracy;
the variable ability evaluation model building module is used for calculating the ability evaluation score of the corresponding variable according to the value of the variable;
the cooperation ability acquisition model construction module is used for calculating a data cooperation ability score according to the ability evaluation scores of a plurality of variables;
the pre-training model construction module is used for combining the variable acquisition model, the capability evaluation model of the variable and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model;
and the training module trains the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
Preferably, the variable acquisition model building module comprises a data preprocessing submodule, and the data preprocessing submodule is used for preprocessing the acquired process data to obtain preprocessed process data.
Preferably, the capability evaluation model building module of the variable comprises a fuzzy algorithm submodule and a capability evaluation score calculating submodule;
the fuzzy algorithm submodule is used for obtaining a membership function corresponding to each variable and parameters thereof through a fuzzy algorithm;
and the capability evaluation score calculating submodule is used for calculating the capability evaluation score of the corresponding variable according to the parameters of the membership function and the value of the variable.
Preferably, the ability-rating-score calculating module calculates the ability-rating score of the variable using a percentile ability index function.
Further features of the present application and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which is to be read in connection with the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart of a modeling method for data synergy assessment provided herein;
fig. 2 is a block diagram of a modeling apparatus for evaluating data collaboration capability according to the present application.
Detailed Description
Various exemplary embodiments of the present application 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, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to those 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 particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that the method and the device are suitable for the data coordination capability of the cigarette cut tobacco products, the cross-process data coordination capability evaluation of the cut stem line products, the cross-department data coordination capability evaluation from the production department to the operation department and the production process of other products, the production data is transparent, and data support is provided for the quality management of the products.
Fig. 1 is a flowchart of a modeling method for evaluating data collaboration capability provided by the present application. As shown in fig. 1, the modeling method for data synergy capability evaluation includes the following steps:
s110: constructing a variable acquisition model for acquiring values of a plurality of variables including process precision C according to the acquired process datapProcess accuracy CaAnd a process capability index Cpk. Wherein the process precision CpProcess accuracy CaFor calculatingData synergy score, Process capability index CpkAnd the method is used for verifying the model.
Process precision is the measure of how well process control meets product quality standards, CpThe larger the variation, the better the process control ability. The process control accuracy is an index for measuring the consistency of the actual central value and the standard central value in the process control. CaThe larger the deviation of the actual center value from the specification center value, the worse the process control capability.
Constructing the variable acquisition model includes taking the process data as a sample, calculating the standard deviation σ and the mean μ of the sample, and further calculating the values of a plurality of variables of the sample.
Wherein,
T＝USL-LSL (3)
Cpk＝(1-|Ca|)·Cp (4)
where USL is the upper standard limit, LSL is the lower standard limit, M is the median standard value, | Ca | is the absolute value of the value of process accuracy.
Preferably, the collected process data is preprocessed before calculating the variables to obtain preprocessed process data. The data preprocessing at least comprises data cleaning and data interception.
Wherein, data cleansing includes but is not limited to: effective data screening is carried out on process original data extracted from a Manufacturing Execution System (MES); filling the missing value by using a hot card filling method; and (3) identifying and eliminating abnormal values by adopting a 3 sigma principle: and carrying out noise detection on the data, and carrying out smoothing processing on the data by a box separation method to remove noise. As an embodiment, in the cross-process or cross-department production link, the process data of the last process or department of the production link is collected as the raw process data in step S110. For example, in the production link of 'loose moisture regain-feeding-leaf storage-cut tobacco drying', raw process data of a 'cut tobacco drying' process is collected.
Data interception includes, but is not limited to, steady-state and non-steady-state identification of process data, and interception of steady-state data as a subsequent data basis.
S120: and constructing a variable ability evaluation model, wherein the ability evaluation model is used for calculating the ability evaluation score of the corresponding variable according to the value of the variable.
Specifically, as an embodiment, constructing a capability evaluation model of a variable includes:
s1201: and acquiring a membership function corresponding to each variable and parameters thereof through a fuzzy algorithm.
Specifically, large-scale or small-scale distribution is selected as a membership function of a fuzzy set in a fuzzy algorithm, a final membership function is selected by adopting a strategy of an optimal membership function, optimal values of all parameters in the membership function are fitted, and meanwhile, a uniform and standard judgment standard is set for the value of a variable of a capability evaluation model.
S1202: and calculating the capability evaluation score of the corresponding variable according to the parameters of the membership function and the value of the variable.
Specifically, as an embodiment, the ability evaluation score of the variable is calculated by using a percentile ability index function, and percentile normalized calculation is performed on the ability index of the variable.
The constructed percentile capacity index function is as follows:
wherein f is1Shows process precision CpX represents CpA, b are andthe parameters of the membership functions corresponding to the program precision;
f2indicating process accuracy CaZ represents the value of | Ca |, and c, d are parameters of the membership function corresponding to the accuracy of the process.
S130: and constructing a cooperation capability obtaining model, wherein the cooperation capability obtaining model is used for calculating the data cooperation capability score according to the capability evaluation scores of the multiple variables.
Specifically, the data synergy capability score F is calculated using the following formula
Wherein f is1Ability evaluation score, f, indicating process precision2The ability evaluation score, which represents the accuracy of the process, and α represents the weight of the precision of the process.
It should be noted that the determination of the weight α mainly takes the control requirement of each process or department as the basic principle, combines the influence degree of two variables in the process or department on the sensory quality, and revises the weight α produced by each cross-process (for example, between multiple processes in the production link of "loose moisture regain-feeding-storing leaf-drying tobacco", "between multiple processes in the production link of" drying tobacco-winnowing-perfuming ") and cross-department (for example, between multiple departments in the production link of" stereo silk bank-wind-feeding-rolling tobacco) through the precision of the delphire method evaluation process. Taking the cross-process of 'loosening and moisture regaining, feeding, storing leaves and drying shreds' as an example, the value of the weight alpha is 0.4 after the research.
S140: and combining the variable acquisition model, the variable capability evaluation model and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model.
S150: and training the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
When the data cooperation capability evaluation model is verified, as an embodiment, two modes of dynamic characterization performance verification and sensitive characterization performance verification are adopted. In a dynamic stateFor process capability index C in the verification of characterization performancepkAnd carrying out correlation analysis on the data synergy ability score. In sensitive characterization performance validation, whether the data synergy score can characterize process precision CpProcess accuracy CaAnd verifying the comprehensive fluctuation condition.
The verification standard of the data synergy capability evaluation model is as follows: and the correlation coefficient in the dynamic characterization performance verification is more than or equal to 0.8, and the sensitive characterization performance verification result passes. And if the data are in accordance with the standards, judging that the evaluation of the data cooperation capability evaluation model is accurate and the evaluation effect is excellent.
Taking the cross-process data synergistic capability evaluation of 'loosening and moisture regaining, feeding, storing leaves and drying tobacco' as an example, the effect verification is carried out on the data synergistic capability evaluation model. In the verification of dynamic characterization performance, the data synergy ability score and the process ability index CpkThe correlation coefficient of (2) is 0.975, which shows that the result of the data synergistic capability evaluation model can objectively represent the level of the synergistic capability of the production parameters. In the sensitive characterization performance verification, a part of batch data with special characteristics is called for verification calculation, and the calculation result shows that the process capability index C is subjected to the verification calculationpkThe data synergy ability scores of the same two different batches fluctuate to different degrees, which shows that the data synergy ability scores can more truly represent the comprehensive fluctuation conditions of the deviation and the dispersion of production parameters in process control, and the process precision CpAnd process accuracy CaWhen mutually offset, the differences of different processes can still be characterized. Therefore, the data synergy ability evaluation method can objectively, scientifically and accurately represent the expression of the data synergy ability of the silk making product, and the model is judged to be accurate in evaluation and excellent in evaluation effect.
When the trained model is used for evaluating the data cooperation ability, the collected process data is input into the data cooperation ability evaluation model to obtain a data cooperation ability score, and whether the data cooperation ability score meets the standard or not is determined according to the grade of the data cooperation ability score.
As an example, the interoperability ranking criteria table is as follows:
TABLE 2 synergetic Power ratings criteria Table
Based on the modeling method for evaluating the data cooperation capability, the application also provides a modeling device for evaluating the data cooperation capability. As shown in fig. 2, the modeling apparatus includes a variable acquisition model construction module 210, a capability evaluation model construction module 220 for variables, a cooperation capability acquisition model construction module 230, a pre-training model construction module 240, and a training module 250.
The variable acquisition model construction module 210 acquires values of a plurality of variables including process precision and process accuracy according to the collected process data.
Preferably, the variable acquisition model building module comprises a data preprocessing module and a variable quantity calculation operator module. The data preprocessing submodule is used for preprocessing the collected process data to obtain preprocessed process data. And the variable calculation submodule is used for taking the preprocessed process data as a sample, calculating the standard deviation and the mean value of the sample, and further calculating the values of a plurality of variables of the sample.
The variable capability evaluation model building module 220 is used for calculating a capability evaluation score of a corresponding variable according to the value of the variable.
As one embodiment, the capability evaluation model building module of the variable comprises a fuzzy algorithm sub-module and a capability evaluation score calculating sub-module.
The fuzzy algorithm submodule is used for obtaining the membership function and the parameter thereof corresponding to each variable through a fuzzy algorithm.
And the capability evaluation score calculating submodule is used for calculating the capability evaluation score of the corresponding variable according to the parameters of the membership function and the value of the variable.
The cooperation ability obtaining model building module 230 is configured to calculate a data cooperation ability score according to the ability evaluation scores of the plurality of variables.
The pre-training model building module 240 is configured to combine the variable obtaining model, the capability evaluation model of the variable, and the cooperation capability obtaining model to form a data cooperation capability evaluation pre-training model.
The training module 250 trains the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
The beneficial effects obtained by the application are as follows:
1. the data coordination capacity is evaluated by adopting a plurality of variables, and the data coordination capacity in the production process is objectively, scientifically and accurately described.
2. Compared with the prior art which only uses a single index CpkThe evaluation method is beneficial to eliminating the homodromous deviation of the process accuracy and the process precision, and the obtained evaluation result is more scientific and accurate.
3. In the application, the capability evaluation score of the variable is in percent, and the numerical resolution is higher than the process capability index CpkAnd can be directly used as a performance index.
4. The data synergy ability evaluation model automatically generates a data synergy ability analysis report, effectively improves the quality of cigarette cut tobacco products, and operators can directly know the cross-process and cross-department performance of products in a certain time according to the analysis report, quickly find batches with insufficient synergy ability, and effectively improve the quality of the cigarette cut tobacco products while improving the working efficiency.
Although some specific embodiments of the present application have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustrative purposes only and are not intended to limit the scope of the present application. 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 present application. The scope of the application is defined by the appended claims.
Claims (10)
1. A modeling method for data synergy capability evaluation is characterized by comprising the following steps:
constructing a variable acquisition model, wherein the variable acquisition model is used for acquiring values of a plurality of variables according to acquired process data, and the variables comprise process precision and process accuracy;
constructing a capability evaluation model of the variable, wherein the capability evaluation model is used for calculating the capability evaluation score of the corresponding variable according to the value of the variable;
constructing a cooperation capability obtaining model, wherein the cooperation capability obtaining model is used for calculating a data cooperation capability score according to the capability evaluation scores of the variables;
combining the variable acquisition model, the capability evaluation model of the variable and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model;
and training the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
2. The modeling method for data synergy potential evaluation according to claim 1, wherein the constructing of the variable acquisition model comprises preprocessing the collected process data to obtain preprocessed process data.
3. The modeling method for data synergy potential evaluation according to claim 2, wherein the constructing a variable acquisition model further comprises taking the preprocessed process data as a sample, calculating a standard deviation and a mean of the sample, and further calculating values of a plurality of variables of the sample.
4. The modeling method for data synergy capability evaluation according to claim 1, wherein the constructing a capability evaluation model of variables comprises:
obtaining a membership function corresponding to each variable and parameters thereof through a fuzzy algorithm;
and calculating the capability evaluation score of the corresponding variable according to the parameters of the membership function and the value of the variable.
5. The method of claim 4, wherein the capability evaluation score of the variable is calculated using a percentile capability index function.
6. The modeling method for data interoperability evaluation according to claim 1, wherein the data interoperability score F is calculated using the following formula
Wherein f is1Ability evaluation score, f, indicating process precision2The ability evaluation score, which represents the accuracy of the process, and α represents the weight of the precision of the process.
7. A modeling device for data cooperation capability evaluation is characterized by comprising a variable acquisition model building module, a variable capability evaluation model building module, a cooperation capability acquisition model building module, a pre-training model building module and a training module;
the variable acquisition model construction module acquires values of a plurality of variables according to the acquired process data, wherein the variables comprise process precision and process accuracy;
the variable ability evaluation model building module is used for calculating the ability evaluation score of the corresponding variable according to the value of the variable;
the cooperation capability acquisition model construction module is used for calculating a data cooperation capability score according to the capability evaluation scores of the variables;
the pre-training model construction module is used for combining the variable acquisition model, the capability evaluation model of the variable and the cooperation capability acquisition model to form a data cooperation capability evaluation pre-training model;
and the training module trains the data cooperation ability evaluation pre-training model to obtain a data cooperation ability evaluation model.
8. The modeling apparatus for data synergy capability evaluation according to claim 7, wherein the variable acquisition model construction module includes a data preprocessing submodule, and the data preprocessing submodule is configured to preprocess the acquired process data to obtain preprocessed process data.
9. The modeling device for data synergy capability evaluation according to claim 7, wherein the capability evaluation model construction module for the variables comprises a fuzzy algorithm sub-module and a capability evaluation score calculation sub-module;
the fuzzy algorithm submodule is used for obtaining a membership function corresponding to each variable and parameters thereof through a fuzzy algorithm;
and the ability evaluation score calculation submodule is used for calculating the ability evaluation score of the corresponding variable according to the parameters of the membership function and the values of the variables.
10. The modeling apparatus for data collaboration capacity evaluation as claimed in claim 9, wherein the capacity evaluation score calculating module calculates the capacity evaluation score of the variable using a percentile capacity index function.
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